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merge into: xsl/change_hair:ubuntu3090
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xsl/change_hair:master xsl/change_hair:ubuntu3090_debug xsl/change_hair:ubuntu3090 xsl/change_hair:xsl5090 xsl/change_hair:feature/inpaint-and-feather
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pull from: xsl/change_hair:feature/inpaint-and-feather
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xsl/change_hair:ubuntu3090_debug xsl/change_hair:ubuntu3090 xsl/change_hair:xsl5090 xsl/change_hair:master xsl/change_hair:feature/inpaint-and-feather
1 Commits
ubuntu3090 .. feature/inpaint-and-feather
Author SHA1 Message Date
xsl 215663177a feat: 新增mask区域重绘功能 + 羽化贴回优化 + 发丝增强 
新增功能：
- inpaint_mask.py: mask区域重绘服务（enhance/pure_inpaint两种模式）
  严格只在mask区做SD inpainting，发丝自然化
- /inpaint 页面 + /api/inpaint 接口：画板手绘mask + 提示词编辑 + denoising可调
- enhance_hair.webui_img2img: 新增denoising_strength参数（原硬编码0.35）

羽化贴回优化（hair_swap_manual.py 步骤⑥）：
- feather_px（羽化范围）+ feather_alpha（羽化强度）拆分为两个独立参数
- 改为只羽化边缘（mask内部保持硬切，仅边缘带渐变）
- 新增黑底羽化对比图（纯黑背景凸显边缘过渡）

其他改动：
- OSS/COS密钥脱敏：改为可选import，未设环境变量时不崩溃
- manual页面：发型列表改为本次训练的8个（含hair_flow）
- enhance二次增强：重绘区改为「原图头发∪手绘mask」并外扩10%+减发际线带3cm
- 发型清单文档更新：179→180个可用发型（+hair_flow）

涉及文件：
- 新增: inpaint_mask.py, inpaint.html
- 修改: app.py, manual.html, hair_swap_manual.py, enhance_hair.py
- 修改: oss_module.py, upload_oss.py, cos_module.py（密钥脱敏）
- 文档: HAIRSTYLES_AVAILABLE.md, hairstyles_available.csv
 2026-07-08 20:38:31 +08:00
83 changed files with 880 additions and 11291 deletions
Expand all files Collapse all files
README.md
+2 -2
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@@ -296,8 +296,8 @@ curl -X POST http://127.0.0.1:32678/api/hair/train \
| 改动 | 说明 |
|------|------|
| `config.json` 的 onediff compiler 路径 | 指向本机 onediff 目录 |
| 启动加 `HF_HUB_OFFLINE=1` | 本机无法稳定访问 huggingface.co，用本地缓存离线加载 CLIP（需预先缓存 `openai/clip-vit-large-patch14`） |
| SD 模型 | `sd_model_checkpoint` 固定为 `v1-5-pruned-emaonly.safetensors`（本机无 majicmix）。仓库内权威副本：`configs/stable-diffusion-webui.config.json`；部署时拷到 `project/onediff/stable-diffusion-webui/config.json`（该目录是独立 git 仓库，无法被本仓直接跟踪） |
| 启动加 `HF_HUB_OFFLINE=1` | 本机无法访问 huggingface.co，用本地缓存离线加载 CLIP |
| SD 模型 | 实际加载 `v1-5-pruned-emaonly`（config 里写的 majicmix 不存在，webui 自动 fallback） |
### 3. 换发型推理改动（`hair_service_sd/gen_super_image.py`）
batch_train_hairstyles.py
+12 -12
View File
@@ -8,8 +8,8 @@
3. 串行执行，记录每个发型成功/失败
用法:
cd /home/ubuntu/change_hair/project/hair_service_sd
python /home/ubuntu/change_hair/batch_train_hairstyles.py --src /home/ubuntu/change_hair/hair_type_images --gender girl [--only 圆-心形] [--start-from 心形-心形]
cd /home/xsl/change_hair/project/hair_service_sd
python /home/xsl/change_hair/batch_train_hairstyles.py --src /home/xsl/change_hair/hair_type_images --gender girl [--only 圆-心形] [--start-from 心形-心形]
注意：
- 中文 hair_id 直接用文件名（去扩展名）作为 ID
@@ -24,14 +24,14 @@ import argparse
import subprocess
from datetime import datetime
SRC_DEFAULT = "/home/ubuntu/change_hair/hair_type_images"
WORK_DIR = "/home/ubuntu/change_hair/data/batch_train_inputs"
LOG_FILE = "/home/ubuntu/change_hair/data/batch_train_log.txt"
TRAIN_SCRIPT = "/home/ubuntu/change_hair/train_hairstyle_full.py"
HAIR_SERVICE_DIR = "/home/ubuntu/change_hair/project/hair_service_sd"
LOCK_FILE = "/home/ubuntu/change_hair/data/batch_train.pid"
DONE_FILE = "/home/ubuntu/change_hair/data/batch_train_done.txt" # 已完成发型清单（断点续跑）
LORA_DIR = "/home/ubuntu/change_hair/data/train_material" # LoRA 输出根目录
SRC_DEFAULT = "/home/xsl/change_hair/hair_type_images"
WORK_DIR = "/home/xsl/change_hair/data/batch_train_inputs"
LOG_FILE = "/home/xsl/change_hair/data/batch_train_log.txt"
TRAIN_SCRIPT = "/home/xsl/change_hair/train_hairstyle_full.py"
HAIR_SERVICE_DIR = "/home/xsl/change_hair/project/hair_service_sd"
LOCK_FILE = "/home/xsl/change_hair/data/batch_train.pid"
DONE_FILE = "/home/xsl/change_hair/data/batch_train_done.txt" # 已完成发型清单（断点续跑）
LORA_DIR = "/home/xsl/change_hair/data/train_material" # LoRA 输出根目录
def acquire_lock():
@@ -125,7 +125,7 @@ def run_one(hair_id, src_img, gender, py, done_set, force=False):
log(f" 调用: {' '.join(cmd[:2])} ... --hair-id {hair_id}")
try:
# 子进程输出实时写到日志文件
proc_log = os.path.join("/home/ubuntu/change_hair/data", f"subprocess_{hair_id}.log")
proc_log = os.path.join("/home/xsl/change_hair/data", f"subprocess_{hair_id}.log")
with open(proc_log, "w", encoding="utf-8") as f:
r = subprocess.run(
cmd, cwd=HAIR_SERVICE_DIR,
@@ -152,7 +152,7 @@ def main():
ap = argparse.ArgumentParser(description="批量训练发型")
ap.add_argument("--src", default=SRC_DEFAULT, help="原图目录")
ap.add_argument("--gender", default="girl", choices=["boy", "girl"])
ap.add_argument("--py", default="/home/ubuntu/miniconda3/envs/my_hair/bin/python",
ap.add_argument("--py", default="/home/xsl/miniconda3/envs/my_hair/bin/python",
help="python 解释器")
ap.add_argument("--only", default=None, help="只训练指定 hair_id（调试用）")
ap.add_argument("--start-from", default=None,
change_hair-hair.service
-19
View File
@@ -1,19 +0,0 @@
[Unit]
Description=change_hair hair_service_sd swapHair (8801)
After=network-online.target change_hair-webui.service change_hair-photo.service
Wants=change_hair-webui.service change_hair-photo.service
[Service]
Type=simple
User=ubuntu
WorkingDirectory=/home/ubuntu/change_hair/project/hair_service_sd
Environment=CRYPTOGRAPHY_OPENSSL_NO_LEGACY=1
Environment=CUDA_VISIBLE_DEVICES=0
Environment=APP_WORKER_ID=1
ExecStart=/home/ubuntu/miniconda3/envs/my_hair/bin/python run_copy_cost_colorb64.py
Restart=on-failure
RestartSec=10
TimeoutStartSec=300
[Install]
WantedBy=multi-user.target
change_hair-photo.service
-17
View File
@@ -1,17 +0,0 @@
[Unit]
Description=change_hair photo_service LoRA scheduler (32678)
After=network-online.target
Wants=network-online.target
[Service]
Type=simple
User=ubuntu
WorkingDirectory=/home/ubuntu/change_hair/project/photo_service
Environment=CUDA_VISIBLE_DEVICES=0
Environment=APP_WORKER_ID=1
ExecStart=/home/ubuntu/miniconda3/envs/py310/bin/python -u lora_train_service_1.py
Restart=on-failure
RestartSec=10
[Install]
WantedBy=multi-user.target
change_hair-webui.service
-20
View File
@@ -1,20 +0,0 @@
[Unit]
Description=change_hair SD WebUI (57860)
After=network-online.target
Wants=network-online.target
[Service]
Type=simple
User=ubuntu
WorkingDirectory=/home/ubuntu/change_hair/project/onediff/stable-diffusion-webui
Environment=CRYPTOGRAPHY_OPENSSL_NO_LEGACY=1
Environment=CUDA_VISIBLE_DEVICES=0
Environment=HF_HUB_OFFLINE=1
Environment=TRANSFORMERS_OFFLINE=1
ExecStart=/home/ubuntu/miniconda3/envs/sdwebui/bin/python webui.py --api --listen --xformers --port 57860
Restart=on-failure
RestartSec=10
TimeoutStartSec=600
[Install]
WantedBy=multi-user.target
configs/stable-diffusion-webui.config.json
-40
View File
@@ -1,40 +0,0 @@
{
"ldsr_steps": 100,
"ldsr_cached": false,
"SCUNET_tile": 256,
"SCUNET_tile_overlap": 8,
"SWIN_tile": 192,
"SWIN_tile_overlap": 8,
"SWIN_torch_compile": false,
"hypertile_enable_unet": false,
"hypertile_enable_unet_secondpass": false,
"hypertile_max_depth_unet": 3,
"hypertile_max_tile_unet": 256,
"hypertile_swap_size_unet": 3,
"hypertile_enable_vae": false,
"hypertile_max_depth_vae": 3,
"hypertile_max_tile_vae": 128,
"hypertile_swap_size_vae": 3,
"onediff_compiler_caches_path": "/home/ubuntu/change_hair/project/onediff/stable-diffusion-webui/extensions/onediff_sd_webui_extensions/compiler_caches",
"onediff_compiler_backend": "oneflow",
"sd_model_checkpoint": "v1-5-pruned-emaonly.safetensors",
"sd_checkpoint_hash": "6ce0161689b3853acaa03779ec93eafe75a02f4ced659bee03f50797806fa2fa",
"control_net_detectedmap_dir": "detected_maps",
"control_net_models_path": "",
"control_net_modules_path": "",
"control_net_unit_count": 3,
"control_net_model_cache_size": 2,
"control_net_inpaint_blur_sigma": 7,
"control_net_no_detectmap": false,
"control_net_detectmap_autosaving": false,
"control_net_allow_script_control": false,
"control_net_sync_field_args": true,
"controlnet_show_batch_images_in_ui": false,
"controlnet_increment_seed_during_batch": false,
"controlnet_disable_openpose_edit": false,
"controlnet_disable_photopea_edit": false,
"controlnet_photopea_warning": true,
"controlnet_ignore_noninpaint_mask": false,
"controlnet_clip_detector_on_cpu": false,
"controlnet_control_type_dropdown": false
}
docs/HAIRSTYLES_AVAILABLE.md
+9 -7
View File
@@ -1,6 +1,6 @@
# 可用发型清单
> 当前共 **179 个发型**可用于换发型功能（hair_flow 训练完成后为 180 个）。
> 当前共 **180 个发型**可用于换发型功能。
> 数据来源：`/api/hairstyles` 接口实时返回，要求发型同时具备「LoRA权重 + ref材质 + config.json」三件齐全。
> 全量清单见同目录 `hairstyles_available.csv`。
@@ -10,11 +10,11 @@
| 类别 | 数量 | 说明 |
|------|------|------|
| **总可用发型** | **179** | 三件齐全，可直接换发型 |
| **总可用发型** | **180** | 三件齐全，可直接换发型 |
| ├ 男款 (boy) | 93 | 适合男性脸型 |
| ├ 女款 (girl) | 86 | 适合女性脸型 |
| ├ 中文名命名 | 7 | 本次按脸型训练的新发型 |
| ├ 英文名命名 | 15 | 历史测试发型 |
| ├ 女款 (girl) | 87 | 适合女性脸型（+hair_flow） |
| ├ 中文名命名 | 7 | 按脸型训练的新发型 |
| ├ 英文名命名 | 16 | 历史测试发型 + hair_flow |
| └ 数字ID命名 | 157 | 生产环境发型（雪花算法ID） |
**性别说明**：gender 字段决定发型被分到"男款/女款"，但换发型时**男女发型都能用**（只是预览图用对应性别的标准脸）。
@@ -55,6 +55,7 @@
| hair_id | gender | 备注 |
|---------|--------|------|
| **hair_flow** | **girl** | **波浪卷发，最新训练（2026-07-07）** |
| huaban1 | girl | 画板1 |
| huaban2 | girl | 画板2 |
| new_test_001 | boy | 测试 |
@@ -104,8 +105,9 @@
### 2. 调试页（带可视化）
- `http://localhost:8888/debug` — 全参数调试台
- `http://localhost:8888/manual` — 手绘 mask 重绘
- `http://localhost:8888/hairline` — 发际线 mask 实验
- `http://localhost:8888/manual` — 手绘 mask 换发型（含 enhance 二次增强 + 羽化贴回）
- `http://localhost:8888/inpaint` — **mask区域重绘**（发丝自然化，enhance/pure_inpaint两种模式）
- `http://localhost:8888/hairline` — 发际线 mask 实验（4种方案对比）
### 3. API 调用
```bash
docs/hairstyles_available.csv
+181 -180
View File
@@ -1,180 +1,181 @@
hair_id,gender,预览图,LoRA大小MB
1905785164224868354,girl,有,144
1907641420518580226,boy,有,144
1907641442341543937,boy,有,144
1907641528173780994,boy,有,144
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1935355868347736065,girl,有,144
1935355934743568386,girl,有,144
1935355969141055490,girl,有,144
1935356042201636865,girl,有,144
1935356092684279809,girl,有,144
1935356131338985473,girl,有,144
1935356178227109890,girl,有,144
1938489620162781185,boy,有,144
1938489674713899010,boy,有,144
1938489739088076801,boy,有,144
1938489840707674114,boy,有,144
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1938841049238970370,girl,有,144
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1980312945301954561,boy,有,144
1980313052302843905,girl,有,144
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1980313819915001858,girl,有,144
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1980314290419441665,girl,有,144
1980314381058351106,girl,有,144
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1980314710101499906,boy,有,144
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1980315054516772866,boy,有,144
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1980413320612847618,girl,有,144
1980415346071609345,girl,有,144
1980415897056354306,girl,有,144
1980422351658196994,boy,有,144
1980422373388886018,boy,有,144
1981177324700549122,girl,有,144
1981177378714796034,boy,有,144
1981177477423546370,boy,有,144
1981177594750812162,boy,有,144
1981177742742634497,boy,有,144
1981177931859607554,boy,有,144
3325266,girl,有,144
huaban1,girl,有,144
huaban2,girl,有,144
new_test_001,boy,有,144
newf_1214,girl,有,144
newf_12141,girl,有,144
newf_121445553,boy,有,144
newf_12314,girl,有,144
newf_1235314,girl,有,144
newf_204,girl,有,144
newf_216,girl,有,144
test001,boy,有,144
test002,girl,有,144
test003,boy,有,144
test0036,boy,有,144
test888,boy,有,144
圆-心形,girl,有,144
圆-椭圆,girl,有,144
圆-波浪,girl,有,144
圆-直线,girl,有,144
圆-花瓣,girl,有,144
心形-心形,girl,有,144
心形-椭圆,girl,有,144
hair_id,gender
1905785164224868354,girl
1907641420518580226,boy
1907641442341543937,boy
1907641528173780994,boy
1907650883958923266,boy
1907650924350070785,boy
1907651105032298498,boy
1907651244006367233,boy
1907651308460236802,girl
1907651348528422914,girl
1907651413477220354,girl
1907651464794529794,girl
1907651525024735234,girl
1907651578753769473,girl
1907651632629604354,girl
1907651680352395265,girl
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1907805226322419713,boy
1929905737381048322,boy
1929905770088230914,boy
1929906202869100546,boy
1929906230224351234,boy
1929906315553271809,boy
1929906349401305089,boy
1929906429839667202,boy
1935354779623858177,boy
1935354812175851522,boy
1935354869738479617,boy
1935354938051108865,boy
1935355030892027905,boy
1935355149217538050,girl
1935355188652384257,girl
1935355258143612929,girl
1935355292960530433,girl
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1935355403207811074,girl
1935355449991077889,girl
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1935355607394918402,girl
1935355698319040514,girl
1935355740559876097,girl
1935355778526715905,girl
1935355868347736065,girl
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huaban2,girl
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圆-心形,girl
圆-椭圆,girl
圆-波浪,girl
圆-直线,girl
圆-花瓣,girl
心形-心形,girl
心形-椭圆,girl
1 hair_id gender 预览图 LoRA大小MB
2 1905785164224868354 girl 有 144
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156 1981177742742634497 boy 有 144
157 1981177931859607554 boy 有 144
158 3325266 girl 有 144
159 huaban1 hair_flow girl 有 144
160 huaban2 huaban1 girl 有 144
161 new_test_001 huaban2 boy girl 有 144
162 newf_1214 new_test_001 girl boy 有 144
163 newf_12141 newf_1214 girl 有 144
164 newf_121445553 newf_12141 boy girl 有 144
165 newf_12314 newf_121445553 girl boy 有 144
166 newf_1235314 newf_12314 girl 有 144
167 newf_204 newf_1235314 girl 有 144
168 newf_216 newf_204 girl 有 144
169 test001 newf_216 boy girl 有 144
170 test002 test001 girl boy 有 144
171 test003 test002 boy girl 有 144
172 test0036 test003 boy 有 144
173 test888 test0036 boy 有 144
174 圆-心形 test888 girl boy 有 144
175 圆-椭圆 圆-心形 girl 有 144
176 圆-波浪 圆-椭圆 girl 有 144
177 圆-直线 圆-波浪 girl 有 144
178 圆-花瓣 圆-直线 girl 有 144
179 心形-心形 圆-花瓣 girl 有 144
180 心形-椭圆 心形-心形 girl 有 144
181 心形-椭圆 girl
docs/换发型集成文档.md
-408
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# 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 <lora:xxx:1.0>
│ 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://<host>: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,<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":"<base64>", "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": "<base64 编码的结果图>" // 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/<hair_id>/` 读模板原图 | <1% |
| 4 | 检查遮挡眼睛 | 从 `hair_template_material/<hair_id>/` 读 `*_matting.png` 和 `.pkl`（关键点） | <1% |
| 5 | 检查发型材质 | 确认 `ref_hairstyle/<hair_id>/` 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/<hair_id>/model/hairstyle_hd_lora.safetensors` 复制到 webui 的 `models/Lora/<hair_id>_hd.safetensors`（已存在则跳过）
3. `photo_service` 在 prompt 前注入 `<lora:<hair_id>_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/<hair_id>_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/<hair_id>/
├── 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/<hair_id>/
├── 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/<hair_id>/
├── first##<uuid>.png # 发型模板原图
├── first##<uuid>_matting.png # 发型 matting（遮挡检测用）
└── first##<uuid>.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/<hair_id>/
└── first##<hair_id>.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://<host>: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 `<hair_id>` 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://<host>: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://<host>:8899/preview/<hair_id>
→ 直接返回预览图（优先 static/previews/<hair_id>.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 <girl|boy> \
--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/<hair_id>/images/1_hairstyle/` 约 20 张 png+txt | ~1 分钟 |
| step2 | LoRA 训练 | kohya 训练 1500 步 → `train_material/<hair_id>/model/hairstyle_hd_lora.safetensors` | ~10 分钟（单卡） |
| step3 | 生成 ref 材质 | 调 hair_service_sd 回调 → `ref_hairstyle/<hair_id>/` 6 个文件 | ~1 秒 |
| step4 | 生成模板材质 | → `hair_template_material/<hair_id>/` 3 个文件 | ~3 秒 |
| step5 | 生成预览图 | → `hair_grow_service/static/previews/<hair_id>.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/<hair_id>/model/` 是否有 LoRA |
| 换发型返回 `发型模板加载失败` | `upload_train_imgs/<hair_id>/` 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/<hair_id>/` 缺 matting/pkl | 检查 5.3-3 目录 |
| 预览图 404 | `ref_hairstyle/<hair_id>/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_<hair_id>.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
```
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# 方案 A：换发型服务本机部署 — 上传文件清单
> 适用场景：从原服务器打包上传 conda 环境 + SD WebUI 代码，在本机完成换发型推理部署。
>
> 本机路径：`/home/xsl/change_hair`（GPU：RTX 5090 32G，CUDA 12.8）
>
> 原服务器参考：`szlc@192.168.101.63`
>
> 更新日期：2026-07-09（核实本机已有资源，更新上传清单）
---
## 一、本机已有资源（已到位，无需再传）✅
以下资源已确认存在于本机项目中：
| 本机路径 | 大小 | 说明 |
|---------|------|------|
| `project/hair_service_sd/weights/` | 9.9G | 核心推理权重（28个文件，含 GAN/分割/关键点/人脸等全部模型） |
| `project/onediff/stable-diffusion-webui/models/` | 4.6G | SD 底模 `v1-5-pruned-emaonly.safetensors` + ESRGAN `4x-UltraSharp.pth` + GFPGAN 全套 |
| `project/data/ref_hairstyle/` | 40M | 5 个发型材质（config.json + npy + png + txt） |
| `project/data/hair_template_material/` | 24M | 5 个发型模板（pkl + png + matting） |
| `project/data/upload_train_imgs/` | 2.5M | 5 个发型原图（jpg） |
| `data/train_material/` | 739M | 5 个发型 LoRA 权重（`hairstyle_hd_lora.safetensors`）+ 训练图像 |
**当前可用发型（5 个）：**
- `chang_bolang`（长波浪）
- `chang_huaban`（长花瓣）
- `chang_tuoyuan`（长椭圆）
- `chang_xinxing`（长心形）
- `chang_zhixian`（长直线）
> 以上发型可做换发型推理。如需更多发型，见第四节可选上传。
**项目代码（已在 git 仓库中，无需上传）：**
- `project/hair_service_sd/`（代码，不含 weights）
- `project/photo_service/`
- `hair_grow_service/`
- 启动脚本、测试脚本等
---
## 二、必须上传（换发型推理最低配置）🔴
以下 4 项需要从原服务器打包上传，**总计约 18–30G**。
### 2.1 Conda 环境 × 3
从原服务器 `/home/szlc/miniconda3/envs/` 打包，上传到本机 `/home/xsl/miniconda3/envs/`。
| 环境名 | 原服务器路径 | 本机目标路径 | 用途 | 预估大小 |
|--------|-------------|-------------|------|---------|
| `my_hair` | `/home/szlc/miniconda3/envs/my_hair/` | `/home/xsl/miniconda3/envs/my_hair/` | hair_service_sd 主服务（端口 8801） | ~5–8G |
| `sdwebui` | `/home/szlc/miniconda3/envs/sdwebui/` | `/home/xsl/miniconda3/envs/sdwebui/` | SD WebUI 推理（端口 57860） | ~8–12G |
| `py310` | `/home/szlc/miniconda3/envs/py310/` | `/home/xsl/miniconda3/envs/py310/` | photo_service LoRA 调度（端口 32678） | ~2–4G |
> ⚠️ **为什么必须上传，不能从公网安装？** 这三个环境包含特定版本的 PyTorch + CUDA 扩展、历史版本的 pip 包、以及编译好的 C++/CUDA 算子，依赖关系复杂。项目中的 `env.yaml` 是旧的 Python 3.7 规格，与实际运行环境（Python 3.10）不一致，无法用于重建。
#### 原服务器打包示例
```bash
# 在原服务器（szlc@192.168.101.63）执行
cd /home/szlc/miniconda3/envs
tar czf my_hair.tar.gz my_hair/
tar czf sdwebui.tar.gz sdwebui/
tar czf py310.tar.gz py310/
```
#### 本机解压示例
```bash
# 在本机执行（上传完成后）
mkdir -p /home/xsl/miniconda3/envs
cd /home/xsl/miniconda3/envs
tar xzf /path/to/my_hair.tar.gz
tar xzf /path/to/sdwebui.tar.gz
tar xzf /path/to/py310.tar.gz
```
---
### 2.2 SD WebUI 代码（不含 models）
从原服务器打包 **webui 程序本体**，**排除 models 目录**（本机已有 4.6G 模型权重）。
| 原服务器路径 | 本机目标路径 | 说明 | 预估大小 |
|-------------|-------------|------|---------|
| `/home/szlc/project/onediff/stable-diffusion-webui/`（排除 `models/`） | `/home/xsl/change_hair/project/onediff/stable-diffusion-webui/` | webui.py、modules/、extensions/、repositories/ 等 | ~2–5G |
> ⚠️ 本机当前 `project/onediff/stable-diffusion-webui/` 目录只有 `models/` 子目录（4.6G 模型权重），缺少 `webui.py`、`modules/`、`extensions/` 等所有代码文件。
**必须包含的关键文件/目录：**
```
stable-diffusion-webui/
├── webui.py # 主入口（必需）
├── webui.sh # 启动脚本
├── webui-user.sh # 用户配置
├── launch.py
├── modules/ # webui 核心模块
├── extensions/ # 扩展（如有）
├── repositories/ # 依赖仓库（如有）
├── config.json # 配置文件
├── ui-config.json # UI 配置（如有）
└── style.css / javascript/ # 前端资源
```
**不要上传（本机已有，勿覆盖）：**
```
models/Stable-diffusion/v1-5-pruned-emaonly.safetensors # 4.0G
models/ESRGAN/4x-UltraSharp.pth # 64M
models/GFPGAN/GFPGANv1.4.pth # 333M
models/GFPGAN/detection_Resnet50_Final.pth # 105M
models/GFPGAN/parsing_parsenet.pth # 82M
```
#### 原服务器打包示例（排除 models）
```bash
# 在原服务器（szlc@192.168.101.63）执行
cd /home/szlc/project/onediff
tar czf webui_code.tar.gz \
--exclude='stable-diffusion-webui/models' \
stable-diffusion-webui/
```
#### 本机解压示例
```bash
# 在本机执行（保留已有 models 目录）
cd /home/xsl/change_hair/project/onediff
tar xzf /path/to/webui_code.tar.gz
# 解压后确认 models/ 仍在且未被覆盖
ls -lh stable-diffusion-webui/models/Stable-diffusion/
```
---
## 三、上传清单速查表
复制以下列表，在原服务器逐项确认后打包：
```
# ===== 必须上传（方案 A，共 4 项，约 18–30G）=====
# 1. Conda 环境（3 个）
/home/szlc/miniconda3/envs/my_hair/ → /home/xsl/miniconda3/envs/my_hair/
/home/szlc/miniconda3/envs/sdwebui/ → /home/xsl/miniconda3/envs/sdwebui/
/home/szlc/miniconda3/envs/py310/ → /home/xsl/miniconda3/envs/py310/
# 2. SD WebUI 代码（排除 models/）
/home/szlc/project/onediff/stable-diffusion-webui/（排除 models/）
→ /home/xsl/change_hair/project/onediff/stable-diffusion-webui/
```
---
## 四、可选上传（非换发型推理必需）🟡
以下功能暂不需要时可跳过，后续按需补传。
### 4.1 训练新发型
| 原服务器路径 | 本机目标路径 | 预估大小 |
|-------------|-------------|---------|
| `/home/szlc/project/kohya_ss_home/` | `/home/xsl/change_hair/project/kohya_ss_home/` | ~13G |
| `/home/szlc/miniconda3/envs/kohya/` | `/home/xsl/miniconda3/envs/kohya/` | ~5G |
### 4.2 换发色功能
| 原服务器路径 | 本机目标路径 |
|-------------|-------------|
| `/home/szlc/project/data/ref_haircolor/` | `/home/xsl/change_hair/project/data/ref_haircolor/` |
| `/home/szlc/project/data/ref_color/` | `/home/xsl/change_hair/project/data/ref_color/` |
| `/home/szlc/project/data/ref_color_imgs/` | `/home/xsl/change_hair/project/data/ref_color_imgs/` |
| `/home/szlc/.../weights/classify_3_2499.pth` | `project/hair_service_sd/weights/classify_3_2499.pth` |
### 4.3 更多发型
当前只有 5 个发型。如需更多，从原服务器追加：
```
/data/train_material/<hair_id>/ → data/train_material/<hair_id>/
/home/szlc/project/data/ref_hairstyle/<hair_id>/ → project/data/ref_hairstyle/<hair_id>/
/home/szlc/project/data/hair_template_material/<hair_id>/ → project/data/hair_template_material/<hair_id>/
/home/szlc/project/data/upload_train_imgs/<hair_id>/ → project/data/upload_train_imgs/<hair_id>/
```
原服务器约有 338 个发型，全量 `train_material` 约 121G。
### 4.4 小文件（可选）
| 文件 | 用途 | 大小 |
|------|------|------|
| `weights/mediapipe/face_landmarker.task` | 发际线检测 | ~3.6M |
---
## 五、上传方式建议
### 方式 1：rsync 直传（推荐，支持断点续传）
```bash
# 从原服务器直传到本机（在本机执行）
rsync -avP --partial \
szlc@192.168.101.63:/home/szlc/miniconda3/envs/my_hair/ \
/home/xsl/miniconda3/envs/my_hair/
rsync -avP --partial \
szlc@192.168.101.63:/home/szlc/miniconda3/envs/sdwebui/ \
/home/xsl/miniconda3/envs/sdwebui/
rsync -avP --partial \
szlc@192.168.101.63:/home/szlc/miniconda3/envs/py310/ \
/home/xsl/miniconda3/envs/py310/
rsync -avP --partial \
--exclude='models/' \
szlc@192.168.101.63:/home/szlc/project/onediff/stable-diffusion-webui/ \
/home/xsl/change_hair/project/onediff/stable-diffusion-webui/
```
### 方式 2：先 tar 再上传到 `/home/xsl/data`
若通过网盘中转，先 tar 再上传到 `/home/xsl/data/`，上传完成后告知我解压。
---
## 六、上传完成后的验证
上传完成后，在本机执行以下命令确认文件到位：
```bash
# 1. Conda 环境
ls /home/xsl/miniconda3/envs/my_hair/bin/python
ls /home/xsl/miniconda3/envs/sdwebui/bin/python
ls /home/xsl/miniconda3/envs/py310/bin/python
# 2. WebUI 代码
ls /home/xsl/change_hair/project/onediff/stable-diffusion-webui/webui.py
# 3. 模型未被覆盖
ls -lh /home/xsl/change_hair/project/onediff/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors
```
期望结果：4 项均存在，SD 底模约 4.0G。
---
## 七、上传完成后我会做的事
你上传完毕并告知后，我会依次执行：
1. **路径适配**：运行 `adapt_paths.sh`，将所有硬编码路径从 `/home/xsl/change_hair` 改为 `/home/xsl/change_hair`，`/home/xsl/miniconda3` 改为 `/home/xsl/miniconda3`
2. **Conda 路径修复**：运行 `fix_conda_paths.py`，修复 3 个环境中二进制文件的 shebang 和内部路径前缀
3. **安装 Miniconda 基础环境**（如尚未安装）：从 `repo.anaconda.com` 下载安装
4. **启动服务**：`bash start_all.sh start`（依赖顺序：webui → photo_service → hair_service_sd）
5. **接口测试**：用 `test_swaphair.py` 验证换发型功能
---
## 八、部署后服务端口
| 服务 | 端口 | 说明 |
|------|------|------|
| webui | 57860 | SD img2img 推理 |
| photo_service | 32678 | LoRA 加载与调度 |
| hair_service_sd | 8801 | 换发型主接口 |
| hair_grow_service | 8899 | 生发前端（独立 Flask） |
换发型接口：
```
POST http://127.0.0.1:8801/api/swapHair/v1
```
---
## 九、常见问题
**Q：能否只上传 miniconda3 整包，不传单个 env？**
可以。上传 `/home/szlc/miniconda3/` 整包到 `/home/xsl/miniconda3/` 也行，体积更大（约 20–30G）但更省事。
**Q：webui 上传时不小心覆盖了 models 怎么办？**
重新从 `/home/xsl/data/sdwebui/models/` 或原服务器补传 models 目录即可。
**Q：本机没有 miniconda 基础安装，只传 envs 够吗？**
不够。3 个 env 内的 python 依赖 miniconda 基础库（如 `libstdc++`、`libgcc` 等）。我会在上传完成后先安装 miniconda，再修复 env 内部路径。
**Q：上传后启动报 xformers 错误？**
webui 启动参数可能需要去掉 `--xformers`，改用 `--opt-sdp-no-mem-attention`（RTX 5090 支持，我会在路径适配时处理）。
**Q：本机磁盘空间是否足够？**
当前 `/home` 剩余约 30G。3 个 conda 环境约 20–25G，解压后可能吃紧。建议解压一个删一个 tar 包，或考虑清理 `/home/xsl/data/`（已有 16G 冗余数据，解压完成后可删除）。
gen_hairline_mask.py
+3 -3
View File
@@ -8,7 +8,7 @@ import sys
import cv2
import numpy as np
HAIR_SERVICE_DIR = "/home/ubuntu/change_hair/project/hair_service_sd"
HAIR_SERVICE_DIR = "/home/xsl/change_hair/project/hair_service_sd"
sys.path.insert(0, HAIR_SERVICE_DIR)
os.environ.setdefault("HF_HUB_OFFLINE", "1")
@@ -87,7 +87,7 @@ def gen_hairline_mask(img_path, mask_path):
if __name__ == "__main__":
img_path = sys.argv[1] if len(sys.argv) > 1 else \
"/home/ubuntu/change_hair/project/data/userImage/8488902485_20250630055547.jpg"
"/home/xsl/change_hair/project/data/userImage/8488902485_20250630055547.jpg"
mask_path = sys.argv[2] if len(sys.argv) > 2 else \
"/home/ubuntu/change_hair/project/logs/hairgrow_test_mask.png"
"/home/xsl/change_hair/project/logs/hairgrow_test_mask.png"
gen_hairline_mask(img_path, mask_path)
gen_hairstyle_previews.py
+6 -6
View File
@@ -14,15 +14,15 @@ import time
import base64
import requests
HAIR_SERVICE_DIR = "/home/ubuntu/change_hair/project/hair_service_sd"
HAIR_SERVICE_DIR = "/home/xsl/change_hair/project/hair_service_sd"
sys.path.insert(0, HAIR_SERVICE_DIR)
os.chdir(HAIR_SERVICE_DIR)
BOY_IMG = "/home/ubuntu/change_hair/images/boy.png"
GIRL_IMG = "/home/ubuntu/change_hair/images/girl.png"
HAIRSTYLE_DIR = "/home/ubuntu/change_hair/project/data/ref_hairstyle"
TRAIN_DIR = "/home/ubuntu/change_hair/data/train_material"
PREVIEW_DIR = "/home/ubuntu/change_hair/hair_grow_service/static/previews" # 预览图存储目录
BOY_IMG = "/home/xsl/change_hair/images/boy.png"
GIRL_IMG = "/home/xsl/change_hair/images/girl.png"
HAIRSTYLE_DIR = "/home/xsl/change_hair/project/data/ref_hairstyle"
TRAIN_DIR = "/home/xsl/change_hair/data/train_material"
PREVIEW_DIR = "/home/xsl/change_hair/hair_grow_service/static/previews" # 预览图存储目录
SWAP_API = "http://127.0.0.1:8801/api/swapHair/v1"
gen_template_material.py
+2 -2
View File
@@ -8,7 +8,7 @@
hair_template_material/<hair_id>/first##<name>.pkl 1k关键点
用法:
cd /home/ubuntu/change_hair/project/hair_service_sd
cd /home/xsl/change_hair/project/hair_service_sd
python gen_template_material.py --hair-id new_test_001 --img /path/to/template.jpg
"""
import os
@@ -19,7 +19,7 @@ import pickle
import argparse
ssl._create_default_https_context = ssl._create_unverified_context
HAIR_SERVICE_DIR = "/home/ubuntu/change_hair/project/hair_service_sd"
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")
hair_grow_cli.py
+4 -4
View File
@@ -6,12 +6,12 @@
需要在 hair_service_sd 目录下运行（依赖其模块导入）。
用法:
cd /home/ubuntu/change_hair/project/hair_service_sd
/home/ubuntu/miniconda3/envs/my_hair/bin/python /home/ubuntu/change_hair/hair_grow_cli.py \
cd /home/xsl/change_hair/project/hair_service_sd
/home/xsl/miniconda3/envs/my_hair/bin/python /home/xsl/change_hair/hair_grow_cli.py \
--img test.jpg --mask mask.png --strength 0.5 -o result.jpg
# 批量跑三档强度对比
/home/ubuntu/miniconda3/envs/my_hair/bin/python /home/ubuntu/change_hair/hair_grow_cli.py \
/home/xsl/miniconda3/envs/my_hair/bin/python /home/xsl/change_hair/hair_grow_cli.py \
--img test.jpg --mask mask.png --compare
"""
import os
@@ -20,7 +20,7 @@ import cv2
import argparse
# 把 hair_service_sd 加入路径，使其模块可被导入
HAIR_SERVICE_DIR = "/home/ubuntu/change_hair/project/hair_service_sd"
HAIR_SERVICE_DIR = "/home/xsl/change_hair/project/hair_service_sd"
sys.path.insert(0, HAIR_SERVICE_DIR)
# 设置离线模式（本机无法访问 huggingface.co）
hair_grow_service/app.py
+81 -19
View File
@@ -24,19 +24,10 @@ os.environ.setdefault("CRYPTOGRAPHY_OPENSSL_NO_LEGACY", "1")
import cv2
import numpy as np
from flask import Flask, request, jsonify, send_from_directory, send_file
from flask import Flask, request, jsonify, send_from_directory
from gevent import pywsgi
# 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
PORT = 8888
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
app = Flask(__name__, static_folder="static", static_url_path="/static")
@@ -96,6 +87,12 @@ def manual_page():
return send_from_directory(os.path.join(BASE_DIR, "static"), "manual.html")
@app.route("/inpaint")
def inpaint_page():
"""mask区域重绘测试页（纯发丝增强/自然重绘）"""
return send_from_directory(os.path.join(BASE_DIR, "static"), "inpaint.html")
@app.route("/api/swap", methods=["POST"])
def api_swap_proxy():
"""代理换发型请求到 8801（避免前端跨域问题）"""
@@ -349,6 +346,7 @@ def api_swap_manual():
blend_dilate=(int(bd[0]), int(bd[1])),
seamless_dilate=(int(sd[0]), int(sd[1])),
feather_px=int(g("feather_px", 0)),
feather_alpha=float(g("feather_alpha", 1.0)),
enhance=bool(g("enhance", False)),
enhance_denoising=float(g("enhance_denoising", 0.35)),
)
@@ -403,7 +401,7 @@ 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(fallback)
return send_file_or_404(fallback)
except Exception:
pass
return ("", 404)
@@ -411,14 +409,25 @@ def preview_img(hair_id):
@app.route("/train_src/<hair_id>")
def train_src_img(hair_id):
"""返回发型的训练原图（hair_type_images/<hair_id>.jpg/.png）。
用于测试页展示发型真实样子，而非套在标准脸上的效果图。
"""返回发型的训练原图，用于测试页展示发型真实样子。
依次查找：hair_type_images/ → images/ → train_material/<id>/images 输入 → 预览图（兜底）。
"""
src_dir = "/home/xsl/change_hair/hair_type_images"
for ext in (".jpg", ".jpeg", ".png"):
path = os.path.join(src_dir, hair_id + ext)
if os.path.exists(path):
return send_from_directory(src_dir, hair_id + ext)
# 候选源目录（按优先级）
candidate_dirs = [
"/home/xsl/change_hair/hair_type_images",
"/home/xsl/change_hair/images",
"/home/xsl/change_hair/data/batch_train_inputs/" + hair_id,
]
for src_dir in candidate_dirs:
for ext in (".jpg", ".jpeg", ".png"):
path = os.path.join(src_dir, hair_id + ext)
if os.path.exists(path):
return send_from_directory(src_dir, hair_id + ext)
# 兜底：返回套脸预览图（previews/<hair_id>.jpg）
preview_dir = os.path.join(BASE_DIR, "static", "previews")
preview_path = os.path.join(preview_dir, hair_id + ".jpg")
if os.path.exists(preview_path):
return send_from_directory(preview_dir, hair_id + ".jpg")
return ("", 404)
@@ -516,6 +525,59 @@ def api_grow():
return jsonify({"state": -1, "msg": f"生发失败: {e}"}), 500
@app.route("/api/inpaint", methods=["POST"])
def api_inpaint():
"""mask区域重绘接口（严格只在mask区做SD inpainting）。
入参 JSON:
img: 原图 base64
mask: 手绘mask base64（白色=重绘区）
prompt: 提示词（空则用模式默认值）
denoising_strength: 重绘强度 0~1（默认0.5）
mode: "enhance" 或 "pure_inpaint"（默认enhance）
返回: {state, result, mask_used, info}
"""
try:
d = request.json
img_b64 = d.get("img", "")
mask_b64 = d.get("mask", "")
if not img_b64 or not mask_b64:
return jsonify({"state": -1, "msg": "img 和 mask 不能为空（请先手绘重绘区域）"}), 400
img = _b64_to_ndarray(img_b64, color=True)
mask = _b64_to_ndarray(mask_b64, color=False)
if img is None or mask is None:
return jsonify({"state": -1, "msg": "img 或 mask 解析失败"}), 400
prompt = d.get("prompt", "")
denoising = float(d.get("denoising_strength", 0.5))
mode = d.get("mode", "enhance")
if mode not in ("enhance", "pure_inpaint"):
mode = "enhance"
print(f"[inpaint] mode={mode}, denoising={denoising}, prompt={prompt[:40]}, img={img.shape}")
import sys
sys.path.insert(0, "/home/xsl/change_hair/project/hair_service_sd")
from inpaint_mask import inpaint_mask as do_inpaint
result, mask_used, info = do_inpaint(
img, mask, prompt=prompt,
denoising_strength=denoising, mode=mode
)
_, buf = cv2.imencode(".jpg", result, [cv2.IMWRITE_JPEG_QUALITY, 95])
result_b64 = base64.b64encode(buf).decode("utf-8")
_, mbuf = cv2.imencode(".png", mask_used)
mask_used_b64 = "data:image/png;base64," + base64.b64encode(mbuf).decode("utf-8")
return jsonify({"state": 0, "result": result_b64,
"mask_used": mask_used_b64, "info": info})
except Exception as e:
print(f"[inpaint] 失败: {e}")
traceback.print_exc()
return jsonify({"state": -1, "msg": f"mask重绘失败: {e}"}), 500
if __name__ == "__main__":
print(f"生发服务启动，端口 {PORT}")
print(f"测试页面: http://0.0.0.0:{PORT}")
hair_grow_service/static/inpaint.html
+323
View File
@@ -0,0 +1,323 @@
<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>mask区域重绘</title>
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
body { font-family: -apple-system, "Segoe UI", "Microsoft YaHei", sans-serif; background: #1a1a2e; color: #eee; min-height: 100vh; font-size: 14px; }
.header { background: #16213e; padding: 16px 28px; border-bottom: 1px solid #0f3460; display: flex; justify-content: space-between; align-items: center; }
.header h1 { font-size: 19px; font-weight: 600; }
.header p { font-size: 12px; color: #888; margin-top: 3px; }
.header .links a { color: #4ecca3; font-size: 12px; text-decoration: none; margin-left: 12px; }
.container { display: flex; gap: 18px; padding: 18px; max-width: 1800px; margin: 0 auto; }
/* 左侧工具+参数 */
.panel { width: 300px; flex-shrink: 0; background: #16213e; border-radius: 10px; padding: 16px; height: fit-content; }
.panel h3 { font-size: 13px; color: #4ecca3; margin: 14px 0 8px; text-transform: uppercase; letter-spacing: 1px; padding-bottom: 5px; border-bottom: 1px solid #0f3460; }
.panel h3:first-child { margin-top: 0; }
.hint { font-size: 11px; color: #888; line-height: 1.6; margin: 6px 0 10px; }
.tool-row { display: flex; gap: 6px; margin-bottom: 10px; }
.btn-tool { flex: 1; padding: 8px; background: #0f3460; color: #ccc; border: none; border-radius: 5px; cursor: pointer; font-size: 12px; }
.btn-tool.active { background: #4ecca3; color: #16213e; font-weight: 600; }
.btn-tool:hover { background: #1a4a7a; }
.btn-tool.active:hover { background: #6ee0bd; }
.btn-danger { width: 100%; padding: 7px; background: #c0392b; color: #fff; border: none; border-radius: 5px; cursor: pointer; font-size: 12px; margin-bottom: 10px; }
.btn-danger:hover { background: #e74c3c; }
.field { margin-bottom: 12px; }
.field-label { display: flex; justify-content: space-between; align-items: center; font-size: 12px; color: #bbb; margin-bottom: 5px; }
.field-label .val { color: #4ecca3; font-family: monospace; font-size: 11px; }
.field input[type="range"] { width: 100%; height: 4px; -webkit-appearance: none; background: #0f3460; border-radius: 2px; outline: none; }
.field input[type="range"]::-webkit-slider-thumb { -webkit-appearance: none; width: 14px; height: 14px; border-radius: 50%; background: #4ecca3; cursor: pointer; }
.field input[type="text"], .field textarea { width: 100%; padding: 6px 8px; background: #0d1b3e; border: 1px solid #0f3460; border-radius: 4px; color: #eee; font-size: 12px; font-family: monospace; }
.field textarea { min-height: 60px; resize: vertical; }
.mode-tabs { display: flex; gap: 6px; margin-bottom: 10px; }
.mode-tab { flex: 1; padding: 8px; background: #0f3460; border: none; border-radius: 5px; color: #aaa; cursor: pointer; font-size: 12px; text-align: center; }
.mode-tab.active { background: #4ecca3; color: #16213e; font-weight: 600; }
.btn { display: block; width: 100%; padding: 12px; border: none; border-radius: 6px; font-size: 15px; cursor: pointer; transition: .15s; }
.btn-primary { background: #4ecca3; color: #16213e; font-weight: 600; margin-top: 8px; }
.btn-primary:hover { background: #6ee0bd; }
.btn-primary:disabled { background: #555; color: #999; cursor: not-allowed; }
/* 右侧画板+结果 */
.workspace { flex: 1; min-width: 0; }
.canvas-wrap { background: #0d0d1a; border-radius: 10px; padding: 18px; text-align: center; min-height: 280px; display: flex; align-items: center; justify-content: center; }
.upload-zone { width: 100%; max-width: 600px; border: 2px dashed #0f3460; border-radius: 10px; padding: 40px 20px; text-align: center; cursor: pointer; transition: .2s; }
.upload-zone:hover { border-color: #4ecca3; background: rgba(78,204,163,.05); }
.upload-zone svg { width: 42px; height: 42px; fill: #4ecca3; margin-bottom: 10px; }
.upload-zone p { color: #888; font-size: 13px; }
.upload-zone p.highlight { color: #4ecca3; }
#canvas-container { position: relative; display: inline-block; max-width: 100%; }
#img-canvas, #mask-canvas { display: block; max-width: 100%; border-radius: 6px; }
#mask-canvas { position: absolute; top: 0; left: 0; cursor: crosshair; touch-action: none; }
#mask-canvas.eraser { cursor: cell; }
.canvas-tip { font-size: 11px; color: #666; margin-top: 8px; }
#status { text-align: center; padding: 24px; color: #4ecca3; display: none; }
.spinner { display: inline-block; width: 18px; height: 18px; border: 3px solid #0f3460; border-top-color: #4ecca3; border-radius: 50%; animation: spin .8s linear infinite; margin-right: 8px; vertical-align: middle; }
@keyframes spin { to { transform: rotate(360deg); } }
#error-msg { color: #e74c3c; padding: 16px; display: none; font-size: 13px; background: rgba(231,76,60,.1); border-radius: 6px; margin-top: 12px; }
.result-section { margin-top: 16px; background: #16213e; border-radius: 10px; padding: 16px; display: none; }
.result-section.show { display: block; }
.result-section h3 { font-size: 14px; color: #4ecca3; margin-bottom: 12px; }
.result-grid { display: flex; gap: 16px; flex-wrap: wrap; }
.result-card { flex: 1; min-width: 240px; }
.result-card h4 { font-size: 12px; color: #aaa; margin-bottom: 6px; text-align: center; }
.result-card img { width: 100%; border-radius: 6px; display: block; cursor: zoom-in; }
.info-box { margin-top: 12px; padding: 10px 12px; background: #0d1b3e; border-radius: 6px; font-size: 11px; color: #888; line-height: 1.7; }
#lightbox { display: none; position: fixed; inset: 0; background: rgba(0,0,0,.92); z-index: 999; justify-content: center; align-items: center; cursor: zoom-out; padding: 20px; }
#lightbox img { max-width: 95%; max-height: 95%; border-radius: 8px; }
#lightbox.show { display: flex; }
</style>
</head>
<body>
<div class="header">
<div>
<h1>🖌️ mask区域重绘（发丝自然化）</h1>
<p>上传图片 → 涂抹要重绘的区域 → 调提示词和强度 → 生成。严格只在涂抹区重绘，其余保留原图</p>
</div>
<div class="links">
<a href="/manual">→ 手绘mask换发型</a>
<a href="/debug">→ 换发型调试</a>
</div>
</div>
<div class="container">
<!-- 左侧 -->
<div class="panel">
<h3>① 画笔工具</h3>
<div class="hint">在右侧图片上涂抹需要重绘的区域（白色=重绘，黑色=保留原图）</div>
<div class="tool-row">
<button class="btn-tool active" id="tool-brush">🖌️ 画笔</button>
<button class="btn-tool" id="tool-eraser">🧹 橡皮</button>
</div>
<div class="field">
<div class="field-label">画笔大小 <span class="val" id="size-val">25px</span></div>
<input type="range" id="brush-size" min="5" max="80" value="25">
</div>
<button class="btn-danger" id="btn-clear">✕ 清除全部涂抹</button>
<h3>② 重绘模式</h3>
<div class="mode-tabs">
<button class="mode-tab active" id="mode-enhance" onclick="setMode('enhance')">发丝增强<br><span style="font-size:10px;opacity:.7">enhance</span></button>
<button class="mode-tab" id="mode-pure" onclick="setMode('pure_inpaint')">自然重绘<br><span style="font-size:10px;opacity:.7">pure_inpaint</span></button>
</div>
<h3>③ 提示词</h3>
<div class="field">
<textarea id="prompt" placeholder="留空用模式默认提示词">high quality, detailed natural hair strands, realistic hair texture, sharp focus, photorealistic</textarea>
</div>
<h3>④ 重绘强度</h3>
<div class="field">
<div class="field-label">denoising_strength <span class="val" id="v-den">0.50</span><span style="color:#666;font-size:10px;margin-left:6px">越低越保留原图</span></div>
<input type="range" id="den" min="0.1" max="1.0" step="0.05" value="0.5" oninput="$('v-den').textContent=parseFloat(this.value).toFixed(2)">
</div>
<button class="btn btn-primary" id="btn-run" disabled>▶ 生成（mask区域重绘）</button>
<div class="hint" style="margin-top:10px">
<b>说明：</b><br>
• 严格只在涂抹区域重绘，mask外完全保留原图<br>
• enhance模式：发丝细节增强（默认0.35强度）<br>
• pure_inpaint模式：自然重绘（默认0.5强度）<br>
• 改提示词可控制重绘方向（如"detailed curly hair"）
</div>
</div>
<!-- 右侧画板+结果 -->
<div class="workspace">
<div class="canvas-wrap">
<div class="upload-zone" id="upload-zone">
<svg viewBox="0 0 24 24"><path d="M19 13v6H5v-6H3v8h18v-8zM6 9l1.41 1.41L11 6.83V18h2V6.83l3.59 3.58L18 9l-6-6z"/></svg>
<p class="highlight">点击上传图片</p>
<p>支持 jpg/png，建议人脸清晰照</p>
</div>
<div id="canvas-container" style="display:none">
<canvas id="img-canvas"></canvas>
<canvas id="mask-canvas"></canvas>
</div>
<input type="file" id="file-input" accept="image/*" style="display:none">
</div>
<div class="canvas-tip" id="canvas-tip" style="display:none;text-align:center">用左侧画笔在图片上涂抹要重绘的区域</div>
<div id="status"><span class="spinner"></span><span id="status-text">重绘中，约15-30秒...</span></div>
<div id="error-msg"></div>
<div class="result-section" id="result-section">
<h3>对比结果</h3>
<div class="result-grid">
<div class="result-card"><h4>原图</h4><img id="r-orig"></div>
<div class="result-card"><h4>使用的mask</h4><img id="r-mask"></div>
<div class="result-card"><h4>重绘结果</h4><img id="r-new"></div>
</div>
<div class="info-box" id="info-box"></div>
</div>
</div>
</div>
<div id="lightbox" onclick="this.classList.remove('show')"><img id="lightbox-img"></div>
<script>
(function(){
const $=id=>document.getElementById(id);
let mode="enhance", imgB64Orig=null;
const imgCanvas=$("img-canvas"),maskCanvas=$("mask-canvas");
const imgCtx=imgCanvas.getContext("2d"),maskCtx=maskCanvas.getContext("2d",{willReadFrequently:true});
let brushSize=25,drawing=false,lastX=0,lastY=0;
// 模式切换
window.setMode=(m)=>{
mode=m;
$("mode-enhance").classList.toggle("active",m==="enhance");
$("mode-pure").classList.toggle("active",m==="pure_inpaint");
// 切换模式时更新默认提示词（如果当前是空的或等于另一模式的默认值）
const defaults={
enhance:"high quality, detailed natural hair strands, realistic hair texture, sharp focus, photorealistic",
pure_inpaint:"natural realistic hair, soft hair strands, photorealistic, high quality, detailed"
};
const cur=$("prompt").value.trim();
if(!cur || Object.values(defaults).includes(cur)){
$("prompt").value=defaults[m];
}
// 切换模式时调整默认denoising
if(m==="enhance" && parseFloat($("den").value)===0.5){ $("den").value=0.35; $("v-den").textContent="0.35"; }
if(m==="pure_inpaint" && parseFloat($("den").value)===0.35){ $("den").value=0.5; $("v-den").textContent="0.50"; }
};
// 上传
$("upload-zone").onclick=()=>$("file-input").click();
$("file-input").onchange=e=>{ if(e.target.files[0]) loadImage(e.target.files[0]); };
$("upload-zone").ondragover=e=>{e.preventDefault();$("upload-zone").style.borderColor="#4ecca3";};
$("upload-zone").ondragleave=()=>$("upload-zone").style.borderColor="#0f3460";
$("upload-zone").ondrop=e=>{e.preventDefault();if(e.dataTransfer.files[0])loadImage(e.dataTransfer.files[0]);};
function loadImage(file){
if(!file.type.startsWith("image/")){alert("请上传图片");return;}
const rd=new FileReader();
rd.onload=e=>{
const im=new Image();
im.onload=()=>{
let w=im.width,h=im.height;
const MAX=1024;
if(Math.max(w,h)>MAX){const sc=MAX/Math.max(w,h);w=Math.round(w*sc);h=Math.round(h*sc);}
imgCanvas.width=w;imgCanvas.height=h;
maskCanvas.width=w;maskCanvas.height=h;
imgCtx.drawImage(im,0,0,w,h);
maskCtx.clearRect(0,0,w,h);
imgB64Orig=imgCanvas.toDataURL("image/jpeg",0.95);
$("canvas-container").style.display="inline-block";
$("upload-zone").style.display="none";
$("canvas-tip").style.display="block";
$("btn-run").disabled=false;
};
im.src=e.target.result;
};
rd.readAsDataURL(file);
}
// 画笔
let drawMode="brush";
$("tool-brush").onclick=()=>{drawMode="brush";$("tool-brush").classList.add("active");$("tool-eraser").classList.remove("active");maskCanvas.classList.remove("eraser");};
$("tool-eraser").onclick=()=>{drawMode="eraser";$("tool-eraser").classList.add("active");$("tool-brush").classList.remove("active");maskCanvas.classList.add("eraser");};
$("brush-size").oninput=e=>{brushSize=+e.target.value;$("size-val").textContent=brushSize+"px";};
$("btn-clear").onclick=()=>{maskCtx.globalCompositeOperation="source-over";maskCtx.clearRect(0,0,maskCanvas.width,maskCanvas.height);};
function getPos(e){
const r=maskCanvas.getBoundingClientRect();
const sc=maskCanvas.width/r.width;
const cx=(e.touches?e.touches[0].clientX:e.clientX)-r.left;
const cy=(e.touches?e.touches[0].clientY:e.clientY)-r.top;
return [cx*sc,cy*sc];
}
function startDraw(e){e.preventDefault();drawing=true;const[x,y]=getPos(e);lastX=x;lastY=y;drawDot(x,y);}
function moveDraw(e){if(!drawing)return;e.preventDefault();const[x,y]=getPos(e);drawLine(lastX,lastY,x,y);lastX=x;lastY=y;}
function endDraw(){drawing=false;}
function drawDot(x,y){
if(drawMode==="brush"){maskCtx.globalCompositeOperation="source-over";maskCtx.fillStyle="rgba(255,60,80,0.5)";}
else{maskCtx.globalCompositeOperation="destination-out";maskCtx.fillStyle="rgba(0,0,0,1)";}
maskCtx.beginPath();maskCtx.arc(x,y,brushSize/2,0,Math.PI*2);maskCtx.fill();
}
function drawLine(x1,y1,x2,y2){
if(drawMode==="brush"){maskCtx.globalCompositeOperation="source-over";maskCtx.strokeStyle="rgba(255,60,80,0.5)";}
else{maskCtx.globalCompositeOperation="destination-out";maskCtx.strokeStyle="rgba(0,0,0,1)";}
maskCtx.beginPath();maskCtx.moveTo(x1,y1);maskCtx.lineTo(x2,y2);
maskCtx.lineWidth=brushSize;maskCtx.lineCap="round";maskCtx.lineJoin="round";maskCtx.stroke();
}
maskCanvas.addEventListener("mousedown",startDraw);
maskCanvas.addEventListener("mousemove",moveDraw);
window.addEventListener("mouseup",endDraw);
maskCanvas.addEventListener("touchstart",startDraw,{passive:false});
maskCanvas.addEventListener("touchmove",moveDraw,{passive:false});
maskCanvas.addEventListener("touchend",endDraw);
// 提取mask为黑白二值PNG
function extractMaskB64(){
const tmp=document.createElement("canvas");
tmp.width=maskCanvas.width;tmp.height=maskCanvas.height;
const tc=tmp.getContext("2d");
const md=maskCtx.getImageData(0,0,maskCanvas.width,maskCanvas.height);
const out=tc.createImageData(tmp.width,tmp.height);
for(let i=0;i<md.data.length;i+=4){
const v=md.data[i+3]>30?255:0;
out.data[i]=v;out.data[i+1]=v;out.data[i+2]=v;out.data[i+3]=255;
}
tc.putImageData(out,0,0);
return tmp.toDataURL("image/png");
}
// 执行
$("btn-run").onclick=async()=>{
if(!imgB64Orig) return;
// 检查mask
const md=maskCtx.getImageData(0,0,maskCanvas.width,maskCanvas.height);
let hasMask=false;
for(let i=3;i<md.data.length;i+=4){if(md.data[i]>30){hasMask=true;break;}}
if(!hasMask){alert("请先用画笔涂抹需要重绘的区域");return;}
const maskB64=extractMaskB64();
$("btn-run").disabled=true;
$("status").style.display="block";
$("status-text").textContent="mask区域重绘中，约15-30秒...";
$("error-msg").style.display="none";
$("result-section").classList.remove("show");
try{
const resp=await fetch("/api/inpaint",{
method:"POST",headers:{"Content-Type":"application/json"},
body:JSON.stringify({
img:imgB64Orig, mask:maskB64,
prompt:$("prompt").value.trim(),
denoising_strength:parseFloat($("den").value),
mode:mode
})
});
const data=await resp.json();
$("status").style.display="none";
if(data.state===0){
$("r-orig").src=imgB64Orig;
$("r-mask").src=data.mask_used;
$("r-new").src="data:image/jpeg;base64,"+data.result;
$("info-box").textContent=data.info;
// 绑定点击放大
["r-orig","r-mask","r-new"].forEach(id=>{
$(id).onclick=(e)=>{ $("lightbox-img").src=e.target.src; $("lightbox").classList.add("show"); };
});
$("result-section").classList.add("show");
$("result-section").scrollIntoView({behavior:"smooth"});
}else{
$("error-msg").textContent="❌ "+(data.msg||"失败");
$("error-msg").style.display="block";
}
}catch(err){
$("status").style.display="none";
$("error-msg").textContent="❌ 请求失败: "+err.message;
$("error-msg").style.display="block";
}
$("btn-run").disabled=false;
};
})();
</script>
</body>
</html>
hair_grow_service/static/manual.html
+13 -11
View File
@@ -159,9 +159,13 @@ body { font-family: -apple-system, "Segoe UI", "Microsoft YaHei", sans-serif; ba
</div>
</div>
<div class="field">
<div class="field-label">feather_px <span class="val" id="v-fp">0</span><span class="tip" title="严格mask贴回的边缘羽化像素。0=硬边缘，>0=高斯模糊边缘平滑过渡（消除贴回接缝）">ⓘ</span></div>
<div class="field-label">feather_px <span class="val" id="v-fp">0</span><span class="tip" title="羽化范围(像素)，边缘带宽度。0=无羽化，越大羽化范围越宽">ⓘ</span></div>
<input type="range" id="fp" min="0" max="60" step="1" value="0" oninput="syncInt('fp')">
</div>
<div class="field">
<div class="field-label">feather_alpha <span class="val" id="v-fa">1.00</span><span class="tip" title="羽化强度(0~1)。0=硬切不羽化，1=最大羽化。控制边缘过渡柔和程度">ⓘ</span></div>
<input type="range" id="fa" min="0" max="1.0" step="0.05" value="1.0" oninput="syncFloat('fa')">
</div>
<div class="readonly-info">
<b style="color:#888">webui端固定：</b><br>
<code>cfg=7</code> <code>steps=20</code> <code>sampler=DPM++ 2M Karras</code>
@@ -182,7 +186,7 @@ body { font-family: -apple-system, "Segoe UI", "Microsoft YaHei", sans-serif; ba
<!-- 中间发型选择 -->
<div class="mid-panel">
<h3>③ 选发型</h3>
<h3>③ 选发型 <span id="count-info" style="font-size:10px;color:#888;font-weight:normal"></span></h3>
<div class="hairstyle-grid" id="hairstyle-grid"><div style="color:#666;font-size:11px">加载中...</div></div>
<div style="font-size:11px;color:#4ecca3;margin-top:8px;text-align:center" id="sel-info">未选择</div>
<button class="btn-tool" style="margin-top:8px;width:100%" onclick="loadStatus()">↻ 刷新</button>
@@ -225,14 +229,6 @@ body { font-family: -apple-system, "Segoe UI", "Microsoft YaHei", sans-serif; ba
<script>
(function(){
const $=id=>document.getElementById(id);
const HAIRSTYLES = [
{face:"圆",name:"圆-心形"},{face:"圆",name:"圆-椭圆"},{face:"圆",name:"圆-波浪"},{face:"圆",name:"圆-直线"},{face:"圆",name:"圆-花瓣"},
{face:"心形",name:"心形-心形"},{face:"心形",name:"心形-椭圆"},{face:"心形",name:"心形-波浪"},{face:"心形",name:"心形-直线"},{face:"心形",name:"心形-花瓣"},
{face:"方脸",name:"方脸-心形"},{face:"方脸",name:"方脸-椭圆"},{face:"方脸",name:"方脸-波浪"},{face:"方脸",name:"方脸-直线"},{face:"方脸",name:"方脸-花瓣"},
{face:"椭圆",name:"椭圆-心形"},{face:"椭圆",name:"椭圆-椭圆"},{face:"椭圆",name:"椭圆-波浪"},{face:"椭圆",name:"椭圆-直线"},{face:"椭圆",name:"椭圆-花瓣"},
{face:"菱形",name:"菱形-心"},{face:"菱形",name:"菱形-椭圆"},{face:"菱形",name:"菱形-波浪"},{face:"菱形",name:"菱形-直线"},{face:"菱形",name:"菱形-花瓣"},
{face:"长",name:"长-心形"},{face:"长",name:"长 -椭圆"},{face:"长",name:"长 -波浪"},{face:"长",name:"长 -直线"},{face:"长",name:"长 -花瓣"}
];
let readySet=new Set(), selectedHair=null, imgB64Orig=null;
// 画板状态
@@ -240,6 +236,11 @@ body { font-family: -apple-system, "Segoe UI", "Microsoft YaHei", sans-serif; ba
const imgCtx=imgCanvas.getContext("2d"),maskCtx=maskCanvas.getContext("2d",{willReadFrequently:true});
let mode="brush",brushSize=25,drawing=false,lastX=0,lastY=0;
// 本次训练的发型（昨天6个 + 今天hair_flow）
const HAIRSTYLES = [
{name:"hair_flow"},{name:"圆-心形"},{name:"圆-椭圆"},{name:"圆-波浪"},
{name:"圆-直线"},{name:"圆-花瓣"},{name:"心形-心形"},{name:"心形-椭圆"}
];
async function loadStatus(){
try{
const r=await fetch("/api/hairstyles");const d=await r.json();
@@ -254,7 +255,7 @@ body { font-family: -apple-system, "Segoe UI", "Microsoft YaHei", sans-serif; ba
const ready=readySet.has(h.name);
const it=document.createElement("div");
it.className="hairstyle-item"+(ready?" ready":" pending")+(h.name===selectedHair?" selected":"");
it.innerHTML=`<img loading="lazy" src="/train_src/${encodeURIComponent(h.name)}" onerror="this.style.objectFit='contain';this.src='data:image/svg+xml;utf8,<svg xmlns=%22http://www.w3.org/2000/svg%22 width=%22100%22 height=%22100%22><text x=%2250%22 y=%2255%22 text-anchor=%22middle%22 fill=%22%23666%22 font-size=%2210%22>训练中</text></svg>'"><div class="name">${h.name}</div>`;
it.innerHTML=`<img loading="lazy" src="/train_src/${encodeURIComponent(h.name)}" onerror="this.style.objectFit='contain';this.src='data:image/svg+xml;utf8,<svg xmlns=%22http://www.w3.org/2000/svg%22 width=%22100%22 height=%22100%22><text x=%2250%22 y=%2255%22 text-anchor=%22middle%22 fill=%22%23666%22 font-size=%2210%22>无图</text></svg>'"><div class="name">${h.name}</div>`;
if(ready) it.onclick=()=>{
selectedHair=h.name;
$("sel-info").textContent="已选: "+h.name;
@@ -366,6 +367,7 @@ body { font-family: -apple-system, "Segoe UI", "Microsoft YaHei", sans-serif; ba
is_hr:$("sw-is_hr").classList.contains("on"),
dilate_kernel:[parseInt($("dk0").value),parseInt($("dk1").value)],
feather_px:parseInt($("fp").value),
feather_alpha:parseFloat($("fa").value),
denoising_strength:parseFloat($("den").value),
enhance:$("sw-enhance").classList.contains("on"),
enhance_denoising:parseFloat($("ed").value),
images/girl/长 -椭圆.jpg
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images/girl/长 -直线.jpg
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prepare_train_data.py
+4 -4
View File
@@ -6,9 +6,9 @@
原始图 → 人脸检测+1k关键点 → Generator_Matte头发抠图 → 白底合成 + 居中裁剪 → 打标签
用法:
cd /home/ubuntu/change_hair/project/hair_service_sd
/home/ubuntu/miniconda3/envs/my_hair/bin/python /home/ubuntu/change_hair/prepare_train_data.py \
--input /home/ubuntu/change_hair/train_images \
cd /home/xsl/change_hair/project/hair_service_sd
/home/xsl/miniconda3/envs/my_hair/bin/python /home/xsl/change_hair/prepare_train_data.py \
--input /home/xsl/change_hair/train_images \
--hair-id new_hairstyle_001 \
--gender boy
"""
@@ -22,7 +22,7 @@ import argparse
ssl._create_default_https_context = ssl._create_unverified_context
# 必须在 hair_service_sd 目录运行（依赖其模块）
HAIR_SERVICE_DIR = "/home/ubuntu/change_hair/project/hair_service_sd"
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")
project/hair_service_sd/change_color.py
+1 -5
View File
@@ -70,11 +70,7 @@ def process_infer(user_img_path, target_color, color_dir, dst_path, ratio=1.0):
# 通过webui对照片进行增强
s4 = time.time()
# cv2.imwrite('/home/student/Downloads/12121.jpg',crop_img)
try:
enhanced_img = enhance_hair.webui_img2img(crop_img, crop_mask, prompt=prompt)
except Exception as e:
print(f"[process_infer] webui enhance skipped: {e}")
enhanced_img = crop_img
enhanced_img = enhance_hair.webui_img2img(crop_img, crop_mask, prompt=prompt)
print("----------------------------webui_img2img", time.time() - s4)
# 写回原图
project/hair_service_sd/config/configure.ini
+16 -15
View File
@@ -1,21 +1,21 @@
[default]
modelDir = weights
hairstyleDir = /home/ubuntu/change_hair/project/data/ref_hairstyle
haircolorDir= /home/ubuntu/change_hair/project/data/ref_haircolor
userDir=/home/ubuntu/change_hair/project/data/userImage
tmp_dir=/home/ubuntu/change_hair/project/data/tmp
res_dir=/home/ubuntu/change_hair/project/data/res_dir
userInfo_dir=/home/ubuntu/change_hair/project/data/user_info
hairstyleDir = /home/xsl/change_hair/project/data/ref_hairstyle
haircolorDir= /home/xsl/change_hair/project/data/ref_haircolor
userDir=/home/xsl/change_hair/project/data/userImage
tmp_dir=/home/xsl/change_hair/project/data/tmp
res_dir=/home/xsl/change_hair/project/data/res_dir
userInfo_dir=/home/xsl/change_hair/project/data/user_info
baseColor_ID=HDR10_443322
Port = 11023
refer_dir = /home/ubuntu/change_hair/project/data/ref_online
ref_user_dir = /home/ubuntu/change_hair/project/data/ref_user_imgs
train_dir = /home/ubuntu/change_hair/data/train_material
refImgDir=/home/ubuntu/change_hair/project/data/refImage
hair_template_material_dir=/home/ubuntu/change_hair/project/data/hair_template_material
ref_color=/home/ubuntu/change_hair/project/data/ref_color
ref_color_img=/home/ubuntu/change_hair/project/data/ref_color_imgs
upload_train_dir=/home/ubuntu/change_hair/project/data/upload_train_imgs
refer_dir = /home/xsl/change_hair/project/data/ref_online
ref_user_dir = /home/xsl/change_hair/project/data/ref_user_imgs
train_dir = /home/xsl/change_hair/data/train_material
refImgDir=/home/xsl/change_hair/project/data/refImage
hair_template_material_dir=/home/xsl/change_hair/project/data/hair_template_material
ref_color=/home/xsl/change_hair/project/data/ref_color
ref_color_img=/home/xsl/change_hair/project/data/ref_color_imgs
upload_train_dir=/home/xsl/change_hair/project/data/upload_train_imgs
;version=local
version=online
@@ -23,7 +23,7 @@ version=online
strength=1
[logger]
logpath = /home/ubuntu/change_hair/project/logs
logpath = /home/xsl/change_hair/project/logs
level=INFO
[timelogger]
@@ -32,3 +32,4 @@ name=watch-time
[errorlogger]
level=ERROR
name=w-error
project/hair_service_sd/core/cos_module.py
+5 -2
View File
@@ -20,8 +20,11 @@ class COS_object():
token = None # 如果使用永久密钥不需要填入 token，如果使用临时密钥需要填入，临时密钥生成和使用指引参见 https://cloud.tencent.com/document/product/436/14048
self.scheme = 'https' # 指定使用 http/https 协议来访问 COS，默认为 https，可不填
self.BucketName= os.getenv('COS_BUCKET', '<your-cos-bucket>')
for param in (secret_id, secret_key, self.region, self.BucketName):
assert '<' not in param, '请设置环境变量 COS_SECRET_ID / COS_SECRET_KEY / COS_REGION / COS_BUCKET'
# 密钥未设置时标记不可用（不抛异常）
if '<' in secret_id or '<' in secret_key:
print('[COS] ⚠️ 未设置 COS 密钥环境变量，COS 上传功能不可用')
self.client = None
return
config = CosConfig(Region=self.region, SecretId=secret_id, SecretKey=secret_key, Token=token, Scheme=self.scheme)
self.client = CosS3Client(config)
project/hair_service_sd/core/models/layers/data/FDDB/img_list.txt
-2845
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project/hair_service_sd/core/models/layers/data/__init__.py
-3
View File
@@ -1,3 +0,0 @@
from .wider_face import WiderFaceDetection, detection_collate
from .data_augment import *
from .config import *
project/hair_service_sd/core/models/layers/data/config.py
-42
View File
@@ -1,42 +0,0 @@
# 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
}
project/hair_service_sd/core/models/layers/data/data_augment.py
-237
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@@ -1,237 +0,0 @@
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
project/hair_service_sd/core/models/layers/data/wider_face.py
-101
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@@ -1,101 +0,0 @@
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)
project/hair_service_sd/core/oss_module.py
+20 -17
View File
@@ -1,28 +1,31 @@
import time
import oss2
import os
try:
import oss2
_OSS2_AVAILABLE = True
except Exception as _e:
print(f'[OSS] ⚠️ oss2 导入失败({_e})，OSS 上传功能不可用')
_OSS2_AVAILABLE = False
class OSS_object():
def __init__(self):
# 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', '<your-access-key-id>')
self.access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', '<your-access-key-secret>')
self.bucket_name = os.getenv('OSS_TEST_BUCKET', '<your-bucket-name>')
self.endpoint = os.getenv('OSS_TEST_ENDPOINT', '<your-endpoint>')
self.bucket = None
def _ensure_bucket(self):
if self.bucket is not None:
if not _OSS2_AVAILABLE:
print('[OSS] oss2 不可用，OSS 功能关闭')
self.bucket = 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)
access_key_id = os.getenv('OSS_TEST_ACCESS_KEY_ID', '<your-access-key-id>')
access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', '<your-access-key-secret>')
bucket_name = os.getenv('OSS_TEST_BUCKET', '<your-bucket-name>')
endpoint = os.getenv('OSS_TEST_ENDPOINT', '<your-endpoint>')
# 密钥未设置时标记不可用（不抛异常，避免服务启动/换发型时崩溃）
if '<' in access_key_id or '<' in access_key_secret:
print('[OSS] ⚠️ 未设置 OSS 密钥环境变量，OSS 上传功能不可用（本地测试用 base64 返回即可）')
self.bucket = None
return
self.bucket = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, 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)
project/hair_service_sd/gunicorn_config.py
+1 -1
View File
@@ -1,4 +1,4 @@
#!/home/xsl/miniconda3/envs/yd/bin/python
#!/home/ubuntu/miniconda3/envs/yd/bin/python
# -*- coding: utf-8 -*-
import os
# 监听本机的端口
project/hair_service_sd/hair_grow_swap.py
-7
View File
@@ -177,17 +177,10 @@ 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")
project/hair_service_sd/hair_swap_manual.py
+46 -12
View File
@@ -36,7 +36,8 @@ def hair_swap_manual(origin_img, hand_mask, hair_id, hairstyle_process, landmark
denoising_strength=0.6, # 重绘强度（唯一能透传到webui的SD参数）
# ===== 贴回/融合（真正生效）=====
blend_dilate=(5, 5), # 步骤⑥ strict mask贴回时mask膨胀核
feather_px=0, # ★ strict贴回边缘羽化像素（0=无羽化硬边缘，>0=高斯模糊边缘）
feather_px=0, # ★ strict贴回边缘羽化范围（像素，边缘带宽度，0=无羽化）
feather_alpha=1.0, # ★ 羽化强度（0~1，0=硬切，1=最大羽化，控制边缘过渡柔和度）
seamless_dilate=(9, 9), # 步骤⑦ 泊松融合mask膨胀核
# ===== enhance 二次增强重绘 =====
enhance=False, # 步骤⑧ 是否对结果再做一次低强度SD重绘（让发丝更清晰）
@@ -230,10 +231,20 @@ def hair_swap_manual(origin_img, hand_mask, hair_id, hairstyle_process, landmark
result_strict_hard[mask_back == 0] = origin_img[mask_back == 0]
if feather_px and feather_px > 0:
# ★ 只羽化边缘：mask 内部保持硬切(255)，只有边缘带做渐变
# 1. 对 mask_back 做高斯模糊（得到边缘的渐变）
k = max(1, int(feather_px)) * 2 + 1 # 高斯核必须是奇数
mask_back_blur = cv2.GaussianBlur(mask_back, (k, k), 0)
mask_blend = (mask_back_blur.astype(np.float32) / 255)[..., None]
feather_info = f"，边缘羽化{feather_px}px"
mask_blur = cv2.GaussianBlur(mask_back, (k, k), 0)
# 2. 取 mask 内部区域（离边缘 > feather_px 的部分），强制为255（不羽化）
erode_kernel = np.ones((k, k), np.uint8)
mask_inner = cv2.erode(mask_back, erode_kernel)
# 3. 合成：内部硬切(255) + 边缘用模糊值
mask_blend_mask = np.maximum(mask_inner, mask_blur).astype(np.float32)
# 4. ★ feather_alpha 控制羽化强度：在「全硬切(原mask)」和「边缘渐变(mask_blend_mask)」之间插值
# alpha=0 → 完全硬切（无羽化），alpha=1 → 最大羽化
mask_blend_mask = mask_back.astype(np.float32) * (1 - feather_alpha) + mask_blend_mask * feather_alpha
mask_blend = (mask_blend_mask / 255)[..., None]
feather_info = f"，边缘羽化{feather_px}px 强度{feather_alpha:.2f}"
else:
mask_blend = hard_blend
result_strict = (sd_result_back.astype(np.float32) * mask_blend +
@@ -241,17 +252,38 @@ def hair_swap_manual(origin_img, hand_mask, hair_id, hairstyle_process, landmark
result_strict = np.clip(result_strict, 0, 255).astype(np.uint8)
result_strict[mask_back == 0] = origin_img[mask_back == 0]
# ★ 羽化对比图：左=硬边缘(feather=0) 右=当前羽化，裁剪mask边界附近放大
# ★ 羽化对比图（黑底版）：纯黑背景 + 贴回图，凸显羽化边缘过渡
# 用黑色背景而非原图，避免原图干扰，纯粹对比羽化效果
feather_compare = None
feather_compare_full = None
if feather_px and feather_px > 0:
# 计算 mask_blend（用于把贴回图叠加到黑底上）
ys, xs = np.where(mask_back > 10)
if len(ys) > 0:
x1 = max(0, xs.min()-20); x2 = min(origin_img.shape[1], xs.max()+20)
y1 = max(0, ys.min()-20); y2 = min(origin_img.shape[0], ys.max()+20)
hard_crop = result_strict_hard[y1:y2, x1:x2]
blur_crop = result_strict[y1:y2, x1:x2]
sep = np.full((hard_crop.shape[0], 3, 3), 128, dtype=np.uint8)
# 黑底版：贴回图 * mask_blend + 黑色(0) * (1-mask_blend)
# result_strict_hard 已是「贴回图+原图」混合，这里重新算黑底版
black_bg = np.zeros_like(origin_img)
# 无羽化版（黑底）
hard_on_black = (sd_result_back.astype(np.float32) * hard_blend +
black_bg.astype(np.float32) * (1 - hard_blend))
hard_on_black = np.clip(hard_on_black, 0, 255).astype(np.uint8)
hard_on_black[mask_back == 0] = 0 # mask外纯黑
# 有羽化版（黑底）
blur_on_black = (sd_result_back.astype(np.float32) * mask_blend +
black_bg.astype(np.float32) * (1 - mask_blend))
blur_on_black = np.clip(blur_on_black, 0, 255).astype(np.uint8)
blur_on_black[mask_back == 0] = 0 # mask外纯黑
# 局部放大版（裁剪mask边界附近）
x1 = max(0, xs.min()-30); x2 = min(origin_img.shape[1], xs.max()+30)
y1 = max(0, ys.min()-30); y2 = min(origin_img.shape[0], ys.max()+30)
hard_crop = hard_on_black[y1:y2, x1:x2]
blur_crop = blur_on_black[y1:y2, x1:x2]
sep = np.full((hard_crop.shape[0], 3, 3), 60, dtype=np.uint8) # 深灰分隔线
feather_compare = np.hstack([hard_crop, sep, blur_crop])
# 完整图版（不裁剪）
sep_full = np.full((origin_img.shape[0], 3, 3), 60, dtype=np.uint8)
feather_compare_full = np.hstack([hard_on_black, sep_full, blur_on_black])
result = result_strict if strict_mask else result_full
@@ -262,7 +294,9 @@ def hair_swap_manual(origin_img, hand_mask, hair_id, hairstyle_process, landmark
{"label": f"当前选用({'严格mask' if strict_mask else '整框覆盖'})", "b64": _enc(result)},
]
if feather_compare is not None:
step6_images.append({"label": f"★ 羽化对比(左=硬边 右=羽化{feather_px}px)", "b64": _enc(feather_compare)})
step6_images.append({"label": f"★ 黑底羽化对比·边缘(左=无羽化 右=羽化{feather_px}pxα{feather_alpha:.1f})", "b64": _enc(feather_compare)})
if feather_compare_full is not None:
step6_images.append({"label": f"★ 黑底羽化对比·完整(左=无羽化 右=羽化{feather_px}pxα{feather_alpha:.1f})", "b64": _enc(feather_compare_full)})
steps.append({
"title": "⑥ 贴回原图",
"desc": f"把SD结果逆warpAffine贴回用户原图。可调：strict_mask={strict_mask}, blend_dilate={bd}{feather_info}。整框覆盖=整个裁剪框覆盖原图；严格mask=只在mask区域融合，mask外保留原图",
@@ -390,7 +424,7 @@ def hair_swap_manual(origin_img, hand_mask, hair_id, hairstyle_process, landmark
"strict_mask": strict_mask, "seamless_blend": seamless_blend,
"is_hr": is_hr, "dilate_kernel": list(dilate_kernel),
"denoising_strength": denoising_strength,
"blend_dilate": list(blend_dilate), "feather_px": feather_px,
"blend_dilate": list(blend_dilate), "feather_px": feather_px, "feather_alpha": feather_alpha,
"seamless_dilate": list(seamless_dilate),
"enhance": enhance, "enhance_denoising": enhance_denoising,
"mask_area_ratio_pct": round(mask_area_ratio, 1),
project/hair_service_sd/hairstyle_model_infer.py
+5 -10
View File
@@ -72,8 +72,6 @@ class HairStyle_Model_Infer(object):
# haircolor_dir = '/home/data/hair/data/ref_color/3628746832766'
face_base, hair_matting, status = self.infer_haircolor_new(user_rgb_8uc3_orisize, haircolor_dir,target_hair_color,
return_matting=True)
if status != 0:
return user_rgb_8uc3_orisize, None, status
user_rgb_8uc3_orisize = face_base
# 构建一个色板
r, g, b = target_hair_color
@@ -1018,7 +1016,7 @@ class HairStyle_Model_Infer(object):
def infer_haircolor_new(self, user_rgb_8uc3_orisize, haircolor_dir, target_hair_color, return_matting=False):
landmarks_origin_img_1k= self.get_landmark.forward(user_rgb_8uc3_orisize)
if landmarks_origin_img_1k is None:
return None, None, 10001
return None, 10001
need_process = (target_hair_color[0] * 0.299 + target_hair_color[1] * 0.587 + target_hair_color[2] * 0.114) > 150
_, user_matting_8uc1_bald_orisize = self.process_data_infer.generator_matte.matte_inference(user_rgb_8uc3_orisize,
landmarks_origin_img_1k)
@@ -1038,13 +1036,10 @@ class HairStyle_Model_Infer(object):
need_process=True
if need_process:
haircolor_dir_tmp = os.path.join(config.get('default', "haircolorDir"), config.get('default', "baseColor_ID"))
if os.path.exists(haircolor_dir_tmp):
face_base, status1 = self.infer_haircolor_tj(user_rgb_8uc3_orisize, haircolor_dir_tmp)
# cv2.imwrite('/home/student/Desktop/tmp_color/need/face_base.png', face_base)
if status1 == 0:
return face_base, user_matting_8uc1_bald_orisize, 0
else:
need_process = False
face_base, status1 = self.infer_haircolor_tj(user_rgb_8uc3_orisize, haircolor_dir_tmp)
# cv2.imwrite('/home/student/Desktop/tmp_color/need/face_base.png', face_base)
if status1 == 0:
return face_base, user_matting_8uc1_bald_orisize, 0
else:
need_process = False
if not need_process:
project/hair_service_sd/inpaint_mask.py
+64
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@@ -0,0 +1,64 @@
# -*- coding: utf-8 -*-
"""mask 区域重绘（inpainting）服务
两种模式（底层都是 webui img2img inpainting，严格只在mask区重绘）：
1. enhance: 用 enhance_hair.webui_img2img（默认提示词=发丝增强，denoising默认0.35）
2. pure_inpaint: 纯SD inpainting（默认提示词=自然头发，denoising默认0.5）
严格只重绘 mask 区域：inpainting_fill=1（mask外保留原图）
"""
import cv2
import numpy as np
from utils import enhance_hair
def inpaint_mask(img, mask, prompt="", denoising_strength=0.5, mode="enhance"):
"""在 mask 区域做 SD inpainting 重绘。
参数:
img: 输入图 BGR
mask: 灰度mask，白色(255)=重绘区，与img同尺寸
prompt: 提示词（空则用模式默认值）
denoising_strength: 重绘强度 0~1
mode: "enhance" 或 "pure_inpaint"
返回:
result: 重绘后的图（mask区已重绘，其余保留原图）
info: 本次参数说明
"""
h, w = img.shape[:2]
# 归一化 mask（保证 0/255，尺寸一致）
if mask.shape[:2] != (h, w):
mask = cv2.resize(mask, (w, h), interpolation=cv2.INTER_NEAREST)
if mask.ndim == 3:
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
_, mask_bin = cv2.threshold(mask, 30, 255, cv2.THRESH_BINARY)
# 模式默认值
if not prompt:
if mode == "enhance":
prompt = "high quality, detailed natural hair strands, realistic hair texture, sharp focus, photorealistic"
else:
prompt = "natural realistic hair, soft hair strands, photorealistic, high quality, detailed"
# 统计 mask 区域占比
mask_ratio = float((mask_bin > 0).sum()) / mask_bin.size * 100
# 调用 enhance_hair.webui_img2img（底层是 inpainting，inpainting_fill=1 严格只在mask区重绘）
result = enhance_hair.webui_img2img(
img, mask_bin, prompt=prompt,
denoising_strength=denoising_strength
)
# webui 可能调整尺寸（调整到8的倍数），resize 回原图尺寸
if result.shape[:2] != (h, w):
result = cv2.resize(result, (w, h), interpolation=cv2.INTER_LANCZOS4)
# 双重保险：强制 mask 外区域用原图（虽然 inpainting_fill=1 已保证，再加一层确保严格）
mask_3c = (mask_bin > 0)[..., None]
result = np.where(mask_3c, result, img).astype(np.uint8)
info = (f"模式={mode}，提示词=\"{prompt[:50]}...\"，denoising={denoising_strength}，"
f"mask区域={mask_ratio:.1f}%。严格只重绘mask区，其余保留原图。")
return result, mask_bin, info
project/hair_service_sd/keypoints/lib/config/__init__.py
-9
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@@ -1,9 +0,0 @@
# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/config/default.py
-160
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@@ -1,160 +0,0 @@
# ------------------------------------------------------------------------------
# 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)
project/hair_service_sd/keypoints/lib/config/models.py
-58
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@@ -1,58 +0,0 @@
# ------------------------------------------------------------------------------
# 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,
}
project/hair_service_sd/keypoints/lib/core/evaluate.py
-73
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@@ -1,73 +0,0 @@
# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/core/function.py
-279
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@@ -1,279 +0,0 @@
# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/core/inference.py
-79
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@@ -1,79 +0,0 @@
# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/core/loss.py
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@@ -1,84 +0,0 @@
# ------------------------------------------------------------------------------
# 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)
project/hair_service_sd/keypoints/lib/dataset/JointsDataset.py
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# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/dataset/__init__.py
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# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/dataset/coco.py
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# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/dataset/mpii.py
-181
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@@ -1,181 +0,0 @@
# ------------------------------------------------------------------------------
# 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']
project/hair_service_sd/keypoints/lib/models/__init__.py
-13
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@@ -1,13 +0,0 @@
# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/models/pose_hrnet.py
-501
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@@ -1,501 +0,0 @@
# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/models/pose_resnet.py
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@@ -1,271 +0,0 @@
# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/nms/__init__.py
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project/hair_service_sd/keypoints/lib/nms/nms.py
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# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/utils/__init__.py
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project/hair_service_sd/keypoints/lib/utils/transforms.py
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@@ -1,121 +0,0 @@
# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/utils/utils.py
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@@ -1,203 +0,0 @@
# ------------------------------------------------------------------------------
# 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
project/hair_service_sd/keypoints/lib/utils/vis.py
-141
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@@ -1,141 +0,0 @@
# ------------------------------------------------------------------------------
# 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)
)
project/hair_service_sd/keypoints/lib/utils/zipreader.py
-70
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@@ -1,70 +0,0 @@
# ------------------------------------------------------------------------------
# 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())
project/hair_service_sd/models/layers/data/FDDB/img_list.txt
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project/hair_service_sd/models/layers/data/__init__.py
-3
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@@ -1,3 +0,0 @@
from .wider_face import WiderFaceDetection, detection_collate
from .data_augment import *
from .config import *
project/hair_service_sd/models/layers/data/config.py
-42
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@@ -1,42 +0,0 @@
# 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
}
project/hair_service_sd/models/layers/data/data_augment.py
-237
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@@ -1,237 +0,0 @@
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
project/hair_service_sd/models/layers/data/wider_face.py
-101
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@@ -1,101 +0,0 @@
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)
project/hair_service_sd/run_copy_cost_colorb64.py
+17 -46
View File
@@ -4,14 +4,6 @@ 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()
@@ -255,8 +247,7 @@ def change_hair_colorv3():
jsonify({'msg': '算法解析错误', 'result': '', 'umd': '', 'state': -1}), 400)
except Exception as e:
print(f"[hairColor ERROR] {e}")
traceback.print_exc()
print(e)
return make_response(
jsonify({'msg': '算法解析错误', 'result': '', 'umd': '', 'state': -1}), 400)
@@ -536,41 +527,21 @@ 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))
# 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)
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)
if not is_hr:
mask_dilate = cv2.dilate(mask, np.ones((3, 9), np.uint8))
else:
@@ -605,7 +576,7 @@ def change_hairstyle_v4():
p_tag = p_tag[p_tag.find("simple background, ") + len("simple background, "):]
else:
p_tag = ""
denoising_strength = float(input_info.get('denoising_strength', 0.6)) # 接口11 可调；默认 0.6
denoising_strength = 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)
project/hair_service_sd/upload_oss.py
+20 -17
View File
@@ -1,28 +1,31 @@
import time
import oss2
import os
try:
import oss2
_OSS2_AVAILABLE = True
except Exception as _e:
print(f'[OSS upload_oss] ⚠️ oss2 导入失败({_e})，OSS 上传功能不可用')
_OSS2_AVAILABLE = False
class OSS_object():
def __init__(self):
# 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', '<your-access-key-id>')
self.access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', '<your-access-key-secret>')
self.bucket_name = os.getenv('OSS_TEST_BUCKET', '<your-bucket-name>')
self.endpoint = os.getenv('OSS_TEST_ENDPOINT', '<your-endpoint>')
self.bucket = None
def _ensure_bucket(self):
if self.bucket is not None:
if not _OSS2_AVAILABLE:
print('[OSS upload_oss] oss2 不可用，OSS 功能关闭')
self.bucket = 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)
access_key_id = os.getenv('OSS_TEST_ACCESS_KEY_ID', '<your-access-key-id>')
access_key_secret = os.getenv('OSS_TEST_ACCESS_KEY_SECRET', '<your-access-key-secret>')
bucket_name = os.getenv('OSS_TEST_BUCKET', '<your-bucket-name>')
endpoint = os.getenv('OSS_TEST_ENDPOINT', '<your-endpoint>')
# 密钥未设置时标记不可用（不抛异常）
if '<' in access_key_id or '<' in access_key_secret:
print('[OSS upload_oss] ⚠️ 未设置 OSS 密钥环境变量，OSS 上传功能不可用')
self.bucket = None
return
self.bucket = oss2.Bucket(oss2.Auth(access_key_id, access_key_secret), endpoint, 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)
project/hair_service_sd/utils/enhance_hair.py
-3
View File
@@ -43,9 +43,6 @@ def webui_img2img(img, mask, prompt='', denoising_strength=0.35):
}
response = requests.post(url=url, json=request_dict)
ret_json = response.json()
if 'images' not in ret_json:
print(f"[webui_img2img ERROR] {ret_json}")
raise Exception(f"webui error: {ret_json.get('error', 'unknown')}")
result = ret_json['images'][0]
img = cv2.imdecode(np.frombuffer(base64.b64decode(result.split(",", 1)[0]), np.uint8), cv2.IMREAD_COLOR)
return img
project/photo_service/lora_train_service_1.py
+6 -6
View File
@@ -37,8 +37,8 @@ if version == "local":
callback_url = 'http://service.aicloud.fit:7395/api/hair/trainCallBack'
else:
current_url = 'http://0.0.0.0:7393/'
kohya_ss_home_dir = '/home/ubuntu/change_hair/project/kohya_ss_home'
webui_lora_dir = '/home/ubuntu/change_hair/project/onediff/stable-diffusion-webui/models/Lora'
kohya_ss_home_dir = '/home/xsl/change_hair/project/kohya_ss_home'
webui_lora_dir = '/home/xsl/change_hair/project/onediff/stable-diffusion-webui/models/Lora'
inference_use_onediff = False
callback_url = 'http://0.0.0.0:8801/api/hair/trainCallBack'
base_webui_port = '57860'
@@ -307,13 +307,13 @@ def train_thread(sq, gpu_id):
# 3. GPU 固定 device=0(单卡)
# 4. 去掉 tokenizer_cache_dir(改用 HF 本地缓存 + 离线模式)
# 5. 设置 HF_HUB_OFFLINE 避免联网检查
kohya_python = '/home/ubuntu/miniconda3/envs/my_hair/bin/python'
kohya_python = '/home/xsl/miniconda3/envs/kohya/bin/python'
kohya_workdir = os.path.join(kohya_ss_home_dir, 'kohya_ss')
base_model = '/home/ubuntu/change_hair/project/onediff/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors'
base_model = '/home/xsl/change_hair/project/onediff/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors'
cmd_train = (
f'cd {kohya_workdir} && '
f'HF_HUB_OFFLINE=1 TRANSFORMERS_OFFLINE=1 CUDA_VISIBLE_DEVICES={device_id} '
f'/home/ubuntu/miniconda3/envs/my_hair/bin/accelerate launch --num_cpu_threads_per_process=2 "./train_network.py" --enable_bucket '
f'/home/xsl/miniconda3/envs/kohya/bin/accelerate 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} '
@@ -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 '
'--sdpa --bucket_no_upscale --noise_offset=0.0')
'--xformers --bucket_no_upscale --noise_offset=0.0')
print("cmd_train:", cmd_train)
os.system(cmd_train)
scripts/sync_data_to_server.sh
-69
View File
@@ -1,69 +0,0 @@
#!/usr/bin/env bash
# 断点续传大文件到云服务器 /home/xsl/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/xsl/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: ${TARGET}"
start_all.sh
+4 -4
View File
@@ -10,16 +10,16 @@
# bash start_all.sh status # 查看状态
# =====================================================================
set -u
BASE="/home/ubuntu/change_hair"
BASE="/home/xsl/change_hair"
PROJ="$BASE/project"
LOGDIR="$PROJ/logs"
PIDD="$LOGDIR/pids"
mkdir -p "$LOGDIR" "$PIDD"
# conda 环境的 python 路径
PY_HAIR="/home/ubuntu/miniconda3/envs/my_hair/bin/python"
PY_SD="/home/ubuntu/miniconda3/envs/sdwebui/bin/python"
PY_PHOTO="/home/ubuntu/miniconda3/envs/py310/bin/python"
PY_HAIR="/home/xsl/miniconda3/envs/my_hair/bin/python"
PY_SD="/home/xsl/miniconda3/envs/sdwebui/bin/python"
PY_PHOTO="/home/xsl/miniconda3/envs/py310/bin/python"
# 公共环境变量
export CRYPTOGRAPHY_OPENSSL_NO_LEGACY=1 # 旧版 cryptography 兼容
start_hair.sh
+2 -2
View File
@@ -2,5 +2,5 @@
export CRYPTOGRAPHY_OPENSSL_NO_LEGACY=1
export CUDA_VISIBLE_DEVICES=0
export APP_WORKER_ID=1
cd /home/ubuntu/change_hair/project/hair_service_sd
exec /home/ubuntu/miniconda3/envs/my_hair/bin/python run_copy_cost_colorb64.py
cd /home/xsl/change_hair/project/hair_service_sd
exec /home/xsl/miniconda3/envs/my_hair/bin/python run_copy_cost_colorb64.py
start_hairgrow.sh
+2 -2
View File
@@ -5,5 +5,5 @@ export CRYPTOGRAPHY_OPENSSL_NO_LEGACY=1
export CUDA_VISIBLE_DEVICES=0
export HF_HUB_OFFLINE=1
export TRANSFORMERS_OFFLINE=1
cd /home/ubuntu/change_hair/hair_grow_service
exec /home/ubuntu/miniconda3/envs/my_hair/bin/python app.py
cd /home/xsl/change_hair/hair_grow_service
exec /home/xsl/miniconda3/envs/my_hair/bin/python app.py
start_photo.sh
+2 -2
View File
@@ -1,5 +1,5 @@
#!/bin/bash
# 独立启动 photo_service，确保脱离会话
export CRYPTOGRAPHY_OPENSSL_NO_LEGACY=1
cd /home/ubuntu/change_hair/project/photo_service
exec /home/ubuntu/miniconda3/envs/py310/bin/python -u lora_train_service_1.py
cd /home/xsl/change_hair/project/photo_service
exec /home/xsl/miniconda3/envs/py310/bin/python -u lora_train_service_1.py
sync_all.sh
+10 -10
View File
@@ -4,7 +4,7 @@
# 已传数据自动续传（--partial）
set -u
LOGDIR="/home/ubuntu/change_hair/project/logs"
LOGDIR="/home/xsl/change_hair/project/logs"
DONE_MARKER="$LOGDIR/sync_all.done"
mkdir -p "$LOGDIR"
@@ -14,15 +14,15 @@ mkdir -p "$LOGDIR"
# 任务定义：名称|源|目标
# 优先级排序：换发色关键链路优先 → 换发型 → 训练相关 → 大数据最后
TASKS=(
"photo_service|szlc@192.168.101.63:/home/szlc/project/photo_service/|/home/ubuntu/change_hair/project/photo_service/"
"project_data|szlc@192.168.101.63:/home/szlc/project/data/|/home/ubuntu/change_hair/project/data/"
"hair_service_sd|szlc@192.168.101.63:/home/szlc/project/hair_service_sd/|/home/ubuntu/change_hair/project/hair_service_sd/"
"conda_py310|szlc@192.168.101.63:/home/szlc/miniconda3/envs/py310/|/home/ubuntu/miniconda3/envs/py310/"
"conda_my_hair|szlc@192.168.101.63:/home/szlc/miniconda3/envs/my_hair/|/home/ubuntu/miniconda3/envs/my_hair/"
"conda_sdwebui|szlc@192.168.101.63:/home/szlc/miniconda3/envs/sdwebui/|/home/ubuntu/miniconda3/envs/sdwebui/"
"kohya_ss_home|szlc@192.168.101.63:/home/szlc/project/kohya_ss_home/|/home/ubuntu/change_hair/project/kohya_ss_home/"
"onediff|szlc@192.168.101.63:/home/szlc/project/onediff/|/home/ubuntu/change_hair/project/onediff/"
"train_material|szlc@192.168.101.63:/data/train_material/|/home/ubuntu/change_hair/data/train_material/"
"photo_service|szlc@192.168.101.63:/home/szlc/project/photo_service/|/home/xsl/change_hair/project/photo_service/"
"project_data|szlc@192.168.101.63:/home/szlc/project/data/|/home/xsl/change_hair/project/data/"
"hair_service_sd|szlc@192.168.101.63:/home/szlc/project/hair_service_sd/|/home/xsl/change_hair/project/hair_service_sd/"
"conda_py310|szlc@192.168.101.63:/home/szlc/miniconda3/envs/py310/|/home/xsl/miniconda3/envs/py310/"
"conda_my_hair|szlc@192.168.101.63:/home/szlc/miniconda3/envs/my_hair/|/home/xsl/miniconda3/envs/my_hair/"
"conda_sdwebui|szlc@192.168.101.63:/home/szlc/miniconda3/envs/sdwebui/|/home/xsl/miniconda3/envs/sdwebui/"
"kohya_ss_home|szlc@192.168.101.63:/home/szlc/project/kohya_ss_home/|/home/xsl/change_hair/project/kohya_ss_home/"
"onediff|szlc@192.168.101.63:/home/szlc/project/onediff/|/home/xsl/change_hair/project/onediff/"
"train_material|szlc@192.168.101.63:/data/train_material/|/home/xsl/change_hair/data/train_material/"
)
echo "========================================" | tee -a "$LOGDIR/sync_main.log"
sync_conda.sh
+2 -2
View File
@@ -1,8 +1,8 @@
#!/bin/bash
# 重新同步 conda 环境（上次被 sed 损坏，这次纯净拷贝）
set -u
CONDA_ENVS="/home/ubuntu/miniconda3/envs"
LOGDIR="/home/ubuntu/change_hair/project/logs"
CONDA_ENVS="/home/xsl/miniconda3/envs"
LOGDIR="/home/xsl/change_hair/project/logs"
mkdir -p "$CONDA_ENVS" "$LOGDIR"
echo "conda 环境重传开始: $(date '+%H:%M:%S')" | tee -a "$LOGDIR/sync_conda.log"
test_grow_swap.py
+3 -3
View File
@@ -2,8 +2,8 @@
"""端到端测试：生发走换发型工作流"""
import base64, requests, time, sys
img_path = "/home/ubuntu/change_hair/project/data/userImage/8488902485_20250630055547.jpg"
mask_path = "/home/ubuntu/change_hair/project/logs/hairgrow_test_mask.png"
img_path = "/home/xsl/change_hair/project/data/userImage/8488902485_20250630055547.jpg"
mask_path = "/home/xsl/change_hair/project/logs/hairgrow_test_mask.png"
with open(img_path, "rb") as f:
img_b64 = "data:image/jpeg;base64," + base64.b64encode(f.read()).decode()
@@ -24,7 +24,7 @@ try:
d = r.json()
print(f"HTTP {r.status_code} | state={d.get('state')} | msg={d.get('msg','')} | 耗时={time.time()-t0:.1f}s")
if d.get("state") == 0:
out = "/home/ubuntu/change_hair/project/logs/test_grow_swap_result.jpg"
out = "/home/xsl/change_hair/project/logs/test_grow_swap_result.jpg"
with open(out, "wb") as f:
f.write(base64.b64decode(d["result"]))
print(f"✅ 生发成功! 结果图: {out}")
test_haircolor.py
+2 -2
View File
@@ -2,7 +2,7 @@
"""换发色接口测试"""
import base64, json, requests, sys
img_path = "/home/ubuntu/change_hair/project/data/userImage/8488902485_20250630055547.jpg"
img_path = "/home/xsl/change_hair/project/data/userImage/8488902485_20250630055547.jpg"
with open(img_path, "rb") as f:
img_b64 = base64.b64encode(f.read()).decode()
@@ -21,7 +21,7 @@ try:
result = d.get("result", "")
if d.get("state") == 0 and result:
# 保存结果图
out = "/home/ubuntu/change_hair/project/logs/test_haircolor_result.jpg"
out = "/home/xsl/change_hair/project/logs/test_haircolor_result.jpg"
with open(out, "wb") as f:
f.write(base64.b64decode(result))
print(f"✅ 换发色成功! 结果图已保存: {out} ({len(result)} bytes base64)")
test_hairgrow_api.py
+3 -3
View File
@@ -2,8 +2,8 @@
"""测试 /api/hairGrow/v1 接口"""
import base64, requests, sys
img_path = "/home/ubuntu/change_hair/project/data/userImage/8488902485_20250630055547.jpg"
mask_path = "/home/ubuntu/change_hair/project/logs/hairgrow_test_mask.png"
img_path = "/home/xsl/change_hair/project/data/userImage/8488902485_20250630055547.jpg"
mask_path = "/home/xsl/change_hair/project/logs/hairgrow_test_mask.png"
with open(img_path, "rb") as f:
img_b64 = "data:image/jpeg;base64," + base64.b64encode(f.read()).decode()
@@ -26,7 +26,7 @@ try:
print(f"HTTP {r.status_code} | msg={d.get('msg')} | state={d.get('state')} | 耗时={time.time()-t0:.1f}s")
result = d.get("result", "")
if d.get("state") == 0 and result:
out = "/home/ubuntu/change_hair/project/logs/test_hairgrow_api_result.jpg"
out = "/home/xsl/change_hair/project/logs/test_hairgrow_api_result.jpg"
with open(out, "wb") as f:
f.write(base64.b64decode(result))
print(f"✅ 生发成功! 结果图: {out}")
test_swaphair.py
+2 -2
View File
@@ -2,7 +2,7 @@
"""换发型接口测试"""
import base64, json, requests, sys, time
img_path = "/home/ubuntu/change_hair/project/data/userImage/8488902485_20250630055547.jpg"
img_path = "/home/xsl/change_hair/project/data/userImage/8488902485_20250630055547.jpg"
with open(img_path, "rb") as f:
img_b64 = base64.b64encode(f.read()).decode()
@@ -22,7 +22,7 @@ try:
print(f"HTTP {r.status_code} | msg={d.get('msg')} | state={d.get('state')} | 耗时={time.time()-t0:.1f}s")
data = d.get("data", "")
if d.get("state") == 0 and data:
out = "/home/ubuntu/change_hair/project/logs/test_swaphair_result.jpg"
out = "/home/xsl/change_hair/project/logs/test_swaphair_result.jpg"
with open(out, "wb") as f:
f.write(base64.b64decode(data))
print(f"✅ 换发型成功! 结果图已保存: {out} ({len(data)} bytes base64)")
test_train_api.py
+1 -1
View File
@@ -4,7 +4,7 @@ import requests, json
# 用一个已有完整训练素材的发型
HAIR_ID = "1905785164224868354"
MATERIAL_DIR = f"/home/ubuntu/change_hair/data/train_material/{HAIR_ID}"
MATERIAL_DIR = f"/home/xsl/change_hair/data/train_material/{HAIR_ID}"
payload = {
"task_id": f"test_train_{HAIR_ID}",
train_batch_stepC.py
-96
View File
@@ -1,96 +0,0 @@
#!/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/ubuntu/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/ubuntu/change_hair/hair_type_images/chang_tuoyuan/chang_tuoyuan.jpg"),
("chang_bolang", "girl", "/home/ubuntu/change_hair/hair_type_images/chang_bolang/chang_bolang.jpg"),
("chang_zhixian", "girl", "/home/ubuntu/change_hair/hair_type_images/chang_zhixian/chang_zhixian.jpg"),
("chang_huaban", "girl", "/home/ubuntu/change_hair/hair_type_images/chang_huaban/chang_huaban.jpg"),
("chang_xinxing", "girl", "/home/ubuntu/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/ubuntu/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/<hair_id>/first##<hair_id>.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()
train_hairstyle_full.py
+6 -10
View File
@@ -6,8 +6,8 @@
串接 prepare_train_data / gen_template_material / photo_service训练 / 回调 / 预览图
用法:
cd /home/ubuntu/change_hair/project/hair_service_sd
python /home/ubuntu/change_hair/train_hairstyle_full.py --hair-id huaban1 --input /home/ubuntu/change_hair/huaban1 --gender girl --template-img /home/ubuntu/change_hair/huaban1/huaban1.jpg
cd /home/xsl/change_hair/project/hair_service_sd
python /home/xsl/change_hair/train_hairstyle_full.py --hair-id huaban1 --input /home/xsl/change_hair/huaban1 --gender girl --template-img /home/xsl/change_hair/huaban1/huaban1.jpg
"""
import os
import sys
@@ -21,7 +21,7 @@ import argparse
import requests as req
ssl._create_default_https_context = ssl._create_unverified_context
HAIR_SERVICE_DIR = "/home/ubuntu/change_hair/project/hair_service_sd"
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")
@@ -48,9 +48,9 @@ RESOLUTIONS = [512, 768, 1024, 1280, 1536]
PHOTO_TRAIN = "http://127.0.0.1:32678/api/hair/train"
HAIR_CALLBACK = "http://127.0.0.1:8801/api/hair/trainCallBack"
SWAP_API = "http://127.0.0.1:8801/api/swapHair/v1"
GIRL_IMG = "/home/ubuntu/change_hair/images/girl.png"
BOY_IMG = "/home/ubuntu/change_hair/images/boy.png"
PREVIEW_DIR = "/home/ubuntu/change_hair/hair_grow_service/static/previews"
GIRL_IMG = "/home/xsl/change_hair/images/girl.png"
BOY_IMG = "/home/xsl/change_hair/images/boy.png"
PREVIEW_DIR = "/home/xsl/change_hair/hair_grow_service/static/previews"
_detector = _aligner = _matte = None
def get_models():
@@ -158,10 +158,6 @@ def step3_wait_and_callback(hair_id, template_img):
else:
print(" ✗ 训练超时"); return False
time.sleep(5) # 等训练进程写完
# 训练时停掉了 hair 服务，回调前需要重启
print(" 重启 change_hair-hair 服务...")
os.system("sudo systemctl restart change_hair-hair")
time.sleep(20)
# 触发回调生成ref材质
print(" 触发回调生成ref材质...")
r = req.post(HAIR_CALLBACK, json={"task_id":f"train_{hair_id}","hair_id":hair_id,
train_hairstyles_parallel.py
-150
View File
@@ -1,150 +0,0 @@
#!/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/ubuntu/change_hair/project/hair_service_sd
python /home/ubuntu/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/ubuntu/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/ubuntu/change_hair/hair_type_images/chang_tuoyuan", "girl", "/home/ubuntu/change_hair/hair_type_images/chang_tuoyuan/chang_tuoyuan.jpg"),
("chang_bolang", "/home/ubuntu/change_hair/hair_type_images/chang_bolang", "girl", "/home/ubuntu/change_hair/hair_type_images/chang_bolang/chang_bolang.jpg"),
("chang_zhixian", "/home/ubuntu/change_hair/hair_type_images/chang_zhixian", "girl", "/home/ubuntu/change_hair/hair_type_images/chang_zhixian/chang_zhixian.jpg"),
("chang_huaban", "/home/ubuntu/change_hair/hair_type_images/chang_huaban", "girl", "/home/ubuntu/change_hair/hair_type_images/chang_huaban/chang_huaban.jpg"),
("chang_xinxing", "/home/ubuntu/change_hair/hair_type_images/chang_xinxing", "girl", "/home/ubuntu/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/ubuntu/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()
train_lora_parallel.py
-124
View File
@@ -1,124 +0,0 @@
#!/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/ubuntu/change_hair/train_lora_parallel.py
前提：step1 训练数据已准备好（data/train_material/<hid>/images/1_hairstyle/*.png）
"""
import os
import sys
import time
import subprocess
from concurrent.futures import ThreadPoolExecutor, as_completed
TRAIN_DIR = "/home/ubuntu/change_hair/data/train_material"
KOHYA_WORKDIR = "/home/ubuntu/change_hair/project/kohya_ss_home/kohya_ss"
BASE_MODEL = "/home/ubuntu/change_hair/project/onediff/stable-diffusion-webui/models/Stable-diffusion/v1-5-pruned-emaonly.safetensors"
KOHYA_ACCEL = "/home/ubuntu/miniconda3/envs/kohya/bin/accelerate"
LOG_DIR = "/home/ubuntu/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()