Update app.py

app.py

@@ -15,15 +15,17 @@ STUMPS_WIDTH = 0.2286  # meters (width of stumps)
 BALL_DIAMETER = 0.073  # meters (approx. cricket ball diameter)
 FRAME_RATE = 30  # Input video frame rate
 SLOW_MOTION_FACTOR = 6  # For very slow motion (6x slower)
-CONF_THRESHOLD = 0.25  # Lowered confidence threshold to improve detection
+CONF_THRESHOLD = 0.25  # Confidence threshold for detection
 IMPACT_ZONE_Y = 0.85  # Fraction of frame height where impact is likely (near stumps)
+IMPACT_DELTA_Y = 50  # Pixels for detecting sudden y-position change (impact)
 
 def process_video(video_path):
     if not os.path.exists(video_path):
-        return [], [], "Error: Video file not found"
+        return [], [], [], "Error: Video file not found"
     cap = cv2.VideoCapture(video_path)
     frames = []
     ball_positions = []
+    detection_frames = []  # Track frames with detections
     debug_log = []
 
     frame_count = 0
@@ -40,6 +42,7 @@ def process_video(video_path):
                 detections += 1
                 x1, y1, x2, y2 = detection.xyxy[0].cpu().numpy()
                 ball_positions.append([(x1 + x2) / 2, (y1 + y2) / 2])
+                detection_frames.append(frame_count - 1)  # Store frame index (0-based)
                 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")
@@ -50,48 +53,34 @@ def process_video(video_path):
     else:
         debug_log.append(f"Total ball detections: {len(ball_positions)}")
 
-    # Interpolate missing detections
-    if ball_positions:
-        ball_positions = interpolate_missing_positions(ball_positions, frame_count)
+    return frames, ball_positions, detection_frames, "\n".join(debug_log)
 
-    return frames, ball_positions, "\n".join(debug_log)
-
-def interpolate_missing_positions(ball_positions, total_frames):
-    if len(ball_positions) < 2:
-        return ball_positions
-    times = np.linspace(0, total_frames / FRAME_RATE, total_frames)
-    detected_times = [i / FRAME_RATE for i, _ in enumerate(ball_positions)]
-    x_coords = [pos[0] for pos in ball_positions]
-    y_coords = [pos[1] for pos in ball_positions]
-
-    try:
-        fx = interp1d(detected_times, x_coords, kind='linear', fill_value="extrapolate")
-        fy = interp1d(detected_times, y_coords, kind='linear', fill_value="extrapolate")
-        interpolated_positions = [(fx(t), fy(t)) for t in times if t <= detected_times[-1]]
-        return interpolated_positions
-    except:
-        return ball_positions
-
-def estimate_trajectory(ball_positions, frames):
+def estimate_trajectory(ball_positions, frames, detection_frames):
     if len(ball_positions) < 2:
-        return None, None, None, "Error: Fewer than 2 ball detections for trajectory"
+        return None, None, None, None, "Error: Fewer than 2 ball detections for trajectory"
     frame_height = frames[0].shape[0]
 
     # Extract x, y coordinates
     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
+    times = np.array(detection_frames) / FRAME_RATE
+
+    # Identify pitch point (first detection)
+    pitch_point = ball_positions[0]
+    pitch_frame = detection_frames[0]
 
-    # Find impact point (closest to batsman, near stumps)
+    # Find impact point (sudden change in y or near stumps)
     impact_idx = None
-    for i, y in enumerate(y_coords):
-        if y > frame_height * IMPACT_ZONE_Y:  # Ball is near stumps/batsman
+    impact_frame = None
+    for i in range(1, len(y_coords)):
+        if y_coords[i] > frame_height * IMPACT_ZONE_Y or abs(y_coords[i] - y_coords[i-1]) > IMPACT_DELTA_Y:
             impact_idx = i
+            impact_frame = detection_frames[i]
             break
     if impact_idx is None:
-        impact_idx = len(ball_positions) - 1  # Fallback to last detection
+        impact_idx = len(ball_positions) - 1
+        impact_frame = detection_frames[-1]
 
-    pitch_point = ball_positions[0]
     impact_point = ball_positions[impact_idx]
 
     # Use positions up to impact for interpolation
@@ -103,15 +92,16 @@ def estimate_trajectory(ball_positions, frames):
         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, None, f"Error in trajectory interpolation: {str(e)}"
+        return None, None, None, None, f"Error in trajectory interpolation: {str(e)}"
 
-    # Project trajectory (detected + future)
+    # Project trajectory (detected + future for LBW decision)
     t_full = np.linspace(times[0], times[-1] + 0.5, len(times) + 10)
     x_full = fx(t_full)
     y_full = fy(t_full)
     trajectory = list(zip(x_full, y_full))
 
-    return trajectory, pitch_point, impact_point, "Trajectory estimated successfully"
+    debug_log = f"Trajectory estimated successfully\nPitch point at frame {pitch_frame + 1}: ({pitch_point[0]:.1f}, {pitch_point[1]:.1f})\nImpact point at frame {impact_frame + 1}: ({impact_point[0]:.1f}, {impact_point[1]:.1f})"
+    return trajectory, pitch_point, impact_point, pitch_frame, impact_frame, debug_log
 
 def lbw_decision(ball_positions, trajectory, frames, pitch_point, impact_point):
     if not frames:
@@ -141,31 +131,33 @@ def lbw_decision(ball_positions, trajectory, frames, pitch_point, impact_point):
             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
     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):
+def generate_slow_motion(frames, trajectory, pitch_point, impact_point, detection_frames, pitch_frame, impact_frame, 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:
-        # Draw full trajectory (blue dots)
-        if trajectory:
-            for x, y in trajectory:
-                cv2.circle(frame, (int(x), int(y)), 5, (255, 0, 0), -1)
+    # Extract trajectory points up to impact for visualization
+    trajectory_points = np.array(trajectory[:len(detection_frames)], dtype=np.int32).reshape((-1, 1, 2))
+
+    for i, frame in enumerate(frames):
+        # Draw trajectory (blue line) only for frames with detections
+        if i in detection_frames and trajectory_points.size > 0:
+            cv2.polylines(frame, [trajectory_points[:detection_frames.index(i) + 1]], False, (255, 0, 0), 2)
 
-        # Draw pitch point (red circle with label)
-        if pitch_point:
+        # Draw pitch point (red circle with label) only in pitch frame
+        if pitch_point and i == pitch_frame:
             x, y = pitch_point
             cv2.circle(frame, (int(x), int(y)), 8, (0, 0, 255), -1)
             cv2.putText(frame, "Pitch Point", (int(x) + 10, int(y) - 10), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
 
-        # Draw impact point (yellow circle with label)
-        if impact_point:
+        # Draw impact point (yellow circle with label) only in impact frame
+        if impact_point and i == impact_frame:
             x, y = impact_point
             cv2.circle(frame, (int(x), int(y)), 8, (0, 255, 255), -1)
             cv2.putText(frame, "Impact Point", (int(x) + 10, int(y) + 20), 
-                       cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
 
         for _ in range(SLOW_MOTION_FACTOR):
             out.write(frame)
@@ -173,14 +165,14 @@ def generate_slow_motion(frames, trajectory, pitch_point, impact_point, output_p
     return output_path
 
 def drs_review(video):
-    frames, ball_positions, debug_log = process_video(video)
+    frames, ball_positions, detection_frames, debug_log = process_video(video)
     if not frames:
         return f"Error: Failed to process video\nDebug Log:\n{debug_log}", None
-    trajectory, pitch_point, impact_point, trajectory_log = estimate_trajectory(ball_positions, frames)
+    trajectory, pitch_point, impact_point, pitch_frame, impact_frame, trajectory_log = estimate_trajectory(ball_positions, frames, detection_frames)
     decision, trajectory, pitch_point, impact_point = lbw_decision(ball_positions, trajectory, frames, pitch_point, impact_point)
 
     output_path = f"output_{uuid.uuid4()}.mp4"
-    slow_motion_path = generate_slow_motion(frames, trajectory, pitch_point, impact_point, output_path)
+    slow_motion_path = generate_slow_motion(frames, trajectory, pitch_point, impact_point, detection_frames, pitch_frame, impact_frame, output_path)
 
     debug_output = f"{debug_log}\n{trajectory_log}"
     return f"DRS Decision: {decision}\nDebug Log:\n{debug_output}", slow_motion_path
@@ -191,10 +183,10 @@ iface = gr.Interface(
     inputs=gr.Video(label="Upload Video Clip"),
     outputs=[
         gr.Textbox(label="DRS Decision and Debug Log"),
-        gr.Video(label="Very 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 Line), Pitch Point (Red), Impact Point (Yellow)")
     ],
     title="AI-Powered DRS for LBW in Local Cricket",
-    description="Upload a video clip of a cricket delivery to get an LBW decision and very slow-motion replay showing ball detection (green boxes), trajectory (blue dots), pitch point (red circle), and impact point (yellow circle)."
+    description="Upload a video clip of a cricket delivery to get an LBW decision and very slow-motion replay showing ball detection (green boxes), trajectory (blue line), pitch point (red circle), and impact point (yellow circle)."
 )
 
 if __name__ == "__main__":