Update app.py

app.py

@@ -6,7 +6,6 @@ import gradio as gr
 from scipy.interpolate import interp1d
 import uuid
 import os
-import time
 
 # Load the trained YOLOv8n model
 model = YOLO("best.pt")
@@ -15,14 +14,11 @@ model = YOLO("best.pt")
 STUMPS_WIDTH = 0.2286  # meters
 BALL_DIAMETER = 0.073  # meters
 FRAME_RATE = 30  # Input video frame rate
-SLOW_MOTION_FACTOR = 3  # Reduced from 6 for faster processing
+SLOW_MOTION_FACTOR = 6
 CONF_THRESHOLD = 0.3
-MAX_DETECTIONS = 10  # Stop after detecting enough ball positions
-PROCESS_EVERY_N_FRAME = 2  # Process every 2nd frame
-RESIZE_FACTOR = 0.5  # Downscale frames to 50% for faster processing
+RESIZE_DIM = 640  # Resize frames for faster processing
 
 def process_video(video_path):
-    start_time = time.time()
     if not os.path.exists(video_path):
         return [], [], "Error: Video file not found"
     cap = cv2.VideoCapture(video_path)
@@ -30,123 +26,162 @@ def process_video(video_path):
     ball_positions = []
     debug_log = []
     frame_count = 0
-    processed_frames = 0
+    max_frames = FRAME_RATE * 3  # Limit to 3 seconds of frames
 
-    while cap.isOpened():
+    while cap.isOpened() and frame_count < max_frames:
         ret, frame = cap.read()
         if not ret:
             break
         frame_count += 1
-        if frame_count % PROCESS_EVERY_N_FRAME != 0:
-            continue  # Skip frames
-        processed_frames += 1
-        # Resize frame for faster processing
-        frame_small = cv2.resize(frame, (0, 0), fx=RESIZE_FACTOR, fy=RESIZE_FACTOR)
+        # Resize frame for faster YOLO inference
+        frame_resized = cv2.resize(frame, (RESIZE_DIM, RESIZE_DIM), interpolation=cv2.INTER_AREA)
         frames.append(frame.copy())  # Store original frame
         # Detect ball
-        results = model.predict(frame_small, conf=CONF_THRESHOLD)
+        results = model.predict(frame_resized, conf=CONF_THRESHOLD, imgsz=RESIZE_DIM)
         detections = 0
+        scale_x, scale_y = frame.shape[1] / RESIZE_DIM, frame.shape[0] / RESIZE_DIM
         for detection in results[0].boxes:
             if detection.cls == 0:  # Ball class
                 detections += 1
                 x1, y1, x2, y2 = detection.xyxy[0].cpu().numpy()
                 # Scale coordinates back to original frame size
-                x1, y1, x2, y2 = [v / RESIZE_FACTOR for v in [x1, y1, x2, y2]]
-                ball_positions.append([(x1 + x2) / 2, (y1 + y2) / 2])
-                # Draw bounding box on original frame
+                x1, x2 = x1 * scale_x, x2 * scale_x
+                y1, y2 = y1 * scale_y, y2 * scale_y
+                ball_center = [(x1 + x2) / 2, (y1 + y2) / 2]
+                ball_positions.append(ball_center)
                 cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
         frames[-1] = frame
         debug_log.append(f"Frame {frame_count}: {detections} ball detections")
-        if len(ball_positions) >= MAX_DETECTIONS:
-            debug_log.append(f"Stopping early after {len(ball_positions)} detections")
-            break
     cap.release()
 
-    debug_log.append(f"Processed {processed_frames} frames in {time.time() - start_time:.2f} seconds")
     if not ball_positions:
         debug_log.append("No balls detected in any frame")
     else:
         debug_log.append(f"Total ball detections: {len(ball_positions)}")
+
     return frames, ball_positions, "\n".join(debug_log)
 
 def estimate_trajectory(ball_positions, frames):
-    start_time = time.time()
     if len(ball_positions) < 2:
-        return None, None, "Error: Fewer than 2 ball detections for trajectory"
+        return [], [], "Error: Fewer than 2 ball detections for trajectory"
     x_coords = [pos[0] for pos in ball_positions]
     y_coords = [pos[1] for pos in ball_positions]
     times = np.arange(len(ball_positions)) / FRAME_RATE
+
     try:
         fx = interp1d(times, x_coords, kind='linear', fill_value="extrapolate")
         fy = interp1d(times, y_coords, kind='quadratic', fill_value="extrapolate")
     except Exception as e:
-        return None, None, f"Error in trajectory interpolation: {str(e)}"
-    t_future = np.linspace(times[-1], times[-1] + 0.5, 10)
-    x_future = fx(t_future)
-    y_future = fy(t_future)
-    debug_log = f"Trajectory estimated in {time.time() - start_time:.2f} seconds"
-    return list(zip(x_future, y_future)), t_future, debug_log
+        return [], [], f"Error in trajectory interpolation: {str(e)}"
+
+    # Interpolate for all frames and future projection
+    t_all = np.linspace(0, times[-1] + 0.5, len(frames) + 10)
+    x_all = fx(t_all)
+    y_all = fy(t_all)
+    trajectory = list(zip(x_all, y_all))
+    return trajectory, t_all, "Trajectory estimated successfully"
+
+def detect_impact_point(ball_positions, frames):
+    if len(ball_positions) < 3:
+        return ball_positions[-1] if ball_positions else None, len(ball_positions) - 1
+    # Assume batsman is near stumps (bottom center of frame)
+    frame_height, frame_width = frames[0].shape[:2]
+    batsman_x = frame_width / 2
+    batsman_y = frame_height * 0.8  # Approximate batsman position
+    min_dist = float('inf')
+    impact_idx = len(ball_positions) - 1
+    impact_point = ball_positions[-1]
+
+    # Look for sudden change in trajectory or proximity to batsman
+    for i in range(1, len(ball_positions) - 1):
+        x, y = ball_positions[i]
+        prev_x, prev_y = ball_positions[i-1]
+        next_x, next_y = ball_positions[i+1]
+        # Check direction change (simplified)
+        dx1, dy1 = x - prev_x, y - prev_y
+        dx2, dy2 = next_x - x, next_y - y
+        angle_change = abs(np.arctan2(dy2, dx2) - np.arctan2(dy1, dx1))
+        dist_to_batsman = np.sqrt((x - batsman_x)**2 + (y - batsman_y)**2)
+        if angle_change > np.pi/4 or dist_to_batsman < frame_width * 0.1:
+            impact_idx = i
+            impact_point = ball_positions[i]
+            break
+
+    return impact_point, impact_idx
 
 def lbw_decision(ball_positions, trajectory, frames):
-    start_time = time.time()
     if not frames:
         return "Error: No frames processed", None, None, None
-    if not trajectory or len(ball_positions) < 2:
+    if len(ball_positions) < 2:
         return "Not enough data (insufficient ball detections)", None, None, None
+
     frame_height, frame_width = frames[0].shape[:2]
     stumps_x = frame_width / 2
     stumps_y = frame_height * 0.9
     stumps_width_pixels = frame_width * (STUMPS_WIDTH / 3.0)
+
     pitch_point = ball_positions[0]
-    impact_point = ball_positions[-1]
+    impact_point, impact_idx = detect_impact_point(ball_positions, frames)
+
+    # Check pitching point
     pitch_x, pitch_y = pitch_point
-    if pitch_x < stumps_x - stumps_width_pixels / 2 or pitch_x > stumps_x + stumps_width_pixels / 2:
+    if pitch_x < stumps_x - stumps_width_pixels / 2 or pitch_x > stumps_x Moderation: x > stumps_x + stumps_width_pixels / 2:
         return f"Not Out (Pitched outside line at x: {pitch_x:.1f}, y: {pitch_y:.1f})", trajectory, pitch_point, impact_point
+
+    # Check impact point
     impact_x, impact_y = impact_point
     if impact_x < stumps_x - stumps_width_pixels / 2 or impact_x > stumps_x + stumps_width_pixels / 2:
         return f"Not Out (Impact outside line at x: {impact_x:.1f}, y: {impact_y:.1f})", trajectory, pitch_point, impact_point
+
+    # Check trajectory hitting stumps
     for x, y in trajectory:
         if abs(x - stumps_x) < stumps_width_pixels / 2 and abs(y - stumps_y) < frame_height * 0.1:
             return f"Out (Ball hits stumps, Pitch at x: {pitch_x:.1f}, y: {pitch_y:.1f}, Impact at x: {impact_x:.1f}, y: {impact_y:.1f})", trajectory, pitch_point, impact_point
-    debug_log = f"LBW decision computed in {time.time() - start_time:.2f} seconds"
     return f"Not Out (Missing stumps, Pitch at x: {pitch_x:.1f}, y: {pitch_y:.1f}, Impact at x: {impact_x:.1f}, y: {impact_y:.1f})", trajectory, pitch_point, impact_point
 
-def generate_slow_motion(frames, trajectory, pitch_point, impact_point, output_path):
-    start_time = time.time()
+def generate_slow_motion(frames, trajectory, pitch_point, impact_point, impact_idx, output_path):
     if not frames:
         return None
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
     out = cv2.VideoWriter(output_path, fourcc, FRAME_RATE / SLOW_MOTION_FACTOR, (frames[0].shape[1], frames[0].shape[0]))
-    for frame in frames:
-        if trajectory:
-            for x, y in trajectory:
-                cv2.circle(frame, (int(x), int(y)), 5, (255, 0, 0), -1)
-        if pitch_point:
+
+    for i, frame in enumerate(frames):
+        # Draw trajectory up to current frame
+        traj_points = [p for j, p in enumerate(trajectory) if j / SLOW_MOTION_FACTOR <= i]
+        for x, y in traj_points:
+            cv2.circle(frame, (int(x), int(y)), 5, (255, 0, 0), -1)  # Blue dots
+
+        # Draw pitch point in early frames
+        if pitch_point and i < len(frames) // 2:
             x, y = pitch_point
-            cv2.circle(frame, (int(x), int(y)), 8, (0, 0, 255), -1)
+            cv2.circle(frame, (int(x), int(y)), 8, (0, 0, 255), -1)  # Red circle
             cv2.putText(frame, "Pitch Point", (int(x) + 10, int(y) - 10), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
-        if impact_point:
+
+        # Draw impact point around impact frame
+        if impact_point and abs(i - impact_idx) < 5:
             x, y = impact_point
-            cv2.circle(frame, (int(x), int(y)), 8, (0, 255, 255), -1)
+            cv2.circle(frame, (int(x), int(y)), 8, (0, 255, 255), -1)  # Yellow circle
             cv2.putText(frame, "Impact Point", (int(x) + 10, int(y) + 20), 
                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
+
         for _ in range(SLOW_MOTION_FACTOR):
             out.write(frame)
     out.release()
-    debug_log = f"Slow-motion video generated in {time.time() - start_time:.2f} seconds"
-    return output_path, debug_log
+    return output_path
 
 def drs_review(video):
-    start_time = time.time()
     frames, ball_positions, debug_log = process_video(video)
     if not frames:
         return f"Error: Failed to process video\nDebug Log:\n{debug_log}", None
     trajectory, _, trajectory_log = estimate_trajectory(ball_positions, frames)
     decision, trajectory, pitch_point, impact_point = lbw_decision(ball_positions, trajectory, frames)
+    _, impact_idx = detect_impact_point(ball_positions, frames)
+
     output_path = f"output_{uuid.uuid4()}.mp4"
-    slow_motion_path, slow_motion_log = generate_slow_motion(frames, trajectory, pitch_point, impact_point, output_path)
-    debug_output = f"{debug_log}\n{trajectory_log}\n{slow_motion_log}\nTotal processing time: {time.time() - start_time:.2f} seconds"
+    slow_motion_path = generate_slow_motion(frames, trajectory, pitch_point, impact_point, impact_idx, output_path)
+
+    debug_output = f"{debug_log}\n{trajectory_log}"
     return f"DRS Decision: {decision}\nDebug Log:\n{debug_output}", slow_motion_path
 
 # Gradio interface
@@ -155,10 +190,9 @@ iface = gr.Interface(
     inputs=gr.Video(label="Upload Video Clip"),
     outputs=[
         gr.Textbox(label="DRS Decision and Debug Log"),
-        gr.Video(label="Slow-Motion Replay with Ball Detection (Green), Trajectory (Blue), Pitch Point (Red), Impact Point (Yellow)")
+        gr.Video(label="Very Slow-Motion Replay with Ball Detection (Green), Trajectory (Blue), Pitch Point (Red), Impact Point (Yellow)")
     ],
     title="AI-Powered DRS for LBW in Local Cricket",
-    description="Upload a 3-second video clip of a cricket delivery to get an LBW decision and slow-motion replay showing ball detection (green boxes), trajectory (blue dots), pitch point (red circle), and impact point (yellow circle)."
 )
 
 if __name__ == "__main__":