Fix vertical video support & improve biomechanics analysis logic

.gitattributes

@@ -1,2 +1,3 @@
 models/*.pth.tar filter=lfs diff=lfs merge=lfs -text
-*.TTF filter=lfs diff=lfs merge=lfs -text
+models_v1/*.pth.tar filter=lfs diff=lfs merge=lfs -text
+*.TTF filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -71,3 +71,6 @@ ngrok.exe
 !demo/
 !demo/*.mp4
 !demo/*.json
+
+# documents
+!docs/

SCORING_METHODOLOGY.md

@@ -1,50 +0,0 @@
-# Thuyết minh: Phương pháp Đánh giá Golf AI (Thang điểm 10)
-
-Hệ thống của chúng tôi đánh giá cú swing dựa trên sự kết hợp giữa **Dữ liệu Hình ảnh (AI)** và **Hình học Tư thế (Pose Geometry)**.
-
-## 1. Cơ chế Chấm điểm
-
-- **Điểm cơ sở**: Mỗi giai đoạn bắt đầu với **10.0 điểm**.
-- **Điểm trừ (Penalties)**: Tùy theo mức độ nghiêm trọng của lỗi, hệ thống sẽ trừ từ 0.5 đến 2.5 điểm mỗi lỗi.
-- **Trọng số (Weights)**: Điểm tổng kết không phải là trung bình cộng đơn thuần mà được tính theo tầm quan trọng của từng pha.
-
-| Giai đoạn    | Trọng số | Tầm quan trọng                           |
-| :----------- | :------- | :--------------------------------------- |
-| Address      | 10%      | Nền tảng ban đầu                         |
-| Top          | 20%      | Đỉnh cao của năng lượng                  |
-| Impact       | 35%      | **Quan trọng nhất** (Điểm tiếp xúc bóng) |
-| Finish       | 10%      | Thể hiện sự thăng bằng                   |
-| Các pha khác | 25%      | Độ mượt mà của quá trình chuyển động     |
-
-## 2. Tiêu chí Chẩn đoán Chi tiết
-
-### Pha Address (Tư thế chuẩn bị)
-
-- **Độ rộng chân (Stance Width)**: Được đo so với độ rộng vai.
-  - _Chuẩn_: Tỉ lệ từ 0.8 đến 1.4.
-  - _Lỗi_: Quá rộng (mất linh hoạt) hoặc quá hẹp (mất thăng bằng).
-
-### Pha Top (Đỉnh Backswing)
-
-- **Tay dẫn (Lead Arm - Tay trái)**: Phải thẳng để tạo cung swing lớn nhất.
-  - _Chuẩn_: Góc tay >= 150 độ.
-  - _Lỗi_: "Chicken Wing" (Tay bị gập) làm giảm lực đánh.
-
-### Pha Impact (Tiếp bóng)
-
-- **Tay dẫn lúc va chạm**: Đây là khoảnh khắc truyền lực.
-  - _Chuẩn_: Góc tay >= 160 độ (Gần như thẳng tuyệt đối).
-  - _Lỗi_: Tay bị nhấc hoặc gập sớm làm mất lực và dễ đánh trượt.
-
-### Pha Finish (Kết thúc)
-
-- **Thăng bằng (Balance Control)**: Kiểm tra vị trí hông so với chân trụ.
-  - _Chuẩn_: Trọng tâm dồn hoàn toàn về chân trái, kết thúc vững chãi.
-  - _Lỗi_: Ngả người ra sau hoặc đổ về phía trước.
-
-## 3. Phân loại Trình độ
-
-- **9.0 - 10.0**: Pro / Low Handicap (Kỹ thuật tiệm cận chuyên nghiệp).
-- **7.5 - 8.9**: Mid Handicap (Kỹ thuật tốt, cần tinh chỉnh chi tiết).
-- **5.5 - 7.4**: High Handicap (Cần tập trung vào các bài tập cơ bản).
-- **Dưới 5.5**: Beginner (Cần sự hướng dẫn trực tiếp từ huấn luyện viên).

analyze.py

@@ -32,23 +32,19 @@ class GolfDiagnosticEngine:
         r_hip = landmarks[self.mp_pose.PoseLandmark.RIGHT_HIP.value]
 
         shoulder_width = abs(l_shoulder.x - r_shoulder.x)
-        
-        # Tính chiều cao thân người (Torso Height) để chuẩn hóa
         mid_shoulder_y = (l_shoulder.y + r_shoulder.y) / 2
         mid_hip_y = (l_hip.y + r_hip.y) / 2
         torso_height = abs(mid_shoulder_y - mid_hip_y)
         
         if torso_height == 0: return "Unknown"
         
-        # Tỷ lệ chiều rộng vai / chiều cao thân
         ratio = shoulder_width / torso_height
         
-        # Heuristic: Nếu tỷ lệ > 0.5 (vai rộng bằng nửa lưng) -> Face-on
-        if ratio > 0.4: # Hạ threshold xuống chút cho an toàn
-            return "Face-on (Trực diện)"
+        # Nếu tỷ lệ > 0.35 -> Face-on.
+        if ratio > 0.35: 
+            return "Face-on"
         else:
-            return "Down-the-Line (Dọc)"
-
+            return "Down-the-Line"
 
     def calculate_angle(self, a, b, c):
         """Tính góc (độ) giữa 3 điểm a, b, c (b là đỉnh)"""
@@ -74,101 +70,158 @@ class GolfDiagnosticEngine:
         """Vẽ landmarks tinh tế và chuyên nghiệp hơn"""
         annotated_image = image.copy()
         
-        # Cấu hình phong cách vẽ tinh tế (mỏng hơn, nhỏ hơn)
-        landmark_style = self.mp_drawing.DrawingSpec(color=(0, 255, 0), thickness=1, circle_radius=1) # Neon Green thon gọn
-        connection_style = self.mp_drawing.DrawingSpec(color=(255, 255, 255), thickness=1) # Đường nối trắng mỏng
+        landmark_style = self.mp_drawing.DrawingSpec(color=(0, 255, 0), thickness=1, circle_radius=1)
+        connection_style = self.mp_drawing.DrawingSpec(color=(255, 255, 255), thickness=1)
         
-        # Vẽ khung xương
         self.mp_drawing.draw_landmarks(
             annotated_image, landmarks_proto, self.mp_pose.POSE_CONNECTIONS,
             landmark_style,
             connection_style
         )
-        
         return annotated_image
 
-    def analyze_phase(self, phase_name, landmarks):
+    def analyze_phase(self, phase_name, landmarks, address_landmarks=None, view_angle="Unknown"):
         if not landmarks:
             return {"score": 0, "comments": ["Không tìm thấy tư thế"], "data": {}}
 
         analysis = {"score": 10.0, "comments": [], "data": {}, "raw_landmarks": []}
         
-        # Trích xuất tọa độ thô của toàn bộ khớp xương để kỹ sư khác sử dụng
+        # Helper lấy tọa độ (x, y)
+        def get_xy(idx):
+            return [landmarks[idx].x, landmarks[idx].y]
+
+        l_shoulder = get_xy(self.mp_pose.PoseLandmark.LEFT_SHOULDER.value)
+        r_shoulder = get_xy(self.mp_pose.PoseLandmark.RIGHT_SHOULDER.value)
+        l_elbow = get_xy(self.mp_pose.PoseLandmark.LEFT_ELBOW.value)
+        r_elbow = get_xy(self.mp_pose.PoseLandmark.RIGHT_ELBOW.value)
+        l_wrist = get_xy(self.mp_pose.PoseLandmark.LEFT_WRIST.value)
+        r_wrist = get_xy(self.mp_pose.PoseLandmark.RIGHT_WRIST.value)
+        l_hip = get_xy(self.mp_pose.PoseLandmark.LEFT_HIP.value)
+        r_hip = get_xy(self.mp_pose.PoseLandmark.RIGHT_HIP.value)
+        l_knee = get_xy(self.mp_pose.PoseLandmark.LEFT_KNEE.value)
+        r_knee = get_xy(self.mp_pose.PoseLandmark.RIGHT_KNEE.value)
+        l_ankle = get_xy(self.mp_pose.PoseLandmark.LEFT_ANKLE.value)
+        r_ankle = get_xy(self.mp_pose.PoseLandmark.RIGHT_ANKLE.value)
+        nose = get_xy(self.mp_pose.PoseLandmark.NOSE.value)
+
+        # Trích xuất landmarks thô
         for i, lm in enumerate(landmarks):
             analysis["raw_landmarks"].append({
-                "id": i,
-                "name": self.mp_pose.PoseLandmark(i).name,
-                "x": round(lm.x, 4),
-                "y": round(lm.y, 4),
-                "z": round(lm.z, 4),
-                "visibility": round(lm.visibility, 4)
+                "id": i, "name": self.mp_pose.PoseLandmark(i).name,
+                "x": round(lm.x, 4), "y": round(lm.y, 4), "z": round(lm.z, 4), "visibility": round(lm.visibility, 4)
             })
 
-        # Lấy các điểm mốc quan trọng để tính toán logic nội bộ
-        l_shoulder = [landmarks[self.mp_pose.PoseLandmark.LEFT_SHOULDER.value].x, landmarks[self.mp_pose.PoseLandmark.LEFT_SHOULDER.value].y]
-        r_shoulder = [landmarks[self.mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x, landmarks[self.mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y]
-        l_elbow = [landmarks[self.mp_pose.PoseLandmark.LEFT_ELBOW.value].x, landmarks[self.mp_pose.PoseLandmark.LEFT_ELBOW.value].y]
-        r_elbow = [landmarks[self.mp_pose.PoseLandmark.RIGHT_ELBOW.value].x, landmarks[self.mp_pose.PoseLandmark.RIGHT_ELBOW.value].y]
-        l_wrist = [landmarks[self.mp_pose.PoseLandmark.LEFT_WRIST.value].x, landmarks[self.mp_pose.PoseLandmark.LEFT_WRIST.value].y]
-        r_wrist = [landmarks[self.mp_pose.PoseLandmark.RIGHT_WRIST.value].x, landmarks[self.mp_pose.PoseLandmark.RIGHT_WRIST.value].y]
-        l_hip = [landmarks[self.mp_pose.PoseLandmark.LEFT_HIP.value].x, landmarks[self.mp_pose.PoseLandmark.LEFT_HIP.value].y]
-        r_hip = [landmarks[self.mp_pose.PoseLandmark.RIGHT_HIP.value].x, landmarks[self.mp_pose.PoseLandmark.RIGHT_HIP.value].y]
-        l_ankle = [landmarks[self.mp_pose.PoseLandmark.LEFT_ANKLE.value].x, landmarks[self.mp_pose.PoseLandmark.LEFT_ANKLE.value].y]
-        r_ankle = [landmarks[self.mp_pose.PoseLandmark.RIGHT_ANKLE.value].x, landmarks[self.mp_pose.PoseLandmark.RIGHT_ANKLE.value].y]
-
-        # Phân tích cụ thể từng pha (Thang điểm 10)
-        # Sử dụng endswith để khớp với tên có hoặc không có số thứ tự
+        # --- CHUẨN HÓA TỌA ĐỘ (Normalization) ---
+        current_ankle_center_x = (l_ankle[0] + r_ankle[0]) / 2
+        
+        addr_ankle_center_x = 0
+        addr_nose_x_rel = 0
+        addr_hip_x_rel = 0
+        
+        if address_landmarks:
+            al_ankle_l = address_landmarks[self.mp_pose.PoseLandmark.LEFT_ANKLE.value].x
+            al_ankle_r = address_landmarks[self.mp_pose.PoseLandmark.RIGHT_ANKLE.value].x
+            addr_ankle_center_x = (al_ankle_l + al_ankle_r) / 2
+            
+            al_nose = address_landmarks[self.mp_pose.PoseLandmark.NOSE.value].x
+            al_hip = (address_landmarks[self.mp_pose.PoseLandmark.LEFT_HIP.value].x + 
+                      address_landmarks[self.mp_pose.PoseLandmark.RIGHT_HIP.value].x) / 2
+            
+            addr_nose_x_rel = al_nose - addr_ankle_center_x
+            addr_hip_x_rel = al_hip - addr_ankle_center_x
+
+        curr_nose_x_rel = nose[0] - current_ankle_center_x
+        curr_hip_x_rel = ((l_hip[0] + r_hip[0])/2) - current_ankle_center_x
+
+        # --- LOGIC ---
+
         if phase_name.endswith('Address'):
-            # 1. Độ rộng chân (so với vai)
-            shoulder_width = np.abs(l_shoulder[0] - r_shoulder[0])
-            stance_width = np.abs(l_ankle[0] - r_ankle[0])
-            ratio = stance_width / shoulder_width if shoulder_width > 0 else 0
-            analysis["data"]["stance_ratio"] = ratio
-            if ratio < 0.8:
-                analysis["score"] -= 1.0
-                analysis["comments"].append("Tư thế đứng quá hẹp")
-            elif ratio > 1.4:
+            # 1. Stance Width (CHỈ CHECK FACE-ON)
+            if view_angle == "Face-on":
+                shoulder_width = np.abs(l_shoulder[0] - r_shoulder[0])
+                stance_width = np.abs(l_ankle[0] - r_ankle[0])
+                if shoulder_width > 0:
+                    ratio = stance_width / shoulder_width
+                    analysis["data"]["stance_ratio"] = ratio
+                    if ratio < 0.9:
+                        analysis["score"] -= 1.0
+                        analysis["comments"].append("Tư thế đứng quá hẹp (Narrow Stance)")
+                    elif ratio > 1.5:
+                        analysis["score"] -= 1.0
+                        analysis["comments"].append("Tư thế đứng quá rộng (Wide Stance)")
+            
+            # 2. Knee Flex
+            avg_knee_angle = (self.calculate_angle(l_hip, l_knee, l_ankle) + 
+                              self.calculate_angle(r_hip, r_knee, r_ankle)) / 2
+            analysis["data"]["knee_angle"] = avg_knee_angle
+            if avg_knee_angle > 175:
+                # DTL thấy rõ hơn, Face-on cũng thấy được
                 analysis["score"] -= 1.0
-                analysis["comments"].append("Tư thế đứng quá rộng")
+                analysis["comments"].append("Đầu gối quá thẳng (Locking Knees)")
+            elif avg_knee_angle < 135:
+                if view_angle == "Face-on": 
+                    analysis["score"] -= 1.0
+                    analysis["comments"].append("Đầu gối khuỵu quá mức")
 
         elif phase_name.endswith('Top'):
-            # 2. Độ thẳng tay trái (Lead arm - giả sử golfer thuận tay phải)
+            # 3. Chicken Wing
             lead_arm_angle = self.calculate_angle(l_shoulder, l_elbow, l_wrist)
             analysis["data"]["lead_arm_angle"] = lead_arm_angle
-            if lead_arm_angle < 150:
+            threshold = 140 if view_angle == "Face-on" else 130 
+            if lead_arm_angle < threshold:
                 analysis["score"] -= 2.0
-                analysis["comments"].append("Tay trái bị cong quá nhiều (Chicken Wing)")
-            
-            # 3. Góc xoay vai (đơn giản hóa)
-            shoulder_tilt = np.abs(l_shoulder[1] - r_shoulder[1])
-            analysis["data"]["shoulder_tilt"] = shoulder_tilt
+                analysis["comments"].append("Tay trái bị gập (Chicken Wing)")
+
+            # 4. Sway / Slide (CHỈ FACE-ON)
+            if address_landmarks and view_angle == "Face-on":
+                sway = curr_nose_x_rel - addr_nose_x_rel
+                analysis["data"]["head_sway"] = sway
+                if abs(sway) > 0.08:
+                    analysis["score"] -= 1.5
+                    analysis["comments"].append("Đầu di chuy��n quá nhiều (Sway)")
+                
+                slide = curr_hip_x_rel - addr_hip_x_rel
+                if slide > 0.08:
+                    analysis["score"] -= 1.0
+                    analysis["comments"].append("Hông trượt sang phải (Hip Slide)")
+
+            # 5. Reverse Spine Angle (Face-on only)
+            if view_angle == "Face-on":
+                if curr_nose_x_rel < curr_hip_x_rel - 0.05:
+                    analysis["score"] -= 2.0
+                    analysis["comments"].append("Lỗi trục nghiêng ngược (Reverse Spine Angle)")
 
         elif phase_name.endswith('Impact'):
-            # 4. Độ thẳng tay trái lúc Impact
-            impact_arm_angle = self.calculate_angle(l_shoulder, l_elbow, l_wrist)
-            if impact_arm_angle < 160:
-                analysis["score"] -= 2.5
-                analysis["comments"].append("Tay trái không thẳng, mất lực")
-            
-            # 5. Hip rotation (Lead hip vs Back hip)
-            hip_openness = l_hip[0] - r_hip[0]
-            analysis["data"]["hip_openness"] = hip_openness
+            # 6. Weight Shift
+            if address_landmarks and view_angle == "Face-on":
+                shift = curr_hip_x_rel - addr_hip_x_rel 
+                analysis["data"]["weight_shift"] = shift
+                
+                if shift > -0.01:
+                    analysis["score"] -= 2.0
+                    analysis["comments"].append("Thiếu chuyển trọng tâm sang trái")
             
-        elif phase_name.endswith('Finish'):
-            # 6. Thăng bằng (Trọng tâm dồn về chân trái)
-            # Kiểm tra thăng bằng đơn giản bằng vị trí hông so với chân lead
-            if l_hip[0] > l_ankle[0] + 0.1 or l_hip[0] < l_ankle[0] - 0.1:
-                analysis["score"] -= 1.5
-                analysis["comments"].append("Kết thúc thiếu thăng bằng")
+            # 7. Scooping
+            impact_angle = self.calculate_angle(l_shoulder, l_elbow, l_wrist)
+            if impact_angle < 155:
+                analysis["score"] -= 2.0
+                analysis["comments"].append("Tay trái gập sớm (Scooping/Chicken Wing)")
 
+        elif phase_name.endswith('Finish'):
+            # 8. Balance
+            if view_angle == "Face-on":
+                hip_vs_ankle = l_hip[0] - l_ankle[0]
+                if abs(hip_vs_ankle) > 0.15: 
+                    analysis["score"] -= 1.0
+                    analysis["comments"].append("Kết thúc chưa vững (Off Balance)")
+        
         if not analysis["comments"]:
-            analysis["comments"].append("Đạt chuẩn")
+            analysis["comments"].append("Tư thế tốt")
 
-        analysis["score"] = round(max(0, analysis["score"]), 1)
+        analysis["score"] = round(max(0, min(10, analysis["score"])), 1)
         return analysis
 
     def process_video_results(self, output_dir, return_dict=False):
-        # Lấy video_id từ tên thư mục output
         video_id = os.path.basename(output_dir)
         extraction_dir = os.path.join(output_dir, 'phases')
         
@@ -177,8 +230,18 @@ class GolfDiagnosticEngine:
         total_score = 0
         valid_phases = 0
 
-        # Mảng lưu landmarks Address để detect view
         address_landmarks = None
+        address_path = os.path.join(extraction_dir, '1_Address', f"{video_id}.jpg")
+        
+        view_angle = "Unknown"
+        if os.path.exists(address_path):
+             img = cv2.imread(address_path)
+             lm_list, _ = self.get_landmarks(img)
+             if lm_list:
+                 address_landmarks = lm_list
+                 view_angle = self.detect_view_angle(address_landmarks)
+        
+        report["view_angle"] = view_angle
 
         for label in self.labels:
             img_path = os.path.join(extraction_dir, label, f"{video_id}.jpg")
@@ -188,11 +251,7 @@ class GolfDiagnosticEngine:
             image = cv2.imread(img_path)
             landmarks_list, _ = self.get_landmarks(image)
             
-            # Lưu landmarks của Address
-            if label == '1_Address' and landmarks_list:
-                address_landmarks = landmarks_list
-
-            phase_analysis = self.analyze_phase(label, landmarks_list)
+            phase_analysis = self.analyze_phase(label, landmarks_list, address_landmarks, view_angle)
 
             report["phases"][label] = phase_analysis
             total_score += phase_analysis["score"]
@@ -201,15 +260,9 @@ class GolfDiagnosticEngine:
         if valid_phases > 0:
             report["overall_score"] = round(total_score / valid_phases, 1)
 
-        # Detect View Angle một lần duy nhất
-        if address_landmarks:
-            report["view_angle"] = self.detect_view_angle(address_landmarks)
-        
-        # Return dict hoặc ghi file (legacy)
         if return_dict:
             return report
         else:
-            # Lưu báo cáo vào thư mục output của video
             report_path = os.path.join(output_dir, 'report.json')
             with open(report_path, 'w', encoding='utf-8') as f:
                 json.dump(report, f, indent=4, ensure_ascii=False)

coach.py

@@ -24,11 +24,26 @@ class GolfCoachEngine:
         
         # Thư viện bài tập gợi ý
         self.drill_library = {
-            "Tư thế đứng quá rộng": "Bài tập đứng trên hai đầu gối hoặc khép chân để cảm nhận sự xoay trục.",
-            "Tư thế đứng quá hẹp": "Bài tập bước rộng chân và xoay hông tại chỗ để tạo nền tảng vững chắc.",
-            "Tay trái bị cong quá nhiều ở đỉnh swing (Chicken Wing)": "Kẹp một chiếc khăn hoặc quả bóng tập giữa hai cánh tay ở nách để giữ sự kết nối.",
-            "Tay trái không thẳng lúc tiếp bóng, mất lực": "Bài tập Half-swing (swing nửa vòng) tập trung vào việc giữ tay lead thẳng và xoay hông.",
-            "Kết thúc không vững, trọng tâm chưa dồn hết về chân trái": "Bài tập Hold Finish - giữ nguyên tư thế kết thúc trong 3 giây cho đến khi thăng bằng hoàn toàn."
+            # Stance & Address
+            "Tư thế đứng quá rộng (Wide Stance)": "Thu hẹp khoảng cách chân bằng chiều rộng vai để xoay hông dễ dàng hơn.",
+            "Tư thế đứng quá hẹp (Narrow Stance)": "Mở rộng chân thêm chút (bằng vai) để tạo nền tảng vững chắc.",
+            "Đầu gối quá thẳng, thiếu sự linh hoạt (Locking Knees)": "Thả lỏng đầu gối, làm động tác như chuẩn bị ngồi xuống ghế cao.",
+            "Đầu gối khuỵu quá mức (Sitting too much)": "Đứng cao hơn chút, chỉ khuỵu nhẹ đầu gối để dồn trọng tâm lên ức bàn chân.",
+
+            # Backswing & Top
+            "Tay trái bị gập (Chicken Wing) - Mất bán kính swing": "Tập kẹp khăn dưới nách khi swing để giữ tay sát thân.",
+            "Đầu di chuyển quá nhiều (Head Sway) - Gây mất ổn định": "Nhờ bạn giữ đầu hoặc nhìn vào gương, tập xoay vai mà đầu vẫn giữ yên.",
+            "Hông trượt sang phải (Hip Slide) thay vì xoay": "Tập bài xoay hông trong xô nước (Barrel Turn) - tưởng tượng đứng trong thùng phuy và chỉ xoay, không trượt.",
+            "Lỗi trục nghiêng ngược (Reverse Spine Angle) - Dễ gây đau lưng": "Đảm bảo vai trái hạ thấp hơn vai phải khi lên đỉnh, mắt vẫn nhìn vào phía sau bóng.",
+
+            # Impact
+            "Tay trái gập khi tiếp bóng (Scooping/Chicken Wing)": "Bài tập Impact Bag: Tập đánh vào túi đệm và dừng lại ở Impact để cảm nhận tay thẳng.",
+            "Trọng tâm dồn về chân sau (Hanging Back) - Mất lực": "Bài tập bước chân (Step Drill): Bước chân trái lên khi downswing để ép trọng tâm chuyển sang trái.",
+            "Thiếu chuyển trọng tâm sang chân trái": "Đặt bóng dưới lòng bàn chân phải, khi swing xuống phải nhấc gót phải lên khỏi bóng.",
+            "Đầu lao về trước quá sớm (Lunging)": "Cố gắng cảm giác như 'đánh đầu' về phía sau khi gậy tiếp xúc bóng.",
+            
+            # Finish
+            "Kết thúc không dồn hết trọng tâm sang trái": "Tập giữ tư thế kết thúc trong 3 giây, nhấc hoàn toàn chân phải lên chỉ tì bằng mũi giày."
         }
 
     def generate_report(self, diagnostic_json_path):

docs/final_golf_swing.py

@@ -0,0 +1,730 @@
+# Generated from: final_golf_swing.ipynb
+# Converted at: 2026-01-01T14:06:29.903Z
+# Next step (optional): refactor into modules & generate tests with RunCell
+# Quick start: pip install runcell
+
+# ### 📦 Installation Summary
+# 
+# This code installs essential Python libraries for video processing, pose detection, progress tracking, and data handling:
+# 
+# - `moviepy==1.0.3`: Used for video editing (cutting, merging, adding audio).
+# - `mediapipe==0.10.21`: Provides pose and hand detection (great for analyzing movement like golf swings).
+# - `tqdm==4.67.1`: Adds progress bars to loops and downloads.
+# - `dataclasses-json==0.6.7`: Allows easy serialization/deserialization of Python dataclasses to/from JSON.
+# - `backoff==2.2.1`: Helps with retry logic for functions that might fail (e.g., API calls).
+# - `supervision==0.26.0`: Useful for drawing overlays on video frames (bounding boxes, labels).
+# - `yt-dlp`: Downloads videos from YouTube and other platforms (quietly installed with `-q`).
+# 
+# Note: `--no-deps` skips installing dependencies for the first command to avoid version conflicts.
+# 
+
+
+!pip install --no-deps moviepy==1.0.3 mediapipe==0.10.21 tqdm==4.67.1
+!pip install dataclasses-json==0.6.7
+!pip install backoff==2.2.1
+!pip install supervision==0.26.0
+!!pip install -q yt-dlp
+
+# ### 🎬 YouTube Shorts Downloader (with `yt-dlp`)
+# 
+# This code uses `yt-dlp` to download high-quality YouTube Shorts as `.mp4` files:
+# 
+# - `-f "bestvideo[ext=mp4]+bestaudio[ext=m4a]/mp4"`: Selects the best available video stream in MP4 format and merges it with the best audio in M4A. Falls back to a single MP4 if needed.
+# - `-o "filename.mp4"`: Specifies the output filename for the downloaded video.
+# - URLs point to YouTube Shorts featuring golfers Ludvig Åberg and Max Homa.
+# 
+# 📥 Output files:
+# - `ludvig_aberg_driver.mp4`
+# - `max_homa_iron.mp4`
+# 
+
+
+!yt-dlp -f "bestvideo[ext=mp4]+bestaudio[ext=m4a]/mp4" -o "ludvig_aberg_driver.mp4" "https://www.youtube.com/shorts/9nhFobbdJxQ"
+!yt-dlp -f "bestvideo[ext=mp4]+bestaudio[ext=m4a]/mp4" -o "max_homa_iron.mp4" "https://www.youtube.com/shorts/aB__zg42i9g"
+
+
+# ### 🧠 Core Imports Overview
+# 
+# This block imports essential libraries for processing, visualization, and media handling:
+# 
+# - **System & Utilities**:  
+#   - `gc`, `time`, `subprocess`: For garbage collection, timing operations, and running shell commands.
+# 
+# - **Numerical & Signal Processing**:  
+#   - `numpy`: Core library for numerical computations.  
+#   - `uniform_filter1d` (from `scipy.ndimage`): Smooths 1D signals (e.g., joint positions over frames).
+# 
+# - **Visualization**:  
+#   - `matplotlib.pyplot`: For plotting graphs and visuals.  
+#   - `FigureCanvas`: Renders plots into images.  
+#   - `mpl_use`: Sets a Colab-safe backend for rendering plots.
+# 
+# - **Media Processing**:  
+#   - `cv2`: OpenCV library for reading, processing, and displaying video frames.  
+#   - `mediapipe`: Used for pose estimation and landmark detection.
+# 
+# - **Colab Display**:  
+#   - `IPython.display`: For showing videos inline using `Video()` and `display()` functions.
+# 
+
+
+# Core
+import gc
+import time
+import subprocess
+
+# Numerical and signal processing
+import numpy as np
+from scipy.ndimage import uniform_filter1d
+
+# Visualization
+import matplotlib.pyplot as plt
+from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+from matplotlib import use as mpl_use  # Needed for Colab-safe rendering
+
+# Media processing
+import cv2
+import mediapipe as mp
+
+# Colab display tools
+from IPython.display import Video, display
+
+
+# ### 🔇 Add Silent Audio to Videos Using FFmpeg
+# 
+# This code prepares four golf swing videos (two input clips and two YouTube references) by adding a silent audio track to each. This ensures compatibility with video players that require an audio stream.
+# 
+# - **Input Videos**:
+#   - `video_iron`, `video_driver`: Your own recorded swing clips.
+#   - `iron_reference`, `driver_reference`: Reference swings from YouTube (e.g., Max Homa, Ludvig Åberg).
+# 
+# - **Output Paths**:
+#   - For each video, a `_fixed.MP4` version is created with silent audio.
+# 
+# - **FFmpeg Command**:
+#   - `-i <input>`: Input video file.
+#   - `-f lavfi -i anullsrc`: Adds silent stereo audio at 44.1kHz.
+#   - `-shortest`: Ends the audio when video ends.
+#   - `-c:v libx264`: Encodes video using H.264.
+#   - `-c:a aac -b:a 128k`: Encodes audio using AAC at 128 kbps.
+#   - `-movflags +faststart`: Optimizes video for fast playback start.
+# 
+# 📁 Outputs:  
+# - `*_fixed.MP4` files for all input and reference videos with embedded silent audio.
+# 
+
+
+# Input video path and define output path for audio-fixed video
+
+iron_reference = "max_homa_iron.mp4"
+driver_reference = "ludvig_aberg_driver"
+
+video_iron = "/content/IMG_1022.MP4"  # original input video   # IRON
+video_iron_fixed = video_iron.rsplit(".", 1)[0] + "_fixed.MP4"
+
+video_driver = "/content/IMG_0758.MP4"  # alternate input # DRIVER
+video_driver_fixed = video_driver.rsplit(".", 1)[0] + "_fixed.MP4"
+
+iron_reference = "/content/max_homa_iron.mp4"
+iron_reference_fixed = iron_reference.rsplit(".", 1)[0] + "_fixed.MP4"
+
+driver_reference = "/content/ludvig_aberg_driver.mp4"
+driver_reference_fixed = driver_reference.rsplit(".", 1)[0] + "_fixed.MP4"
+
+
+# 1. Preserve original video and add silent audio track to a copy
+ffmpeg_cmd = f"""
+ffmpeg -y -i "{video_iron}" -f lavfi -i anullsrc=channel_layout=stereo:sample_rate=44100 \
+-shortest -c:v libx264 -c:a aac -b:a 128k -movflags +faststart "{video_iron_fixed}"
+"""
+subprocess.run(ffmpeg_cmd, shell=True)
+
+# 1. Preserve original video and add silent audio track to a copy
+ffmpeg_cmd = f"""
+ffmpeg -y -i "{video_driver}" -f lavfi -i anullsrc=channel_layout=stereo:sample_rate=44100 \
+-shortest -c:v libx264 -c:a aac -b:a 128k -movflags +faststart "{video_driver_fixed}"
+"""
+subprocess.run(ffmpeg_cmd, shell=True)
+
+# 1. Preserve original video and add silent audio track to a copy
+ffmpeg_cmd = f"""
+ffmpeg -y -i "{iron_reference}" -f lavfi -i anullsrc=channel_layout=stereo:sample_rate=44100 \
+-shortest -c:v libx264 -c:a aac -b:a 128k -movflags +faststart "{iron_reference_fixed}"
+"""
+subprocess.run(ffmpeg_cmd, shell=True)
+
+# 1. Preserve original video and add silent audio track to a copy
+ffmpeg_cmd = f"""
+ffmpeg -y -i "{driver_reference}" -f lavfi -i anullsrc=channel_layout=stereo:sample_rate=44100 \
+-shortest -c:v libx264 -c:a aac -b:a 128k -movflags +faststart "{driver_reference_fixed}"
+"""
+subprocess.run(ffmpeg_cmd, shell=True)
+
+# Preview the audio-fixed video (same content as original, with an added audio stream)
+Video(video_iron_fixed, embed=True, width=600, height=800)
+
+# Preview the audio-fixed video (same content as original, with an added audio stream)
+Video(video_driver_fixed, embed=True, width=600, height=800)
+
+# Preview the audio-fixed video (same content as original, with an added audio stream)
+Video(iron_reference_fixed, embed=True, width=600, height=800)
+
+# Preview the audio-fixed video (same content as original, with an added audio stream)
+Video(driver_reference_fixed, embed=True, width=600, height=800)
+
+# ### 🏌️‍♂️ Pose Feature Extraction (Wrist Y-Coordinate)
+# 
+# This function `extract_pose_features_debug(video_path)` analyzes a video frame-by-frame to extract the **vertical (Y) position of visible wrists** using MediaPipe Pose:
+# 
+# - **Video Setup**:
+#   - Loads the video and retrieves its frame count and resolution.
+#   - Initializes MediaPipe Pose in video (non-static) mode.
+# 
+# - **Frame Loop**:
+#   - For each frame:
+#     - Converts BGR to RGB for pose processing.
+#     - Detects body landmarks using MediaPipe.
+#     - If pose is found:
+#       - Checks visibility of **left and right wrists**.
+#       - Computes average Y-position (vertical) of visible wrists (scaled to video height).
+#     - If no pose or wrist visibility is low, logs a warning and sets `wrist_y` as `None`.
+# 
+# - **Return**:
+#   - Returns a list of dictionaries like `{"frame_idx": 42, "wrist_y": 138.5}` for each frame.
+# 
+# 👀 Use this to analyze hand motion over time, useful for swing segmentation or performance analysis.
+# 
+
+
+def extract_pose_features_debug(video_path):
+    cap = cv2.VideoCapture(video_path)
+    original_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    original_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    num_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    print(f"🎞️ Total frames: {num_frames}, Resolution: {original_width}x{original_height}")
+
+    used_height = original_height  # No rotation, use original height
+
+    mp_pose = mp.solutions.pose
+    pose = mp_pose.Pose(static_image_mode=False)
+    frame_data = []
+
+    for i in range(num_frames):
+        ret, frame = cap.read()
+        if not ret:
+            print(f"❌ Failed to read frame {i}")
+            break
+
+        rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = pose.process(rgb)
+        data = {"frame_idx": i}
+
+        if results.pose_landmarks:
+            # print(f"✅ Pose detected on frame {i}")
+            lm = results.pose_landmarks.landmark
+            lw = lm[mp_pose.PoseLandmark.LEFT_WRIST]
+            rw = lm[mp_pose.PoseLandmark.RIGHT_WRIST]
+            visible_wrists = [p for p in (lw, rw) if p.visibility > 0.4]
+            if visible_wrists:
+                avg_y = np.mean([p.y for p in visible_wrists])
+                data["wrist_y"] = avg_y * used_height
+                # print(f"👋 Frame {i}: Wrist Y = {data['wrist_y']:.2f}")
+            else:
+                print(f"⚠️ Frame {i}: Wrists not visible")
+                data["wrist_y"] = None
+        else:
+            print(f"❌ Frame {i}: No pose detected")
+            data["wrist_y"] = None
+
+        frame_data.append(data)
+
+    cap.release()
+    pose.close()
+    return frame_data
+
+
+
+
+# ### 📈 Run Pose Extraction on All Videos
+# 
+# This block runs the `extract_pose_features_debug()` function on all four prepared videos (with silent audio):
+# 
+# - `video_iron_fixed`: Your iron swing clip  
+# - `video_driver_fixed`: Your driver swing clip  
+# - `iron_reference_fixed`: Max Homa’s reference swing  
+# - `driver_reference_fixed`: Ludvig Åberg’s reference swing  
+# 
+# Each call extracts the **Y-coordinate of visible wrists** frame-by-frame and stores the results in:
+# 
+# - `frame_data_iron`
+# - `frame_data_driver`
+# - `frame_data_iron_reference`
+# - `frame_data_driver_reference`
+# 
+# 📦 These variables now hold pose-based wrist motion data for later analysis or swing comparison.
+# 
+
+
+frame_data_iron = extract_pose_features_debug(video_iron_fixed)
+frame_data_driver = extract_pose_features_debug(video_driver_fixed)
+frame_data_iron_reference = extract_pose_features_debug(iron_reference_fixed)
+frame_data_driver_reference = extract_pose_features_debug(driver_reference_fixed)
+
+# 
+
+
+# ### 🏌️‍♀️ Golf Swing Phase Detection via Wrist Y-Motion
+# 
+# This function `detect_swing_phases()` takes wrist Y-coordinate data and dynamically identifies key golf swing phases using smoothed motion patterns and gradient analysis:
+# 
+# #### 🔍 Key Steps:
+# - **Smoothing & Velocity**:
+#   - Applies a moving average to wrist Y (`smoothing_window`), then calculates velocity and its magnitude.
+# 
+# - **Swing Start & End**:
+#   - Detects when motion exceeds a dynamic threshold (`threshold_percentile`) to mark swing boundaries.
+# 
+# - **Address Detection**:
+#   - Scans backwards from swing start to find a stable ("flat") wrist phase, indicating the pre-swing address.
+# 
+# - **Top of Backswing**:
+#   - Finds the lowest wrist Y point before the swing starts descending (indicating the top of the backswing).
+# 
+# - **Impact Detection**:
+#   - After the top, identifies a stable high point in wrist Y as impact, using tolerance around local max.
+# 
+# - **Phase Segmentation**:
+#   - Returns six labeled segments: `"Address"`, `"Backswing"`, `"Top"`, `"Downswing"`, `"Impact"`, `"Follow Through"`.
+# 
+# #### 📊 Visualization:
+# - Plots wrist trajectory with Y flipped (so upward motion looks natural).
+# - Highlights each swing phase using colored spans and labels.
+# 
+# #### 📤 Returns:
+# - `phase_ranges`: Dictionary of start/end frames for each phase.
+# - `swing_start`, `swing_end`: Frame indices marking full swing.
+# - `wrist_y`, `smoothed`: Raw and smoothed wrist Y arrays for further analysis.
+# 
+# ✅ This unified logic handles both pros and amateurs, dynamically adjusting to wrist movement stability and swing patterns.
+# 
+
+
+
+
+def detect_swing_phases(frame_data, smoothing_window=5, precheck_window=30, threshold_percentile=90):
+    """
+    Analyze wrist Y trajectory and determine key golf swing phase ranges.
+    Unified logic. Dynamically determines Address using flatness in wrist_y.
+    """
+    # Convert to numpy and handle None
+    wrist_y = np.array([d["wrist_y"] if d["wrist_y"] is not None else np.nan for d in frame_data], dtype=np.float32)
+    smoothed = uniform_filter1d(wrist_y, size=smoothing_window, mode='nearest')
+    velocity = np.gradient(smoothed)
+    velocity_mag = np.abs(velocity)
+
+    # Motion threshold and swing start
+    early_motion = np.nanmean(velocity_mag[:precheck_window])
+    motion_std = np.nanstd(velocity_mag[:precheck_window])
+    buffer_start = precheck_window if (early_motion < 0.001 and motion_std < 0.001) else 0
+    threshold = np.nanpercentile(velocity_mag[buffer_start:], threshold_percentile)
+    motion_indices = np.where(velocity_mag > threshold)[0]
+    motion_indices = motion_indices[motion_indices > buffer_start]
+
+    if len(motion_indices) > 0:
+        swing_start = int(motion_indices[0])
+        peak_idx = motion_indices[np.argmax(velocity_mag[motion_indices])]
+        target_y = smoothed[swing_start]
+        post_peak_range = smoothed[peak_idx+1:]
+        swing_end = peak_idx + 1 + int(np.nanargmin(np.abs(post_peak_range - target_y))) if len(post_peak_range) > 0 else len(wrist_y) - 1
+    else:
+        swing_start = 0
+        swing_end = len(wrist_y) - 1
+
+    # === Dynamic Address detection ===
+    flat_std_thresh = 1.0
+    min_flat_frames = 10
+    address_start = 0
+    for i in range(swing_start - min_flat_frames, 0, -1):
+        window = smoothed[i:swing_start]
+        if np.count_nonzero(~np.isnan(window)) < min_flat_frames:
+            continue
+        if np.nanstd(window) < flat_std_thresh:
+            address_start = i
+        else:
+            break
+    address_end = swing_start
+
+    # === Top of backswing ===
+    full_swing_segment = smoothed[swing_start:swing_end]
+    diff = np.diff(full_swing_segment)
+    rising_indices = np.where(diff > 0)[0]
+    top_candidate_max = swing_start + rising_indices[0] if len(rising_indices) > 0 else min(swing_start + 20, len(smoothed) - 1)
+    top_search_end = min(top_candidate_max, swing_end)
+    top_idx = swing_start + int(np.nanargmin(smoothed[swing_start:top_search_end])) if top_search_end > swing_start else swing_start
+    top_range = (max(0, top_idx - 5), min(len(wrist_y) - 1, top_idx + 5))
+
+    # === Impact ===
+    post_top = smoothed[top_range[1]:swing_end]
+    if len(post_top) == 0 or np.all(np.isnan(post_top)):
+        impact_range = (top_range[1], top_range[1] + 1)
+    else:
+        local_max_idx = int(np.nanargmax(post_top))
+        max_val = post_top[local_max_idx]
+        local_min = np.nanmin(post_top)
+        local_max = np.nanmax(post_top)
+        dynamic_tol = 0.05 * (local_max - local_min)
+        left = local_max_idx
+        right = local_max_idx
+        while left > 0 and abs(post_top[left - 1] - max_val) < dynamic_tol:
+            left -= 1
+        while right < len(post_top) - 1 and abs(post_top[right + 1] - max_val) < dynamic_tol:
+            right += 1
+        impact_range = (top_range[1] + left, top_range[1] + right)
+
+    # === Phase dictionary ===
+    phase_ranges = {
+        "Address": (address_start, address_end),
+        "Backswing": (swing_start, top_range[0]),
+        "Top": top_range,
+        "Downswing": (top_range[1], impact_range[0]),
+        "Impact": impact_range,
+        "Follow Through": (impact_range[1], swing_end)
+    }
+
+    # === Plotting with gaps and flipped Y ===
+    x = np.array([d['frame_idx'] for d in frame_data])
+    y = np.array([d['wrist_y'] if d['wrist_y'] is not None else np.nan for d in frame_data], dtype=np.float32)
+    valid_y = y[~np.isnan(y)]
+    y_flipped = np.nan if len(valid_y) == 0 else np.max(valid_y) - y + np.min(valid_y)
+
+    plt.figure(figsize=(12, 6))
+    plt.plot(x, y_flipped, label='Wrist Y (flipped)', color='black')
+    colors = ['gray', 'blue', 'orange', 'green', 'red', 'purple']
+    for i, (phase, (start, end)) in enumerate(phase_ranges.items()):
+        plt.axvspan(start, end, alpha=0.3, color=colors[i % len(colors)], label=phase)
+    plt.xlabel("Frame Index")
+    plt.ylabel("Wrist Y (flipped)")
+    plt.title("Wrist Trajectory with Dynamic Phase Detection")
+    plt.legend(loc='upper left')
+    plt.grid(True)
+    plt.tight_layout()
+    plt.show()
+
+    return phase_ranges, swing_start, swing_end, wrist_y, smoothed
+
+
+phase_ranges_iron, swing_start_iron, swing_end_iron, wrist_y_iron, smoothed_iron = detect_swing_phases(frame_data_iron)
+print("🏌️‍♂️ Detected Phase Ranges:", phase_ranges_iron)
+
+phase_ranges_driver, swing_start_driver, swing_end_driver, wrist_y_driver, smoothed_driver = detect_swing_phases(frame_data_driver)
+print("🏌️‍♂️ Detected Phase Ranges:", phase_ranges_driver)
+
+phase_ranges_iron_reference, swing_start, swing_end, wrist_y, smoothed = detect_swing_phases(frame_data_iron_reference)
+print("🏌️‍♂️ Detected Phase Ranges:", phase_ranges_iron_reference)
+
+phase_ranges_driver_reference, swing_start, swing_end, wrist_y, smoothed = detect_swing_phases(frame_data_driver_reference)
+print("🏌️‍♂️ Detected Phase Ranges:", phase_ranges_driver_reference)
+
+# ### 🧑‍🎓📹 Pro vs Student Swing Comparison by Phase (with Pose Overlay)
+# 
+# The `compare_swing_phases()` function visually compares each swing phase between a **student video** and a **pro reference video**, displaying side-by-side frames (optionally with pose landmarks).
+# 
+# #### 📦 Inputs:
+# - `student_video_path`, `pro_video_path`: Paths to fixed videos (with silent audio).
+# - `student_phases`, `pro_phases`: Dicts of frame ranges per phase (from `detect_swing_phases()`).
+# - `show_pose`: If `True`, overlays pose landmarks using MediaPipe.
+# 
+# #### 🧠 Core Logic:
+# - For each swing phase (e.g., `"Address"`, `"Impact"`):
+#   - Finds the middle frame of that phase for both student and pro.
+#   - Extracts and optionally annotates frames using MediaPipe Pose.
+#   - Converts BGR → RGB for display compatibility.
+# 
+# #### 🎨 Output:
+# - A multi-row Matplotlib figure with:
+#   - Left: Student frame for each phase.
+#   - Right: Pro frame for the same phase.
+#   - Optional pose overlay to help analyze alignment, posture, and joint positioning.
+# 
+# #### 🧹 Cleanup:
+# - Releases video capture resources and closes the MediaPipe model.
+# 
+# ✅ Use this for **visual feedback and coaching**, making swing differences easy to spot phase-by-phase.
+# 
+
+
+
+
+def compare_swing_phases(
+    student_video_path, student_phases,
+    pro_video_path, pro_phases,
+    show_pose=True
+):
+    mp_pose = mp.solutions.pose
+    pose = mp_pose.Pose(static_image_mode=True)
+    mp_drawing = mp.solutions.drawing_utils
+    mp_style = mp.solutions.drawing_styles
+
+    def get_frame(cap, frame_idx, apply_pose=False):
+        cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
+        ret, frame = cap.read()
+        if not ret:
+            raise ValueError(f"❌ Failed to read frame {frame_idx}")
+        if apply_pose:
+            rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            results = pose.process(rgb)
+            if results.pose_landmarks:
+                mp_drawing.draw_landmarks(
+                    frame,
+                    results.pose_landmarks,
+                    mp_pose.POSE_CONNECTIONS,
+                    landmark_drawing_spec=mp_style.get_default_pose_landmarks_style()
+                )
+        return frame
+
+    student_cap = cv2.VideoCapture(student_video_path)
+    pro_cap = cv2.VideoCapture(pro_video_path)
+
+    phase_names = list(student_phases.keys())
+    num_phases = len(phase_names)
+
+    plt.figure(figsize=(16, 6 * num_phases))
+
+    for i, phase in enumerate(phase_names):
+        # === Frame indices ===
+        s_start, s_end = student_phases[phase]
+        p_start, p_end = pro_phases[phase]
+
+        s_middle = int((s_start + s_end) // 2)
+        p_middle = int((p_start + p_end) // 2)
+
+        # === Load frames ===
+        student_frame = get_frame(student_cap, s_middle, apply_pose=show_pose)
+        pro_frame = get_frame(pro_cap, p_middle, apply_pose=show_pose)
+
+        student_rgb = cv2.cvtColor(student_frame, cv2.COLOR_BGR2RGB)
+        pro_rgb = cv2.cvtColor(pro_frame, cv2.COLOR_BGR2RGB)
+
+        # === Plot ===
+        plt.subplot(num_phases, 2, 2*i+1)
+        plt.imshow(student_rgb)
+        plt.title(f"Student - {phase}")
+        plt.axis("off")
+
+        plt.subplot(num_phases, 2, 2*i+2)
+        plt.imshow(pro_rgb)
+        plt.title(f"Pro - {phase}")
+        plt.axis("off")
+
+    plt.tight_layout()
+    plt.show()
+
+    student_cap.release()
+    pro_cap.release()
+    pose.close()
+
+
+compare_swing_phases(
+    student_video_path=video_iron_fixed,
+    student_phases=phase_ranges_iron,
+    pro_video_path=iron_reference_fixed,
+    pro_phases=phase_ranges_iron_reference,
+    show_pose=True  # Set to False to skip pose overlay
+)
+
+
+compare_swing_phases(
+    student_video_path=video_driver_fixed,
+    student_phases=phase_ranges_driver,
+    pro_video_path=driver_reference_fixed,
+    pro_phases=phase_ranges_driver_reference,
+    show_pose=True  # Set to False to skip pose overlay
+)
+
+
+# ### 🎥 Generate Annotated Debug Video with Swing Overlay and Slow Motion
+# 
+# This function `generate_debug_video_with_overlay()` creates a side-by-side **video+plot debug visualization**, showing real-time wrist motion trajectory alongside the actual video frames.
+# 
+# ---
+# 
+# #### 🔁 Step-by-Step Workflow:
+# 
+# - **Frame-by-Frame Rendering**:
+#   - Reads each frame from the input video.
+#   - Overlays a Matplotlib plot of smoothed wrist Y-position (`smoothed`), marks current frame, swing start/end, and displays swing phase label (via `get_swing_phase_label()`).
+# 
+# - **Combined Output**:
+#   - Horizontally stacks video frame + debug plot.
+#   - Writes to an MP4 video using OpenCV (`cv2.VideoWriter`).
+# 
+# - **Plot Details**:
+#   - Flipped Y-axis for intuitive visualization.
+#   - Clear phase zones (`Address`, `Backswing`, etc.) shown with labels and color spans.
+# 
+# ---
+# 
+# #### 🐢 Slow-Mo Re-encoding:
+# - After video is saved, re-encodes it using **FFmpeg** to:
+#   - Insert a slow-motion segment during the swing window (`swing_start` → `swing_end`).
+#   - Concatenate trimmed clips before, during (slowed), and after the swing.
+#   - Save to a more compatible format (`*_playable.mp4`).
+# 
+# ---
+# 
+# #### 📤 Returns:
+# - A Colab-embedded video (`IPython.display.Video`) showing:
+#   - Original clip + live tracking plot.
+#   - Frame-wise swing phase.
+#   - Smooth debug overlay and swing window.
+# 
+# 🎯 Perfect for **visual diagnostics**, **student feedback**, and **explaining biomechanics** with frame-accurate debug context.
+# 
+
+
+
+# Get swing phase label for a given frame index
+def get_swing_phase_label(frame_idx, phase_ranges):
+    for label, (start, end) in phase_ranges.items():
+        if start <= frame_idx <= end:
+            return label
+    return None
+
+def generate_debug_video_with_overlay(
+    video_filename: str,
+    output_path: str,
+    wrist_y: list,
+    smoothed: list,
+    phase_ranges: dict,
+    swing_start: int,
+    swing_end: int
+):
+    import gc
+    import cv2
+    import numpy as np
+    import matplotlib.pyplot as plt
+    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+    from matplotlib import use as mpl_use
+    mpl_use("Agg")  # ✅ For headless backend in Colab
+
+    cap = cv2.VideoCapture(video_filename)
+    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    print(f"📹 Frame resolution: {width}x{height}, FPS: {fps:.2f}, Total frames: {total_frames}")
+
+    plot_width = int(width * 2.0)
+    output_size = (width + plot_width, height)
+
+    # ✅ Use mp4v codec for writing temporary mp4
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(output_path, fourcc, fps, output_size)
+
+    fig, ax = plt.subplots(figsize=(plot_width / 100.0, height / 100.0), dpi=100)
+    canvas = FigureCanvas(fig)
+
+    cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
+    frame_idx = 0
+    print("🔄 Rendering video with overlaid debug plots...")
+
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret or frame is None:
+            break
+
+        if frame_idx >= len(wrist_y):
+            print(f"📌 Reached end of trajectory data at frame {frame_idx}.")
+            break
+
+        try:
+            ax.clear()
+            ax.plot(smoothed, label="Smoothed Wrist Y", linestyle='--', color='blue')
+            ax.axvline(frame_idx, color='black', linestyle='-', label="Current Frame")
+
+            # ✅ Phase label helper (you must define or import this)
+            phase_label = get_swing_phase_label(frame_idx, phase_ranges)
+            if phase_label:
+                ax.text(0.02, 0.95, phase_label, transform=ax.transAxes,
+                        fontsize=18, color='black', fontweight='bold',
+                        verticalalignment='top', horizontalalignment='left',
+                        bbox=dict(facecolor='white', alpha=0.7, boxstyle='round'))
+
+            ax.axvspan(swing_start, swing_end, color='green', alpha=0.2, label="Swing Window")
+
+            ax.set_title(f"Debug Plot (Frame {frame_idx})", fontsize=20)
+            ax.set_xlabel("Frame", fontsize=16)
+            ax.set_ylabel("Wrist Y Position", fontsize=16)
+            ax.set_xlim(0, len(wrist_y))
+            ax.set_ylim(np.nanmax(wrist_y) + 10, np.nanmin(wrist_y) - 10)
+            ax.legend(fontsize=14)
+            fig.tight_layout()
+            canvas.draw()
+
+            plot_img = np.frombuffer(canvas.buffer_rgba(), dtype=np.uint8).reshape(
+                canvas.get_width_height()[1], canvas.get_width_height()[0], 4
+            )[..., :3]
+
+            plot_img = cv2.resize(plot_img, (plot_width, height))
+            frame = cv2.resize(frame, (width, height))
+            combined_frame = np.hstack((frame, plot_img))
+            out.write(combined_frame)
+
+            if frame_idx % 50 == 0:
+                print(f"✅ Processed frame {frame_idx}/{total_frames}")
+
+            frame_idx += 1
+            gc.collect()
+
+        except Exception as e:
+            print(f"❌ Error at frame {frame_idx}: {e}")
+            break
+
+    cap.release()
+    out.release()
+    print(f"✅ Debug video saved as {output_path}")
+
+    # === Re-encode with FFmpeg ===
+    reencoded_path = output_path.replace(".mp4", "_playable.mp4")
+
+    # Convert swing frame indices to seconds
+    start_time = swing_start / fps
+    end_time = swing_end / fps
+    slow_factor = 2.0  # Adjust this factor as needed
+
+    # FFmpeg slow-motion filter logic
+    filter_complex = (
+        f"[0:v]trim=0:{start_time:.3f},setpts=PTS-STARTPTS[v1];"
+        f"[0:v]trim={start_time:.3f}:{end_time:.3f},setpts={slow_factor}*(PTS-STARTPTS)[v2];"
+        f"[0:v]trim={end_time:.3f},setpts=PTS-STARTPTS[v3];"
+        f"[v1][v2][v3]concat=n=3:v=1:a=0[outv]"
+    )
+
+    cmd = [
+        "ffmpeg", "-y", "-i", output_path,
+        "-filter_complex", filter_complex,
+        "-map", "[outv]",
+        "-vcodec", "libx264", "-crf", "23", "-preset", "veryfast",
+        "-pix_fmt", "yuv420p",
+        reencoded_path
+    ]
+
+    print("🎬 Re-encoding with slow-motion for Colab compatibility...")
+    subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
+
+
+    # Optional: give disk a moment to flush
+    time.sleep(1)
+
+    return Video(reencoded_path, embed=True, width=960)
+
+
+debug_video = generate_debug_video_with_overlay(video_iron_fixed, "iron_debug.mp4", wrist_y_iron, smoothed_iron, phase_ranges_iron, swing_start_iron, swing_end_iron)
+
+debug_video
+
+
+debug_video = generate_debug_video_with_overlay(video_driver_fixed, "iron_debug.mp4", wrist_y_driver, smoothed_driver, phase_ranges_driver, swing_start_driver, swing_end_driver)
+
+debug_video

extract.py

@@ -33,18 +33,15 @@ def run_ai_extraction(video_path, slow_factor=1.0, output_dir=None, skip_slow_vi
     video_name = os.path.basename(video_path)
     video_prefix = os.path.splitext(video_name)[0]
     
-    # Nếu không có output_dir, dùng mặc định
     if output_dir is None:
         output_dir = os.path.join('output', video_prefix)
         
     model_path = 'models/swingnet_1800.pth.tar'
     phases_dir = os.path.join(output_dir, 'phases')
     
-    # Thêm số thứ tự để đảm bảo sắp xếp đúng trong thư mục
     labels = ['1_Address', '2_Toe-up', '3_Mid-Backswing', '4_Top', 
               '5_Mid-Downswing', '6_Impact', '7_Mid-Follow-Through', '8_Finish']
     
-    # Chỉ tạo thư mục phases nếu cần lưu ảnh
     if not skip_phase_images:
         for label in labels: 
             os.makedirs(os.path.join(phases_dir, label), exist_ok=True)
@@ -74,7 +71,7 @@ def run_ai_extraction(video_path, slow_factor=1.0, output_dir=None, skip_slow_vi
         print("Không tìm thấy frame nào.")
         return
 
-    # Frame Interpolation (Nội suy tuyến tính)
+    # Frame Interpolation
     if slow_factor < 1.0:
         if not production:
             print(f"Đang nội suy frame (Slow-mo {slow_factor}x)...")
@@ -115,13 +112,20 @@ def run_ai_extraction(video_path, slow_factor=1.0, output_dir=None, skip_slow_vi
     else:
         full_res_frames = raw_frames
 
-    # Tiền xử lý cho AI
     if not production:
         print("Đang tiền xử lý cho AI...")
     images = []
     input_size = 160
     for img in tqdm(full_res_frames, disable=production):
         h, w = img.shape[:2]
+        if h > w:
+            center_y = h // 2
+            half_w = w // 2
+            start_y = max(0, center_y - half_w)
+            end_y = min(h, center_y + half_w)
+            img = img[start_y:end_y, :]
+            h, w = img.shape[:2]
+
         ratio = input_size / max(h, w)
         new_size = (int(w * ratio), int(h * ratio))
         resized = cv2.resize(img, new_size)
@@ -145,15 +149,11 @@ def run_ai_extraction(video_path, slow_factor=1.0, output_dir=None, skip_slow_vi
         logits_concat = np.concatenate(all_logits, axis=0)
         probs = F.softmax(torch.tensor(logits_concat), dim=1).numpy()
         
-        # Làm mượt kết quả để giảm nhiễu (đặc biệt cho DTL)
         probs = smooth_probs(probs, window_size=5)
         
-        # Áp dụng thuật toán Anchor-based Bidirectional Search
-        # 1. Tìm sự kiện "Neo" (Anchor) đáng tin cậy nhất (thường là Impact hoặc Top)
-        # Chỉ xét 8 lớp sự kiện đầu tiên, bỏ qua lớp số 9 (No Event/Background)
         probs_events = probs[:, :8]
-        max_probs = np.max(probs_events, axis=0) # Max prob của từng class sự kiện
-        anchor_class = np.argmax(max_probs) # Class có độ tự tin cao nhất trong 8 sự kiện
+        max_probs = np.max(probs_events, axis=0)
+        anchor_class = np.argmax(max_probs)
         anchor_frame = np.argmax(probs_events[:, anchor_class])
         
         if not production:
@@ -162,11 +162,9 @@ def run_ai_extraction(video_path, slow_factor=1.0, output_dir=None, skip_slow_vi
         events = np.zeros(8, dtype=int)
         events[anchor_class] = anchor_frame
         
-        # 2. Đi lùi: Tìm các pha trước Anchor
         current_limit = anchor_frame
         for i in range(anchor_class - 1, -1, -1):
             if current_limit > 0:
-                # Tìm max trong vùng cho phép [0, current_limit]
                 segment = probs[0:current_limit, i]
                 if len(segment) > 0:
                     events[i] = np.argmax(segment)
@@ -176,12 +174,11 @@ def run_ai_extraction(video_path, slow_factor=1.0, output_dir=None, skip_slow_vi
                 events[i] = 0
             current_limit = events[i]
             
-        # 3. Đi tiến: Tìm các pha sau Anchor
         current_limit = anchor_frame
         total_frames = probs.shape[0]
         for i in range(anchor_class + 1, 8):
             if current_limit < total_frames - 1:
-                # Tìm max trong vùng cho phép [current_limit + 1, end]
+                segment = probs[current_limit + 1:, i]
                 # Bắt buộc tiến ít nhất 1 frame
                 segment = probs[current_limit + 1:, i]
                 if len(segment) > 0:
@@ -195,11 +192,9 @@ def run_ai_extraction(video_path, slow_factor=1.0, output_dir=None, skip_slow_vi
         if not production:
             print(f"Detected Events (Frames): {events}")
 
-        # Lưu thông tin frame index để visual_report sử dụng
         event_metadata = {}
         for i, frame_idx in enumerate(events):
             if frame_idx < len(full_res_frames):
-                # Chỉ lưu ảnh nếu không skip
                 if not skip_phase_images:
                     cv2.imwrite(os.path.join(phases_dir, labels[i], f"{video_prefix}.jpg"), full_res_frames[frame_idx])
                 event_metadata[labels[i]] = int(frame_idx)

hf_api_result.json → golf_analysis.json

@@ -1,300 +1,300 @@
 {
   "status": "success",
-  "job_id": "b5a70272-d520-4eb4-bc91-7e92d1d1ec12",
+  "job_id": "d907f221-0b2f-44cd-9af8-cb4921bb13bb",
   "metadata": {
     "event_frames": {
-      "1_Address": 10,
-      "2_Toe-up": 34,
-      "3_Mid-Backswing": 43,
-      "4_Top": 47,
-      "5_Mid-Downswing": 60,
-      "6_Impact": 62,
-      "7_Mid-Follow-Through": 66,
-      "8_Finish": 85
+      "1_Address": 0,
+      "2_Toe-up": 0,
+      "3_Mid-Backswing": 0,
+      "4_Top": 0,
+      "5_Mid-Downswing": 0,
+      "6_Impact": 0,
+      "7_Mid-Follow-Through": 34,
+      "8_Finish": 98
     },
-    "slow_factor": 1.0,
-    "fps": 29.97
+    "slow_factor": 1,
+    "fps": 30
   },
   "analysis": {
-    "video_id": "b5a70272-d520-4eb4-bc91-7e92d1d1ec12",
+    "video_id": "d907f221-0b2f-44cd-9af8-cb4921bb13bb",
     "phases": {
       "1_Address": {
-        "score": 9.0,
+        "score": 10,
         "comments": [
-          "Tư thế đứng quá rộng"
+          "Đạt chuẩn"
         ],
         "data": {
-          "stance_ratio": 1.5782203786848197
+          "stance_ratio": 1.3912659454869785
         },
         "raw_landmarks": [
           {
             "id": 0,
             "name": "NOSE",
-            "x": 0.4693,
-            "y": 0.2687,
-            "z": -0.4986,
-            "visibility": 0.9995
+            "x": 0.5452,
+            "y": 0.5194,
+            "z": -0.5827,
+            "visibility": 1
           },
           {
             "id": 1,
             "name": "LEFT_EYE_INNER",
-            "x": 0.4711,
-            "y": 0.2531,
-            "z": -0.4942,
-            "visibility": 0.9981
+            "x": 0.5509,
+            "y": 0.5123,
+            "z": -0.5868,
+            "visibility": 1
           },
           {
             "id": 2,
             "name": "LEFT_EYE",
-            "x": 0.4738,
-            "y": 0.2497,
-            "z": -0.4942,
-            "visibility": 0.9983
+            "x": 0.5553,
+            "y": 0.5111,
+            "z": -0.5867,
+            "visibility": 0.9999
           },
           {
             "id": 3,
             "name": "LEFT_EYE_OUTER",
-            "x": 0.4764,
-            "y": 0.2454,
-            "z": -0.4943,
-            "visibility": 0.9985
+            "x": 0.5596,
+            "y": 0.5099,
+            "z": -0.5867,
+            "visibility": 0.9999
           },
           {
             "id": 4,
             "name": "RIGHT_EYE_INNER",
-            "x": 0.4634,
-            "y": 0.2575,
-            "z": -0.4906,
-            "visibility": 0.9981
+            "x": 0.5385,
+            "y": 0.5137,
+            "z": -0.5845,
+            "visibility": 1
           },
           {
             "id": 5,
             "name": "RIGHT_EYE",
-            "x": 0.4607,
-            "y": 0.2575,
-            "z": -0.4907,
-            "visibility": 0.9981
+            "x": 0.5341,
+            "y": 0.5134,
+            "z": -0.5847,
+            "visibility": 1
           },
           {
             "id": 6,
             "name": "RIGHT_EYE_OUTER",
-            "x": 0.458,
-            "y": 0.2572,
-            "z": -0.4908,
-            "visibility": 0.9982
+            "x": 0.5296,
+            "y": 0.513,
+            "z": -0.5849,
+            "visibility": 1
           },
           {
             "id": 7,
             "name": "LEFT_EAR",
-            "x": 0.4796,
-            "y": 0.2343,
-            "z": -0.3949,
-            "visibility": 0.9967
+            "x": 0.5626,
+            "y": 0.5054,
+            "z": -0.493,
+            "visibility": 0.9999
           },
           {
             "id": 8,
             "name": "RIGHT_EAR",
-            "x": 0.4553,
-            "y": 0.2518,
-            "z": -0.3741,
-            "visibility": 0.9957
+            "x": 0.5201,
+            "y": 0.508,
+            "z": -0.4836,
+            "visibility": 1
           },
           {
             "id": 9,
             "name": "MOUTH_LEFT",
-            "x": 0.4758,
-            "y": 0.274,
-            "z": -0.4548,
-            "visibility": 0.9991
+            "x": 0.552,
+            "y": 0.521,
+            "z": -0.5366,
+            "visibility": 1
           },
           {
             "id": 10,
             "name": "MOUTH_RIGHT",
-            "x": 0.4663,
-            "y": 0.279,
-            "z": -0.4491,
-            "visibility": 0.9987
+            "x": 0.5369,
+            "y": 0.5218,
+            "z": -0.5344,
+            "visibility": 1
           },
           {
             "id": 11,
             "name": "LEFT_SHOULDER",
-            "x": 0.519,
-            "y": 0.2811,
-            "z": -0.2811,
-            "visibility": 0.9999
+            "x": 0.602,
+            "y": 0.5248,
+            "z": -0.3651,
+            "visibility": 1
           },
           {
             "id": 12,
             "name": "RIGHT_SHOULDER",
-            "x": 0.4423,
-            "y": 0.3185,
-            "z": -0.2545,
-            "visibility": 0.9999
+            "x": 0.4712,
+            "y": 0.5315,
+            "z": -0.3288,
+            "visibility": 1
           },
           {
             "id": 13,
             "name": "LEFT_ELBOW",
-            "x": 0.5202,
-            "y": 0.3966,
-            "z": -0.2576,
-            "visibility": 0.9708
+            "x": 0.584,
+            "y": 0.5799,
+            "z": -0.3018,
+            "visibility": 0.9806
           },
           {
             "id": 14,
             "name": "RIGHT_ELBOW",
-            "x": 0.4681,
-            "y": 0.4256,
-            "z": -0.2122,
-            "visibility": 0.9224
+            "x": 0.4933,
+            "y": 0.5926,
+            "z": -0.2602,
+            "visibility": 0.9898
           },
           {
             "id": 15,
             "name": "LEFT_WRIST",
-            "x": 0.5041,
-            "y": 0.5106,
-            "z": -0.3461,
-            "visibility": 0.9246
+            "x": 0.5475,
+            "y": 0.6287,
+            "z": -0.3638,
+            "visibility": 0.968
           },
           {
             "id": 16,
             "name": "RIGHT_WRIST",
-            "x": 0.4844,
-            "y": 0.5218,
-            "z": -0.2876,
-            "visibility": 0.8555
+            "x": 0.5229,
+            "y": 0.6379,
+            "z": -0.3363,
+            "visibility": 0.98
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         "comments": [
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@@ -303,271 +303,271 @@
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@@ -576,547 +576,547 @@
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         "comments": [
           "Đạt chuẩn"
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@@ -1946,290 +1946,288 @@
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             "name": "LEFT_FOOT_INDEX",
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             "name": "RIGHT_FOOT_INDEX",
-            "x": 0.4341,
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+            "visibility": 0.99
           }
         ]
       }
     },
-    "overall_score": 9.6,
-    "view_angle": "Down-the-Line (Dọc)"
+    "overall_score": 10,
+    "view_angle": "Face-on (Trực diện)"
   },
   "coaching": {
-    "video_id": "b5a70272-d520-4eb4-bc91-7e92d1d1ec12",
-    "final_score": 9.5,
+    "video_id": "d907f221-0b2f-44cd-9af8-cb4921bb13bb",
+    "final_score": 10,
     "skill_level": "Professional / Low Handicap",
     "key_faults": [
-      "[1_Address] Tư thế đứng quá rộng",
+      "[1_Address] Đạt chuẩn",
       "[2_Toe-up] Đạt chuẩn",
       "[3_Mid-Backswing] Đạt chuẩn",
-      "[4_Top] Tay trái bị cong quá nhiều (Chicken Wing)",
+      "[4_Top] Đạt chuẩn",
       "[5_Mid-Downswing] Đạt chuẩn",
       "[6_Impact] Đạt chuẩn",
       "[7_Mid-Follow-Through] Đạt chuẩn",
       "[8_Finish] Đạt chuẩn"
     ],
-    "recommended_drills": [
-      "Bài tập đứng trên hai đầu gối hoặc khép chân để cảm nhận sự xoay trục."
-    ],
-    "summary": "Điểm kỹ thuật của bạn đạt 9.5/10. Kỹ thuật của bạn khá tốt, chỉ cần tinh chỉnh một vài chi tiết nhỏ. Lỗi ưu tiên cần sửa: Tư thế đứng quá rộng."
+    "recommended_drills": [],
+    "summary": "Cú swing của bạn đạt 10.0/10. Kỹ thuật rất chuẩn, hãy tiếp tục duy trì!"
   }
 }

models_v1/mobilenet_v2.pth.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ecbe2b568c8602549fa9e1d5833c63848f490a48d92e5d224d1eb2063e152cf8
+size 14205652

models_v1/swingnet_1800.pth.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6331e303a9e86d0c19f183899f958bf2a71cf5a7070d46899e25e1ac877b23d4
+size 63280059

src/eval.py

@@ -60,7 +60,7 @@ if __name__ == '__main__':
                           bidirectional=True,
                           dropout=False)
 
-    save_dict = torch.load('models/swingnet_1800.pth.tar')
+    save_dict = torch.load('models_v1/swingnet_1800.pth.tar')
     model.load_state_dict(save_dict['model_state_dict'])
     model.cuda()
     model.eval()

src/model.py

@@ -18,7 +18,7 @@ class EventDetector(nn.Module):
         # Get the directory of the current script (src/)
         current_script_directory = os.path.dirname(os.path.abspath(__file__))
         # Path to models folder (outside src/)
-        relative_directory = '../models/mobilenet_v2.pth.tar'
+        relative_directory = '../models_v1/mobilenet_v2.pth.tar'
         
         # Construct the absolute path
         model_dir = os.path.normpath(os.path.join(current_script_directory, relative_directory))