Quick and Dirty Selective Blur with OpenCV + MTCNN

selective_blur.py

import cv2
import numpy as np
from mtcnn import MTCNN

def create_blurred_video(input_video, output_video):
    cap = cv2.VideoCapture(input_video)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = cap.get(cv2.CAP_PROP_FPS)
    
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_video, fourcc, fps, (width, height))
    
    detector = MTCNN()
    
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break
        
        faces = detector.detect_faces(frame)
        for face in faces:
            x, y, w, h = face['box']
            roi = frame[y:y+h, x:x+w]
            roi = cv2.GaussianBlur(roi, (23, 23), 30)
            frame[y:y+h, x:x+w] = roi
        
        out.write(frame)
    
    cap.release()
    out.release()

def apply_mask_and_combine(original_video, blurred_video, output_video, face_to_keep):
    cap_original = cv2.VideoCapture(original_video)
    cap_blurred = cv2.VideoCapture(blurred_video)
    
    width = int(cap_original.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap_original.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps = cap_original.get(cv2.CAP_PROP_FPS)
    
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter(output_video, fourcc, fps, (width, height))
    
    detector = MTCNN()
    
    while cap_original.isOpened() and cap_blurred.isOpened():
        ret_original, frame_original = cap_original.read()
        ret_blurred, frame_blurred = cap_blurred.read()
        
        if not ret_original or not ret_blurred:
            break
        
        faces = detector.detect_faces(frame_original)
        
        mask = np.zeros(frame_original.shape[:2], dtype=np.uint8)
        if len(faces) > face_to_keep:
            x, y, w, h = faces[face_to_keep]['box']
            cv2.rectangle(mask, (x, y), (x+w, y+h), 255, -1)
        
        # Invert the mask
        mask = cv2.bitwise_not(mask)
        
        # Combine the videos using the mask
        result = cv2.bitwise_and(frame_blurred, frame_blurred, mask=mask)
        result += cv2.bitwise_and(frame_original, frame_original, mask=cv2.bitwise_not(mask))
        
        out.write(result)
    
    cap_original.release()
    cap_blurred.release()
    out.release()

# Main process
input_video = 'input_video.mp4'
blurred_video = 'blurred_video.mp4'
final_output = 'final_output.mp4'
face_to_keep = 0  # Index of the face to keep unblurred (0 for the first detected face)

# Step 1: Create a blurred version of the video
create_blurred_video(input_video, blurred_video)

# Step 2 & 3: Apply mask to keep one face visible and combine videos
apply_mask_and_combine(input_video, blurred_video, final_output, face_to_keep)

print("Video processing complete. Output saved as", final_output)