app.py

import streamlit as st
import os
import mimetypes
from deface.deface import anonymize_frame
from deface.centerface import CenterFace
from PIL import Image
import imageio.v2 as imageio
import cv2
import tempfile
import os

def streamlit_video_deface_cli(uploaded_file, keep_audio):
    temp_video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
    processed_video_temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name

    with open(temp_video_path, 'wb') as f:
        f.write(uploaded_file.read())

    if keep_audio:
        command = f"deface {temp_video_path} --keep-audio -o {processed_video_temp_path}"
    else:
        command = f"deface {temp_video_path} -o {processed_video_temp_path}"
    
    os.system(command)

    return processed_video_temp_path


def process_video_frame(frame, centerface, threshold, replacewith, mask_scale, ellipse, draw_scores, replaceimg=None, mosaicsize=20):
    dets, _ = centerface(frame, threshold=threshold)
    anonymize_frame(dets, frame, mask_scale=mask_scale, replacewith=replacewith, ellipse=ellipse, draw_scores=draw_scores, replaceimg=replaceimg, mosaicsize=mosaicsize)
    return frame


def streamlit_video_detect(uploaded_file, centerface, threshold, replacewith, mask_scale, ellipse, draw_scores, enable_preview, keep_audio, replaceimg=None, mosaicsize=20):
    temp_video_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
    processed_video_temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
    output_with_audio_temp_path = tempfile.NamedTemporaryFile(delete=False, suffix=".mp4").name
    with open(temp_video_path, 'wb') as f:
        f.write(uploaded_file.read())
    
    vidcap = cv2.VideoCapture(temp_video_path)
    
    # Get video properties
    width  = int(vidcap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(vidcap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    fps    = vidcap.get(cv2.CAP_PROP_FPS)
    total_frames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))

    
    # Create a video writer object
    out = cv2.VideoWriter(processed_video_temp_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (width, height))
    
    # Progress Bar setup
    progress_text = "Processing video. Please wait."
    my_bar = st.progress(0, text=progress_text)
    frames_processed = 0
    
    while True:
        ret, frame = vidcap.read()
        if not ret:
            break
        
        processed_frame = process_video_frame(frame, centerface, threshold, replacewith, mask_scale, ellipse, draw_scores, replaceimg, mosaicsize)
        out.write(processed_frame)

        # Progress Bar update
        frames_processed += 1
        progress_percent = int((frames_processed / total_frames) * 100)
        my_bar.progress(progress_percent, text=progress_text)
    
    vidcap.release()
    out.release()
    
    # Empty the progress bar after completion
    my_bar.empty()
    st.write("Re-encoding video. Please wait...")
    os.system(f"ffmpeg -y -i {processed_video_temp_path} -c:v libx264 {processed_video_temp_path.split('.')[0]}_processed_.mp4")
    # If keep_audio is checked, use FFmpeg to overlay the original audio on the processed video
    if keep_audio:
        try:
            st.write("Overlaying audio. Please wait...")
            command = f"ffmpeg -y -i {processed_video_temp_path.split('.')[0]}_processed_.mp4 -i {temp_video_path} -c:v copy -c:a aac -strict experimental {output_with_audio_temp_path}"
            os.system(command)
            return output_with_audio_temp_path
        except:
            return f"{processed_video_temp_path.split('.')[0]}_processed_.mp4"
    else:
        return f"{processed_video_temp_path.split('.')[0]}_processed_.mp4"


def streamlit_image_detect(uploaded_file, centerface, threshold, replacewith, mask_scale, ellipse, draw_scores, enable_preview, keep_metadata, replaceimg=None, mosaicsize=20):
    # Read the uploaded image into a numpy array
    frame = imageio.imread(uploaded_file)

    # For the sake of this example, I'm skipping the metadata part
    
    dets, _ = centerface(frame, threshold=threshold)
    anonymize_frame(dets, frame, mask_scale=mask_scale, replacewith=replacewith, ellipse=ellipse, draw_scores=draw_scores, replaceimg=replaceimg, mosaicsize=mosaicsize)

    # Convert numpy array back to a PIL.Image so that we can display/save it easily in Streamlit
    result_img = Image.fromarray(frame)
    return result_img

def get_file_type(uploaded_file):
    mime = mimetypes.guess_type(uploaded_file.name)[0]
    if mime is None:
        return None
    if mime.startswith('video'):
        return 'video'
    if mime.startswith('image'):
        return 'image'
    return mime

def main():
    st.title("☢️🧑📷 Media Anonymizer 📹🧑‍🦳☢️")
    # description
    st.write("""
    You can upload images or videos and the application will automatically detect faces and blur them out.\nAudio is removed by deafult. Check the box below to keep audio.
    """)
    
    uploaded_files = st.file_uploader("Upload media files (images,videos)", accept_multiple_files=True)
    keep_audio = st.checkbox("Keep Audio")

    for uploaded_file in uploaded_files:
        file_type = get_file_type(uploaded_file)
        
        if file_type == 'image':
            # Initialize centerface or other configurations if needed
            centerface = CenterFace() 
            result_img = streamlit_image_detect(uploaded_file, centerface, threshold=0.2, replacewith='blur', mask_scale=1.0, ellipse=True, draw_scores=False, enable_preview=False, keep_metadata=False)
            st.image(result_img, caption=f'Anonymized {uploaded_file.name}', use_column_width=True)
        elif file_type == 'video':
            centerface = CenterFace()
            processed_video_path = streamlit_video_detect(uploaded_file, centerface, threshold=0.2, replacewith='blur', mask_scale=1.0, ellipse=True, draw_scores=False, enable_preview=False, keep_audio=keep_audio)
            st.video(processed_video_path, format='video/mp4', start_time=0)
        else:
            st.write(f"Unsupported file type for {uploaded_file.name}")

if __name__ == "__main__":
    main()