Update app.py

app.py

@@ -18,13 +18,13 @@ STUMPS_WIDTH = 0.2286  # meters (width of stumps)
 BALL_DIAMETER = 0.073  # meters (approx. cricket ball diameter)
 FRAME_RATE = 20  # Default frame rate, updated dynamically
 SLOW_MOTION_FACTOR = 1.5  # Faster replay (e.g., 30 / 1.5 = 20 FPS)
-CONF_THRESHOLD = 0.2  # Lowered for better detection
+CONF_THRESHOLD = 0.15  # Lowered for better detection
 IMPACT_ZONE_Y = 0.9  # Adjusted to 90% of frame height for impact zone
 PITCH_LENGTH = 20.12  # meters (standard cricket pitch length)
 STUMPS_HEIGHT = 0.71  # meters (stumps height)
 CAMERA_HEIGHT = 2.0  # meters (assumed camera height)
 CAMERA_DISTANCE = 10.0  # meters (assumed camera distance from pitch)
-MAX_POSITION_JUMP = 200  # Increased to include more detections
+MAX_POSITION_JUMP = 250  # Increased to include more detections
 
 def process_video(video_path):
     if not os.path.exists(video_path):
@@ -50,27 +50,34 @@ def process_video(video_path):
             break
         frame_count += 1
         frames.append(frame.copy())
-        # Enhance frame contrast and apply adaptive thresholding
-        frame = cv2.convertScaleAbs(frame, alpha=1.3, beta=10)
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-        frame = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
-        frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
-        results = model.predict(frame, conf=CONF_THRESHOLD, imgsz=(img_height, img_width), iou=0.5, max_det=3)
+        # Enhance frame contrast and sharpness
+        frame = cv2.convertScaleAbs(frame, alpha=1.5, beta=20)
+        kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]])
+        frame = cv2.filter2D(frame, -1, kernel)
+        results = model.predict(frame, conf=CONF_THRESHOLD, imgsz=(img_height, img_width), iou=0.5, max_det=5)
         detections = sum(1 for detection in results[0].boxes if detection.cls == 0)
-        if detections == 1:  # Only process frames with exactly one ball detection
+        if detections >= 1:  # Process frames with at least one ball detection
+            max_conf = 0
+            best_detection = None
+            conf_scores = []
             for detection in results[0].boxes:
                 if detection.cls == 0:  # Class 0 is the ball
                     conf = detection.conf.cpu().numpy()[0]
-                    x1, y1, x2, y2 = detection.xyxy[0].cpu().numpy()
-                    # Scale coordinates back to original frame size
-                    x1 = x1 * frame_width / img_width
-                    x2 = x2 * frame_width / img_width
-                    y1 = y1 * frame_height / img_height
-                    y2 = y2 * frame_height / img_height
-                    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)
-                    debug_log.append(f"Frame {frame_count}: 1 ball detection, confidence={conf:.3f}")
+                    conf_scores.append(conf)
+                    if conf > max_conf:
+                        max_conf = conf
+                        best_detection = detection
+            if best_detection:
+                x1, y1, x2, y2 = best_detection.xyxy[0].cpu().numpy()
+                # Scale coordinates back to original frame size
+                x1 = x1 * frame_width / img_width
+                x2 = x2 * frame_width / img_width
+                y1 = y1 * frame_height / img_height
+                y2 = y2 * frame_height / img_height
+                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)
+                debug_log.append(f"Frame {frame_count}: {detections} ball detections, selected confidence={max_conf:.3f}, all confidences={conf_scores}")
         else:
             debug_log.append(f"Frame {frame_count}: {detections} ball detections")
         frames[-1] = frame
@@ -79,9 +86,9 @@ def process_video(video_path):
     cap.release()
 
     if not ball_positions:
-        debug_log.append("No frames with exactly one ball detection")
+        debug_log.append("No frames with ball detection")
     else:
-        debug_log.append(f"Total frames with one ball detection: {len(ball_positions)}")
+        debug_log.append(f"Total frames with ball detection: {len(ball_positions)}")
         debug_log.append(f"Video resolution: {frame_width}x{frame_height}")
         debug_log.append(f"Video frame rate: {FRAME_RATE}")
 
@@ -152,7 +159,7 @@ def estimate_trajectory(ball_positions, frames, detection_frames):
 
     # Generate dense points for all frames between first and last detection
     total_frames = max(detection_frames) - min(detection_frames) + 1
-    t_full = np.linspace(times[0], times[-1], total_frames * SLOW_MOTION_FACTOR)
+    t_full = np.linspace(min(detection_frames) / FRAME_RATE, max(detection_frames) / FRAME_RATE, int(total_frames * SLOW_MOTION_FACTOR))
     x_full = fx(t_full)
     y_full = fy(t_full)
     trajectory_2d = list(zip(x_full, y_full))