xslandClaude Opus 4.8
554b64a916
第一步：按性别把发际线类型贴图渲染到照片，输出 N 张发际线叠加预览图。 - hairline/render.py: 解析 face_ext.obj(502 UV + 64 ribbon扩展面) + OpenCV 逐三角 仿射 warp 渲染器；关键修复——face_ext.obj 是 OBJ序，用 INDEX_MAP_468 把 MP序 502点重排成 OBJ序后再投影，否则 ribbon 会错贴到中脸 - hairline/service.py: FaceLandmarker+SegFormer 单例 + 性别贴图映射(扫描去空格) + generate_previews 管线(female5/male4) - 集成点修复: face_landmarks DEFAULT_MODEL_PATH 改 hairline/models/; constants HF_FACE_PARSER_MODEL 改本地路径(离线) - app.py: /api/v1/hair/grow 接真实实现，gender 必填(非法→1004)，返回 results[].image_base64(不落盘)，校验/鉴权同接口1；lifespan 预热接口2单例； 补 logging.basicConfig - 依赖: transformers==4.45.2；SegFormer 权重走 hf-mirror 下载(见 OFFLINE_ASSETS) - 测试: tests/test_hairline.py(mesh/重排/贴图映射) + test_api 接口2用例，31 全绿 注：SegFormer 受 5090/torch 限制走 CPU(~2.5s/张)，换 cu128 可 SEG_DEVICE=cuda。 Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
114 lines
3.8 KiB
Python
114 lines
3.8 KiB
Python
"""接口集成测试（FastAPI TestClient）：错误码 + 鉴权 + 正常用例结构。"""
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import base64
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import pytest
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from fastapi.testclient import TestClient
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from conftest import fixture
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import app as app_module
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# 测试自带固定密码，避免依赖 worker_config.json 的实际值（中间件运行期读模块全局）
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app_module.ACCEPT_PASSWORDS = ["testpass"]
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URL = "/api/v1/face/measure"
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H = {"X-Internal-Token": "testpass"}
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@pytest.fixture(scope="module")
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def client():
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# with 触发 lifespan：加载模型单例（detector + 尽力加载 segmenter）
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with TestClient(app_module.app) as c:
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yield c
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def _post(client, fixture_name=None, headers=H, data=None, extra_files=None):
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files = {}
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if fixture_name:
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files["image_file"] = (fixture_name, open(fixture(fixture_name), "rb"), "application/octet-stream")
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if extra_files:
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files.update(extra_files)
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return client.post(URL, headers=headers, files=files or None, data=data)
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def test_auth_missing_token_401(client):
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r = _post(client, "frontal.jpg", headers={})
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assert r.status_code == 401
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def test_health_no_token_200(client):
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r = client.get("/health")
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assert r.status_code == 200
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assert r.json()["status"] == "ok"
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def test_param_none_provided_1007(client):
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r = client.post(URL, headers=H)
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assert r.json()["code"] == 1007
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def test_param_multiple_provided_1007(client):
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r = _post(client, "frontal.jpg", data={"image_url": "http://example.com/x.jpg"})
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assert r.json()["code"] == 1007
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def test_oversize_1006(client, oversize_file):
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files = {"image_file": ("oversize.bin", open(oversize_file, "rb"), "application/octet-stream")}
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r = client.post(URL, headers=H, files=files)
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assert r.json()["code"] == 1006
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def test_lowres_1002(client):
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r = _post(client, "lowres.png")
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assert r.json()["code"] == 1002
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def test_no_face_1001(client):
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r = _post(client, "landscape.jpg")
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assert r.json()["code"] == 1001
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def test_corrupt_1008(client):
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r = _post(client, "corrupt.bin")
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assert r.json()["code"] == 1008
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GROW = "/api/v1/hair/grow"
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def test_grow_missing_gender_1004(client):
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files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
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r = client.post(GROW, headers=H, files=files)
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assert r.json()["code"] == 1004
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def test_grow_female_returns_5(client):
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files = {"image_file": ("frontal.jpg", open(fixture("frontal.jpg"), "rb"), "application/octet-stream")}
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r = client.post(GROW, headers=H, files=files, data={"gender": "female"})
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body = r.json()
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assert body["code"] == 0, body
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results = body["data"]["results"]
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assert [x["hairline_type"] for x in results] == ["ellipse", "flower", "heart", "straight", "wave"]
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assert [x["order"] for x in results] == [1, 2, 3, 4, 5]
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assert base64.b64decode(results[0]["image_base64"])[:8] == b"\x89PNG\r\n\x1a\n"
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assert "image_url" not in results[0]
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def test_success_structure(client):
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r = _post(client, "frontal.jpg")
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body = r.json()
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assert body["code"] == 0, body
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data = body["data"]
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# 业务字段对齐文档
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assert set(["face_total_height_cm", "four_courts", "seven_eyes",
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"landmarks", "hairline_source", "head_pose",
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"annotated_image_base64"]).issubset(data.keys())
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assert set(["top_court_cm", "upper_court_cm", "middle_court_cm",
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"lower_court_cm", "ratios"]).issubset(data["four_courts"].keys())
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assert set(["eye_width_cm", "face_width_cm", "inter_eye_distance_cm",
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"ratios"]).issubset(data["seven_eyes"].keys())
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assert data["hairline_source"] in ("segmentation", "estimated")
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# base64 解码为合法 PNG（非 URL）
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assert "annotated_image_url" not in data
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png = base64.b64decode(data["annotated_image_base64"])
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assert png[:8] == b"\x89PNG\r\n\x1a\n"
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