Update app.py

app.py

@@ -13,8 +13,8 @@ model = YOLO("best.pt")
 # Constants for LBW decision and video processing
 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)
+FRAME_RATE = 20  # Input video frame rate (reduced to 20 FPS)
+SLOW_MOTION_FACTOR = 3  # Adjusted for 20 FPS (slower playback without being too slow)
 CONF_THRESHOLD = 0.25  # Confidence threshold for detection
 IMPACT_ZONE_Y = 0.85  # Fraction of frame height where impact is likely (near stumps)
 
@@ -103,17 +103,17 @@ def lbw_decision(ball_positions, trajectory, frames, pitch_point, impact_point):
 
     frame_height, frame_width = frames[0].shape[:2]
     stumps_x = frame_width / 2
-    stumps_y = frame_height * 0.9
+    stumps_y = frame_height * 0.9  # Position of the stumps at the bottom of the frame
     stumps_width_pixels = frame_width * (STUMPS_WIDTH / 3.0)
 
     pitch_x, pitch_y = pitch_point
     impact_x, impact_y = impact_point
 
-    # Check pitching point
+    # Check pitching point - the ball should land between stumps
     if pitch_x < stumps_x - stumps_width_pixels / 2 or pitch_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
+    # Check impact point - the ball should hit within the stumps area
     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
 
@@ -121,6 +121,7 @@ def lbw_decision(ball_positions, trajectory, frames, pitch_point, impact_point):
     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
+
     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, detection_frames, output_path):
@@ -129,7 +130,6 @@ def generate_slow_motion(frames, trajectory, pitch_point, impact_point, detectio
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
     out = cv2.VideoWriter(output_path, fourcc, FRAME_RATE / SLOW_MOTION_FACTOR, (frames[0].shape[1], frames[0].shape[0]))
 
-    # 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):
@@ -137,19 +137,21 @@ def generate_slow_motion(frames, trajectory, pitch_point, impact_point, detectio
         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 and i >= detection_frames[0]:
+        # Draw pitch point (red circle with label) when the ball touches the ground (y < ground threshold)
+        if pitch_point and impact_point and i >= detection_frames[0]:
             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)
+            if y > frame.shape[0] * 0.75:  # Threshold for ground contact (adjust as necessary)
+                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)
 
-        # Draw impact point (yellow circle with label) only in frames after impact
+        # Draw impact point (yellow circle with label) when ball is near stumps (y near bottom)
         if impact_point and i >= detection_frames[min(len(detection_frames) - 1, detection_frames.index(detection_frames[-1]))]:
             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)
+            if y > frame.shape[0] * 0.85:  # Threshold for impact (adjust as necessary)
+                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)
 
         for _ in range(SLOW_MOTION_FACTOR):
             out.write(frame)
@@ -175,11 +177,11 @@ 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 Line), Pitch Point (Red), Impact Point (Yellow)")
+        gr.Video(label="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 line), pitch point (red circle), and impact point (yellow circle)."
+    description="Upload a video clip of a cricket delivery to get an LBW decision and slow-motion replay showing ball detection (green boxes), trajectory (blue line), pitch point (red circle), and impact point (yellow circle)."
 )
 
 if __name__ == "__main__":
-    iface.launch()  
+    iface.launch()