i am trying to replace background of a video using open-cv. my problem is a white borders that are appearing in the edges of a person.enter image description here. this is a frame of video i am trying to replace background frame by frame in a video. u guys can test this code and give me results too, heres the full code: import cv2 from rembg import remove from PIL import Image, ImageFilter import os import numpy as np from moviepy.editor import VideoFileClip, ImageSequenceClip def smooth_edges(image): # Apply Gaussian blur to the alpha channel for smoothing alpha_channel = image[:, :, 3] blurred_alpha = cv2.GaussianBlur(alpha_channel, (15, 15), 0) # Apply dilation to the alpha channel to reduce white borders kernel = np.ones((5, 5), np.uint8) dilated_alpha = cv2.dilate(alpha_channel, kernel, iterations=1) # Update the alpha channel with the smoothed and dilated alpha image[:, :, 3] = dilated_alpha return image def process_video_with_audio(input_video_path, output_video_path='output_video.mp4', frame_limit=40): # Check if the video file exists if not os.path.exists(input_video_path): print(f"Video file '{input_video_path}' not found.") return None # Open the video capture video_capture = cv2.VideoCapture(input_video_path) # Check if the video capture is open if not video_capture.isOpened(): print(f"Failed to open the video '{input_video_path}'.") return None frame_count = 0 # Initialize a frame count frame_list = [] # List to store processed frames while frame_count < frame_limit: # Read one frame from the video ret, frame = video_capture.read() # Check if the frame was successfully read if not ret: print(f"Failed to read frame {frame_count} from the video.") break # Exit the loop if there are no more frames # Specify the output path for the processed frame frame_name = f'frame{frame_count}.png' frame_path = 'masked/' + frame_name # Save the frame as an image cv2.imwrite(frame_path, frame) # Output path for the processed image output_image_path = f'masked/processed_frame{frame_count}.png' # Save the frame as an image before processing it cv2.imwrite(output_image_path, frame) with open(output_image_path, 'rb') as f: input_image = f.read() subject = remove(input_image, alpha_matting=True, alpha_matting_foreground_threshold=50) with open(f'masked/background{frame_count}.png', 'wb') as output_file: output_file.write(subject) background_img_path = 'Cover.jpg' if os.path.exists(background_img_path): background_img = Image.open(background_img_path) # Load the processed frame as the foreground image foreground_img = Image.open(f'masked/background{frame_count}.png').convert("RGBA") # Resize the background image to match the dimensions of the video frame background_img = background_img.resize((foreground_img.width, foreground_img.height)) # Ensure the foreground image has an 'RGBA' mode with an alpha channel foreground_img = foreground_img.convert("RGBA") # Apply Gaussian blur to the alpha channel for smoothing blurred_alpha = foreground_img.split()[3].filter(ImageFilter.GaussianBlur(radius=5)) # Apply dilation to the alpha channel to reduce white borders dilated_alpha = np.array(blurred_alpha) kernel = np.ones((5, 5), np.uint8) dilated_alpha = cv2.dilate(dilated_alpha, kernel, iterations=1) blurred_alpha = Image.fromarray(dilated_alpha) # Composite the images with the smoothed and dilated alpha channel composite_img = Image.merge("RGBA", foreground_img.split()[:3] + (blurred_alpha,)) # Save the final composite image as PNG (supports transparency) result = Image.alpha_composite(background_img.convert("RGBA"), composite_img) # Append the processed frame to the list frame_list.append(np.array(result)) # Convert to NumPy array frame_count += 1 # Increment the frame count # DEBUG: Print statement for debugging print(f"Processing frame {frame_count}...") else: print(f"Background image '{background_img_path}' not found") # Check if there are processed frames to create a video if frame_list: # Get the audio from the original video original_clip = VideoFileClip(input_video_path) audio = original_clip.audio # Create an ImageSequenceClip from the processed frames processed_clip = ImageSequenceClip(frame_list, fps=30) # Set the audio of the processed video to the original audio processed_clip = processed_clip.set_audio(audio) # Set the duration of the processed video to match the specified number of frames processed_clip = processed_clip.subclip(0, processed_clip.duration) # Save the final video with audio using MoviePy processed_clip.write_videofile(output_video_path, codec='libx264', audio_codec='aac') print(f"Video created at '{output_video_path}' with audio.") return output_video_path else: print("No processed frames to create a video.") return None # Example usage: video_path = 'alex.mp4' # Change this to the actual path of your video output_path = process_video_with_audio(video_path) # You can use the 'output_path' variable as needed. already tried dilation and gaussian blur but it didnt work Continue reading...
