Video Processing.py

import cv2
import numpy as np 
import moviepy.editor as mp
from deepfill import DeepFill # Make sure you have DeepFill setup
from multiprocessing import Pool
import os 

# Path to the video file 
input_video = 'input_video.mp4'
output_video = 'output_video.mp4'

# Load video using moviepy 
def load_video(input_path):
    return mp.VideoFileClip(input_path)

# Function to detect and remove captions/emojis/images using object detection (simplified here)
def detect_and_remove_objects(frame):
    # Convert to grayscale for object detection
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # Simulated detection of unwanted regions (this can be replaced with any object detection method)
    # Here we manually simulate a mask for removal; ideally, you'd use a trained model
    mask = np.zeros_like(gray_frame)
    
    # For demo purposes, assume we detect something in a specific region (e.g., emoji or caption)
    # Update this with the actual detected coordinates of objects
    cv2.rectangle(mask, (100, 100), (400,  200), 255, -1) # Example bounding box

    return mask

# Inpainting removed objects using deepfill (or any other inpainting technique)
def inpaint_objects(frame, mask):
    # Inpainting using Deepfill method
    result_frame = DeepFill.inpaint(frame, mask)

    # If DeepFill is not available, fall back to OpenCV inpainting (telea or navier-stokes)
    if result_frame is None:
        result_frame = cv2.inpaint(frame, mask, inpaintRadius=3, flags=cv2.INPAINT_TELEA)

    return result_frame

# Function to process each video frame: remove objects and enhance quality
def process_frame(frame):
    # Detect objects (e.g., captions, emojis) and create a mask
    mask = detect_and_remove_objects(frame)

    # Inpaint to remove unwanted objects
    clean_frame = inpaint_objects(frame, mask)

    # Enhance quality (e.g., denoising)
    clean_frame = cv2.fastNlMeansDenoisingColored(clean_frame, None, 10, 10, 7, 21)

    return clean_frame

# Process the video: frame by frame
def process_video(video_clip):
    frames = [process_frame(frame) for frame in video_clip.iter_frames()]

    # Create a processed video from frames
    fps = video_clip.fps
    height, width, _ = frames[0].shape
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    video_writer = cv2.VideoWriter(output_video, fourcc, fps, (width, height))

    for frame in frames:
        video_writer.write(frame)

    video_writer.release()

# Optimized processing using parallelism (optional)
def process_frame_parallel(frame_idx, video_clip):
    frame = video_clip.get_frame(frame_idx / video_clip.fps)
    return process_frame(frame)

def process_video_parallel(video_clip):
    with Pool(os.cpu_count()) as p:
        frames = p.starmap(process_frame_parallel, [(i, video_clip) for i in range(int(video_clip.fps * video_clip.duration))])

    fps = video_clip.fps
    height, width, _ = frames[0].shape
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    video_writer = cv2.VideoWriter(output_video, fourcc, fps, (width, height))

    for frame in frames:
        video_writer.write(frame)

    video_writer.release()

# Main execution
if __name__ == "__main__":
    # Load video
    video_clip = load_video(input_video)

    # Process video (choose between parallel and sequential processing)
    use_parallel_processing = True # Set to False if you don't want parallelism
    if use_parallel_processing:
        process_video_parallel(video_clip)
    else:
        process_video(video_clip)

    print(f"Video processing completed. Output saved at {output_video}")