Update app.py

app.py

@@ -1,69 +1,79 @@
 import cv2
-import numpy as np
 import mediapipe as mp
+import numpy as np
 import gradio as gr
 
-# Initialize Mediapipe Pose
 mp_pose = mp.solutions.pose
 mp_drawing = mp.solutions.drawing_utils
-pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.5)
 
-def calculate_angle(a, b, c):
-    """Calculate angle between three points (for body extension)."""
-    a = np.array(a)  # Point A
-    b = np.array(b)  # Joint (Point B)
-    c = np.array(c)  # Point C
+pose = mp_pose.Pose(static_image_mode=False, min_detection_confidence=0.5, min_tracking_confidence=0.5)
 
+def calculate_angle(a, b, c):
+    a = np.array(a)
+    b = np.array(b)
+    c = np.array(c)
     ba = a - b
     bc = c - b
-
     cosine_angle = np.dot(ba, bc) / (np.linalg.norm(ba) * np.linalg.norm(bc))
     angle = np.arccos(cosine_angle)
     return np.degrees(angle)
 
-def detect_pose(image):
-    """Detect pose in an image and return annotated image + example angle."""
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-    results = pose.process(image_rgb)
+def detect_pose_video(video_path):
+    cap = cv2.VideoCapture(video_path)
+    output_frames = []
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        results = pose.process(frame_rgb)
+
+        if results.pose_landmarks:
+            mp_drawing.draw_landmarks(
+                frame,
+                results.pose_landmarks,
+                mp_pose.POSE_CONNECTIONS,
+                mp_drawing.DrawingSpec(color=(0,255,0), thickness=2, circle_radius=2),
+                mp_drawing.DrawingSpec(color=(0,0,255), thickness=2)
+            )
 
-    annotated = image.copy()
-    angle_text = "No person detected"
+            # Example: left elbow angle
+            h, w, _ = frame.shape
+            landmarks = results.pose_landmarks.landmark
+            shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x * w,
+                        landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y * h]
+            elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x * w,
+                     landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y * h]
+            wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x * w,
+                     landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y * h]
 
-    if results.pose_landmarks:
-        # Draw pose landmarks
-        mp_drawing.draw_landmarks(
-            annotated,
-            results.pose_landmarks,
-            mp_pose.POSE_CONNECTIONS,
-            mp_drawing.DrawingSpec(color=(0, 255, 0), thickness=2, circle_radius=2),
-            mp_drawing.DrawingSpec(color=(0, 0, 255), thickness=2)
-        )
+            angle = calculate_angle(shoulder, elbow, wrist)
+            cv2.putText(frame, f"Left Elbow: {int(angle)} deg", (20,40),
+                        cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,0), 2)
 
-        # Example: Calculate elbow angle (shoulder-elbow-wrist)
-        h, w, _ = image.shape
-        landmarks = results.pose_landmarks.landmark
-        shoulder = [landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x * w,
-                    landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y * h]
-        elbow = [landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].x * w,
-                 landmarks[mp_pose.PoseLandmark.LEFT_ELBOW.value].y * h]
-        wrist = [landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].x * w,
-                 landmarks[mp_pose.PoseLandmark.LEFT_WRIST.value].y * h]
+        output_frames.append(frame)
 
-        angle = calculate_angle(shoulder, elbow, wrist)
-        angle_text = f"Left Elbow Angle: {int(angle)}°"
+    cap.release()
 
-        cv2.putText(annotated, angle_text, (20, 40),
-                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)
+    # Convert frames to video file
+    out_path = "annotated_video.mp4"
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter(out_path, fourcc, 20.0, (frame.shape[1], frame.shape[0]))
+    for f in output_frames:
+        out.write(f)
+    out.release()
 
-    return annotated[:, :, ::-1], angle_text  # Convert BGR → RGB for Gradio
+    return out_path
 
-# Gradio Interface
+# Gradio interface
 demo = gr.Interface(
-    fn=detect_pose,
-    inputs=gr.Image(type="numpy", label="Upload Image"),
-    outputs=[gr.Image(type="numpy", label="Pose Estimation"), gr.Textbox(label="Body Extension Angle")],
-    title="Human Pose Estimation with MediaPipe",
-    description="Upload an image with a person doing exercise. The app will detect body pose and calculate joint angles."
+    fn=detect_pose_video,
+    inputs=gr.Video(label="Upload Video"),
+    outputs=gr.Video(label="Annotated Video"),
+    title="Human Pose Estimation on Video",
+    description="Upload a video and see pose landmarks & joint angles detected in real-time."
 )
 
 if __name__ == "__main__":