From ce445b64a6d90baed5c51df250fb5354c48bb373 Mon Sep 17 00:00:00 2001 From: xsl Date: Thu, 9 Jul 2026 20:45:08 +0800 Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E5=96=84=E9=83=A8=E7=BD=B2=E5=B9=B6?= =?UTF-8?q?=E8=AE=AD=E7=BB=835=E4=B8=AA=E6=96=B0=E5=8F=91=E5=9E=8B=20+=20?= =?UTF-8?q?=E6=8D=A2=E5=8F=91=E5=9E=8B=E9=9B=86=E6=88=90=E6=96=87=E6=A1=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 部署修复： - torch.load 增加 weights_only=False patch，兼容 PyTorch 2.6+ 加载旧权重 - OSS 改为懒加载，本地用 output_format=base64 无需配凭证即可启动 - 补全被 gitignore 误排除的必需代码：core/models/layers/data、models/layers/data、keypoints/lib - webui 训练命令 --xformers 改 --sdpa（修复 xformers 无 CUDA 支持报错） 功能调整： - hair_grow_service 端口改 8899、preview 路由修复（send_file） - list_hairstyles 增加发型白名单，测试页只展示当前5个发型 新增脚本： - train_lora_parallel.py：直接调 kohya 并行训练 LoRA（绕过 photo_service 串行限制） - train_hairstyles_parallel.py / train_batch_stepC.py：批量训练辅助脚本 - scripts/sync_data_to_server.sh：大文件断点续传到云服务器 文档： - docs/换发型集成文档.md：换发型完整流程、服务架构、资源依赖、训练方法、集成步骤 --- docs/换发型集成文档.md | 408 +++ hair_grow_service/app.py | 15 +- .../core/models/layers/data/FDDB/img_list.txt | 2845 +++++++++++++++++ .../core/models/layers/data/__init__.py | 3 + .../core/models/layers/data/config.py | 42 + .../core/models/layers/data/data_augment.py | 237 ++ .../core/models/layers/data/wider_face.py | 101 + project/hair_service_sd/core/oss_module.py | 22 +- project/hair_service_sd/hair_grow_swap.py | 7 + .../keypoints/lib/config/__init__.py | 9 + .../keypoints/lib/config/default.py | 160 + .../keypoints/lib/config/models.py | 58 + .../keypoints/lib/core/evaluate.py | 73 + .../keypoints/lib/core/function.py | 279 ++ .../keypoints/lib/core/inference.py | 79 + .../keypoints/lib/core/loss.py | 84 + .../keypoints/lib/dataset/JointsDataset.py | 292 ++ .../keypoints/lib/dataset/__init__.py | 12 + .../keypoints/lib/dataset/coco.py | 445 +++ .../keypoints/lib/dataset/mpii.py | 181 ++ .../keypoints/lib/models/__init__.py | 13 + .../keypoints/lib/models/pose_hrnet.py | 501 +++ .../keypoints/lib/models/pose_resnet.py | 271 ++ .../keypoints/lib/nms/__init__.py | 0 .../hair_service_sd/keypoints/lib/nms/nms.py | 164 + .../keypoints/lib/utils/__init__.py | 0 .../keypoints/lib/utils/transforms.py | 121 + .../keypoints/lib/utils/utils.py | 203 ++ .../keypoints/lib/utils/vis.py | 141 + .../keypoints/lib/utils/zipreader.py | 70 + .../models/layers/data/FDDB/img_list.txt | 2845 +++++++++++++++++ .../models/layers/data/__init__.py | 3 + .../models/layers/data/config.py | 42 + .../models/layers/data/data_augment.py | 237 ++ .../models/layers/data/wider_face.py | 101 + .../hair_service_sd/run_copy_cost_colorb64.py | 60 +- project/hair_service_sd/upload_oss.py | 22 +- project/photo_service/lora_train_service_1.py | 2 +- scripts/sync_data_to_server.sh | 69 + train_batch_stepC.py | 96 + train_hairstyles_parallel.py | 150 + train_lora_parallel.py | 124 + 42 files changed, 10553 insertions(+), 34 deletions(-) create mode 100644 docs/换发型集成文档.md create mode 100644 project/hair_service_sd/core/models/layers/data/FDDB/img_list.txt create mode 100644 project/hair_service_sd/core/models/layers/data/__init__.py create mode 100644 project/hair_service_sd/core/models/layers/data/config.py create mode 100644 project/hair_service_sd/core/models/layers/data/data_augment.py create mode 100644 project/hair_service_sd/core/models/layers/data/wider_face.py create mode 100644 project/hair_service_sd/keypoints/lib/config/__init__.py create mode 100644 project/hair_service_sd/keypoints/lib/config/default.py create mode 100644 project/hair_service_sd/keypoints/lib/config/models.py create mode 100644 project/hair_service_sd/keypoints/lib/core/evaluate.py create mode 100644 project/hair_service_sd/keypoints/lib/core/function.py create mode 100644 project/hair_service_sd/keypoints/lib/core/inference.py create mode 100644 project/hair_service_sd/keypoints/lib/core/loss.py create mode 100644 project/hair_service_sd/keypoints/lib/dataset/JointsDataset.py create mode 100644 project/hair_service_sd/keypoints/lib/dataset/__init__.py create mode 100644 project/hair_service_sd/keypoints/lib/dataset/coco.py create mode 100644 project/hair_service_sd/keypoints/lib/dataset/mpii.py create mode 100644 project/hair_service_sd/keypoints/lib/models/__init__.py create mode 100644 project/hair_service_sd/keypoints/lib/models/pose_hrnet.py create mode 100644 project/hair_service_sd/keypoints/lib/models/pose_resnet.py create mode 100644 project/hair_service_sd/keypoints/lib/nms/__init__.py create mode 100644 project/hair_service_sd/keypoints/lib/nms/nms.py create mode 100644 project/hair_service_sd/keypoints/lib/utils/__init__.py create mode 100644 project/hair_service_sd/keypoints/lib/utils/transforms.py create mode 100644 project/hair_service_sd/keypoints/lib/utils/utils.py create mode 100644 project/hair_service_sd/keypoints/lib/utils/vis.py create mode 100644 project/hair_service_sd/keypoints/lib/utils/zipreader.py create mode 100644 project/hair_service_sd/models/layers/data/FDDB/img_list.txt create mode 100644 project/hair_service_sd/models/layers/data/__init__.py create mode 100644 project/hair_service_sd/models/layers/data/config.py create mode 100644 project/hair_service_sd/models/layers/data/data_augment.py create mode 100644 project/hair_service_sd/models/layers/data/wider_face.py create mode 100755 scripts/sync_data_to_server.sh create mode 100644 train_batch_stepC.py create mode 100644 train_hairstyles_parallel.py create mode 100644 train_lora_parallel.py diff --git a/docs/换发型集成文档.md b/docs/换发型集成文档.md new file mode 100644 index 0000000..721cfeb --- /dev/null +++ b/docs/换发型集成文档.md @@ -0,0 +1,408 @@ +# 换发型功能集成文档 + +本文档说明"换发型"功能的完整工作流程、服务依赖、资源结构，供其他项目集成参考。 + +--- + +## 一、功能概述 + +**换发型**：用户上传一张人像照片，选择一个目标发型（hair_id），系统把用户的头发替换成目标发型，保留人脸和身体。 + +- **输入**：用户人像图（base64 或 URL） + 目标发型 ID +- **输出**：换好发型的结果图（base64 或 OSS URL） +- **耗时**：单次约 10-12 秒（GPU: RTX 5090） + +### 同类功能（同套服务支撑） +- **换发色**（`/hairColor/v2`）：把头发颜色换成指定 RGB，额外依赖 `ref_color` 颜色查找表 +- **训练新发型**：用发型图片训练新的 LoRA，详见第七节 + +--- + +## 二、服务架构 + +换发型由 **3 个后端服务**协同完成（缺一不可），另有 1 个前端页面服务（可选）。 + +| 服务 | 端口 | conda 环境 | 职责 | +|------|------|-----------|------| +| **webui** | 57860 | `sdwebui` | Stable Diffusion img2img 推理引擎（基于 AUTOMATIC1111 webui） | +| **photo_service** | 32678 | `py310` | LoRA 调度：推理时把对应发型的 LoRA 复制到 webui 并注入 prompt；训练时调度 kohya | +| **hair_service_sd** | 8801 | `my_hair` | 换发型主服务：人脸检测、关键点、头发抠图、材质推理、图像合成 | +| hair_grow_service | 8899 | `my_hair` | （可选）前端测试页面，提供发型列表/预览/换发型 UI | + +### 服务间调用链（换发型） + +``` +调用方 + │ POST /api/swapHair/v1 + ▼ +hair_service_sd (8801) ← 人脸检测/关键点/抠图/材质推理/图像合成 + │ POST /api/hair/inference (转发 img2img 请求) + ▼ +photo_service (32678) ← 复制 LoRA 到 webui、prompt 注入 + │ POST /sdapi/v1/img2img + ▼ +webui (57860) ← SD img2img 真正推理 +``` + +### 启动方式 + +```bash +cd /home/xsl/change_hair +bash start_all.sh start # 启动 webui + photo_service + hair_service_sd +# 等待 1-2 分钟（webui 加载 SD 模型） + +# 可选：前端页面 +nohup bash start_hairgrow.sh > project/logs/hair_grow_service.log 2>&1 & +``` + +> 三个核心服务有启动顺序依赖：webui 必须先启动并加载完模型（约 1-2 分钟），photo_service 和 hair_service_sd 才能正常工作。 + +--- + +## 三、换发型 API 接口 + +### 请求 + +``` +POST http://:8801/api/swapHair/v1 +Content-Type: application/json +``` + +**请求体参数：** + +| 参数 | 类型 | 必填 | 说明 | +|------|------|:---:|------| +| `hair_id` | string | ✓ | 目标发型 ID（如 `chang_tuoyuan`），对应发型资源目录名 | +| `task_id` | string | ✓ | 任务唯一标识，用于临时文件命名 | +| `user_img_path` | string | ✓ | 用户人像图。支持 `data:image/jpeg;base64,` 或图片 URL | +| `is_hr` | string | ✓ | 是否高清模式。`"true"` 输出 1152×1536，`"false"` 输出 576×768 | +| `output_format` | string | ✗ | 输出格式。`"base64"`（推荐，直接返回图）或 `"url"`（上传 OSS 返回链接，需配 OSS 凭证） | + +**请求示例：** + +```bash +curl -X POST http://127.0.0.1:8801/api/swapHair/v1 \ + -H "Content-Type: application/json" \ + -d '{ + "hair_id": "chang_tuoyuan", + "task_id": "req_001", + "is_hr": "true", + "user_img_path": "data:image/jpeg;base64,/9j/4AAQ...", + "output_format": "base64" + }' +``` + +```python +# Python 示例 +import base64, requests +with open("user.jpg", "rb") as f: + img_b64 = base64.b64encode(f.read()).decode() +r = requests.post("http://127.0.0.1:8801/api/swapHair/v1", json={ + "hair_id": "chang_tuoyuan", + "task_id": "req_001", + "is_hr": "true", + "user_img_path": "data:image/jpeg;base64," + img_b64, + "output_format": "base64", +}, timeout=300) +result = r.json() # {"state":0, "data":"", "msg":"success"} +with open("out.jpg", "wb") as f: + f.write(base64.b64decode(result["data"])) +``` + +### 响应 + +**成功（HTTP 200）：** +```json +{ + "state": 0, + "msg": "success", + "data": "" // output_format=url 时为 OSS 图片链接 +} +``` + +**失败（HTTP 400）：** +```json +{ + "state": -1, + "msg": "推理发型失败", // 具体错误信息，如：参数错误/用户图像加载失败/发型模板加载失败/推理发型失败 + "data": "", + "task_id": "req_001" +} +``` + +--- + +## 四、换发型内部流程（9 个功能步骤） + +理解内部流程有助于排查问题和性能优化。`hair_service_sd` 收到请求后依次执行： + +| 步骤 | 功能 | 说明 | 耗时占比 | +|------|------|------|---------| +| 1 | 获取请求参数 | 解析参数，base64/URL 图片解码落盘 | <1% | +| 2 | 加载用户图像 | 复制到工作目录 | <1% | +| 3 | 加载发型模板图像 | 从 `upload_train_imgs//` 读模板原图 | <1% | +| 4 | 检查遮挡眼睛 | 从 `hair_template_material//` 读 `*_matting.png` 和 `.pkl`（关键点） | <1% | +| 5 | 检查发型材质 | 确认 `ref_hairstyle//` 存在 | <1% | +| 6 | **推理发型** | 用 GAN 模型把用户图和 `ref_rgb_8uc3_768.png` 融合，生成初步换发图 | ~30% | +| 7 | 判断遮挡眼睛 | 检查头发是否遮挡眼睛（决定后续处理路径） | <1% | +| 8 | 处理发型区域 | 头发区域裁剪、mask 生成、透视变换到 576×768/1152×1536 | ~5% | +| 9 | **webui img2img** | 经 photo_service 调 webui，加载 LoRA 做 SD 重绘（核心画质步骤） | ~60% | + +**关键：LoRA 加载机制**（步骤 9） +1. `hair_service_sd` 把裁剪好的头发区域图 + mask 发给 `photo_service` +2. `photo_service` 把 `train_material//model/hairstyle_hd_lora.safetensors` 复制到 webui 的 `models/Lora/_hd.safetensors`（已存在则跳过） +3. `photo_service` 在 prompt 前注入 `_hd:1.0>` +4. webui 收到 img2img 请求，自动加载该 LoRA 做局部重绘 +5. 结果返回 `hair_service_sd`，贴回原图对应位置 + +--- + +## 五、必需资源清单 + +换发型功能依赖以下资源。集成时需保证这些文件到位。 + +### 5.1 核心模型权重（全局共享，约 10G） + +位置：`project/hair_service_sd/weights/` + +| 文件/目录 | 用途 | +|-----------|------| +| `yolov5l.pt` | RetinaFace/人脸检测 | +| `Resnet50_Final.pth` | 人脸检测辅助 | +| `79999_iter.pth` | 关键点检测（MomocvFaceAlignment1K） | +| `master_hair_v8_onlyhair_nowarp_all_0709.pt` | 头发迁移 GAN（发型融合） | +| `zxm_v7_gen_bald_add_changed_hair_pairdata_5seg_0611.pt` | 秃头分割 + 发型生成 GAN | +| `parser_model.pth` | 头发抠图（matting） | +| `gender_models/` | 性别识别 | +| `keypoints/` | 关键点模型库 | + +### 5.2 Stable Diffusion 推理资源（约 5G） + +位置：`project/onediff/stable-diffusion-webui/models/` + +| 文件/目录 | 用途 | +|-----------|------| +| `Stable-diffusion/v1-5-pruned-emaonly.safetensors` | **SD 1.5 底模**（必需，4G） | +| `Lora/_hd.safetensors` | 各发型的 LoRA（推理时自动生成，见 5.3） | +| `ESRGAN/4x-UltraSharp.pth` | 超分辨率（高清模式用） | +| `GFPGAN/` | 人脸修复（可选） | + +### 5.3 单个发型的资源（每个发型约 160M） + +**一个可用的发型需要 4 个目录的资源配套：** + +``` +# 1. 发型材质（换发型必需，由训练 step3 生成） +project/data/ref_hairstyle// +├── config.json # {"gender":"girl","version":"20221206","ratio":"1"} +├── ref_rgb_8uc3_768.png # 发型 RGB 图（GAN 融合参考图，必需） +├── ref_matting_8uc3_768.png # 发型 mask +├── ref_matting_fg_8uc3_768.png # 发型前景 +├── ref_baldseg_8uc3_768.png # 秃头分割 +├── ref_landmark_f1k2_768.txt # 1000 个关键点 +└── input_another_pose_hair_image.npy + +# 2. LoRA 权重 + 训练图（换发型必需，由训练 step1+step2 生成） +data/train_material// +├── model/ +│ └── hairstyle_hd_lora.safetensors # LoRA 权重（~145M，SD 重绘用） +└── images/ + └── 1_hairstyle/ # 训练图 + caption + ├── 0001.png + ├── 0001.txt # caption tag（webui 推理 prompt 用） + └── ... (约20张) + +# 3. 模板材质（换发型必需，由训练 step4 生成） +project/data/hair_template_material// +├── first##.png # 发型模板原图 +├── first##_matting.png # 发型 matting（遮挡检测用） +└── first##.pkl # 关键点（遮挡检测用） + +# 4. 发型模板原图（换发型必需，由训练准备） +project/data/upload_train_imgs// +└── first##.jpg # 发型模板原图（步骤3读取） +``` + +> **注意**：`images/1_hairstyle/` 目录名必须以数字开头（`1_hairstyle`），代码用 `os.listdir(images)[0]` 取子目录并校验数字开头。 + +### 5.4 路径配置 + +所有路径在 `project/hair_service_sd/config/configure.ini` 集中配置，迁移时改这里的路径即可： + +```ini +[default] +modelDir = weights # 核心模型目录（相对 hair_service_sd） +hairstyleDir = /home/xsl/change_hair/project/data/ref_hairstyle # 发型材质（5.3-1） +haircolorDir= /home/xsl/change_hair/project/data/ref_haircolor # 换发色用 +userDir=/home/xsl/change_hair/project/data/userImage # 用户图工作目录 +tmp_dir=/home/xsl/change_hair/project/data/tmp # 临时目录 +res_dir=/home/xsl/change_hair/project/data/res_dir # 结果输出目录 +userInfo_dir=/home/xsl/change_hair/project/data/user_info # 用户关键点缓存 +train_dir = /home/xsl/change_hair/data/train_material # LoRA+训练图（5.3-2） +refImgDir=/home/xsl/change_hair/project/data/refImage # 参考图缓存 +hair_template_material_dir=/home/xsl/change_hair/project/data/hair_template_material # 5.3-3 +upload_train_dir=/home/xsl/change_hair/project/data/upload_train_imgs # 5.3-4 +version=online # online 用 0.0.0.0 地址 +``` + +`photo_service` 的 webui/LoRA 路径在 `project/photo_service/lora_train_service_1.py` 第 41 行： +```python +webui_lora_dir = '/home/xsl/change_hair/project/onediff/stable-diffusion-webui/models/Lora' +``` + +--- + +## 六、集成步骤（接入新项目） + +### 1. 部署服务端 +按第二节启动 webui + photo_service + hair_service_sd 三个服务，确认 `http://:8801` 可达。 + +### 2. 调用换发型接口 +新项目只需调 `hair_service_sd` 的 `/api/swapHair/v1`，无需关心内部服务调用。 + +### 3. 准备发型资源 +每个要支持的发型，必须备齐第五节 5.3 的 4 个目录资源。两种获取方式： +- **训练新发型**（见第七节）：用发型图训练，自动生成全部资源 +- **复制现有发型**：从已部署环境复制 `` 的 4 个目录 + +### 4. 查询可用发型列表 +调 `hair_grow_service` 的接口（需启动 8899）： +``` +GET http://:8899/api/hairstyles +→ {"state":0, "count":5, "data":[{"hair_id":"chang_tuoyuan","gender":"girl"}, ...]} +``` +> 注意：该接口在 `hair_grow_swap.py:list_hairstyles()` 内有白名单过滤（当前只返回 5 个 chang_* 发型），集成时按需调整或去掉。 + +### 5. 获取发型预览图 +``` +GET http://:8899/preview/ +→ 直接返回预览图（优先 static/previews/.jpg，回退到 ref_rgb_8uc3_768.png） +``` + +--- + +## 七、训练新发型 + +### 7.1 一键训练脚本 + +```bash +cd /home/xsl/change_hair/project/hair_service_sd +python /home/xsl/change_hair/train_hairstyle_full.py \ + --hair-id <新发型ID> \ + --input <训练图目录> \ # 目录里放该发型的 jpg/png（1张即可，多张效果更好） + --gender \ + --template-img <模板图> # 用于生成材质和预览，通常是该发型最代表性的图 +``` + +### 7.2 训练 5 个阶段 + +| 阶段 | 功能 | 产物 | 耗时 | +|------|------|------|------| +| step1 | 准备训练数据 | 抠图 + 数据增广（水平翻转 × 5 分辨率）→ `train_material//images/1_hairstyle/` 约 20 张 png+txt | ~1 分钟 | +| step2 | LoRA 训练 | kohya 训练 1500 步 → `train_material//model/hairstyle_hd_lora.safetensors` | ~10 分钟（单卡） | +| step3 | 生成 ref 材质 | 调 hair_service_sd 回调 → `ref_hairstyle//` 6 个文件 | ~1 秒 | +| step4 | 生成模板材质 | → `hair_template_material//` 3 个文件 | ~3 秒 | +| step5 | 生成预览图 | → `hair_grow_service/static/previews/.jpg` | ~10 秒 | + +### 7.3 批量并行训练（多个发型） + +用 `train_lora_parallel.py` 直接调 kohya，支持并发： + +```python +# 编辑 train_lora_parallel.py 顶部的 HAIRSTYLES 列表 +HAIRSTYLES = ["新发型1", "新发型2", "新发型3"] +PARALLEL = 2 # 并发数（按显存调整，5090 32G 建议 2） + +# step1 数据需先用 train_hairstyles_parallel.py 或逐个跑 step1 准备好 +python train_lora_parallel.py +``` + +### 7.4 训练注意事项 +- **hair_id 命名**：只能用英文/数字/下划线，不能含中文或特殊字符（会作目录名、文件名、LoRA 名） +- **单图训练**：1 张图经增广出 ~20 张训练图，LoRA 能用但泛化有限；多图（不同角度/光照）效果更好 +- **GPU 显存**：训练峰值约 13G/卡，推理约 6G。并行训练需控制并发数避免 OOM +- **训练前提**：webui + photo_service + hair_service_sd 必须在运行（step3 回调依赖） + +--- + +## 八、环境依赖 + +### Conda 环境 + +| 环境 | Python | 用途 | 关键依赖 | +|------|--------|------|---------| +| `my_hair` | 3.10 | hair_service_sd / hair_grow_service / 训练 step1 | torch, cv2, flask, gevent, oss2 | +| `sdwebui` | 3.10 | webui | torch, gradio, transformers（AUTOMATIC1111 全套） | +| `py310` | 3.10 | photo_service | torch, flask, requests | +| `kohya` | 3.10 | LoRA 训练（kohya_ss） | torch, accelerate, safetensors | + +### 关键兼容性处理（迁移时注意） + +1. **PyTorch 2.6+ 的 `weights_only`**：加载旧权重（yolov5l.pt 等）需 `torch.load(..., weights_only=False)`。代码已在 `run_copy_cost_colorb64.py` 和 `hair_grow_service/app.py` 顶部做了全局 patch。 + +2. **OSS 上传（可选）**：`output_format=url` 时需配 OSS 凭证环境变量： + ```bash + export OSS_TEST_ACCESS_KEY_ID=<...> + export OSS_TEST_ACCESS_KEY_SECRET=<...> + export OSS_TEST_BUCKET=<...> + export OSS_TEST_ENDPOINT=<...> + ``` + 用 `output_format=base64` 则完全不需要 OSS。 + +3. **webui xformers**：kohya 训练命令用 `--sdpa`（PyTorch 原生），不要用 `--xformers`（若 xformers 与 torch 版本不匹配会报 CUDA 错误）。 + +--- + +## 九、故障排查 + +| 现象 | 可能原因 | 排查 | +|------|---------|------| +| 换发型返回 `推理发型失败` | webui 没启动 / LoRA 文件缺失 | 检查 webui 57860 端口；检查 `train_material//model/` 是否有 LoRA | +| 换发型返回 `发型模板加载失败` | `upload_train_imgs//` 缺 `first##` 开头的文件 | 检查 5.3-4 目录 | +| 换发型返回 `检查遮挡失败` | `hair_template_material//` 缺 matting/pkl | 检查 5.3-3 目录 | +| 预览图 404 | `ref_hairstyle//ref_rgb_8uc3_768.png` 不存在 | 检查 5.3-1 目录 | +| 发型列表少发型 | `hair_grow_swap.py:list_hairstyles()` 白名单过滤 | 调整 `_VISIBLE_HAIRSTYLES` 集合 | +| webui 启动报 xformers 错 | xformers 与 torch 版本不匹配 | webui 启动去掉 `--xformers`，或用 `--sdpa` | +| 训练报 CUDA error | 显存不足或 xformers 问题 | 训练命令用 `--sdpa`；降低并发数 | + +### 日志位置 +- webui：`project/logs/webui.log` +- photo_service：`project/logs/photo_service.log` +- hair_service_sd：`project/logs/hair_service.log` +- hair_grow_service：`project/logs/hair_grow_service.log` +- 训练：`project/logs/train_.log` + +--- + +## 十、目录结构总览 + +``` +/home/xsl/change_hair/ +├── project/ +│ ├── hair_service_sd/ # 换发型主服务 +│ │ ├── weights/ # 核心模型权重（10G） +│ │ ├── config/configure.ini # 路径配置 +│ │ ├── run_copy_cost_colorb64.py # 主入口（API 定义） +│ │ ├── gen_super_image.py # webui img2img 调用 +│ │ └── core/ # 发型推理核心逻辑 +│ ├── photo_service/ # LoRA 调度服务 +│ │ └── lora_train_service_1.py # 训练调度 + 推理 LoRA 切换 +│ ├── onediff/ # SD webui（推理引擎，14G） +│ │ └── stable-diffusion-webui/models/ +│ │ ├── Stable-diffusion/ # SD 底模 +│ │ └── Lora/ # 各发型 LoRA（自动生成） +│ ├── kohya_ss_home/ # LoRA 训练框架（13G） +│ ├── data/ # 数据目录（由 configure.ini 指向） +│ │ ├── ref_hairstyle/ # 发型材质（5.3-1） +│ │ ├── hair_template_material/ # 模板材质（5.3-3） +│ │ ├── upload_train_imgs/ # 发型模板原图（5.3-4） +│ │ └── ... +│ └── logs/ +├── data/ +│ └── train_material/ # LoRA + 训练图（5.3-2） +├── hair_grow_service/ # 前端页面（可选） +├── train_hairstyle_full.py # 单发型一键训练 +├── train_lora_parallel.py # 批量并行训练 +├── start_all.sh # 服务启停脚本 +└── docs/换发型集成文档.md # 本文档 +``` diff --git a/hair_grow_service/app.py b/hair_grow_service/app.py index 124e53d..27054d0 100644 --- a/hair_grow_service/app.py +++ b/hair_grow_service/app.py @@ -24,10 +24,19 @@ os.environ.setdefault("CRYPTOGRAPHY_OPENSSL_NO_LEGACY", "1") import cv2 import numpy as np -from flask import Flask, request, jsonify, send_from_directory +from flask import Flask, request, jsonify, send_from_directory, send_file from gevent import pywsgi -PORT = 8888 +# PyTorch 2.6+ 默认 weights_only=True，无法加载含 numpy 对象的旧权重（yolov5l.pt 等）。 +# 统一改回 False（权重均为本机自有可信文件）。 +import torch +_orig_torch_load = torch.load +def _torch_load(*args, **kwargs): + kwargs.setdefault("weights_only", False) + return _orig_torch_load(*args, **kwargs) +torch.load = _torch_load + +PORT = 8899 BASE_DIR = os.path.dirname(os.path.abspath(__file__)) app = Flask(__name__, static_folder="static", static_url_path="/static") @@ -394,7 +403,7 @@ def preview_img(hair_id): hairstyle_dir = config.get('default', 'hairstyleDir') fallback = os.path.join(hairstyle_dir, hair_id, "ref_rgb_8uc3_768.png") if os.path.exists(fallback): - return send_file_or_404(fallback) + return send_file(fallback) except Exception: pass return ("", 404) diff --git a/project/hair_service_sd/core/models/layers/data/FDDB/img_list.txt b/project/hair_service_sd/core/models/layers/data/FDDB/img_list.txt new file mode 100644 index 0000000..5cf3d31 --- /dev/null +++ b/project/hair_service_sd/core/models/layers/data/FDDB/img_list.txt @@ -0,0 +1,2845 @@ +2002/08/11/big/img_591 +2002/08/26/big/img_265 +2002/07/19/big/img_423 +2002/08/24/big/img_490 +2002/08/31/big/img_17676 +2002/07/31/big/img_228 +2002/07/24/big/img_402 +2002/08/04/big/img_769 +2002/07/19/big/img_581 +2002/08/13/big/img_723 +2002/08/12/big/img_821 +2003/01/17/big/img_610 +2002/08/13/big/img_1116 +2002/08/28/big/img_19238 +2002/08/21/big/img_660 +2002/08/14/big/img_607 +2002/08/05/big/img_3708 +2002/08/19/big/img_511 +2002/08/07/big/img_1316 +2002/07/25/big/img_1047 +2002/07/23/big/img_474 +2002/07/27/big/img_970 +2002/09/02/big/img_15752 +2002/09/01/big/img_16378 +2002/09/01/big/img_16189 +2002/08/26/big/img_276 +2002/07/24/big/img_518 +2002/08/14/big/img_1027 +2002/08/24/big/img_733 +2002/08/15/big/img_249 +2003/01/15/big/img_1371 +2002/08/07/big/img_1348 +2003/01/01/big/img_331 +2002/08/23/big/img_536 +2002/07/30/big/img_224 +2002/08/10/big/img_763 +2002/08/21/big/img_293 +2002/08/15/big/img_1211 +2002/08/15/big/img_1194 +2003/01/15/big/img_390 +2002/08/06/big/img_2893 +2002/08/17/big/img_691 +2002/08/07/big/img_1695 +2002/08/16/big/img_829 +2002/07/25/big/img_201 +2002/08/23/big/img_36 +2003/01/15/big/img_763 +2003/01/15/big/img_637 +2002/08/22/big/img_592 +2002/07/25/big/img_817 +2003/01/15/big/img_1219 +2002/08/05/big/img_3508 +2002/08/15/big/img_1108 +2002/07/19/big/img_488 +2003/01/16/big/img_704 +2003/01/13/big/img_1087 +2002/08/10/big/img_670 +2002/07/24/big/img_104 +2002/08/27/big/img_19823 +2002/09/01/big/img_16229 +2003/01/13/big/img_846 +2002/08/04/big/img_412 +2002/07/22/big/img_554 +2002/08/12/big/img_331 +2002/08/02/big/img_533 +2002/08/12/big/img_259 +2002/08/18/big/img_328 +2003/01/14/big/img_630 +2002/08/05/big/img_3541 +2002/08/06/big/img_2390 +2002/08/20/big/img_150 +2002/08/02/big/img_1231 +2002/08/16/big/img_710 +2002/08/19/big/img_591 +2002/07/22/big/img_725 +2002/07/24/big/img_820 +2003/01/13/big/img_568 +2002/08/22/big/img_853 +2002/08/09/big/img_648 +2002/08/23/big/img_528 +2003/01/14/big/img_888 +2002/08/30/big/img_18201 +2002/08/13/big/img_965 +2003/01/14/big/img_660 +2002/07/19/big/img_517 +2003/01/14/big/img_406 +2002/08/30/big/img_18433 +2002/08/07/big/img_1630 +2002/08/06/big/img_2717 +2002/08/21/big/img_470 +2002/07/23/big/img_633 +2002/08/20/big/img_915 +2002/08/16/big/img_893 +2002/07/29/big/img_644 +2002/08/15/big/img_529 +2002/08/16/big/img_668 +2002/08/07/big/img_1871 +2002/07/25/big/img_192 +2002/07/31/big/img_961 +2002/08/19/big/img_738 +2002/07/31/big/img_382 +2002/08/19/big/img_298 +2003/01/17/big/img_608 +2002/08/21/big/img_514 +2002/07/23/big/img_183 +2003/01/17/big/img_536 +2002/07/24/big/img_478 +2002/08/06/big/img_2997 +2002/09/02/big/img_15380 +2002/08/07/big/img_1153 +2002/07/31/big/img_967 +2002/07/31/big/img_711 +2002/08/26/big/img_664 +2003/01/01/big/img_326 +2002/08/24/big/img_775 +2002/08/08/big/img_961 +2002/08/16/big/img_77 +2002/08/12/big/img_296 +2002/07/22/big/img_905 +2003/01/13/big/img_284 +2002/08/13/big/img_887 +2002/08/24/big/img_849 +2002/07/30/big/img_345 +2002/08/18/big/img_419 +2002/08/01/big/img_1347 +2002/08/05/big/img_3670 +2002/07/21/big/img_479 +2002/08/08/big/img_913 +2002/09/02/big/img_15828 +2002/08/30/big/img_18194 +2002/08/08/big/img_471 +2002/08/22/big/img_734 +2002/08/09/big/img_586 +2002/08/09/big/img_454 +2002/07/29/big/img_47 +2002/07/19/big/img_381 +2002/07/29/big/img_733 +2002/08/20/big/img_327 +2002/07/21/big/img_96 +2002/08/06/big/img_2680 +2002/07/25/big/img_919 +2002/07/21/big/img_158 +2002/07/22/big/img_801 +2002/07/22/big/img_567 +2002/07/24/big/img_804 +2002/07/24/big/img_690 +2003/01/15/big/img_576 +2002/08/14/big/img_335 +2003/01/13/big/img_390 +2002/08/11/big/img_258 +2002/07/23/big/img_917 +2002/08/15/big/img_525 +2003/01/15/big/img_505 +2002/07/30/big/img_886 +2003/01/16/big/img_640 +2003/01/14/big/img_642 +2003/01/17/big/img_844 +2002/08/04/big/img_571 +2002/08/29/big/img_18702 +2003/01/15/big/img_240 +2002/07/29/big/img_553 +2002/08/10/big/img_354 +2002/08/18/big/img_17 +2003/01/15/big/img_782 +2002/07/27/big/img_382 +2002/08/14/big/img_970 +2003/01/16/big/img_70 +2003/01/16/big/img_625 +2002/08/18/big/img_341 +2002/08/26/big/img_188 +2002/08/09/big/img_405 +2002/08/02/big/img_37 +2002/08/13/big/img_748 +2002/07/22/big/img_399 +2002/07/25/big/img_844 +2002/08/12/big/img_340 +2003/01/13/big/img_815 +2002/08/26/big/img_5 +2002/08/10/big/img_158 +2002/08/18/big/img_95 +2002/07/29/big/img_1297 +2003/01/13/big/img_508 +2002/09/01/big/img_16680 +2003/01/16/big/img_338 +2002/08/13/big/img_517 +2002/07/22/big/img_626 +2002/08/06/big/img_3024 +2002/07/26/big/img_499 +2003/01/13/big/img_387 +2002/08/31/big/img_18025 +2002/08/13/big/img_520 +2003/01/16/big/img_576 +2002/07/26/big/img_121 +2002/08/25/big/img_703 +2002/08/26/big/img_615 +2002/08/17/big/img_434 +2002/08/02/big/img_677 +2002/08/18/big/img_276 +2002/08/05/big/img_3672 +2002/07/26/big/img_700 +2002/07/31/big/img_277 +2003/01/14/big/img_220 +2002/08/23/big/img_232 +2002/08/31/big/img_17422 +2002/07/22/big/img_508 +2002/08/13/big/img_681 +2003/01/15/big/img_638 +2002/08/30/big/img_18408 +2003/01/14/big/img_533 +2003/01/17/big/img_12 +2002/08/28/big/img_19388 +2002/08/08/big/img_133 +2002/07/26/big/img_885 +2002/08/19/big/img_387 +2002/08/27/big/img_19976 +2002/08/26/big/img_118 +2002/08/28/big/img_19146 +2002/08/05/big/img_3259 +2002/08/15/big/img_536 +2002/07/22/big/img_279 +2002/07/22/big/img_9 +2002/08/13/big/img_301 +2002/08/15/big/img_974 +2002/08/06/big/img_2355 +2002/08/01/big/img_1526 +2002/08/03/big/img_417 +2002/08/04/big/img_407 +2002/08/15/big/img_1029 +2002/07/29/big/img_700 +2002/08/01/big/img_1463 +2002/08/31/big/img_17365 +2002/07/28/big/img_223 +2002/07/19/big/img_827 +2002/07/27/big/img_531 +2002/07/19/big/img_845 +2002/08/20/big/img_382 +2002/07/31/big/img_268 +2002/08/27/big/img_19705 +2002/08/02/big/img_830 +2002/08/23/big/img_250 +2002/07/20/big/img_777 +2002/08/21/big/img_879 +2002/08/26/big/img_20146 +2002/08/23/big/img_789 +2002/08/06/big/img_2683 +2002/08/25/big/img_576 +2002/08/09/big/img_498 +2002/08/08/big/img_384 +2002/08/26/big/img_592 +2002/07/29/big/img_1470 +2002/08/21/big/img_452 +2002/08/30/big/img_18395 +2002/08/15/big/img_215 +2002/07/21/big/img_643 +2002/07/22/big/img_209 +2003/01/17/big/img_346 +2002/08/25/big/img_658 +2002/08/21/big/img_221 +2002/08/14/big/img_60 +2003/01/17/big/img_885 +2003/01/16/big/img_482 +2002/08/19/big/img_593 +2002/08/08/big/img_233 +2002/07/30/big/img_458 +2002/07/23/big/img_384 +2003/01/15/big/img_670 +2003/01/15/big/img_267 +2002/08/26/big/img_540 +2002/07/29/big/img_552 +2002/07/30/big/img_997 +2003/01/17/big/img_377 +2002/08/21/big/img_265 +2002/08/09/big/img_561 +2002/07/31/big/img_945 +2002/09/02/big/img_15252 +2002/08/11/big/img_276 +2002/07/22/big/img_491 +2002/07/26/big/img_517 +2002/08/14/big/img_726 +2002/08/08/big/img_46 +2002/08/28/big/img_19458 +2002/08/06/big/img_2935 +2002/07/29/big/img_1392 +2002/08/13/big/img_776 +2002/08/24/big/img_616 +2002/08/14/big/img_1065 +2002/07/29/big/img_889 +2002/08/18/big/img_188 +2002/08/07/big/img_1453 +2002/08/02/big/img_760 +2002/07/28/big/img_416 +2002/08/07/big/img_1393 +2002/08/26/big/img_292 +2002/08/26/big/img_301 +2003/01/13/big/img_195 +2002/07/26/big/img_532 +2002/08/20/big/img_550 +2002/08/05/big/img_3658 +2002/08/26/big/img_738 +2002/09/02/big/img_15750 +2003/01/17/big/img_451 +2002/07/23/big/img_339 +2002/08/16/big/img_637 +2002/08/14/big/img_748 +2002/08/06/big/img_2739 +2002/07/25/big/img_482 +2002/08/19/big/img_191 +2002/08/26/big/img_537 +2003/01/15/big/img_716 +2003/01/15/big/img_767 +2002/08/02/big/img_452 +2002/08/08/big/img_1011 +2002/08/10/big/img_144 +2003/01/14/big/img_122 +2002/07/24/big/img_586 +2002/07/24/big/img_762 +2002/08/20/big/img_369 +2002/07/30/big/img_146 +2002/08/23/big/img_396 +2003/01/15/big/img_200 +2002/08/15/big/img_1183 +2003/01/14/big/img_698 +2002/08/09/big/img_792 +2002/08/06/big/img_2347 +2002/07/31/big/img_911 +2002/08/26/big/img_722 +2002/08/23/big/img_621 +2002/08/05/big/img_3790 +2003/01/13/big/img_633 +2002/08/09/big/img_224 +2002/07/24/big/img_454 +2002/07/21/big/img_202 +2002/08/02/big/img_630 +2002/08/30/big/img_18315 +2002/07/19/big/img_491 +2002/09/01/big/img_16456 +2002/08/09/big/img_242 +2002/07/25/big/img_595 +2002/07/22/big/img_522 +2002/08/01/big/img_1593 +2002/07/29/big/img_336 +2002/08/15/big/img_448 +2002/08/28/big/img_19281 +2002/07/29/big/img_342 +2002/08/12/big/img_78 +2003/01/14/big/img_525 +2002/07/28/big/img_147 +2002/08/11/big/img_353 +2002/08/22/big/img_513 +2002/08/04/big/img_721 +2002/08/17/big/img_247 +2003/01/14/big/img_891 +2002/08/20/big/img_853 +2002/07/19/big/img_414 +2002/08/01/big/img_1530 +2003/01/14/big/img_924 +2002/08/22/big/img_468 +2002/08/18/big/img_354 +2002/08/30/big/img_18193 +2002/08/23/big/img_492 +2002/08/15/big/img_871 +2002/08/12/big/img_494 +2002/08/06/big/img_2470 +2002/07/23/big/img_923 +2002/08/26/big/img_155 +2002/08/08/big/img_669 +2002/07/23/big/img_404 +2002/08/28/big/img_19421 +2002/08/29/big/img_18993 +2002/08/25/big/img_416 +2003/01/17/big/img_434 +2002/07/29/big/img_1370 +2002/07/28/big/img_483 +2002/08/11/big/img_50 +2002/08/10/big/img_404 +2002/09/02/big/img_15057 +2003/01/14/big/img_911 +2002/09/01/big/img_16697 +2003/01/16/big/img_665 +2002/09/01/big/img_16708 +2002/08/22/big/img_612 +2002/08/28/big/img_19471 +2002/08/02/big/img_198 +2003/01/16/big/img_527 +2002/08/22/big/img_209 +2002/08/30/big/img_18205 +2003/01/14/big/img_114 +2003/01/14/big/img_1028 +2003/01/16/big/img_894 +2003/01/14/big/img_837 +2002/07/30/big/img_9 +2002/08/06/big/img_2821 +2002/08/04/big/img_85 +2003/01/13/big/img_884 +2002/07/22/big/img_570 +2002/08/07/big/img_1773 +2002/07/26/big/img_208 +2003/01/17/big/img_946 +2002/07/19/big/img_930 +2003/01/01/big/img_698 +2003/01/17/big/img_612 +2002/07/19/big/img_372 +2002/07/30/big/img_721 +2003/01/14/big/img_649 +2002/08/19/big/img_4 +2002/07/25/big/img_1024 +2003/01/15/big/img_601 +2002/08/30/big/img_18470 +2002/07/22/big/img_29 +2002/08/07/big/img_1686 +2002/07/20/big/img_294 +2002/08/14/big/img_800 +2002/08/19/big/img_353 +2002/08/19/big/img_350 +2002/08/05/big/img_3392 +2002/08/09/big/img_622 +2003/01/15/big/img_236 +2002/08/11/big/img_643 +2002/08/05/big/img_3458 +2002/08/12/big/img_413 +2002/08/22/big/img_415 +2002/08/13/big/img_635 +2002/08/07/big/img_1198 +2002/08/04/big/img_873 +2002/08/12/big/img_407 +2003/01/15/big/img_346 +2002/08/02/big/img_275 +2002/08/17/big/img_997 +2002/08/21/big/img_958 +2002/08/20/big/img_579 +2002/07/29/big/img_142 +2003/01/14/big/img_1115 +2002/08/16/big/img_365 +2002/07/29/big/img_1414 +2002/08/17/big/img_489 +2002/08/13/big/img_1010 +2002/07/31/big/img_276 +2002/07/25/big/img_1000 +2002/08/23/big/img_524 +2002/08/28/big/img_19147 +2003/01/13/big/img_433 +2002/08/20/big/img_205 +2003/01/01/big/img_458 +2002/07/29/big/img_1449 +2003/01/16/big/img_696 +2002/08/28/big/img_19296 +2002/08/29/big/img_18688 +2002/08/21/big/img_767 +2002/08/20/big/img_532 +2002/08/26/big/img_187 +2002/07/26/big/img_183 +2002/07/27/big/img_890 +2003/01/13/big/img_576 +2002/07/30/big/img_15 +2002/07/31/big/img_889 +2002/08/31/big/img_17759 +2003/01/14/big/img_1114 +2002/07/19/big/img_445 +2002/08/03/big/img_593 +2002/07/24/big/img_750 +2002/07/30/big/img_133 +2002/08/25/big/img_671 +2002/07/20/big/img_351 +2002/08/31/big/img_17276 +2002/08/05/big/img_3231 +2002/09/02/big/img_15882 +2002/08/14/big/img_115 +2002/08/02/big/img_1148 +2002/07/25/big/img_936 +2002/07/31/big/img_639 +2002/08/04/big/img_427 +2002/08/22/big/img_843 +2003/01/17/big/img_17 +2003/01/13/big/img_690 +2002/08/13/big/img_472 +2002/08/09/big/img_425 +2002/08/05/big/img_3450 +2003/01/17/big/img_439 +2002/08/13/big/img_539 +2002/07/28/big/img_35 +2002/08/16/big/img_241 +2002/08/06/big/img_2898 +2003/01/16/big/img_429 +2002/08/05/big/img_3817 +2002/08/27/big/img_19919 +2002/07/19/big/img_422 +2002/08/15/big/img_560 +2002/07/23/big/img_750 +2002/07/30/big/img_353 +2002/08/05/big/img_43 +2002/08/23/big/img_305 +2002/08/01/big/img_2137 +2002/08/30/big/img_18097 +2002/08/01/big/img_1389 +2002/08/02/big/img_308 +2003/01/14/big/img_652 +2002/08/01/big/img_1798 +2003/01/14/big/img_732 +2003/01/16/big/img_294 +2002/08/26/big/img_213 +2002/07/24/big/img_842 +2003/01/13/big/img_630 +2003/01/13/big/img_634 +2002/08/06/big/img_2285 +2002/08/01/big/img_2162 +2002/08/30/big/img_18134 +2002/08/02/big/img_1045 +2002/08/01/big/img_2143 +2002/07/25/big/img_135 +2002/07/20/big/img_645 +2002/08/05/big/img_3666 +2002/08/14/big/img_523 +2002/08/04/big/img_425 +2003/01/14/big/img_137 +2003/01/01/big/img_176 +2002/08/15/big/img_505 +2002/08/24/big/img_386 +2002/08/05/big/img_3187 +2002/08/15/big/img_419 +2003/01/13/big/img_520 +2002/08/04/big/img_444 +2002/08/26/big/img_483 +2002/08/05/big/img_3449 +2002/08/30/big/img_18409 +2002/08/28/big/img_19455 +2002/08/27/big/img_20090 +2002/07/23/big/img_625 +2002/08/24/big/img_205 +2002/08/08/big/img_938 +2003/01/13/big/img_527 +2002/08/07/big/img_1712 +2002/07/24/big/img_801 +2002/08/09/big/img_579 +2003/01/14/big/img_41 +2003/01/15/big/img_1130 +2002/07/21/big/img_672 +2002/08/07/big/img_1590 +2003/01/01/big/img_532 +2002/08/02/big/img_529 +2002/08/05/big/img_3591 +2002/08/23/big/img_5 +2003/01/14/big/img_882 +2002/08/28/big/img_19234 +2002/07/24/big/img_398 +2003/01/14/big/img_592 +2002/08/22/big/img_548 +2002/08/12/big/img_761 +2003/01/16/big/img_497 +2002/08/18/big/img_133 +2002/08/08/big/img_874 +2002/07/19/big/img_247 +2002/08/15/big/img_170 +2002/08/27/big/img_19679 +2002/08/20/big/img_246 +2002/08/24/big/img_358 +2002/07/29/big/img_599 +2002/08/01/big/img_1555 +2002/07/30/big/img_491 +2002/07/30/big/img_371 +2003/01/16/big/img_682 +2002/07/25/big/img_619 +2003/01/15/big/img_587 +2002/08/02/big/img_1212 +2002/08/01/big/img_2152 +2002/07/25/big/img_668 +2003/01/16/big/img_574 +2002/08/28/big/img_19464 +2002/08/11/big/img_536 +2002/07/24/big/img_201 +2002/08/05/big/img_3488 +2002/07/25/big/img_887 +2002/07/22/big/img_789 +2002/07/30/big/img_432 +2002/08/16/big/img_166 +2002/09/01/big/img_16333 +2002/07/26/big/img_1010 +2002/07/21/big/img_793 +2002/07/22/big/img_720 +2002/07/31/big/img_337 +2002/07/27/big/img_185 +2002/08/23/big/img_440 +2002/07/31/big/img_801 +2002/07/25/big/img_478 +2003/01/14/big/img_171 +2002/08/07/big/img_1054 +2002/09/02/big/img_15659 +2002/07/29/big/img_1348 +2002/08/09/big/img_337 +2002/08/26/big/img_684 +2002/07/31/big/img_537 +2002/08/15/big/img_808 +2003/01/13/big/img_740 +2002/08/07/big/img_1667 +2002/08/03/big/img_404 +2002/08/06/big/img_2520 +2002/07/19/big/img_230 +2002/07/19/big/img_356 +2003/01/16/big/img_627 +2002/08/04/big/img_474 +2002/07/29/big/img_833 +2002/07/25/big/img_176 +2002/08/01/big/img_1684 +2002/08/21/big/img_643 +2002/08/27/big/img_19673 +2002/08/02/big/img_838 +2002/08/06/big/img_2378 +2003/01/15/big/img_48 +2002/07/30/big/img_470 +2002/08/15/big/img_963 +2002/08/24/big/img_444 +2002/08/16/big/img_662 +2002/08/15/big/img_1209 +2002/07/24/big/img_25 +2002/08/06/big/img_2740 +2002/07/29/big/img_996 +2002/08/31/big/img_18074 +2002/08/04/big/img_343 +2003/01/17/big/img_509 +2003/01/13/big/img_726 +2002/08/07/big/img_1466 +2002/07/26/big/img_307 +2002/08/10/big/img_598 +2002/08/13/big/img_890 +2002/08/14/big/img_997 +2002/07/19/big/img_392 +2002/08/02/big/img_475 +2002/08/29/big/img_19038 +2002/07/29/big/img_538 +2002/07/29/big/img_502 +2002/08/02/big/img_364 +2002/08/31/big/img_17353 +2002/08/08/big/img_539 +2002/08/01/big/img_1449 +2002/07/22/big/img_363 +2002/08/02/big/img_90 +2002/09/01/big/img_16867 +2002/08/05/big/img_3371 +2002/07/30/big/img_342 +2002/08/07/big/img_1363 +2002/08/22/big/img_790 +2003/01/15/big/img_404 +2002/08/05/big/img_3447 +2002/09/01/big/img_16167 +2003/01/13/big/img_840 +2002/08/22/big/img_1001 +2002/08/09/big/img_431 +2002/07/27/big/img_618 +2002/07/31/big/img_741 +2002/07/30/big/img_964 +2002/07/25/big/img_86 +2002/07/29/big/img_275 +2002/08/21/big/img_921 +2002/07/26/big/img_892 +2002/08/21/big/img_663 +2003/01/13/big/img_567 +2003/01/14/big/img_719 +2002/07/28/big/img_251 +2003/01/15/big/img_1123 +2002/07/29/big/img_260 +2002/08/24/big/img_337 +2002/08/01/big/img_1914 +2002/08/13/big/img_373 +2003/01/15/big/img_589 +2002/08/13/big/img_906 +2002/07/26/big/img_270 +2002/08/26/big/img_313 +2002/08/25/big/img_694 +2003/01/01/big/img_327 +2002/07/23/big/img_261 +2002/08/26/big/img_642 +2002/07/29/big/img_918 +2002/07/23/big/img_455 +2002/07/24/big/img_612 +2002/07/23/big/img_534 +2002/07/19/big/img_534 +2002/07/19/big/img_726 +2002/08/01/big/img_2146 +2002/08/02/big/img_543 +2003/01/16/big/img_777 +2002/07/30/big/img_484 +2002/08/13/big/img_1161 +2002/07/21/big/img_390 +2002/08/06/big/img_2288 +2002/08/21/big/img_677 +2002/08/13/big/img_747 +2002/08/15/big/img_1248 +2002/07/31/big/img_416 +2002/09/02/big/img_15259 +2002/08/16/big/img_781 +2002/08/24/big/img_754 +2002/07/24/big/img_803 +2002/08/20/big/img_609 +2002/08/28/big/img_19571 +2002/09/01/big/img_16140 +2002/08/26/big/img_769 +2002/07/20/big/img_588 +2002/08/02/big/img_898 +2002/07/21/big/img_466 +2002/08/14/big/img_1046 +2002/07/25/big/img_212 +2002/08/26/big/img_353 +2002/08/19/big/img_810 +2002/08/31/big/img_17824 +2002/08/12/big/img_631 +2002/07/19/big/img_828 +2002/07/24/big/img_130 +2002/08/25/big/img_580 +2002/07/31/big/img_699 +2002/07/23/big/img_808 +2002/07/31/big/img_377 +2003/01/16/big/img_570 +2002/09/01/big/img_16254 +2002/07/21/big/img_471 +2002/08/01/big/img_1548 +2002/08/18/big/img_252 +2002/08/19/big/img_576 +2002/08/20/big/img_464 +2002/07/27/big/img_735 +2002/08/21/big/img_589 +2003/01/15/big/img_1192 +2002/08/09/big/img_302 +2002/07/31/big/img_594 +2002/08/23/big/img_19 +2002/08/29/big/img_18819 +2002/08/19/big/img_293 +2002/07/30/big/img_331 +2002/08/23/big/img_607 +2002/07/30/big/img_363 +2002/08/16/big/img_766 +2003/01/13/big/img_481 +2002/08/06/big/img_2515 +2002/09/02/big/img_15913 +2002/09/02/big/img_15827 +2002/09/02/big/img_15053 +2002/08/07/big/img_1576 +2002/07/23/big/img_268 +2002/08/21/big/img_152 +2003/01/15/big/img_578 +2002/07/21/big/img_589 +2002/07/20/big/img_548 +2002/08/27/big/img_19693 +2002/08/31/big/img_17252 +2002/07/31/big/img_138 +2002/07/23/big/img_372 +2002/08/16/big/img_695 +2002/07/27/big/img_287 +2002/08/15/big/img_315 +2002/08/10/big/img_361 +2002/07/29/big/img_899 +2002/08/13/big/img_771 +2002/08/21/big/img_92 +2003/01/15/big/img_425 +2003/01/16/big/img_450 +2002/09/01/big/img_16942 +2002/08/02/big/img_51 +2002/09/02/big/img_15379 +2002/08/24/big/img_147 +2002/08/30/big/img_18122 +2002/07/26/big/img_950 +2002/08/07/big/img_1400 +2002/08/17/big/img_468 +2002/08/15/big/img_470 +2002/07/30/big/img_318 +2002/07/22/big/img_644 +2002/08/27/big/img_19732 +2002/07/23/big/img_601 +2002/08/26/big/img_398 +2002/08/21/big/img_428 +2002/08/06/big/img_2119 +2002/08/29/big/img_19103 +2003/01/14/big/img_933 +2002/08/11/big/img_674 +2002/08/28/big/img_19420 +2002/08/03/big/img_418 +2002/08/17/big/img_312 +2002/07/25/big/img_1044 +2003/01/17/big/img_671 +2002/08/30/big/img_18297 +2002/07/25/big/img_755 +2002/07/23/big/img_471 +2002/08/21/big/img_39 +2002/07/26/big/img_699 +2003/01/14/big/img_33 +2002/07/31/big/img_411 +2002/08/16/big/img_645 +2003/01/17/big/img_116 +2002/09/02/big/img_15903 +2002/08/20/big/img_120 +2002/08/22/big/img_176 +2002/07/29/big/img_1316 +2002/08/27/big/img_19914 +2002/07/22/big/img_719 +2002/08/28/big/img_19239 +2003/01/13/big/img_385 +2002/08/08/big/img_525 +2002/07/19/big/img_782 +2002/08/13/big/img_843 +2002/07/30/big/img_107 +2002/08/11/big/img_752 +2002/07/29/big/img_383 +2002/08/26/big/img_249 +2002/08/29/big/img_18860 +2002/07/30/big/img_70 +2002/07/26/big/img_194 +2002/08/15/big/img_530 +2002/08/08/big/img_816 +2002/07/31/big/img_286 +2003/01/13/big/img_294 +2002/07/31/big/img_251 +2002/07/24/big/img_13 +2002/08/31/big/img_17938 +2002/07/22/big/img_642 +2003/01/14/big/img_728 +2002/08/18/big/img_47 +2002/08/22/big/img_306 +2002/08/20/big/img_348 +2002/08/15/big/img_764 +2002/08/08/big/img_163 +2002/07/23/big/img_531 +2002/07/23/big/img_467 +2003/01/16/big/img_743 +2003/01/13/big/img_535 +2002/08/02/big/img_523 +2002/08/22/big/img_120 +2002/08/11/big/img_496 +2002/08/29/big/img_19075 +2002/08/08/big/img_465 +2002/08/09/big/img_790 +2002/08/19/big/img_588 +2002/08/23/big/img_407 +2003/01/17/big/img_435 +2002/08/24/big/img_398 +2002/08/27/big/img_19899 +2003/01/15/big/img_335 +2002/08/13/big/img_493 +2002/09/02/big/img_15460 +2002/07/31/big/img_470 +2002/08/05/big/img_3550 +2002/07/28/big/img_123 +2002/08/01/big/img_1498 +2002/08/04/big/img_504 +2003/01/17/big/img_427 +2002/08/27/big/img_19708 +2002/07/27/big/img_861 +2002/07/25/big/img_685 +2002/07/31/big/img_207 +2003/01/14/big/img_745 +2002/08/31/big/img_17756 +2002/08/24/big/img_288 +2002/08/18/big/img_181 +2002/08/10/big/img_520 +2002/08/25/big/img_705 +2002/08/23/big/img_226 +2002/08/04/big/img_727 +2002/07/24/big/img_625 +2002/08/28/big/img_19157 +2002/08/23/big/img_586 +2002/07/31/big/img_232 +2003/01/13/big/img_240 +2003/01/14/big/img_321 +2003/01/15/big/img_533 +2002/07/23/big/img_480 +2002/07/24/big/img_371 +2002/08/21/big/img_702 +2002/08/31/big/img_17075 +2002/09/02/big/img_15278 +2002/07/29/big/img_246 +2003/01/15/big/img_829 +2003/01/15/big/img_1213 +2003/01/16/big/img_441 +2002/08/14/big/img_921 +2002/07/23/big/img_425 +2002/08/15/big/img_296 +2002/07/19/big/img_135 +2002/07/26/big/img_402 +2003/01/17/big/img_88 +2002/08/20/big/img_872 +2002/08/13/big/img_1110 +2003/01/16/big/img_1040 +2002/07/23/big/img_9 +2002/08/13/big/img_700 +2002/08/16/big/img_371 +2002/08/27/big/img_19966 +2003/01/17/big/img_391 +2002/08/18/big/img_426 +2002/08/01/big/img_1618 +2002/07/21/big/img_754 +2003/01/14/big/img_1101 +2003/01/16/big/img_1022 +2002/07/22/big/img_275 +2002/08/24/big/img_86 +2002/08/17/big/img_582 +2003/01/15/big/img_765 +2003/01/17/big/img_449 +2002/07/28/big/img_265 +2003/01/13/big/img_552 +2002/07/28/big/img_115 +2003/01/16/big/img_56 +2002/08/02/big/img_1232 +2003/01/17/big/img_925 +2002/07/22/big/img_445 +2002/07/25/big/img_957 +2002/07/20/big/img_589 +2002/08/31/big/img_17107 +2002/07/29/big/img_483 +2002/08/14/big/img_1063 +2002/08/07/big/img_1545 +2002/08/14/big/img_680 +2002/09/01/big/img_16694 +2002/08/14/big/img_257 +2002/08/11/big/img_726 +2002/07/26/big/img_681 +2002/07/25/big/img_481 +2003/01/14/big/img_737 +2002/08/28/big/img_19480 +2003/01/16/big/img_362 +2002/08/27/big/img_19865 +2003/01/01/big/img_547 +2002/09/02/big/img_15074 +2002/08/01/big/img_1453 +2002/08/22/big/img_594 +2002/08/28/big/img_19263 +2002/08/13/big/img_478 +2002/07/29/big/img_1358 +2003/01/14/big/img_1022 +2002/08/16/big/img_450 +2002/08/02/big/img_159 +2002/07/26/big/img_781 +2003/01/13/big/img_601 +2002/08/20/big/img_407 +2002/08/15/big/img_468 +2002/08/31/big/img_17902 +2002/08/16/big/img_81 +2002/07/25/big/img_987 +2002/07/25/big/img_500 +2002/08/02/big/img_31 +2002/08/18/big/img_538 +2002/08/08/big/img_54 +2002/07/23/big/img_686 +2002/07/24/big/img_836 +2003/01/17/big/img_734 +2002/08/16/big/img_1055 +2003/01/16/big/img_521 +2002/07/25/big/img_612 +2002/08/22/big/img_778 +2002/08/03/big/img_251 +2002/08/12/big/img_436 +2002/08/23/big/img_705 +2002/07/28/big/img_243 +2002/07/25/big/img_1029 +2002/08/20/big/img_287 +2002/08/29/big/img_18739 +2002/08/05/big/img_3272 +2002/07/27/big/img_214 +2003/01/14/big/img_5 +2002/08/01/big/img_1380 +2002/08/29/big/img_19097 +2002/07/30/big/img_486 +2002/08/29/big/img_18707 +2002/08/10/big/img_559 +2002/08/15/big/img_365 +2002/08/09/big/img_525 +2002/08/10/big/img_689 +2002/07/25/big/img_502 +2002/08/03/big/img_667 +2002/08/10/big/img_855 +2002/08/10/big/img_706 +2002/08/18/big/img_603 +2003/01/16/big/img_1055 +2002/08/31/big/img_17890 +2002/08/15/big/img_761 +2003/01/15/big/img_489 +2002/08/26/big/img_351 +2002/08/01/big/img_1772 +2002/08/31/big/img_17729 +2002/07/25/big/img_609 +2003/01/13/big/img_539 +2002/07/27/big/img_686 +2002/07/31/big/img_311 +2002/08/22/big/img_799 +2003/01/16/big/img_936 +2002/08/31/big/img_17813 +2002/08/04/big/img_862 +2002/08/09/big/img_332 +2002/07/20/big/img_148 +2002/08/12/big/img_426 +2002/07/24/big/img_69 +2002/07/27/big/img_685 +2002/08/02/big/img_480 +2002/08/26/big/img_154 +2002/07/24/big/img_598 +2002/08/01/big/img_1881 +2002/08/20/big/img_667 +2003/01/14/big/img_495 +2002/07/21/big/img_744 +2002/07/30/big/img_150 +2002/07/23/big/img_924 +2002/08/08/big/img_272 +2002/07/23/big/img_310 +2002/07/25/big/img_1011 +2002/09/02/big/img_15725 +2002/07/19/big/img_814 +2002/08/20/big/img_936 +2002/07/25/big/img_85 +2002/08/24/big/img_662 +2002/08/09/big/img_495 +2003/01/15/big/img_196 +2002/08/16/big/img_707 +2002/08/28/big/img_19370 +2002/08/06/big/img_2366 +2002/08/06/big/img_3012 +2002/08/01/big/img_1452 +2002/07/31/big/img_742 +2002/07/27/big/img_914 +2003/01/13/big/img_290 +2002/07/31/big/img_288 +2002/08/02/big/img_171 +2002/08/22/big/img_191 +2002/07/27/big/img_1066 +2002/08/12/big/img_383 +2003/01/17/big/img_1018 +2002/08/01/big/img_1785 +2002/08/11/big/img_390 +2002/08/27/big/img_20037 +2002/08/12/big/img_38 +2003/01/15/big/img_103 +2002/08/26/big/img_31 +2002/08/18/big/img_660 +2002/07/22/big/img_694 +2002/08/15/big/img_24 +2002/07/27/big/img_1077 +2002/08/01/big/img_1943 +2002/07/22/big/img_292 +2002/09/01/big/img_16857 +2002/07/22/big/img_892 +2003/01/14/big/img_46 +2002/08/09/big/img_469 +2002/08/09/big/img_414 +2003/01/16/big/img_40 +2002/08/28/big/img_19231 +2002/07/27/big/img_978 +2002/07/23/big/img_475 +2002/07/25/big/img_92 +2002/08/09/big/img_799 +2002/07/25/big/img_491 +2002/08/03/big/img_654 +2003/01/15/big/img_687 +2002/08/11/big/img_478 +2002/08/07/big/img_1664 +2002/08/20/big/img_362 +2002/08/01/big/img_1298 +2003/01/13/big/img_500 +2002/08/06/big/img_2896 +2002/08/30/big/img_18529 +2002/08/16/big/img_1020 +2002/07/29/big/img_892 +2002/08/29/big/img_18726 +2002/07/21/big/img_453 +2002/08/17/big/img_437 +2002/07/19/big/img_665 +2002/07/22/big/img_440 +2002/07/19/big/img_582 +2002/07/21/big/img_233 +2003/01/01/big/img_82 +2002/07/25/big/img_341 +2002/07/29/big/img_864 +2002/08/02/big/img_276 +2002/08/29/big/img_18654 +2002/07/27/big/img_1024 +2002/08/19/big/img_373 +2003/01/15/big/img_241 +2002/07/25/big/img_84 +2002/08/13/big/img_834 +2002/08/10/big/img_511 +2002/08/01/big/img_1627 +2002/08/08/big/img_607 +2002/08/06/big/img_2083 +2002/08/01/big/img_1486 +2002/08/08/big/img_700 +2002/08/01/big/img_1954 +2002/08/21/big/img_54 +2002/07/30/big/img_847 +2002/08/28/big/img_19169 +2002/07/21/big/img_549 +2002/08/03/big/img_693 +2002/07/31/big/img_1002 +2003/01/14/big/img_1035 +2003/01/16/big/img_622 +2002/07/30/big/img_1201 +2002/08/10/big/img_444 +2002/07/31/big/img_374 +2002/08/21/big/img_301 +2002/08/13/big/img_1095 +2003/01/13/big/img_288 +2002/07/25/big/img_232 +2003/01/13/big/img_967 +2002/08/26/big/img_360 +2002/08/05/big/img_67 +2002/08/29/big/img_18969 +2002/07/28/big/img_16 +2002/08/16/big/img_515 +2002/07/20/big/img_708 +2002/08/18/big/img_178 +2003/01/15/big/img_509 +2002/07/25/big/img_430 +2002/08/21/big/img_738 +2002/08/16/big/img_886 +2002/09/02/big/img_15605 +2002/09/01/big/img_16242 +2002/08/24/big/img_711 +2002/07/25/big/img_90 +2002/08/09/big/img_491 +2002/07/30/big/img_534 +2003/01/13/big/img_474 +2002/08/25/big/img_510 +2002/08/15/big/img_555 +2002/08/02/big/img_775 +2002/07/23/big/img_975 +2002/08/19/big/img_229 +2003/01/17/big/img_860 +2003/01/02/big/img_10 +2002/07/23/big/img_542 +2002/08/06/big/img_2535 +2002/07/22/big/img_37 +2002/08/06/big/img_2342 +2002/08/25/big/img_515 +2002/08/25/big/img_336 +2002/08/18/big/img_837 +2002/08/21/big/img_616 +2003/01/17/big/img_24 +2002/07/26/big/img_936 +2002/08/14/big/img_896 +2002/07/29/big/img_465 +2002/07/31/big/img_543 +2002/08/01/big/img_1411 +2002/08/02/big/img_423 +2002/08/21/big/img_44 +2002/07/31/big/img_11 +2003/01/15/big/img_628 +2003/01/15/big/img_605 +2002/07/30/big/img_571 +2002/07/23/big/img_428 +2002/08/15/big/img_942 +2002/07/26/big/img_531 +2003/01/16/big/img_59 +2002/08/02/big/img_410 +2002/07/31/big/img_230 +2002/08/19/big/img_806 +2003/01/14/big/img_462 +2002/08/16/big/img_370 +2002/08/13/big/img_380 +2002/08/16/big/img_932 +2002/07/19/big/img_393 +2002/08/20/big/img_764 +2002/08/15/big/img_616 +2002/07/26/big/img_267 +2002/07/27/big/img_1069 +2002/08/14/big/img_1041 +2003/01/13/big/img_594 +2002/09/01/big/img_16845 +2002/08/09/big/img_229 +2003/01/16/big/img_639 +2002/08/19/big/img_398 +2002/08/18/big/img_978 +2002/08/24/big/img_296 +2002/07/29/big/img_415 +2002/07/30/big/img_923 +2002/08/18/big/img_575 +2002/08/22/big/img_182 +2002/07/25/big/img_806 +2002/07/22/big/img_49 +2002/07/29/big/img_989 +2003/01/17/big/img_789 +2003/01/15/big/img_503 +2002/09/01/big/img_16062 +2003/01/17/big/img_794 +2002/08/15/big/img_564 +2003/01/15/big/img_222 +2002/08/01/big/img_1656 +2003/01/13/big/img_432 +2002/07/19/big/img_426 +2002/08/17/big/img_244 +2002/08/13/big/img_805 +2002/09/02/big/img_15067 +2002/08/11/big/img_58 +2002/08/22/big/img_636 +2002/07/22/big/img_416 +2002/08/13/big/img_836 +2002/08/26/big/img_363 +2002/07/30/big/img_917 +2003/01/14/big/img_206 +2002/08/12/big/img_311 +2002/08/31/big/img_17623 +2002/07/29/big/img_661 +2003/01/13/big/img_417 +2002/08/02/big/img_463 +2002/08/02/big/img_669 +2002/08/26/big/img_670 +2002/08/02/big/img_375 +2002/07/19/big/img_209 +2002/08/08/big/img_115 +2002/08/21/big/img_399 +2002/08/20/big/img_911 +2002/08/07/big/img_1212 +2002/08/20/big/img_578 +2002/08/22/big/img_554 +2002/08/21/big/img_484 +2002/07/25/big/img_450 +2002/08/03/big/img_542 +2002/08/15/big/img_561 +2002/07/23/big/img_360 +2002/08/30/big/img_18137 +2002/07/25/big/img_250 +2002/08/03/big/img_647 +2002/08/20/big/img_375 +2002/08/14/big/img_387 +2002/09/01/big/img_16990 +2002/08/28/big/img_19341 +2003/01/15/big/img_239 +2002/08/20/big/img_528 +2002/08/12/big/img_130 +2002/09/02/big/img_15108 +2003/01/15/big/img_372 +2002/08/16/big/img_678 +2002/08/04/big/img_623 +2002/07/23/big/img_477 +2002/08/28/big/img_19590 +2003/01/17/big/img_978 +2002/09/01/big/img_16692 +2002/07/20/big/img_109 +2002/08/06/big/img_2660 +2003/01/14/big/img_464 +2002/08/09/big/img_618 +2002/07/22/big/img_722 +2002/08/25/big/img_419 +2002/08/03/big/img_314 +2002/08/25/big/img_40 +2002/07/27/big/img_430 +2002/08/10/big/img_569 +2002/08/23/big/img_398 +2002/07/23/big/img_893 +2002/08/16/big/img_261 +2002/08/06/big/img_2668 +2002/07/22/big/img_835 +2002/09/02/big/img_15093 +2003/01/16/big/img_65 +2002/08/21/big/img_448 +2003/01/14/big/img_351 +2003/01/17/big/img_133 +2002/07/28/big/img_493 +2003/01/15/big/img_640 +2002/09/01/big/img_16880 +2002/08/15/big/img_350 +2002/08/20/big/img_624 +2002/08/25/big/img_604 +2002/08/06/big/img_2200 +2002/08/23/big/img_290 +2002/08/13/big/img_1152 +2003/01/14/big/img_251 +2002/08/02/big/img_538 +2002/08/22/big/img_613 +2003/01/13/big/img_351 +2002/08/18/big/img_368 +2002/07/23/big/img_392 +2002/07/25/big/img_198 +2002/07/25/big/img_418 +2002/08/26/big/img_614 +2002/07/23/big/img_405 +2003/01/14/big/img_445 +2002/07/25/big/img_326 +2002/08/10/big/img_734 +2003/01/14/big/img_530 +2002/08/08/big/img_561 +2002/08/29/big/img_18990 +2002/08/10/big/img_576 +2002/07/29/big/img_1494 +2002/07/19/big/img_198 +2002/08/10/big/img_562 +2002/07/22/big/img_901 +2003/01/14/big/img_37 +2002/09/02/big/img_15629 +2003/01/14/big/img_58 +2002/08/01/big/img_1364 +2002/07/27/big/img_636 +2003/01/13/big/img_241 +2002/09/01/big/img_16988 +2003/01/13/big/img_560 +2002/08/09/big/img_533 +2002/07/31/big/img_249 +2003/01/17/big/img_1007 +2002/07/21/big/img_64 +2003/01/13/big/img_537 +2003/01/15/big/img_606 +2002/08/18/big/img_651 +2002/08/24/big/img_405 +2002/07/26/big/img_837 +2002/08/09/big/img_562 +2002/08/01/big/img_1983 +2002/08/03/big/img_514 +2002/07/29/big/img_314 +2002/08/12/big/img_493 +2003/01/14/big/img_121 +2003/01/14/big/img_479 +2002/08/04/big/img_410 +2002/07/22/big/img_607 +2003/01/17/big/img_417 +2002/07/20/big/img_547 +2002/08/13/big/img_396 +2002/08/31/big/img_17538 +2002/08/13/big/img_187 +2002/08/12/big/img_328 +2003/01/14/big/img_569 +2002/07/27/big/img_1081 +2002/08/14/big/img_504 +2002/08/23/big/img_785 +2002/07/26/big/img_339 +2002/08/07/big/img_1156 +2002/08/07/big/img_1456 +2002/08/23/big/img_378 +2002/08/27/big/img_19719 +2002/07/31/big/img_39 +2002/07/31/big/img_883 +2003/01/14/big/img_676 +2002/07/29/big/img_214 +2002/07/26/big/img_669 +2002/07/25/big/img_202 +2002/08/08/big/img_259 +2003/01/17/big/img_943 +2003/01/15/big/img_512 +2002/08/05/big/img_3295 +2002/08/27/big/img_19685 +2002/08/08/big/img_277 +2002/08/30/big/img_18154 +2002/07/22/big/img_663 +2002/08/29/big/img_18914 +2002/07/31/big/img_908 +2002/08/27/big/img_19926 +2003/01/13/big/img_791 +2003/01/15/big/img_827 +2002/08/18/big/img_878 +2002/08/14/big/img_670 +2002/07/20/big/img_182 +2002/08/15/big/img_291 +2002/08/06/big/img_2600 +2002/07/23/big/img_587 +2002/08/14/big/img_577 +2003/01/15/big/img_585 +2002/07/30/big/img_310 +2002/08/03/big/img_658 +2002/08/10/big/img_157 +2002/08/19/big/img_811 +2002/07/29/big/img_1318 +2002/08/04/big/img_104 +2002/07/30/big/img_332 +2002/07/24/big/img_789 +2002/07/29/big/img_516 +2002/07/23/big/img_843 +2002/08/01/big/img_1528 +2002/08/13/big/img_798 +2002/08/07/big/img_1729 +2002/08/28/big/img_19448 +2003/01/16/big/img_95 +2002/08/12/big/img_473 +2002/07/27/big/img_269 +2003/01/16/big/img_621 +2002/07/29/big/img_772 +2002/07/24/big/img_171 +2002/07/19/big/img_429 +2002/08/07/big/img_1933 +2002/08/27/big/img_19629 +2002/08/05/big/img_3688 +2002/08/07/big/img_1691 +2002/07/23/big/img_600 +2002/07/29/big/img_666 +2002/08/25/big/img_566 +2002/08/06/big/img_2659 +2002/08/29/big/img_18929 +2002/08/16/big/img_407 +2002/08/18/big/img_774 +2002/08/19/big/img_249 +2002/08/06/big/img_2427 +2002/08/29/big/img_18899 +2002/08/01/big/img_1818 +2002/07/31/big/img_108 +2002/07/29/big/img_500 +2002/08/11/big/img_115 +2002/07/19/big/img_521 +2002/08/02/big/img_1163 +2002/07/22/big/img_62 +2002/08/13/big/img_466 +2002/08/21/big/img_956 +2002/08/23/big/img_602 +2002/08/20/big/img_858 +2002/07/25/big/img_690 +2002/07/19/big/img_130 +2002/08/04/big/img_874 +2002/07/26/big/img_489 +2002/07/22/big/img_548 +2002/08/10/big/img_191 +2002/07/25/big/img_1051 +2002/08/18/big/img_473 +2002/08/12/big/img_755 +2002/08/18/big/img_413 +2002/08/08/big/img_1044 +2002/08/17/big/img_680 +2002/08/26/big/img_235 +2002/08/20/big/img_330 +2002/08/22/big/img_344 +2002/08/09/big/img_593 +2002/07/31/big/img_1006 +2002/08/14/big/img_337 +2002/08/16/big/img_728 +2002/07/24/big/img_834 +2002/08/04/big/img_552 +2002/09/02/big/img_15213 +2002/07/25/big/img_725 +2002/08/30/big/img_18290 +2003/01/01/big/img_475 +2002/07/27/big/img_1083 +2002/08/29/big/img_18955 +2002/08/31/big/img_17232 +2002/08/08/big/img_480 +2002/08/01/big/img_1311 +2002/07/30/big/img_745 +2002/08/03/big/img_649 +2002/08/12/big/img_193 +2002/07/29/big/img_228 +2002/07/25/big/img_836 +2002/08/20/big/img_400 +2002/07/30/big/img_507 +2002/09/02/big/img_15072 +2002/07/26/big/img_658 +2002/07/28/big/img_503 +2002/08/05/big/img_3814 +2002/08/24/big/img_745 +2003/01/13/big/img_817 +2002/08/08/big/img_579 +2002/07/22/big/img_251 +2003/01/13/big/img_689 +2002/07/25/big/img_407 +2002/08/13/big/img_1050 +2002/08/14/big/img_733 +2002/07/24/big/img_82 +2003/01/17/big/img_288 +2003/01/15/big/img_475 +2002/08/14/big/img_620 +2002/08/21/big/img_167 +2002/07/19/big/img_300 +2002/07/26/big/img_219 +2002/08/01/big/img_1468 +2002/07/23/big/img_260 +2002/08/09/big/img_555 +2002/07/19/big/img_160 +2002/08/02/big/img_1060 +2003/01/14/big/img_149 +2002/08/15/big/img_346 +2002/08/24/big/img_597 +2002/08/22/big/img_502 +2002/08/30/big/img_18228 +2002/07/21/big/img_766 +2003/01/15/big/img_841 +2002/07/24/big/img_516 +2002/08/02/big/img_265 +2002/08/15/big/img_1243 +2003/01/15/big/img_223 +2002/08/04/big/img_236 +2002/07/22/big/img_309 +2002/07/20/big/img_656 +2002/07/31/big/img_412 +2002/09/01/big/img_16462 +2003/01/16/big/img_431 +2002/07/22/big/img_793 +2002/08/15/big/img_877 +2002/07/26/big/img_282 +2002/07/25/big/img_529 +2002/08/24/big/img_613 +2003/01/17/big/img_700 +2002/08/06/big/img_2526 +2002/08/24/big/img_394 +2002/08/21/big/img_521 +2002/08/25/big/img_560 +2002/07/29/big/img_966 +2002/07/25/big/img_448 +2003/01/13/big/img_782 +2002/08/21/big/img_296 +2002/09/01/big/img_16755 +2002/08/05/big/img_3552 +2002/09/02/big/img_15823 +2003/01/14/big/img_193 +2002/07/21/big/img_159 +2002/08/02/big/img_564 +2002/08/16/big/img_300 +2002/07/19/big/img_269 +2002/08/13/big/img_676 +2002/07/28/big/img_57 +2002/08/05/big/img_3318 +2002/07/31/big/img_218 +2002/08/21/big/img_898 +2002/07/29/big/img_109 +2002/07/19/big/img_854 +2002/08/23/big/img_311 +2002/08/14/big/img_318 +2002/07/25/big/img_523 +2002/07/21/big/img_678 +2003/01/17/big/img_690 +2002/08/28/big/img_19503 +2002/08/18/big/img_251 +2002/08/22/big/img_672 +2002/08/20/big/img_663 +2002/08/02/big/img_148 +2002/09/02/big/img_15580 +2002/07/25/big/img_778 +2002/08/14/big/img_565 +2002/08/12/big/img_374 +2002/08/13/big/img_1018 +2002/08/20/big/img_474 +2002/08/25/big/img_33 +2002/08/02/big/img_1190 +2002/08/08/big/img_864 +2002/08/14/big/img_1071 +2002/08/30/big/img_18103 +2002/08/18/big/img_533 +2003/01/16/big/img_650 +2002/07/25/big/img_108 +2002/07/26/big/img_81 +2002/07/27/big/img_543 +2002/07/29/big/img_521 +2003/01/13/big/img_434 +2002/08/26/big/img_674 +2002/08/06/big/img_2932 +2002/08/07/big/img_1262 +2003/01/15/big/img_201 +2003/01/16/big/img_673 +2002/09/02/big/img_15988 +2002/07/29/big/img_1306 +2003/01/14/big/img_1072 +2002/08/30/big/img_18232 +2002/08/05/big/img_3711 +2002/07/23/big/img_775 +2002/08/01/big/img_16 +2003/01/16/big/img_630 +2002/08/22/big/img_695 +2002/08/14/big/img_51 +2002/08/14/big/img_782 +2002/08/24/big/img_742 +2003/01/14/big/img_512 +2003/01/15/big/img_1183 +2003/01/15/big/img_714 +2002/08/01/big/img_2078 +2002/07/31/big/img_682 +2002/09/02/big/img_15687 +2002/07/26/big/img_518 +2002/08/27/big/img_19676 +2002/09/02/big/img_15969 +2002/08/02/big/img_931 +2002/08/25/big/img_508 +2002/08/29/big/img_18616 +2002/07/22/big/img_839 +2002/07/28/big/img_313 +2003/01/14/big/img_155 +2002/08/02/big/img_1105 +2002/08/09/big/img_53 +2002/08/16/big/img_469 +2002/08/15/big/img_502 +2002/08/20/big/img_575 +2002/07/25/big/img_138 +2003/01/16/big/img_579 +2002/07/19/big/img_352 +2003/01/14/big/img_762 +2003/01/01/big/img_588 +2002/08/02/big/img_981 +2002/08/21/big/img_447 +2002/09/01/big/img_16151 +2003/01/14/big/img_769 +2002/08/23/big/img_461 +2002/08/17/big/img_240 +2002/09/02/big/img_15220 +2002/07/19/big/img_408 +2002/09/02/big/img_15496 +2002/07/29/big/img_758 +2002/08/28/big/img_19392 +2002/08/06/big/img_2723 +2002/08/31/big/img_17752 +2002/08/23/big/img_469 +2002/08/13/big/img_515 +2002/09/02/big/img_15551 +2002/08/03/big/img_462 +2002/07/24/big/img_613 +2002/07/22/big/img_61 +2002/08/08/big/img_171 +2002/08/21/big/img_177 +2003/01/14/big/img_105 +2002/08/02/big/img_1017 +2002/08/22/big/img_106 +2002/07/27/big/img_542 +2002/07/21/big/img_665 +2002/07/23/big/img_595 +2002/08/04/big/img_657 +2002/08/29/big/img_19002 +2003/01/15/big/img_550 +2002/08/14/big/img_662 +2002/07/20/big/img_425 +2002/08/30/big/img_18528 +2002/07/26/big/img_611 +2002/07/22/big/img_849 +2002/08/07/big/img_1655 +2002/08/21/big/img_638 +2003/01/17/big/img_732 +2003/01/01/big/img_496 +2002/08/18/big/img_713 +2002/08/08/big/img_109 +2002/07/27/big/img_1008 +2002/07/20/big/img_559 +2002/08/16/big/img_699 +2002/08/31/big/img_17702 +2002/07/31/big/img_1013 +2002/08/01/big/img_2027 +2002/08/02/big/img_1001 +2002/08/03/big/img_210 +2002/08/01/big/img_2087 +2003/01/14/big/img_199 +2002/07/29/big/img_48 +2002/07/19/big/img_727 +2002/08/09/big/img_249 +2002/08/04/big/img_632 +2002/08/22/big/img_620 +2003/01/01/big/img_457 +2002/08/05/big/img_3223 +2002/07/27/big/img_240 +2002/07/25/big/img_797 +2002/08/13/big/img_430 +2002/07/25/big/img_615 +2002/08/12/big/img_28 +2002/07/30/big/img_220 +2002/07/24/big/img_89 +2002/08/21/big/img_357 +2002/08/09/big/img_590 +2003/01/13/big/img_525 +2002/08/17/big/img_818 +2003/01/02/big/img_7 +2002/07/26/big/img_636 +2003/01/13/big/img_1122 +2002/07/23/big/img_810 +2002/08/20/big/img_888 +2002/07/27/big/img_3 +2002/08/15/big/img_451 +2002/09/02/big/img_15787 +2002/07/31/big/img_281 +2002/08/05/big/img_3274 +2002/08/07/big/img_1254 +2002/07/31/big/img_27 +2002/08/01/big/img_1366 +2002/07/30/big/img_182 +2002/08/27/big/img_19690 +2002/07/29/big/img_68 +2002/08/23/big/img_754 +2002/07/30/big/img_540 +2002/08/27/big/img_20063 +2002/08/14/big/img_471 +2002/08/02/big/img_615 +2002/07/30/big/img_186 +2002/08/25/big/img_150 +2002/07/27/big/img_626 +2002/07/20/big/img_225 +2003/01/15/big/img_1252 +2002/07/19/big/img_367 +2003/01/15/big/img_582 +2002/08/09/big/img_572 +2002/08/08/big/img_428 +2003/01/15/big/img_639 +2002/08/28/big/img_19245 +2002/07/24/big/img_321 +2002/08/02/big/img_662 +2002/08/08/big/img_1033 +2003/01/17/big/img_867 +2002/07/22/big/img_652 +2003/01/14/big/img_224 +2002/08/18/big/img_49 +2002/07/26/big/img_46 +2002/08/31/big/img_18021 +2002/07/25/big/img_151 +2002/08/23/big/img_540 +2002/08/25/big/img_693 +2002/07/23/big/img_340 +2002/07/28/big/img_117 +2002/09/02/big/img_15768 +2002/08/26/big/img_562 +2002/07/24/big/img_480 +2003/01/15/big/img_341 +2002/08/10/big/img_783 +2002/08/20/big/img_132 +2003/01/14/big/img_370 +2002/07/20/big/img_720 +2002/08/03/big/img_144 +2002/08/20/big/img_538 +2002/08/01/big/img_1745 +2002/08/11/big/img_683 +2002/08/03/big/img_328 +2002/08/10/big/img_793 +2002/08/14/big/img_689 +2002/08/02/big/img_162 +2003/01/17/big/img_411 +2002/07/31/big/img_361 +2002/08/15/big/img_289 +2002/08/08/big/img_254 +2002/08/15/big/img_996 +2002/08/20/big/img_785 +2002/07/24/big/img_511 +2002/08/06/big/img_2614 +2002/08/29/big/img_18733 +2002/08/17/big/img_78 +2002/07/30/big/img_378 +2002/08/31/big/img_17947 +2002/08/26/big/img_88 +2002/07/30/big/img_558 +2002/08/02/big/img_67 +2003/01/14/big/img_325 +2002/07/29/big/img_1357 +2002/07/19/big/img_391 +2002/07/30/big/img_307 +2003/01/13/big/img_219 +2002/07/24/big/img_807 +2002/08/23/big/img_543 +2002/08/29/big/img_18620 +2002/07/22/big/img_769 +2002/08/26/big/img_503 +2002/07/30/big/img_78 +2002/08/14/big/img_1036 +2002/08/09/big/img_58 +2002/07/24/big/img_616 +2002/08/02/big/img_464 +2002/07/26/big/img_576 +2002/07/22/big/img_273 +2003/01/16/big/img_470 +2002/07/29/big/img_329 +2002/07/30/big/img_1086 +2002/07/31/big/img_353 +2002/09/02/big/img_15275 +2003/01/17/big/img_555 +2002/08/26/big/img_212 +2002/08/01/big/img_1692 +2003/01/15/big/img_600 +2002/07/29/big/img_825 +2002/08/08/big/img_68 +2002/08/10/big/img_719 +2002/07/31/big/img_636 +2002/07/29/big/img_325 +2002/07/21/big/img_515 +2002/07/22/big/img_705 +2003/01/13/big/img_818 +2002/08/09/big/img_486 +2002/08/22/big/img_141 +2002/07/22/big/img_303 +2002/08/09/big/img_393 +2002/07/29/big/img_963 +2002/08/02/big/img_1215 +2002/08/19/big/img_674 +2002/08/12/big/img_690 +2002/08/21/big/img_637 +2002/08/21/big/img_841 +2002/08/24/big/img_71 +2002/07/25/big/img_596 +2002/07/24/big/img_864 +2002/08/18/big/img_293 +2003/01/14/big/img_657 +2002/08/15/big/img_411 +2002/08/16/big/img_348 +2002/08/05/big/img_3157 +2002/07/20/big/img_663 +2003/01/13/big/img_654 +2003/01/16/big/img_433 +2002/08/30/big/img_18200 +2002/08/12/big/img_226 +2003/01/16/big/img_491 +2002/08/08/big/img_666 +2002/07/19/big/img_576 +2003/01/15/big/img_776 +2003/01/16/big/img_899 +2002/07/19/big/img_397 +2002/08/14/big/img_44 +2003/01/15/big/img_762 +2002/08/02/big/img_982 +2002/09/02/big/img_15234 +2002/08/17/big/img_556 +2002/08/21/big/img_410 +2002/08/21/big/img_386 +2002/07/19/big/img_690 +2002/08/05/big/img_3052 +2002/08/14/big/img_219 +2002/08/16/big/img_273 +2003/01/15/big/img_752 +2002/08/08/big/img_184 +2002/07/31/big/img_743 +2002/08/23/big/img_338 +2003/01/14/big/img_1055 +2002/08/05/big/img_3405 +2003/01/15/big/img_17 +2002/08/03/big/img_141 +2002/08/14/big/img_549 +2002/07/27/big/img_1034 +2002/07/31/big/img_932 +2002/08/30/big/img_18487 +2002/09/02/big/img_15814 +2002/08/01/big/img_2086 +2002/09/01/big/img_16535 +2002/07/22/big/img_500 +2003/01/13/big/img_400 +2002/08/25/big/img_607 +2002/08/30/big/img_18384 +2003/01/14/big/img_951 +2002/08/13/big/img_1150 +2002/08/08/big/img_1022 +2002/08/10/big/img_428 +2002/08/28/big/img_19242 +2002/08/05/big/img_3098 +2002/07/23/big/img_400 +2002/08/26/big/img_365 +2002/07/20/big/img_318 +2002/08/13/big/img_740 +2003/01/16/big/img_37 +2002/08/26/big/img_274 +2002/08/02/big/img_205 +2002/08/21/big/img_695 +2002/08/06/big/img_2289 +2002/08/20/big/img_794 +2002/08/18/big/img_438 +2002/08/07/big/img_1380 +2002/08/02/big/img_737 +2002/08/07/big/img_1651 +2002/08/15/big/img_1238 +2002/08/01/big/img_1681 +2002/08/06/big/img_3017 +2002/07/23/big/img_706 +2002/07/31/big/img_392 +2002/08/09/big/img_539 +2002/07/29/big/img_835 +2002/08/26/big/img_723 +2002/08/28/big/img_19235 +2003/01/16/big/img_353 +2002/08/10/big/img_150 +2002/08/29/big/img_19025 +2002/08/21/big/img_310 +2002/08/10/big/img_823 +2002/07/26/big/img_981 +2002/08/11/big/img_288 +2002/08/19/big/img_534 +2002/08/21/big/img_300 +2002/07/31/big/img_49 +2002/07/30/big/img_469 +2002/08/28/big/img_19197 +2002/08/25/big/img_205 +2002/08/10/big/img_390 +2002/08/23/big/img_291 +2002/08/26/big/img_230 +2002/08/18/big/img_76 +2002/07/23/big/img_409 +2002/08/14/big/img_1053 +2003/01/14/big/img_291 +2002/08/10/big/img_503 +2002/08/27/big/img_19928 +2002/08/03/big/img_563 +2002/08/17/big/img_250 +2002/08/06/big/img_2381 +2002/08/17/big/img_948 +2002/08/06/big/img_2710 +2002/07/22/big/img_696 +2002/07/31/big/img_670 +2002/08/12/big/img_594 +2002/07/29/big/img_624 +2003/01/17/big/img_934 +2002/08/03/big/img_584 +2002/08/22/big/img_1003 +2002/08/05/big/img_3396 +2003/01/13/big/img_570 +2002/08/02/big/img_219 +2002/09/02/big/img_15774 +2002/08/16/big/img_818 +2002/08/23/big/img_402 +2003/01/14/big/img_552 +2002/07/29/big/img_71 +2002/08/05/big/img_3592 +2002/08/16/big/img_80 +2002/07/27/big/img_672 +2003/01/13/big/img_470 +2003/01/16/big/img_702 +2002/09/01/big/img_16130 +2002/08/08/big/img_240 +2002/09/01/big/img_16338 +2002/07/26/big/img_312 +2003/01/14/big/img_538 +2002/07/20/big/img_695 +2002/08/30/big/img_18098 +2002/08/25/big/img_259 +2002/08/16/big/img_1042 +2002/08/09/big/img_837 +2002/08/31/big/img_17760 +2002/07/31/big/img_14 +2002/08/09/big/img_361 +2003/01/16/big/img_107 +2002/08/14/big/img_124 +2002/07/19/big/img_463 +2003/01/15/big/img_275 +2002/07/25/big/img_1151 +2002/07/29/big/img_1501 +2002/08/27/big/img_19889 +2002/08/29/big/img_18603 +2003/01/17/big/img_601 +2002/08/25/big/img_355 +2002/08/08/big/img_297 +2002/08/20/big/img_290 +2002/07/31/big/img_195 +2003/01/01/big/img_336 +2002/08/18/big/img_369 +2002/07/25/big/img_621 +2002/08/11/big/img_508 +2003/01/14/big/img_458 +2003/01/15/big/img_795 +2002/08/12/big/img_498 +2002/08/01/big/img_1734 +2002/08/02/big/img_246 +2002/08/16/big/img_565 +2002/08/11/big/img_475 +2002/08/22/big/img_408 +2002/07/28/big/img_78 +2002/07/21/big/img_81 +2003/01/14/big/img_697 +2002/08/14/big/img_661 +2002/08/15/big/img_507 +2002/08/19/big/img_55 +2002/07/22/big/img_152 +2003/01/14/big/img_470 +2002/08/03/big/img_379 +2002/08/22/big/img_506 +2003/01/16/big/img_966 +2002/08/18/big/img_698 +2002/08/24/big/img_528 +2002/08/23/big/img_10 +2002/08/01/big/img_1655 +2002/08/22/big/img_953 +2002/07/19/big/img_630 +2002/07/22/big/img_889 +2002/08/16/big/img_351 +2003/01/16/big/img_83 +2002/07/19/big/img_805 +2002/08/14/big/img_704 +2002/07/19/big/img_389 +2002/08/31/big/img_17765 +2002/07/29/big/img_606 +2003/01/17/big/img_939 +2002/09/02/big/img_15081 +2002/08/21/big/img_181 +2002/07/29/big/img_1321 +2002/07/21/big/img_497 +2002/07/20/big/img_539 +2002/08/24/big/img_119 +2002/08/01/big/img_1281 +2002/07/26/big/img_207 +2002/07/26/big/img_432 +2002/07/27/big/img_1006 +2002/08/05/big/img_3087 +2002/08/14/big/img_252 +2002/08/14/big/img_798 +2002/07/24/big/img_538 +2002/09/02/big/img_15507 +2002/08/08/big/img_901 +2003/01/14/big/img_557 +2002/08/07/big/img_1819 +2002/08/04/big/img_470 +2002/08/01/big/img_1504 +2002/08/16/big/img_1070 +2002/08/16/big/img_372 +2002/08/23/big/img_416 +2002/08/30/big/img_18208 +2002/08/01/big/img_2043 +2002/07/22/big/img_385 +2002/08/22/big/img_466 +2002/08/21/big/img_869 +2002/08/28/big/img_19429 +2002/08/02/big/img_770 +2002/07/23/big/img_433 +2003/01/14/big/img_13 +2002/07/27/big/img_953 +2002/09/02/big/img_15728 +2002/08/01/big/img_1361 +2002/08/29/big/img_18897 +2002/08/26/big/img_534 +2002/08/11/big/img_121 +2002/08/26/big/img_20130 +2002/07/31/big/img_363 +2002/08/13/big/img_978 +2002/07/25/big/img_835 +2002/08/02/big/img_906 +2003/01/14/big/img_548 +2002/07/30/big/img_80 +2002/07/26/big/img_982 +2003/01/16/big/img_99 +2002/08/19/big/img_362 +2002/08/24/big/img_376 +2002/08/07/big/img_1264 +2002/07/27/big/img_938 +2003/01/17/big/img_535 +2002/07/26/big/img_457 +2002/08/08/big/img_848 +2003/01/15/big/img_859 +2003/01/15/big/img_622 +2002/07/30/big/img_403 +2002/07/29/big/img_217 +2002/07/26/big/img_891 +2002/07/24/big/img_70 +2002/08/25/big/img_619 +2002/08/05/big/img_3375 +2002/08/01/big/img_2160 +2002/08/06/big/img_2227 +2003/01/14/big/img_117 +2002/08/14/big/img_227 +2002/08/13/big/img_565 +2002/08/19/big/img_625 +2002/08/03/big/img_812 +2002/07/24/big/img_41 +2002/08/16/big/img_235 +2002/07/29/big/img_759 +2002/07/21/big/img_433 +2002/07/29/big/img_190 +2003/01/16/big/img_435 +2003/01/13/big/img_708 +2002/07/30/big/img_57 +2002/08/22/big/img_162 +2003/01/01/big/img_558 +2003/01/15/big/img_604 +2002/08/16/big/img_935 +2002/08/20/big/img_394 +2002/07/28/big/img_465 +2002/09/02/big/img_15534 +2002/08/16/big/img_87 +2002/07/22/big/img_469 +2002/08/12/big/img_245 +2003/01/13/big/img_236 +2002/08/06/big/img_2736 +2002/08/03/big/img_348 +2003/01/14/big/img_218 +2002/07/26/big/img_232 +2003/01/15/big/img_244 +2002/07/25/big/img_1121 +2002/08/01/big/img_1484 +2002/07/26/big/img_541 +2002/08/07/big/img_1244 +2002/07/31/big/img_3 +2002/08/30/big/img_18437 +2002/08/29/big/img_19094 +2002/08/01/big/img_1355 +2002/08/19/big/img_338 +2002/07/19/big/img_255 +2002/07/21/big/img_76 +2002/08/25/big/img_199 +2002/08/12/big/img_740 +2002/07/30/big/img_852 +2002/08/15/big/img_599 +2002/08/23/big/img_254 +2002/08/19/big/img_125 +2002/07/24/big/img_2 +2002/08/04/big/img_145 +2002/08/05/big/img_3137 +2002/07/28/big/img_463 +2003/01/14/big/img_801 +2002/07/23/big/img_366 +2002/08/26/big/img_600 +2002/08/26/big/img_649 +2002/09/02/big/img_15849 +2002/07/26/big/img_248 +2003/01/13/big/img_200 +2002/08/07/big/img_1794 +2002/08/31/big/img_17270 +2002/08/23/big/img_608 +2003/01/13/big/img_837 +2002/08/23/big/img_581 +2002/08/20/big/img_754 +2002/08/18/big/img_183 +2002/08/20/big/img_328 +2002/07/22/big/img_494 +2002/07/29/big/img_399 +2002/08/28/big/img_19284 +2002/08/08/big/img_566 +2002/07/25/big/img_376 +2002/07/23/big/img_138 +2002/07/25/big/img_435 +2002/08/17/big/img_685 +2002/07/19/big/img_90 +2002/07/20/big/img_716 +2002/08/31/big/img_17458 +2002/08/26/big/img_461 +2002/07/25/big/img_355 +2002/08/06/big/img_2152 +2002/07/27/big/img_932 +2002/07/23/big/img_232 +2002/08/08/big/img_1020 +2002/07/31/big/img_366 +2002/08/06/big/img_2667 +2002/08/21/big/img_465 +2002/08/15/big/img_305 +2002/08/02/big/img_247 +2002/07/28/big/img_46 +2002/08/27/big/img_19922 +2002/08/23/big/img_643 +2003/01/13/big/img_624 +2002/08/23/big/img_625 +2002/08/05/big/img_3787 +2003/01/13/big/img_627 +2002/09/01/big/img_16381 +2002/08/05/big/img_3668 +2002/07/21/big/img_535 +2002/08/27/big/img_19680 +2002/07/22/big/img_413 +2002/07/29/big/img_481 +2003/01/15/big/img_496 +2002/07/23/big/img_701 +2002/08/29/big/img_18670 +2002/07/28/big/img_319 +2003/01/14/big/img_517 +2002/07/26/big/img_256 +2003/01/16/big/img_593 +2002/07/30/big/img_956 +2002/07/30/big/img_667 +2002/07/25/big/img_100 +2002/08/11/big/img_570 +2002/07/26/big/img_745 +2002/08/04/big/img_834 +2002/08/25/big/img_521 +2002/08/01/big/img_2148 +2002/09/02/big/img_15183 +2002/08/22/big/img_514 +2002/08/23/big/img_477 +2002/07/23/big/img_336 +2002/07/26/big/img_481 +2002/08/20/big/img_409 +2002/07/23/big/img_918 +2002/08/09/big/img_474 +2002/08/02/big/img_929 +2002/08/31/big/img_17932 +2002/08/19/big/img_161 +2002/08/09/big/img_667 +2002/07/31/big/img_805 +2002/09/02/big/img_15678 +2002/08/31/big/img_17509 +2002/08/29/big/img_18998 +2002/07/23/big/img_301 +2002/08/07/big/img_1612 +2002/08/06/big/img_2472 +2002/07/23/big/img_466 +2002/08/27/big/img_19634 +2003/01/16/big/img_16 +2002/08/14/big/img_193 +2002/08/21/big/img_340 +2002/08/27/big/img_19799 +2002/08/01/big/img_1345 +2002/08/07/big/img_1448 +2002/08/11/big/img_324 +2003/01/16/big/img_754 +2002/08/13/big/img_418 +2003/01/16/big/img_544 +2002/08/19/big/img_135 +2002/08/10/big/img_455 +2002/08/10/big/img_693 +2002/08/31/big/img_17967 +2002/08/28/big/img_19229 +2002/08/04/big/img_811 +2002/09/01/big/img_16225 +2003/01/16/big/img_428 +2002/09/02/big/img_15295 +2002/07/26/big/img_108 +2002/07/21/big/img_477 +2002/08/07/big/img_1354 +2002/08/23/big/img_246 +2002/08/16/big/img_652 +2002/07/27/big/img_553 +2002/07/31/big/img_346 +2002/08/04/big/img_537 +2002/08/08/big/img_498 +2002/08/29/big/img_18956 +2003/01/13/big/img_922 +2002/08/31/big/img_17425 +2002/07/26/big/img_438 +2002/08/19/big/img_185 +2003/01/16/big/img_33 +2002/08/10/big/img_252 +2002/07/29/big/img_598 +2002/08/27/big/img_19820 +2002/08/06/big/img_2664 +2002/08/20/big/img_705 +2003/01/14/big/img_816 +2002/08/03/big/img_552 +2002/07/25/big/img_561 +2002/07/25/big/img_934 +2002/08/01/big/img_1893 +2003/01/14/big/img_746 +2003/01/16/big/img_519 +2002/08/03/big/img_681 +2002/07/24/big/img_808 +2002/08/14/big/img_803 +2002/08/25/big/img_155 +2002/07/30/big/img_1107 +2002/08/29/big/img_18882 +2003/01/15/big/img_598 +2002/08/19/big/img_122 +2002/07/30/big/img_428 +2002/07/24/big/img_684 +2002/08/22/big/img_192 +2002/08/22/big/img_543 +2002/08/07/big/img_1318 +2002/08/18/big/img_25 +2002/07/26/big/img_583 +2002/07/20/big/img_464 +2002/08/19/big/img_664 +2002/08/24/big/img_861 +2002/09/01/big/img_16136 +2002/08/22/big/img_400 +2002/08/12/big/img_445 +2003/01/14/big/img_174 +2002/08/27/big/img_19677 +2002/08/31/big/img_17214 +2002/08/30/big/img_18175 +2003/01/17/big/img_402 +2002/08/06/big/img_2396 +2002/08/18/big/img_448 +2002/08/21/big/img_165 +2002/08/31/big/img_17609 +2003/01/01/big/img_151 +2002/08/26/big/img_372 +2002/09/02/big/img_15994 +2002/07/26/big/img_660 +2002/09/02/big/img_15197 +2002/07/29/big/img_258 +2002/08/30/big/img_18525 +2003/01/13/big/img_368 +2002/07/29/big/img_1538 +2002/07/21/big/img_787 +2002/08/18/big/img_152 +2002/08/06/big/img_2379 +2003/01/17/big/img_864 +2002/08/27/big/img_19998 +2002/08/01/big/img_1634 +2002/07/25/big/img_414 +2002/08/22/big/img_627 +2002/08/07/big/img_1669 +2002/08/16/big/img_1052 +2002/08/31/big/img_17796 +2002/08/18/big/img_199 +2002/09/02/big/img_15147 +2002/08/09/big/img_460 +2002/08/14/big/img_581 +2002/08/30/big/img_18286 +2002/07/26/big/img_337 +2002/08/18/big/img_589 +2003/01/14/big/img_866 +2002/07/20/big/img_624 +2002/08/01/big/img_1801 +2002/07/24/big/img_683 +2002/08/09/big/img_725 +2003/01/14/big/img_34 +2002/07/30/big/img_144 +2002/07/30/big/img_706 +2002/08/08/big/img_394 +2002/08/19/big/img_619 +2002/08/06/big/img_2703 +2002/08/29/big/img_19034 +2002/07/24/big/img_67 +2002/08/27/big/img_19841 +2002/08/19/big/img_427 +2003/01/14/big/img_333 +2002/09/01/big/img_16406 +2002/07/19/big/img_882 +2002/08/17/big/img_238 +2003/01/14/big/img_739 +2002/07/22/big/img_151 +2002/08/21/big/img_743 +2002/07/25/big/img_1048 +2002/07/30/big/img_395 +2003/01/13/big/img_584 +2002/08/13/big/img_742 +2002/08/13/big/img_1168 +2003/01/14/big/img_147 +2002/07/26/big/img_803 +2002/08/05/big/img_3298 +2002/08/07/big/img_1451 +2002/08/16/big/img_424 +2002/07/29/big/img_1069 +2002/09/01/big/img_16735 +2002/07/21/big/img_637 +2003/01/14/big/img_585 +2002/08/02/big/img_358 +2003/01/13/big/img_358 +2002/08/14/big/img_198 +2002/08/17/big/img_935 +2002/08/04/big/img_42 +2002/08/30/big/img_18245 +2002/07/25/big/img_158 +2002/08/22/big/img_744 +2002/08/06/big/img_2291 +2002/08/05/big/img_3044 +2002/07/30/big/img_272 +2002/08/23/big/img_641 +2002/07/24/big/img_797 +2002/07/30/big/img_392 +2003/01/14/big/img_447 +2002/07/31/big/img_898 +2002/08/06/big/img_2812 +2002/08/13/big/img_564 +2002/07/22/big/img_43 +2002/07/26/big/img_634 +2002/07/19/big/img_843 +2002/08/26/big/img_58 +2002/07/21/big/img_375 +2002/08/25/big/img_729 +2002/07/19/big/img_561 +2003/01/15/big/img_884 +2002/07/25/big/img_891 +2002/08/09/big/img_558 +2002/08/26/big/img_587 +2002/08/13/big/img_1146 +2002/09/02/big/img_15153 +2002/07/26/big/img_316 +2002/08/01/big/img_1940 +2002/08/26/big/img_90 +2003/01/13/big/img_347 +2002/07/25/big/img_520 +2002/08/29/big/img_18718 +2002/08/28/big/img_19219 +2002/08/13/big/img_375 +2002/07/20/big/img_719 +2002/08/31/big/img_17431 +2002/07/28/big/img_192 +2002/08/26/big/img_259 +2002/08/18/big/img_484 +2002/07/29/big/img_580 +2002/07/26/big/img_84 +2002/08/02/big/img_302 +2002/08/31/big/img_17007 +2003/01/15/big/img_543 +2002/09/01/big/img_16488 +2002/08/22/big/img_798 +2002/07/30/big/img_383 +2002/08/04/big/img_668 +2002/08/13/big/img_156 +2002/08/07/big/img_1353 +2002/07/25/big/img_281 +2003/01/14/big/img_587 +2003/01/15/big/img_524 +2002/08/19/big/img_726 +2002/08/21/big/img_709 +2002/08/26/big/img_465 +2002/07/31/big/img_658 +2002/08/28/big/img_19148 +2002/07/23/big/img_423 +2002/08/16/big/img_758 +2002/08/22/big/img_523 +2002/08/16/big/img_591 +2002/08/23/big/img_845 +2002/07/26/big/img_678 +2002/08/09/big/img_806 +2002/08/06/big/img_2369 +2002/07/29/big/img_457 +2002/07/19/big/img_278 +2002/08/30/big/img_18107 +2002/07/26/big/img_444 +2002/08/20/big/img_278 +2002/08/26/big/img_92 +2002/08/26/big/img_257 +2002/07/25/big/img_266 +2002/08/05/big/img_3829 +2002/07/26/big/img_757 +2002/07/29/big/img_1536 +2002/08/09/big/img_472 +2003/01/17/big/img_480 +2002/08/28/big/img_19355 +2002/07/26/big/img_97 +2002/08/06/big/img_2503 +2002/07/19/big/img_254 +2002/08/01/big/img_1470 +2002/08/21/big/img_42 +2002/08/20/big/img_217 +2002/08/06/big/img_2459 +2002/07/19/big/img_552 +2002/08/13/big/img_717 +2002/08/12/big/img_586 +2002/08/20/big/img_411 +2003/01/13/big/img_768 +2002/08/07/big/img_1747 +2002/08/15/big/img_385 +2002/08/01/big/img_1648 +2002/08/15/big/img_311 +2002/08/21/big/img_95 +2002/08/09/big/img_108 +2002/08/21/big/img_398 +2002/08/17/big/img_340 +2002/08/14/big/img_474 +2002/08/13/big/img_294 +2002/08/24/big/img_840 +2002/08/09/big/img_808 +2002/08/23/big/img_491 +2002/07/28/big/img_33 +2003/01/13/big/img_664 +2002/08/02/big/img_261 +2002/08/09/big/img_591 +2002/07/26/big/img_309 +2003/01/14/big/img_372 +2002/08/19/big/img_581 +2002/08/19/big/img_168 +2002/08/26/big/img_422 +2002/07/24/big/img_106 +2002/08/01/big/img_1936 +2002/08/05/big/img_3764 +2002/08/21/big/img_266 +2002/08/31/big/img_17968 +2002/08/01/big/img_1941 +2002/08/15/big/img_550 +2002/08/14/big/img_13 +2002/07/30/big/img_171 +2003/01/13/big/img_490 +2002/07/25/big/img_427 +2002/07/19/big/img_770 +2002/08/12/big/img_759 +2003/01/15/big/img_1360 +2002/08/05/big/img_3692 +2003/01/16/big/img_30 +2002/07/25/big/img_1026 +2002/07/22/big/img_288 +2002/08/29/big/img_18801 +2002/07/24/big/img_793 +2002/08/13/big/img_178 +2002/08/06/big/img_2322 +2003/01/14/big/img_560 +2002/08/18/big/img_408 +2003/01/16/big/img_915 +2003/01/16/big/img_679 +2002/08/07/big/img_1552 +2002/08/29/big/img_19050 +2002/08/01/big/img_2172 +2002/07/31/big/img_30 +2002/07/30/big/img_1019 +2002/07/30/big/img_587 +2003/01/13/big/img_773 +2002/07/30/big/img_410 +2002/07/28/big/img_65 +2002/08/05/big/img_3138 +2002/07/23/big/img_541 +2002/08/22/big/img_963 +2002/07/27/big/img_657 +2002/07/30/big/img_1051 +2003/01/16/big/img_150 +2002/07/31/big/img_519 +2002/08/01/big/img_1961 +2002/08/05/big/img_3752 +2002/07/23/big/img_631 +2003/01/14/big/img_237 +2002/07/28/big/img_21 +2002/07/22/big/img_813 +2002/08/05/big/img_3563 +2003/01/17/big/img_620 +2002/07/19/big/img_523 +2002/07/30/big/img_904 +2002/08/29/big/img_18642 +2002/08/11/big/img_492 +2002/08/01/big/img_2130 +2002/07/25/big/img_618 +2002/08/17/big/img_305 +2003/01/16/big/img_520 +2002/07/26/big/img_495 +2002/08/17/big/img_164 +2002/08/03/big/img_440 +2002/07/24/big/img_441 +2002/08/06/big/img_2146 +2002/08/11/big/img_558 +2002/08/02/big/img_545 +2002/08/31/big/img_18090 +2003/01/01/big/img_136 +2002/07/25/big/img_1099 +2003/01/13/big/img_728 +2003/01/16/big/img_197 +2002/07/26/big/img_651 +2002/08/11/big/img_676 +2003/01/15/big/img_10 +2002/08/21/big/img_250 +2002/08/14/big/img_325 +2002/08/04/big/img_390 +2002/07/24/big/img_554 +2003/01/16/big/img_333 +2002/07/31/big/img_922 +2002/09/02/big/img_15586 +2003/01/16/big/img_184 +2002/07/22/big/img_766 +2002/07/21/big/img_608 +2002/08/07/big/img_1578 +2002/08/17/big/img_961 +2002/07/27/big/img_324 +2002/08/05/big/img_3765 +2002/08/23/big/img_462 +2003/01/16/big/img_382 +2002/08/27/big/img_19838 +2002/08/01/big/img_1505 +2002/08/21/big/img_662 +2002/08/14/big/img_605 +2002/08/19/big/img_816 +2002/07/29/big/img_136 +2002/08/20/big/img_719 +2002/08/06/big/img_2826 +2002/08/10/big/img_630 +2003/01/17/big/img_973 +2002/08/14/big/img_116 +2002/08/02/big/img_666 +2002/08/21/big/img_710 +2002/08/05/big/img_55 +2002/07/31/big/img_229 +2002/08/01/big/img_1549 +2002/07/23/big/img_432 +2002/07/21/big/img_430 +2002/08/21/big/img_549 +2002/08/08/big/img_985 +2002/07/20/big/img_610 +2002/07/23/big/img_978 +2002/08/23/big/img_219 +2002/07/25/big/img_175 +2003/01/15/big/img_230 +2002/08/23/big/img_385 +2002/07/31/big/img_879 +2002/08/12/big/img_495 +2002/08/22/big/img_499 +2002/08/30/big/img_18322 +2002/08/15/big/img_795 +2002/08/13/big/img_835 +2003/01/17/big/img_930 +2002/07/30/big/img_873 +2002/08/11/big/img_257 +2002/07/31/big/img_593 +2002/08/21/big/img_916 +2003/01/13/big/img_814 +2002/07/25/big/img_722 +2002/08/16/big/img_379 +2002/07/31/big/img_497 +2002/07/22/big/img_602 +2002/08/21/big/img_642 +2002/08/21/big/img_614 +2002/08/23/big/img_482 +2002/07/29/big/img_603 +2002/08/13/big/img_705 +2002/07/23/big/img_833 +2003/01/14/big/img_511 +2002/07/24/big/img_376 +2002/08/17/big/img_1030 +2002/08/05/big/img_3576 +2002/08/16/big/img_540 +2002/07/22/big/img_630 +2002/08/10/big/img_180 +2002/08/14/big/img_905 +2002/08/29/big/img_18777 +2002/08/22/big/img_693 +2003/01/16/big/img_933 +2002/08/20/big/img_555 +2002/08/15/big/img_549 +2003/01/14/big/img_830 +2003/01/16/big/img_64 +2002/08/27/big/img_19670 +2002/08/22/big/img_729 +2002/07/27/big/img_981 +2002/08/09/big/img_458 +2003/01/17/big/img_884 +2002/07/25/big/img_639 +2002/08/31/big/img_18008 +2002/08/22/big/img_249 +2002/08/17/big/img_971 +2002/08/04/big/img_308 +2002/07/28/big/img_362 +2002/08/12/big/img_142 +2002/08/26/big/img_61 +2002/08/14/big/img_422 +2002/07/19/big/img_607 +2003/01/15/big/img_717 +2002/08/01/big/img_1475 +2002/08/29/big/img_19061 +2003/01/01/big/img_346 +2002/07/20/big/img_315 +2003/01/15/big/img_756 +2002/08/15/big/img_879 +2002/08/08/big/img_615 +2003/01/13/big/img_431 +2002/08/05/big/img_3233 +2002/08/24/big/img_526 +2003/01/13/big/img_717 +2002/09/01/big/img_16408 +2002/07/22/big/img_217 +2002/07/31/big/img_960 +2002/08/21/big/img_610 +2002/08/05/big/img_3753 +2002/08/03/big/img_151 +2002/08/21/big/img_267 +2002/08/01/big/img_2175 +2002/08/04/big/img_556 +2002/08/21/big/img_527 +2002/09/02/big/img_15800 +2002/07/27/big/img_156 +2002/07/20/big/img_590 +2002/08/15/big/img_700 +2002/08/08/big/img_444 +2002/07/25/big/img_94 +2002/07/24/big/img_778 +2002/08/14/big/img_694 +2002/07/20/big/img_666 +2002/08/02/big/img_200 +2002/08/02/big/img_578 +2003/01/17/big/img_332 +2002/09/01/big/img_16352 +2002/08/27/big/img_19668 +2002/07/23/big/img_823 +2002/08/13/big/img_431 +2003/01/16/big/img_463 +2002/08/27/big/img_19711 +2002/08/23/big/img_154 +2002/07/31/big/img_360 +2002/08/23/big/img_555 +2002/08/10/big/img_561 +2003/01/14/big/img_550 +2002/08/07/big/img_1370 +2002/07/30/big/img_1184 +2002/08/01/big/img_1445 +2002/08/23/big/img_22 +2002/07/30/big/img_606 +2003/01/17/big/img_271 +2002/08/31/big/img_17316 +2002/08/16/big/img_973 +2002/07/26/big/img_77 +2002/07/20/big/img_788 +2002/08/06/big/img_2426 +2002/08/07/big/img_1498 +2002/08/16/big/img_358 +2002/08/06/big/img_2851 +2002/08/12/big/img_359 +2002/08/01/big/img_1521 +2002/08/02/big/img_709 +2002/08/20/big/img_935 +2002/08/12/big/img_188 +2002/08/24/big/img_411 +2002/08/22/big/img_680 +2002/08/06/big/img_2480 +2002/07/20/big/img_627 +2002/07/30/big/img_214 +2002/07/25/big/img_354 +2002/08/02/big/img_636 +2003/01/15/big/img_661 +2002/08/07/big/img_1327 +2002/08/01/big/img_2108 +2002/08/31/big/img_17919 +2002/08/29/big/img_18768 +2002/08/05/big/img_3840 +2002/07/26/big/img_242 +2003/01/14/big/img_451 +2002/08/20/big/img_923 +2002/08/27/big/img_19908 +2002/08/16/big/img_282 +2002/08/19/big/img_440 +2003/01/01/big/img_230 +2002/08/08/big/img_212 +2002/07/20/big/img_443 +2002/08/25/big/img_635 +2003/01/13/big/img_1169 +2002/07/26/big/img_998 +2002/08/15/big/img_995 +2002/08/06/big/img_3002 +2002/07/29/big/img_460 +2003/01/14/big/img_925 +2002/07/23/big/img_539 +2002/08/16/big/img_694 +2003/01/13/big/img_459 +2002/07/23/big/img_249 +2002/08/20/big/img_539 +2002/08/04/big/img_186 +2002/08/26/big/img_264 +2002/07/22/big/img_704 +2002/08/25/big/img_277 +2002/08/22/big/img_988 +2002/07/29/big/img_504 +2002/08/05/big/img_3600 +2002/08/30/big/img_18380 +2003/01/14/big/img_937 +2002/08/21/big/img_254 +2002/08/10/big/img_130 +2002/08/20/big/img_339 +2003/01/14/big/img_428 +2002/08/20/big/img_889 +2002/08/31/big/img_17637 +2002/07/26/big/img_644 +2002/09/01/big/img_16776 +2002/08/06/big/img_2239 +2002/08/06/big/img_2646 +2003/01/13/big/img_491 +2002/08/10/big/img_579 +2002/08/21/big/img_713 +2002/08/22/big/img_482 +2002/07/22/big/img_167 +2002/07/24/big/img_539 +2002/08/14/big/img_721 +2002/07/25/big/img_389 +2002/09/01/big/img_16591 +2002/08/13/big/img_543 +2003/01/14/big/img_432 +2002/08/09/big/img_287 +2002/07/26/big/img_126 +2002/08/23/big/img_412 +2002/08/15/big/img_1034 +2002/08/28/big/img_19485 +2002/07/31/big/img_236 +2002/07/30/big/img_523 +2002/07/19/big/img_141 +2003/01/17/big/img_957 +2002/08/04/big/img_81 +2002/07/25/big/img_206 +2002/08/15/big/img_716 +2002/08/13/big/img_403 +2002/08/15/big/img_685 +2002/07/26/big/img_884 +2002/07/19/big/img_499 +2002/07/23/big/img_772 +2002/07/27/big/img_752 +2003/01/14/big/img_493 +2002/08/25/big/img_664 +2002/07/31/big/img_334 +2002/08/26/big/img_678 +2002/09/01/big/img_16541 +2003/01/14/big/img_347 +2002/07/23/big/img_187 +2002/07/30/big/img_1163 +2002/08/05/big/img_35 +2002/08/22/big/img_944 +2002/08/07/big/img_1239 +2002/07/29/big/img_1215 +2002/08/03/big/img_312 +2002/08/05/big/img_3523 +2002/07/29/big/img_218 +2002/08/13/big/img_672 +2002/08/16/big/img_205 +2002/08/17/big/img_594 +2002/07/29/big/img_1411 +2002/07/30/big/img_942 +2003/01/16/big/img_312 +2002/08/08/big/img_312 +2002/07/25/big/img_15 +2002/08/09/big/img_839 +2002/08/01/big/img_2069 +2002/08/31/big/img_17512 +2002/08/01/big/img_3 +2002/07/31/big/img_320 +2003/01/15/big/img_1265 +2002/08/14/big/img_563 +2002/07/31/big/img_167 +2002/08/20/big/img_374 +2002/08/13/big/img_406 +2002/08/08/big/img_625 +2002/08/02/big/img_314 +2002/08/27/big/img_19964 +2002/09/01/big/img_16670 +2002/07/31/big/img_599 +2002/08/29/big/img_18906 +2002/07/24/big/img_373 +2002/07/26/big/img_513 +2002/09/02/big/img_15497 +2002/08/19/big/img_117 +2003/01/01/big/img_158 +2002/08/24/big/img_178 +2003/01/13/big/img_935 +2002/08/13/big/img_609 +2002/08/30/big/img_18341 +2002/08/25/big/img_674 +2003/01/13/big/img_209 +2002/08/13/big/img_258 +2002/08/05/big/img_3543 +2002/08/07/big/img_1970 +2002/08/06/big/img_3004 +2003/01/17/big/img_487 +2002/08/24/big/img_873 +2002/08/29/big/img_18730 +2002/08/09/big/img_375 +2003/01/16/big/img_751 +2002/08/02/big/img_603 +2002/08/19/big/img_325 +2002/09/01/big/img_16420 +2002/08/05/big/img_3633 +2002/08/21/big/img_516 +2002/07/19/big/img_501 +2002/07/26/big/img_688 +2002/07/24/big/img_256 +2002/07/25/big/img_438 +2002/07/31/big/img_1017 +2002/08/22/big/img_512 +2002/07/21/big/img_543 +2002/08/08/big/img_223 +2002/08/19/big/img_189 +2002/08/12/big/img_630 +2002/07/30/big/img_958 +2002/07/28/big/img_208 +2002/08/31/big/img_17691 +2002/07/22/big/img_542 +2002/07/19/big/img_741 +2002/07/19/big/img_158 +2002/08/15/big/img_399 +2002/08/01/big/img_2159 +2002/08/14/big/img_455 +2002/08/17/big/img_1011 +2002/08/26/big/img_744 +2002/08/12/big/img_624 +2003/01/17/big/img_821 +2002/08/16/big/img_980 +2002/07/28/big/img_281 +2002/07/25/big/img_171 +2002/08/03/big/img_116 +2002/07/22/big/img_467 +2002/07/31/big/img_750 +2002/07/26/big/img_435 +2002/07/19/big/img_822 +2002/08/13/big/img_626 +2002/08/11/big/img_344 +2002/08/02/big/img_473 +2002/09/01/big/img_16817 +2002/08/01/big/img_1275 +2002/08/28/big/img_19270 +2002/07/23/big/img_607 +2002/08/09/big/img_316 +2002/07/29/big/img_626 +2002/07/24/big/img_824 +2002/07/22/big/img_342 +2002/08/08/big/img_794 +2002/08/07/big/img_1209 +2002/07/19/big/img_18 +2002/08/25/big/img_634 +2002/07/24/big/img_730 +2003/01/17/big/img_356 +2002/07/23/big/img_305 +2002/07/30/big/img_453 +2003/01/13/big/img_972 +2002/08/06/big/img_2610 +2002/08/29/big/img_18920 +2002/07/31/big/img_123 +2002/07/26/big/img_979 +2002/08/24/big/img_635 +2002/08/05/big/img_3704 +2002/08/07/big/img_1358 +2002/07/22/big/img_306 +2002/08/13/big/img_619 +2002/08/02/big/img_366 diff --git a/project/hair_service_sd/core/models/layers/data/__init__.py b/project/hair_service_sd/core/models/layers/data/__init__.py new file mode 100644 index 0000000..ea50eba --- /dev/null +++ b/project/hair_service_sd/core/models/layers/data/__init__.py @@ -0,0 +1,3 @@ +from .wider_face import WiderFaceDetection, detection_collate +from .data_augment import * +from .config import * diff --git a/project/hair_service_sd/core/models/layers/data/config.py b/project/hair_service_sd/core/models/layers/data/config.py new file mode 100644 index 0000000..591f349 --- /dev/null +++ b/project/hair_service_sd/core/models/layers/data/config.py @@ -0,0 +1,42 @@ +# config.py + +cfg_mnet = { + 'name': 'mobilenet0.25', + 'min_sizes': [[16, 32], [64, 128], [256, 512]], + 'steps': [8, 16, 32], + 'variance': [0.1, 0.2], + 'clip': False, + 'loc_weight': 2.0, + 'gpu_train': True, + 'batch_size': 32, + 'ngpu': 1, + 'epoch': 250, + 'decay1': 190, + 'decay2': 220, + 'image_size': 640, + 'pretrain': True, + 'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3}, + 'in_channel': 32, + 'out_channel': 64 +} + +cfg_re50 = { + 'name': 'Resnet50', + 'min_sizes': [[16, 32], [64, 128], [256, 512]], + 'steps': [8, 16, 32], + 'variance': [0.1, 0.2], + 'clip': False, + 'loc_weight': 2.0, + 'gpu_train': True, + 'batch_size': 24, + 'ngpu': 4, + 'epoch': 100, + 'decay1': 70, + 'decay2': 90, + 'image_size': 840, + 'pretrain': True, + 'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3}, + 'in_channel': 256, + 'out_channel': 256 +} + diff --git a/project/hair_service_sd/core/models/layers/data/data_augment.py b/project/hair_service_sd/core/models/layers/data/data_augment.py new file mode 100644 index 0000000..2980fe8 --- /dev/null +++ b/project/hair_service_sd/core/models/layers/data/data_augment.py @@ -0,0 +1,237 @@ +import cv2 +import numpy as np +import random +from core.utils.box_utils_Retina import matrix_iof + + +def _crop(image, boxes, labels, landm, img_dim): + height, width, _ = image.shape + pad_image_flag = True + + for _ in range(250): + """ + if random.uniform(0, 1) <= 0.2: + scale = 1.0 + else: + scale = random.uniform(0.3, 1.0) + """ + PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0] + scale = random.choice(PRE_SCALES) + short_side = min(width, height) + w = int(scale * short_side) + h = w + + if width == w: + l = 0 + else: + l = random.randrange(width - w) + if height == h: + t = 0 + else: + t = random.randrange(height - h) + roi = np.array((l, t, l + w, t + h)) + + value = matrix_iof(boxes, roi[np.newaxis]) + flag = (value >= 1) + if not flag.any(): + continue + + centers = (boxes[:, :2] + boxes[:, 2:]) / 2 + mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1) + boxes_t = boxes[mask_a].copy() + labels_t = labels[mask_a].copy() + landms_t = landm[mask_a].copy() + landms_t = landms_t.reshape([-1, 5, 2]) + + if boxes_t.shape[0] == 0: + continue + + image_t = image[roi[1]:roi[3], roi[0]:roi[2]] + + boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2]) + boxes_t[:, :2] -= roi[:2] + boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:]) + boxes_t[:, 2:] -= roi[:2] + + # landm + landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2] + landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0])) + landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2]) + landms_t = landms_t.reshape([-1, 10]) + + + # make sure that the cropped image contains at least one face > 16 pixel at training image scale + b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim + b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim + mask_b = np.minimum(b_w_t, b_h_t) > 0.0 + boxes_t = boxes_t[mask_b] + labels_t = labels_t[mask_b] + landms_t = landms_t[mask_b] + + if boxes_t.shape[0] == 0: + continue + + pad_image_flag = False + + return image_t, boxes_t, labels_t, landms_t, pad_image_flag + return image, boxes, labels, landm, pad_image_flag + + +def _distort(image): + + def _convert(image, alpha=1, beta=0): + tmp = image.astype(float) * alpha + beta + tmp[tmp < 0] = 0 + tmp[tmp > 255] = 255 + image[:] = tmp + + image = image.copy() + + if random.randrange(2): + + #brightness distortion + if random.randrange(2): + _convert(image, beta=random.uniform(-32, 32)) + + #contrast distortion + if random.randrange(2): + _convert(image, alpha=random.uniform(0.5, 1.5)) + + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + #saturation distortion + if random.randrange(2): + _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) + + #hue distortion + if random.randrange(2): + tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) + tmp %= 180 + image[:, :, 0] = tmp + + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + + else: + + #brightness distortion + if random.randrange(2): + _convert(image, beta=random.uniform(-32, 32)) + + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + #saturation distortion + if random.randrange(2): + _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) + + #hue distortion + if random.randrange(2): + tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) + tmp %= 180 + image[:, :, 0] = tmp + + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + + #contrast distortion + if random.randrange(2): + _convert(image, alpha=random.uniform(0.5, 1.5)) + + return image + + +def _expand(image, boxes, fill, p): + if random.randrange(2): + return image, boxes + + height, width, depth = image.shape + + scale = random.uniform(1, p) + w = int(scale * width) + h = int(scale * height) + + left = random.randint(0, w - width) + top = random.randint(0, h - height) + + boxes_t = boxes.copy() + boxes_t[:, :2] += (left, top) + boxes_t[:, 2:] += (left, top) + expand_image = np.empty( + (h, w, depth), + dtype=image.dtype) + expand_image[:, :] = fill + expand_image[top:top + height, left:left + width] = image + image = expand_image + + return image, boxes_t + + +def _mirror(image, boxes, landms): + _, width, _ = image.shape + if random.randrange(2): + image = image[:, ::-1] + boxes = boxes.copy() + boxes[:, 0::2] = width - boxes[:, 2::-2] + + # landm + landms = landms.copy() + landms = landms.reshape([-1, 5, 2]) + landms[:, :, 0] = width - landms[:, :, 0] + tmp = landms[:, 1, :].copy() + landms[:, 1, :] = landms[:, 0, :] + landms[:, 0, :] = tmp + tmp1 = landms[:, 4, :].copy() + landms[:, 4, :] = landms[:, 3, :] + landms[:, 3, :] = tmp1 + landms = landms.reshape([-1, 10]) + + return image, boxes, landms + + +def _pad_to_square(image, rgb_mean, pad_image_flag): + if not pad_image_flag: + return image + height, width, _ = image.shape + long_side = max(width, height) + image_t = np.empty((long_side, long_side, 3), dtype=image.dtype) + image_t[:, :] = rgb_mean + image_t[0:0 + height, 0:0 + width] = image + return image_t + + +def _resize_subtract_mean(image, insize, rgb_mean): + interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4] + interp_method = interp_methods[random.randrange(5)] + image = cv2.resize(image, (insize, insize), interpolation=interp_method) + image = image.astype(np.float32) + image -= rgb_mean + return image.transpose(2, 0, 1) + + +class preproc(object): + + def __init__(self, img_dim, rgb_means): + self.img_dim = img_dim + self.rgb_means = rgb_means + + def __call__(self, image, targets): + assert targets.shape[0] > 0, "this image does not have gt" + + boxes = targets[:, :4].copy() + labels = targets[:, -1].copy() + landm = targets[:, 4:-1].copy() + + image_t, boxes_t, labels_t, landm_t, pad_image_flag = _crop(image, boxes, labels, landm, self.img_dim) + image_t = _distort(image_t) + image_t = _pad_to_square(image_t,self.rgb_means, pad_image_flag) + image_t, boxes_t, landm_t = _mirror(image_t, boxes_t, landm_t) + height, width, _ = image_t.shape + image_t = _resize_subtract_mean(image_t, self.img_dim, self.rgb_means) + boxes_t[:, 0::2] /= width + boxes_t[:, 1::2] /= height + + landm_t[:, 0::2] /= width + landm_t[:, 1::2] /= height + + labels_t = np.expand_dims(labels_t, 1) + targets_t = np.hstack((boxes_t, landm_t, labels_t)) + + return image_t, targets_t diff --git a/project/hair_service_sd/core/models/layers/data/wider_face.py b/project/hair_service_sd/core/models/layers/data/wider_face.py new file mode 100644 index 0000000..22f56ef --- /dev/null +++ b/project/hair_service_sd/core/models/layers/data/wider_face.py @@ -0,0 +1,101 @@ +import os +import os.path +import sys +import torch +import torch.utils.data as data +import cv2 +import numpy as np + +class WiderFaceDetection(data.Dataset): + def __init__(self, txt_path, preproc=None): + self.preproc = preproc + self.imgs_path = [] + self.words = [] + f = open(txt_path,'r') + lines = f.readlines() + isFirst = True + labels = [] + for line in lines: + line = line.rstrip() + if line.startswith('#'): + if isFirst is True: + isFirst = False + else: + labels_copy = labels.copy() + self.words.append(labels_copy) + labels.clear() + path = line[2:] + path = txt_path.replace('label.txt','images/') + path + self.imgs_path.append(path) + else: + line = line.split(' ') + label = [float(x) for x in line] + labels.append(label) + + self.words.append(labels) + + def __len__(self): + return len(self.imgs_path) + + def __getitem__(self, index): + img = cv2.imread(self.imgs_path[index]) + height, width, _ = img.shape + + labels = self.words[index] + annotations = np.zeros((0, 15)) + if len(labels) == 0: + return annotations + for idx, label in enumerate(labels): + annotation = np.zeros((1, 15)) + # bbox + annotation[0, 0] = label[0] # x1 + annotation[0, 1] = label[1] # y1 + annotation[0, 2] = label[0] + label[2] # x2 + annotation[0, 3] = label[1] + label[3] # y2 + + # landmarks + annotation[0, 4] = label[4] # l0_x + annotation[0, 5] = label[5] # l0_y + annotation[0, 6] = label[7] # l1_x + annotation[0, 7] = label[8] # l1_y + annotation[0, 8] = label[10] # l2_x + annotation[0, 9] = label[11] # l2_y + annotation[0, 10] = label[13] # l3_x + annotation[0, 11] = label[14] # l3_y + annotation[0, 12] = label[16] # l4_x + annotation[0, 13] = label[17] # l4_y + if (annotation[0, 4]<0): + annotation[0, 14] = -1 + else: + annotation[0, 14] = 1 + + annotations = np.append(annotations, annotation, axis=0) + target = np.array(annotations) + if self.preproc is not None: + img, target = self.preproc(img, target) + + return torch.from_numpy(img), target + +def detection_collate(batch): + """Custom collate fn for dealing with batches of images that have a different + number of associated object annotations (bounding boxes). + + Arguments: + batch: (tuple) A tuple of tensor images and lists of annotations + + Return: + A tuple containing: + 1) (tensor) batch of images stacked on their 0 dim + 2) (list of tensors) annotations for a given image are stacked on 0 dim + """ + targets = [] + imgs = [] + for _, sample in enumerate(batch): + for _, tup in enumerate(sample): + if torch.is_tensor(tup): + imgs.append(tup) + elif isinstance(tup, type(np.empty(0))): + annos = torch.from_numpy(tup).float() + targets.append(annos) + + return (torch.stack(imgs, 0), targets) diff --git a/project/hair_service_sd/core/oss_module.py b/project/hair_service_sd/core/oss_module.py index e8d8306..22ab556 100644 --- a/project/hair_service_sd/core/oss_module.py +++ b/project/hair_service_sd/core/oss_module.py @@ -5,16 +5,24 @@ import os class OSS_object(): def __init__(self): - access_key_id = os.getenv('OSS_TEST_ACCESS_KEY_ID', '') - access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', '') - bucket_name = os.getenv('OSS_TEST_BUCKET', '') - endpoint = os.getenv('OSS_TEST_ENDPOINT', '') - for param in (access_key_id, access_key_secret, bucket_name, endpoint): - assert '<' not in param, '请设置环境变量 OSS_TEST_ACCESS_KEY_ID / OSS_TEST_ACCESS_KEY_SECRET / OSS_TEST_BUCKET / OSS_TEST_ENDPOINT' + # 懒加载：只读取凭证，不立即连接 OSS。 + # 这样本地用 output_format=base64 时不配 OSS 凭证也能启动服务； + # 仅在真正调用 upload_file 时才校验并连接。 + self.access_key_id = os.getenv('OSS_TEST_ACCESS_KEY_ID', '') + self.access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', '') + self.bucket_name = os.getenv('OSS_TEST_BUCKET', '') + self.endpoint = os.getenv('OSS_TEST_ENDPOINT', '') + self.bucket = None - self.bucket = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, bucket_name) + def _ensure_bucket(self): + if self.bucket is not None: + return + for param in (self.access_key_id, self.access_key_secret, self.bucket_name, self.endpoint): + assert '<' not in param, '请设置环境变量 OSS_TEST_ACCESS_KEY_ID / OSS_TEST_ACCESS_KEY_SECRET / OSS_TEST_BUCKET / OSS_TEST_ENDPOINT' + self.bucket = oss2.Bucket(oss2.Auth(self.access_key_id, self.access_key_secret), self.endpoint, self.bucket_name) def upload_file(self, file, target_name): + self._ensure_bucket() t0 = time.time() with open(oss2.to_unicode(file), 'rb') as f: ret = self.bucket.put_object(target_name, f) diff --git a/project/hair_service_sd/hair_grow_swap.py b/project/hair_service_sd/hair_grow_swap.py index 3cda439..281671d 100644 --- a/project/hair_service_sd/hair_grow_swap.py +++ b/project/hair_service_sd/hair_grow_swap.py @@ -177,10 +177,17 @@ def list_hairstyles(hairstyle_dir, train_dir, upload_dir, limit=None): 返回: [{hair_id, gender, has_lora}, ...] """ + # 仅展示这些发型（其余数据保留在磁盘，但不在列表中显示） + _VISIBLE_HAIRSTYLES = { + "chang_tuoyuan", "chang_bolang", "chang_zhixian", + "chang_huaban", "chang_xinxing", + } styles = [] if not os.path.isdir(hairstyle_dir): return styles for hair_id in os.listdir(hairstyle_dir): + if hair_id not in _VISIBLE_HAIRSTYLES: + continue cfg_path = os.path.join(hairstyle_dir, hair_id, "config.json") ref_path = os.path.join(hairstyle_dir, hair_id, "ref_rgb_8uc3_768.png") lora_path = os.path.join(train_dir, hair_id, "model", "hairstyle_hd_lora.safetensors") diff --git a/project/hair_service_sd/keypoints/lib/config/__init__.py b/project/hair_service_sd/keypoints/lib/config/__init__.py new file mode 100644 index 0000000..a44e926 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/config/__init__.py @@ -0,0 +1,9 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from .default import _C as cfg +from .default import update_config +from .models import MODEL_EXTRAS diff --git a/project/hair_service_sd/keypoints/lib/config/default.py b/project/hair_service_sd/keypoints/lib/config/default.py new file mode 100644 index 0000000..72d3faf --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/config/default.py @@ -0,0 +1,160 @@ + +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from yacs.config import CfgNode as CN + + +_C = CN() + +_C.OUTPUT_DIR = '' +_C.LOG_DIR = '' +_C.DATA_DIR = '' +_C.GPUS = (0,) +_C.WORKERS = 4 +_C.PRINT_FREQ = 20 +_C.AUTO_RESUME = False +_C.PIN_MEMORY = True +_C.RANK = 0 + +# Cudnn related params +_C.CUDNN = CN() +_C.CUDNN.BENCHMARK = True +_C.CUDNN.DETERMINISTIC = False +_C.CUDNN.ENABLED = True + +# common params for NETWORK +_C.MODEL = CN() +_C.MODEL.NAME = 'pose_hrnet' +_C.MODEL.INIT_WEIGHTS = True +_C.MODEL.PRETRAINED = '' +_C.MODEL.NUM_JOINTS = 17 +_C.MODEL.TAG_PER_JOINT = True +_C.MODEL.TARGET_TYPE = 'gaussian' +_C.MODEL.IMAGE_SIZE = [256, 256] # width * height, ex: 192 * 256 +_C.MODEL.HEATMAP_SIZE = [64, 64] # width * height, ex: 24 * 32 +_C.MODEL.SIGMA = 2 +_C.MODEL.EXTRA = CN(new_allowed=True) + +_C.LOSS = CN() +_C.LOSS.USE_OHKM = False +_C.LOSS.TOPK = 8 +_C.LOSS.USE_TARGET_WEIGHT = True +_C.LOSS.USE_DIFFERENT_JOINTS_WEIGHT = False + +# DATASET related params +_C.DATASET = CN() +_C.DATASET.ROOT = '' +_C.DATASET.DATASET = 'mpii' +_C.DATASET.TRAIN_SET = 'train' +_C.DATASET.TEST_SET = 'valid' +_C.DATASET.DATA_FORMAT = 'jpg' +_C.DATASET.HYBRID_JOINTS_TYPE = '' +_C.DATASET.SELECT_DATA = False + +# training data augmentation +_C.DATASET.FLIP = True +_C.DATASET.SCALE_FACTOR = 0.25 +_C.DATASET.ROT_FACTOR = 30 +_C.DATASET.PROB_HALF_BODY = 0.0 +_C.DATASET.NUM_JOINTS_HALF_BODY = 8 +_C.DATASET.COLOR_RGB = False + +# train +_C.TRAIN = CN() + +_C.TRAIN.LR_FACTOR = 0.1 +_C.TRAIN.LR_STEP = [90, 110] +_C.TRAIN.LR = 0.001 + +_C.TRAIN.OPTIMIZER = 'adam' +_C.TRAIN.MOMENTUM = 0.9 +_C.TRAIN.WD = 0.0001 +_C.TRAIN.NESTEROV = False +_C.TRAIN.GAMMA1 = 0.99 +_C.TRAIN.GAMMA2 = 0.0 + +_C.TRAIN.BEGIN_EPOCH = 0 +_C.TRAIN.END_EPOCH = 140 + +_C.TRAIN.RESUME = False +_C.TRAIN.CHECKPOINT = '' + +_C.TRAIN.BATCH_SIZE_PER_GPU = 32 +_C.TRAIN.SHUFFLE = True + +# testing +_C.TEST = CN() + +# size of images for each device +_C.TEST.BATCH_SIZE_PER_GPU = 32 +# Test Model Epoch +_C.TEST.FLIP_TEST = False +_C.TEST.POST_PROCESS = False +_C.TEST.SHIFT_HEATMAP = False + +_C.TEST.USE_GT_BBOX = False + +# nms +_C.TEST.IMAGE_THRE = 0.1 +_C.TEST.NMS_THRE = 0.6 +_C.TEST.SOFT_NMS = False +_C.TEST.OKS_THRE = 0.5 +_C.TEST.IN_VIS_THRE = 0.0 +_C.TEST.COCO_BBOX_FILE = '' +_C.TEST.BBOX_THRE = 1.0 +_C.TEST.MODEL_FILE = '' + +# debug +_C.DEBUG = CN() +_C.DEBUG.DEBUG = False +_C.DEBUG.SAVE_BATCH_IMAGES_GT = False +_C.DEBUG.SAVE_BATCH_IMAGES_PRED = False +_C.DEBUG.SAVE_HEATMAPS_GT = False +_C.DEBUG.SAVE_HEATMAPS_PRED = False + + +def update_config(cfg, args): + cfg.defrost() + cfg.merge_from_file(args.cfg) + cfg.merge_from_list(args.opts) + + if args.modelDir: + cfg.OUTPUT_DIR = args.modelDir + + if args.logDir: + cfg.LOG_DIR = args.logDir + + if args.dataDir: + cfg.DATA_DIR = args.dataDir + + cfg.DATASET.ROOT = os.path.join( + cfg.DATA_DIR, cfg.DATASET.ROOT + ) + + cfg.MODEL.PRETRAINED = os.path.join( + cfg.DATA_DIR, cfg.MODEL.PRETRAINED + ) + + if cfg.TEST.MODEL_FILE: + cfg.TEST.MODEL_FILE = os.path.join( + cfg.DATA_DIR, cfg.TEST.MODEL_FILE + ) + + cfg.freeze() + + +if __name__ == '__main__': + import sys + with open(sys.argv[1], 'w') as f: + print(_C, file=f) + diff --git a/project/hair_service_sd/keypoints/lib/config/models.py b/project/hair_service_sd/keypoints/lib/config/models.py new file mode 100644 index 0000000..8e04c4f --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/config/models.py @@ -0,0 +1,58 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from yacs.config import CfgNode as CN + + +# pose_resnet related params +POSE_RESNET = CN() +POSE_RESNET.NUM_LAYERS = 50 +POSE_RESNET.DECONV_WITH_BIAS = False +POSE_RESNET.NUM_DECONV_LAYERS = 3 +POSE_RESNET.NUM_DECONV_FILTERS = [256, 256, 256] +POSE_RESNET.NUM_DECONV_KERNELS = [4, 4, 4] +POSE_RESNET.FINAL_CONV_KERNEL = 1 +POSE_RESNET.PRETRAINED_LAYERS = ['*'] + +# pose_multi_resoluton_net related params +POSE_HIGH_RESOLUTION_NET = CN() +POSE_HIGH_RESOLUTION_NET.PRETRAINED_LAYERS = ['*'] +POSE_HIGH_RESOLUTION_NET.STEM_INPLANES = 64 +POSE_HIGH_RESOLUTION_NET.FINAL_CONV_KERNEL = 1 + +POSE_HIGH_RESOLUTION_NET.STAGE2 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BRANCHES = 2 +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_BLOCKS = [4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE2.NUM_CHANNELS = [32, 64] +POSE_HIGH_RESOLUTION_NET.STAGE2.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE2.FUSE_METHOD = 'SUM' + +POSE_HIGH_RESOLUTION_NET.STAGE3 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BRANCHES = 3 +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_BLOCKS = [4, 4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE3.NUM_CHANNELS = [32, 64, 128] +POSE_HIGH_RESOLUTION_NET.STAGE3.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE3.FUSE_METHOD = 'SUM' + +POSE_HIGH_RESOLUTION_NET.STAGE4 = CN() +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_MODULES = 1 +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BRANCHES = 4 +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4] +POSE_HIGH_RESOLUTION_NET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256] +POSE_HIGH_RESOLUTION_NET.STAGE4.BLOCK = 'BASIC' +POSE_HIGH_RESOLUTION_NET.STAGE4.FUSE_METHOD = 'SUM' + + +MODEL_EXTRAS = { + 'pose_resnet': POSE_RESNET, + 'pose_high_resolution_net': POSE_HIGH_RESOLUTION_NET, +} diff --git a/project/hair_service_sd/keypoints/lib/core/evaluate.py b/project/hair_service_sd/keypoints/lib/core/evaluate.py new file mode 100644 index 0000000..cf72285 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/core/evaluate.py @@ -0,0 +1,73 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from core.inference import get_max_preds + + +def calc_dists(preds, target, normalize): + preds = preds.astype(np.float32) + target = target.astype(np.float32) + dists = np.zeros((preds.shape[1], preds.shape[0])) + for n in range(preds.shape[0]): + for c in range(preds.shape[1]): + if target[n, c, 0] > 1 and target[n, c, 1] > 1: + normed_preds = preds[n, c, :] / normalize[n] + normed_targets = target[n, c, :] / normalize[n] + dists[c, n] = np.linalg.norm(normed_preds - normed_targets) + else: + dists[c, n] = -1 + return dists + + +def dist_acc(dists, thr=0.5): + ''' Return percentage below threshold while ignoring values with a -1 ''' + dist_cal = np.not_equal(dists, -1) + num_dist_cal = dist_cal.sum() + if num_dist_cal > 0: + return np.less(dists[dist_cal], thr).sum() * 1.0 / num_dist_cal + else: + return -1 + + +def accuracy(output, target, hm_type='gaussian', thr=0.5): + ''' + Calculate accuracy according to PCK, + but uses ground truth heatmap rather than x,y locations + First value to be returned is average accuracy across 'idxs', + followed by individual accuracies + ''' + idx = list(range(output.shape[1])) + norm = 1.0 + if hm_type == 'gaussian': + pred, _ = get_max_preds(output) + target, _ = get_max_preds(target) + h = output.shape[2] + w = output.shape[3] + norm = np.ones((pred.shape[0], 2)) * np.array([h, w]) / 10 + dists = calc_dists(pred, target, norm) + + acc = np.zeros((len(idx) + 1)) + avg_acc = 0 + cnt = 0 + + for i in range(len(idx)): + acc[i + 1] = dist_acc(dists[idx[i]]) + if acc[i + 1] >= 0: + avg_acc = avg_acc + acc[i + 1] + cnt += 1 + + avg_acc = avg_acc / cnt if cnt != 0 else 0 + if cnt != 0: + acc[0] = avg_acc + return acc, avg_acc, cnt, pred + + diff --git a/project/hair_service_sd/keypoints/lib/core/function.py b/project/hair_service_sd/keypoints/lib/core/function.py new file mode 100644 index 0000000..dadff0f --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/core/function.py @@ -0,0 +1,279 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import time +import logging +import os + +import numpy as np +import torch + +from core.evaluate import accuracy +from core.inference import get_final_preds +from utils.transforms import flip_back +from utils.vis import save_debug_images + + +logger = logging.getLogger(__name__) + + +def train(config, train_loader, model, criterion, optimizer, epoch, + output_dir, tb_log_dir, writer_dict): + batch_time = AverageMeter() + data_time = AverageMeter() + losses = AverageMeter() + acc = AverageMeter() + + # switch to train mode + model.train() + + end = time.time() + for i, (input, target, target_weight, meta) in enumerate(train_loader): + # measure data loading time + data_time.update(time.time() - end) + + # compute output + outputs = model(input) + + target = target.cuda(non_blocking=True) + target_weight = target_weight.cuda(non_blocking=True) + + if isinstance(outputs, list): + loss = criterion(outputs[0], target, target_weight) + for output in outputs[1:]: + loss += criterion(output, target, target_weight) + else: + output = outputs + loss = criterion(output, target, target_weight) + + # loss = criterion(output, target, target_weight) + + # compute gradient and do update step + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # measure accuracy and record loss + losses.update(loss.item(), input.size(0)) + + _, avg_acc, cnt, pred = accuracy(output.detach().cpu().numpy(), + target.detach().cpu().numpy()) + acc.update(avg_acc, cnt) + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + if i % config.PRINT_FREQ == 0: + msg = 'Epoch: [{0}][{1}/{2}]\t' \ + 'Time {batch_time.val:.3f}s ({batch_time.avg:.3f}s)\t' \ + 'Speed {speed:.1f} samples/s\t' \ + 'Data {data_time.val:.3f}s ({data_time.avg:.3f}s)\t' \ + 'Loss {loss.val:.5f} ({loss.avg:.5f})\t' \ + 'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format( + epoch, i, len(train_loader), batch_time=batch_time, + speed=input.size(0)/batch_time.val, + data_time=data_time, loss=losses, acc=acc) + logger.info(msg) + + writer = writer_dict['writer'] + global_steps = writer_dict['train_global_steps'] + writer.add_scalar('train_loss', losses.val, global_steps) + writer.add_scalar('train_acc', acc.val, global_steps) + writer_dict['train_global_steps'] = global_steps + 1 + + prefix = '{}_{}'.format(os.path.join(output_dir, 'train'), i) + save_debug_images(config, input, meta, target, pred*4, output, + prefix) + + +def validate(config, val_loader, val_dataset, model, criterion, output_dir, + tb_log_dir, writer_dict=None): + batch_time = AverageMeter() + losses = AverageMeter() + acc = AverageMeter() + + # switch to evaluate mode + model.eval() + + num_samples = len(val_dataset) + all_preds = np.zeros( + (num_samples, config.MODEL.NUM_JOINTS, 3), + dtype=np.float32 + ) + all_boxes = np.zeros((num_samples, 6)) + image_path = [] + filenames = [] + imgnums = [] + idx = 0 + with torch.no_grad(): + end = time.time() + for i, (input, target, target_weight, meta) in enumerate(val_loader): + # compute output + outputs = model(input) + if isinstance(outputs, list): + output = outputs[-1] + else: + output = outputs + + if config.TEST.FLIP_TEST: + # this part is ugly, because pytorch has not supported negative index + # input_flipped = model(input[:, :, :, ::-1]) + input_flipped = np.flip(input.cpu().numpy(), 3).copy() + input_flipped = torch.from_numpy(input_flipped).cuda() + outputs_flipped = model(input_flipped) + + if isinstance(outputs_flipped, list): + output_flipped = outputs_flipped[-1] + else: + output_flipped = outputs_flipped + + output_flipped = flip_back(output_flipped.cpu().numpy(), + val_dataset.flip_pairs) + output_flipped = torch.from_numpy(output_flipped.copy()).cuda() + + + # feature is not aligned, shift flipped heatmap for higher accuracy + if config.TEST.SHIFT_HEATMAP: + output_flipped[:, :, :, 1:] = \ + output_flipped.clone()[:, :, :, 0:-1] + + output = (output + output_flipped) * 0.5 + + target = target.cuda(non_blocking=True) + target_weight = target_weight.cuda(non_blocking=True) + + loss = criterion(output, target, target_weight) + + num_images = input.size(0) + # measure accuracy and record loss + losses.update(loss.item(), num_images) + _, avg_acc, cnt, pred = accuracy(output.cpu().numpy(), + target.cpu().numpy()) + + acc.update(avg_acc, cnt) + + # measure elapsed time + batch_time.update(time.time() - end) + end = time.time() + + c = meta['center'].numpy() + s = meta['scale'].numpy() + score = meta['score'].numpy() + + preds, maxvals = get_final_preds( + config, output.clone().cpu().numpy(), c, s) + + all_preds[idx:idx + num_images, :, 0:2] = preds[:, :, 0:2] + all_preds[idx:idx + num_images, :, 2:3] = maxvals + # double check this all_boxes parts + all_boxes[idx:idx + num_images, 0:2] = c[:, 0:2] + all_boxes[idx:idx + num_images, 2:4] = s[:, 0:2] + all_boxes[idx:idx + num_images, 4] = np.prod(s*200, 1) + all_boxes[idx:idx + num_images, 5] = score + image_path.extend(meta['image']) + + idx += num_images + + if i % config.PRINT_FREQ == 0: + msg = 'Test: [{0}/{1}]\t' \ + 'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t' \ + 'Loss {loss.val:.4f} ({loss.avg:.4f})\t' \ + 'Accuracy {acc.val:.3f} ({acc.avg:.3f})'.format( + i, len(val_loader), batch_time=batch_time, + loss=losses, acc=acc) + logger.info(msg) + + prefix = '{}_{}'.format( + os.path.join(output_dir, 'val'), i + ) + save_debug_images(config, input, meta, target, pred*4, output, + prefix) + + name_values, perf_indicator = val_dataset.evaluate( + config, all_preds, output_dir, all_boxes, image_path, + filenames, imgnums + ) + + model_name = config.MODEL.NAME + if isinstance(name_values, list): + for name_value in name_values: + _print_name_value(name_value, model_name) + else: + _print_name_value(name_values, model_name) + + if writer_dict: + writer = writer_dict['writer'] + global_steps = writer_dict['valid_global_steps'] + writer.add_scalar( + 'valid_loss', + losses.avg, + global_steps + ) + writer.add_scalar( + 'valid_acc', + acc.avg, + global_steps + ) + if isinstance(name_values, list): + for name_value in name_values: + writer.add_scalars( + 'valid', + dict(name_value), + global_steps + ) + else: + writer.add_scalars( + 'valid', + dict(name_values), + global_steps + ) + writer_dict['valid_global_steps'] = global_steps + 1 + + return perf_indicator + + +# markdown format output +def _print_name_value(name_value, full_arch_name): + names = name_value.keys() + values = name_value.values() + num_values = len(name_value) + logger.info( + '| Arch ' + + ' '.join(['| {}'.format(name) for name in names]) + + ' |' + ) + logger.info('|---' * (num_values+1) + '|') + + if len(full_arch_name) > 15: + full_arch_name = full_arch_name[:8] + '...' + logger.info( + '| ' + full_arch_name + ' ' + + ' '.join(['| {:.3f}'.format(value) for value in values]) + + ' |' + ) + + +class AverageMeter(object): + """Computes and stores the average and current value""" + def __init__(self): + self.reset() + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count if self.count != 0 else 0 diff --git a/project/hair_service_sd/keypoints/lib/core/inference.py b/project/hair_service_sd/keypoints/lib/core/inference.py new file mode 100644 index 0000000..ad0058c --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/core/inference.py @@ -0,0 +1,79 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +import numpy as np + +from keypoints.lib.utils.transforms import transform_preds + + +def get_max_preds(batch_heatmaps): + ''' + get predictions from score maps + heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) + ''' + assert isinstance(batch_heatmaps, np.ndarray), \ + 'batch_heatmaps should be numpy.ndarray' + assert batch_heatmaps.ndim == 4, 'batch_images should be 4-ndim' + + batch_size = batch_heatmaps.shape[0] + num_joints = batch_heatmaps.shape[1] + width = batch_heatmaps.shape[3] + heatmaps_reshaped = batch_heatmaps.reshape((batch_size, num_joints, -1)) + idx = np.argmax(heatmaps_reshaped, 2) + maxvals = np.amax(heatmaps_reshaped, 2) + + maxvals = maxvals.reshape((batch_size, num_joints, 1)) + idx = idx.reshape((batch_size, num_joints, 1)) + + preds = np.tile(idx, (1, 1, 2)).astype(np.float32) + + preds[:, :, 0] = (preds[:, :, 0]) % width + preds[:, :, 1] = np.floor((preds[:, :, 1]) / width) + + pred_mask = np.tile(np.greater(maxvals, 0.0), (1, 1, 2)) + pred_mask = pred_mask.astype(np.float32) + + preds *= pred_mask + return preds, maxvals + + +def get_final_preds(config, batch_heatmaps, center, scale): + coords, maxvals = get_max_preds(batch_heatmaps) + + heatmap_height = batch_heatmaps.shape[2] + heatmap_width = batch_heatmaps.shape[3] + + # post-processing + if config.TEST.POST_PROCESS: + for n in range(coords.shape[0]): + for p in range(coords.shape[1]): + hm = batch_heatmaps[n][p] + px = int(math.floor(coords[n][p][0] + 0.5)) + py = int(math.floor(coords[n][p][1] + 0.5)) + if 1 < px < heatmap_width-1 and 1 < py < heatmap_height-1: + diff = np.array( + [ + hm[py][px+1] - hm[py][px-1], + hm[py+1][px]-hm[py-1][px] + ] + ) + coords[n][p] += np.sign(diff) * .25 + + preds = coords.copy() + + # Transform back + for i in range(coords.shape[0]): + preds[i] = transform_preds( + coords[i], center[i], scale[i], [heatmap_width, heatmap_height] + ) + + return preds, maxvals diff --git a/project/hair_service_sd/keypoints/lib/core/loss.py b/project/hair_service_sd/keypoints/lib/core/loss.py new file mode 100644 index 0000000..b879495 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/core/loss.py @@ -0,0 +1,84 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import torch +import torch.nn as nn + + +class JointsMSELoss(nn.Module): + def __init__(self, use_target_weight): + super(JointsMSELoss, self).__init__() + self.criterion = nn.MSELoss(reduction='mean') + self.use_target_weight = use_target_weight + + def forward(self, output, target, target_weight): + batch_size = output.size(0) + num_joints = output.size(1) + heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, 1) + heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1) + loss = 0 + + for idx in range(num_joints): + heatmap_pred = heatmaps_pred[idx].squeeze() + heatmap_gt = heatmaps_gt[idx].squeeze() + if self.use_target_weight: + loss += 0.5 * self.criterion( + heatmap_pred.mul(target_weight[:, idx]), + heatmap_gt.mul(target_weight[:, idx]) + ) + else: + loss += 0.5 * self.criterion(heatmap_pred, heatmap_gt) + + return loss / num_joints + + +class JointsOHKMMSELoss(nn.Module): + def __init__(self, use_target_weight, topk=8): + super(JointsOHKMMSELoss, self).__init__() + self.criterion = nn.MSELoss(reduction='none') + self.use_target_weight = use_target_weight + self.topk = topk + + def ohkm(self, loss): + ohkm_loss = 0. + for i in range(loss.size()[0]): + sub_loss = loss[i] + topk_val, topk_idx = torch.topk( + sub_loss, k=self.topk, dim=0, sorted=False + ) + tmp_loss = torch.gather(sub_loss, 0, topk_idx) + ohkm_loss += torch.sum(tmp_loss) / self.topk + ohkm_loss /= loss.size()[0] + return ohkm_loss + + def forward(self, output, target, target_weight): + batch_size = output.size(0) + num_joints = output.size(1) + heatmaps_pred = output.reshape((batch_size, num_joints, -1)).split(1, 1) + heatmaps_gt = target.reshape((batch_size, num_joints, -1)).split(1, 1) + + loss = [] + for idx in range(num_joints): + heatmap_pred = heatmaps_pred[idx].squeeze() + heatmap_gt = heatmaps_gt[idx].squeeze() + if self.use_target_weight: + loss.append(0.5 * self.criterion( + heatmap_pred.mul(target_weight[:, idx]), + heatmap_gt.mul(target_weight[:, idx]) + )) + else: + loss.append( + 0.5 * self.criterion(heatmap_pred, heatmap_gt) + ) + + loss = [l.mean(dim=1).unsqueeze(dim=1) for l in loss] + loss = torch.cat(loss, dim=1) + + return self.ohkm(loss) diff --git a/project/hair_service_sd/keypoints/lib/dataset/JointsDataset.py b/project/hair_service_sd/keypoints/lib/dataset/JointsDataset.py new file mode 100644 index 0000000..987f680 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/dataset/JointsDataset.py @@ -0,0 +1,292 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy +import logging +import random + +import cv2 +import numpy as np +import torch +from torch.utils.data import Dataset + +from utils.transforms import get_affine_transform +from utils.transforms import affine_transform +from utils.transforms import fliplr_joints + + +logger = logging.getLogger(__name__) + + +class JointsDataset(Dataset): + def __init__(self, cfg, root, image_set, is_train, transform=None): + self.num_joints = 0 + self.pixel_std = 200 + self.flip_pairs = [] + self.parent_ids = [] + + self.is_train = is_train + self.root = root + self.image_set = image_set + + self.output_path = cfg.OUTPUT_DIR + self.data_format = cfg.DATASET.DATA_FORMAT + + self.scale_factor = cfg.DATASET.SCALE_FACTOR + self.rotation_factor = cfg.DATASET.ROT_FACTOR + self.flip = cfg.DATASET.FLIP + self.num_joints_half_body = cfg.DATASET.NUM_JOINTS_HALF_BODY + self.prob_half_body = cfg.DATASET.PROB_HALF_BODY + self.color_rgb = cfg.DATASET.COLOR_RGB + + self.target_type = cfg.MODEL.TARGET_TYPE + self.image_size = np.array(cfg.MODEL.IMAGE_SIZE) + self.heatmap_size = np.array(cfg.MODEL.HEATMAP_SIZE) + self.sigma = cfg.MODEL.SIGMA + self.use_different_joints_weight = cfg.LOSS.USE_DIFFERENT_JOINTS_WEIGHT + self.joints_weight = 1 + + self.transform = transform + self.db = [] + + def _get_db(self): + raise NotImplementedError + + def evaluate(self, cfg, preds, output_dir, *args, **kwargs): + raise NotImplementedError + + def half_body_transform(self, joints, joints_vis): + upper_joints = [] + lower_joints = [] + for joint_id in range(self.num_joints): + if joints_vis[joint_id][0] > 0: + if joint_id in self.upper_body_ids: + upper_joints.append(joints[joint_id]) + else: + lower_joints.append(joints[joint_id]) + + if np.random.randn() < 0.5 and len(upper_joints) > 2: + selected_joints = upper_joints + else: + selected_joints = lower_joints \ + if len(lower_joints) > 2 else upper_joints + + if len(selected_joints) < 2: + return None, None + + selected_joints = np.array(selected_joints, dtype=np.float32) + center = selected_joints.mean(axis=0)[:2] + + left_top = np.amin(selected_joints, axis=0) + right_bottom = np.amax(selected_joints, axis=0) + + w = right_bottom[0] - left_top[0] + h = right_bottom[1] - left_top[1] + + if w > self.aspect_ratio * h: + h = w * 1.0 / self.aspect_ratio + elif w < self.aspect_ratio * h: + w = h * self.aspect_ratio + + scale = np.array( + [ + w * 1.0 / self.pixel_std, + h * 1.0 / self.pixel_std + ], + dtype=np.float32 + ) + + scale = scale * 1.5 + + return center, scale + + def __len__(self,): + return len(self.db) + + def __getitem__(self, idx): + db_rec = copy.deepcopy(self.db[idx]) + + image_file = db_rec['image'] + filename = db_rec['filename'] if 'filename' in db_rec else '' + imgnum = db_rec['imgnum'] if 'imgnum' in db_rec else '' + + if self.data_format == 'zip': + from utils import zipreader + data_numpy = zipreader.imread( + image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION + ) + else: + data_numpy = cv2.imread( + image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION + ) + + if self.color_rgb: + data_numpy = cv2.cvtColor(data_numpy, cv2.COLOR_BGR2RGB) + + if data_numpy is None: + logger.error('=> fail to read {}'.format(image_file)) + raise ValueError('Fail to read {}'.format(image_file)) + + joints = db_rec['joints_3d'] + joints_vis = db_rec['joints_3d_vis'] + + c = db_rec['center'] + s = db_rec['scale'] + score = db_rec['score'] if 'score' in db_rec else 1 + r = 0 + + if self.is_train: + if (np.sum(joints_vis[:, 0]) > self.num_joints_half_body + and np.random.rand() < self.prob_half_body): + c_half_body, s_half_body = self.half_body_transform( + joints, joints_vis + ) + + if c_half_body is not None and s_half_body is not None: + c, s = c_half_body, s_half_body + + sf = self.scale_factor + rf = self.rotation_factor + s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf) + r = np.clip(np.random.randn()*rf, -rf*2, rf*2) \ + if random.random() <= 0.6 else 0 + + if self.flip and random.random() <= 0.5: + data_numpy = data_numpy[:, ::-1, :] + joints, joints_vis = fliplr_joints( + joints, joints_vis, data_numpy.shape[1], self.flip_pairs) + c[0] = data_numpy.shape[1] - c[0] - 1 + + trans = get_affine_transform(c, s, r, self.image_size) + input = cv2.warpAffine( + data_numpy, + trans, + (int(self.image_size[0]), int(self.image_size[1])), + flags=cv2.INTER_LINEAR) + + cv2.imshow('input', input) + cv2.waitKey() + + if self.transform: + input = self.transform(input) + + for i in range(self.num_joints): + if joints_vis[i, 0] > 0.0: + joints[i, 0:2] = affine_transform(joints[i, 0:2], trans) + + target, target_weight = self.generate_target(joints, joints_vis) + + target = torch.from_numpy(target) + target_weight = torch.from_numpy(target_weight) + + meta = { + 'image': image_file, + 'filename': filename, + 'imgnum': imgnum, + 'joints': joints, + 'joints_vis': joints_vis, + 'center': c, + 'scale': s, + 'rotation': r, + 'score': score + } + + return input, target, target_weight, meta + + def select_data(self, db): + db_selected = [] + for rec in db: + num_vis = 0 + joints_x = 0.0 + joints_y = 0.0 + for joint, joint_vis in zip( + rec['joints_3d'], rec['joints_3d_vis']): + if joint_vis[0] <= 0: + continue + num_vis += 1 + + joints_x += joint[0] + joints_y += joint[1] + if num_vis == 0: + continue + + joints_x, joints_y = joints_x / num_vis, joints_y / num_vis + + area = rec['scale'][0] * rec['scale'][1] * (self.pixel_std**2) + joints_center = np.array([joints_x, joints_y]) + bbox_center = np.array(rec['center']) + diff_norm2 = np.linalg.norm((joints_center-bbox_center), 2) + ks = np.exp(-1.0*(diff_norm2**2) / ((0.2)**2*2.0*area)) + + metric = (0.2 / 16) * num_vis + 0.45 - 0.2 / 16 + if ks > metric: + db_selected.append(rec) + + logger.info('=> num db: {}'.format(len(db))) + logger.info('=> num selected db: {}'.format(len(db_selected))) + return db_selected + + def generate_target(self, joints, joints_vis): + ''' + :param joints: [num_joints, 3] + :param joints_vis: [num_joints, 3] + :return: target, target_weight(1: visible, 0: invisible) + ''' + target_weight = np.ones((self.num_joints, 1), dtype=np.float32) + target_weight[:, 0] = joints_vis[:, 0] + + assert self.target_type == 'gaussian', \ + 'Only support gaussian map now!' + + if self.target_type == 'gaussian': + target = np.zeros((self.num_joints, + self.heatmap_size[1], + self.heatmap_size[0]), + dtype=np.float32) + + tmp_size = self.sigma * 3 + + for joint_id in range(self.num_joints): + feat_stride = self.image_size / self.heatmap_size + mu_x = int(joints[joint_id][0] / feat_stride[0] + 0.5) + mu_y = int(joints[joint_id][1] / feat_stride[1] + 0.5) + # Check that any part of the gaussian is in-bounds + ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)] + br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)] + if ul[0] >= self.heatmap_size[0] or ul[1] >= self.heatmap_size[1] \ + or br[0] < 0 or br[1] < 0: + # If not, just return the image as is + target_weight[joint_id] = 0 + continue + + # # Generate gaussian + size = 2 * tmp_size + 1 + x = np.arange(0, size, 1, np.float32) + y = x[:, np.newaxis] + x0 = y0 = size // 2 + # The gaussian is not normalized, we want the center value to equal 1 + g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * self.sigma ** 2)) + + # Usable gaussian range + g_x = max(0, -ul[0]), min(br[0], self.heatmap_size[0]) - ul[0] + g_y = max(0, -ul[1]), min(br[1], self.heatmap_size[1]) - ul[1] + # Image range + img_x = max(0, ul[0]), min(br[0], self.heatmap_size[0]) + img_y = max(0, ul[1]), min(br[1], self.heatmap_size[1]) + + v = target_weight[joint_id] + if v > 0.5: + target[joint_id][img_y[0]:img_y[1], img_x[0]:img_x[1]] = \ + g[g_y[0]:g_y[1], g_x[0]:g_x[1]] + + if self.use_different_joints_weight: + target_weight = np.multiply(target_weight, self.joints_weight) + + return target, target_weight diff --git a/project/hair_service_sd/keypoints/lib/dataset/__init__.py b/project/hair_service_sd/keypoints/lib/dataset/__init__.py new file mode 100644 index 0000000..16e2413 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/dataset/__init__.py @@ -0,0 +1,12 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from .mpii import MPIIDataset as mpii +from .coco import COCODataset as coco diff --git a/project/hair_service_sd/keypoints/lib/dataset/coco.py b/project/hair_service_sd/keypoints/lib/dataset/coco.py new file mode 100644 index 0000000..1f95f23 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/dataset/coco.py @@ -0,0 +1,445 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from collections import defaultdict +from collections import OrderedDict +import logging +import os + +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +import json_tricks as json +import numpy as np + +from dataset.JointsDataset import JointsDataset +from nms.nms import oks_nms +from nms.nms import soft_oks_nms + + +logger = logging.getLogger(__name__) + + +class COCODataset(JointsDataset): + ''' + "keypoints": { + 0: "nose", + 1: "left_eye", + 2: "right_eye", + 3: "left_ear", + 4: "right_ear", + 5: "left_shoulder", + 6: "right_shoulder", + 7: "left_elbow", + 8: "right_elbow", + 9: "left_wrist", + 10: "right_wrist", + 11: "left_hip", + 12: "right_hip", + 13: "left_knee", + 14: "right_knee", + 15: "left_ankle", + 16: "right_ankle" + }, + "skeleton": [ + [16,14],[14,12],[17,15],[15,13],[12,13],[6,12],[7,13], [6,7],[6,8], + [7,9],[8,10],[9,11],[2,3],[1,2],[1,3],[2,4],[3,5],[4,6],[5,7]] + ''' + def __init__(self, cfg, root, image_set, is_train, transform=None): + super().__init__(cfg, root, image_set, is_train, transform) + self.nms_thre = cfg.TEST.NMS_THRE + self.image_thre = cfg.TEST.IMAGE_THRE + self.soft_nms = cfg.TEST.SOFT_NMS + self.oks_thre = cfg.TEST.OKS_THRE + self.in_vis_thre = cfg.TEST.IN_VIS_THRE + self.bbox_file = cfg.TEST.COCO_BBOX_FILE + self.use_gt_bbox = cfg.TEST.USE_GT_BBOX + self.image_width = cfg.MODEL.IMAGE_SIZE[0] + self.image_height = cfg.MODEL.IMAGE_SIZE[1] + self.aspect_ratio = self.image_width * 1.0 / self.image_height + self.pixel_std = 200 + + self.coco = COCO(self._get_ann_file_keypoint()) + + # deal with class names + cats = [cat['name'] + for cat in self.coco.loadCats(self.coco.getCatIds())] + self.classes = ['__background__'] + cats + logger.info('=> classes: {}'.format(self.classes)) + self.num_classes = len(self.classes) + self._class_to_ind = dict(zip(self.classes, range(self.num_classes))) + self._class_to_coco_ind = dict(zip(cats, self.coco.getCatIds())) + self._coco_ind_to_class_ind = dict( + [ + (self._class_to_coco_ind[cls], self._class_to_ind[cls]) + for cls in self.classes[1:] + ] + ) + + # load image file names + self.image_set_index = self._load_image_set_index() + self.num_images = len(self.image_set_index) + logger.info('=> num_images: {}'.format(self.num_images)) + + self.num_joints = 17 + self.flip_pairs = [[1, 2], [3, 4], [5, 6], [7, 8], + [9, 10], [11, 12], [13, 14], [15, 16]] + self.parent_ids = None + self.upper_body_ids = (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10) + self.lower_body_ids = (11, 12, 13, 14, 15, 16) + + self.joints_weight = np.array( + [ + 1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, + 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, 1.5 + ], + dtype=np.float32 + ).reshape((self.num_joints, 1)) + + self.db = self._get_db() + + if is_train and cfg.DATASET.SELECT_DATA: + self.db = self.select_data(self.db) + + logger.info('=> load {} samples'.format(len(self.db))) + + def _get_ann_file_keypoint(self): + """ self.root / annotations / person_keypoints_train2017.json """ + prefix = 'person_keypoints' \ + if 'test' not in self.image_set else 'image_info' + return os.path.join( + self.root, + 'annotations', + prefix + '_' + self.image_set + '.json' + ) + + def _load_image_set_index(self): + """ image id: int """ + image_ids = self.coco.getImgIds() + return image_ids + + def _get_db(self): + if self.is_train or self.use_gt_bbox: + # use ground truth bbox + gt_db = self._load_coco_keypoint_annotations() + else: + # use bbox from detection + gt_db = self._load_coco_person_detection_results() + return gt_db + + def _load_coco_keypoint_annotations(self): + """ ground truth bbox and keypoints """ + gt_db = [] + for index in self.image_set_index: + gt_db.extend(self._load_coco_keypoint_annotation_kernal(index)) + return gt_db + + def _load_coco_keypoint_annotation_kernal(self, index): + """ + coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id'] + iscrowd: + crowd instances are handled by marking their overlaps with all categories to -1 + and later excluded in training + bbox: + [x1, y1, w, h] + :param index: coco image id + :return: db entry + """ + im_ann = self.coco.loadImgs(index)[0] + width = im_ann['width'] + height = im_ann['height'] + + annIds = self.coco.getAnnIds(imgIds=index, iscrowd=False) + objs = self.coco.loadAnns(annIds) + + # sanitize bboxes + valid_objs = [] + for obj in objs: + x, y, w, h = obj['bbox'] + x1 = np.max((0, x)) + y1 = np.max((0, y)) + x2 = np.min((width - 1, x1 + np.max((0, w - 1)))) + y2 = np.min((height - 1, y1 + np.max((0, h - 1)))) + if obj['area'] > 0 and x2 >= x1 and y2 >= y1: + obj['clean_bbox'] = [x1, y1, x2-x1, y2-y1] + valid_objs.append(obj) + objs = valid_objs + + rec = [] + for obj in objs: + cls = self._coco_ind_to_class_ind[obj['category_id']] + if cls != 1: + continue + + # ignore objs without keypoints annotation + if max(obj['keypoints']) == 0: + continue + + joints_3d = np.zeros((self.num_joints, 3), dtype=np.float) + joints_3d_vis = np.zeros((self.num_joints, 3), dtype=np.float) + for ipt in range(self.num_joints): + joints_3d[ipt, 0] = obj['keypoints'][ipt * 3 + 0] + joints_3d[ipt, 1] = obj['keypoints'][ipt * 3 + 1] + joints_3d[ipt, 2] = 0 + t_vis = obj['keypoints'][ipt * 3 + 2] + if t_vis > 1: + t_vis = 1 + joints_3d_vis[ipt, 0] = t_vis + joints_3d_vis[ipt, 1] = t_vis + joints_3d_vis[ipt, 2] = 0 + + center, scale = self._box2cs(obj['clean_bbox'][:4]) + rec.append({ + 'image': self.image_path_from_index(index), + 'center': center, + 'scale': scale, + 'joints_3d': joints_3d, + 'joints_3d_vis': joints_3d_vis, + 'filename': '', + 'imgnum': 0, + }) + + return rec + + def _box2cs(self, box): + x, y, w, h = box[:4] + return self._xywh2cs(x, y, w, h) + + def _xywh2cs(self, x, y, w, h): + center = np.zeros((2), dtype=np.float32) + center[0] = x + w * 0.5 + center[1] = y + h * 0.5 + + if w > self.aspect_ratio * h: + h = w * 1.0 / self.aspect_ratio + elif w < self.aspect_ratio * h: + w = h * self.aspect_ratio + scale = np.array( + [w * 1.0 / self.pixel_std, h * 1.0 / self.pixel_std], + dtype=np.float32) + if center[0] != -1: + scale = scale * 1.25 + + return center, scale + + def image_path_from_index(self, index): + """ example: images / train2017 / 000000119993.jpg """ + file_name = '%012d.jpg' % index + if '2014' in self.image_set: + file_name = 'COCO_%s_' % self.image_set + file_name + + prefix = 'test2017' if 'test' in self.image_set else self.image_set + + data_name = prefix + '.zip@' if self.data_format == 'zip' else prefix + + image_path = os.path.join( + self.root, 'images', data_name, file_name) + + return image_path + + def _load_coco_person_detection_results(self): + all_boxes = None + with open(self.bbox_file, 'r') as f: + all_boxes = json.load(f)[:10] + + if not all_boxes: + logger.error('=> Load %s fail!' % self.bbox_file) + return None + + logger.info('=> Total boxes: {}'.format(len(all_boxes))) + + kpt_db = [] + num_boxes = 0 + for n_img in range(0, len(all_boxes)): + det_res = all_boxes[n_img] + if det_res['category_id'] != 1: + continue + img_name = self.image_path_from_index(det_res['image_id']) + box = det_res['bbox'] + score = det_res['score'] + + if score < self.image_thre: + continue + + num_boxes = num_boxes + 1 + + center, scale = self._box2cs(box) + joints_3d = np.zeros((self.num_joints, 3), dtype=np.float) + joints_3d_vis = np.ones( + (self.num_joints, 3), dtype=np.float) + kpt_db.append({ + 'image': img_name, + 'center': center, + 'scale': scale, + 'score': score, + 'joints_3d': joints_3d, + 'joints_3d_vis': joints_3d_vis, + }) + + logger.info('=> Total boxes after fliter low score@{}: {}'.format( + self.image_thre, num_boxes)) + return kpt_db + + def evaluate(self, cfg, preds, output_dir, all_boxes, img_path, + *args, **kwargs): + rank = cfg.RANK + + res_folder = os.path.join(output_dir, 'results') + if not os.path.exists(res_folder): + try: + os.makedirs(res_folder) + except Exception: + logger.error('Fail to make {}'.format(res_folder)) + + res_file = os.path.join( + res_folder, 'keypoints_{}_results_{}.json'.format( + self.image_set, rank) + ) + + # person x (keypoints) + _kpts = [] + for idx, kpt in enumerate(preds): + _kpts.append({ + 'keypoints': kpt, + 'center': all_boxes[idx][0:2], + 'scale': all_boxes[idx][2:4], + 'area': all_boxes[idx][4], + 'score': all_boxes[idx][5], + 'image': int(img_path[idx][-16:-4]) + }) + # image x person x (keypoints) + kpts = defaultdict(list) + for kpt in _kpts: + kpts[kpt['image']].append(kpt) + + # rescoring and oks nms + num_joints = self.num_joints + in_vis_thre = self.in_vis_thre + oks_thre = self.oks_thre + oks_nmsed_kpts = [] + for img in kpts.keys(): + img_kpts = kpts[img] + for n_p in img_kpts: + box_score = n_p['score'] + kpt_score = 0 + valid_num = 0 + for n_jt in range(0, num_joints): + t_s = n_p['keypoints'][n_jt][2] + if t_s > in_vis_thre: + kpt_score = kpt_score + t_s + valid_num = valid_num + 1 + if valid_num != 0: + kpt_score = kpt_score / valid_num + # rescoring + n_p['score'] = kpt_score * box_score + + if self.soft_nms: + keep = soft_oks_nms( + [img_kpts[i] for i in range(len(img_kpts))], + oks_thre + ) + else: + keep = oks_nms( + [img_kpts[i] for i in range(len(img_kpts))], + oks_thre + ) + + if len(keep) == 0: + oks_nmsed_kpts.append(img_kpts) + else: + oks_nmsed_kpts.append([img_kpts[_keep] for _keep in keep]) + + self._write_coco_keypoint_results( + oks_nmsed_kpts, res_file) + if 'test' not in self.image_set: + info_str = self._do_python_keypoint_eval( + res_file, res_folder) + name_value = OrderedDict(info_str) + return name_value, name_value['AP'] + else: + return {'Null': 0}, 0 + + def _write_coco_keypoint_results(self, keypoints, res_file): + data_pack = [ + { + 'cat_id': self._class_to_coco_ind[cls], + 'cls_ind': cls_ind, + 'cls': cls, + 'ann_type': 'keypoints', + 'keypoints': keypoints + } + for cls_ind, cls in enumerate(self.classes) if not cls == '__background__' + ] + + results = self._coco_keypoint_results_one_category_kernel(data_pack[0]) + logger.info('=> writing results json to %s' % res_file) + with open(res_file, 'w') as f: + json.dump(results, f, sort_keys=True, indent=4) + try: + json.load(open(res_file)) + except Exception: + content = [] + with open(res_file, 'r') as f: + for line in f: + content.append(line) + content[-1] = ']' + with open(res_file, 'w') as f: + for c in content: + f.write(c) + + def _coco_keypoint_results_one_category_kernel(self, data_pack): + cat_id = data_pack['cat_id'] + keypoints = data_pack['keypoints'] + cat_results = [] + + for img_kpts in keypoints: + if len(img_kpts) == 0: + continue + + _key_points = np.array([img_kpts[k]['keypoints'] + for k in range(len(img_kpts))]) + key_points = np.zeros( + (_key_points.shape[0], self.num_joints * 3), dtype=np.float + ) + + for ipt in range(self.num_joints): + key_points[:, ipt * 3 + 0] = _key_points[:, ipt, 0] + key_points[:, ipt * 3 + 1] = _key_points[:, ipt, 1] + key_points[:, ipt * 3 + 2] = _key_points[:, ipt, 2] # keypoints score. + + result = [ + { + 'image_id': img_kpts[k]['image'], + 'category_id': cat_id, + 'keypoints': list(key_points[k]), + 'score': img_kpts[k]['score'], + 'center': list(img_kpts[k]['center']), + 'scale': list(img_kpts[k]['scale']) + } + for k in range(len(img_kpts)) + ] + cat_results.extend(result) + + return cat_results + + def _do_python_keypoint_eval(self, res_file, res_folder): + coco_dt = self.coco.loadRes(res_file) + coco_eval = COCOeval(self.coco, coco_dt, 'keypoints') + coco_eval.params.useSegm = None + coco_eval.evaluate() + coco_eval.accumulate() + coco_eval.summarize() + + stats_names = ['AP', 'Ap .5', 'AP .75', 'AP (M)', 'AP (L)', 'AR', 'AR .5', 'AR .75', 'AR (M)', 'AR (L)'] + + info_str = [] + for ind, name in enumerate(stats_names): + info_str.append((name, coco_eval.stats[ind])) + + return info_str diff --git a/project/hair_service_sd/keypoints/lib/dataset/mpii.py b/project/hair_service_sd/keypoints/lib/dataset/mpii.py new file mode 100644 index 0000000..c935001 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/dataset/mpii.py @@ -0,0 +1,181 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import logging +import os +import json_tricks as json +from collections import OrderedDict + +import numpy as np +from scipy.io import loadmat, savemat + +from dataset.JointsDataset import JointsDataset + + +logger = logging.getLogger(__name__) + + +class MPIIDataset(JointsDataset): + def __init__(self, cfg, root, image_set, is_train, transform=None): + super().__init__(cfg, root, image_set, is_train, transform) + + self.num_joints = 16 + self.flip_pairs = [[0, 5], [1, 4], [2, 3], [10, 15], [11, 14], [12, 13]] + self.parent_ids = [1, 2, 6, 6, 3, 4, 6, 6, 7, 8, 11, 12, 7, 7, 13, 14] + + self.upper_body_ids = (7, 8, 9, 10, 11, 12, 13, 14, 15) + self.lower_body_ids = (0, 1, 2, 3, 4, 5, 6) + + self.db = self._get_db() + + if is_train and cfg.DATASET.SELECT_DATA: + self.db = self.select_data(self.db) + + logger.info('=> load {} samples'.format(len(self.db))) + + def _get_db(self): + # create train/val split + file_name = os.path.join( + self.root, 'annot', self.image_set+'.json' + ) + with open(file_name) as anno_file: + anno = json.load(anno_file) + + gt_db = [] + for a in anno: + image_name = a['image'] + + c = np.array(a['center'], dtype=np.float) + s = np.array([a['scale'], a['scale']], dtype=np.float) + + # Adjust center/scale slightly to avoid cropping limbs + if c[0] != -1: + c[1] = c[1] + 15 * s[1] + s = s * 1.25 + + # MPII uses matlab format, index is based 1, + # we should first convert to 0-based index + c = c - 1 + + joints_3d = np.zeros((self.num_joints, 3), dtype=np.float) + joints_3d_vis = np.zeros((self.num_joints, 3), dtype=np.float) + if self.image_set != 'test': + joints = np.array(a['joints']) + joints[:, 0:2] = joints[:, 0:2] - 1 + joints_vis = np.array(a['joints_vis']) + assert len(joints) == self.num_joints, \ + 'joint num diff: {} vs {}'.format(len(joints), + self.num_joints) + + joints_3d[:, 0:2] = joints[:, 0:2] + joints_3d_vis[:, 0] = joints_vis[:] + joints_3d_vis[:, 1] = joints_vis[:] + + image_dir = 'images.zip@' if self.data_format == 'zip' else 'images' + gt_db.append( + { + 'image': os.path.join(self.root, image_dir, image_name), + 'center': c, + 'scale': s, + 'joints_3d': joints_3d, + 'joints_3d_vis': joints_3d_vis, + 'filename': '', + 'imgnum': 0, + } + ) + + return gt_db + + def evaluate(self, cfg, preds, output_dir, *args, **kwargs): + # convert 0-based index to 1-based index + preds = preds[:, :, 0:2] + 1.0 + + if output_dir: + pred_file = os.path.join(output_dir, 'pred.mat') + savemat(pred_file, mdict={'preds': preds}) + + if 'test' in cfg.DATASET.TEST_SET: + return {'Null': 0.0}, 0.0 + + SC_BIAS = 0.6 + threshold = 0.5 + + gt_file = os.path.join(cfg.DATASET.ROOT, + 'annot', + 'gt_{}.mat'.format(cfg.DATASET.TEST_SET)) + gt_dict = loadmat(gt_file) + dataset_joints = gt_dict['dataset_joints'] + jnt_missing = gt_dict['jnt_missing'] + pos_gt_src = gt_dict['pos_gt_src'] + headboxes_src = gt_dict['headboxes_src'] + + pos_pred_src = np.transpose(preds, [1, 2, 0]) + + head = np.where(dataset_joints == 'head')[1][0] + lsho = np.where(dataset_joints == 'lsho')[1][0] + lelb = np.where(dataset_joints == 'lelb')[1][0] + lwri = np.where(dataset_joints == 'lwri')[1][0] + lhip = np.where(dataset_joints == 'lhip')[1][0] + lkne = np.where(dataset_joints == 'lkne')[1][0] + lank = np.where(dataset_joints == 'lank')[1][0] + + rsho = np.where(dataset_joints == 'rsho')[1][0] + relb = np.where(dataset_joints == 'relb')[1][0] + rwri = np.where(dataset_joints == 'rwri')[1][0] + rkne = np.where(dataset_joints == 'rkne')[1][0] + rank = np.where(dataset_joints == 'rank')[1][0] + rhip = np.where(dataset_joints == 'rhip')[1][0] + + jnt_visible = 1 - jnt_missing + uv_error = pos_pred_src - pos_gt_src + uv_err = np.linalg.norm(uv_error, axis=1) + headsizes = headboxes_src[1, :, :] - headboxes_src[0, :, :] + headsizes = np.linalg.norm(headsizes, axis=0) + headsizes *= SC_BIAS + scale = np.multiply(headsizes, np.ones((len(uv_err), 1))) + scaled_uv_err = np.divide(uv_err, scale) + scaled_uv_err = np.multiply(scaled_uv_err, jnt_visible) + jnt_count = np.sum(jnt_visible, axis=1) + less_than_threshold = np.multiply((scaled_uv_err <= threshold), + jnt_visible) + PCKh = np.divide(100.*np.sum(less_than_threshold, axis=1), jnt_count) + + # save + rng = np.arange(0, 0.5+0.01, 0.01) + pckAll = np.zeros((len(rng), 16)) + + for r in range(len(rng)): + threshold = rng[r] + less_than_threshold = np.multiply(scaled_uv_err <= threshold, + jnt_visible) + pckAll[r, :] = np.divide(100.*np.sum(less_than_threshold, axis=1), + jnt_count) + + PCKh = np.ma.array(PCKh, mask=False) + PCKh.mask[6:8] = True + + jnt_count = np.ma.array(jnt_count, mask=False) + jnt_count.mask[6:8] = True + jnt_ratio = jnt_count / np.sum(jnt_count).astype(np.float64) + + name_value = [ + ('Head', PCKh[head]), + ('Shoulder', 0.5 * (PCKh[lsho] + PCKh[rsho])), + ('Elbow', 0.5 * (PCKh[lelb] + PCKh[relb])), + ('Wrist', 0.5 * (PCKh[lwri] + PCKh[rwri])), + ('Hip', 0.5 * (PCKh[lhip] + PCKh[rhip])), + ('Knee', 0.5 * (PCKh[lkne] + PCKh[rkne])), + ('Ankle', 0.5 * (PCKh[lank] + PCKh[rank])), + ('Mean', np.sum(PCKh * jnt_ratio)), + ('Mean@0.1', np.sum(pckAll[11, :] * jnt_ratio)) + ] + name_value = OrderedDict(name_value) + + return name_value, name_value['Mean'] diff --git a/project/hair_service_sd/keypoints/lib/models/__init__.py b/project/hair_service_sd/keypoints/lib/models/__init__.py new file mode 100644 index 0000000..9fa06b5 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/models/__init__.py @@ -0,0 +1,13 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function \ No newline at end of file diff --git a/project/hair_service_sd/keypoints/lib/models/pose_hrnet.py b/project/hair_service_sd/keypoints/lib/models/pose_hrnet.py new file mode 100644 index 0000000..ea65f41 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/models/pose_hrnet.py @@ -0,0 +1,501 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import logging + +import torch +import torch.nn as nn + + +BN_MOMENTUM = 0.1 +logger = logging.getLogger(__name__) + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, + bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion, + momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HighResolutionModule(nn.Module): + def __init__(self, num_branches, blocks, num_blocks, num_inchannels, + num_channels, fuse_method, multi_scale_output=True): + super(HighResolutionModule, self).__init__() + self._check_branches( + num_branches, blocks, num_blocks, num_inchannels, num_channels) + + self.num_inchannels = num_inchannels + self.fuse_method = fuse_method + self.num_branches = num_branches + + self.multi_scale_output = multi_scale_output + + self.branches = self._make_branches( + num_branches, blocks, num_blocks, num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(True) + + def _check_branches(self, num_branches, blocks, num_blocks, + num_inchannels, num_channels): + if num_branches != len(num_blocks): + error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format( + num_branches, len(num_blocks)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format( + num_branches, len(num_channels)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_inchannels): + error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format( + num_branches, len(num_inchannels)) + logger.error(error_msg) + raise ValueError(error_msg) + + def _make_one_branch(self, branch_index, block, num_blocks, num_channels, + stride=1): + downsample = None + if stride != 1 or \ + self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.num_inchannels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, stride=stride, bias=False + ), + nn.BatchNorm2d( + num_channels[branch_index] * block.expansion, + momentum=BN_MOMENTUM + ), + ) + + layers = [] + layers.append( + block( + self.num_inchannels[branch_index], + num_channels[branch_index], + stride, + downsample + ) + ) + self.num_inchannels[branch_index] = \ + num_channels[branch_index] * block.expansion + for i in range(1, num_blocks[branch_index]): + layers.append( + block( + self.num_inchannels[branch_index], + num_channels[branch_index] + ) + ) + + return nn.Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + branches = [] + + for i in range(num_branches): + branches.append( + self._make_one_branch(i, block, num_blocks, num_channels) + ) + + return nn.ModuleList(branches) + + def _make_fuse_layers(self): + if self.num_branches == 1: + return None + + num_branches = self.num_branches + num_inchannels = self.num_inchannels + fuse_layers = [] + for i in range(num_branches if self.multi_scale_output else 1): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_inchannels[i], + 1, 1, 0, bias=False + ), + nn.BatchNorm2d(num_inchannels[i]), + nn.Upsample(scale_factor=2**(j-i), mode='nearest') + ) + ) + elif j == i: + fuse_layer.append(None) + else: + conv3x3s = [] + for k in range(i-j): + if k == i - j - 1: + num_outchannels_conv3x3 = num_inchannels[i] + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False + ), + nn.BatchNorm2d(num_outchannels_conv3x3) + ) + ) + else: + num_outchannels_conv3x3 = num_inchannels[j] + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False + ), + nn.BatchNorm2d(num_outchannels_conv3x3), + nn.ReLU(True) + ) + ) + fuse_layer.append(nn.Sequential(*conv3x3s)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def get_num_inchannels(self): + return self.num_inchannels + + def forward(self, x): + if self.num_branches == 1: + return [self.branches[0](x[0])] + + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + + for i in range(len(self.fuse_layers)): + y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) + for j in range(1, self.num_branches): + if i == j: + y = y + x[j] + else: + y = y + self.fuse_layers[i][j](x[j]) + x_fuse.append(self.relu(y)) + + return x_fuse + + +blocks_dict = { + 'BASIC': BasicBlock, + 'BOTTLENECK': Bottleneck +} + + +class PoseHighResolutionNet(nn.Module): + + def __init__(self, cfg, **kwargs): + self.inplanes = 64 + extra = cfg.MODEL.EXTRA + super(PoseHighResolutionNet, self).__init__() + + # stem net + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.layer1 = self._make_layer(Bottleneck, 64, 4) + + self.stage2_cfg = cfg['MODEL']['EXTRA']['STAGE2'] + num_channels = self.stage2_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage2_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels)) + ] + self.transition1 = self._make_transition_layer([256], num_channels) + self.stage2, pre_stage_channels = self._make_stage( + self.stage2_cfg, num_channels) + + self.stage3_cfg = cfg['MODEL']['EXTRA']['STAGE3'] + num_channels = self.stage3_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage3_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels)) + ] + self.transition2 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage3, pre_stage_channels = self._make_stage( + self.stage3_cfg, num_channels) + + self.stage4_cfg = cfg['MODEL']['EXTRA']['STAGE4'] + num_channels = self.stage4_cfg['NUM_CHANNELS'] + block = blocks_dict[self.stage4_cfg['BLOCK']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels)) + ] + self.transition3 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels, multi_scale_output=False) + + self.final_layer = nn.Conv2d( + in_channels=pre_stage_channels[0], + out_channels=cfg.MODEL.NUM_JOINTS, + kernel_size=extra.FINAL_CONV_KERNEL, + stride=1, + padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0 + ) + + self.pretrained_layers = cfg['MODEL']['EXTRA']['PRETRAINED_LAYERS'] + + def _make_transition_layer( + self, num_channels_pre_layer, num_channels_cur_layer): + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append( + nn.Sequential( + nn.Conv2d( + num_channels_pre_layer[i], + num_channels_cur_layer[i], + 3, 1, 1, bias=False + ), + nn.BatchNorm2d(num_channels_cur_layer[i]), + nn.ReLU(inplace=True) + ) + ) + else: + transition_layers.append(None) + else: + conv3x3s = [] + for j in range(i+1-num_branches_pre): + inchannels = num_channels_pre_layer[-1] + outchannels = num_channels_cur_layer[i] \ + if j == i-num_branches_pre else inchannels + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + inchannels, outchannels, 3, 2, 1, bias=False + ), + nn.BatchNorm2d(outchannels), + nn.ReLU(inplace=True) + ) + ) + transition_layers.append(nn.Sequential(*conv3x3s)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False + ), + nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def _make_stage(self, layer_config, num_inchannels, + multi_scale_output=True): + num_modules = layer_config['NUM_MODULES'] + num_branches = layer_config['NUM_BRANCHES'] + num_blocks = layer_config['NUM_BLOCKS'] + num_channels = layer_config['NUM_CHANNELS'] + block = blocks_dict[layer_config['BLOCK']] + fuse_method = layer_config['FUSE_METHOD'] + + modules = [] + for i in range(num_modules): + # multi_scale_output is only used last module + if not multi_scale_output and i == num_modules - 1: + reset_multi_scale_output = False + else: + reset_multi_scale_output = True + + modules.append( + HighResolutionModule( + num_branches, + block, + num_blocks, + num_inchannels, + num_channels, + fuse_method, + reset_multi_scale_output + ) + ) + num_inchannels = modules[-1].get_num_inchannels() + + return nn.Sequential(*modules), num_inchannels + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg['NUM_BRANCHES']): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg['NUM_BRANCHES']): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg['NUM_BRANCHES']): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage4(x_list) + + x = self.final_layer(y_list[0]) + + return x + + def init_weights(self, pretrained=''): + logger.info('=> init weights from normal distribution') + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + nn.init.normal_(m.weight, std=0.001) + for name, _ in m.named_parameters(): + if name in ['bias']: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + nn.init.normal_(m.weight, std=0.001) + for name, _ in m.named_parameters(): + if name in ['bias']: + nn.init.constant_(m.bias, 0) + + if os.path.isfile(pretrained): + pretrained_state_dict = torch.load(pretrained) + logger.info('=> loading pretrained model {}'.format(pretrained)) + + need_init_state_dict = {} + for name, m in pretrained_state_dict.items(): + if name.split('.')[0] in self.pretrained_layers \ + or self.pretrained_layers[0] is '*': + need_init_state_dict[name] = m + self.load_state_dict(need_init_state_dict, strict=False) + elif pretrained: + logger.error('=> please download pre-trained models first!') + raise ValueError('{} is not exist!'.format(pretrained)) + + +def get_pose_net(cfg, is_train, **kwargs): + model = PoseHighResolutionNet(cfg, **kwargs) + + if is_train and cfg.MODEL.INIT_WEIGHTS: + model.init_weights(cfg.MODEL.PRETRAINED) + + return model diff --git a/project/hair_service_sd/keypoints/lib/models/pose_resnet.py b/project/hair_service_sd/keypoints/lib/models/pose_resnet.py new file mode 100644 index 0000000..f264dee --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/models/pose_resnet.py @@ -0,0 +1,271 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import logging + +import torch +import torch.nn as nn + + +BN_MOMENTUM = 0.1 +logger = logging.getLogger(__name__) + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d( + in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False + ) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(inplanes, planes, stride) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(planes, planes) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class Bottleneck(nn.Module): + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM) + self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, + bias=False) + self.bn3 = nn.BatchNorm2d(planes * self.expansion, + momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class PoseResNet(nn.Module): + + def __init__(self, block, layers, cfg, **kwargs): + self.inplanes = 64 + extra = cfg.MODEL.EXTRA + self.deconv_with_bias = extra.DECONV_WITH_BIAS + + super(PoseResNet, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + + # used for deconv layers + self.deconv_layers = self._make_deconv_layer( + extra.NUM_DECONV_LAYERS, + extra.NUM_DECONV_FILTERS, + extra.NUM_DECONV_KERNELS, + ) + + self.final_layer = nn.Conv2d( + in_channels=extra.NUM_DECONV_FILTERS[-1], + out_channels=cfg.MODEL.NUM_JOINTS, + kernel_size=extra.FINAL_CONV_KERNEL, + stride=1, + padding=1 if extra.FINAL_CONV_KERNEL == 3 else 0 + ) + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def _get_deconv_cfg(self, deconv_kernel, index): + if deconv_kernel == 4: + padding = 1 + output_padding = 0 + elif deconv_kernel == 3: + padding = 1 + output_padding = 1 + elif deconv_kernel == 2: + padding = 0 + output_padding = 0 + + return deconv_kernel, padding, output_padding + + def _make_deconv_layer(self, num_layers, num_filters, num_kernels): + assert num_layers == len(num_filters), \ + 'ERROR: num_deconv_layers is different len(num_deconv_filters)' + assert num_layers == len(num_kernels), \ + 'ERROR: num_deconv_layers is different len(num_deconv_filters)' + + layers = [] + for i in range(num_layers): + kernel, padding, output_padding = \ + self._get_deconv_cfg(num_kernels[i], i) + + planes = num_filters[i] + layers.append( + nn.ConvTranspose2d( + in_channels=self.inplanes, + out_channels=planes, + kernel_size=kernel, + stride=2, + padding=padding, + output_padding=output_padding, + bias=self.deconv_with_bias)) + layers.append(nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)) + layers.append(nn.ReLU(inplace=True)) + self.inplanes = planes + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.deconv_layers(x) + x = self.final_layer(x) + + return x + + def init_weights(self, pretrained=''): + if os.path.isfile(pretrained): + logger.info('=> init deconv weights from normal distribution') + for name, m in self.deconv_layers.named_modules(): + if isinstance(m, nn.ConvTranspose2d): + logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) + logger.info('=> init {}.bias as 0'.format(name)) + nn.init.normal_(m.weight, std=0.001) + if self.deconv_with_bias: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + logger.info('=> init {}.weight as 1'.format(name)) + logger.info('=> init {}.bias as 0'.format(name)) + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + logger.info('=> init final conv weights from normal distribution') + for m in self.final_layer.modules(): + if isinstance(m, nn.Conv2d): + # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + logger.info('=> init {}.weight as normal(0, 0.001)'.format(name)) + logger.info('=> init {}.bias as 0'.format(name)) + nn.init.normal_(m.weight, std=0.001) + nn.init.constant_(m.bias, 0) + + pretrained_state_dict = torch.load(pretrained) + logger.info('=> loading pretrained model {}'.format(pretrained)) + self.load_state_dict(pretrained_state_dict, strict=False) + else: + logger.info('=> init weights from normal distribution') + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + nn.init.normal_(m.weight, std=0.001) + # nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + nn.init.normal_(m.weight, std=0.001) + if self.deconv_with_bias: + nn.init.constant_(m.bias, 0) + + +resnet_spec = { + 18: (BasicBlock, [2, 2, 2, 2]), + 34: (BasicBlock, [3, 4, 6, 3]), + 50: (Bottleneck, [3, 4, 6, 3]), + 101: (Bottleneck, [3, 4, 23, 3]), + 152: (Bottleneck, [3, 8, 36, 3]) +} + + +def get_pose_net(cfg, is_train, **kwargs): + num_layers = cfg.MODEL.EXTRA.NUM_LAYERS + + block_class, layers = resnet_spec[num_layers] + + model = PoseResNet(block_class, layers, cfg, **kwargs) + + if is_train and cfg.MODEL.INIT_WEIGHTS: + model.init_weights(cfg.MODEL.PRETRAINED) + + return model diff --git a/project/hair_service_sd/keypoints/lib/nms/__init__.py b/project/hair_service_sd/keypoints/lib/nms/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/project/hair_service_sd/keypoints/lib/nms/nms.py b/project/hair_service_sd/keypoints/lib/nms/nms.py new file mode 100644 index 0000000..dd70c09 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/nms/nms.py @@ -0,0 +1,164 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Modified from py-faster-rcnn (https://github.com/rbgirshick/py-faster-rcnn) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +def py_nms_wrapper(thresh): + def _nms(dets): + return nms(dets, thresh) + return _nms + + +def nms(dets, thresh): + """ + greedily select boxes with high confidence and overlap with current maximum <= thresh + rule out overlap >= thresh + :param dets: [[x1, y1, x2, y2 score]] + :param thresh: retain overlap < thresh + :return: indexes to keep + """ + if dets.shape[0] == 0: + return [] + + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + +def oks_iou(g, d, a_g, a_d, sigmas=None, in_vis_thre=None): + if not isinstance(sigmas, np.ndarray): + sigmas = np.array([.26, .25, .25, .35, .35, .79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89]) / 10.0 + vars = (sigmas * 2) ** 2 + xg = g[0::3] + yg = g[1::3] + vg = g[2::3] + ious = np.zeros((d.shape[0])) + for n_d in range(0, d.shape[0]): + xd = d[n_d, 0::3] + yd = d[n_d, 1::3] + vd = d[n_d, 2::3] + dx = xd - xg + dy = yd - yg + e = (dx ** 2 + dy ** 2) / vars / ((a_g + a_d[n_d]) / 2 + np.spacing(1)) / 2 + if in_vis_thre is not None: + ind = list(vg > in_vis_thre) and list(vd > in_vis_thre) + e = e[ind] + ious[n_d] = np.sum(np.exp(-e)) / e.shape[0] if e.shape[0] != 0 else 0.0 + return ious + + +def oks_nms(kpts_db, thresh, sigmas=None, in_vis_thre=None): + """ + greedily select boxes with high confidence and overlap with current maximum <= thresh + rule out overlap >= thresh, overlap = oks + :param kpts_db + :param thresh: retain overlap < thresh + :return: indexes to keep + """ + if len(kpts_db) == 0: + return [] + + scores = np.array([kpts_db[i]['score'] for i in range(len(kpts_db))]) + kpts = np.array([kpts_db[i]['keypoints'].flatten() for i in range(len(kpts_db))]) + areas = np.array([kpts_db[i]['area'] for i in range(len(kpts_db))]) + + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + + oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]], sigmas, in_vis_thre) + + inds = np.where(oks_ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + +def rescore(overlap, scores, thresh, type='gaussian'): + assert overlap.shape[0] == scores.shape[0] + if type == 'linear': + inds = np.where(overlap >= thresh)[0] + scores[inds] = scores[inds] * (1 - overlap[inds]) + else: + scores = scores * np.exp(- overlap**2 / thresh) + + return scores + + +def soft_oks_nms(kpts_db, thresh, sigmas=None, in_vis_thre=None): + """ + greedily select boxes with high confidence and overlap with current maximum <= thresh + rule out overlap >= thresh, overlap = oks + :param kpts_db + :param thresh: retain overlap < thresh + :return: indexes to keep + """ + if len(kpts_db) == 0: + return [] + + scores = np.array([kpts_db[i]['score'] for i in range(len(kpts_db))]) + kpts = np.array([kpts_db[i]['keypoints'].flatten() for i in range(len(kpts_db))]) + areas = np.array([kpts_db[i]['area'] for i in range(len(kpts_db))]) + + order = scores.argsort()[::-1] + scores = scores[order] + + # max_dets = order.size + max_dets = 20 + keep = np.zeros(max_dets, dtype=np.intp) + keep_cnt = 0 + while order.size > 0 and keep_cnt < max_dets: + i = order[0] + + oks_ovr = oks_iou(kpts[i], kpts[order[1:]], areas[i], areas[order[1:]], sigmas, in_vis_thre) + + order = order[1:] + scores = rescore(oks_ovr, scores[1:], thresh) + + tmp = scores.argsort()[::-1] + order = order[tmp] + scores = scores[tmp] + + keep[keep_cnt] = i + keep_cnt += 1 + + keep = keep[:keep_cnt] + + return keep + # kpts_db = kpts_db[:keep_cnt] + + # return kpts_db diff --git a/project/hair_service_sd/keypoints/lib/utils/__init__.py b/project/hair_service_sd/keypoints/lib/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/project/hair_service_sd/keypoints/lib/utils/transforms.py b/project/hair_service_sd/keypoints/lib/utils/transforms.py new file mode 100644 index 0000000..6b12f44 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/utils/transforms.py @@ -0,0 +1,121 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import cv2 + + +def flip_back(output_flipped, matched_parts): + ''' + ouput_flipped: numpy.ndarray(batch_size, num_joints, height, width) + ''' + assert output_flipped.ndim == 4,\ + 'output_flipped should be [batch_size, num_joints, height, width]' + + output_flipped = output_flipped[:, :, :, ::-1] + + for pair in matched_parts: + tmp = output_flipped[:, pair[0], :, :].copy() + output_flipped[:, pair[0], :, :] = output_flipped[:, pair[1], :, :] + output_flipped[:, pair[1], :, :] = tmp + + return output_flipped + + +def fliplr_joints(joints, joints_vis, width, matched_parts): + """ + flip coords + """ + # Flip horizontal + joints[:, 0] = width - joints[:, 0] - 1 + + # Change left-right parts + for pair in matched_parts: + joints[pair[0], :], joints[pair[1], :] = \ + joints[pair[1], :], joints[pair[0], :].copy() + joints_vis[pair[0], :], joints_vis[pair[1], :] = \ + joints_vis[pair[1], :], joints_vis[pair[0], :].copy() + + return joints*joints_vis, joints_vis + + +def transform_preds(coords, center, scale, output_size): + target_coords = np.zeros(coords.shape) + trans = get_affine_transform(center, scale, 0, output_size, inv=1) + for p in range(coords.shape[0]): + target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans) + return target_coords + + +def get_affine_transform( + center, scale, rot, output_size, + shift=np.array([0, 0], dtype=np.float32), inv=0 +): + if not isinstance(scale, np.ndarray) and not isinstance(scale, list): + print(scale) + scale = np.array([scale, scale]) + + scale_tmp = scale * 200.0 + src_w = scale_tmp[0] + dst_w = output_size[0] + dst_h = output_size[1] + + rot_rad = np.pi * rot / 180 + src_dir = get_dir([0, src_w * -0.5], rot_rad) + dst_dir = np.array([0, dst_w * -0.5], np.float32) + + src = np.zeros((3, 2), dtype=np.float32) + dst = np.zeros((3, 2), dtype=np.float32) + src[0, :] = center + scale_tmp * shift + src[1, :] = center + src_dir + scale_tmp * shift + dst[0, :] = [dst_w * 0.5, dst_h * 0.5] + dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5]) + dst_dir + + src[2:, :] = get_3rd_point(src[0, :], src[1, :]) + dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :]) + + if inv: + trans = cv2.getAffineTransform(np.float32(dst), np.float32(src)) + else: + trans = cv2.getAffineTransform(np.float32(src), np.float32(dst)) + + return trans + + +def affine_transform(pt, t): + new_pt = np.array([pt[0], pt[1], 1.]).T + new_pt = np.dot(t, new_pt) + return new_pt[:2] + + +def get_3rd_point(a, b): + direct = a - b + return b + np.array([-direct[1], direct[0]], dtype=np.float32) + + +def get_dir(src_point, rot_rad): + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + + src_result = [0, 0] + src_result[0] = src_point[0] * cs - src_point[1] * sn + src_result[1] = src_point[0] * sn + src_point[1] * cs + + return src_result + + +def crop(img, center, scale, output_size, rot=0): + trans = get_affine_transform(center, scale, rot, output_size) + + dst_img = cv2.warpAffine( + img, trans, (int(output_size[0]), int(output_size[1])), + flags=cv2.INTER_LINEAR + ) + + return dst_img diff --git a/project/hair_service_sd/keypoints/lib/utils/utils.py b/project/hair_service_sd/keypoints/lib/utils/utils.py new file mode 100644 index 0000000..5c31ca1 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/utils/utils.py @@ -0,0 +1,203 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import logging +import time +from collections import namedtuple +from pathlib import Path + +import torch +import torch.optim as optim +import torch.nn as nn + + +def create_logger(cfg, cfg_name, phase='train'): + root_output_dir = Path(cfg.OUTPUT_DIR) + # set up logger + if not root_output_dir.exists(): + print('=> creating {}'.format(root_output_dir)) + root_output_dir.mkdir() + + dataset = cfg.DATASET.DATASET + '_' + cfg.DATASET.HYBRID_JOINTS_TYPE \ + if cfg.DATASET.HYBRID_JOINTS_TYPE else cfg.DATASET.DATASET + dataset = dataset.replace(':', '_') + model = cfg.MODEL.NAME + cfg_name = os.path.basename(cfg_name).split('.')[0] + + final_output_dir = root_output_dir / dataset / model / cfg_name + + print('=> creating {}'.format(final_output_dir)) + final_output_dir.mkdir(parents=True, exist_ok=True) + + time_str = time.strftime('%Y-%m-%d-%H-%M') + log_file = '{}_{}_{}.log'.format(cfg_name, time_str, phase) + final_log_file = final_output_dir / log_file + head = '%(asctime)-15s %(message)s' + logging.basicConfig(filename=str(final_log_file), + format=head) + logger = logging.getLogger() + logger.setLevel(logging.INFO) + console = logging.StreamHandler() + logging.getLogger('').addHandler(console) + + tensorboard_log_dir = Path(cfg.LOG_DIR) / dataset / model / \ + (cfg_name + '_' + time_str) + + print('=> creating {}'.format(tensorboard_log_dir)) + tensorboard_log_dir.mkdir(parents=True, exist_ok=True) + + return logger, str(final_output_dir), str(tensorboard_log_dir) + + +def get_optimizer(cfg, model): + optimizer = None + if cfg.TRAIN.OPTIMIZER == 'sgd': + optimizer = optim.SGD( + model.parameters(), + lr=cfg.TRAIN.LR, + momentum=cfg.TRAIN.MOMENTUM, + weight_decay=cfg.TRAIN.WD, + nesterov=cfg.TRAIN.NESTEROV + ) + elif cfg.TRAIN.OPTIMIZER == 'adam': + optimizer = optim.Adam( + model.parameters(), + lr=cfg.TRAIN.LR + ) + + return optimizer + + +def save_checkpoint(states, is_best, output_dir, + filename='checkpoint.pth'): + torch.save(states, os.path.join(output_dir, filename)) + if is_best and 'state_dict' in states: + torch.save(states['best_state_dict'], + os.path.join(output_dir, 'model_best.pth')) + + +def get_model_summary(model, *input_tensors, item_length=26, verbose=False): + """ + :param model: + :param input_tensors: + :param item_length: + :return: + """ + + summary = [] + + ModuleDetails = namedtuple( + "Layer", ["name", "input_size", "output_size", "num_parameters", "multiply_adds"]) + hooks = [] + layer_instances = {} + + def add_hooks(module): + + def hook(module, input, output): + class_name = str(module.__class__.__name__) + + instance_index = 1 + if class_name not in layer_instances: + layer_instances[class_name] = instance_index + else: + instance_index = layer_instances[class_name] + 1 + layer_instances[class_name] = instance_index + + layer_name = class_name + "_" + str(instance_index) + + params = 0 + + if class_name.find("Conv") != -1 or class_name.find("BatchNorm") != -1 or \ + class_name.find("Linear") != -1: + for param_ in module.parameters(): + params += param_.view(-1).size(0) + + flops = "Not Available" + if class_name.find("Conv") != -1 and hasattr(module, "weight"): + flops = ( + torch.prod( + torch.LongTensor(list(module.weight.data.size()))) * + torch.prod( + torch.LongTensor(list(output.size())[2:]))).item() + elif isinstance(module, nn.Linear): + flops = (torch.prod(torch.LongTensor(list(output.size()))) \ + * input[0].size(1)).item() + + if isinstance(input[0], list): + input = input[0] + if isinstance(output, list): + output = output[0] + + summary.append( + ModuleDetails( + name=layer_name, + input_size=list(input[0].size()), + output_size=list(output.size()), + num_parameters=params, + multiply_adds=flops) + ) + + if not isinstance(module, nn.ModuleList) \ + and not isinstance(module, nn.Sequential) \ + and module != model: + hooks.append(module.register_forward_hook(hook)) + + model.eval() + model.apply(add_hooks) + + space_len = item_length + + model(*input_tensors) + for hook in hooks: + hook.remove() + + details = '' + if verbose: + details = "Model Summary" + \ + os.linesep + \ + "Name{}Input Size{}Output Size{}Parameters{}Multiply Adds (Flops){}".format( + ' ' * (space_len - len("Name")), + ' ' * (space_len - len("Input Size")), + ' ' * (space_len - len("Output Size")), + ' ' * (space_len - len("Parameters")), + ' ' * (space_len - len("Multiply Adds (Flops)"))) \ + + os.linesep + '-' * space_len * 5 + os.linesep + + params_sum = 0 + flops_sum = 0 + for layer in summary: + params_sum += layer.num_parameters + if layer.multiply_adds != "Not Available": + flops_sum += layer.multiply_adds + if verbose: + details += "{}{}{}{}{}{}{}{}{}{}".format( + layer.name, + ' ' * (space_len - len(layer.name)), + layer.input_size, + ' ' * (space_len - len(str(layer.input_size))), + layer.output_size, + ' ' * (space_len - len(str(layer.output_size))), + layer.num_parameters, + ' ' * (space_len - len(str(layer.num_parameters))), + layer.multiply_adds, + ' ' * (space_len - len(str(layer.multiply_adds)))) \ + + os.linesep + '-' * space_len * 5 + os.linesep + + details += os.linesep \ + + "Total Parameters: {:,}".format(params_sum) \ + + os.linesep + '-' * space_len * 5 + os.linesep + details += "Total Multiply Adds (For Convolution and Linear Layers only): {:,} GFLOPs".format(flops_sum/(1024**3)) \ + + os.linesep + '-' * space_len * 5 + os.linesep + details += "Number of Layers" + os.linesep + for layer in layer_instances: + details += "{} : {} layers ".format(layer, layer_instances[layer]) + + return details diff --git a/project/hair_service_sd/keypoints/lib/utils/vis.py b/project/hair_service_sd/keypoints/lib/utils/vis.py new file mode 100644 index 0000000..adc0947 --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/utils/vis.py @@ -0,0 +1,141 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import math + +import numpy as np +import torchvision +import cv2 + +from core.inference import get_max_preds + + +def save_batch_image_with_joints(batch_image, batch_joints, batch_joints_vis, + file_name, nrow=8, padding=2): + ''' + batch_image: [batch_size, channel, height, width] + batch_joints: [batch_size, num_joints, 3], + batch_joints_vis: [batch_size, num_joints, 1], + } + ''' + grid = torchvision.utils.make_grid(batch_image, nrow, padding, True) + ndarr = grid.mul(255).clamp(0, 255).byte().permute(1, 2, 0).cpu().numpy() + ndarr = ndarr.copy() + + nmaps = batch_image.size(0) + xmaps = min(nrow, nmaps) + ymaps = int(math.ceil(float(nmaps) / xmaps)) + height = int(batch_image.size(2) + padding) + width = int(batch_image.size(3) + padding) + k = 0 + for y in range(ymaps): + for x in range(xmaps): + if k >= nmaps: + break + joints = batch_joints[k] + joints_vis = batch_joints_vis[k] + + for joint, joint_vis in zip(joints, joints_vis): + joint[0] = x * width + padding + joint[0] + joint[1] = y * height + padding + joint[1] + if joint_vis[0]: + cv2.circle(ndarr, (int(joint[0]), int(joint[1])), 2, [255, 0, 0], 2) + k = k + 1 + cv2.imwrite(file_name, ndarr) + + +def save_batch_heatmaps(batch_image, batch_heatmaps, file_name, + normalize=True): + ''' + batch_image: [batch_size, channel, height, width] + batch_heatmaps: ['batch_size, num_joints, height, width] + file_name: saved file name + ''' + if normalize: + batch_image = batch_image.clone() + min = float(batch_image.min()) + max = float(batch_image.max()) + + batch_image.add_(-min).div_(max - min + 1e-5) + + batch_size = batch_heatmaps.size(0) + num_joints = batch_heatmaps.size(1) + heatmap_height = batch_heatmaps.size(2) + heatmap_width = batch_heatmaps.size(3) + + grid_image = np.zeros((batch_size*heatmap_height, + (num_joints+1)*heatmap_width, + 3), + dtype=np.uint8) + + preds, maxvals = get_max_preds(batch_heatmaps.detach().cpu().numpy()) + + for i in range(batch_size): + image = batch_image[i].mul(255)\ + .clamp(0, 255)\ + .byte()\ + .permute(1, 2, 0)\ + .cpu().numpy() + heatmaps = batch_heatmaps[i].mul(255)\ + .clamp(0, 255)\ + .byte()\ + .cpu().numpy() + + resized_image = cv2.resize(image, + (int(heatmap_width), int(heatmap_height))) + + height_begin = heatmap_height * i + height_end = heatmap_height * (i + 1) + for j in range(num_joints): + cv2.circle(resized_image, + (int(preds[i][j][0]), int(preds[i][j][1])), + 1, [0, 0, 255], 1) + heatmap = heatmaps[j, :, :] + colored_heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_JET) + masked_image = colored_heatmap*0.7 + resized_image*0.3 + cv2.circle(masked_image, + (int(preds[i][j][0]), int(preds[i][j][1])), + 1, [0, 0, 255], 1) + + width_begin = heatmap_width * (j+1) + width_end = heatmap_width * (j+2) + grid_image[height_begin:height_end, width_begin:width_end, :] = \ + masked_image + # grid_image[height_begin:height_end, width_begin:width_end, :] = \ + # colored_heatmap*0.7 + resized_image*0.3 + + grid_image[height_begin:height_end, 0:heatmap_width, :] = resized_image + + cv2.imwrite(file_name, grid_image) + + +def save_debug_images(config, input, meta, target, joints_pred, output, + prefix): + if not config.DEBUG.DEBUG: + return + + if config.DEBUG.SAVE_BATCH_IMAGES_GT: + save_batch_image_with_joints( + input, meta['joints'], meta['joints_vis'], + '{}_gt.jpg'.format(prefix) + ) + if config.DEBUG.SAVE_BATCH_IMAGES_PRED: + save_batch_image_with_joints( + input, joints_pred, meta['joints_vis'], + '{}_pred.jpg'.format(prefix) + ) + if config.DEBUG.SAVE_HEATMAPS_GT: + save_batch_heatmaps( + input, target, '{}_hm_gt.jpg'.format(prefix) + ) + if config.DEBUG.SAVE_HEATMAPS_PRED: + save_batch_heatmaps( + input, output, '{}_hm_pred.jpg'.format(prefix) + ) diff --git a/project/hair_service_sd/keypoints/lib/utils/zipreader.py b/project/hair_service_sd/keypoints/lib/utils/zipreader.py new file mode 100644 index 0000000..dab919f --- /dev/null +++ b/project/hair_service_sd/keypoints/lib/utils/zipreader.py @@ -0,0 +1,70 @@ +# ------------------------------------------------------------------------------ +# Copyright (c) Microsoft +# Licensed under the MIT License. +# Written by Bin Xiao (Bin.Xiao@microsoft.com) +# ------------------------------------------------------------------------------ + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import zipfile +import xml.etree.ElementTree as ET + +import cv2 +import numpy as np + +_im_zfile = [] +_xml_path_zip = [] +_xml_zfile = [] + + +def imread(filename, flags=cv2.IMREAD_COLOR): + global _im_zfile + path = filename + pos_at = path.index('@') + if pos_at == -1: + print("character '@' is not found from the given path '%s'"%(path)) + assert 0 + path_zip = path[0: pos_at] + path_img = path[pos_at + 2:] + if not os.path.isfile(path_zip): + print("zip file '%s' is not found"%(path_zip)) + assert 0 + for i in range(len(_im_zfile)): + if _im_zfile[i]['path'] == path_zip: + data = _im_zfile[i]['zipfile'].read(path_img) + return cv2.imdecode(np.frombuffer(data, np.uint8), flags) + + _im_zfile.append({ + 'path': path_zip, + 'zipfile': zipfile.ZipFile(path_zip, 'r') + }) + data = _im_zfile[-1]['zipfile'].read(path_img) + + return cv2.imdecode(np.frombuffer(data, np.uint8), flags) + + +def xmlread(filename): + global _xml_path_zip + global _xml_zfile + path = filename + pos_at = path.index('@') + if pos_at == -1: + print("character '@' is not found from the given path '%s'"%(path)) + assert 0 + path_zip = path[0: pos_at] + path_xml = path[pos_at + 2:] + if not os.path.isfile(path_zip): + print("zip file '%s' is not found"%(path_zip)) + assert 0 + for i in xrange(len(_xml_path_zip)): + if _xml_path_zip[i] == path_zip: + data = _xml_zfile[i].open(path_xml) + return ET.fromstring(data.read()) + _xml_path_zip.append(path_zip) + print("read new xml file '%s'"%(path_zip)) + _xml_zfile.append(zipfile.ZipFile(path_zip, 'r')) + data = _xml_zfile[-1].open(path_xml) + return ET.fromstring(data.read()) diff --git a/project/hair_service_sd/models/layers/data/FDDB/img_list.txt b/project/hair_service_sd/models/layers/data/FDDB/img_list.txt new file mode 100644 index 0000000..5cf3d31 --- /dev/null +++ b/project/hair_service_sd/models/layers/data/FDDB/img_list.txt @@ -0,0 +1,2845 @@ +2002/08/11/big/img_591 +2002/08/26/big/img_265 +2002/07/19/big/img_423 +2002/08/24/big/img_490 +2002/08/31/big/img_17676 +2002/07/31/big/img_228 +2002/07/24/big/img_402 +2002/08/04/big/img_769 +2002/07/19/big/img_581 +2002/08/13/big/img_723 +2002/08/12/big/img_821 +2003/01/17/big/img_610 +2002/08/13/big/img_1116 +2002/08/28/big/img_19238 +2002/08/21/big/img_660 +2002/08/14/big/img_607 +2002/08/05/big/img_3708 +2002/08/19/big/img_511 +2002/08/07/big/img_1316 +2002/07/25/big/img_1047 +2002/07/23/big/img_474 +2002/07/27/big/img_970 +2002/09/02/big/img_15752 +2002/09/01/big/img_16378 +2002/09/01/big/img_16189 +2002/08/26/big/img_276 +2002/07/24/big/img_518 +2002/08/14/big/img_1027 +2002/08/24/big/img_733 +2002/08/15/big/img_249 +2003/01/15/big/img_1371 +2002/08/07/big/img_1348 +2003/01/01/big/img_331 +2002/08/23/big/img_536 +2002/07/30/big/img_224 +2002/08/10/big/img_763 +2002/08/21/big/img_293 +2002/08/15/big/img_1211 +2002/08/15/big/img_1194 +2003/01/15/big/img_390 +2002/08/06/big/img_2893 +2002/08/17/big/img_691 +2002/08/07/big/img_1695 +2002/08/16/big/img_829 +2002/07/25/big/img_201 +2002/08/23/big/img_36 +2003/01/15/big/img_763 +2003/01/15/big/img_637 +2002/08/22/big/img_592 +2002/07/25/big/img_817 +2003/01/15/big/img_1219 +2002/08/05/big/img_3508 +2002/08/15/big/img_1108 +2002/07/19/big/img_488 +2003/01/16/big/img_704 +2003/01/13/big/img_1087 +2002/08/10/big/img_670 +2002/07/24/big/img_104 +2002/08/27/big/img_19823 +2002/09/01/big/img_16229 +2003/01/13/big/img_846 +2002/08/04/big/img_412 +2002/07/22/big/img_554 +2002/08/12/big/img_331 +2002/08/02/big/img_533 +2002/08/12/big/img_259 +2002/08/18/big/img_328 +2003/01/14/big/img_630 +2002/08/05/big/img_3541 +2002/08/06/big/img_2390 +2002/08/20/big/img_150 +2002/08/02/big/img_1231 +2002/08/16/big/img_710 +2002/08/19/big/img_591 +2002/07/22/big/img_725 +2002/07/24/big/img_820 +2003/01/13/big/img_568 +2002/08/22/big/img_853 +2002/08/09/big/img_648 +2002/08/23/big/img_528 +2003/01/14/big/img_888 +2002/08/30/big/img_18201 +2002/08/13/big/img_965 +2003/01/14/big/img_660 +2002/07/19/big/img_517 +2003/01/14/big/img_406 +2002/08/30/big/img_18433 +2002/08/07/big/img_1630 +2002/08/06/big/img_2717 +2002/08/21/big/img_470 +2002/07/23/big/img_633 +2002/08/20/big/img_915 +2002/08/16/big/img_893 +2002/07/29/big/img_644 +2002/08/15/big/img_529 +2002/08/16/big/img_668 +2002/08/07/big/img_1871 +2002/07/25/big/img_192 +2002/07/31/big/img_961 +2002/08/19/big/img_738 +2002/07/31/big/img_382 +2002/08/19/big/img_298 +2003/01/17/big/img_608 +2002/08/21/big/img_514 +2002/07/23/big/img_183 +2003/01/17/big/img_536 +2002/07/24/big/img_478 +2002/08/06/big/img_2997 +2002/09/02/big/img_15380 +2002/08/07/big/img_1153 +2002/07/31/big/img_967 +2002/07/31/big/img_711 +2002/08/26/big/img_664 +2003/01/01/big/img_326 +2002/08/24/big/img_775 +2002/08/08/big/img_961 +2002/08/16/big/img_77 +2002/08/12/big/img_296 +2002/07/22/big/img_905 +2003/01/13/big/img_284 +2002/08/13/big/img_887 +2002/08/24/big/img_849 +2002/07/30/big/img_345 +2002/08/18/big/img_419 +2002/08/01/big/img_1347 +2002/08/05/big/img_3670 +2002/07/21/big/img_479 +2002/08/08/big/img_913 +2002/09/02/big/img_15828 +2002/08/30/big/img_18194 +2002/08/08/big/img_471 +2002/08/22/big/img_734 +2002/08/09/big/img_586 +2002/08/09/big/img_454 +2002/07/29/big/img_47 +2002/07/19/big/img_381 +2002/07/29/big/img_733 +2002/08/20/big/img_327 +2002/07/21/big/img_96 +2002/08/06/big/img_2680 +2002/07/25/big/img_919 +2002/07/21/big/img_158 +2002/07/22/big/img_801 +2002/07/22/big/img_567 +2002/07/24/big/img_804 +2002/07/24/big/img_690 +2003/01/15/big/img_576 +2002/08/14/big/img_335 +2003/01/13/big/img_390 +2002/08/11/big/img_258 +2002/07/23/big/img_917 +2002/08/15/big/img_525 +2003/01/15/big/img_505 +2002/07/30/big/img_886 +2003/01/16/big/img_640 +2003/01/14/big/img_642 +2003/01/17/big/img_844 +2002/08/04/big/img_571 +2002/08/29/big/img_18702 +2003/01/15/big/img_240 +2002/07/29/big/img_553 +2002/08/10/big/img_354 +2002/08/18/big/img_17 +2003/01/15/big/img_782 +2002/07/27/big/img_382 +2002/08/14/big/img_970 +2003/01/16/big/img_70 +2003/01/16/big/img_625 +2002/08/18/big/img_341 +2002/08/26/big/img_188 +2002/08/09/big/img_405 +2002/08/02/big/img_37 +2002/08/13/big/img_748 +2002/07/22/big/img_399 +2002/07/25/big/img_844 +2002/08/12/big/img_340 +2003/01/13/big/img_815 +2002/08/26/big/img_5 +2002/08/10/big/img_158 +2002/08/18/big/img_95 +2002/07/29/big/img_1297 +2003/01/13/big/img_508 +2002/09/01/big/img_16680 +2003/01/16/big/img_338 +2002/08/13/big/img_517 +2002/07/22/big/img_626 +2002/08/06/big/img_3024 +2002/07/26/big/img_499 +2003/01/13/big/img_387 +2002/08/31/big/img_18025 +2002/08/13/big/img_520 +2003/01/16/big/img_576 +2002/07/26/big/img_121 +2002/08/25/big/img_703 +2002/08/26/big/img_615 +2002/08/17/big/img_434 +2002/08/02/big/img_677 +2002/08/18/big/img_276 +2002/08/05/big/img_3672 +2002/07/26/big/img_700 +2002/07/31/big/img_277 +2003/01/14/big/img_220 +2002/08/23/big/img_232 +2002/08/31/big/img_17422 +2002/07/22/big/img_508 +2002/08/13/big/img_681 +2003/01/15/big/img_638 +2002/08/30/big/img_18408 +2003/01/14/big/img_533 +2003/01/17/big/img_12 +2002/08/28/big/img_19388 +2002/08/08/big/img_133 +2002/07/26/big/img_885 +2002/08/19/big/img_387 +2002/08/27/big/img_19976 +2002/08/26/big/img_118 +2002/08/28/big/img_19146 +2002/08/05/big/img_3259 +2002/08/15/big/img_536 +2002/07/22/big/img_279 +2002/07/22/big/img_9 +2002/08/13/big/img_301 +2002/08/15/big/img_974 +2002/08/06/big/img_2355 +2002/08/01/big/img_1526 +2002/08/03/big/img_417 +2002/08/04/big/img_407 +2002/08/15/big/img_1029 +2002/07/29/big/img_700 +2002/08/01/big/img_1463 +2002/08/31/big/img_17365 +2002/07/28/big/img_223 +2002/07/19/big/img_827 +2002/07/27/big/img_531 +2002/07/19/big/img_845 +2002/08/20/big/img_382 +2002/07/31/big/img_268 +2002/08/27/big/img_19705 +2002/08/02/big/img_830 +2002/08/23/big/img_250 +2002/07/20/big/img_777 +2002/08/21/big/img_879 +2002/08/26/big/img_20146 +2002/08/23/big/img_789 +2002/08/06/big/img_2683 +2002/08/25/big/img_576 +2002/08/09/big/img_498 +2002/08/08/big/img_384 +2002/08/26/big/img_592 +2002/07/29/big/img_1470 +2002/08/21/big/img_452 +2002/08/30/big/img_18395 +2002/08/15/big/img_215 +2002/07/21/big/img_643 +2002/07/22/big/img_209 +2003/01/17/big/img_346 +2002/08/25/big/img_658 +2002/08/21/big/img_221 +2002/08/14/big/img_60 +2003/01/17/big/img_885 +2003/01/16/big/img_482 +2002/08/19/big/img_593 +2002/08/08/big/img_233 +2002/07/30/big/img_458 +2002/07/23/big/img_384 +2003/01/15/big/img_670 +2003/01/15/big/img_267 +2002/08/26/big/img_540 +2002/07/29/big/img_552 +2002/07/30/big/img_997 +2003/01/17/big/img_377 +2002/08/21/big/img_265 +2002/08/09/big/img_561 +2002/07/31/big/img_945 +2002/09/02/big/img_15252 +2002/08/11/big/img_276 +2002/07/22/big/img_491 +2002/07/26/big/img_517 +2002/08/14/big/img_726 +2002/08/08/big/img_46 +2002/08/28/big/img_19458 +2002/08/06/big/img_2935 +2002/07/29/big/img_1392 +2002/08/13/big/img_776 +2002/08/24/big/img_616 +2002/08/14/big/img_1065 +2002/07/29/big/img_889 +2002/08/18/big/img_188 +2002/08/07/big/img_1453 +2002/08/02/big/img_760 +2002/07/28/big/img_416 +2002/08/07/big/img_1393 +2002/08/26/big/img_292 +2002/08/26/big/img_301 +2003/01/13/big/img_195 +2002/07/26/big/img_532 +2002/08/20/big/img_550 +2002/08/05/big/img_3658 +2002/08/26/big/img_738 +2002/09/02/big/img_15750 +2003/01/17/big/img_451 +2002/07/23/big/img_339 +2002/08/16/big/img_637 +2002/08/14/big/img_748 +2002/08/06/big/img_2739 +2002/07/25/big/img_482 +2002/08/19/big/img_191 +2002/08/26/big/img_537 +2003/01/15/big/img_716 +2003/01/15/big/img_767 +2002/08/02/big/img_452 +2002/08/08/big/img_1011 +2002/08/10/big/img_144 +2003/01/14/big/img_122 +2002/07/24/big/img_586 +2002/07/24/big/img_762 +2002/08/20/big/img_369 +2002/07/30/big/img_146 +2002/08/23/big/img_396 +2003/01/15/big/img_200 +2002/08/15/big/img_1183 +2003/01/14/big/img_698 +2002/08/09/big/img_792 +2002/08/06/big/img_2347 +2002/07/31/big/img_911 +2002/08/26/big/img_722 +2002/08/23/big/img_621 +2002/08/05/big/img_3790 +2003/01/13/big/img_633 +2002/08/09/big/img_224 +2002/07/24/big/img_454 +2002/07/21/big/img_202 +2002/08/02/big/img_630 +2002/08/30/big/img_18315 +2002/07/19/big/img_491 +2002/09/01/big/img_16456 +2002/08/09/big/img_242 +2002/07/25/big/img_595 +2002/07/22/big/img_522 +2002/08/01/big/img_1593 +2002/07/29/big/img_336 +2002/08/15/big/img_448 +2002/08/28/big/img_19281 +2002/07/29/big/img_342 +2002/08/12/big/img_78 +2003/01/14/big/img_525 +2002/07/28/big/img_147 +2002/08/11/big/img_353 +2002/08/22/big/img_513 +2002/08/04/big/img_721 +2002/08/17/big/img_247 +2003/01/14/big/img_891 +2002/08/20/big/img_853 +2002/07/19/big/img_414 +2002/08/01/big/img_1530 +2003/01/14/big/img_924 +2002/08/22/big/img_468 +2002/08/18/big/img_354 +2002/08/30/big/img_18193 +2002/08/23/big/img_492 +2002/08/15/big/img_871 +2002/08/12/big/img_494 +2002/08/06/big/img_2470 +2002/07/23/big/img_923 +2002/08/26/big/img_155 +2002/08/08/big/img_669 +2002/07/23/big/img_404 +2002/08/28/big/img_19421 +2002/08/29/big/img_18993 +2002/08/25/big/img_416 +2003/01/17/big/img_434 +2002/07/29/big/img_1370 +2002/07/28/big/img_483 +2002/08/11/big/img_50 +2002/08/10/big/img_404 +2002/09/02/big/img_15057 +2003/01/14/big/img_911 +2002/09/01/big/img_16697 +2003/01/16/big/img_665 +2002/09/01/big/img_16708 +2002/08/22/big/img_612 +2002/08/28/big/img_19471 +2002/08/02/big/img_198 +2003/01/16/big/img_527 +2002/08/22/big/img_209 +2002/08/30/big/img_18205 +2003/01/14/big/img_114 +2003/01/14/big/img_1028 +2003/01/16/big/img_894 +2003/01/14/big/img_837 +2002/07/30/big/img_9 +2002/08/06/big/img_2821 +2002/08/04/big/img_85 +2003/01/13/big/img_884 +2002/07/22/big/img_570 +2002/08/07/big/img_1773 +2002/07/26/big/img_208 +2003/01/17/big/img_946 +2002/07/19/big/img_930 +2003/01/01/big/img_698 +2003/01/17/big/img_612 +2002/07/19/big/img_372 +2002/07/30/big/img_721 +2003/01/14/big/img_649 +2002/08/19/big/img_4 +2002/07/25/big/img_1024 +2003/01/15/big/img_601 +2002/08/30/big/img_18470 +2002/07/22/big/img_29 +2002/08/07/big/img_1686 +2002/07/20/big/img_294 +2002/08/14/big/img_800 +2002/08/19/big/img_353 +2002/08/19/big/img_350 +2002/08/05/big/img_3392 +2002/08/09/big/img_622 +2003/01/15/big/img_236 +2002/08/11/big/img_643 +2002/08/05/big/img_3458 +2002/08/12/big/img_413 +2002/08/22/big/img_415 +2002/08/13/big/img_635 +2002/08/07/big/img_1198 +2002/08/04/big/img_873 +2002/08/12/big/img_407 +2003/01/15/big/img_346 +2002/08/02/big/img_275 +2002/08/17/big/img_997 +2002/08/21/big/img_958 +2002/08/20/big/img_579 +2002/07/29/big/img_142 +2003/01/14/big/img_1115 +2002/08/16/big/img_365 +2002/07/29/big/img_1414 +2002/08/17/big/img_489 +2002/08/13/big/img_1010 +2002/07/31/big/img_276 +2002/07/25/big/img_1000 +2002/08/23/big/img_524 +2002/08/28/big/img_19147 +2003/01/13/big/img_433 +2002/08/20/big/img_205 +2003/01/01/big/img_458 +2002/07/29/big/img_1449 +2003/01/16/big/img_696 +2002/08/28/big/img_19296 +2002/08/29/big/img_18688 +2002/08/21/big/img_767 +2002/08/20/big/img_532 +2002/08/26/big/img_187 +2002/07/26/big/img_183 +2002/07/27/big/img_890 +2003/01/13/big/img_576 +2002/07/30/big/img_15 +2002/07/31/big/img_889 +2002/08/31/big/img_17759 +2003/01/14/big/img_1114 +2002/07/19/big/img_445 +2002/08/03/big/img_593 +2002/07/24/big/img_750 +2002/07/30/big/img_133 +2002/08/25/big/img_671 +2002/07/20/big/img_351 +2002/08/31/big/img_17276 +2002/08/05/big/img_3231 +2002/09/02/big/img_15882 +2002/08/14/big/img_115 +2002/08/02/big/img_1148 +2002/07/25/big/img_936 +2002/07/31/big/img_639 +2002/08/04/big/img_427 +2002/08/22/big/img_843 +2003/01/17/big/img_17 +2003/01/13/big/img_690 +2002/08/13/big/img_472 +2002/08/09/big/img_425 +2002/08/05/big/img_3450 +2003/01/17/big/img_439 +2002/08/13/big/img_539 +2002/07/28/big/img_35 +2002/08/16/big/img_241 +2002/08/06/big/img_2898 +2003/01/16/big/img_429 +2002/08/05/big/img_3817 +2002/08/27/big/img_19919 +2002/07/19/big/img_422 +2002/08/15/big/img_560 +2002/07/23/big/img_750 +2002/07/30/big/img_353 +2002/08/05/big/img_43 +2002/08/23/big/img_305 +2002/08/01/big/img_2137 +2002/08/30/big/img_18097 +2002/08/01/big/img_1389 +2002/08/02/big/img_308 +2003/01/14/big/img_652 +2002/08/01/big/img_1798 +2003/01/14/big/img_732 +2003/01/16/big/img_294 +2002/08/26/big/img_213 +2002/07/24/big/img_842 +2003/01/13/big/img_630 +2003/01/13/big/img_634 +2002/08/06/big/img_2285 +2002/08/01/big/img_2162 +2002/08/30/big/img_18134 +2002/08/02/big/img_1045 +2002/08/01/big/img_2143 +2002/07/25/big/img_135 +2002/07/20/big/img_645 +2002/08/05/big/img_3666 +2002/08/14/big/img_523 +2002/08/04/big/img_425 +2003/01/14/big/img_137 +2003/01/01/big/img_176 +2002/08/15/big/img_505 +2002/08/24/big/img_386 +2002/08/05/big/img_3187 +2002/08/15/big/img_419 +2003/01/13/big/img_520 +2002/08/04/big/img_444 +2002/08/26/big/img_483 +2002/08/05/big/img_3449 +2002/08/30/big/img_18409 +2002/08/28/big/img_19455 +2002/08/27/big/img_20090 +2002/07/23/big/img_625 +2002/08/24/big/img_205 +2002/08/08/big/img_938 +2003/01/13/big/img_527 +2002/08/07/big/img_1712 +2002/07/24/big/img_801 +2002/08/09/big/img_579 +2003/01/14/big/img_41 +2003/01/15/big/img_1130 +2002/07/21/big/img_672 +2002/08/07/big/img_1590 +2003/01/01/big/img_532 +2002/08/02/big/img_529 +2002/08/05/big/img_3591 +2002/08/23/big/img_5 +2003/01/14/big/img_882 +2002/08/28/big/img_19234 +2002/07/24/big/img_398 +2003/01/14/big/img_592 +2002/08/22/big/img_548 +2002/08/12/big/img_761 +2003/01/16/big/img_497 +2002/08/18/big/img_133 +2002/08/08/big/img_874 +2002/07/19/big/img_247 +2002/08/15/big/img_170 +2002/08/27/big/img_19679 +2002/08/20/big/img_246 +2002/08/24/big/img_358 +2002/07/29/big/img_599 +2002/08/01/big/img_1555 +2002/07/30/big/img_491 +2002/07/30/big/img_371 +2003/01/16/big/img_682 +2002/07/25/big/img_619 +2003/01/15/big/img_587 +2002/08/02/big/img_1212 +2002/08/01/big/img_2152 +2002/07/25/big/img_668 +2003/01/16/big/img_574 +2002/08/28/big/img_19464 +2002/08/11/big/img_536 +2002/07/24/big/img_201 +2002/08/05/big/img_3488 +2002/07/25/big/img_887 +2002/07/22/big/img_789 +2002/07/30/big/img_432 +2002/08/16/big/img_166 +2002/09/01/big/img_16333 +2002/07/26/big/img_1010 +2002/07/21/big/img_793 +2002/07/22/big/img_720 +2002/07/31/big/img_337 +2002/07/27/big/img_185 +2002/08/23/big/img_440 +2002/07/31/big/img_801 +2002/07/25/big/img_478 +2003/01/14/big/img_171 +2002/08/07/big/img_1054 +2002/09/02/big/img_15659 +2002/07/29/big/img_1348 +2002/08/09/big/img_337 +2002/08/26/big/img_684 +2002/07/31/big/img_537 +2002/08/15/big/img_808 +2003/01/13/big/img_740 +2002/08/07/big/img_1667 +2002/08/03/big/img_404 +2002/08/06/big/img_2520 +2002/07/19/big/img_230 +2002/07/19/big/img_356 +2003/01/16/big/img_627 +2002/08/04/big/img_474 +2002/07/29/big/img_833 +2002/07/25/big/img_176 +2002/08/01/big/img_1684 +2002/08/21/big/img_643 +2002/08/27/big/img_19673 +2002/08/02/big/img_838 +2002/08/06/big/img_2378 +2003/01/15/big/img_48 +2002/07/30/big/img_470 +2002/08/15/big/img_963 +2002/08/24/big/img_444 +2002/08/16/big/img_662 +2002/08/15/big/img_1209 +2002/07/24/big/img_25 +2002/08/06/big/img_2740 +2002/07/29/big/img_996 +2002/08/31/big/img_18074 +2002/08/04/big/img_343 +2003/01/17/big/img_509 +2003/01/13/big/img_726 +2002/08/07/big/img_1466 +2002/07/26/big/img_307 +2002/08/10/big/img_598 +2002/08/13/big/img_890 +2002/08/14/big/img_997 +2002/07/19/big/img_392 +2002/08/02/big/img_475 +2002/08/29/big/img_19038 +2002/07/29/big/img_538 +2002/07/29/big/img_502 +2002/08/02/big/img_364 +2002/08/31/big/img_17353 +2002/08/08/big/img_539 +2002/08/01/big/img_1449 +2002/07/22/big/img_363 +2002/08/02/big/img_90 +2002/09/01/big/img_16867 +2002/08/05/big/img_3371 +2002/07/30/big/img_342 +2002/08/07/big/img_1363 +2002/08/22/big/img_790 +2003/01/15/big/img_404 +2002/08/05/big/img_3447 +2002/09/01/big/img_16167 +2003/01/13/big/img_840 +2002/08/22/big/img_1001 +2002/08/09/big/img_431 +2002/07/27/big/img_618 +2002/07/31/big/img_741 +2002/07/30/big/img_964 +2002/07/25/big/img_86 +2002/07/29/big/img_275 +2002/08/21/big/img_921 +2002/07/26/big/img_892 +2002/08/21/big/img_663 +2003/01/13/big/img_567 +2003/01/14/big/img_719 +2002/07/28/big/img_251 +2003/01/15/big/img_1123 +2002/07/29/big/img_260 +2002/08/24/big/img_337 +2002/08/01/big/img_1914 +2002/08/13/big/img_373 +2003/01/15/big/img_589 +2002/08/13/big/img_906 +2002/07/26/big/img_270 +2002/08/26/big/img_313 +2002/08/25/big/img_694 +2003/01/01/big/img_327 +2002/07/23/big/img_261 +2002/08/26/big/img_642 +2002/07/29/big/img_918 +2002/07/23/big/img_455 +2002/07/24/big/img_612 +2002/07/23/big/img_534 +2002/07/19/big/img_534 +2002/07/19/big/img_726 +2002/08/01/big/img_2146 +2002/08/02/big/img_543 +2003/01/16/big/img_777 +2002/07/30/big/img_484 +2002/08/13/big/img_1161 +2002/07/21/big/img_390 +2002/08/06/big/img_2288 +2002/08/21/big/img_677 +2002/08/13/big/img_747 +2002/08/15/big/img_1248 +2002/07/31/big/img_416 +2002/09/02/big/img_15259 +2002/08/16/big/img_781 +2002/08/24/big/img_754 +2002/07/24/big/img_803 +2002/08/20/big/img_609 +2002/08/28/big/img_19571 +2002/09/01/big/img_16140 +2002/08/26/big/img_769 +2002/07/20/big/img_588 +2002/08/02/big/img_898 +2002/07/21/big/img_466 +2002/08/14/big/img_1046 +2002/07/25/big/img_212 +2002/08/26/big/img_353 +2002/08/19/big/img_810 +2002/08/31/big/img_17824 +2002/08/12/big/img_631 +2002/07/19/big/img_828 +2002/07/24/big/img_130 +2002/08/25/big/img_580 +2002/07/31/big/img_699 +2002/07/23/big/img_808 +2002/07/31/big/img_377 +2003/01/16/big/img_570 +2002/09/01/big/img_16254 +2002/07/21/big/img_471 +2002/08/01/big/img_1548 +2002/08/18/big/img_252 +2002/08/19/big/img_576 +2002/08/20/big/img_464 +2002/07/27/big/img_735 +2002/08/21/big/img_589 +2003/01/15/big/img_1192 +2002/08/09/big/img_302 +2002/07/31/big/img_594 +2002/08/23/big/img_19 +2002/08/29/big/img_18819 +2002/08/19/big/img_293 +2002/07/30/big/img_331 +2002/08/23/big/img_607 +2002/07/30/big/img_363 +2002/08/16/big/img_766 +2003/01/13/big/img_481 +2002/08/06/big/img_2515 +2002/09/02/big/img_15913 +2002/09/02/big/img_15827 +2002/09/02/big/img_15053 +2002/08/07/big/img_1576 +2002/07/23/big/img_268 +2002/08/21/big/img_152 +2003/01/15/big/img_578 +2002/07/21/big/img_589 +2002/07/20/big/img_548 +2002/08/27/big/img_19693 +2002/08/31/big/img_17252 +2002/07/31/big/img_138 +2002/07/23/big/img_372 +2002/08/16/big/img_695 +2002/07/27/big/img_287 +2002/08/15/big/img_315 +2002/08/10/big/img_361 +2002/07/29/big/img_899 +2002/08/13/big/img_771 +2002/08/21/big/img_92 +2003/01/15/big/img_425 +2003/01/16/big/img_450 +2002/09/01/big/img_16942 +2002/08/02/big/img_51 +2002/09/02/big/img_15379 +2002/08/24/big/img_147 +2002/08/30/big/img_18122 +2002/07/26/big/img_950 +2002/08/07/big/img_1400 +2002/08/17/big/img_468 +2002/08/15/big/img_470 +2002/07/30/big/img_318 +2002/07/22/big/img_644 +2002/08/27/big/img_19732 +2002/07/23/big/img_601 +2002/08/26/big/img_398 +2002/08/21/big/img_428 +2002/08/06/big/img_2119 +2002/08/29/big/img_19103 +2003/01/14/big/img_933 +2002/08/11/big/img_674 +2002/08/28/big/img_19420 +2002/08/03/big/img_418 +2002/08/17/big/img_312 +2002/07/25/big/img_1044 +2003/01/17/big/img_671 +2002/08/30/big/img_18297 +2002/07/25/big/img_755 +2002/07/23/big/img_471 +2002/08/21/big/img_39 +2002/07/26/big/img_699 +2003/01/14/big/img_33 +2002/07/31/big/img_411 +2002/08/16/big/img_645 +2003/01/17/big/img_116 +2002/09/02/big/img_15903 +2002/08/20/big/img_120 +2002/08/22/big/img_176 +2002/07/29/big/img_1316 +2002/08/27/big/img_19914 +2002/07/22/big/img_719 +2002/08/28/big/img_19239 +2003/01/13/big/img_385 +2002/08/08/big/img_525 +2002/07/19/big/img_782 +2002/08/13/big/img_843 +2002/07/30/big/img_107 +2002/08/11/big/img_752 +2002/07/29/big/img_383 +2002/08/26/big/img_249 +2002/08/29/big/img_18860 +2002/07/30/big/img_70 +2002/07/26/big/img_194 +2002/08/15/big/img_530 +2002/08/08/big/img_816 +2002/07/31/big/img_286 +2003/01/13/big/img_294 +2002/07/31/big/img_251 +2002/07/24/big/img_13 +2002/08/31/big/img_17938 +2002/07/22/big/img_642 +2003/01/14/big/img_728 +2002/08/18/big/img_47 +2002/08/22/big/img_306 +2002/08/20/big/img_348 +2002/08/15/big/img_764 +2002/08/08/big/img_163 +2002/07/23/big/img_531 +2002/07/23/big/img_467 +2003/01/16/big/img_743 +2003/01/13/big/img_535 +2002/08/02/big/img_523 +2002/08/22/big/img_120 +2002/08/11/big/img_496 +2002/08/29/big/img_19075 +2002/08/08/big/img_465 +2002/08/09/big/img_790 +2002/08/19/big/img_588 +2002/08/23/big/img_407 +2003/01/17/big/img_435 +2002/08/24/big/img_398 +2002/08/27/big/img_19899 +2003/01/15/big/img_335 +2002/08/13/big/img_493 +2002/09/02/big/img_15460 +2002/07/31/big/img_470 +2002/08/05/big/img_3550 +2002/07/28/big/img_123 +2002/08/01/big/img_1498 +2002/08/04/big/img_504 +2003/01/17/big/img_427 +2002/08/27/big/img_19708 +2002/07/27/big/img_861 +2002/07/25/big/img_685 +2002/07/31/big/img_207 +2003/01/14/big/img_745 +2002/08/31/big/img_17756 +2002/08/24/big/img_288 +2002/08/18/big/img_181 +2002/08/10/big/img_520 +2002/08/25/big/img_705 +2002/08/23/big/img_226 +2002/08/04/big/img_727 +2002/07/24/big/img_625 +2002/08/28/big/img_19157 +2002/08/23/big/img_586 +2002/07/31/big/img_232 +2003/01/13/big/img_240 +2003/01/14/big/img_321 +2003/01/15/big/img_533 +2002/07/23/big/img_480 +2002/07/24/big/img_371 +2002/08/21/big/img_702 +2002/08/31/big/img_17075 +2002/09/02/big/img_15278 +2002/07/29/big/img_246 +2003/01/15/big/img_829 +2003/01/15/big/img_1213 +2003/01/16/big/img_441 +2002/08/14/big/img_921 +2002/07/23/big/img_425 +2002/08/15/big/img_296 +2002/07/19/big/img_135 +2002/07/26/big/img_402 +2003/01/17/big/img_88 +2002/08/20/big/img_872 +2002/08/13/big/img_1110 +2003/01/16/big/img_1040 +2002/07/23/big/img_9 +2002/08/13/big/img_700 +2002/08/16/big/img_371 +2002/08/27/big/img_19966 +2003/01/17/big/img_391 +2002/08/18/big/img_426 +2002/08/01/big/img_1618 +2002/07/21/big/img_754 +2003/01/14/big/img_1101 +2003/01/16/big/img_1022 +2002/07/22/big/img_275 +2002/08/24/big/img_86 +2002/08/17/big/img_582 +2003/01/15/big/img_765 +2003/01/17/big/img_449 +2002/07/28/big/img_265 +2003/01/13/big/img_552 +2002/07/28/big/img_115 +2003/01/16/big/img_56 +2002/08/02/big/img_1232 +2003/01/17/big/img_925 +2002/07/22/big/img_445 +2002/07/25/big/img_957 +2002/07/20/big/img_589 +2002/08/31/big/img_17107 +2002/07/29/big/img_483 +2002/08/14/big/img_1063 +2002/08/07/big/img_1545 +2002/08/14/big/img_680 +2002/09/01/big/img_16694 +2002/08/14/big/img_257 +2002/08/11/big/img_726 +2002/07/26/big/img_681 +2002/07/25/big/img_481 +2003/01/14/big/img_737 +2002/08/28/big/img_19480 +2003/01/16/big/img_362 +2002/08/27/big/img_19865 +2003/01/01/big/img_547 +2002/09/02/big/img_15074 +2002/08/01/big/img_1453 +2002/08/22/big/img_594 +2002/08/28/big/img_19263 +2002/08/13/big/img_478 +2002/07/29/big/img_1358 +2003/01/14/big/img_1022 +2002/08/16/big/img_450 +2002/08/02/big/img_159 +2002/07/26/big/img_781 +2003/01/13/big/img_601 +2002/08/20/big/img_407 +2002/08/15/big/img_468 +2002/08/31/big/img_17902 +2002/08/16/big/img_81 +2002/07/25/big/img_987 +2002/07/25/big/img_500 +2002/08/02/big/img_31 +2002/08/18/big/img_538 +2002/08/08/big/img_54 +2002/07/23/big/img_686 +2002/07/24/big/img_836 +2003/01/17/big/img_734 +2002/08/16/big/img_1055 +2003/01/16/big/img_521 +2002/07/25/big/img_612 +2002/08/22/big/img_778 +2002/08/03/big/img_251 +2002/08/12/big/img_436 +2002/08/23/big/img_705 +2002/07/28/big/img_243 +2002/07/25/big/img_1029 +2002/08/20/big/img_287 +2002/08/29/big/img_18739 +2002/08/05/big/img_3272 +2002/07/27/big/img_214 +2003/01/14/big/img_5 +2002/08/01/big/img_1380 +2002/08/29/big/img_19097 +2002/07/30/big/img_486 +2002/08/29/big/img_18707 +2002/08/10/big/img_559 +2002/08/15/big/img_365 +2002/08/09/big/img_525 +2002/08/10/big/img_689 +2002/07/25/big/img_502 +2002/08/03/big/img_667 +2002/08/10/big/img_855 +2002/08/10/big/img_706 +2002/08/18/big/img_603 +2003/01/16/big/img_1055 +2002/08/31/big/img_17890 +2002/08/15/big/img_761 +2003/01/15/big/img_489 +2002/08/26/big/img_351 +2002/08/01/big/img_1772 +2002/08/31/big/img_17729 +2002/07/25/big/img_609 +2003/01/13/big/img_539 +2002/07/27/big/img_686 +2002/07/31/big/img_311 +2002/08/22/big/img_799 +2003/01/16/big/img_936 +2002/08/31/big/img_17813 +2002/08/04/big/img_862 +2002/08/09/big/img_332 +2002/07/20/big/img_148 +2002/08/12/big/img_426 +2002/07/24/big/img_69 +2002/07/27/big/img_685 +2002/08/02/big/img_480 +2002/08/26/big/img_154 +2002/07/24/big/img_598 +2002/08/01/big/img_1881 +2002/08/20/big/img_667 +2003/01/14/big/img_495 +2002/07/21/big/img_744 +2002/07/30/big/img_150 +2002/07/23/big/img_924 +2002/08/08/big/img_272 +2002/07/23/big/img_310 +2002/07/25/big/img_1011 +2002/09/02/big/img_15725 +2002/07/19/big/img_814 +2002/08/20/big/img_936 +2002/07/25/big/img_85 +2002/08/24/big/img_662 +2002/08/09/big/img_495 +2003/01/15/big/img_196 +2002/08/16/big/img_707 +2002/08/28/big/img_19370 +2002/08/06/big/img_2366 +2002/08/06/big/img_3012 +2002/08/01/big/img_1452 +2002/07/31/big/img_742 +2002/07/27/big/img_914 +2003/01/13/big/img_290 +2002/07/31/big/img_288 +2002/08/02/big/img_171 +2002/08/22/big/img_191 +2002/07/27/big/img_1066 +2002/08/12/big/img_383 +2003/01/17/big/img_1018 +2002/08/01/big/img_1785 +2002/08/11/big/img_390 +2002/08/27/big/img_20037 +2002/08/12/big/img_38 +2003/01/15/big/img_103 +2002/08/26/big/img_31 +2002/08/18/big/img_660 +2002/07/22/big/img_694 +2002/08/15/big/img_24 +2002/07/27/big/img_1077 +2002/08/01/big/img_1943 +2002/07/22/big/img_292 +2002/09/01/big/img_16857 +2002/07/22/big/img_892 +2003/01/14/big/img_46 +2002/08/09/big/img_469 +2002/08/09/big/img_414 +2003/01/16/big/img_40 +2002/08/28/big/img_19231 +2002/07/27/big/img_978 +2002/07/23/big/img_475 +2002/07/25/big/img_92 +2002/08/09/big/img_799 +2002/07/25/big/img_491 +2002/08/03/big/img_654 +2003/01/15/big/img_687 +2002/08/11/big/img_478 +2002/08/07/big/img_1664 +2002/08/20/big/img_362 +2002/08/01/big/img_1298 +2003/01/13/big/img_500 +2002/08/06/big/img_2896 +2002/08/30/big/img_18529 +2002/08/16/big/img_1020 +2002/07/29/big/img_892 +2002/08/29/big/img_18726 +2002/07/21/big/img_453 +2002/08/17/big/img_437 +2002/07/19/big/img_665 +2002/07/22/big/img_440 +2002/07/19/big/img_582 +2002/07/21/big/img_233 +2003/01/01/big/img_82 +2002/07/25/big/img_341 +2002/07/29/big/img_864 +2002/08/02/big/img_276 +2002/08/29/big/img_18654 +2002/07/27/big/img_1024 +2002/08/19/big/img_373 +2003/01/15/big/img_241 +2002/07/25/big/img_84 +2002/08/13/big/img_834 +2002/08/10/big/img_511 +2002/08/01/big/img_1627 +2002/08/08/big/img_607 +2002/08/06/big/img_2083 +2002/08/01/big/img_1486 +2002/08/08/big/img_700 +2002/08/01/big/img_1954 +2002/08/21/big/img_54 +2002/07/30/big/img_847 +2002/08/28/big/img_19169 +2002/07/21/big/img_549 +2002/08/03/big/img_693 +2002/07/31/big/img_1002 +2003/01/14/big/img_1035 +2003/01/16/big/img_622 +2002/07/30/big/img_1201 +2002/08/10/big/img_444 +2002/07/31/big/img_374 +2002/08/21/big/img_301 +2002/08/13/big/img_1095 +2003/01/13/big/img_288 +2002/07/25/big/img_232 +2003/01/13/big/img_967 +2002/08/26/big/img_360 +2002/08/05/big/img_67 +2002/08/29/big/img_18969 +2002/07/28/big/img_16 +2002/08/16/big/img_515 +2002/07/20/big/img_708 +2002/08/18/big/img_178 +2003/01/15/big/img_509 +2002/07/25/big/img_430 +2002/08/21/big/img_738 +2002/08/16/big/img_886 +2002/09/02/big/img_15605 +2002/09/01/big/img_16242 +2002/08/24/big/img_711 +2002/07/25/big/img_90 +2002/08/09/big/img_491 +2002/07/30/big/img_534 +2003/01/13/big/img_474 +2002/08/25/big/img_510 +2002/08/15/big/img_555 +2002/08/02/big/img_775 +2002/07/23/big/img_975 +2002/08/19/big/img_229 +2003/01/17/big/img_860 +2003/01/02/big/img_10 +2002/07/23/big/img_542 +2002/08/06/big/img_2535 +2002/07/22/big/img_37 +2002/08/06/big/img_2342 +2002/08/25/big/img_515 +2002/08/25/big/img_336 +2002/08/18/big/img_837 +2002/08/21/big/img_616 +2003/01/17/big/img_24 +2002/07/26/big/img_936 +2002/08/14/big/img_896 +2002/07/29/big/img_465 +2002/07/31/big/img_543 +2002/08/01/big/img_1411 +2002/08/02/big/img_423 +2002/08/21/big/img_44 +2002/07/31/big/img_11 +2003/01/15/big/img_628 +2003/01/15/big/img_605 +2002/07/30/big/img_571 +2002/07/23/big/img_428 +2002/08/15/big/img_942 +2002/07/26/big/img_531 +2003/01/16/big/img_59 +2002/08/02/big/img_410 +2002/07/31/big/img_230 +2002/08/19/big/img_806 +2003/01/14/big/img_462 +2002/08/16/big/img_370 +2002/08/13/big/img_380 +2002/08/16/big/img_932 +2002/07/19/big/img_393 +2002/08/20/big/img_764 +2002/08/15/big/img_616 +2002/07/26/big/img_267 +2002/07/27/big/img_1069 +2002/08/14/big/img_1041 +2003/01/13/big/img_594 +2002/09/01/big/img_16845 +2002/08/09/big/img_229 +2003/01/16/big/img_639 +2002/08/19/big/img_398 +2002/08/18/big/img_978 +2002/08/24/big/img_296 +2002/07/29/big/img_415 +2002/07/30/big/img_923 +2002/08/18/big/img_575 +2002/08/22/big/img_182 +2002/07/25/big/img_806 +2002/07/22/big/img_49 +2002/07/29/big/img_989 +2003/01/17/big/img_789 +2003/01/15/big/img_503 +2002/09/01/big/img_16062 +2003/01/17/big/img_794 +2002/08/15/big/img_564 +2003/01/15/big/img_222 +2002/08/01/big/img_1656 +2003/01/13/big/img_432 +2002/07/19/big/img_426 +2002/08/17/big/img_244 +2002/08/13/big/img_805 +2002/09/02/big/img_15067 +2002/08/11/big/img_58 +2002/08/22/big/img_636 +2002/07/22/big/img_416 +2002/08/13/big/img_836 +2002/08/26/big/img_363 +2002/07/30/big/img_917 +2003/01/14/big/img_206 +2002/08/12/big/img_311 +2002/08/31/big/img_17623 +2002/07/29/big/img_661 +2003/01/13/big/img_417 +2002/08/02/big/img_463 +2002/08/02/big/img_669 +2002/08/26/big/img_670 +2002/08/02/big/img_375 +2002/07/19/big/img_209 +2002/08/08/big/img_115 +2002/08/21/big/img_399 +2002/08/20/big/img_911 +2002/08/07/big/img_1212 +2002/08/20/big/img_578 +2002/08/22/big/img_554 +2002/08/21/big/img_484 +2002/07/25/big/img_450 +2002/08/03/big/img_542 +2002/08/15/big/img_561 +2002/07/23/big/img_360 +2002/08/30/big/img_18137 +2002/07/25/big/img_250 +2002/08/03/big/img_647 +2002/08/20/big/img_375 +2002/08/14/big/img_387 +2002/09/01/big/img_16990 +2002/08/28/big/img_19341 +2003/01/15/big/img_239 +2002/08/20/big/img_528 +2002/08/12/big/img_130 +2002/09/02/big/img_15108 +2003/01/15/big/img_372 +2002/08/16/big/img_678 +2002/08/04/big/img_623 +2002/07/23/big/img_477 +2002/08/28/big/img_19590 +2003/01/17/big/img_978 +2002/09/01/big/img_16692 +2002/07/20/big/img_109 +2002/08/06/big/img_2660 +2003/01/14/big/img_464 +2002/08/09/big/img_618 +2002/07/22/big/img_722 +2002/08/25/big/img_419 +2002/08/03/big/img_314 +2002/08/25/big/img_40 +2002/07/27/big/img_430 +2002/08/10/big/img_569 +2002/08/23/big/img_398 +2002/07/23/big/img_893 +2002/08/16/big/img_261 +2002/08/06/big/img_2668 +2002/07/22/big/img_835 +2002/09/02/big/img_15093 +2003/01/16/big/img_65 +2002/08/21/big/img_448 +2003/01/14/big/img_351 +2003/01/17/big/img_133 +2002/07/28/big/img_493 +2003/01/15/big/img_640 +2002/09/01/big/img_16880 +2002/08/15/big/img_350 +2002/08/20/big/img_624 +2002/08/25/big/img_604 +2002/08/06/big/img_2200 +2002/08/23/big/img_290 +2002/08/13/big/img_1152 +2003/01/14/big/img_251 +2002/08/02/big/img_538 +2002/08/22/big/img_613 +2003/01/13/big/img_351 +2002/08/18/big/img_368 +2002/07/23/big/img_392 +2002/07/25/big/img_198 +2002/07/25/big/img_418 +2002/08/26/big/img_614 +2002/07/23/big/img_405 +2003/01/14/big/img_445 +2002/07/25/big/img_326 +2002/08/10/big/img_734 +2003/01/14/big/img_530 +2002/08/08/big/img_561 +2002/08/29/big/img_18990 +2002/08/10/big/img_576 +2002/07/29/big/img_1494 +2002/07/19/big/img_198 +2002/08/10/big/img_562 +2002/07/22/big/img_901 +2003/01/14/big/img_37 +2002/09/02/big/img_15629 +2003/01/14/big/img_58 +2002/08/01/big/img_1364 +2002/07/27/big/img_636 +2003/01/13/big/img_241 +2002/09/01/big/img_16988 +2003/01/13/big/img_560 +2002/08/09/big/img_533 +2002/07/31/big/img_249 +2003/01/17/big/img_1007 +2002/07/21/big/img_64 +2003/01/13/big/img_537 +2003/01/15/big/img_606 +2002/08/18/big/img_651 +2002/08/24/big/img_405 +2002/07/26/big/img_837 +2002/08/09/big/img_562 +2002/08/01/big/img_1983 +2002/08/03/big/img_514 +2002/07/29/big/img_314 +2002/08/12/big/img_493 +2003/01/14/big/img_121 +2003/01/14/big/img_479 +2002/08/04/big/img_410 +2002/07/22/big/img_607 +2003/01/17/big/img_417 +2002/07/20/big/img_547 +2002/08/13/big/img_396 +2002/08/31/big/img_17538 +2002/08/13/big/img_187 +2002/08/12/big/img_328 +2003/01/14/big/img_569 +2002/07/27/big/img_1081 +2002/08/14/big/img_504 +2002/08/23/big/img_785 +2002/07/26/big/img_339 +2002/08/07/big/img_1156 +2002/08/07/big/img_1456 +2002/08/23/big/img_378 +2002/08/27/big/img_19719 +2002/07/31/big/img_39 +2002/07/31/big/img_883 +2003/01/14/big/img_676 +2002/07/29/big/img_214 +2002/07/26/big/img_669 +2002/07/25/big/img_202 +2002/08/08/big/img_259 +2003/01/17/big/img_943 +2003/01/15/big/img_512 +2002/08/05/big/img_3295 +2002/08/27/big/img_19685 +2002/08/08/big/img_277 +2002/08/30/big/img_18154 +2002/07/22/big/img_663 +2002/08/29/big/img_18914 +2002/07/31/big/img_908 +2002/08/27/big/img_19926 +2003/01/13/big/img_791 +2003/01/15/big/img_827 +2002/08/18/big/img_878 +2002/08/14/big/img_670 +2002/07/20/big/img_182 +2002/08/15/big/img_291 +2002/08/06/big/img_2600 +2002/07/23/big/img_587 +2002/08/14/big/img_577 +2003/01/15/big/img_585 +2002/07/30/big/img_310 +2002/08/03/big/img_658 +2002/08/10/big/img_157 +2002/08/19/big/img_811 +2002/07/29/big/img_1318 +2002/08/04/big/img_104 +2002/07/30/big/img_332 +2002/07/24/big/img_789 +2002/07/29/big/img_516 +2002/07/23/big/img_843 +2002/08/01/big/img_1528 +2002/08/13/big/img_798 +2002/08/07/big/img_1729 +2002/08/28/big/img_19448 +2003/01/16/big/img_95 +2002/08/12/big/img_473 +2002/07/27/big/img_269 +2003/01/16/big/img_621 +2002/07/29/big/img_772 +2002/07/24/big/img_171 +2002/07/19/big/img_429 +2002/08/07/big/img_1933 +2002/08/27/big/img_19629 +2002/08/05/big/img_3688 +2002/08/07/big/img_1691 +2002/07/23/big/img_600 +2002/07/29/big/img_666 +2002/08/25/big/img_566 +2002/08/06/big/img_2659 +2002/08/29/big/img_18929 +2002/08/16/big/img_407 +2002/08/18/big/img_774 +2002/08/19/big/img_249 +2002/08/06/big/img_2427 +2002/08/29/big/img_18899 +2002/08/01/big/img_1818 +2002/07/31/big/img_108 +2002/07/29/big/img_500 +2002/08/11/big/img_115 +2002/07/19/big/img_521 +2002/08/02/big/img_1163 +2002/07/22/big/img_62 +2002/08/13/big/img_466 +2002/08/21/big/img_956 +2002/08/23/big/img_602 +2002/08/20/big/img_858 +2002/07/25/big/img_690 +2002/07/19/big/img_130 +2002/08/04/big/img_874 +2002/07/26/big/img_489 +2002/07/22/big/img_548 +2002/08/10/big/img_191 +2002/07/25/big/img_1051 +2002/08/18/big/img_473 +2002/08/12/big/img_755 +2002/08/18/big/img_413 +2002/08/08/big/img_1044 +2002/08/17/big/img_680 +2002/08/26/big/img_235 +2002/08/20/big/img_330 +2002/08/22/big/img_344 +2002/08/09/big/img_593 +2002/07/31/big/img_1006 +2002/08/14/big/img_337 +2002/08/16/big/img_728 +2002/07/24/big/img_834 +2002/08/04/big/img_552 +2002/09/02/big/img_15213 +2002/07/25/big/img_725 +2002/08/30/big/img_18290 +2003/01/01/big/img_475 +2002/07/27/big/img_1083 +2002/08/29/big/img_18955 +2002/08/31/big/img_17232 +2002/08/08/big/img_480 +2002/08/01/big/img_1311 +2002/07/30/big/img_745 +2002/08/03/big/img_649 +2002/08/12/big/img_193 +2002/07/29/big/img_228 +2002/07/25/big/img_836 +2002/08/20/big/img_400 +2002/07/30/big/img_507 +2002/09/02/big/img_15072 +2002/07/26/big/img_658 +2002/07/28/big/img_503 +2002/08/05/big/img_3814 +2002/08/24/big/img_745 +2003/01/13/big/img_817 +2002/08/08/big/img_579 +2002/07/22/big/img_251 +2003/01/13/big/img_689 +2002/07/25/big/img_407 +2002/08/13/big/img_1050 +2002/08/14/big/img_733 +2002/07/24/big/img_82 +2003/01/17/big/img_288 +2003/01/15/big/img_475 +2002/08/14/big/img_620 +2002/08/21/big/img_167 +2002/07/19/big/img_300 +2002/07/26/big/img_219 +2002/08/01/big/img_1468 +2002/07/23/big/img_260 +2002/08/09/big/img_555 +2002/07/19/big/img_160 +2002/08/02/big/img_1060 +2003/01/14/big/img_149 +2002/08/15/big/img_346 +2002/08/24/big/img_597 +2002/08/22/big/img_502 +2002/08/30/big/img_18228 +2002/07/21/big/img_766 +2003/01/15/big/img_841 +2002/07/24/big/img_516 +2002/08/02/big/img_265 +2002/08/15/big/img_1243 +2003/01/15/big/img_223 +2002/08/04/big/img_236 +2002/07/22/big/img_309 +2002/07/20/big/img_656 +2002/07/31/big/img_412 +2002/09/01/big/img_16462 +2003/01/16/big/img_431 +2002/07/22/big/img_793 +2002/08/15/big/img_877 +2002/07/26/big/img_282 +2002/07/25/big/img_529 +2002/08/24/big/img_613 +2003/01/17/big/img_700 +2002/08/06/big/img_2526 +2002/08/24/big/img_394 +2002/08/21/big/img_521 +2002/08/25/big/img_560 +2002/07/29/big/img_966 +2002/07/25/big/img_448 +2003/01/13/big/img_782 +2002/08/21/big/img_296 +2002/09/01/big/img_16755 +2002/08/05/big/img_3552 +2002/09/02/big/img_15823 +2003/01/14/big/img_193 +2002/07/21/big/img_159 +2002/08/02/big/img_564 +2002/08/16/big/img_300 +2002/07/19/big/img_269 +2002/08/13/big/img_676 +2002/07/28/big/img_57 +2002/08/05/big/img_3318 +2002/07/31/big/img_218 +2002/08/21/big/img_898 +2002/07/29/big/img_109 +2002/07/19/big/img_854 +2002/08/23/big/img_311 +2002/08/14/big/img_318 +2002/07/25/big/img_523 +2002/07/21/big/img_678 +2003/01/17/big/img_690 +2002/08/28/big/img_19503 +2002/08/18/big/img_251 +2002/08/22/big/img_672 +2002/08/20/big/img_663 +2002/08/02/big/img_148 +2002/09/02/big/img_15580 +2002/07/25/big/img_778 +2002/08/14/big/img_565 +2002/08/12/big/img_374 +2002/08/13/big/img_1018 +2002/08/20/big/img_474 +2002/08/25/big/img_33 +2002/08/02/big/img_1190 +2002/08/08/big/img_864 +2002/08/14/big/img_1071 +2002/08/30/big/img_18103 +2002/08/18/big/img_533 +2003/01/16/big/img_650 +2002/07/25/big/img_108 +2002/07/26/big/img_81 +2002/07/27/big/img_543 +2002/07/29/big/img_521 +2003/01/13/big/img_434 +2002/08/26/big/img_674 +2002/08/06/big/img_2932 +2002/08/07/big/img_1262 +2003/01/15/big/img_201 +2003/01/16/big/img_673 +2002/09/02/big/img_15988 +2002/07/29/big/img_1306 +2003/01/14/big/img_1072 +2002/08/30/big/img_18232 +2002/08/05/big/img_3711 +2002/07/23/big/img_775 +2002/08/01/big/img_16 +2003/01/16/big/img_630 +2002/08/22/big/img_695 +2002/08/14/big/img_51 +2002/08/14/big/img_782 +2002/08/24/big/img_742 +2003/01/14/big/img_512 +2003/01/15/big/img_1183 +2003/01/15/big/img_714 +2002/08/01/big/img_2078 +2002/07/31/big/img_682 +2002/09/02/big/img_15687 +2002/07/26/big/img_518 +2002/08/27/big/img_19676 +2002/09/02/big/img_15969 +2002/08/02/big/img_931 +2002/08/25/big/img_508 +2002/08/29/big/img_18616 +2002/07/22/big/img_839 +2002/07/28/big/img_313 +2003/01/14/big/img_155 +2002/08/02/big/img_1105 +2002/08/09/big/img_53 +2002/08/16/big/img_469 +2002/08/15/big/img_502 +2002/08/20/big/img_575 +2002/07/25/big/img_138 +2003/01/16/big/img_579 +2002/07/19/big/img_352 +2003/01/14/big/img_762 +2003/01/01/big/img_588 +2002/08/02/big/img_981 +2002/08/21/big/img_447 +2002/09/01/big/img_16151 +2003/01/14/big/img_769 +2002/08/23/big/img_461 +2002/08/17/big/img_240 +2002/09/02/big/img_15220 +2002/07/19/big/img_408 +2002/09/02/big/img_15496 +2002/07/29/big/img_758 +2002/08/28/big/img_19392 +2002/08/06/big/img_2723 +2002/08/31/big/img_17752 +2002/08/23/big/img_469 +2002/08/13/big/img_515 +2002/09/02/big/img_15551 +2002/08/03/big/img_462 +2002/07/24/big/img_613 +2002/07/22/big/img_61 +2002/08/08/big/img_171 +2002/08/21/big/img_177 +2003/01/14/big/img_105 +2002/08/02/big/img_1017 +2002/08/22/big/img_106 +2002/07/27/big/img_542 +2002/07/21/big/img_665 +2002/07/23/big/img_595 +2002/08/04/big/img_657 +2002/08/29/big/img_19002 +2003/01/15/big/img_550 +2002/08/14/big/img_662 +2002/07/20/big/img_425 +2002/08/30/big/img_18528 +2002/07/26/big/img_611 +2002/07/22/big/img_849 +2002/08/07/big/img_1655 +2002/08/21/big/img_638 +2003/01/17/big/img_732 +2003/01/01/big/img_496 +2002/08/18/big/img_713 +2002/08/08/big/img_109 +2002/07/27/big/img_1008 +2002/07/20/big/img_559 +2002/08/16/big/img_699 +2002/08/31/big/img_17702 +2002/07/31/big/img_1013 +2002/08/01/big/img_2027 +2002/08/02/big/img_1001 +2002/08/03/big/img_210 +2002/08/01/big/img_2087 +2003/01/14/big/img_199 +2002/07/29/big/img_48 +2002/07/19/big/img_727 +2002/08/09/big/img_249 +2002/08/04/big/img_632 +2002/08/22/big/img_620 +2003/01/01/big/img_457 +2002/08/05/big/img_3223 +2002/07/27/big/img_240 +2002/07/25/big/img_797 +2002/08/13/big/img_430 +2002/07/25/big/img_615 +2002/08/12/big/img_28 +2002/07/30/big/img_220 +2002/07/24/big/img_89 +2002/08/21/big/img_357 +2002/08/09/big/img_590 +2003/01/13/big/img_525 +2002/08/17/big/img_818 +2003/01/02/big/img_7 +2002/07/26/big/img_636 +2003/01/13/big/img_1122 +2002/07/23/big/img_810 +2002/08/20/big/img_888 +2002/07/27/big/img_3 +2002/08/15/big/img_451 +2002/09/02/big/img_15787 +2002/07/31/big/img_281 +2002/08/05/big/img_3274 +2002/08/07/big/img_1254 +2002/07/31/big/img_27 +2002/08/01/big/img_1366 +2002/07/30/big/img_182 +2002/08/27/big/img_19690 +2002/07/29/big/img_68 +2002/08/23/big/img_754 +2002/07/30/big/img_540 +2002/08/27/big/img_20063 +2002/08/14/big/img_471 +2002/08/02/big/img_615 +2002/07/30/big/img_186 +2002/08/25/big/img_150 +2002/07/27/big/img_626 +2002/07/20/big/img_225 +2003/01/15/big/img_1252 +2002/07/19/big/img_367 +2003/01/15/big/img_582 +2002/08/09/big/img_572 +2002/08/08/big/img_428 +2003/01/15/big/img_639 +2002/08/28/big/img_19245 +2002/07/24/big/img_321 +2002/08/02/big/img_662 +2002/08/08/big/img_1033 +2003/01/17/big/img_867 +2002/07/22/big/img_652 +2003/01/14/big/img_224 +2002/08/18/big/img_49 +2002/07/26/big/img_46 +2002/08/31/big/img_18021 +2002/07/25/big/img_151 +2002/08/23/big/img_540 +2002/08/25/big/img_693 +2002/07/23/big/img_340 +2002/07/28/big/img_117 +2002/09/02/big/img_15768 +2002/08/26/big/img_562 +2002/07/24/big/img_480 +2003/01/15/big/img_341 +2002/08/10/big/img_783 +2002/08/20/big/img_132 +2003/01/14/big/img_370 +2002/07/20/big/img_720 +2002/08/03/big/img_144 +2002/08/20/big/img_538 +2002/08/01/big/img_1745 +2002/08/11/big/img_683 +2002/08/03/big/img_328 +2002/08/10/big/img_793 +2002/08/14/big/img_689 +2002/08/02/big/img_162 +2003/01/17/big/img_411 +2002/07/31/big/img_361 +2002/08/15/big/img_289 +2002/08/08/big/img_254 +2002/08/15/big/img_996 +2002/08/20/big/img_785 +2002/07/24/big/img_511 +2002/08/06/big/img_2614 +2002/08/29/big/img_18733 +2002/08/17/big/img_78 +2002/07/30/big/img_378 +2002/08/31/big/img_17947 +2002/08/26/big/img_88 +2002/07/30/big/img_558 +2002/08/02/big/img_67 +2003/01/14/big/img_325 +2002/07/29/big/img_1357 +2002/07/19/big/img_391 +2002/07/30/big/img_307 +2003/01/13/big/img_219 +2002/07/24/big/img_807 +2002/08/23/big/img_543 +2002/08/29/big/img_18620 +2002/07/22/big/img_769 +2002/08/26/big/img_503 +2002/07/30/big/img_78 +2002/08/14/big/img_1036 +2002/08/09/big/img_58 +2002/07/24/big/img_616 +2002/08/02/big/img_464 +2002/07/26/big/img_576 +2002/07/22/big/img_273 +2003/01/16/big/img_470 +2002/07/29/big/img_329 +2002/07/30/big/img_1086 +2002/07/31/big/img_353 +2002/09/02/big/img_15275 +2003/01/17/big/img_555 +2002/08/26/big/img_212 +2002/08/01/big/img_1692 +2003/01/15/big/img_600 +2002/07/29/big/img_825 +2002/08/08/big/img_68 +2002/08/10/big/img_719 +2002/07/31/big/img_636 +2002/07/29/big/img_325 +2002/07/21/big/img_515 +2002/07/22/big/img_705 +2003/01/13/big/img_818 +2002/08/09/big/img_486 +2002/08/22/big/img_141 +2002/07/22/big/img_303 +2002/08/09/big/img_393 +2002/07/29/big/img_963 +2002/08/02/big/img_1215 +2002/08/19/big/img_674 +2002/08/12/big/img_690 +2002/08/21/big/img_637 +2002/08/21/big/img_841 +2002/08/24/big/img_71 +2002/07/25/big/img_596 +2002/07/24/big/img_864 +2002/08/18/big/img_293 +2003/01/14/big/img_657 +2002/08/15/big/img_411 +2002/08/16/big/img_348 +2002/08/05/big/img_3157 +2002/07/20/big/img_663 +2003/01/13/big/img_654 +2003/01/16/big/img_433 +2002/08/30/big/img_18200 +2002/08/12/big/img_226 +2003/01/16/big/img_491 +2002/08/08/big/img_666 +2002/07/19/big/img_576 +2003/01/15/big/img_776 +2003/01/16/big/img_899 +2002/07/19/big/img_397 +2002/08/14/big/img_44 +2003/01/15/big/img_762 +2002/08/02/big/img_982 +2002/09/02/big/img_15234 +2002/08/17/big/img_556 +2002/08/21/big/img_410 +2002/08/21/big/img_386 +2002/07/19/big/img_690 +2002/08/05/big/img_3052 +2002/08/14/big/img_219 +2002/08/16/big/img_273 +2003/01/15/big/img_752 +2002/08/08/big/img_184 +2002/07/31/big/img_743 +2002/08/23/big/img_338 +2003/01/14/big/img_1055 +2002/08/05/big/img_3405 +2003/01/15/big/img_17 +2002/08/03/big/img_141 +2002/08/14/big/img_549 +2002/07/27/big/img_1034 +2002/07/31/big/img_932 +2002/08/30/big/img_18487 +2002/09/02/big/img_15814 +2002/08/01/big/img_2086 +2002/09/01/big/img_16535 +2002/07/22/big/img_500 +2003/01/13/big/img_400 +2002/08/25/big/img_607 +2002/08/30/big/img_18384 +2003/01/14/big/img_951 +2002/08/13/big/img_1150 +2002/08/08/big/img_1022 +2002/08/10/big/img_428 +2002/08/28/big/img_19242 +2002/08/05/big/img_3098 +2002/07/23/big/img_400 +2002/08/26/big/img_365 +2002/07/20/big/img_318 +2002/08/13/big/img_740 +2003/01/16/big/img_37 +2002/08/26/big/img_274 +2002/08/02/big/img_205 +2002/08/21/big/img_695 +2002/08/06/big/img_2289 +2002/08/20/big/img_794 +2002/08/18/big/img_438 +2002/08/07/big/img_1380 +2002/08/02/big/img_737 +2002/08/07/big/img_1651 +2002/08/15/big/img_1238 +2002/08/01/big/img_1681 +2002/08/06/big/img_3017 +2002/07/23/big/img_706 +2002/07/31/big/img_392 +2002/08/09/big/img_539 +2002/07/29/big/img_835 +2002/08/26/big/img_723 +2002/08/28/big/img_19235 +2003/01/16/big/img_353 +2002/08/10/big/img_150 +2002/08/29/big/img_19025 +2002/08/21/big/img_310 +2002/08/10/big/img_823 +2002/07/26/big/img_981 +2002/08/11/big/img_288 +2002/08/19/big/img_534 +2002/08/21/big/img_300 +2002/07/31/big/img_49 +2002/07/30/big/img_469 +2002/08/28/big/img_19197 +2002/08/25/big/img_205 +2002/08/10/big/img_390 +2002/08/23/big/img_291 +2002/08/26/big/img_230 +2002/08/18/big/img_76 +2002/07/23/big/img_409 +2002/08/14/big/img_1053 +2003/01/14/big/img_291 +2002/08/10/big/img_503 +2002/08/27/big/img_19928 +2002/08/03/big/img_563 +2002/08/17/big/img_250 +2002/08/06/big/img_2381 +2002/08/17/big/img_948 +2002/08/06/big/img_2710 +2002/07/22/big/img_696 +2002/07/31/big/img_670 +2002/08/12/big/img_594 +2002/07/29/big/img_624 +2003/01/17/big/img_934 +2002/08/03/big/img_584 +2002/08/22/big/img_1003 +2002/08/05/big/img_3396 +2003/01/13/big/img_570 +2002/08/02/big/img_219 +2002/09/02/big/img_15774 +2002/08/16/big/img_818 +2002/08/23/big/img_402 +2003/01/14/big/img_552 +2002/07/29/big/img_71 +2002/08/05/big/img_3592 +2002/08/16/big/img_80 +2002/07/27/big/img_672 +2003/01/13/big/img_470 +2003/01/16/big/img_702 +2002/09/01/big/img_16130 +2002/08/08/big/img_240 +2002/09/01/big/img_16338 +2002/07/26/big/img_312 +2003/01/14/big/img_538 +2002/07/20/big/img_695 +2002/08/30/big/img_18098 +2002/08/25/big/img_259 +2002/08/16/big/img_1042 +2002/08/09/big/img_837 +2002/08/31/big/img_17760 +2002/07/31/big/img_14 +2002/08/09/big/img_361 +2003/01/16/big/img_107 +2002/08/14/big/img_124 +2002/07/19/big/img_463 +2003/01/15/big/img_275 +2002/07/25/big/img_1151 +2002/07/29/big/img_1501 +2002/08/27/big/img_19889 +2002/08/29/big/img_18603 +2003/01/17/big/img_601 +2002/08/25/big/img_355 +2002/08/08/big/img_297 +2002/08/20/big/img_290 +2002/07/31/big/img_195 +2003/01/01/big/img_336 +2002/08/18/big/img_369 +2002/07/25/big/img_621 +2002/08/11/big/img_508 +2003/01/14/big/img_458 +2003/01/15/big/img_795 +2002/08/12/big/img_498 +2002/08/01/big/img_1734 +2002/08/02/big/img_246 +2002/08/16/big/img_565 +2002/08/11/big/img_475 +2002/08/22/big/img_408 +2002/07/28/big/img_78 +2002/07/21/big/img_81 +2003/01/14/big/img_697 +2002/08/14/big/img_661 +2002/08/15/big/img_507 +2002/08/19/big/img_55 +2002/07/22/big/img_152 +2003/01/14/big/img_470 +2002/08/03/big/img_379 +2002/08/22/big/img_506 +2003/01/16/big/img_966 +2002/08/18/big/img_698 +2002/08/24/big/img_528 +2002/08/23/big/img_10 +2002/08/01/big/img_1655 +2002/08/22/big/img_953 +2002/07/19/big/img_630 +2002/07/22/big/img_889 +2002/08/16/big/img_351 +2003/01/16/big/img_83 +2002/07/19/big/img_805 +2002/08/14/big/img_704 +2002/07/19/big/img_389 +2002/08/31/big/img_17765 +2002/07/29/big/img_606 +2003/01/17/big/img_939 +2002/09/02/big/img_15081 +2002/08/21/big/img_181 +2002/07/29/big/img_1321 +2002/07/21/big/img_497 +2002/07/20/big/img_539 +2002/08/24/big/img_119 +2002/08/01/big/img_1281 +2002/07/26/big/img_207 +2002/07/26/big/img_432 +2002/07/27/big/img_1006 +2002/08/05/big/img_3087 +2002/08/14/big/img_252 +2002/08/14/big/img_798 +2002/07/24/big/img_538 +2002/09/02/big/img_15507 +2002/08/08/big/img_901 +2003/01/14/big/img_557 +2002/08/07/big/img_1819 +2002/08/04/big/img_470 +2002/08/01/big/img_1504 +2002/08/16/big/img_1070 +2002/08/16/big/img_372 +2002/08/23/big/img_416 +2002/08/30/big/img_18208 +2002/08/01/big/img_2043 +2002/07/22/big/img_385 +2002/08/22/big/img_466 +2002/08/21/big/img_869 +2002/08/28/big/img_19429 +2002/08/02/big/img_770 +2002/07/23/big/img_433 +2003/01/14/big/img_13 +2002/07/27/big/img_953 +2002/09/02/big/img_15728 +2002/08/01/big/img_1361 +2002/08/29/big/img_18897 +2002/08/26/big/img_534 +2002/08/11/big/img_121 +2002/08/26/big/img_20130 +2002/07/31/big/img_363 +2002/08/13/big/img_978 +2002/07/25/big/img_835 +2002/08/02/big/img_906 +2003/01/14/big/img_548 +2002/07/30/big/img_80 +2002/07/26/big/img_982 +2003/01/16/big/img_99 +2002/08/19/big/img_362 +2002/08/24/big/img_376 +2002/08/07/big/img_1264 +2002/07/27/big/img_938 +2003/01/17/big/img_535 +2002/07/26/big/img_457 +2002/08/08/big/img_848 +2003/01/15/big/img_859 +2003/01/15/big/img_622 +2002/07/30/big/img_403 +2002/07/29/big/img_217 +2002/07/26/big/img_891 +2002/07/24/big/img_70 +2002/08/25/big/img_619 +2002/08/05/big/img_3375 +2002/08/01/big/img_2160 +2002/08/06/big/img_2227 +2003/01/14/big/img_117 +2002/08/14/big/img_227 +2002/08/13/big/img_565 +2002/08/19/big/img_625 +2002/08/03/big/img_812 +2002/07/24/big/img_41 +2002/08/16/big/img_235 +2002/07/29/big/img_759 +2002/07/21/big/img_433 +2002/07/29/big/img_190 +2003/01/16/big/img_435 +2003/01/13/big/img_708 +2002/07/30/big/img_57 +2002/08/22/big/img_162 +2003/01/01/big/img_558 +2003/01/15/big/img_604 +2002/08/16/big/img_935 +2002/08/20/big/img_394 +2002/07/28/big/img_465 +2002/09/02/big/img_15534 +2002/08/16/big/img_87 +2002/07/22/big/img_469 +2002/08/12/big/img_245 +2003/01/13/big/img_236 +2002/08/06/big/img_2736 +2002/08/03/big/img_348 +2003/01/14/big/img_218 +2002/07/26/big/img_232 +2003/01/15/big/img_244 +2002/07/25/big/img_1121 +2002/08/01/big/img_1484 +2002/07/26/big/img_541 +2002/08/07/big/img_1244 +2002/07/31/big/img_3 +2002/08/30/big/img_18437 +2002/08/29/big/img_19094 +2002/08/01/big/img_1355 +2002/08/19/big/img_338 +2002/07/19/big/img_255 +2002/07/21/big/img_76 +2002/08/25/big/img_199 +2002/08/12/big/img_740 +2002/07/30/big/img_852 +2002/08/15/big/img_599 +2002/08/23/big/img_254 +2002/08/19/big/img_125 +2002/07/24/big/img_2 +2002/08/04/big/img_145 +2002/08/05/big/img_3137 +2002/07/28/big/img_463 +2003/01/14/big/img_801 +2002/07/23/big/img_366 +2002/08/26/big/img_600 +2002/08/26/big/img_649 +2002/09/02/big/img_15849 +2002/07/26/big/img_248 +2003/01/13/big/img_200 +2002/08/07/big/img_1794 +2002/08/31/big/img_17270 +2002/08/23/big/img_608 +2003/01/13/big/img_837 +2002/08/23/big/img_581 +2002/08/20/big/img_754 +2002/08/18/big/img_183 +2002/08/20/big/img_328 +2002/07/22/big/img_494 +2002/07/29/big/img_399 +2002/08/28/big/img_19284 +2002/08/08/big/img_566 +2002/07/25/big/img_376 +2002/07/23/big/img_138 +2002/07/25/big/img_435 +2002/08/17/big/img_685 +2002/07/19/big/img_90 +2002/07/20/big/img_716 +2002/08/31/big/img_17458 +2002/08/26/big/img_461 +2002/07/25/big/img_355 +2002/08/06/big/img_2152 +2002/07/27/big/img_932 +2002/07/23/big/img_232 +2002/08/08/big/img_1020 +2002/07/31/big/img_366 +2002/08/06/big/img_2667 +2002/08/21/big/img_465 +2002/08/15/big/img_305 +2002/08/02/big/img_247 +2002/07/28/big/img_46 +2002/08/27/big/img_19922 +2002/08/23/big/img_643 +2003/01/13/big/img_624 +2002/08/23/big/img_625 +2002/08/05/big/img_3787 +2003/01/13/big/img_627 +2002/09/01/big/img_16381 +2002/08/05/big/img_3668 +2002/07/21/big/img_535 +2002/08/27/big/img_19680 +2002/07/22/big/img_413 +2002/07/29/big/img_481 +2003/01/15/big/img_496 +2002/07/23/big/img_701 +2002/08/29/big/img_18670 +2002/07/28/big/img_319 +2003/01/14/big/img_517 +2002/07/26/big/img_256 +2003/01/16/big/img_593 +2002/07/30/big/img_956 +2002/07/30/big/img_667 +2002/07/25/big/img_100 +2002/08/11/big/img_570 +2002/07/26/big/img_745 +2002/08/04/big/img_834 +2002/08/25/big/img_521 +2002/08/01/big/img_2148 +2002/09/02/big/img_15183 +2002/08/22/big/img_514 +2002/08/23/big/img_477 +2002/07/23/big/img_336 +2002/07/26/big/img_481 +2002/08/20/big/img_409 +2002/07/23/big/img_918 +2002/08/09/big/img_474 +2002/08/02/big/img_929 +2002/08/31/big/img_17932 +2002/08/19/big/img_161 +2002/08/09/big/img_667 +2002/07/31/big/img_805 +2002/09/02/big/img_15678 +2002/08/31/big/img_17509 +2002/08/29/big/img_18998 +2002/07/23/big/img_301 +2002/08/07/big/img_1612 +2002/08/06/big/img_2472 +2002/07/23/big/img_466 +2002/08/27/big/img_19634 +2003/01/16/big/img_16 +2002/08/14/big/img_193 +2002/08/21/big/img_340 +2002/08/27/big/img_19799 +2002/08/01/big/img_1345 +2002/08/07/big/img_1448 +2002/08/11/big/img_324 +2003/01/16/big/img_754 +2002/08/13/big/img_418 +2003/01/16/big/img_544 +2002/08/19/big/img_135 +2002/08/10/big/img_455 +2002/08/10/big/img_693 +2002/08/31/big/img_17967 +2002/08/28/big/img_19229 +2002/08/04/big/img_811 +2002/09/01/big/img_16225 +2003/01/16/big/img_428 +2002/09/02/big/img_15295 +2002/07/26/big/img_108 +2002/07/21/big/img_477 +2002/08/07/big/img_1354 +2002/08/23/big/img_246 +2002/08/16/big/img_652 +2002/07/27/big/img_553 +2002/07/31/big/img_346 +2002/08/04/big/img_537 +2002/08/08/big/img_498 +2002/08/29/big/img_18956 +2003/01/13/big/img_922 +2002/08/31/big/img_17425 +2002/07/26/big/img_438 +2002/08/19/big/img_185 +2003/01/16/big/img_33 +2002/08/10/big/img_252 +2002/07/29/big/img_598 +2002/08/27/big/img_19820 +2002/08/06/big/img_2664 +2002/08/20/big/img_705 +2003/01/14/big/img_816 +2002/08/03/big/img_552 +2002/07/25/big/img_561 +2002/07/25/big/img_934 +2002/08/01/big/img_1893 +2003/01/14/big/img_746 +2003/01/16/big/img_519 +2002/08/03/big/img_681 +2002/07/24/big/img_808 +2002/08/14/big/img_803 +2002/08/25/big/img_155 +2002/07/30/big/img_1107 +2002/08/29/big/img_18882 +2003/01/15/big/img_598 +2002/08/19/big/img_122 +2002/07/30/big/img_428 +2002/07/24/big/img_684 +2002/08/22/big/img_192 +2002/08/22/big/img_543 +2002/08/07/big/img_1318 +2002/08/18/big/img_25 +2002/07/26/big/img_583 +2002/07/20/big/img_464 +2002/08/19/big/img_664 +2002/08/24/big/img_861 +2002/09/01/big/img_16136 +2002/08/22/big/img_400 +2002/08/12/big/img_445 +2003/01/14/big/img_174 +2002/08/27/big/img_19677 +2002/08/31/big/img_17214 +2002/08/30/big/img_18175 +2003/01/17/big/img_402 +2002/08/06/big/img_2396 +2002/08/18/big/img_448 +2002/08/21/big/img_165 +2002/08/31/big/img_17609 +2003/01/01/big/img_151 +2002/08/26/big/img_372 +2002/09/02/big/img_15994 +2002/07/26/big/img_660 +2002/09/02/big/img_15197 +2002/07/29/big/img_258 +2002/08/30/big/img_18525 +2003/01/13/big/img_368 +2002/07/29/big/img_1538 +2002/07/21/big/img_787 +2002/08/18/big/img_152 +2002/08/06/big/img_2379 +2003/01/17/big/img_864 +2002/08/27/big/img_19998 +2002/08/01/big/img_1634 +2002/07/25/big/img_414 +2002/08/22/big/img_627 +2002/08/07/big/img_1669 +2002/08/16/big/img_1052 +2002/08/31/big/img_17796 +2002/08/18/big/img_199 +2002/09/02/big/img_15147 +2002/08/09/big/img_460 +2002/08/14/big/img_581 +2002/08/30/big/img_18286 +2002/07/26/big/img_337 +2002/08/18/big/img_589 +2003/01/14/big/img_866 +2002/07/20/big/img_624 +2002/08/01/big/img_1801 +2002/07/24/big/img_683 +2002/08/09/big/img_725 +2003/01/14/big/img_34 +2002/07/30/big/img_144 +2002/07/30/big/img_706 +2002/08/08/big/img_394 +2002/08/19/big/img_619 +2002/08/06/big/img_2703 +2002/08/29/big/img_19034 +2002/07/24/big/img_67 +2002/08/27/big/img_19841 +2002/08/19/big/img_427 +2003/01/14/big/img_333 +2002/09/01/big/img_16406 +2002/07/19/big/img_882 +2002/08/17/big/img_238 +2003/01/14/big/img_739 +2002/07/22/big/img_151 +2002/08/21/big/img_743 +2002/07/25/big/img_1048 +2002/07/30/big/img_395 +2003/01/13/big/img_584 +2002/08/13/big/img_742 +2002/08/13/big/img_1168 +2003/01/14/big/img_147 +2002/07/26/big/img_803 +2002/08/05/big/img_3298 +2002/08/07/big/img_1451 +2002/08/16/big/img_424 +2002/07/29/big/img_1069 +2002/09/01/big/img_16735 +2002/07/21/big/img_637 +2003/01/14/big/img_585 +2002/08/02/big/img_358 +2003/01/13/big/img_358 +2002/08/14/big/img_198 +2002/08/17/big/img_935 +2002/08/04/big/img_42 +2002/08/30/big/img_18245 +2002/07/25/big/img_158 +2002/08/22/big/img_744 +2002/08/06/big/img_2291 +2002/08/05/big/img_3044 +2002/07/30/big/img_272 +2002/08/23/big/img_641 +2002/07/24/big/img_797 +2002/07/30/big/img_392 +2003/01/14/big/img_447 +2002/07/31/big/img_898 +2002/08/06/big/img_2812 +2002/08/13/big/img_564 +2002/07/22/big/img_43 +2002/07/26/big/img_634 +2002/07/19/big/img_843 +2002/08/26/big/img_58 +2002/07/21/big/img_375 +2002/08/25/big/img_729 +2002/07/19/big/img_561 +2003/01/15/big/img_884 +2002/07/25/big/img_891 +2002/08/09/big/img_558 +2002/08/26/big/img_587 +2002/08/13/big/img_1146 +2002/09/02/big/img_15153 +2002/07/26/big/img_316 +2002/08/01/big/img_1940 +2002/08/26/big/img_90 +2003/01/13/big/img_347 +2002/07/25/big/img_520 +2002/08/29/big/img_18718 +2002/08/28/big/img_19219 +2002/08/13/big/img_375 +2002/07/20/big/img_719 +2002/08/31/big/img_17431 +2002/07/28/big/img_192 +2002/08/26/big/img_259 +2002/08/18/big/img_484 +2002/07/29/big/img_580 +2002/07/26/big/img_84 +2002/08/02/big/img_302 +2002/08/31/big/img_17007 +2003/01/15/big/img_543 +2002/09/01/big/img_16488 +2002/08/22/big/img_798 +2002/07/30/big/img_383 +2002/08/04/big/img_668 +2002/08/13/big/img_156 +2002/08/07/big/img_1353 +2002/07/25/big/img_281 +2003/01/14/big/img_587 +2003/01/15/big/img_524 +2002/08/19/big/img_726 +2002/08/21/big/img_709 +2002/08/26/big/img_465 +2002/07/31/big/img_658 +2002/08/28/big/img_19148 +2002/07/23/big/img_423 +2002/08/16/big/img_758 +2002/08/22/big/img_523 +2002/08/16/big/img_591 +2002/08/23/big/img_845 +2002/07/26/big/img_678 +2002/08/09/big/img_806 +2002/08/06/big/img_2369 +2002/07/29/big/img_457 +2002/07/19/big/img_278 +2002/08/30/big/img_18107 +2002/07/26/big/img_444 +2002/08/20/big/img_278 +2002/08/26/big/img_92 +2002/08/26/big/img_257 +2002/07/25/big/img_266 +2002/08/05/big/img_3829 +2002/07/26/big/img_757 +2002/07/29/big/img_1536 +2002/08/09/big/img_472 +2003/01/17/big/img_480 +2002/08/28/big/img_19355 +2002/07/26/big/img_97 +2002/08/06/big/img_2503 +2002/07/19/big/img_254 +2002/08/01/big/img_1470 +2002/08/21/big/img_42 +2002/08/20/big/img_217 +2002/08/06/big/img_2459 +2002/07/19/big/img_552 +2002/08/13/big/img_717 +2002/08/12/big/img_586 +2002/08/20/big/img_411 +2003/01/13/big/img_768 +2002/08/07/big/img_1747 +2002/08/15/big/img_385 +2002/08/01/big/img_1648 +2002/08/15/big/img_311 +2002/08/21/big/img_95 +2002/08/09/big/img_108 +2002/08/21/big/img_398 +2002/08/17/big/img_340 +2002/08/14/big/img_474 +2002/08/13/big/img_294 +2002/08/24/big/img_840 +2002/08/09/big/img_808 +2002/08/23/big/img_491 +2002/07/28/big/img_33 +2003/01/13/big/img_664 +2002/08/02/big/img_261 +2002/08/09/big/img_591 +2002/07/26/big/img_309 +2003/01/14/big/img_372 +2002/08/19/big/img_581 +2002/08/19/big/img_168 +2002/08/26/big/img_422 +2002/07/24/big/img_106 +2002/08/01/big/img_1936 +2002/08/05/big/img_3764 +2002/08/21/big/img_266 +2002/08/31/big/img_17968 +2002/08/01/big/img_1941 +2002/08/15/big/img_550 +2002/08/14/big/img_13 +2002/07/30/big/img_171 +2003/01/13/big/img_490 +2002/07/25/big/img_427 +2002/07/19/big/img_770 +2002/08/12/big/img_759 +2003/01/15/big/img_1360 +2002/08/05/big/img_3692 +2003/01/16/big/img_30 +2002/07/25/big/img_1026 +2002/07/22/big/img_288 +2002/08/29/big/img_18801 +2002/07/24/big/img_793 +2002/08/13/big/img_178 +2002/08/06/big/img_2322 +2003/01/14/big/img_560 +2002/08/18/big/img_408 +2003/01/16/big/img_915 +2003/01/16/big/img_679 +2002/08/07/big/img_1552 +2002/08/29/big/img_19050 +2002/08/01/big/img_2172 +2002/07/31/big/img_30 +2002/07/30/big/img_1019 +2002/07/30/big/img_587 +2003/01/13/big/img_773 +2002/07/30/big/img_410 +2002/07/28/big/img_65 +2002/08/05/big/img_3138 +2002/07/23/big/img_541 +2002/08/22/big/img_963 +2002/07/27/big/img_657 +2002/07/30/big/img_1051 +2003/01/16/big/img_150 +2002/07/31/big/img_519 +2002/08/01/big/img_1961 +2002/08/05/big/img_3752 +2002/07/23/big/img_631 +2003/01/14/big/img_237 +2002/07/28/big/img_21 +2002/07/22/big/img_813 +2002/08/05/big/img_3563 +2003/01/17/big/img_620 +2002/07/19/big/img_523 +2002/07/30/big/img_904 +2002/08/29/big/img_18642 +2002/08/11/big/img_492 +2002/08/01/big/img_2130 +2002/07/25/big/img_618 +2002/08/17/big/img_305 +2003/01/16/big/img_520 +2002/07/26/big/img_495 +2002/08/17/big/img_164 +2002/08/03/big/img_440 +2002/07/24/big/img_441 +2002/08/06/big/img_2146 +2002/08/11/big/img_558 +2002/08/02/big/img_545 +2002/08/31/big/img_18090 +2003/01/01/big/img_136 +2002/07/25/big/img_1099 +2003/01/13/big/img_728 +2003/01/16/big/img_197 +2002/07/26/big/img_651 +2002/08/11/big/img_676 +2003/01/15/big/img_10 +2002/08/21/big/img_250 +2002/08/14/big/img_325 +2002/08/04/big/img_390 +2002/07/24/big/img_554 +2003/01/16/big/img_333 +2002/07/31/big/img_922 +2002/09/02/big/img_15586 +2003/01/16/big/img_184 +2002/07/22/big/img_766 +2002/07/21/big/img_608 +2002/08/07/big/img_1578 +2002/08/17/big/img_961 +2002/07/27/big/img_324 +2002/08/05/big/img_3765 +2002/08/23/big/img_462 +2003/01/16/big/img_382 +2002/08/27/big/img_19838 +2002/08/01/big/img_1505 +2002/08/21/big/img_662 +2002/08/14/big/img_605 +2002/08/19/big/img_816 +2002/07/29/big/img_136 +2002/08/20/big/img_719 +2002/08/06/big/img_2826 +2002/08/10/big/img_630 +2003/01/17/big/img_973 +2002/08/14/big/img_116 +2002/08/02/big/img_666 +2002/08/21/big/img_710 +2002/08/05/big/img_55 +2002/07/31/big/img_229 +2002/08/01/big/img_1549 +2002/07/23/big/img_432 +2002/07/21/big/img_430 +2002/08/21/big/img_549 +2002/08/08/big/img_985 +2002/07/20/big/img_610 +2002/07/23/big/img_978 +2002/08/23/big/img_219 +2002/07/25/big/img_175 +2003/01/15/big/img_230 +2002/08/23/big/img_385 +2002/07/31/big/img_879 +2002/08/12/big/img_495 +2002/08/22/big/img_499 +2002/08/30/big/img_18322 +2002/08/15/big/img_795 +2002/08/13/big/img_835 +2003/01/17/big/img_930 +2002/07/30/big/img_873 +2002/08/11/big/img_257 +2002/07/31/big/img_593 +2002/08/21/big/img_916 +2003/01/13/big/img_814 +2002/07/25/big/img_722 +2002/08/16/big/img_379 +2002/07/31/big/img_497 +2002/07/22/big/img_602 +2002/08/21/big/img_642 +2002/08/21/big/img_614 +2002/08/23/big/img_482 +2002/07/29/big/img_603 +2002/08/13/big/img_705 +2002/07/23/big/img_833 +2003/01/14/big/img_511 +2002/07/24/big/img_376 +2002/08/17/big/img_1030 +2002/08/05/big/img_3576 +2002/08/16/big/img_540 +2002/07/22/big/img_630 +2002/08/10/big/img_180 +2002/08/14/big/img_905 +2002/08/29/big/img_18777 +2002/08/22/big/img_693 +2003/01/16/big/img_933 +2002/08/20/big/img_555 +2002/08/15/big/img_549 +2003/01/14/big/img_830 +2003/01/16/big/img_64 +2002/08/27/big/img_19670 +2002/08/22/big/img_729 +2002/07/27/big/img_981 +2002/08/09/big/img_458 +2003/01/17/big/img_884 +2002/07/25/big/img_639 +2002/08/31/big/img_18008 +2002/08/22/big/img_249 +2002/08/17/big/img_971 +2002/08/04/big/img_308 +2002/07/28/big/img_362 +2002/08/12/big/img_142 +2002/08/26/big/img_61 +2002/08/14/big/img_422 +2002/07/19/big/img_607 +2003/01/15/big/img_717 +2002/08/01/big/img_1475 +2002/08/29/big/img_19061 +2003/01/01/big/img_346 +2002/07/20/big/img_315 +2003/01/15/big/img_756 +2002/08/15/big/img_879 +2002/08/08/big/img_615 +2003/01/13/big/img_431 +2002/08/05/big/img_3233 +2002/08/24/big/img_526 +2003/01/13/big/img_717 +2002/09/01/big/img_16408 +2002/07/22/big/img_217 +2002/07/31/big/img_960 +2002/08/21/big/img_610 +2002/08/05/big/img_3753 +2002/08/03/big/img_151 +2002/08/21/big/img_267 +2002/08/01/big/img_2175 +2002/08/04/big/img_556 +2002/08/21/big/img_527 +2002/09/02/big/img_15800 +2002/07/27/big/img_156 +2002/07/20/big/img_590 +2002/08/15/big/img_700 +2002/08/08/big/img_444 +2002/07/25/big/img_94 +2002/07/24/big/img_778 +2002/08/14/big/img_694 +2002/07/20/big/img_666 +2002/08/02/big/img_200 +2002/08/02/big/img_578 +2003/01/17/big/img_332 +2002/09/01/big/img_16352 +2002/08/27/big/img_19668 +2002/07/23/big/img_823 +2002/08/13/big/img_431 +2003/01/16/big/img_463 +2002/08/27/big/img_19711 +2002/08/23/big/img_154 +2002/07/31/big/img_360 +2002/08/23/big/img_555 +2002/08/10/big/img_561 +2003/01/14/big/img_550 +2002/08/07/big/img_1370 +2002/07/30/big/img_1184 +2002/08/01/big/img_1445 +2002/08/23/big/img_22 +2002/07/30/big/img_606 +2003/01/17/big/img_271 +2002/08/31/big/img_17316 +2002/08/16/big/img_973 +2002/07/26/big/img_77 +2002/07/20/big/img_788 +2002/08/06/big/img_2426 +2002/08/07/big/img_1498 +2002/08/16/big/img_358 +2002/08/06/big/img_2851 +2002/08/12/big/img_359 +2002/08/01/big/img_1521 +2002/08/02/big/img_709 +2002/08/20/big/img_935 +2002/08/12/big/img_188 +2002/08/24/big/img_411 +2002/08/22/big/img_680 +2002/08/06/big/img_2480 +2002/07/20/big/img_627 +2002/07/30/big/img_214 +2002/07/25/big/img_354 +2002/08/02/big/img_636 +2003/01/15/big/img_661 +2002/08/07/big/img_1327 +2002/08/01/big/img_2108 +2002/08/31/big/img_17919 +2002/08/29/big/img_18768 +2002/08/05/big/img_3840 +2002/07/26/big/img_242 +2003/01/14/big/img_451 +2002/08/20/big/img_923 +2002/08/27/big/img_19908 +2002/08/16/big/img_282 +2002/08/19/big/img_440 +2003/01/01/big/img_230 +2002/08/08/big/img_212 +2002/07/20/big/img_443 +2002/08/25/big/img_635 +2003/01/13/big/img_1169 +2002/07/26/big/img_998 +2002/08/15/big/img_995 +2002/08/06/big/img_3002 +2002/07/29/big/img_460 +2003/01/14/big/img_925 +2002/07/23/big/img_539 +2002/08/16/big/img_694 +2003/01/13/big/img_459 +2002/07/23/big/img_249 +2002/08/20/big/img_539 +2002/08/04/big/img_186 +2002/08/26/big/img_264 +2002/07/22/big/img_704 +2002/08/25/big/img_277 +2002/08/22/big/img_988 +2002/07/29/big/img_504 +2002/08/05/big/img_3600 +2002/08/30/big/img_18380 +2003/01/14/big/img_937 +2002/08/21/big/img_254 +2002/08/10/big/img_130 +2002/08/20/big/img_339 +2003/01/14/big/img_428 +2002/08/20/big/img_889 +2002/08/31/big/img_17637 +2002/07/26/big/img_644 +2002/09/01/big/img_16776 +2002/08/06/big/img_2239 +2002/08/06/big/img_2646 +2003/01/13/big/img_491 +2002/08/10/big/img_579 +2002/08/21/big/img_713 +2002/08/22/big/img_482 +2002/07/22/big/img_167 +2002/07/24/big/img_539 +2002/08/14/big/img_721 +2002/07/25/big/img_389 +2002/09/01/big/img_16591 +2002/08/13/big/img_543 +2003/01/14/big/img_432 +2002/08/09/big/img_287 +2002/07/26/big/img_126 +2002/08/23/big/img_412 +2002/08/15/big/img_1034 +2002/08/28/big/img_19485 +2002/07/31/big/img_236 +2002/07/30/big/img_523 +2002/07/19/big/img_141 +2003/01/17/big/img_957 +2002/08/04/big/img_81 +2002/07/25/big/img_206 +2002/08/15/big/img_716 +2002/08/13/big/img_403 +2002/08/15/big/img_685 +2002/07/26/big/img_884 +2002/07/19/big/img_499 +2002/07/23/big/img_772 +2002/07/27/big/img_752 +2003/01/14/big/img_493 +2002/08/25/big/img_664 +2002/07/31/big/img_334 +2002/08/26/big/img_678 +2002/09/01/big/img_16541 +2003/01/14/big/img_347 +2002/07/23/big/img_187 +2002/07/30/big/img_1163 +2002/08/05/big/img_35 +2002/08/22/big/img_944 +2002/08/07/big/img_1239 +2002/07/29/big/img_1215 +2002/08/03/big/img_312 +2002/08/05/big/img_3523 +2002/07/29/big/img_218 +2002/08/13/big/img_672 +2002/08/16/big/img_205 +2002/08/17/big/img_594 +2002/07/29/big/img_1411 +2002/07/30/big/img_942 +2003/01/16/big/img_312 +2002/08/08/big/img_312 +2002/07/25/big/img_15 +2002/08/09/big/img_839 +2002/08/01/big/img_2069 +2002/08/31/big/img_17512 +2002/08/01/big/img_3 +2002/07/31/big/img_320 +2003/01/15/big/img_1265 +2002/08/14/big/img_563 +2002/07/31/big/img_167 +2002/08/20/big/img_374 +2002/08/13/big/img_406 +2002/08/08/big/img_625 +2002/08/02/big/img_314 +2002/08/27/big/img_19964 +2002/09/01/big/img_16670 +2002/07/31/big/img_599 +2002/08/29/big/img_18906 +2002/07/24/big/img_373 +2002/07/26/big/img_513 +2002/09/02/big/img_15497 +2002/08/19/big/img_117 +2003/01/01/big/img_158 +2002/08/24/big/img_178 +2003/01/13/big/img_935 +2002/08/13/big/img_609 +2002/08/30/big/img_18341 +2002/08/25/big/img_674 +2003/01/13/big/img_209 +2002/08/13/big/img_258 +2002/08/05/big/img_3543 +2002/08/07/big/img_1970 +2002/08/06/big/img_3004 +2003/01/17/big/img_487 +2002/08/24/big/img_873 +2002/08/29/big/img_18730 +2002/08/09/big/img_375 +2003/01/16/big/img_751 +2002/08/02/big/img_603 +2002/08/19/big/img_325 +2002/09/01/big/img_16420 +2002/08/05/big/img_3633 +2002/08/21/big/img_516 +2002/07/19/big/img_501 +2002/07/26/big/img_688 +2002/07/24/big/img_256 +2002/07/25/big/img_438 +2002/07/31/big/img_1017 +2002/08/22/big/img_512 +2002/07/21/big/img_543 +2002/08/08/big/img_223 +2002/08/19/big/img_189 +2002/08/12/big/img_630 +2002/07/30/big/img_958 +2002/07/28/big/img_208 +2002/08/31/big/img_17691 +2002/07/22/big/img_542 +2002/07/19/big/img_741 +2002/07/19/big/img_158 +2002/08/15/big/img_399 +2002/08/01/big/img_2159 +2002/08/14/big/img_455 +2002/08/17/big/img_1011 +2002/08/26/big/img_744 +2002/08/12/big/img_624 +2003/01/17/big/img_821 +2002/08/16/big/img_980 +2002/07/28/big/img_281 +2002/07/25/big/img_171 +2002/08/03/big/img_116 +2002/07/22/big/img_467 +2002/07/31/big/img_750 +2002/07/26/big/img_435 +2002/07/19/big/img_822 +2002/08/13/big/img_626 +2002/08/11/big/img_344 +2002/08/02/big/img_473 +2002/09/01/big/img_16817 +2002/08/01/big/img_1275 +2002/08/28/big/img_19270 +2002/07/23/big/img_607 +2002/08/09/big/img_316 +2002/07/29/big/img_626 +2002/07/24/big/img_824 +2002/07/22/big/img_342 +2002/08/08/big/img_794 +2002/08/07/big/img_1209 +2002/07/19/big/img_18 +2002/08/25/big/img_634 +2002/07/24/big/img_730 +2003/01/17/big/img_356 +2002/07/23/big/img_305 +2002/07/30/big/img_453 +2003/01/13/big/img_972 +2002/08/06/big/img_2610 +2002/08/29/big/img_18920 +2002/07/31/big/img_123 +2002/07/26/big/img_979 +2002/08/24/big/img_635 +2002/08/05/big/img_3704 +2002/08/07/big/img_1358 +2002/07/22/big/img_306 +2002/08/13/big/img_619 +2002/08/02/big/img_366 diff --git a/project/hair_service_sd/models/layers/data/__init__.py b/project/hair_service_sd/models/layers/data/__init__.py new file mode 100644 index 0000000..ea50eba --- /dev/null +++ b/project/hair_service_sd/models/layers/data/__init__.py @@ -0,0 +1,3 @@ +from .wider_face import WiderFaceDetection, detection_collate +from .data_augment import * +from .config import * diff --git a/project/hair_service_sd/models/layers/data/config.py b/project/hair_service_sd/models/layers/data/config.py new file mode 100644 index 0000000..591f349 --- /dev/null +++ b/project/hair_service_sd/models/layers/data/config.py @@ -0,0 +1,42 @@ +# config.py + +cfg_mnet = { + 'name': 'mobilenet0.25', + 'min_sizes': [[16, 32], [64, 128], [256, 512]], + 'steps': [8, 16, 32], + 'variance': [0.1, 0.2], + 'clip': False, + 'loc_weight': 2.0, + 'gpu_train': True, + 'batch_size': 32, + 'ngpu': 1, + 'epoch': 250, + 'decay1': 190, + 'decay2': 220, + 'image_size': 640, + 'pretrain': True, + 'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3}, + 'in_channel': 32, + 'out_channel': 64 +} + +cfg_re50 = { + 'name': 'Resnet50', + 'min_sizes': [[16, 32], [64, 128], [256, 512]], + 'steps': [8, 16, 32], + 'variance': [0.1, 0.2], + 'clip': False, + 'loc_weight': 2.0, + 'gpu_train': True, + 'batch_size': 24, + 'ngpu': 4, + 'epoch': 100, + 'decay1': 70, + 'decay2': 90, + 'image_size': 840, + 'pretrain': True, + 'return_layers': {'layer2': 1, 'layer3': 2, 'layer4': 3}, + 'in_channel': 256, + 'out_channel': 256 +} + diff --git a/project/hair_service_sd/models/layers/data/data_augment.py b/project/hair_service_sd/models/layers/data/data_augment.py new file mode 100644 index 0000000..0c925b1 --- /dev/null +++ b/project/hair_service_sd/models/layers/data/data_augment.py @@ -0,0 +1,237 @@ +import cv2 +import numpy as np +import random +from utils.box_utils_Retina import matrix_iof + + +def _crop(image, boxes, labels, landm, img_dim): + height, width, _ = image.shape + pad_image_flag = True + + for _ in range(250): + """ + if random.uniform(0, 1) <= 0.2: + scale = 1.0 + else: + scale = random.uniform(0.3, 1.0) + """ + PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0] + scale = random.choice(PRE_SCALES) + short_side = min(width, height) + w = int(scale * short_side) + h = w + + if width == w: + l = 0 + else: + l = random.randrange(width - w) + if height == h: + t = 0 + else: + t = random.randrange(height - h) + roi = np.array((l, t, l + w, t + h)) + + value = matrix_iof(boxes, roi[np.newaxis]) + flag = (value >= 1) + if not flag.any(): + continue + + centers = (boxes[:, :2] + boxes[:, 2:]) / 2 + mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1) + boxes_t = boxes[mask_a].copy() + labels_t = labels[mask_a].copy() + landms_t = landm[mask_a].copy() + landms_t = landms_t.reshape([-1, 5, 2]) + + if boxes_t.shape[0] == 0: + continue + + image_t = image[roi[1]:roi[3], roi[0]:roi[2]] + + boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2]) + boxes_t[:, :2] -= roi[:2] + boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:]) + boxes_t[:, 2:] -= roi[:2] + + # landm + landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2] + landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0])) + landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2]) + landms_t = landms_t.reshape([-1, 10]) + + + # make sure that the cropped image contains at least one face > 16 pixel at training image scale + b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim + b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim + mask_b = np.minimum(b_w_t, b_h_t) > 0.0 + boxes_t = boxes_t[mask_b] + labels_t = labels_t[mask_b] + landms_t = landms_t[mask_b] + + if boxes_t.shape[0] == 0: + continue + + pad_image_flag = False + + return image_t, boxes_t, labels_t, landms_t, pad_image_flag + return image, boxes, labels, landm, pad_image_flag + + +def _distort(image): + + def _convert(image, alpha=1, beta=0): + tmp = image.astype(float) * alpha + beta + tmp[tmp < 0] = 0 + tmp[tmp > 255] = 255 + image[:] = tmp + + image = image.copy() + + if random.randrange(2): + + #brightness distortion + if random.randrange(2): + _convert(image, beta=random.uniform(-32, 32)) + + #contrast distortion + if random.randrange(2): + _convert(image, alpha=random.uniform(0.5, 1.5)) + + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + #saturation distortion + if random.randrange(2): + _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) + + #hue distortion + if random.randrange(2): + tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) + tmp %= 180 + image[:, :, 0] = tmp + + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + + else: + + #brightness distortion + if random.randrange(2): + _convert(image, beta=random.uniform(-32, 32)) + + image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV) + + #saturation distortion + if random.randrange(2): + _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5)) + + #hue distortion + if random.randrange(2): + tmp = image[:, :, 0].astype(int) + random.randint(-18, 18) + tmp %= 180 + image[:, :, 0] = tmp + + image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR) + + #contrast distortion + if random.randrange(2): + _convert(image, alpha=random.uniform(0.5, 1.5)) + + return image + + +def _expand(image, boxes, fill, p): + if random.randrange(2): + return image, boxes + + height, width, depth = image.shape + + scale = random.uniform(1, p) + w = int(scale * width) + h = int(scale * height) + + left = random.randint(0, w - width) + top = random.randint(0, h - height) + + boxes_t = boxes.copy() + boxes_t[:, :2] += (left, top) + boxes_t[:, 2:] += (left, top) + expand_image = np.empty( + (h, w, depth), + dtype=image.dtype) + expand_image[:, :] = fill + expand_image[top:top + height, left:left + width] = image + image = expand_image + + return image, boxes_t + + +def _mirror(image, boxes, landms): + _, width, _ = image.shape + if random.randrange(2): + image = image[:, ::-1] + boxes = boxes.copy() + boxes[:, 0::2] = width - boxes[:, 2::-2] + + # landm + landms = landms.copy() + landms = landms.reshape([-1, 5, 2]) + landms[:, :, 0] = width - landms[:, :, 0] + tmp = landms[:, 1, :].copy() + landms[:, 1, :] = landms[:, 0, :] + landms[:, 0, :] = tmp + tmp1 = landms[:, 4, :].copy() + landms[:, 4, :] = landms[:, 3, :] + landms[:, 3, :] = tmp1 + landms = landms.reshape([-1, 10]) + + return image, boxes, landms + + +def _pad_to_square(image, rgb_mean, pad_image_flag): + if not pad_image_flag: + return image + height, width, _ = image.shape + long_side = max(width, height) + image_t = np.empty((long_side, long_side, 3), dtype=image.dtype) + image_t[:, :] = rgb_mean + image_t[0:0 + height, 0:0 + width] = image + return image_t + + +def _resize_subtract_mean(image, insize, rgb_mean): + interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4] + interp_method = interp_methods[random.randrange(5)] + image = cv2.resize(image, (insize, insize), interpolation=interp_method) + image = image.astype(np.float32) + image -= rgb_mean + return image.transpose(2, 0, 1) + + +class preproc(object): + + def __init__(self, img_dim, rgb_means): + self.img_dim = img_dim + self.rgb_means = rgb_means + + def __call__(self, image, targets): + assert targets.shape[0] > 0, "this image does not have gt" + + boxes = targets[:, :4].copy() + labels = targets[:, -1].copy() + landm = targets[:, 4:-1].copy() + + image_t, boxes_t, labels_t, landm_t, pad_image_flag = _crop(image, boxes, labels, landm, self.img_dim) + image_t = _distort(image_t) + image_t = _pad_to_square(image_t,self.rgb_means, pad_image_flag) + image_t, boxes_t, landm_t = _mirror(image_t, boxes_t, landm_t) + height, width, _ = image_t.shape + image_t = _resize_subtract_mean(image_t, self.img_dim, self.rgb_means) + boxes_t[:, 0::2] /= width + boxes_t[:, 1::2] /= height + + landm_t[:, 0::2] /= width + landm_t[:, 1::2] /= height + + labels_t = np.expand_dims(labels_t, 1) + targets_t = np.hstack((boxes_t, landm_t, labels_t)) + + return image_t, targets_t diff --git a/project/hair_service_sd/models/layers/data/wider_face.py b/project/hair_service_sd/models/layers/data/wider_face.py new file mode 100644 index 0000000..22f56ef --- /dev/null +++ b/project/hair_service_sd/models/layers/data/wider_face.py @@ -0,0 +1,101 @@ +import os +import os.path +import sys +import torch +import torch.utils.data as data +import cv2 +import numpy as np + +class WiderFaceDetection(data.Dataset): + def __init__(self, txt_path, preproc=None): + self.preproc = preproc + self.imgs_path = [] + self.words = [] + f = open(txt_path,'r') + lines = f.readlines() + isFirst = True + labels = [] + for line in lines: + line = line.rstrip() + if line.startswith('#'): + if isFirst is True: + isFirst = False + else: + labels_copy = labels.copy() + self.words.append(labels_copy) + labels.clear() + path = line[2:] + path = txt_path.replace('label.txt','images/') + path + self.imgs_path.append(path) + else: + line = line.split(' ') + label = [float(x) for x in line] + labels.append(label) + + self.words.append(labels) + + def __len__(self): + return len(self.imgs_path) + + def __getitem__(self, index): + img = cv2.imread(self.imgs_path[index]) + height, width, _ = img.shape + + labels = self.words[index] + annotations = np.zeros((0, 15)) + if len(labels) == 0: + return annotations + for idx, label in enumerate(labels): + annotation = np.zeros((1, 15)) + # bbox + annotation[0, 0] = label[0] # x1 + annotation[0, 1] = label[1] # y1 + annotation[0, 2] = label[0] + label[2] # x2 + annotation[0, 3] = label[1] + label[3] # y2 + + # landmarks + annotation[0, 4] = label[4] # l0_x + annotation[0, 5] = label[5] # l0_y + annotation[0, 6] = label[7] # l1_x + annotation[0, 7] = label[8] # l1_y + annotation[0, 8] = label[10] # l2_x + annotation[0, 9] = label[11] # l2_y + annotation[0, 10] = label[13] # l3_x + annotation[0, 11] = label[14] # l3_y + annotation[0, 12] = label[16] # l4_x + annotation[0, 13] = label[17] # l4_y + if (annotation[0, 4]<0): + annotation[0, 14] = -1 + else: + annotation[0, 14] = 1 + + annotations = np.append(annotations, annotation, axis=0) + target = np.array(annotations) + if self.preproc is not None: + img, target = self.preproc(img, target) + + return torch.from_numpy(img), target + +def detection_collate(batch): + """Custom collate fn for dealing with batches of images that have a different + number of associated object annotations (bounding boxes). + + Arguments: + batch: (tuple) A tuple of tensor images and lists of annotations + + Return: + A tuple containing: + 1) (tensor) batch of images stacked on their 0 dim + 2) (list of tensors) annotations for a given image are stacked on 0 dim + """ + targets = [] + imgs = [] + for _, sample in enumerate(batch): + for _, tup in enumerate(sample): + if torch.is_tensor(tup): + imgs.append(tup) + elif isinstance(tup, type(np.empty(0))): + annos = torch.from_numpy(tup).float() + targets.append(annos) + + return (torch.stack(imgs, 0), targets) diff --git a/project/hair_service_sd/run_copy_cost_colorb64.py b/project/hair_service_sd/run_copy_cost_colorb64.py index d946075..1ba0c59 100644 --- a/project/hair_service_sd/run_copy_cost_colorb64.py +++ b/project/hair_service_sd/run_copy_cost_colorb64.py @@ -4,6 +4,14 @@ from uuid import uuid4 import imghdr import traceback import torch +# PyTorch 2.6+ 默认 weights_only=True，无法加载含 numpy 对象的旧权重 +# （yolov5l.pt、各 *.pth 等）。统一改回 False（权重均为本机自有可信文件）。 +_orig_torch_load = torch.load +def _torch_load(*args, **kwargs): + kwargs.setdefault("weights_only", False) + return _orig_torch_load(*args, **kwargs) +torch.load = _torch_load + from gevent import monkey monkey.patch_all() @@ -527,21 +535,41 @@ def change_hairstyle_v4(): crop_result = landmark_processor.high_quality_warpAffine(img_res, M, dst_size) # crop_result = landmark_processor.high_quality_warpAffine(img_res, M, (w, h)) - origin_matting = cv2.imread(user_orig_mask_path, cv2.IMREAD_GRAYSCALE) - new_matting = cv2.imread(hair_matting_path, cv2.IMREAD_GRAYSCALE) - left_point = user_landmarks_origin_img_137[14] # (x1, y1) 获取刘海区域的圆心和半径 - right_point = user_landmarks_origin_img_137[7] # (x2, y2) - center_x = int((left_point[0] + right_point[0]) // 2) - center_y = int((left_point[1] + right_point[1]) // 2) - radius = int(np.sqrt((right_point[0] - left_point[0]) ** 2 + (right_point[1] - left_point[1]) ** 2) / 2) - h, w = origin_matting.shape # 初始化刘海区域为空（全黑色） - part_bangs = np.zeros((h, w), dtype=np.uint8) - cv2.circle(part_bangs, (center_x, center_y), radius, 255, -1) # 在 part_bangs 上绘制圆作为刘海区域 圆形区域设为白色 - no_bang_result = cv2.subtract(origin_matting, part_bangs) # 去除刘海区域 - matting_merge = np.max( np.stack([no_bang_result, new_matting], axis=2), axis=2).astype(np.uint8)# 合并 origin_matting 和 new_matting - crop_matting = cv2.warpAffine(matting_merge, M, dst_size) - # crop_matting = cv2.warpAffine(matting_merge, M, (w, h)) - mask = (crop_matting > 10).astype(np.float32) + # 接口11：可选外部遮罩 ext_mask（接口9 头发遮罩，原图坐标、白=生发区）。 + # 传入则经 M 变换到 crop 坐标后替换内部 matting 合成遮罩，webui 精确重绘该区域； + # 不传则保持原 matting_merge 逻辑（向后兼容，其它功能不受影响）。 + ext_mask_b64 = input_info.get('ext_mask', '') + if ext_mask_b64: + if 'data:image/' in ext_mask_b64: + ext_mask_b64 = ext_mask_b64.split(',')[1] + ext_mask_np = cv2.imdecode( + np.frombuffer(base64.b64decode(ext_mask_b64), np.uint8), + cv2.IMREAD_GRAYSCALE) + if ext_mask_np is None: + raise RuntimeError("ext_mask 解析失败") + if ext_mask_np.shape[:2] != origin_img.shape[:2]: + ext_mask_np = cv2.resize( + ext_mask_np, (origin_img.shape[1], origin_img.shape[0]), + interpolation=cv2.INTER_NEAREST) + print(f"[swapHair] 使用外部遮罩 ext_mask, shape={ext_mask_np.shape}, " + f"白像素={int((ext_mask_np > 10).sum())}", flush=True) + crop_matting = cv2.warpAffine(ext_mask_np, M, dst_size) + mask = (crop_matting > 10).astype(np.float32) + else: + origin_matting = cv2.imread(user_orig_mask_path, cv2.IMREAD_GRAYSCALE) + new_matting = cv2.imread(hair_matting_path, cv2.IMREAD_GRAYSCALE) + left_point = user_landmarks_origin_img_137[14] # (x1, y1) 获取刘海区域的圆心和半径 + right_point = user_landmarks_origin_img_137[7] # (x2, y2) + center_x = int((left_point[0] + right_point[0]) // 2) + center_y = int((left_point[1] + right_point[1]) // 2) + radius = int(np.sqrt((right_point[0] - left_point[0]) ** 2 + (right_point[1] - left_point[1]) ** 2) / 2) + h, w = origin_matting.shape # 初始化刘海区域为空（全黑色） + part_bangs = np.zeros((h, w), dtype=np.uint8) + cv2.circle(part_bangs, (center_x, center_y), radius, 255, -1) # 在 part_bangs 上绘制圆作为刘海区域 圆形区域设为白色 + no_bang_result = cv2.subtract(origin_matting, part_bangs) # 去除刘海区域 + matting_merge = np.max( np.stack([no_bang_result, new_matting], axis=2), axis=2).astype(np.uint8)# 合并 origin_matting 和 new_matting + crop_matting = cv2.warpAffine(matting_merge, M, dst_size) + mask = (crop_matting > 10).astype(np.float32) if not is_hr: mask_dilate = cv2.dilate(mask, np.ones((3, 9), np.uint8)) else: @@ -576,7 +604,7 @@ def change_hairstyle_v4(): p_tag = p_tag[p_tag.find("simple background, ") + len("simple background, "):] else: p_tag = "" - denoising_strength = 0.6 + denoising_strength = float(input_info.get('denoising_strength', 0.6)) # 接口11 可调；默认 0.6 print(f"功能8：处理发型区域，耗时：{time.time() - start_time:.3f}s") # cv2.imwrite(f"{task_id}_mask_dilate.jpg", mask_dilate) # cv2.imwrite(f"{task_id}_final_img.jpg", final_img) diff --git a/project/hair_service_sd/upload_oss.py b/project/hair_service_sd/upload_oss.py index c37fde7..6a17376 100644 --- a/project/hair_service_sd/upload_oss.py +++ b/project/hair_service_sd/upload_oss.py @@ -5,16 +5,24 @@ import os class OSS_object(): def __init__(self): - access_key_id = os.getenv('OSS_TEST_ACCESS_KEY_ID', '') - access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', '') - bucket_name = os.getenv('OSS_TEST_BUCKET', '') - endpoint = os.getenv('OSS_TEST_ENDPOINT', '') - for param in (access_key_id, access_key_secret, bucket_name, endpoint): - assert '<' not in param, '请设置环境变量 OSS_TEST_ACCESS_KEY_ID / OSS_TEST_ACCESS_KEY_SECRET / OSS_TEST_BUCKET / OSS_TEST_ENDPOINT' + # 懒加载：只读取凭证，不立即连接 OSS。 + # 本地用 output_format=base64 时不配 OSS 凭证也能启动服务； + # 仅在真正调用 upload_file 时才校验并连接。 + self.access_key_id = os.getenv('OSS_TEST_ACCESS_KEY_ID', '') + self.access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', '') + self.bucket_name = os.getenv('OSS_TEST_BUCKET', '') + self.endpoint = os.getenv('OSS_TEST_ENDPOINT', '') + self.bucket = None - self.bucket = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, bucket_name) + def _ensure_bucket(self): + if self.bucket is not None: + return + for param in (self.access_key_id, self.access_key_secret, self.bucket_name, self.endpoint): + assert '<' not in param, '请设置环境变量 OSS_TEST_ACCESS_KEY_ID / OSS_TEST_ACCESS_KEY_SECRET / OSS_TEST_BUCKET / OSS_TEST_ENDPOINT' + self.bucket = oss2.Bucket(oss2.Auth(self.access_key_id, self.access_key_secret), self.endpoint, self.bucket_name) def upload_file(self, file, target_name): + self._ensure_bucket() t0 = time.time() with open(oss2.to_unicode(file), 'rb') as f: ret = self.bucket.put_object(target_name, f) diff --git a/project/photo_service/lora_train_service_1.py b/project/photo_service/lora_train_service_1.py index d4bc7e1..d1d11f8 100644 --- a/project/photo_service/lora_train_service_1.py +++ b/project/photo_service/lora_train_service_1.py @@ -324,7 +324,7 @@ def train_thread(sq, gpu_id): '--caption_extension=".txt" --sample_sampler=ddim ' f'--sample_prompts={sample_txt} --sample_every_n_epochs="1000" ' '--seed="1234" --cache_latents --optimizer_type="AdamW" --max_data_loader_n_workers="0" --bucket_reso_steps=64 ' - '--xformers --bucket_no_upscale --noise_offset=0.0') + '--sdpa --bucket_no_upscale --noise_offset=0.0') print("cmd_train:", cmd_train) os.system(cmd_train) diff --git a/scripts/sync_data_to_server.sh b/scripts/sync_data_to_server.sh new file mode 100755 index 0000000..c790b37 --- /dev/null +++ b/scripts/sync_data_to_server.sh @@ -0,0 +1,69 @@ +#!/usr/bin/env bash +# 断点续传大文件到云服务器 /home/ubuntu/data +# 用法: bash scripts/sync_data_to_server.sh [all|weights|sd|hairstyles] +set -euo pipefail + +REMOTE="ubuntu@117.50.213.111" +REMOTE_BASE="/home/ubuntu/data" +LOCAL_BASE="/home/xsl/change_hair" +LOG_DIR="${LOCAL_BASE}/project/logs" +mkdir -p "$LOG_DIR" + +RSYNC_OPTS=(-avP --partial --info=progress2) + +sync_weights() { + echo ">>> [1/3] 同步 hair_service_sd/weights (~10G) ..." + rsync "${RSYNC_OPTS[@]}" \ + "${LOCAL_BASE}/project/hair_service_sd/weights/" \ + "${REMOTE}:${REMOTE_BASE}/hair_service_sd/weights/" +} + +sync_sd_models() { + echo ">>> [2/3] 同步 SD 底模 + ESRGAN + GFPGAN (~4.6G) ..." + rsync "${RSYNC_OPTS[@]}" \ + "${LOCAL_BASE}/project/onediff/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors" \ + "${REMOTE}:${REMOTE_BASE}/sdwebui/models/Stable-diffusion/" + rsync "${RSYNC_OPTS[@]}" \ + "${LOCAL_BASE}/project/onediff/stable-diffusion-webui/models/ESRGAN/" \ + "${REMOTE}:${REMOTE_BASE}/sdwebui/models/ESRGAN/" + rsync "${RSYNC_OPTS[@]}" \ + "${LOCAL_BASE}/project/onediff/stable-diffusion-webui/models/GFPGAN/" \ + "${REMOTE}:${REMOTE_BASE}/sdwebui/models/GFPGAN/" +} + +sync_hairstyles() { + echo ">>> [3/3] 同步 5 个 chang_* 发型资源 (~0.8G) ..." + for id in chang_tuoyuan chang_bolang chang_zhixian chang_huaban chang_xinxing; do + echo " - $id" + rsync "${RSYNC_OPTS[@]}" \ + "${LOCAL_BASE}/project/data/ref_hairstyle/${id}/" \ + "${REMOTE}:${REMOTE_BASE}/project/data/ref_hairstyle/${id}/" + rsync "${RSYNC_OPTS[@]}" \ + "${LOCAL_BASE}/project/data/hair_template_material/${id}/" \ + "${REMOTE}:${REMOTE_BASE}/project/data/hair_template_material/${id}/" + rsync "${RSYNC_OPTS[@]}" \ + "${LOCAL_BASE}/project/data/upload_train_imgs/${id}/" \ + "${REMOTE}:${REMOTE_BASE}/project/data/upload_train_imgs/${id}/" + rsync "${RSYNC_OPTS[@]}" \ + "${LOCAL_BASE}/data/train_material/${id}/" \ + "${REMOTE}:${REMOTE_BASE}/train_material/${id}/" + done +} + +TARGET="${1:-all}" +case "$TARGET" in + weights) sync_weights ;; + sd) sync_sd_models ;; + hairstyles) sync_hairstyles ;; + all) + sync_weights + sync_sd_models + sync_hairstyles + ;; + *) + echo "用法: $0 [all|weights|sd|hairstyles]" + exit 1 + ;; +esac + +echo ">>> 同步完成: ${TARGET}" diff --git a/train_batch_stepC.py b/train_batch_stepC.py new file mode 100644 index 0000000..074fde8 --- /dev/null +++ b/train_batch_stepC.py @@ -0,0 +1,96 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""阶段C：为已训练的发型生成 ref材质 + 模板材质 + 预览图 + +前置：LoRA 已训练完成，webui/photo_service/hair_service_sd 已启动。 + +对每个发型依次： + step3: 调 hair_service_sd trainCallBack 回调生成 ref 材质 + step4: 生成模板材质 + step5: 生成预览图 +""" +import os +import sys +import time +import json +import shutil + +HAIR_SERVICE_DIR = "/home/xsl/change_hair/project/hair_service_sd" +os.chdir(HAIR_SERVICE_DIR) +sys.path.insert(0, HAIR_SERVICE_DIR) +os.environ.setdefault("HF_HUB_OFFLINE", "1") +os.environ.setdefault("TRANSFORMERS_OFFLINE", "1") +os.environ.setdefault("CRYPTOGRAPHY_OPENSSL_NO_LEGACY", "1") + +import requests as req + +# (hair_id, gender, template_img) +HAIRSTYLES = [ + ("chang_tuoyuan", "girl", "/home/xsl/change_hair/hair_type_images/chang_tuoyuan/chang_tuoyuan.jpg"), + ("chang_bolang", "girl", "/home/xsl/change_hair/hair_type_images/chang_bolang/chang_bolang.jpg"), + ("chang_zhixian", "girl", "/home/xsl/change_hair/hair_type_images/chang_zhixian/chang_zhixian.jpg"), + ("chang_huaban", "girl", "/home/xsl/change_hair/hair_type_images/chang_huaban/chang_huaban.jpg"), + ("chang_xinxing", "girl", "/home/xsl/change_hair/hair_type_images/chang_xinxing/chang_xinxing.jpg"), +] + +HAIR_CALLBACK = "http://127.0.0.1:8801/api/hair/trainCallBack" +UPLOAD_TRAIN_DIR = "/home/xsl/change_hair/project/data/upload_train_imgs" + +# 导入 step4/step5 函数（会触发模型加载） +from train_hairstyle_full import step4_template_material, step5_preview +from common.logger import config + + +def log(msg): + print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True) + + +def step3_callback(hair_id, template_img): + """调 trainCallBack 生成 ref 材质。需要先把模板图放到 upload_train_imgs""" + log(f" --- step3: {hair_id} 生成 ref 材质 ---") + # 准备 upload_train_imgs//first##.jpg + src_dir = os.path.join(UPLOAD_TRAIN_DIR, hair_id) + os.makedirs(src_dir, exist_ok=True) + dst = os.path.join(src_dir, f"first##{hair_id}.jpg") + shutil.copy(template_img, dst) + + try: + r = req.post(HAIR_CALLBACK, json={ + "task_id": f"train_{hair_id}", "hair_id": hair_id, + "state": 0, "msg": "头发lora训练成功", "is_tj": "1" + }, timeout=300) + d = r.json() + ref_dir = os.path.join(config.get('default', 'hairstyleDir'), hair_id) + ok = d.get("state") == 0 and os.path.exists(os.path.join(ref_dir, "ref_rgb_8uc3_768.png")) + log(f" {'✓' if ok else '✗'} {hair_id} ref材质 {'成功' if ok else '失败: ' + str(d.get('msg'))}") + return ok + except Exception as e: + log(f" ✗ {hair_id} ref材质异常: {e}") + return False + + +def main(): + t0 = time.time() + log(f"阶段C: 为 {len(HAIRSTYLES)} 个发型生成材质和预览") + + results = {} + for hair_id, gender, template_img in HAIRSTYLES: + log(f"\n{'='*50}") + log(f"处理: {hair_id}") + log(f"{'='*50}") + ok3 = step3_callback(hair_id, template_img) + ok4 = step4_template_material(hair_id, template_img) + ok5 = step5_preview(hair_id, gender) + results[hair_id] = (ok3, ok4, ok5) + log(f" {hair_id}: ref={'✓' if ok3 else '✗'} 模板={'✓' if ok4 else '✗'} 预览={'✓' if ok5 else '✗'}") + + log(f"\n{'='*60}") + log(f"阶段C完成，耗时 {time.time()-t0:.0f}s") + log(f"{'='*60}") + for hair_id, (ok3, ok4, ok5) in results.items(): + status = "✓全部成功" if (ok3 and ok4 and ok5) else f"⚠️ ref={'✓' if ok3 else '✗'} 模板={'✓' if ok4 else '✗'} 预览={'✓' if ok5 else '✗'}" + log(f" {hair_id}: {status}") + + +if __name__ == "__main__": + main() diff --git a/train_hairstyles_parallel.py b/train_hairstyles_parallel.py new file mode 100644 index 0000000..bbcebea --- /dev/null +++ b/train_hairstyles_parallel.py @@ -0,0 +1,150 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""批量并行训练多个发型 + +流程： + 阶段A: 串行 step1（准备训练数据，共用GPU模型，避免重复加载） + 阶段B: 并行 step2（LoRA训练，kohya独立进程，限制并发数） + 阶段C: 启动服务后串行 step3(回调生成ref材质) + step4(模板) + step5(预览) + +用法: + cd /home/xsl/change_hair/project/hair_service_sd + python /home/xsl/change_hair/train_hairstyles_parallel.py +""" +import os +import sys +import time +import json +import subprocess +from concurrent.futures import ThreadPoolExecutor, as_completed + +HAIR_SERVICE_DIR = "/home/xsl/change_hair/project/hair_service_sd" +os.chdir(HAIR_SERVICE_DIR) +sys.path.insert(0, HAIR_SERVICE_DIR) +os.environ.setdefault("HF_HUB_OFFLINE", "1") +os.environ.setdefault("TRANSFORMERS_OFFLINE", "1") +os.environ.setdefault("CRYPTOGRAPHY_OPENSSL_NO_LEGACY", "1") +os.environ.setdefault("CUDA_VISIBLE_DEVICES", "0") + +# ====== 配置：本次要训练的发型 ====== +# (hair_id, input_dir, gender, template_img) +HAIRSTYLES = [ + ("chang_tuoyuan", "/home/xsl/change_hair/hair_type_images/chang_tuoyuan", "girl", "/home/xsl/change_hair/hair_type_images/chang_tuoyuan/chang_tuoyuan.jpg"), + ("chang_bolang", "/home/xsl/change_hair/hair_type_images/chang_bolang", "girl", "/home/xsl/change_hair/hair_type_images/chang_bolang/chang_bolang.jpg"), + ("chang_zhixian", "/home/xsl/change_hair/hair_type_images/chang_zhixian", "girl", "/home/xsl/change_hair/hair_type_images/chang_zhixian/chang_zhixian.jpg"), + ("chang_huaban", "/home/xsl/change_hair/hair_type_images/chang_huaban", "girl", "/home/xsl/change_hair/hair_type_images/chang_huaban/chang_huaban.jpg"), + ("chang_xinxing", "/home/xsl/change_hair/hair_type_images/chang_xinxing", "girl", "/home/xsl/change_hair/hair_type_images/chang_xinxing/chang_xinxing.jpg"), +] +PARALLEL = 2 # LoRA 训练并发数 + +# 导入训练模块（复用其 step 函数） +from train_hairstyle_full import ( + step1_prepare_data, step2_train, step3_wait_and_callback, + step4_template_material, step5_preview, get_models +) +import train_hairstyle_full as TH +from common.logger import config + + +def log(msg): + print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True) + + +def main(): + t0 = time.time() + ids = [h[0] for h in HAIRSTYLES] + log(f"批量训练 {len(ids)} 个发型: {ids} (并发={PARALLEL})") + + # ============ 阶段A: 串行 step1（准备数据）============ + log("=" * 60) + log("阶段A: 串行准备训练数据 (step1)") + log("=" * 60) + log(" 预加载模型...") + get_models() # 预加载，5个发型共用 + step1_ok, step1_fail = [], [] + for hair_id, input_dir, gender, _ in HAIRSTYLES: + # 清理可能的历史训练数据，确保干净 + train_dir = config.get('default', 'train_dir') + out_dir = os.path.join(train_dir, hair_id, "images", "1_hairstyle") + os.makedirs(out_dir, exist_ok=True) + os.makedirs(os.path.join(train_dir, hair_id, "model"), exist_ok=True) + + log(f" --- step1: {hair_id} ---") + try: + ok = step1_prepare_data(hair_id, input_dir, gender) + if ok: + step1_ok.append(hair_id) + log(f" ✓ {hair_id} 数据准备完成") + else: + step1_fail.append(hair_id) + log(f" ✗ {hair_id} 数据准备失败") + except Exception as e: + step1_fail.append(hair_id) + log(f" ✗ {hair_id} 数据准备异常: {e}") + log(f"阶段A完成: 成功 {len(step1_ok)}/{len(ids)}，失败 {step1_fail}") + if not step1_ok: + log("全部 step1 失败，退出"); return + + # 释放 step1 用的 GPU 模型，给 step2 训练腾显存 + log("释放 step1 模型显存...") + import torch + try: + for m in [TH._detector, TH._aligner, TH._matte]: + if m is not None and hasattr(m, 'model'): + pass + torch.cuda.empty_cache() + except Exception: + pass + + # ============ 阶段B: 并行 step2（LoRA 训练）============ + log("=" * 60) + log(f"阶段B: 并行 LoRA 训练 (step2, 并发={PARALLEL})") + log("=" * 60) + step2_results = {} # hair_id -> bool + with ThreadPoolExecutor(max_workers=PARALLEL) as ex: + futures = {ex.submit(step2_train, hid): hid for hid in step1_ok} + for fut in as_completed(futures): + hid = futures[fut] + try: + ok = fut.result() + step2_results[hid] = ok + log(f" {'✓' if ok else '✗'} {hid} step2 (训练启动)={'成功' if ok else '失败'}") + except Exception as e: + step2_results[hid] = False + log(f" ✗ {hid} step2 异常: {e}") + + # 等待所有 LoRA 训练真正完成（step2 只是启动，训练在 photo_service 后台跑） + log("等待所有 LoRA 训练完成（检查权重文件）...") + train_dir = config.get('default', 'train_dir') + pending = [hid for hid in step1_ok if step2_results.get(hid)] + completed = set() + deadline = time.time() + 3600 # 最多等1小时 + while pending and time.time() < deadline: + still = [] + for hid in pending: + lora = os.path.join(train_dir, hid, "model", "hairstyle_hd_lora.safetensors") + if os.path.exists(lora) and os.path.getsize(lora) > 100000000: + completed.add(hid) + log(f" ✓ {hid} LoRA 训练完成 ({os.path.getsize(lora)//1024//1024}MB)") + else: + still.append(hid) + pending = still + if pending: + log(f" ...等待中: {pending}") + time.sleep(30) + if pending: + log(f"⚠️ 超时未完成: {pending}") + log(f"阶段B完成: LoRA 训练完成 {len(completed)}/{len(step1_ok)}") + + # 写一个完成清单文件，供阶段C脚本读取 + done_file = "/home/xsl/change_hair/project/logs/train_batch_done.json" + with open(done_file, "w") as f: + json.dump({"completed": sorted(completed), "all": ids}, f) + log(f"已写入完成清单: {done_file}") + log(f"\n批量训练阶段A+B总耗时 {time.time()-t0:.0f}s") + log(f"完成训练的发型: {sorted(completed)}") + log("接下来需启动服务跑阶段C（step3/4/5 生成材质和预览）") + + +if __name__ == "__main__": + main() diff --git a/train_lora_parallel.py b/train_lora_parallel.py new file mode 100644 index 0000000..ebd1882 --- /dev/null +++ b/train_lora_parallel.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +"""直接调 kohya train_network.py 真正并行训练 LoRA + +绕过 photo_service 的串行阻塞，用 subprocess 直接起 train_network.py 进程， +通过 ThreadPoolExecutor 控制并发数。 + +用法: + python /home/xsl/change_hair/train_lora_parallel.py + +前提：step1 训练数据已准备好（data/train_material//images/1_hairstyle/*.png） +""" +import os +import sys +import time +import subprocess +from concurrent.futures import ThreadPoolExecutor, as_completed + +TRAIN_DIR = "/home/xsl/change_hair/data/train_material" +KOHYA_WORKDIR = "/home/xsl/change_hair/project/kohya_ss_home/kohya_ss" +BASE_MODEL = "/home/xsl/change_hair/project/onediff/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors" +KOHYA_ACCEL = "/home/xsl/miniconda3/envs/kohya/bin/accelerate" +LOG_DIR = "/home/xsl/change_hair/project/logs" + +# 本次要训练的发型 +HAIRSTYLES = ["chang_tuoyuan", "chang_zhixian", "chang_huaban", "chang_xinxing"] +# chang_bolang 已完成，跳过 +PARALLEL = 2 + + +def log(msg): + print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True) + + +def train_one(hair_id): + """训练单个发型的 LoRA，返回 (hair_id, success, log_path)""" + images_dir = os.path.join(TRAIN_DIR, hair_id, "images") + model_dir = os.path.join(TRAIN_DIR, hair_id, "model") + os.makedirs(model_dir, exist_ok=True) + + # 准备 sample prompt 文件 + sample_dir = os.path.join(model_dir, "sample") + os.makedirs(sample_dir, exist_ok=True) + sample_txt = os.path.join(sample_dir, "prompt.txt") + with open(sample_txt, "w") as f: + f.write('titor hairstyle, easyphoto, faceless, no human, white background, simple background, ' + ' --n low quality, worst quality, bad anatomy, bad composition, poor, low effort --h 768 ' + '--w 768 --s 30 --l 7') + + log_path = os.path.join(LOG_DIR, f"train_{hair_id}.log") + + cmd = [ + "bash", "-c", + f'cd {KOHYA_WORKDIR} && ' + f'HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 CUDA_VISIBLE_DEVICES=0 ' + f'{KOHYA_ACCEL} launch --num_cpu_threads_per_process=2 "./train_network.py" --enable_bucket ' + f'--min_bucket_reso=256 --max_bucket_reso=2048 --pretrained_model_name_or_path="{BASE_MODEL}" ' + f'--train_data_dir={images_dir} --resolution="2000,2000" ' + f'--output_dir={model_dir} ' + '--network_alpha="64" --save_model_as=safetensors --network_module=networks.lora --text_encoder_lr=5e-05 ' + '--unet_lr=0.0001 --network_dim=128 --output_name="hairstyle_hd_lora" --lr_scheduler_num_cycles="20" ' + '--no_half_vae --learning_rate="0.0001" --lr_scheduler="cosine" --lr_warmup_steps="650" --train_batch_size="1" ' + '--max_train_steps="1500" --save_every_n_epochs="100" --mixed_precision="fp16" --save_precision="fp16" ' + '--caption_extension=".txt" --sample_sampler=ddim ' + f'--sample_prompts={sample_txt} --sample_every_n_epochs="1000" ' + '--seed="1234" --cache_latents --optimizer_type="AdamW" --max_data_loader_n_workers="0" --bucket_reso_steps=64 ' + '--sdpa --bucket_no_upscale --noise_offset=0.0' + ] + + log(f" ▶ {hair_id} 开始训练，日志: {log_path}") + t0 = time.time() + try: + with open(log_path, "w") as lf: + proc = subprocess.run(cmd, stdout=lf, stderr=subprocess.STDOUT, timeout=1800) + ok = proc.returncode == 0 + lora = os.path.join(model_dir, "hairstyle_hd_lora.safetensors") + ok = ok and os.path.exists(lora) and os.path.getsize(lora) > 100000000 + elapsed = time.time() - t0 + sz = os.path.getsize(lora) // 1024 // 1024 if os.path.exists(lora) else 0 + log(f" {'✓' if ok else '✗'} {hair_id} {'训练完成' if ok else '训练失败'} ({sz}MB, {elapsed:.0f}s)") + return (hair_id, ok, log_path) + except subprocess.TimeoutExpired: + log(f" ✗ {hair_id} 训练超时") + return (hair_id, False, log_path) + except Exception as e: + log(f" ✗ {hair_id} 训练异常: {e}") + return (hair_id, False, log_path) + + +def main(): + t0 = time.time() + log(f"并行 LoRA 训练: {HAIRSTYLES} (并发={PARALLEL})") + + results = {} + with ThreadPoolExecutor(max_workers=PARALLEL) as ex: + futures = {ex.submit(train_one, hid): hid for hid in HAIRSTYLES} + for fut in as_completed(futures): + hid = futures[fut] + try: + hair_id, ok, log_path = fut.result() + results[hair_id] = ok + except Exception as e: + log(f" ✗ {hid} 异常: {e}") + results[hid] = False + + log(f"\n{'='*60}") + log(f"全部完成，耗时 {time.time()-t0:.0f}s") + for hid, ok in results.items(): + lora = os.path.join(TRAIN_DIR, hid, "model", "hairstyle_hd_lora.safetensors") + sz = os.path.getsize(lora) // 1024 // 1024 if os.path.exists(lora) else 0 + log(f" {'✓' if ok else '✗'} {hid} ({sz}MB)") + success = [h for h, ok in results.items() if ok] + log(f"成功: {success}") + + # 写完成清单 + done_file = os.path.join(LOG_DIR, "train_lora_done.json") + import json + with open(done_file, "w") as f: + json.dump({"completed": success, "all": HAIRSTYLES}, f) + log(f"完成清单: {done_file}") + + +if __name__ == "__main__": + main()