admin/Pre_Seg_Server
1
1
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases 1 Wiki Activity
Files
4c1d3dba73d466e1af192710447d84e098308482
Pre_Seg_Server/backend/tests/test_ai.py
admin 4c1d3dba73 feat: 完善 mask 编辑、传播平滑与开发重启闭环 
功能增加：

- 新增后端 /api/ai/smooth-mask 接口，对当前 mask polygon 执行 Chaikin 边缘平滑，并返回 polygon、bbox、area 与拓扑锚点。

- 在右侧实例属性面板加入边缘平滑强度和应用边缘平滑操作，应用后将 mask 标记为 draft/dirty，并通过正常保存链路落库。

- 保存标注与传播 seed 时保留 geometry_smoothing 元数据，自动传播 forward/backward 结果保存前应用同一平滑参数。

- 自动传播 seed signature 纳入平滑参数，修改平滑强度后会触发旧同源传播结果清理并重新传播。

- 支持跨帧跟随同一传播链 mask，AI 推送回工作区时保留当前帧视角。

Bugfix：

- 修复中间帧向前传播时旧 forward/backward 同物体结果未被清理导致双重 mask 的问题。

- 修复 propagation worker 写入目标帧前只按旧方向清理导致 backward 重传残留的问题。

- 修复多边形顶点拖拽和编辑后画布视口异常移动的问题，并补充拖拽状态回写。

- 修复实例属性标题跟随全局 active class 而不是当前 mask label 的问题，并移除后端模型置信度展示。

开发与部署：

- 新增 restart_dev_services.sh，使用 setsid 独立后台重启 FastAPI、Celery 和前端，写入 pid/log 文件并做 3000/8000 健康检查。

- 明确后端或 Celery 相关改动完成后需要运行重启脚本，保证运行态加载最新代码。

测试与文档：

- 补充后端 smooth-mask、传播平滑 metadata、seed signature、传播去重方向覆盖等测试。

- 补充前端 OntologyInspector、VideoWorkspace、CanvasArea 和 api 契约测试，覆盖边缘平滑、传播参数、跨帧选区跟随和画布编辑行为。

- 更新 README、AGENTS、安装文档、前端元素审计、需求冻结、设计冻结和测试计划，记录当前真实行为与重启要求。
2026-05-02 17:04:02 +08:00

1087 lines
41 KiB
Python
Raw Blame History

import numpy as np
import cv2
from pathlib import Path
from models import Annotation, ProcessingTask
from services.propagation_task_runner import _seed_signature, run_propagate_project_task
def _create_project_and_frame(client):
project = client.post("/api/projects", json={"name": "AI Project"}).json()
frame = client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": 0,
"image_url": "frames/0.jpg",
"width": 640,
"height": 360,
}).json()
template = client.post("/api/templates", json={
"name": "Template",
"color": "#06b6d4",
"z_index": 0,
"classes": [],
"rules": [],
}).json()
return project, frame, template
def test_predict_accepts_point_object_with_labels(client, monkeypatch):
_, frame, _ = _create_project_and_frame(client)
calls = {}
monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))
def fake_predict_points(image, points, labels):
calls["args"] = (points, labels)
return (
[[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]],
[0.95],
)
monkeypatch.setattr("routers.ai.sam_registry.predict_points", lambda model, image, points, labels: fake_predict_points(image, points, labels))
response = client.post("/api/ai/predict", json={
"image_id": frame["id"],
"prompt_type": "point",
"prompt_data": {"points": [[0.5, 0.5], [0.1, 0.1]], "labels": [1, 0]},
})
assert response.status_code == 200
assert response.json()["scores"] == [0.95]
assert calls["args"] == ([[0.5, 0.5], [0.1, 0.1]], [1, 0])
def test_predict_applies_crop_and_background_filter_options(client, monkeypatch):
_, frame, _ = _create_project_and_frame(client)
calls = {}
monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((100, 200, 3), dtype=np.uint8))
def fake_predict_points(model, image, points, labels):
calls["shape"] = image.shape
calls["points"] = points
calls["labels"] = labels
return (
[
[[0.0, 0.0], [0.2, 0.0], [0.2, 0.2]],
[[0.45, 0.45], [0.55, 0.45], [0.55, 0.55]],
],
[0.9, 0.01],
)
monkeypatch.setattr("routers.ai.sam_registry.predict_points", fake_predict_points)
response = client.post("/api/ai/predict", json={
"image_id": frame["id"],
"prompt_type": "point",
"prompt_data": {"points": [[0.5, 0.5], [0.52, 0.52]], "labels": [1, 0]},
"options": {
"crop_to_prompt": True,
"crop_margin": 0.1,
"auto_filter_background": True,
"min_score": 0.05,
},
})
assert response.status_code == 200
assert calls["shape"][0] < 100
assert calls["shape"][1] < 200
assert calls["labels"] == [1, 0]
assert response.json()["scores"] == [0.9]
polygon = response.json()["polygons"][0]
assert all(0.0 <= coord <= 1.0 for point in polygon for coord in point)
def test_predict_box_and_rejects_semantic_prompt(client, monkeypatch):
_, frame, _ = _create_project_and_frame(client)
monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))
monkeypatch.setattr("routers.ai.sam_registry.predict_box", lambda model, image, box: (
[[[0.2, 0.2], [0.8, 0.2], [0.8, 0.8]]],
[0.8],
))
box_response = client.post("/api/ai/predict", json={
"image_id": frame["id"],
"prompt_type": "box",
"prompt_data": [0.2, 0.2, 0.8, 0.8],
})
semantic_response = client.post("/api/ai/predict", json={
"image_id": frame["id"],
"prompt_type": "semantic",
"prompt_data": "胆囊",
"model": "sam3",
"options": {"min_score": 0.05},
})
assert box_response.status_code == 200
assert box_response.json()["scores"] == [0.8]
assert semantic_response.status_code == 400
assert "Unsupported model: sam3" in semantic_response.json()["detail"]
def test_predict_interactive_combines_box_and_points(client, monkeypatch):
_, frame, _ = _create_project_and_frame(client)
calls = {}
monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))
def fake_predict_interactive(model, image, box, points, labels):
calls["model"] = model
calls["box"] = box
calls["points"] = points
calls["labels"] = labels
return (
[[[0.2, 0.2], [0.8, 0.2], [0.8, 0.8]]],
[0.88],
)
monkeypatch.setattr("routers.ai.sam_registry.predict_interactive", fake_predict_interactive)
response = client.post("/api/ai/predict", json={
"image_id": frame["id"],
"prompt_type": "interactive",
"prompt_data": {
"box": [0.1, 0.1, 0.9, 0.9],
"points": [[0.5, 0.5], [0.2, 0.2]],
"labels": [1, 0],
},
"model": "sam2.1_hiera_small",
})
assert response.status_code == 200
assert response.json()["scores"] == [0.88]
assert calls == {
"model": "sam2.1_hiera_small",
"box": [0.1, 0.1, 0.9, 0.9],
"points": [[0.5, 0.5], [0.2, 0.2]],
"labels": [1, 0],
}
def test_model_status_reports_runtime(client, monkeypatch):
monkeypatch.setattr("routers.ai.sam_registry.runtime_status", lambda selected_model=None: {
"selected_model": "sam2.1_hiera_tiny",
"gpu": {
"available": False,
"device": "cpu",
"name": None,
"torch_available": True,
"torch_version": "2.x",
"cuda_version": None,
},
"models": [
{
"id": "sam2.1_hiera_tiny",
"label": "SAM 2.1 Tiny",
"available": True,
"loaded": False,
"device": "cpu",
"supports": ["point", "box", "auto"],
"message": "ready",
"package_available": True,
"checkpoint_exists": True,
"checkpoint_path": "model.pt",
"python_ok": True,
"torch_ok": True,
"cuda_required": False,
},
],
})
response = client.get("/api/ai/models/status")
assert response.status_code == 200
body = response.json()
assert body["selected_model"] == "sam2.1_hiera_tiny"
assert len(body["models"]) == 1
assert body["models"][0]["id"] == "sam2.1_hiera_tiny"
def test_model_status_rejects_disabled_sam3(client):
response = client.get("/api/ai/models/status?selected_model=sam3")
assert response.status_code == 400
assert "Unsupported model" in response.json()["detail"]
def test_analyze_mask_returns_backend_geometry_properties(client):
_, frame, _ = _create_project_and_frame(client)
response = client.post("/api/ai/analyze-mask", json={
"frame_id": frame["id"],
"mask_data": {
"polygons": [[[0.1, 0.1], [0.3, 0.1], [0.3, 0.3], [0.1, 0.3]]],
"source": "sam2.1_hiera_tiny",
"score": 0.87,
},
"extract_skeleton": True,
})
assert response.status_code == 200
body = response.json()
assert body["confidence"] == 0.87
assert body["confidence_source"] == "model_score"
assert body["topology_anchor_count"] == 4
assert body["area"] > 0
assert body["message"] == "已从后端重新提取几何拓扑锚点"
def test_smooth_mask_returns_backend_smoothed_geometry(client):
_, frame, _ = _create_project_and_frame(client)
response = client.post("/api/ai/smooth-mask", json={
"frame_id": frame["id"],
"mask_data": {
"polygons": [[[0.1, 0.1], [0.3, 0.1], [0.3, 0.3], [0.1, 0.3]]],
"label": "胆囊",
"color": "#ff0000",
},
"strength": 45,
})
assert response.status_code == 200
body = response.json()
assert body["smoothing"] == {"strength": 45.0, "method": "chaikin"}
assert len(body["polygons"]) == 1
assert len(body["polygons"][0]) > 4
assert body["topology_anchor_count"] > 0
assert body["message"] == "已应用边缘平滑强度 45"
def test_seed_signature_includes_smoothing_parameters():
seed = {
"polygons": [[[0.1, 0.1], [0.3, 0.1], [0.3, 0.3]]],
"label": "胆囊",
"color": "#ff0000",
}
assert _seed_signature({**seed, "smoothing": {"strength": 20, "method": "chaikin"}}) != _seed_signature({
**seed,
"smoothing": {"strength": 40, "method": "chaikin"},
})
def test_propagate_saves_tracked_annotations(client, monkeypatch):
project = client.post("/api/projects", json={"name": "Video Project"}).json()
frames = [
client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": idx,
"image_url": f"frames/{idx}.jpg",
"width": 640,
"height": 360,
}).json()
for idx in range(3)
]
calls = {}
monkeypatch.setattr("routers.ai.download_file", lambda object_name: b"jpeg")
def fake_propagate_video(model, frame_paths, source_frame_index, seed, direction, max_frames):
calls["model"] = model
calls["source_frame_index"] = source_frame_index
calls["seed"] = seed
calls["direction"] = direction
calls["max_frames"] = max_frames
calls["frame_count"] = len(frame_paths)
return [
{
"frame_index": 0,
"polygons": [[[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]],
"scores": [0.9],
"object_ids": [1],
},
{
"frame_index": 1,
"polygons": [[[0.15, 0.15], [0.25, 0.15], [0.25, 0.25]]],
"scores": [0.8],
"object_ids": [1],
},
]
monkeypatch.setattr("routers.ai.sam_registry.propagate_video", fake_propagate_video)
response = client.post("/api/ai/propagate", json={
"project_id": project["id"],
"frame_id": frames[0]["id"],
"model": "sam2.1_hiera_tiny",
"direction": "forward",
"max_frames": 2,
"include_source": False,
"seed": {
"polygons": [[[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]],
"bbox": [0.1, 0.1, 0.1, 0.1],
"label": "胆囊",
"color": "#ff0000",
"class_metadata": {"id": "c1", "name": "胆囊", "color": "#ff0000", "zIndex": 20},
"template_id": None,
},
})
assert response.status_code == 200
body = response.json()
assert body["created_annotation_count"] == 1
assert body["processed_frame_count"] == 2
assert calls["model"] == "sam2.1_hiera_tiny"
assert calls["source_frame_index"] == 0
assert calls["direction"] == "forward"
assert calls["frame_count"] == 2
saved = body["annotations"][0]
assert saved["frame_id"] == frames[1]["id"]
assert saved["mask_data"]["source"] == "sam2.1_hiera_tiny_propagation"
assert saved["mask_data"]["class"]["name"] == "胆囊"
assert saved["mask_data"]["score"] == 0.8
listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
assert len(listing.json()) == 1
def test_queue_propagation_task_creates_processing_task(client, monkeypatch):
project = client.post("/api/projects", json={"name": "Queued Propagation"}).json()
frame = client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": 0,
"image_url": "frames/0.jpg",
"width": 640,
"height": 360,
}).json()
class FakeAsyncResult:
id = "celery-propagate-1"
queued = []
monkeypatch.setattr("routers.ai.propagate_project_masks.delay", lambda task_id: queued.append(task_id) or FakeAsyncResult())
monkeypatch.setattr("routers.ai.publish_task_progress_event", lambda task: None)
response = client.post("/api/ai/propagate/task", json={
"project_id": project["id"],
"frame_id": frame["id"],
"model": "sam2.1_hiera_tiny",
"steps": [{
"direction": "forward",
"max_frames": 2,
"seed": {
"polygons": [[[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]],
"label": "胆囊",
"smoothing": {"strength": 35, "method": "chaikin"},
},
}],
})
assert response.status_code == 202
body = response.json()
assert body["task_type"] == "propagate_masks"
assert body["status"] == "queued"
assert body["celery_task_id"] == "celery-propagate-1"
assert body["payload"]["model"] == "sam2.1_hiera_tiny"
assert body["payload"]["steps"][0]["seed"]["label"] == "胆囊"
assert body["payload"]["steps"][0]["seed"]["smoothing"] == {"strength": 35, "method": "chaikin"}
assert queued == [body["id"]]
def test_queue_propagation_task_normalizes_model_and_rejects_unsupported(client, monkeypatch):
project = client.post("/api/projects", json={"name": "Propagation Model"}).json()
frame = client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": 0,
"image_url": "frames/0.jpg",
"width": 640,
"height": 360,
}).json()
class FakeAsyncResult:
id = "celery-propagate-model"
monkeypatch.setattr("routers.ai.propagate_project_masks.delay", lambda task_id: FakeAsyncResult())
monkeypatch.setattr("routers.ai.publish_task_progress_event", lambda task: None)
response = client.post("/api/ai/propagate/task", json={
"project_id": project["id"],
"frame_id": frame["id"],
"model": "sam2",
"steps": [{
"direction": "forward",
"max_frames": 2,
"seed": {
"polygons": [[[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]],
},
}],
})
assert response.status_code == 202
assert response.json()["payload"]["model"] == "sam2.1_hiera_tiny"
unsupported = client.post("/api/ai/propagate/task", json={
"project_id": project["id"],
"frame_id": frame["id"],
"model": "sam3",
"steps": [{
"direction": "forward",
"max_frames": 2,
"seed": {
"polygons": [[[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]],
},
}],
})
assert unsupported.status_code == 400
assert "Unsupported model" in unsupported.json()["detail"]
def test_propagation_task_runner_saves_annotations_and_progress(client, db_session, monkeypatch):
project = client.post("/api/projects", json={"name": "Propagation Worker"}).json()
frames = [
client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": idx,
"image_url": f"frames/{idx}.jpg",
"width": 640,
"height": 360,
}).json()
for idx in range(2)
]
task = ProcessingTask(
task_type="propagate_masks",
status="queued",
progress=0,
project_id=project["id"],
payload={
"project_id": project["id"],
"frame_id": frames[0]["id"],
"model": "sam2.1_hiera_tiny",
"include_source": False,
"save_annotations": True,
"steps": [{
"direction": "forward",
"max_frames": 2,
"seed": {
"polygons": [[[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]],
"label": "胆囊",
"color": "#ff0000",
"class_metadata": {"id": "c1", "name": "胆囊"},
"smoothing": {"strength": 40, "method": "chaikin"},
},
}],
},
)
db_session.add(task)
db_session.commit()
db_session.refresh(task)
published = []
monkeypatch.setattr("services.propagation_task_runner.download_file", lambda object_name: b"jpeg")
monkeypatch.setattr("services.propagation_task_runner.publish_task_progress_event", lambda event_task: published.append((event_task.status, event_task.progress)))
def fake_propagate_video(model, frame_paths, source_frame_index, seed, direction, max_frames):
assert [Path(path).name for path in frame_paths] == ["000000.jpg", "000001.jpg"]
return [
{"frame_index": 0, "polygons": [[[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]], "scores": [0.9]},
{"frame_index": 1, "polygons": [[[0.15, 0.15], [0.25, 0.15], [0.25, 0.25]]], "scores": [0.8]},
]
monkeypatch.setattr("services.propagation_task_runner.sam_registry.propagate_video", fake_propagate_video)
result = run_propagate_project_task(db_session, task.id)
db_session.refresh(task)
assert task.status == "success"
assert task.progress == 100
assert task.result["model"] == "sam2.1_hiera_tiny"
assert task.result["steps"][0]["model"] == "sam2.1_hiera_tiny"
assert result["created_annotation_count"] == 1
assert result["processed_frame_count"] == 2
assert published[0][0] == "running"
assert published[-1] == ("success", 100)
listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
assert listing.json()[0]["frame_id"] == frames[1]["id"]
assert listing.json()[0]["mask_data"]["source"] == "sam2.1_hiera_tiny_propagation"
assert listing.json()[0]["mask_data"]["geometry_smoothing"] == {"strength": 40.0, "method": "chaikin"}
assert len(listing.json()[0]["mask_data"]["polygons"][0]) > 3
def test_propagation_task_runner_skips_unchanged_seed_and_replaces_changed_seed(client, db_session, monkeypatch):
project = client.post("/api/projects", json={"name": "Propagation Dedupe"}).json()
frames = [
client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": idx,
"image_url": f"frames/{idx}.jpg",
"width": 640,
"height": 360,
}).json()
for idx in range(2)
]
def make_task(seed_polygon):
task = ProcessingTask(
task_type="propagate_masks",
status="queued",
progress=0,
project_id=project["id"],
payload={
"project_id": project["id"],
"frame_id": frames[0]["id"],
"model": "sam2.1_hiera_tiny",
"include_source": False,
"save_annotations": True,
"steps": [{
"direction": "forward",
"max_frames": 2,
"seed": {
"polygons": [seed_polygon],
"label": "胆囊",
"color": "#ff0000",
"source_annotation_id": 7,
"source_mask_id": "annotation-7",
},
}],
},
)
db_session.add(task)
db_session.commit()
db_session.refresh(task)
return task
seed_polygon = [[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]
first_output_polygon = [[0.15, 0.15], [0.25, 0.15], [0.25, 0.25]]
changed_seed_polygon = [[0.2, 0.2], [0.3, 0.2], [0.3, 0.3]]
replacement_output_polygon = [[0.22, 0.22], [0.32, 0.22], [0.32, 0.32]]
monkeypatch.setattr("services.propagation_task_runner.download_file", lambda object_name: b"jpeg")
monkeypatch.setattr("services.propagation_task_runner.publish_task_progress_event", lambda event_task: None)
propagate_calls = []
def fake_propagate_video(model, frame_paths, source_frame_index, seed, direction, max_frames):
propagate_calls.append(seed["polygons"][0])
output_polygon = replacement_output_polygon if seed["polygons"][0] == changed_seed_polygon else first_output_polygon
return [
{"frame_index": 0, "polygons": [seed["polygons"][0]], "scores": [0.9]},
{"frame_index": 1, "polygons": [output_polygon], "scores": [0.8]},
]
monkeypatch.setattr("services.propagation_task_runner.sam_registry.propagate_video", fake_propagate_video)
first_result = run_propagate_project_task(db_session, make_task(seed_polygon).id)
assert first_result["created_annotation_count"] == 1
assert len(propagate_calls) == 1
unchanged_result = run_propagate_project_task(db_session, make_task(seed_polygon).id)
assert unchanged_result["created_annotation_count"] == 0
assert unchanged_result["skipped_seed_count"] == 1
assert len(propagate_calls) == 1
assert db_session.query(Annotation).filter(Annotation.project_id == project["id"]).count() == 1
changed_result = run_propagate_project_task(db_session, make_task(changed_seed_polygon).id)
assert changed_result["created_annotation_count"] == 1
assert changed_result["deleted_annotation_count"] == 1
assert len(propagate_calls) == 2
annotations = db_session.query(Annotation).filter(Annotation.project_id == project["id"]).all()
assert len(annotations) == 1
assert annotations[0].mask_data["polygons"] == [replacement_output_polygon]
assert annotations[0].mask_data["source_annotation_id"] == 7
def test_propagation_task_runner_replaces_legacy_or_different_weight_results(client, db_session, monkeypatch):
project = client.post("/api/projects", json={"name": "Propagation Legacy Cleanup"}).json()
frames = [
client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": idx,
"image_url": f"frames/{idx}.jpg",
"width": 640,
"height": 360,
}).json()
for idx in range(2)
]
seed_polygon = [[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]
output_polygon = [[0.15, 0.15], [0.25, 0.15], [0.25, 0.25]]
db_session.add(Annotation(
project_id=project["id"],
frame_id=frames[1]["id"],
mask_data={
"polygons": [[[0.12, 0.12], [0.22, 0.12], [0.22, 0.22]]],
"label": "胆囊",
"color": "#ff0000",
"source": "sam2.1_hiera_tiny_propagation",
"propagated_from_frame_id": frames[0]["id"],
"propagation_seed_key": "mask:temporary-front-end-id",
"propagation_direction": "forward",
},
bbox=[0.12, 0.12, 0.1, 0.1],
))
db_session.commit()
task = ProcessingTask(
task_type="propagate_masks",
status="queued",
progress=0,
project_id=project["id"],
payload={
"project_id": project["id"],
"frame_id": frames[0]["id"],
"model": "sam2.1_hiera_small",
"include_source": False,
"save_annotations": True,
"steps": [{
"direction": "forward",
"max_frames": 2,
"seed": {
"polygons": [seed_polygon],
"label": "胆囊",
"color": "#ff0000",
"source_annotation_id": 7,
"source_mask_id": "annotation-7",
},
}],
},
)
db_session.add(task)
db_session.commit()
db_session.refresh(task)
monkeypatch.setattr("services.propagation_task_runner.download_file", lambda object_name: b"jpeg")
monkeypatch.setattr("services.propagation_task_runner.publish_task_progress_event", lambda event_task: None)
monkeypatch.setattr("services.propagation_task_runner.sam_registry.propagate_video", lambda model, frame_paths, source_frame_index, seed, direction, max_frames: [
{"frame_index": 0, "polygons": [seed["polygons"][0]], "scores": [0.9]},
{"frame_index": 1, "polygons": [output_polygon], "scores": [0.8]},
])
result = run_propagate_project_task(db_session, task.id)
assert result["created_annotation_count"] == 1
assert result["deleted_annotation_count"] == 1
annotations = db_session.query(Annotation).filter(Annotation.project_id == project["id"]).all()
assert len(annotations) == 1
assert annotations[0].mask_data["source"] == "sam2.1_hiera_small_propagation"
assert annotations[0].mask_data["source_annotation_id"] == 7
assert annotations[0].mask_data["polygons"] == [output_polygon]
def test_propagation_task_runner_replaces_downstream_result_from_middle_frame_manual_seed(client, db_session, monkeypatch):
project = client.post("/api/projects", json={"name": "Propagation Middle Frame Replacement"}).json()
frames = [
client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": idx,
"image_url": f"frames/{idx}.jpg",
"width": 640,
"height": 360,
}).json()
for idx in range(3)
]
old_downstream_polygon = [[0.18, 0.18], [0.28, 0.18], [0.28, 0.28]]
replacement_seed_polygon = [[0.16, 0.16], [0.26, 0.16], [0.26, 0.26]]
replacement_downstream_polygon = [[0.19, 0.19], [0.29, 0.19], [0.29, 0.29]]
db_session.add(Annotation(
project_id=project["id"],
frame_id=frames[2]["id"],
template_id=3,
mask_data={
"polygons": [old_downstream_polygon],
"label": "胆囊",
"color": "#ff0000",
"class": {"id": "c1", "name": "胆囊", "color": "#ff0000"},
"source": "sam2.1_hiera_tiny_propagation",
"propagated_from_frame_id": frames[0]["id"],
"propagation_seed_key": "annotation:7",
"propagation_seed_signature": "old-signature",
"propagation_direction": "forward",
"source_annotation_id": 7,
"source_mask_id": "annotation-7",
},
bbox=[0.18, 0.18, 0.1, 0.1],
))
db_session.commit()
task = ProcessingTask(
task_type="propagate_masks",
status="queued",
progress=0,
project_id=project["id"],
payload={
"project_id": project["id"],
"frame_id": frames[1]["id"],
"model": "sam2.1_hiera_tiny",
"include_source": False,
"save_annotations": True,
"steps": [{
"direction": "forward",
"max_frames": 2,
"seed": {
"polygons": [replacement_seed_polygon],
"label": "胆囊",
"color": "#ff0000",
"source_annotation_id": 20,
"source_mask_id": "annotation-20",
},
}],
},
)
db_session.add(task)
db_session.commit()
db_session.refresh(task)
monkeypatch.setattr("services.propagation_task_runner.download_file", lambda object_name: b"jpeg")
monkeypatch.setattr("services.propagation_task_runner.publish_task_progress_event", lambda event_task: None)
monkeypatch.setattr("services.propagation_task_runner.sam_registry.propagate_video", lambda model, frame_paths, source_frame_index, seed, direction, max_frames: [
{"frame_index": 0, "polygons": [seed["polygons"][0]], "scores": [0.9]},
{"frame_index": 1, "polygons": [replacement_downstream_polygon], "scores": [0.8]},
])
result = run_propagate_project_task(db_session, task.id)
assert result["created_annotation_count"] == 1
assert result["deleted_annotation_count"] == 1
annotations = db_session.query(Annotation).filter(Annotation.project_id == project["id"]).all()
assert len(annotations) == 1
assert annotations[0].frame_id == frames[2]["id"]
assert annotations[0].mask_data["polygons"] == [replacement_downstream_polygon]
assert annotations[0].mask_data["source_annotation_id"] == 20
assert annotations[0].mask_data["source_mask_id"] == "annotation-20"
def test_propagation_task_runner_replaces_forward_result_when_middle_frame_propagates_backward(client, db_session, monkeypatch):
project = client.post("/api/projects", json={"name": "Propagation Backward Middle Replacement"}).json()
frames = [
client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": idx,
"image_url": f"frames/{idx}.jpg",
"width": 640,
"height": 360,
}).json()
for idx in range(3)
]
old_upstream_polygon = [[0.12, 0.12], [0.22, 0.12], [0.22, 0.22]]
replacement_seed_polygon = [[0.16, 0.16], [0.26, 0.16], [0.26, 0.26]]
replacement_upstream_polygon = [[0.13, 0.13], [0.23, 0.13], [0.23, 0.23]]
db_session.add(Annotation(
project_id=project["id"],
frame_id=frames[0]["id"],
mask_data={
"polygons": [old_upstream_polygon],
"label": "胆囊",
"color": "#ff0000",
"source": "sam2.1_hiera_tiny_propagation",
"propagated_from_frame_id": frames[0]["id"],
"propagation_seed_key": "annotation:7",
"propagation_seed_signature": "old-signature",
"propagation_direction": "forward",
"source_annotation_id": 7,
"source_mask_id": "annotation-7",
},
bbox=[0.12, 0.12, 0.1, 0.1],
))
db_session.commit()
task = ProcessingTask(
task_type="propagate_masks",
status="queued",
progress=0,
project_id=project["id"],
payload={
"project_id": project["id"],
"frame_id": frames[1]["id"],
"model": "sam2.1_hiera_tiny",
"include_source": False,
"save_annotations": True,
"steps": [{
"direction": "backward",
"max_frames": 2,
"seed": {
"polygons": [replacement_seed_polygon],
"label": "胆囊",
"color": "#ff0000",
"source_annotation_id": 20,
"source_mask_id": "annotation-20",
},
}],
},
)
db_session.add(task)
db_session.commit()
db_session.refresh(task)
monkeypatch.setattr("services.propagation_task_runner.download_file", lambda object_name: b"jpeg")
monkeypatch.setattr("services.propagation_task_runner.publish_task_progress_event", lambda event_task: None)
monkeypatch.setattr("services.propagation_task_runner.sam_registry.propagate_video", lambda model, frame_paths, source_frame_index, seed, direction, max_frames: [
{"frame_index": 0, "polygons": [replacement_upstream_polygon], "scores": [0.8]},
{"frame_index": 1, "polygons": [seed["polygons"][0]], "scores": [0.9]},
])
result = run_propagate_project_task(db_session, task.id)
assert result["created_annotation_count"] == 1
assert result["deleted_annotation_count"] == 1
annotations = db_session.query(Annotation).filter(Annotation.project_id == project["id"]).all()
assert len(annotations) == 1
assert annotations[0].frame_id == frames[0]["id"]
assert annotations[0].mask_data["polygons"] == [replacement_upstream_polygon]
assert annotations[0].mask_data["propagation_direction"] == "backward"
assert annotations[0].mask_data["source_annotation_id"] == 20
def test_propagation_task_runner_skips_unmodified_propagated_seed_on_overlapping_frames(client, db_session, monkeypatch):
project = client.post("/api/projects", json={"name": "Propagation Overlap Skip"}).json()
frames = [
client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": idx,
"image_url": f"frames/{idx}.jpg",
"width": 640,
"height": 360,
}).json()
for idx in range(3)
]
original_seed_polygon = [[0.1, 0.1], [0.2, 0.1], [0.2, 0.2]]
propagated_seed_polygon = [[0.14, 0.14], [0.24, 0.14], [0.24, 0.24]]
downstream_polygon = [[0.18, 0.18], [0.28, 0.18], [0.28, 0.28]]
inherited_signature = _seed_signature({
"polygons": [original_seed_polygon],
"label": "胆囊",
"color": "#ff0000",
"source_annotation_id": 7,
"source_mask_id": "annotation-7",
})
db_session.add(Annotation(
project_id=project["id"],
frame_id=frames[2]["id"],
mask_data={
"polygons": [downstream_polygon],
"label": "胆囊",
"color": "#ff0000",
"source": "sam2.1_hiera_tiny_propagation",
"propagated_from_frame_id": frames[0]["id"],
"propagation_seed_key": "annotation:7",
"propagation_seed_signature": inherited_signature,
"propagation_direction": "forward",
"source_annotation_id": 7,
"source_mask_id": "annotation-7",
},
bbox=[0.18, 0.18, 0.1, 0.1],
))
db_session.commit()
task = ProcessingTask(
task_type="propagate_masks",
status="queued",
progress=0,
project_id=project["id"],
payload={
"project_id": project["id"],
"frame_id": frames[1]["id"],
"model": "sam2.1_hiera_tiny",
"include_source": False,
"save_annotations": True,
"steps": [{
"direction": "forward",
"max_frames": 2,
"seed": {
"polygons": [propagated_seed_polygon],
"label": "胆囊",
"color": "#ff0000",
"source_annotation_id": 7,
"source_mask_id": "annotation-7",
"propagation_seed_signature": inherited_signature,
},
}],
},
)
db_session.add(task)
db_session.commit()
db_session.refresh(task)
propagate_calls = []
monkeypatch.setattr("services.propagation_task_runner.download_file", lambda object_name: b"jpeg")
monkeypatch.setattr("services.propagation_task_runner.publish_task_progress_event", lambda event_task: None)
monkeypatch.setattr("services.propagation_task_runner.sam_registry.propagate_video", lambda *args, **kwargs: propagate_calls.append(args) or [])
result = run_propagate_project_task(db_session, task.id)
assert result["created_annotation_count"] == 0
assert result["deleted_annotation_count"] == 0
assert result["skipped_seed_count"] == 1
assert propagate_calls == []
annotations = db_session.query(Annotation).filter(Annotation.project_id == project["id"]).all()
assert len(annotations) == 1
assert annotations[0].mask_data["polygons"] == [downstream_polygon]
def test_predict_validation_errors(client, monkeypatch):
project, _, _ = _create_project_and_frame(client)
assert client.post("/api/ai/predict", json={
"image_id": 999,
"prompt_type": "point",
"prompt_data": [[0.5, 0.5]],
}).status_code == 404
frame = client.post(f"/api/projects/{project['id']}/frames", json={
"project_id": project["id"],
"frame_index": 1,
"image_url": "frames/1.jpg",
}).json()
monkeypatch.setattr("routers.ai._load_frame_image", lambda frame: np.zeros((10, 10, 3), dtype=np.uint8))
assert client.post("/api/ai/predict", json={
"image_id": frame["id"],
"prompt_type": "box",
"prompt_data": [0.1, 0.2],
}).status_code == 400
def test_save_annotation_validates_project_and_frame(client):
project, frame, template = _create_project_and_frame(client)
saved = client.post("/api/ai/annotate", json={
"project_id": project["id"],
"frame_id": frame["id"],
"template_id": template["id"],
"mask_data": {"polygons": [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]]},
"points": [[0.5, 0.5]],
"bbox": [0.1, 0.1, 0.8, 0.8],
})
assert saved.status_code == 201
assert saved.json()["project_id"] == project["id"]
listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
assert listing.status_code == 200
assert listing.json()[0]["id"] == saved.json()["id"]
frame_listing = client.get(f"/api/ai/annotations?project_id={project['id']}&frame_id={frame['id']}")
assert frame_listing.status_code == 200
assert len(frame_listing.json()) == 1
missing_project = client.post("/api/ai/annotate", json={"project_id": 999})
assert missing_project.status_code == 404
missing_frame = client.post("/api/ai/annotate", json={
"project_id": project["id"],
"frame_id": 999,
})
assert missing_frame.status_code == 404
missing_project_list = client.get("/api/ai/annotations?project_id=999")
assert missing_project_list.status_code == 404
def test_update_and_delete_annotation(client):
project, frame, template = _create_project_and_frame(client)
saved = client.post("/api/ai/annotate", json={
"project_id": project["id"],
"frame_id": frame["id"],
"template_id": template["id"],
"mask_data": {
"polygons": [[[0.1, 0.1], [0.9, 0.1], [0.9, 0.9]]],
"label": "AI Mask",
"color": "#06b6d4",
},
"points": [[0.5, 0.5]],
"bbox": [0.1, 0.1, 0.8, 0.8],
}).json()
updated = client.patch(f"/api/ai/annotations/{saved['id']}", json={
"template_id": template["id"],
"mask_data": {
"polygons": [[[0.2, 0.2], [0.8, 0.2], [0.8, 0.8]]],
"label": "胆囊",
"color": "#ff0000",
"class": {"id": "c1", "name": "胆囊", "color": "#ff0000", "zIndex": 20},
},
"points": [[0.4, 0.4]],
"bbox": [0.2, 0.2, 0.6, 0.6],
})
assert updated.status_code == 200
body = updated.json()
assert body["mask_data"]["label"] == "胆囊"
assert body["mask_data"]["class"]["id"] == "c1"
assert body["points"] == [[0.4, 0.4]]
assert body["bbox"] == [0.2, 0.2, 0.6, 0.6]
listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
assert listing.status_code == 200
assert listing.json()[0]["mask_data"]["class"]["name"] == "胆囊"
deleted = client.delete(f"/api/ai/annotations/{saved['id']}")
assert deleted.status_code == 204
empty_listing = client.get(f"/api/ai/annotations?project_id={project['id']}")
assert empty_listing.status_code == 200
assert empty_listing.json() == []
def test_update_and_delete_annotation_validation(client):
project, frame, template = _create_project_and_frame(client)
saved = client.post("/api/ai/annotate", json={
"project_id": project["id"],
"frame_id": frame["id"],
"template_id": template["id"],
}).json()
assert client.patch("/api/ai/annotations/999", json={"bbox": [0, 0, 1, 1]}).status_code == 404
assert client.delete("/api/ai/annotations/999").status_code == 404
assert client.patch(
f"/api/ai/annotations/{saved['id']}",
json={"template_id": 999},
).status_code == 404
def test_import_gt_mask_creates_annotations_with_seed_points(client):
project, frame, template = _create_project_and_frame(client)
mask = np.zeros((360, 640), dtype=np.uint8)
cv2.rectangle(mask, (100, 80), (260, 220), 255, thickness=-1)
ok, encoded = cv2.imencode(".png", mask)
assert ok
response = client.post(
"/api/ai/import-gt-mask",
data={
"project_id": str(project["id"]),
"frame_id": str(frame["id"]),
"template_id": str(template["id"]),
"label": "Imported GT",
"color": "#22c55e",
},
files={"file": ("mask.png", encoded.tobytes(), "image/png")},
)
assert response.status_code == 201
body = response.json()
assert len(body) == 1
assert body[0]["project_id"] == project["id"]
assert body[0]["frame_id"] == frame["id"]
assert body[0]["template_id"] == template["id"]
assert body[0]["mask_data"]["label"] == "Imported GT"
assert body[0]["mask_data"]["source"] == "gt_mask"
assert body[0]["mask_data"]["gt_label_value"] == 255
assert len(body[0]["mask_data"]["polygons"][0]) >= 3
assert len(body[0]["points"]) == 1
assert 0.0 <= body[0]["points"][0][0] <= 1.0
assert 0.0 <= body[0]["points"][0][1] <= 1.0
def test_import_gt_mask_splits_label_values(client):
project, frame, _ = _create_project_and_frame(client)
mask = np.zeros((360, 640), dtype=np.uint8)
cv2.rectangle(mask, (20, 20), (120, 120), 1, thickness=-1)
cv2.rectangle(mask, (220, 80), (320, 180), 2, thickness=-1)
ok, encoded = cv2.imencode(".png", mask)
assert ok
response = client.post(
"/api/ai/import-gt-mask",
data={
"project_id": str(project["id"]),
"frame_id": str(frame["id"]),
"label": "GT Class",
},
files={"file": ("labels.png", encoded.tobytes(), "image/png")},
)
assert response.status_code == 201
body = sorted(response.json(), key=lambda item: item["mask_data"]["gt_label_value"])
assert [item["mask_data"]["gt_label_value"] for item in body] == [1, 2]
assert [item["mask_data"]["label"] for item in body] == ["GT Class 1", "GT Class 2"]
assert all(len(item["points"]) == 1 for item in body)
Reference in New Issue View Git Blame Copy Permalink