Update app.py

app.py

@@ -1,3 +1,4 @@
+```python
 import cv2
 import numpy as np
 import torch
@@ -12,12 +13,11 @@ 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 = 20  # Input video frame rate
-SLOW_MOTION_FACTOR = 3  # Adjusted for 20 FPS
+SLOW_MOTION_FACTOR = 2  # Reduced for faster output
 CONF_THRESHOLD = 0.25  # Confidence threshold for detection
-PITCH_ZONE_Y = 0.9  # Fraction of frame height for pitch zone (near stumps base)
-IMPACT_ZONE_Y = 0.8  # Fraction of frame height for impact zone (near batsman)
+PITCH_ZONE_Y = 0.9  # Fraction of frame height for pitch zone
+IMPACT_ZONE_Y = 0.8  # Fraction of frame height for impact zone
 IMPACT_DELTA_Y = 50  # Pixels for detecting sudden y-position change
 STUMPS_HEIGHT = 0.711  # meters (height of stumps)
 
@@ -35,17 +35,18 @@ def process_video(video_path):
         ret, frame = cap.read()
         if not ret:
             break
-        frame_count += 1
-        frames.append(frame.copy())
-        results = model.predict(frame, conf=CONF_THRESHOLD)
-        detections = [det for det in results[0].boxes if det.cls == 0]
-        if len(detections) == 1:
-            x1, y1, x2, y2 = detections[0].xyxy[0].cpu().numpy()
-            ball_positions.append([(x1 + x2) / 2, (y1 + y2) / 2])
-            detection_frames.append(frame_count - 1)
-            cv2.rectangle(frame, (int(x1), int(y1)), (int(x2), int(y2)), (0, 255, 0), 2)
-        frames[-1] = frame
+        if frame_count % 2 == 0:  # Process every 2nd frame
+            frames.append(frame.copy())
+            results = model.predict(frame, conf=CONF_THRESHOLD)
+            detections = [det for det in results[0].boxes if det.cls == 0]
+            if len(detections) == 1:
+                x1, y1, x2, y2 = detections[0].xyxy[0].cpu().numpy()
+                ball_positions.append([(x1 + x2) / 2, (y1 + y2) / 2])
+                detection_frames.append(len(frames) - 1)
+                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}: {len(detections)} ball detections")
+        frame_count += 1
     cap.release()
 
     if not ball_positions:
@@ -60,16 +61,21 @@ def estimate_trajectory(ball_positions, detection_frames, frames):
         return None, None, None, None, None, None, "Error: Fewer than 2 valid single-ball detections for trajectory"
     frame_height = frames[0].shape[0]
 
-    x_coords = [pos[0] for pos in ball_positions]
-    y_coords = [pos[1] for pos in ball_positions]
-    times = np.array(detection_frames) / FRAME_RATE
+    # Filter to unique positions to reduce interpolation points
+    unique_positions = [ball_positions[0]]
+    for pos in ball_positions[1:]:
+        if abs(pos[0] - unique_positions[-1][0]) > 10 or abs(pos[1] - unique_positions[-1][1]) > 10:
+            unique_positions.append(pos)
+    x_coords = [pos[0] for pos in unique_positions]
+    y_coords = [pos[1] for pos in unique_positions]
+    times = np.array([i / FRAME_RATE for i in range(len(unique_positions))])
 
     pitch_idx = 0
     for i, y in enumerate(y_coords):
         if y > frame_height * PITCH_ZONE_Y:
             pitch_idx = i
             break
-    pitch_point = ball_positions[pitch_idx]
+    pitch_point = unique_positions[pitch_idx]
     pitch_frame = detection_frames[pitch_idx]
 
     impact_idx = None
@@ -79,8 +85,8 @@ def estimate_trajectory(ball_positions, detection_frames, frames):
             impact_idx = i
             break
     if impact_idx is None:
-        impact_idx = len(ball_positions) - 1
-    impact_point = ball_positions[impact_idx]
+        impact_idx = len(y_coords) - 1
+    impact_point = unique_positions[impact_idx]
     impact_frame = detection_frames[impact_idx]
 
     x_coords = x_coords[:impact_idx + 1]
@@ -94,8 +100,7 @@ def estimate_trajectory(ball_positions, detection_frames, frames):
         return None, None, None, None, None, None, f"Error in trajectory interpolation: {str(e)}"
 
     vis_trajectory = list(zip(x_coords, y_coords))
-
-    t_full = np.linspace(times[0], times[-1] + 0.5, len(times) + 10)
+    t_full = np.linspace(times[0], times[-1] + 0.5, len(times) + 5)  # Reduced points
     x_full = fx(t_full)
     y_full = fy(t_full)
     full_trajectory = list(zip(x_full, y_full))
@@ -115,12 +120,11 @@ def lbw_decision(ball_positions, full_trajectory, frames, pitch_point, impact_po
     stumps_x = frame_width / 2
     stumps_y = frame_height * 0.9
     stumps_width_pixels = frame_width * (STUMPS_WIDTH / 3.0)
-    batsman_area_y = frame_height * 0.8  # Approximate batsman leg area
+    batsman_area_y = frame_height * 0.8
 
     pitch_x, pitch_y = pitch_point
     impact_x, impact_y = impact_point
 
-    # LBW Conditions
     in_line_threshold = stumps_width_pixels / 2
     if pitch_x < stumps_x - in_line_threshold or pitch_x > stumps_x + in_line_threshold:
         return f"Not Out (Pitched outside line at x: {pitch_x:.1f}, y: {pitch_y:.1f})", full_trajectory, pitch_point, impact_point
@@ -136,8 +140,7 @@ def lbw_decision(ball_positions, full_trajectory, frames, pitch_point, impact_po
             break
 
     if hit_stumps:
-        # Check if clipping (Umpire's Call)
-        if abs(x - stumps_x) < in_line_threshold * 0.1:  # Arbitrary small margin for clipping
+        if abs(x - stumps_x) < in_line_threshold * 0.1:
             return f"Umpire's Call - Not Out (Ball clips stumps, Pitch at x: {pitch_x:.1f}, y: {pitch_y:.1f}, Impact at x: {impact_x:.1f}, y: {impact_y:.1f})", full_trajectory, 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})", full_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})", full_trajectory, pitch_point, impact_point
@@ -157,17 +160,12 @@ def generate_slow_motion(frames, vis_trajectory, pitch_point, pitch_frame, impac
     trajectory_points = np.array(vis_trajectory, dtype=np.int32).reshape((-1, 1, 2))
 
     for i, frame in enumerate(frames):
-        # Draw stumps (three white vertical lines)
-        stump_positions = [
-            (stumps_x - stumps_width_pixels / 2, stumps_y),
-            (stumps_x, stumps_y),
-            (stumps_x + stumps_width_pixels / 2, stumps_y)
-        ]
-        for x, y in stump_positions:
-            cv2.line(frame, (int(x), int(y)), (int(x), int(y - stumps_height_pixels)), (255, 255, 255), 2)
-
-        # Draw crease line (striker to non-striker)
-        cv2.line(frame, (0, int(stumps_y)), (frame_width, int(stumps_y)), (255, 255, 0), 2)  # Yellow line
+        # Draw stumps (single line for efficiency)
+        cv2.line(frame, (int(stumps_x - stumps_width_pixels / 2), int(stumps_y)),
+                 (int(stumps_x + stumps_width_pixels / 2), int(stumps_y)), (255, 255, 255), 2)
+
+        # Draw crease line
+        cv2.line(frame, (0, int(stumps_y)), (frame_width, int(stumps_y)), (255, 255, 0), 2)
 
         if i in detection_frames and trajectory_points.size > 0:
             idx = detection_frames.index(i) + 1
@@ -176,20 +174,19 @@ def generate_slow_motion(frames, vis_trajectory, pitch_point, pitch_frame, impac
 
         if pitch_point and i == pitch_frame:
             x, y = pitch_point
-            cv2.circle(frame, (int(x), int(y)), 8, (0, 255, 0), -1)  # Green for pitch
+            cv2.circle(frame, (int(x), int(y)), 8, (0, 255, 0), -1)
             cv2.putText(frame, "Pitching Factor", (int(x) + 10, int(y) - 10),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
 
         if impact_point and i == impact_frame:
             x, y = impact_point
-            cv2.circle(frame, (int(x), int(y)), 8, (0, 0, 255), -1)  # Red for impact
+            cv2.circle(frame, (int(x), int(y)), 8, (0, 0, 255), -1)
             cv2.putText(frame, "Impact Factor", (int(x) + 10, int(y) + 20),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1)
 
-        # Wicket factor (show at impact frame if hitting stumps)
         if impact_point and i == impact_frame and "Out" in lbw_decision(ball_positions, full_trajectory, frames, pitch_point, impact_point)[0]:
             cv2.putText(frame, "Wicket Factor", (int(stumps_x) - 50, int(stumps_y) - 20),
-                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 165, 255), 2)  # Orange
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 165, 255), 1)
 
         for _ in range(SLOW_MOTION_FACTOR):
             out.write(frame)
@@ -215,11 +212,12 @@ 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 Pitching Factor (Green), Impact Factor (Red), Wicket Factor (Orange), Stumps (White), Crease (Yellow)")
+        gr.Video(label="Optimized Slow-Motion Replay with Pitching Factor (Green), Impact Factor (Red), Wicket Factor (Orange), Stumps (White), Crease (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 slow-motion replay showing pitching factor (green circle), impact factor (red circle), wicket factor (orange text), stumps (white lines), and crease line (yellow line)."
+    description="Upload a video clip of a cricket delivery to get an LBW decision and optimized slow-motion replay showing pitching factor (green circle), impact factor (red circle), wicket factor (orange text), stumps (white lines), and crease line (yellow line)."
 )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()
+```