基于 AI 动作识别技术的
匹克球运动技能教学模式创新研究

cap = cv2.VideoCapture("serve_001.mp4")
fps = cap.get(cv2.CAP_PROP_FPS)  # 60fps
frames = []
while cap.isOpened():
    ret, frame = cap.read()
    if not ret: break
    frame = cv2.resize(frame, (1280, 720))
    frames.append(frame)

import mediapipe as mp
pose = mp.solutions.pose.Pose(
    model_complexity=2,
    min_detection_confidence=0.5,
    min_tracking_confidence=0.5
)
results = pose.process(
    cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
)
landmarks = results.pose_landmarks.landmark

def calc_angle(a: np.ndarray,
               b: np.ndarray,
               c: np.ndarray) -> float:
    ba = a - b; bc = c - b
    cos = np.dot(ba, bc) / (
        np.linalg.norm(ba) * np.linalg.norm(bc)
    )
    return np.degrees(np.arccos(np.clip(cos,-1,1)))

elbow_angle = calc_angle(
    shoulder_pt, elbow_pt, wrist_pt
)

model = ActionClassifier.load("model_v2.pt")
features = extract_temporal_features(sequence)
action_probs = model(features)          # [4]
action_id = ACTIONS[action_probs.argmax()]

scores = score_against_template(
    joint_angles=measured_angles,
    template=STANDARDS[action_id],
    weights=EVAL_WEIGHTS[action_id]
)
total = sum(s*w for s,w in scores.items())

report = FeedbackReport(
    student_id="TS2026_001",
    action=action_id,
    scores=scores,
    errors=detect_errors(measured, template),
    skill_level=classify_level(total),
    improvement_plan=suggest_drills(errors)
)
report.export_pdf("feedback_001.pdf")
report.push_to_dashboard()