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| # Pose-Think: AI-Powered Movement Analysis Suite | |
| # Hugging Face Spaces Compatible Version | |
| import cv2 | |
| import mediapipe as mp | |
| import gradio as gr | |
| import numpy as np | |
| # MediaPipe başlatma / Initialize MediaPipe | |
| mp_pose = mp.solutions.pose | |
| mp_hands = mp.solutions.hands | |
| mp_drawing = mp.solutions.drawing_utils | |
| class PostureAnalyzer: | |
| """Birleşik postür analiz sınıfı / Unified posture analyzer class""" | |
| def __init__(self): | |
| self.pose = mp_pose.Pose( | |
| static_image_mode=False, | |
| model_complexity=1, | |
| enable_segmentation=False, | |
| min_detection_confidence=0.5, | |
| min_tracking_confidence=0.5 | |
| ) | |
| self.hands = mp_hands.Hands( | |
| static_image_mode=False, | |
| max_num_hands=2, | |
| min_detection_confidence=0.5, | |
| min_tracking_confidence=0.5 | |
| ) | |
| def calculate_angle(self, a, b, c): | |
| """Üç nokta arasındaki açıyı hesapla / Calculate angle between three points""" | |
| try: | |
| a = np.array(a) | |
| b = np.array(b) | |
| c = np.array(c) | |
| radians = np.arctan2(c[1] - b[1], c[0] - b[0]) - np.arctan2(a[1] - b[1], a[0] - b[0]) | |
| angle = np.abs(radians * 180.0 / np.pi) | |
| if angle > 180.0: | |
| angle = 360 - angle | |
| return angle | |
| except: | |
| return 0 | |
| def analyze_posture(self, image, analysis_type="basic", age=None, height=None, weight=None): | |
| """Ana analiz fonksiyonu / Main analysis function""" | |
| if image is None: | |
| return None, "❌ Görüntü yok / No image" | |
| # BGR'den RGB'ye çevir / Convert BGR to RGB | |
| rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| # Analiz türüne göre işlem / Process based on analysis type | |
| if analysis_type == "hand": | |
| return self._analyze_hands(rgb_image) | |
| else: | |
| return self._analyze_body_posture(rgb_image, analysis_type, age, height, weight) | |
| def _analyze_hands(self, rgb_image): | |
| """El analizi / Hand analysis""" | |
| results = self.hands.process(rgb_image) | |
| output_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR) | |
| feedback = [] | |
| if results.multi_hand_landmarks: | |
| hand_count = len(results.multi_hand_landmarks) | |
| feedback.append(f"✅ {hand_count} el tespit edildi / {hand_count} hand(s) detected") | |
| for idx, hand_landmarks in enumerate(results.multi_hand_landmarks): | |
| # El landmark'larını çiz / Draw hand landmarks | |
| mp_drawing.draw_landmarks( | |
| output_image, hand_landmarks, mp_hands.HAND_CONNECTIONS) | |
| # Parmak durumları / Finger states | |
| landmarks = hand_landmarks.landmark | |
| # Başparmak / Thumb | |
| thumb_tip = landmarks[mp_hands.HandLandmark.THUMB_TIP] | |
| thumb_ip = landmarks[mp_hands.HandLandmark.THUMB_IP] | |
| # Diğer parmaklar / Other fingers | |
| fingers_up = 0 | |
| finger_names = ["Başparmak/Thumb", "İşaret/Index", "Orta/Middle", "Yüzük/Ring", "Serçe/Pinky"] | |
| # Basit parmak sayma / Simple finger counting | |
| tip_ids = [4, 8, 12, 16, 20] | |
| pip_ids = [3, 6, 10, 14, 18] | |
| for i in range(5): | |
| if landmarks[tip_ids[i]].y < landmarks[pip_ids[i]].y: | |
| fingers_up += 1 | |
| feedback.append(f"🖐️ El {idx+1}: {fingers_up} parmak yukarıda / Hand {idx+1}: {fingers_up} fingers up") | |
| else: | |
| feedback.append("❌ El tespit edilemedi / No hands detected") | |
| feedback.append("🖐️ Ellerinizi kameraya gösterin / Show your hands to the camera") | |
| return output_image, "\n".join(feedback) | |
| def _analyze_body_posture(self, rgb_image, analysis_type, age, height, weight): | |
| """Vücut postür analizi / Body posture analysis""" | |
| results = self.pose.process(rgb_image) | |
| output_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR) | |
| feedback = [] | |
| # Profil bilgisi varsa ekle / Add profile info if available | |
| if analysis_type == "enhanced" and (age or height or weight): | |
| profile_info = [] | |
| if age: | |
| profile_info.append(f"Yaş/Age: {age}") | |
| if height and weight: | |
| bmi = weight / ((height/100) ** 2) | |
| profile_info.append(f"BMI: {bmi:.1f}") | |
| if bmi > 25: | |
| feedback.append("⚠️ BMI yüksek - postür üzerinde ekstra yük / High BMI - extra load on posture") | |
| if profile_info: | |
| feedback.append(f"👤 Profil / Profile: {' | '.join(profile_info)}") | |
| feedback.append("") | |
| if results.pose_landmarks: | |
| # Landmark'ları çiz / Draw landmarks | |
| mp_drawing.draw_landmarks( | |
| output_image, | |
| results.pose_landmarks, | |
| mp_pose.POSE_CONNECTIONS | |
| ) | |
| landmarks = results.pose_landmarks.landmark | |
| # Görünür parçaları kontrol et / Check visible parts | |
| visible_parts = [] | |
| # Baş / Head | |
| if landmarks[mp_pose.PoseLandmark.NOSE.value].visibility > 0.5: | |
| visible_parts.append("Baş/Head") | |
| # Omuzlar / Shoulders | |
| left_shoulder = landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value] | |
| right_shoulder = landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value] | |
| if left_shoulder.visibility > 0.5 and right_shoulder.visibility > 0.5: | |
| visible_parts.append("Omuzlar/Shoulders") | |
| # Omuz seviyesi / Shoulder level | |
| shoulder_diff = abs(left_shoulder.y - right_shoulder.y) | |
| if shoulder_diff > 0.05: | |
| if left_shoulder.y < right_shoulder.y: | |
| feedback.append("⚠️ Sol omuz yüksek / Left shoulder high") | |
| else: | |
| feedback.append("⚠️ Sağ omuz yüksek / Right shoulder high") | |
| else: | |
| feedback.append("✅ Omuzlar seviyeli / Shoulders level") | |
| # Dirsekler ve açıları / Elbows and angles | |
| left_elbow = landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value] | |
| right_elbow = landmarks[mp_pose.PoseLandmark.RIGHT_ELBOW.value] | |
| if left_elbow.visibility > 0.5 and right_elbow.visibility > 0.5: | |
| visible_parts.append("Dirsekler/Elbows") | |
| # Dirsek açıları hesapla / Calculate elbow angles | |
| try: | |
| # Sol dirsek / Left elbow | |
| left_shoulder_pos = [left_shoulder.x, left_shoulder.y] | |
| left_elbow_pos = [left_elbow.x, left_elbow.y] | |
| left_wrist_pos = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x, | |
| landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y] | |
| left_elbow_angle = self.calculate_angle(left_shoulder_pos, left_elbow_pos, left_wrist_pos) | |
| if left_elbow_angle > 0: | |
| feedback.append(f"📐 Sol dirsek açısı / Left elbow: {left_elbow_angle:.1f}°") | |
| # Sağ dirsek / Right elbow | |
| right_shoulder_pos = [right_shoulder.x, right_shoulder.y] | |
| right_elbow_pos = [right_elbow.x, right_elbow.y] | |
| right_wrist_pos = [landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].x, | |
| landmarks[mp_pose.PoseLandmark.RIGHT_WRIST.value].y] | |
| right_elbow_angle = self.calculate_angle(right_shoulder_pos, right_elbow_pos, right_wrist_pos) | |
| if right_elbow_angle > 0: | |
| feedback.append(f"📐 Sağ dirsek açısı / Right elbow: {right_elbow_angle:.1f}°") | |
| except: | |
| feedback.append("⚠️ Dirsek açısı hesaplanamadı / Cannot calculate elbow angles") | |
| # Kalçalar / Hips | |
| left_hip = landmarks[mp_pose.PoseLandmark.LEFT_HIP.value] | |
| right_hip = landmarks[mp_pose.PoseLandmark.RIGHT_HIP.value] | |
| if left_hip.visibility > 0.5 and right_hip.visibility > 0.5: | |
| visible_parts.append("Kalçalar/Hips") | |
| # Kalça seviyesi / Hip level | |
| hip_diff = abs(left_hip.y - right_hip.y) | |
| if hip_diff > 0.03: | |
| if left_hip.y < right_hip.y: | |
| feedback.append("⚠️ Sol kalça yüksek / Left hip high") | |
| else: | |
| feedback.append("⚠️ Sağ kalça yüksek / Right hip high") | |
| else: | |
| feedback.append("✅ Kalçalar seviyeli / Hips level") | |
| # Boyun pozisyonu / Neck position | |
| nose = landmarks[mp_pose.PoseLandmark.NOSE.value] | |
| if nose.visibility > 0.5 and left_shoulder.visibility > 0.5 and right_shoulder.visibility > 0.5: | |
| shoulder_center_x = (left_shoulder.x + right_shoulder.x) / 2 | |
| head_offset = abs(nose.x - shoulder_center_x) | |
| if head_offset > 0.08: | |
| if nose.x < shoulder_center_x: | |
| feedback.append("🔍 Boyun sola eğik / Neck tilted left") | |
| else: | |
| feedback.append("🔍 Boyun sağa eğik / Neck tilted right") | |
| else: | |
| feedback.append("🔍 Boyun merkezi / Neck centered") | |
| # Yaşa özel öneriler (enhanced modda) / Age-specific recommendations (in enhanced mode) | |
| if analysis_type == "enhanced" and age: | |
| feedback.append("") | |
| feedback.append("🎯 Yaşınıza Özel Öneriler / Age-Specific Recommendations:") | |
| if age < 25: | |
| feedback.append("💡 Genç yaş: Postür alışkanlıkları şimdi oluşturun / Young age: Form posture habits now") | |
| elif age < 45: | |
| feedback.append("💡 Orta yaş: Düzenli egzersiz önemli / Middle age: Regular exercise important") | |
| else: | |
| feedback.append("💡 Olgun yaş: Kemik sağlığına dikkat / Mature age: Focus on bone health") | |
| # Görünür parçaları listele / List visible parts | |
| if visible_parts: | |
| feedback.insert(0, f"✅ Görünen / Visible: {', '.join(visible_parts)}") | |
| feedback.insert(1, "") | |
| else: | |
| feedback.append("❌ Vücut tespit edilemedi / Body not detected") | |
| feedback.append("📍 Kameraya tam vücut görünecek şekilde durun / Stand so full body is visible") | |
| return output_image, "\n".join(feedback) | |
| # Global analyzer | |
| analyzer = PostureAnalyzer() | |
| def analyze_movement(image, analysis_type, age, height, weight): | |
| """Hareket analizi ana fonksiyonu / Main movement analysis function""" | |
| return analyzer.analyze_posture(image, analysis_type, age, height, weight) | |
| # Basit Gradio arayüzü / Simple Gradio interface | |
| def create_interface(): | |
| def process_with_settings(image, analysis_type, age, height, weight): | |
| """Ayarlarla birlikte işle / Process with settings""" | |
| # Boş değerleri None'a çevir / Convert empty values to None | |
| age = int(age) if age and str(age).strip() else None | |
| height = int(height) if height and str(height).strip() else None | |
| weight = int(weight) if weight and str(weight).strip() else None | |
| return analyze_movement(image, analysis_type, age, height, weight) | |
| # Ana interface / Main interface | |
| with gr.Blocks(title="Pose-Think: AI Movement Analysis") as demo: | |
| gr.Markdown(""" | |
| # 🎯 Pose-Think: AI-Powered Movement Analysis Suite | |
| ## Real-time posture and movement analysis with multiple modes | |
| **Choose your analysis type and get instant feedback on what the camera sees!** | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| # Analiz türü / Analysis type | |
| analysis_type = gr.Radio( | |
| choices=[ | |
| ("🎯 Basic Posture", "basic"), | |
| ("🎯 Enhanced Posture", "enhanced"), | |
| ("🤚 Hand Tracking", "hand") | |
| ], | |
| value="basic", | |
| label="Analysis Type" | |
| ) | |
| # Profil bilgileri / Profile info | |
| gr.Markdown("### 👤 Optional Profile (for Enhanced mode)") | |
| age_input = gr.Number(label="Age", minimum=10, maximum=100, value=None) | |
| height_input = gr.Number(label="Height (cm)", minimum=100, maximum=250, value=None) | |
| weight_input = gr.Number(label="Weight (kg)", minimum=30, maximum=200, value=None) | |
| # Kamera / Camera | |
| input_image = gr.Image(sources=["webcam"], streaming=True, label="📹 Camera") | |
| # Analiz modu seçimi / Analysis mode selection | |
| realtime_mode = gr.Checkbox(label="🔄 Real-time Analysis / Gerçek Zamanlı Analiz", value=True) | |
| # Buton (sadece real-time kapalıysa) / Button (only when real-time is off) | |
| analyze_btn = gr.Button("🔍 Analyze", variant="primary", visible=False) | |
| with gr.Column(): | |
| # Çıktılar / Outputs | |
| output_image = gr.Image(label="🎯 Analysis Result") | |
| feedback_text = gr.Textbox( | |
| label="📊 Detailed Feedback", | |
| lines=15, | |
| interactive=False | |
| ) | |
| # Real-time modu toggle / Real-time mode toggle | |
| def toggle_realtime(realtime_enabled): | |
| return gr.update(visible=not realtime_enabled) | |
| realtime_mode.change( | |
| fn=toggle_realtime, | |
| inputs=[realtime_mode], | |
| outputs=[analyze_btn] | |
| ) | |
| # Gerçek zamanlı analiz / Real-time analysis | |
| input_image.stream( | |
| fn=process_with_settings, | |
| inputs=[input_image, analysis_type, age_input, height_input, weight_input], | |
| outputs=[output_image, feedback_text], | |
| stream_every=0.5 # Her 0.5 saniyede bir analiz / Analyze every 0.5 seconds | |
| ) | |
| # Manuel analiz butonu / Manual analysis button | |
| analyze_btn.click( | |
| fn=process_with_settings, | |
| inputs=[input_image, analysis_type, age_input, height_input, weight_input], | |
| outputs=[output_image, feedback_text] | |
| ) | |
| # Kullanım talimatları / Usage instructions | |
| gr.Markdown(""" | |
| ## 📋 How to Use | |
| ### 🎯 **Analysis Types:** | |
| - **Basic Posture**: Body parts, joint angles, alignment | |
| - **Enhanced Posture**: Basic + age/BMI insights | |
| - **Hand Tracking**: Hand detection and finger counting | |
| ### � **Analysis Modes:** | |
| - **Real-time**: Continuous analysis (default) - automatic feedback every 0.5 seconds | |
| - **Manual**: Click "Analyze" button for single analysis | |
| ### �📝 **Instructions:** | |
| 1. Choose analysis type | |
| 2. Allow camera access when prompted | |
| 3. Position yourself 2-3 meters from camera | |
| 4. **Real-time mode**: Get continuous feedback automatically | |
| 5. **Manual mode**: Uncheck real-time and click Analyze button | |
| 6. For Enhanced: Enter age/height/weight for personalized insights | |
| ### 🎯 **Feedback Symbols:** | |
| - ✅ Good alignment | ⚠️ Issues detected | 📐 Joint angles | 🔍 Position info | |
| """) | |
| return demo | |
| demo = create_interface() | |
| if __name__ == "__main__": | |
| demo.launch() | |