Update app.py

app.py

@@ -1,6 +1,7 @@
 import gradio as gr
 import cv2
 import numpy as np
+from PIL import Image
 from transparent_background import Remover
 
 remover = Remover(mode='fast')  # Custom setting
@@ -17,19 +18,17 @@ def doo(video):
         if ret is False:
             break
 
-        # Assuming frame is a NumPy array (e.g., shape: (height, width, 3))
-        # Perform background removal using the model
-        # Replace this placeholder code with actual model inference
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        img = Image.fromarray(frame).convert('RGB')
 
-        # Example: Apply a simple threshold to create a binary mask
-        gray_frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
-        _, mask = cv2.threshold(gray_frame, 200, 255, cv2.THRESH_BINARY)
+        # Process the frame using the transparent-background model
+        out = remover.process(img, type='map')  # Same as image, except for 'rgba'
 
-        # Create a masked frame
-        masked_frame = cv2.bitwise_and(frame, frame, mask=mask)
+        # Convert the processed frame back to a NumPy array
+        processed_frame = np.array(out)
 
-        # Append the processed frame to the output
-        processed_frames.append(masked_frame)
+        # Append the processed frame to the list
+        processed_frames.append(processed_frame)
 
     cap.release()