Vids and Photos

app.py

@@ -1,4 +1,133 @@
 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
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+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()
+    
+    # If keep_audio is checked, use FFmpeg to overlay the original audio on the processed video
+    if keep_audio:
+        st.write("Overlaying audio. Please wait...")
+        command = f"ffmpeg -i {processed_video_temp_path} -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
+    else:
+        return processed_video_temp_path
+
+
+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")
+    
+    uploaded_files = st.file_uploader("Upload media files", 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()

requirements.txt

@@ -1 +1,5 @@
-deface
+deface==1.5.0
+imageio==2.31.1
+opencv_python==4.5.5.64
+Pillow==10.1.0
+streamlit==1.27.2