Update app.py

app.py

@@ -28,6 +28,7 @@ MODEL_INPUT_SIZE = (640, 640)  # (height, width)
 FRAME_SKIP = 2  # Process every 2nd frame
 MIN_DETECTIONS = 10  # Stop after 10 detections
 BATCH_SIZE = 4  # Process 4 frames at a time
+SLOW_MOTION_FACTOR = 3  # Duplicate each frame 3 times for slow motion
 
 # Load model
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -181,7 +182,7 @@ def estimate_speed(positions, frame_numbers, frame_rate, frame_width):
     avg_speed_kmh = np.mean(speeds) * 3.6
     return avg_speed_kmh, "Speed calculated successfully"
 
-# Main Gradio function with video overlay
+# Main Gradio function with video overlay and slow motion
 def drs_analysis(video):
     # Video is a file path (string) in Hugging Face Spaces
     video_path = video if isinstance(video, str) else "temp_video.mp4"
@@ -202,7 +203,7 @@ def drs_analysis(video):
     pitch_x, pitch_y = map_pitch(bounce_point, frame_width, frame_height)
     speed_kmh, speed_status = estimate_speed(positions, frame_numbers, frame_rate, frame_width)
 
-    # Create output video with overlays
+    # Create output video with overlays and slow motion
     output_path = "output_video.mp4"
     cap = cv2.VideoCapture(video_path)
     fourcc = cv2.VideoWriter_fourcc(*'mp4v')
@@ -257,7 +258,10 @@ def drs_analysis(video):
         text = f"LBW: {lbw_decision}\nSpeed: {speed_kmh:.2f} km/h"
         cv2.putText(frame, text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
 
-        out.write(frame)
+        # Write frame multiple times for slow motion
+        for _ in range(SLOW_MOTION_FACTOR):
+            out.write(frame)
+
         frame_count += 1
 
     cap.release()