Update app.py

app.py

@@ -1,7 +1,127 @@
-import gradio as gr
+import cv2
+import mediapipe as mp
+import numpy as np
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+def list_connected_cameras():
+    """
+    Lists all connected cameras by attempting to open camera indexes sequentially.
+
+    Returns:
+        list[int]: A list of valid camera indexes.
+    """
+    index = 0
+    valid_cameras = []
+    while True:
+        cap = cv2.VideoCapture(index)
+        if cap.isOpened():
+            ret, _ = cap.read()
+            if ret:
+                valid_cameras.append(index)
+            cap.release()
+        else:
+            break
+        index += 1
+    return valid_cameras
+
+
+def main():
+    """
+    Main function for real-time background removal. It allows the user to:
+    - Select a camera index.
+    - Perform segmentation on the video feed.
+    - Replace the background with a solid green screen.
+    - View the processed output in real-time.
+    """
+    # Step 1: Detect available cameras
+    cameras = list_connected_cameras()
+    if not cameras:
+        print("No cameras found!")
+        return
+
+    print("Available camera indexes:", cameras)
+
+    # Step 2: Prompt the user to select a camera
+    cam_index = None
+    while cam_index not in cameras:
+        try:
+            cam_index = int(input(f"Select a camera index from the above list: "))
+        except ValueError:
+            print("Invalid input. Please enter a valid camera index.")
+
+    # Step 3: Open the selected camera
+    cap = cv2.VideoCapture(cam_index)
+    if not cap.isOpened():
+        print(f"Failed to open camera with index {cam_index}")
+        return
+
+    # Step 4: Initialize MediaPipe Selfie Segmentation
+    mp_selfie_segmentation = mp.solutions.selfie_segmentation
+    selfie_segmentation = mp_selfie_segmentation.SelfieSegmentation(model_selection=1)
+
+    # Background color (green screen)
+    bg_color = (0, 255, 0)
+
+    # Temporal smoothing setup
+    prev_blurred_mask = None  # Store the previous mask for smoothing
+    alpha = 0.6  # Blending factor for smoothing
+
+    # Morphological erosion parameters
+    erosion_kernel_size = 5  # Kernel size for erosion
+    erosion_iterations = 1  # Number of erosion iterations
+    erosion_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (erosion_kernel_size, erosion_kernel_size))
+
+    print("Press 'q' to exit.")
+
+    while True:
+        # Step 5: Capture a frame from the camera
+        ret, frame = cap.read()
+        if not ret:
+            print("Failed to read from camera. Exiting...")
+            break
+
+        # Step 6: Convert BGR to RGB for MediaPipe processing
+        rgb_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+        # Step 7: Perform segmentation to obtain the mask
+        results = selfie_segmentation.process(rgb_frame)
+        mask = results.segmentation_mask  # Values range from 0.0 to 1.0
+
+        # Step 8: Smooth the mask with Gaussian blur
+        blurred_mask = cv2.GaussianBlur(mask, (15, 15), 0)
+
+        # Step 9: Apply temporal smoothing
+        if prev_blurred_mask is not None:
+            blurred_mask = alpha * prev_blurred_mask + (1 - alpha) * blurred_mask
+        prev_blurred_mask = blurred_mask
+
+        # Step 10: Threshold the mask to create a binary condition
+        threshold_value = 0.5
+        condition = blurred_mask > threshold_value
+
+        # Step 11: Refine the mask using morphological erosion
+        mask_uint8 = (condition.astype(np.uint8)) * 255  # Convert to 0-255
+        eroded_mask = cv2.erode(mask_uint8, erosion_kernel, iterations=erosion_iterations)
+        final_condition = eroded_mask > 128  # Convert back to a boolean condition
+
+        # Step 12: Create a green background
+        bg_frame = np.zeros(frame.shape, dtype=np.uint8)
+        bg_frame[:] = bg_color
+
+        # Step 13: Blend the original frame and the green background
+        output_frame = np.where(final_condition[..., None], frame, bg_frame)
+
+        # Step 14: Display the output frame
+        cv2.imshow("Green Background - Eroded Mask", output_frame)
+
+        # Exit the loop when 'q' is pressed
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    # Cleanup resources
+    cap.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == "__main__":
+    main()