admin
8a9247075e
功能增加: - 将视频导入和生成帧拆成两个明确动作，项目库生成帧时选择 FPS，工作区不再自动触发拆帧。 - 为工作区新增调整多边形工具，支持选中 mask、拖动顶点、边中点插点、双击边界按位置插点，并保留多 polygon 子区域编辑。 - 打通 AI 页 SAM2/SAM3 结果到工作区的联动，生成 mask 后自动选中，可在右侧分类树换标签，并推送到工作区继续编辑。 - 增强 Dashboard WebSocket 连接状态与心跳，使用真实 onopen/onclose/onerror 状态驱动前端显示。 - 完善 SAM3 external worker 适配，支持 box prompt、semantic 请求级阈值和 video tracker 路径。 bugfix: - 修复 SAM2 文本语义误走自动分割的问题，改为提示使用点提示或切换 SAM3。 - 修复 SAM2 多候选重叠显示的问题，点提示和 auto fallback 默认只采用最高分候选。 - 修复 SAM2 反向点看起来无效的问题，带负点时启用背景过滤，过滤为空时移除旧候选。 - 修复 SAM3 单个 2D mask 结果无法转 polygon、低阈值 semantic 返回被默认阈值吞掉的问题。 - 修复 AI 页 mask 未选中导致分类树无法修改 SAM2 结果标签的问题。 测试和文档: - 补充 CanvasArea、AISegmentation、ProjectLibrary、VideoWorkspace、Dashboard、websocket 和 SAM engine/API 测试。 - 新增 backend/tests/test_sam2_engine.py，覆盖 SAM2 单候选请求和 auto fallback 行为。 - 更新 README、AGENTS 和 doc 需求/设计/接口/测试矩阵，按当前实现冻结功能状态。
721 lines
28 KiB
Python
721 lines
28 KiB
Python
"""AI inference endpoints using selectable SAM runtimes."""
|
|
|
|
import logging
|
|
import tempfile
|
|
from pathlib import Path
|
|
from typing import Any, List
|
|
|
|
import cv2
|
|
import numpy as np
|
|
from fastapi import APIRouter, Depends, File, Form, HTTPException, Response, UploadFile, status
|
|
from sqlalchemy.orm import Session
|
|
|
|
from database import get_db
|
|
from minio_client import download_file
|
|
from models import Project, Frame, Template, Annotation
|
|
from schemas import (
|
|
AiRuntimeStatus,
|
|
PredictRequest,
|
|
PredictResponse,
|
|
PropagateRequest,
|
|
PropagateResponse,
|
|
AnnotationOut,
|
|
AnnotationCreate,
|
|
AnnotationUpdate,
|
|
)
|
|
from services.sam_registry import ModelUnavailableError, sam_registry
|
|
|
|
logger = logging.getLogger(__name__)
|
|
router = APIRouter(prefix="/api/ai", tags=["AI"])
|
|
|
|
|
|
def _load_frame_image(frame: Frame) -> np.ndarray:
|
|
"""Download a frame from MinIO and decode it to an RGB numpy array."""
|
|
try:
|
|
data = download_file(frame.image_url)
|
|
arr = np.frombuffer(data, dtype=np.uint8)
|
|
img = cv2.imdecode(arr, cv2.IMREAD_COLOR)
|
|
if img is None:
|
|
raise ValueError("OpenCV could not decode image")
|
|
return cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
|
except Exception as exc: # noqa: BLE001
|
|
logger.error("Failed to load frame image: %s", exc)
|
|
raise HTTPException(status_code=500, detail="Failed to load frame image") from exc
|
|
|
|
|
|
def _normalized_contour(contour: np.ndarray, width: int, height: int) -> list[list[float]]:
|
|
"""Approximate a contour and convert it to normalized polygon coordinates."""
|
|
arc_length = cv2.arcLength(contour, True)
|
|
epsilon = max(1.0, arc_length * 0.01)
|
|
approx = cv2.approxPolyDP(contour, epsilon, True)
|
|
points = approx.reshape(-1, 2)
|
|
if len(points) < 3:
|
|
points = contour.reshape(-1, 2)
|
|
return [
|
|
[
|
|
min(max(float(x) / max(width, 1), 0.0), 1.0),
|
|
min(max(float(y) / max(height, 1), 0.0), 1.0),
|
|
]
|
|
for x, y in points
|
|
]
|
|
|
|
|
|
def _contour_bbox(contour: np.ndarray, width: int, height: int) -> list[float]:
|
|
x, y, w, h = cv2.boundingRect(contour)
|
|
return [
|
|
min(max(float(x) / max(width, 1), 0.0), 1.0),
|
|
min(max(float(y) / max(height, 1), 0.0), 1.0),
|
|
min(max(float(w) / max(width, 1), 0.0), 1.0),
|
|
min(max(float(h) / max(height, 1), 0.0), 1.0),
|
|
]
|
|
|
|
|
|
def _polygon_bbox(polygon: list[list[float]]) -> list[float]:
|
|
xs = [_clamp01(point[0]) for point in polygon]
|
|
ys = [_clamp01(point[1]) for point in polygon]
|
|
left, right = min(xs), max(xs)
|
|
top, bottom = min(ys), max(ys)
|
|
return [left, top, max(right - left, 0.0), max(bottom - top, 0.0)]
|
|
|
|
|
|
def _frame_window(
|
|
frames: list[Frame],
|
|
source_position: int,
|
|
direction: str,
|
|
max_frames: int,
|
|
) -> tuple[list[Frame], int]:
|
|
count = max(1, min(max_frames, len(frames)))
|
|
if direction == "backward":
|
|
start = max(0, source_position - count + 1)
|
|
return frames[start:source_position + 1], source_position - start
|
|
if direction == "both":
|
|
before = (count - 1) // 2
|
|
after = count - 1 - before
|
|
start = max(0, source_position - before)
|
|
end = min(len(frames), source_position + after + 1)
|
|
while end - start < count and start > 0:
|
|
start -= 1
|
|
while end - start < count and end < len(frames):
|
|
end += 1
|
|
return frames[start:end], source_position - start
|
|
end = min(len(frames), source_position + count)
|
|
return frames[source_position:end], 0
|
|
|
|
|
|
def _write_frame_sequence(frames: list[Frame], directory: Path) -> list[str]:
|
|
paths = []
|
|
for index, frame in enumerate(frames):
|
|
data = download_file(frame.image_url)
|
|
path = directory / f"frame_{index:06d}.jpg"
|
|
path.write_bytes(data)
|
|
paths.append(str(path))
|
|
return paths
|
|
|
|
|
|
def _component_seed_point(component_mask: np.ndarray, width: int, height: int) -> list[float]:
|
|
"""Reduce a binary component to one positive prompt point using distance transform."""
|
|
dist = cv2.distanceTransform(component_mask.astype(np.uint8), cv2.DIST_L2, 5)
|
|
_, _, _, max_loc = cv2.minMaxLoc(dist)
|
|
x, y = max_loc
|
|
return [
|
|
min(max(float(x) / max(width, 1), 0.0), 1.0),
|
|
min(max(float(y) / max(height, 1), 0.0), 1.0),
|
|
]
|
|
|
|
|
|
def _clamp01(value: float) -> float:
|
|
return min(max(float(value), 0.0), 1.0)
|
|
|
|
|
|
def _point_in_polygon(point: list[float], polygon: list[list[float]]) -> bool:
|
|
"""Return whether a normalized point is inside a normalized polygon."""
|
|
if len(polygon) < 3:
|
|
return False
|
|
x, y = point
|
|
inside = False
|
|
j = len(polygon) - 1
|
|
for i, current in enumerate(polygon):
|
|
xi, yi = current
|
|
xj, yj = polygon[j]
|
|
intersects = ((yi > y) != (yj > y)) and (
|
|
x < (xj - xi) * (y - yi) / ((yj - yi) or 1e-9) + xi
|
|
)
|
|
if intersects:
|
|
inside = not inside
|
|
j = i
|
|
return inside
|
|
|
|
|
|
def _crop_bounds_from_points(points: list[list[float]], margin: float) -> tuple[float, float, float, float]:
|
|
xs = [_clamp01(point[0]) for point in points]
|
|
ys = [_clamp01(point[1]) for point in points]
|
|
x1 = max(0.0, min(xs) - margin)
|
|
y1 = max(0.0, min(ys) - margin)
|
|
x2 = min(1.0, max(xs) + margin)
|
|
y2 = min(1.0, max(ys) + margin)
|
|
if x2 - x1 < 0.05:
|
|
center = (x1 + x2) / 2
|
|
x1 = max(0.0, center - 0.025)
|
|
x2 = min(1.0, center + 0.025)
|
|
if y2 - y1 < 0.05:
|
|
center = (y1 + y2) / 2
|
|
y1 = max(0.0, center - 0.025)
|
|
y2 = min(1.0, center + 0.025)
|
|
return x1, y1, x2, y2
|
|
|
|
|
|
def _crop_image(image: np.ndarray, bounds: tuple[float, float, float, float]) -> np.ndarray:
|
|
height, width = image.shape[:2]
|
|
x1, y1, x2, y2 = bounds
|
|
left = int(round(x1 * width))
|
|
top = int(round(y1 * height))
|
|
right = max(left + 1, int(round(x2 * width)))
|
|
bottom = max(top + 1, int(round(y2 * height)))
|
|
return image[top:bottom, left:right]
|
|
|
|
|
|
def _to_crop_point(point: list[float], bounds: tuple[float, float, float, float]) -> list[float]:
|
|
x1, y1, x2, y2 = bounds
|
|
return [
|
|
_clamp01((float(point[0]) - x1) / max(x2 - x1, 1e-9)),
|
|
_clamp01((float(point[1]) - y1) / max(y2 - y1, 1e-9)),
|
|
]
|
|
|
|
|
|
def _from_crop_polygon(
|
|
polygon: list[list[float]],
|
|
bounds: tuple[float, float, float, float],
|
|
) -> list[list[float]]:
|
|
x1, y1, x2, y2 = bounds
|
|
return [
|
|
[
|
|
_clamp01(x1 + float(point[0]) * (x2 - x1)),
|
|
_clamp01(y1 + float(point[1]) * (y2 - y1)),
|
|
]
|
|
for point in polygon
|
|
]
|
|
|
|
|
|
def _filter_predictions(
|
|
polygons: list[list[list[float]]],
|
|
scores: list[float],
|
|
options: dict[str, Any],
|
|
negative_points: list[list[float]] | None = None,
|
|
) -> tuple[list[list[list[float]]], list[float]]:
|
|
if not options.get("auto_filter_background"):
|
|
return polygons, scores
|
|
|
|
min_score = float(options.get("min_score", 0.0) or 0.0)
|
|
next_polygons: list[list[list[float]]] = []
|
|
next_scores: list[float] = []
|
|
for index, polygon in enumerate(polygons):
|
|
score = scores[index] if index < len(scores) else 0.0
|
|
if score < min_score:
|
|
continue
|
|
if negative_points and any(_point_in_polygon(point, polygon) for point in negative_points):
|
|
continue
|
|
next_polygons.append(polygon)
|
|
next_scores.append(score)
|
|
return next_polygons, next_scores
|
|
|
|
|
|
@router.post(
|
|
"/predict",
|
|
response_model=PredictResponse,
|
|
summary="Run SAM inference with a prompt",
|
|
)
|
|
def predict(payload: PredictRequest, db: Session = Depends(get_db)) -> dict:
|
|
"""Execute selected SAM segmentation given an image and a prompt.
|
|
|
|
- **point**: `prompt_data` is either a list of `[[x, y], ...]` normalized
|
|
coordinates or `{ "points": [[x, y], ...], "labels": [1, 0, ...] }`.
|
|
- **box**: `prompt_data` is `[x1, y1, x2, y2]` normalized coordinates.
|
|
- **interactive**: `prompt_data` is `{ "box": [...], "points": [[x, y]], "labels": [1, 0] }`.
|
|
- **semantic**: SAM 3 text prompt when model=`sam3`; SAM 2 falls back to auto.
|
|
"""
|
|
frame = db.query(Frame).filter(Frame.id == payload.image_id).first()
|
|
if not frame:
|
|
raise HTTPException(status_code=404, detail="Frame not found")
|
|
|
|
image = _load_frame_image(frame)
|
|
prompt_type = payload.prompt_type.lower()
|
|
options = payload.options or {}
|
|
|
|
polygons: List[List[List[float]]] = []
|
|
scores: List[float] = []
|
|
negative_points: list[list[float]] = []
|
|
|
|
try:
|
|
if prompt_type == "point":
|
|
point_payload = payload.prompt_data
|
|
if isinstance(point_payload, dict):
|
|
points = point_payload.get("points")
|
|
labels = point_payload.get("labels")
|
|
else:
|
|
points = point_payload
|
|
labels = None
|
|
|
|
if not isinstance(points, list) or len(points) == 0:
|
|
raise HTTPException(status_code=400, detail="Invalid point prompt data")
|
|
if not isinstance(labels, list) or len(labels) != len(points):
|
|
labels = [1] * len(points)
|
|
negative_points = [
|
|
point for point, label in zip(points, labels) if label == 0
|
|
]
|
|
inference_image = image
|
|
inference_points = points
|
|
crop_bounds = None
|
|
if options.get("crop_to_prompt"):
|
|
margin = float(options.get("crop_margin", 0.25) or 0.25)
|
|
crop_bounds = _crop_bounds_from_points(points, margin)
|
|
inference_image = _crop_image(image, crop_bounds)
|
|
inference_points = [_to_crop_point(point, crop_bounds) for point in points]
|
|
polygons, scores = sam_registry.predict_points(payload.model, inference_image, inference_points, labels)
|
|
if crop_bounds:
|
|
polygons = [_from_crop_polygon(polygon, crop_bounds) for polygon in polygons]
|
|
|
|
elif prompt_type == "box":
|
|
box = payload.prompt_data
|
|
if not isinstance(box, list) or len(box) != 4:
|
|
raise HTTPException(status_code=400, detail="Invalid box prompt data")
|
|
inference_image = image
|
|
inference_box = box
|
|
crop_bounds = None
|
|
if options.get("crop_to_prompt"):
|
|
margin = float(options.get("crop_margin", 0.05) or 0.05)
|
|
crop_bounds = _crop_bounds_from_points([[box[0], box[1]], [box[2], box[3]]], margin)
|
|
inference_image = _crop_image(image, crop_bounds)
|
|
inference_box = [
|
|
*_to_crop_point([box[0], box[1]], crop_bounds),
|
|
*_to_crop_point([box[2], box[3]], crop_bounds),
|
|
]
|
|
polygons, scores = sam_registry.predict_box(payload.model, inference_image, inference_box)
|
|
if crop_bounds:
|
|
polygons = [_from_crop_polygon(polygon, crop_bounds) for polygon in polygons]
|
|
|
|
elif prompt_type == "interactive":
|
|
prompt = payload.prompt_data
|
|
if not isinstance(prompt, dict):
|
|
raise HTTPException(status_code=400, detail="Invalid interactive prompt data")
|
|
box = prompt.get("box")
|
|
points = prompt.get("points") or []
|
|
labels = prompt.get("labels")
|
|
if box is not None and (not isinstance(box, list) or len(box) != 4):
|
|
raise HTTPException(status_code=400, detail="Invalid interactive box prompt data")
|
|
if not isinstance(points, list):
|
|
raise HTTPException(status_code=400, detail="Invalid interactive point prompt data")
|
|
if not box and len(points) == 0:
|
|
raise HTTPException(status_code=400, detail="Interactive prompt requires a box or points")
|
|
if not isinstance(labels, list) or len(labels) != len(points):
|
|
labels = [1] * len(points)
|
|
negative_points = [
|
|
point for point, label in zip(points, labels) if label == 0
|
|
]
|
|
inference_image = image
|
|
inference_box = box
|
|
inference_points = points
|
|
crop_bounds = None
|
|
if options.get("crop_to_prompt"):
|
|
margin = float(options.get("crop_margin", 0.05) or 0.05)
|
|
crop_points = list(points)
|
|
if box:
|
|
crop_points.extend([[box[0], box[1]], [box[2], box[3]]])
|
|
crop_bounds = _crop_bounds_from_points(crop_points, margin)
|
|
inference_image = _crop_image(image, crop_bounds)
|
|
inference_points = [_to_crop_point(point, crop_bounds) for point in points]
|
|
if box:
|
|
inference_box = [
|
|
*_to_crop_point([box[0], box[1]], crop_bounds),
|
|
*_to_crop_point([box[2], box[3]], crop_bounds),
|
|
]
|
|
polygons, scores = sam_registry.predict_interactive(
|
|
payload.model,
|
|
inference_image,
|
|
inference_box,
|
|
inference_points,
|
|
labels,
|
|
)
|
|
if crop_bounds:
|
|
polygons = [_from_crop_polygon(polygon, crop_bounds) for polygon in polygons]
|
|
|
|
elif prompt_type == "semantic":
|
|
text = payload.prompt_data if isinstance(payload.prompt_data, str) else ""
|
|
min_score = options.get("min_score")
|
|
confidence_threshold = None
|
|
if min_score is not None:
|
|
try:
|
|
parsed_min_score = float(min_score)
|
|
if parsed_min_score > 0:
|
|
confidence_threshold = parsed_min_score
|
|
except (TypeError, ValueError):
|
|
confidence_threshold = None
|
|
polygons, scores = sam_registry.predict_semantic(
|
|
payload.model,
|
|
image,
|
|
text,
|
|
confidence_threshold=confidence_threshold,
|
|
)
|
|
|
|
else:
|
|
raise HTTPException(status_code=400, detail=f"Unsupported prompt_type: {prompt_type}")
|
|
except ModelUnavailableError as exc:
|
|
raise HTTPException(status_code=503, detail=str(exc)) from exc
|
|
except NotImplementedError as exc:
|
|
raise HTTPException(status_code=400, detail=str(exc)) from exc
|
|
except ValueError as exc:
|
|
raise HTTPException(status_code=400, detail=str(exc)) from exc
|
|
|
|
polygons, scores = _filter_predictions(polygons, scores, options, negative_points)
|
|
logger.info(
|
|
"AI predict completed model=%s prompt_type=%s frame_id=%s polygons=%d",
|
|
payload.model or "default",
|
|
prompt_type,
|
|
payload.image_id,
|
|
len(polygons),
|
|
)
|
|
return {"polygons": polygons, "scores": scores}
|
|
|
|
|
|
@router.get(
|
|
"/models/status",
|
|
response_model=AiRuntimeStatus,
|
|
summary="Get SAM model and GPU runtime status",
|
|
)
|
|
def model_status(selected_model: str | None = None) -> dict:
|
|
"""Return real runtime availability for GPU, SAM 2, and SAM 3."""
|
|
try:
|
|
return sam_registry.runtime_status(selected_model)
|
|
except ValueError as exc:
|
|
raise HTTPException(status_code=400, detail=str(exc)) from exc
|
|
|
|
|
|
@router.post(
|
|
"/propagate",
|
|
response_model=PropagateResponse,
|
|
summary="Propagate one current-frame region across a video frame segment",
|
|
)
|
|
def propagate(payload: PropagateRequest, db: Session = Depends(get_db)) -> dict:
|
|
"""Track one selected region from the current frame across nearby frames.
|
|
|
|
SAM 2 uses the official video predictor with the selected mask as the seed.
|
|
SAM 3 uses the external Python 3.12 video tracker with the seed bbox.
|
|
"""
|
|
direction = payload.direction.lower()
|
|
if direction not in {"forward", "backward", "both"}:
|
|
raise HTTPException(status_code=400, detail="direction must be forward, backward, or both")
|
|
max_frames = max(1, min(int(payload.max_frames or 30), 500))
|
|
|
|
project = db.query(Project).filter(Project.id == payload.project_id).first()
|
|
if not project:
|
|
raise HTTPException(status_code=404, detail="Project not found")
|
|
|
|
source_frame = db.query(Frame).filter(
|
|
Frame.id == payload.frame_id,
|
|
Frame.project_id == payload.project_id,
|
|
).first()
|
|
if not source_frame:
|
|
raise HTTPException(status_code=404, detail="Frame not found")
|
|
|
|
seed = payload.seed.model_dump(exclude_none=True)
|
|
polygons = seed.get("polygons") or []
|
|
bbox = seed.get("bbox")
|
|
points = seed.get("points") or []
|
|
if not polygons and not bbox and not points:
|
|
raise HTTPException(status_code=400, detail="Propagation requires seed polygons, bbox, or points")
|
|
|
|
frames = db.query(Frame).filter(Frame.project_id == payload.project_id).order_by(Frame.frame_index).all()
|
|
source_position = next((index for index, frame in enumerate(frames) if frame.id == source_frame.id), None)
|
|
if source_position is None:
|
|
raise HTTPException(status_code=404, detail="Source frame is not in project frame sequence")
|
|
|
|
selected_frames, source_relative_index = _frame_window(frames, source_position, direction, max_frames)
|
|
if len(selected_frames) == 0:
|
|
raise HTTPException(status_code=400, detail="No frames available for propagation")
|
|
|
|
try:
|
|
with tempfile.TemporaryDirectory(prefix=f"seg_propagate_{payload.project_id}_") as tmpdir:
|
|
frame_paths = _write_frame_sequence(selected_frames, Path(tmpdir))
|
|
propagated = sam_registry.propagate_video(
|
|
payload.model,
|
|
frame_paths,
|
|
source_relative_index,
|
|
seed,
|
|
direction,
|
|
len(selected_frames),
|
|
)
|
|
except ModelUnavailableError as exc:
|
|
raise HTTPException(status_code=503, detail=str(exc)) from exc
|
|
except NotImplementedError as exc:
|
|
raise HTTPException(status_code=400, detail=str(exc)) from exc
|
|
except ValueError as exc:
|
|
raise HTTPException(status_code=400, detail=str(exc)) from exc
|
|
except Exception as exc: # noqa: BLE001
|
|
logger.error("Video propagation failed: %s", exc)
|
|
raise HTTPException(status_code=500, detail=f"Video propagation failed: {exc}") from exc
|
|
|
|
created: list[Annotation] = []
|
|
if payload.save_annotations:
|
|
class_metadata = seed.get("class_metadata")
|
|
template_id = seed.get("template_id")
|
|
label = seed.get("label") or "Propagated Mask"
|
|
color = seed.get("color") or "#06b6d4"
|
|
model_id = sam_registry.normalize_model_id(payload.model)
|
|
|
|
for frame_result in propagated:
|
|
relative_index = int(frame_result.get("frame_index", -1))
|
|
if relative_index < 0 or relative_index >= len(selected_frames):
|
|
continue
|
|
frame = selected_frames[relative_index]
|
|
if not payload.include_source and frame.id == source_frame.id:
|
|
continue
|
|
result_polygons = frame_result.get("polygons") or []
|
|
scores = frame_result.get("scores") or []
|
|
for polygon_index, polygon in enumerate(result_polygons):
|
|
if len(polygon) < 3:
|
|
continue
|
|
annotation = Annotation(
|
|
project_id=payload.project_id,
|
|
frame_id=frame.id,
|
|
template_id=template_id,
|
|
mask_data={
|
|
"polygons": [polygon],
|
|
"label": label,
|
|
"color": color,
|
|
"source": f"{model_id}_propagation",
|
|
"propagated_from_frame_id": source_frame.id,
|
|
"propagated_from_frame_index": source_frame.frame_index,
|
|
"score": scores[polygon_index] if polygon_index < len(scores) else None,
|
|
**({"class": class_metadata} if class_metadata else {}),
|
|
},
|
|
points=None,
|
|
bbox=_polygon_bbox(polygon),
|
|
)
|
|
db.add(annotation)
|
|
created.append(annotation)
|
|
|
|
db.commit()
|
|
for annotation in created:
|
|
db.refresh(annotation)
|
|
|
|
return {
|
|
"model": sam_registry.normalize_model_id(payload.model),
|
|
"direction": direction,
|
|
"source_frame_id": source_frame.id,
|
|
"processed_frame_count": len(selected_frames),
|
|
"created_annotation_count": len(created),
|
|
"annotations": created,
|
|
}
|
|
|
|
|
|
@router.post(
|
|
"/auto",
|
|
response_model=PredictResponse,
|
|
summary="Run automatic segmentation",
|
|
)
|
|
def auto_segment(image_id: int, db: Session = Depends(get_db)) -> dict:
|
|
"""Run automatic mask generation on a frame using a grid of point prompts."""
|
|
frame = db.query(Frame).filter(Frame.id == image_id).first()
|
|
if not frame:
|
|
raise HTTPException(status_code=404, detail="Frame not found")
|
|
|
|
image = _load_frame_image(frame)
|
|
try:
|
|
polygons, scores = sam_registry.predict_auto(None, image)
|
|
except ModelUnavailableError as exc:
|
|
raise HTTPException(status_code=503, detail=str(exc)) from exc
|
|
|
|
return {"polygons": polygons, "scores": scores}
|
|
|
|
|
|
@router.post(
|
|
"/annotate",
|
|
response_model=AnnotationOut,
|
|
status_code=status.HTTP_201_CREATED,
|
|
summary="Save an AI-generated annotation",
|
|
)
|
|
def save_annotation(
|
|
payload: AnnotationCreate,
|
|
db: Session = Depends(get_db),
|
|
) -> Annotation:
|
|
"""Persist an annotation (mask, points, bbox) into the database."""
|
|
project = db.query(Project).filter(Project.id == payload.project_id).first()
|
|
if not project:
|
|
raise HTTPException(status_code=404, detail="Project not found")
|
|
|
|
if payload.frame_id:
|
|
frame = db.query(Frame).filter(Frame.id == payload.frame_id).first()
|
|
if not frame:
|
|
raise HTTPException(status_code=404, detail="Frame not found")
|
|
|
|
annotation = Annotation(**payload.model_dump())
|
|
db.add(annotation)
|
|
db.commit()
|
|
db.refresh(annotation)
|
|
logger.info("Saved annotation id=%s project_id=%s", annotation.id, annotation.project_id)
|
|
return annotation
|
|
|
|
|
|
@router.post(
|
|
"/import-gt-mask",
|
|
response_model=List[AnnotationOut],
|
|
status_code=status.HTTP_201_CREATED,
|
|
summary="Import a GT mask and reduce components to editable point regions",
|
|
)
|
|
async def import_gt_mask(
|
|
project_id: int = Form(...),
|
|
frame_id: int = Form(...),
|
|
template_id: int | None = Form(None),
|
|
label: str = Form("GT Mask"),
|
|
color: str = Form("#22c55e"),
|
|
file: UploadFile = File(...),
|
|
db: Session = Depends(get_db),
|
|
) -> List[Annotation]:
|
|
"""Convert a binary/label mask image into persisted polygon annotations.
|
|
|
|
Each connected component becomes one annotation. The `points` field stores a
|
|
positive seed point at the component's distance-transform center, which gives
|
|
the frontend an editable point-region representation instead of a static
|
|
bitmap layer.
|
|
"""
|
|
project = db.query(Project).filter(Project.id == project_id).first()
|
|
if not project:
|
|
raise HTTPException(status_code=404, detail="Project not found")
|
|
|
|
frame = db.query(Frame).filter(Frame.id == frame_id, Frame.project_id == project_id).first()
|
|
if not frame:
|
|
raise HTTPException(status_code=404, detail="Frame not found")
|
|
|
|
if template_id is not None:
|
|
template = db.query(Template).filter(Template.id == template_id).first()
|
|
if not template:
|
|
raise HTTPException(status_code=404, detail="Template not found")
|
|
|
|
data = await file.read()
|
|
image = cv2.imdecode(np.frombuffer(data, dtype=np.uint8), cv2.IMREAD_GRAYSCALE)
|
|
if image is None:
|
|
raise HTTPException(status_code=400, detail="Invalid mask image")
|
|
|
|
width = int(frame.width or image.shape[1])
|
|
height = int(frame.height or image.shape[0])
|
|
label_values = [int(value) for value in np.unique(image) if int(value) > 0]
|
|
if not label_values:
|
|
raise HTTPException(status_code=400, detail="No foreground mask regions found")
|
|
has_multiple_labels = len(label_values) > 1
|
|
|
|
annotations: list[Annotation] = []
|
|
for label_value in label_values:
|
|
binary = np.where(image == label_value, 255, 0).astype(np.uint8)
|
|
contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
|
|
annotation_label = f"{label} {label_value}" if has_multiple_labels else label
|
|
|
|
for contour in contours:
|
|
if cv2.contourArea(contour) < 1:
|
|
continue
|
|
|
|
polygon = _normalized_contour(contour, image.shape[1], image.shape[0])
|
|
if len(polygon) < 3:
|
|
continue
|
|
|
|
component = np.zeros_like(binary, dtype=np.uint8)
|
|
cv2.drawContours(component, [contour], -1, 1, thickness=-1)
|
|
seed_point = _component_seed_point(component, image.shape[1], image.shape[0])
|
|
bbox = _contour_bbox(contour, image.shape[1], image.shape[0])
|
|
|
|
annotation = Annotation(
|
|
project_id=project_id,
|
|
frame_id=frame_id,
|
|
template_id=template_id,
|
|
mask_data={
|
|
"polygons": [polygon],
|
|
"label": annotation_label,
|
|
"color": color,
|
|
"source": "gt_mask",
|
|
"gt_label_value": label_value,
|
|
"image_size": {"width": width, "height": height},
|
|
},
|
|
points=[seed_point],
|
|
bbox=bbox,
|
|
)
|
|
db.add(annotation)
|
|
annotations.append(annotation)
|
|
|
|
if not annotations:
|
|
raise HTTPException(status_code=400, detail="No foreground mask regions found")
|
|
|
|
db.commit()
|
|
for annotation in annotations:
|
|
db.refresh(annotation)
|
|
logger.info("Imported %s GT mask annotations for project_id=%s frame_id=%s", len(annotations), project_id, frame_id)
|
|
return annotations
|
|
|
|
|
|
@router.get(
|
|
"/annotations",
|
|
response_model=List[AnnotationOut],
|
|
summary="List saved annotations for a project",
|
|
)
|
|
def list_annotations(
|
|
project_id: int,
|
|
frame_id: int | None = None,
|
|
db: Session = Depends(get_db),
|
|
) -> List[Annotation]:
|
|
"""Return persisted annotations for a project, optionally scoped to one frame."""
|
|
project = db.query(Project).filter(Project.id == project_id).first()
|
|
if not project:
|
|
raise HTTPException(status_code=404, detail="Project not found")
|
|
|
|
query = db.query(Annotation).filter(Annotation.project_id == project_id)
|
|
if frame_id is not None:
|
|
query = query.filter(Annotation.frame_id == frame_id)
|
|
return query.order_by(Annotation.id).all()
|
|
|
|
|
|
@router.patch(
|
|
"/annotations/{annotation_id}",
|
|
response_model=AnnotationOut,
|
|
summary="Update a saved annotation",
|
|
)
|
|
def update_annotation(
|
|
annotation_id: int,
|
|
payload: AnnotationUpdate,
|
|
db: Session = Depends(get_db),
|
|
) -> Annotation:
|
|
"""Update mutable annotation fields persisted in the database."""
|
|
annotation = db.query(Annotation).filter(Annotation.id == annotation_id).first()
|
|
if not annotation:
|
|
raise HTTPException(status_code=404, detail="Annotation not found")
|
|
|
|
updates = payload.model_dump(exclude_unset=True)
|
|
if "template_id" in updates and updates["template_id"] is not None:
|
|
template = db.query(Template).filter(Template.id == updates["template_id"]).first()
|
|
if not template:
|
|
raise HTTPException(status_code=404, detail="Template not found")
|
|
|
|
for field, value in updates.items():
|
|
setattr(annotation, field, value)
|
|
|
|
db.commit()
|
|
db.refresh(annotation)
|
|
logger.info("Updated annotation id=%s", annotation.id)
|
|
return annotation
|
|
|
|
|
|
@router.delete(
|
|
"/annotations/{annotation_id}",
|
|
status_code=status.HTTP_204_NO_CONTENT,
|
|
summary="Delete a saved annotation",
|
|
)
|
|
def delete_annotation(
|
|
annotation_id: int,
|
|
db: Session = Depends(get_db),
|
|
) -> Response:
|
|
"""Delete an annotation and its derived mask rows through ORM cascade."""
|
|
annotation = db.query(Annotation).filter(Annotation.id == annotation_id).first()
|
|
if not annotation:
|
|
raise HTTPException(status_code=404, detail="Annotation not found")
|
|
|
|
db.delete(annotation)
|
|
db.commit()
|
|
logger.info("Deleted annotation id=%s", annotation_id)
|
|
return Response(status_code=status.HTTP_204_NO_CONTENT)
|