Initial commit: Happy Up monorepo through Sprint 5.

Document-driven MVP with FastAPI backend, Vue H5, WeChat mini shell, product demo, and Docker dev stack. Co-authored-by: Cursor <cursoragent@cursor.com>
2026-07-23 11:42:40 +08:00
commit 1aaef71f52
116 changed files with 10550 additions and 0 deletions
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+"""Pose analysis pipeline — MediaPipe frame extraction with mock fallback."""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from app.ai.pose_metrics import FrameLandmarks, build_movement_report, build_screening_report
+from app.mock_data import MOCK_MOVEMENT_REPORT, MOCK_REPORT
+
+MAX_FRAMES = 32
+FRAME_STRIDE = 6
+
+
+def analyze_task(task_type: str, video_path: Path | None = None) -> dict:
+    if video_path and video_path.exists():
+        mediapipe_result = _analyze_video(video_path, task_type)
+        if mediapipe_result:
+            return mediapipe_result
+
+    return _mock_result(task_type)
+
+
+def _mock_result(task_type: str) -> dict:
+    if task_type == "movement_scoring":
+        return {
+            "engine": "mock",
+            "confidence": 0.88,
+            "report": {**MOCK_MOVEMENT_REPORT},
+            "modelVersion": "mock-movement-v1",
+        }
+    return {
+        "engine": "mock",
+        "confidence": 0.88,
+        "report": {**MOCK_REPORT},
+        "modelVersion": "mock-screening-v1",
+    }
+
+
+def _analyze_video(video_path: Path, task_type: str) -> dict | None:
+    frames = _extract_pose_frames(video_path)
+    if not frames:
+        return None
+
+    try:
+        if task_type == "movement_scoring":
+            report = build_movement_report(frames)
+        else:
+            report = build_screening_report(frames)
+    except ValueError:
+        return None
+
+    confidences = []
+    if task_type != "movement_scoring":
+        for metric in report.get("metrics", []):
+            if "confidence" in metric:
+                confidences.append(metric["confidence"])
+
+    return {
+        "engine": "mediapipe",
+        "confidence": round(mean(confidences) if confidences else 0.9, 2),
+        "report": report,
+        "modelVersion": "mediapipe-pose-v2",
+        "frameCount": len(frames),
+        "sourceFile": video_path.name,
+    }
+
+
+def _extract_pose_frames(video_path: Path) -> list[FrameLandmarks]:
+    try:
+        import cv2
+        import mediapipe as mp
+    except ImportError:
+        return []
+
+    capture = cv2.VideoCapture(str(video_path))
+    if not capture.isOpened():
+        return []
+
+    pose = mp.solutions.pose.Pose(
+        static_image_mode=False,
+        model_complexity=1,
+        min_detection_confidence=0.5,
+        min_tracking_confidence=0.5,
+    )
+
+    frames: list[FrameLandmarks] = []
+    index = 0
+    try:
+        while capture.isOpened() and len(frames) < MAX_FRAMES:
+            ok, image = capture.read()
+            if not ok:
+                break
+            if index % FRAME_STRIDE != 0:
+                index += 1
+                continue
+            index += 1
+
+            rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+            result = pose.process(rgb)
+            if not result.pose_landmarks:
+                continue
+
+            frame: FrameLandmarks = {}
+            for idx, landmark in enumerate(result.pose_landmarks.landmark):
+                frame[idx] = {
+                    "x": landmark.x,
+                    "y": landmark.y,
+                    "visibility": landmark.visibility,
+                }
+            frames.append(frame)
+    finally:
+        pose.close()
+        capture.release()
+
+    return frames
+
+
+def mean(values: list[float]) -> float:
+    return sum(values) / len(values) if values else 0.0