admin
afcddfaeb9
- 新增基于 JWT 当前用户的登录恢复、角色权限、用户管理、审计日志和演示出厂重置后台接口与前端管理页。 - 重串 GT_label 导出和 GT Mask 导入逻辑：导出保留类别真实 maskid，导入仅接受灰度或 RGB 等通道 maskid 图，支持未知 maskid 策略、尺寸最近邻拉伸和导入预览。 - 统一分割结果导出体验：默认当前帧，按项目抽帧顺序和 XhXXmXXsXXXms 时间戳命名 ZIP 与图片，补齐 GT/Pro/Mix/分开 Mask 输出和映射 JSON。 - 调整工作区左侧工具栏：移除创建点/线段入口，新增画笔、橡皮擦及尺寸控制，并按绘制、布尔、导入/AI 工具分组分隔。 - 扩展 Canvas 编辑能力：画笔按语义分类绘制并可自动并入连通选中 mask，橡皮擦对选中区域扣除，优化布尔操作、选区、撤销重做和保存状态联动。 - 优化自动传播时间轴显示：同一蓝色系按传播新旧递进变暗，老传播记录达到阈值后统一旧记录色，并维护范围选择与清空后的历史显示。 - 将 AI 智能分割入口替换为更明确的 AI 元素图标，并同步侧栏、工作区和 AI 页面入口表现。 - 完善模板分类、maskid 工具函数、分类树联动、遮罩透明度、边缘平滑和传播链同步相关前端状态。 - 扩展后端项目、媒体、任务、Dashboard、模板和传播 runner 的用户隔离、任务控制、进度事件与兼容处理。 - 补充前后端测试，覆盖用户管理、GT_label 往返导入导出、GT Mask 校验和预览、画笔/橡皮擦、时间轴传播历史、导出范围、WebSocket 与 API 封装。 - 更新 AGENTS、README 和 doc 文档，记录当前接口契约、实现状态、测试计划、安装说明和 maskid/GT_label 规则。
453 lines
19 KiB
Python
453 lines
19 KiB
Python
import zipfile
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import json
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from io import BytesIO
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from urllib.parse import unquote
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import cv2
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import numpy as np
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def _fake_image_bytes(width=100, height=50, color=(255, 255, 255)):
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image = np.full((height, width, 3), color, dtype=np.uint8)
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_, encoded = cv2.imencode(".jpg", image)
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return encoded.tobytes()
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def _seed_export_data(client):
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project = client.post("/api/projects", json={
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"name": "Export Project",
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"video_path": "uploads/1/clip.mp4",
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}).json()
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frame = client.post(f"/api/projects/{project['id']}/frames", json={
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"project_id": project["id"],
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"frame_index": 0,
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"image_url": "frames/0.jpg",
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"width": 100,
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"height": 50,
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"timestamp_ms": 1250.0,
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"source_frame_number": 37,
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}).json()
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template = client.post("/api/templates", json={
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"name": "Category",
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"color": "#06b6d4",
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"z_index": 0,
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"classes": [],
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"rules": [],
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}).json()
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annotation = client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": frame["id"],
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"template_id": template["id"],
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"mask_data": {"polygons": [[[0.1, 0.2], [0.9, 0.2], [0.9, 0.8], [0.1, 0.8]]]},
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"points": [[0.5, 0.5]],
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"bbox": [0.1, 0.2, 0.8, 0.6],
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}).json()
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return project, frame, template, annotation
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def test_export_coco_json_structure(client):
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project, frame, _, _ = _seed_export_data(client)
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response = client.get(f"/api/export/{project['id']}/coco")
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assert response.status_code == 200
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assert response.headers["content-type"].startswith("application/json")
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data = response.json()
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assert data["info"]["description"] == "Annotations for Export Project"
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assert data["images"][0] == {
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"id": frame["id"],
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"file_name": "frames/0.jpg",
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"width": 100,
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"height": 50,
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"frame_index": 0,
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}
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assert data["annotations"][0]["segmentation"] == [[10.0, 10.0, 90.0, 10.0, 90.0, 40.0, 10.0, 40.0]]
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assert data["annotations"][0]["bbox"] == [10.0, 10.0, 80.0, 30.000000000000004]
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assert data["categories"][0]["name"] == "Category"
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def test_export_masks_zip(client):
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project, _, _, annotation = _seed_export_data(client)
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response = client.get(f"/api/export/{project['id']}/masks")
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assert response.status_code == 200
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assert response.headers["content-type"].startswith("application/zip")
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with zipfile.ZipFile(BytesIO(response.content)) as archive:
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assert archive.namelist() == [
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f"mask_{annotation['id']:06d}.png",
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"semantic_frame_000000.png",
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"semantic_classes.json",
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]
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def test_export_masks_uses_z_index_for_semantic_fusion(client):
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project = client.post("/api/projects", json={"name": "Fusion Project"}).json()
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frame = client.post(f"/api/projects/{project['id']}/frames", json={
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"project_id": project["id"],
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"frame_index": 0,
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"image_url": "frames/0.jpg",
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"width": 20,
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"height": 20,
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}).json()
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low = client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": frame["id"],
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"mask_data": {
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"polygons": [[[0.1, 0.1], [0.8, 0.1], [0.8, 0.8], [0.1, 0.8]]],
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"label": "Low",
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"color": "#00ff00",
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"class": {"id": "low", "name": "Low", "color": "#00ff00", "zIndex": 10},
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},
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}).json()
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high = client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": frame["id"],
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"mask_data": {
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"polygons": [[[0.4, 0.4], [0.9, 0.4], [0.9, 0.9], [0.4, 0.9]]],
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"label": "High",
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"color": "#ff0000",
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"class": {"id": "high", "name": "High", "color": "#ff0000", "zIndex": 20},
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},
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}).json()
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response = client.get(f"/api/export/{project['id']}/masks")
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assert response.status_code == 200
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with zipfile.ZipFile(BytesIO(response.content)) as archive:
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assert f"mask_{low['id']:06d}.png" in archive.namelist()
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assert f"mask_{high['id']:06d}.png" in archive.namelist()
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legend = json.loads(archive.read("semantic_classes.json"))
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high_value = next(item["value"] for item in legend["classes"] if item["key"] == "class:high")
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semantic_bytes = np.frombuffer(archive.read("semantic_frame_000000.png"), dtype=np.uint8)
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semantic = cv2.imdecode(semantic_bytes, cv2.IMREAD_GRAYSCALE)
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assert semantic[10, 10] == high_value
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def test_export_results_zip_contains_coco_original_images_and_selected_mask_outputs(client, monkeypatch):
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project, _, _, annotation = _seed_export_data(client)
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monkeypatch.setattr("routers.export.download_file", lambda object_name: _fake_image_bytes())
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response = client.get(f"/api/export/{project['id']}/results?scope=all&mask_type=both")
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assert response.status_code == 200
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assert response.headers["content-type"].startswith("application/zip")
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with zipfile.ZipFile(BytesIO(response.content)) as archive:
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names = archive.namelist()
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frame_stem = "clip_0h00m01s250ms_frame000001"
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assert "annotations_coco.json" in names
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assert "maskid_GT像素值_类别映射.json" in names
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assert f"原始图片/{frame_stem}.jpg" in names
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assert f"分开Mask分割结果/{frame_stem}_分别导出/{frame_stem}_Category_maskid1.png" in names
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assert f"GT_label图/{frame_stem}.png" in names
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assert f"Pro_label彩色分割结果/{frame_stem}.png" in names
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assert f"Mix_label重叠覆盖彩色分割结果/{frame_stem}.png" in names
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coco = json.loads(archive.read("annotations_coco.json"))
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mapping = json.loads(archive.read("maskid_GT像素值_类别映射.json"))
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label_bytes = np.frombuffer(archive.read(f"GT_label图/{frame_stem}.png"), dtype=np.uint8)
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gt_label = cv2.imdecode(label_bytes, cv2.IMREAD_UNCHANGED)
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pro_label = cv2.imdecode(
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np.frombuffer(archive.read(f"Pro_label彩色分割结果/{frame_stem}.png"), dtype=np.uint8),
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cv2.IMREAD_COLOR,
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)
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mix_label = cv2.imdecode(
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np.frombuffer(archive.read(f"Mix_label重叠覆盖彩色分割结果/{frame_stem}.png"), dtype=np.uint8),
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cv2.IMREAD_COLOR,
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)
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assert coco["images"][0]["frame_index"] == 0
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assert coco["annotations"][0]["image_id"] == annotation["frame_id"]
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assert mapping["classes"] == [{
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"gt_pixel_value": 1,
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"maskid": 1,
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"chineseName": "Category",
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"className": "Category",
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"categoryName": "Category",
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"rgb": [6, 182, 212],
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"color": "#06b6d4",
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"key": f"template:{annotation['template_id']}",
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"template_id": annotation["template_id"],
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}]
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assert gt_label[0, 0] == 0
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assert gt_label[20, 50] == 1
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assert pro_label[20, 50].tolist() == [212, 182, 6]
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assert pro_label[0, 0].tolist() == [0, 0, 0]
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assert mix_label[20, 50].tolist() != [255, 255, 255]
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def test_export_results_uses_internal_layer_order_for_gt_pro_and_mix_outputs(client, monkeypatch):
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monkeypatch.setattr("routers.export.download_file", lambda object_name: _fake_image_bytes(20, 20))
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project = client.post("/api/projects", json={
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"name": "Layered Export Project",
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"video_path": "uploads/2/layered.mp4",
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}).json()
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frame = client.post(f"/api/projects/{project['id']}/frames", json={
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"project_id": project["id"],
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"frame_index": 0,
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"image_url": "frames/layered.jpg",
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"width": 20,
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"height": 20,
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"timestamp_ms": 0,
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"source_frame_number": 0,
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}).json()
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client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": frame["id"],
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"mask_data": {
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"polygons": [[[0.1, 0.1], [0.8, 0.1], [0.8, 0.8], [0.1, 0.8]]],
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"label": "Low",
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"color": "#00ff00",
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"class": {"id": "low", "name": "Low", "color": "#00ff00", "zIndex": 10},
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},
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})
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client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": frame["id"],
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"mask_data": {
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"polygons": [[[0.4, 0.4], [0.9, 0.4], [0.9, 0.9], [0.4, 0.9]]],
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"label": "High",
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"color": "#ff0000",
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"class": {"id": "high", "name": "High", "color": "#ff0000", "zIndex": 20},
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},
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})
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response = client.get(
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f"/api/export/{project['id']}/results?scope=all&outputs=gt_label,pro_label,mix_label&mix_opacity=0.5",
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)
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assert response.status_code == 200
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with zipfile.ZipFile(BytesIO(response.content)) as archive:
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mapping = json.loads(archive.read("maskid_GT像素值_类别映射.json"))
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high_value = next(item["maskid"] for item in mapping["classes"] if item["key"] == "class:high")
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stem = "layered_0h00m00s000ms_frame000001"
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gt_label = cv2.imdecode(
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np.frombuffer(archive.read(f"GT_label图/{stem}.png"), dtype=np.uint8),
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cv2.IMREAD_UNCHANGED,
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)
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pro_label = cv2.imdecode(
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np.frombuffer(archive.read(f"Pro_label彩色分割结果/{stem}.png"), dtype=np.uint8),
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cv2.IMREAD_COLOR,
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)
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mix_label = cv2.imdecode(
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np.frombuffer(archive.read(f"Mix_label重叠覆盖彩色分割结果/{stem}.png"), dtype=np.uint8),
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cv2.IMREAD_COLOR,
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)
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assert gt_label[10, 10] == high_value
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assert pro_label[10, 10].tolist() == [0, 0, 255]
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assert mix_label[10, 10].tolist() == [127, 127, 255]
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def test_export_results_supports_range_and_current_scope(client, monkeypatch):
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monkeypatch.setattr("routers.export.download_file", lambda object_name: _fake_image_bytes(20, 20))
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project = client.post("/api/projects", json={
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"name": "Scoped Export Project",
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"video_path": "uploads/9/scope.mp4",
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"parse_fps": 2,
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}).json()
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template = client.post("/api/templates", json={
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"name": "Scoped Category",
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"color": "#06b6d4",
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"z_index": 0,
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"classes": [],
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"rules": [],
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}).json()
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frames = []
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annotations = []
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for idx in range(3):
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frame = client.post(f"/api/projects/{project['id']}/frames", json={
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"project_id": project["id"],
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"frame_index": idx,
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"image_url": f"frames/{idx}.jpg",
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"width": 20,
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"height": 20,
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"timestamp_ms": idx * 500.0,
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"source_frame_number": idx * 10,
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}).json()
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frames.append(frame)
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annotations.append(client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": frame["id"],
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"template_id": template["id"],
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"mask_data": {"polygons": [[[0.1, 0.1], [0.8, 0.1], [0.8, 0.8], [0.1, 0.8]]]},
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}).json())
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range_response = client.get(
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f"/api/export/{project['id']}/results?scope=range&start_frame=2&end_frame=3&mask_type=gt_label",
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)
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current_response = client.get(
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f"/api/export/{project['id']}/results?scope=current&frame_id={frames[1]['id']}&mask_type=separate",
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)
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assert range_response.status_code == 200
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assert "Scoped_Export_Project_seg_T_0h00m00s500ms-0h00m01s000ms_P_2-3.zip" in unquote(
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range_response.headers["content-disposition"],
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)
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with zipfile.ZipFile(BytesIO(range_response.content)) as archive:
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names = archive.namelist()
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coco = json.loads(archive.read("annotations_coco.json"))
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assert "原始图片/scope_0h00m00s500ms_frame000002.jpg" in names
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assert "原始图片/scope_0h00m01s000ms_frame000003.jpg" in names
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assert "原始图片/scope_0h00m00s000ms_frame000001.jpg" not in names
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assert "GT_label图/scope_0h00m00s500ms_frame000002.png" in names
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assert "GT_label图/scope_0h00m01s000ms_frame000003.png" in names
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assert "GT_label图/scope_0h00m00s000ms_frame000001.png" not in names
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assert not any(name.startswith("分开Mask分割结果/") for name in names)
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assert not any(name.startswith("Pro_label彩色分割结果/") for name in names)
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assert not any(name.startswith("Mix_label重叠覆盖彩色分割结果/") for name in names)
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assert [image["frame_index"] for image in coco["images"]] == [1, 2]
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assert current_response.status_code == 200
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with zipfile.ZipFile(BytesIO(current_response.content)) as archive:
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names = archive.namelist()
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coco = json.loads(archive.read("annotations_coco.json"))
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current_stem = "scope_0h00m00s500ms_frame000002"
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assert f"原始图片/{current_stem}.jpg" in names
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assert f"分开Mask分割结果/{current_stem}_分别导出/{current_stem}_Scoped_Category_maskid1.png" in names
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assert f"分开Mask分割结果/scope_0h00m00s000ms_frame000001_分别导出/scope_0h00m00s000ms_frame000001_Scoped_Category_maskid1.png" not in names
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assert not any(name.startswith("GT_label图/") for name in names)
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assert not any(name.startswith("Pro_label彩色分割结果/") for name in names)
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assert not any(name.startswith("Mix_label重叠覆盖彩色分割结果/") for name in names)
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assert [image["id"] for image in coco["images"]] == [frames[1]["id"]]
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def test_export_results_preserves_template_maskid_consistently_across_frames(client, monkeypatch):
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monkeypatch.setattr("routers.export.download_file", lambda object_name: _fake_image_bytes(20, 20))
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project = client.post("/api/projects", json={
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"name": "MaskId Export Project",
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"video_path": "uploads/8/maskid-demo.mp4",
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"parse_fps": 1,
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}).json()
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frames = []
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for idx in range(2):
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frames.append(client.post(f"/api/projects/{project['id']}/frames", json={
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"project_id": project["id"],
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"frame_index": idx,
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"image_url": f"frames/{idx}.jpg",
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"width": 20,
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"height": 20,
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"timestamp_ms": idx * 1000.0,
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"source_frame_number": idx,
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}).json())
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client.post("/api/ai/annotate", json={
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"project_id": project["id"],
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"frame_id": frames[-1]["id"],
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"mask_data": {
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"polygons": [[[0.1, 0.1], [0.8, 0.1], [0.8, 0.8], [0.1, 0.8]]],
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"label": "Tumor",
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"color": "#ff0000",
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"class": {"id": "tumor", "name": "Tumor", "color": "#ff0000", "maskId": 7, "zIndex": 30},
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},
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})
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response = client.get(f"/api/export/{project['id']}/results?scope=all&mask_type=both")
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assert response.status_code == 200
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with zipfile.ZipFile(BytesIO(response.content)) as archive:
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names = archive.namelist()
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mapping = json.loads(archive.read("maskid_GT像素值_类别映射.json"))
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first_stem = "maskid-demo_0h00m00s000ms_frame000001"
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second_stem = "maskid-demo_0h00m01s000ms_frame000002"
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assert f"分开Mask分割结果/{first_stem}_分别导出/{first_stem}_Tumor_maskid7.png" in names
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assert f"分开Mask分割结果/{second_stem}_分别导出/{second_stem}_Tumor_maskid7.png" in names
|
|
first_label = cv2.imdecode(np.frombuffer(archive.read(f"GT_label图/{first_stem}.png"), dtype=np.uint8), cv2.IMREAD_UNCHANGED)
|
|
second_label = cv2.imdecode(np.frombuffer(archive.read(f"GT_label图/{second_stem}.png"), dtype=np.uint8), cv2.IMREAD_UNCHANGED)
|
|
|
|
assert mapping["classes"] == [{
|
|
"gt_pixel_value": 7,
|
|
"maskid": 7,
|
|
"chineseName": "Tumor",
|
|
"className": "Tumor",
|
|
"categoryName": "",
|
|
"rgb": [255, 0, 0],
|
|
"color": "#ff0000",
|
|
"key": "class:tumor",
|
|
"template_id": None,
|
|
}]
|
|
assert first_label[5, 5] == 7
|
|
assert second_label[5, 5] == 7
|
|
|
|
|
|
def test_exported_gtlabel_round_trips_through_gt_mask_import_with_template_maskid(client, monkeypatch):
|
|
monkeypatch.setattr("routers.export.download_file", lambda object_name: _fake_image_bytes(20, 20))
|
|
project = client.post("/api/projects", json={
|
|
"name": "GT Roundtrip Project",
|
|
"video_path": "uploads/8/roundtrip.mp4",
|
|
}).json()
|
|
template = client.post("/api/templates", json={
|
|
"name": "Roundtrip Template",
|
|
"color": "#06b6d4",
|
|
"z_index": 0,
|
|
"classes": [
|
|
{"id": "tumor", "name": "Tumor", "color": "#ff0000", "zIndex": 30, "maskId": 7},
|
|
],
|
|
"rules": [],
|
|
}).json()
|
|
source_frame = client.post(f"/api/projects/{project['id']}/frames", json={
|
|
"project_id": project["id"],
|
|
"frame_index": 0,
|
|
"image_url": "frames/source.jpg",
|
|
"width": 20,
|
|
"height": 20,
|
|
"timestamp_ms": 0,
|
|
}).json()
|
|
target_frame = client.post(f"/api/projects/{project['id']}/frames", json={
|
|
"project_id": project["id"],
|
|
"frame_index": 1,
|
|
"image_url": "frames/target.jpg",
|
|
"width": 20,
|
|
"height": 20,
|
|
"timestamp_ms": 1000,
|
|
}).json()
|
|
client.post("/api/ai/annotate", json={
|
|
"project_id": project["id"],
|
|
"frame_id": source_frame["id"],
|
|
"template_id": template["id"],
|
|
"mask_data": {
|
|
"polygons": [[[0.1, 0.1], [0.8, 0.1], [0.8, 0.8], [0.1, 0.8]]],
|
|
"label": "Tumor",
|
|
"color": "#ff0000",
|
|
"class": {"id": "tumor", "name": "Tumor", "color": "#ff0000", "maskId": 7, "zIndex": 30},
|
|
},
|
|
})
|
|
|
|
export_response = client.get(
|
|
f"/api/export/{project['id']}/results?scope=current&frame_id={source_frame['id']}&outputs=gt_label",
|
|
)
|
|
|
|
assert export_response.status_code == 200
|
|
with zipfile.ZipFile(BytesIO(export_response.content)) as archive:
|
|
stem = "roundtrip_0h00m00s000ms_frame000001"
|
|
exported_gt_label = archive.read(f"GT_label图/{stem}.png")
|
|
gt_label = cv2.imdecode(np.frombuffer(exported_gt_label, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
|
|
mapping = json.loads(archive.read("maskid_GT像素值_类别映射.json"))
|
|
|
|
assert gt_label[5, 5] == 7
|
|
assert mapping["classes"][0]["maskid"] == 7
|
|
|
|
import_response = client.post(
|
|
"/api/ai/import-gt-mask",
|
|
data={
|
|
"project_id": str(project["id"]),
|
|
"frame_id": str(target_frame["id"]),
|
|
"template_id": str(template["id"]),
|
|
"unknown_color_policy": "discard",
|
|
},
|
|
files={"file": ("exported_gt_label.png", exported_gt_label, "image/png")},
|
|
)
|
|
|
|
assert import_response.status_code == 201
|
|
imported = import_response.json()
|
|
assert len(imported) == 1
|
|
assert imported[0]["frame_id"] == target_frame["id"]
|
|
assert imported[0]["mask_data"]["gt_label_value"] == 7
|
|
assert imported[0]["mask_data"]["label"] == "Tumor"
|
|
assert imported[0]["mask_data"]["class"]["maskId"] == 7
|
|
|
|
|
|
def test_export_missing_project_returns_404(client):
|
|
assert client.get("/api/export/999/coco").status_code == 404
|
|
assert client.get("/api/export/999/masks").status_code == 404
|
|
assert client.get("/api/export/999/results").status_code == 404
|