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| import base64 | |
| import json | |
| import os, shutil | |
| import re | |
| import time | |
| import uuid | |
| import cv2 | |
| import numpy as np | |
| import streamlit as st | |
| from PIL import Image | |
| # from extract_video import extract_method_single_video | |
| from utils import st_file_selector, img2base64 | |
| from main import FaceFakePipelineImage, FaceFakePipelineVideo | |
| import os | |
| DEBUG = True | |
| def main(): | |
| st.markdown("###") | |
| uploaded_file = st.file_uploader('Upload a picture', type=['mp4', 'jpg', 'jpeg', 'png'], accept_multiple_files=False) | |
| with st.spinner(f'Loading samples...'): | |
| while not os.path.isdir("sample_files"): | |
| time.sleep(1) | |
| st.markdown("### or") | |
| selected_file = st_file_selector(st, path='sample_files', key = 'selected', label = 'Choose a sample image/video') | |
| if uploaded_file: | |
| random_id = uuid.uuid1() | |
| base_folder = "temps" | |
| filename = "{}.{}".format(random_id, uploaded_file.type.split("/")[-1]) | |
| file_type = uploaded_file.type.split("/")[0] | |
| filepath = f"{base_folder}/{filename}" | |
| faces_folder = f"{base_folder}/images/{random_id}" | |
| st.write(filepath) | |
| if uploaded_file.type == 'video/mp4': | |
| with open(f"temps/{filename}", mode='wb') as f: | |
| f.write(uploaded_file.read()) | |
| video_path = filepath | |
| st.video(uploaded_file) | |
| else: | |
| img = Image.open(uploaded_file).convert('RGB') | |
| ext = uploaded_file.type.split("/")[-1] | |
| st.image(img) | |
| elif selected_file: | |
| base_folder = "sample_files" | |
| file_type = selected_file.split(".")[-1] | |
| filename = selected_file.split("/")[-1] | |
| filepath = f"{base_folder}/{selected_file}" | |
| if file_type == 'mp4': | |
| video_file = open(filepath, 'rb') | |
| video_bytes = video_file.read() | |
| st.video(video_bytes) | |
| video_path = filepath | |
| else: | |
| img = Image.open(filepath).convert('RGB') | |
| st.image(img) | |
| else: | |
| return | |
| with st.spinner(f'Analyzing {file_type}...'): | |
| if file_type == 'video' or file_type == 'mp4': | |
| result = video_pipeline(video_path, config_payload=config_payload) | |
| else: | |
| result = image_pipeline({'images': [img2base64(np.array(img))]}, config_payload=config_payload) | |
| if 'Real' in result['message']: | |
| st.success(result['message'], icon="✅") | |
| else: | |
| st.error(result['message'], icon="🚨") | |
| st.divider() | |
| st.write('## Response JSON') | |
| st.write(result) | |
| def setup(): | |
| if not os.path.isdir("temps"): | |
| os.makedirs("temps") | |
| if __name__ == "__main__": | |
| image_pipeline = FaceFakePipelineImage() | |
| video_pipeline = FaceFakePipelineVideo() | |
| with st.sidebar: | |
| config_payload = { | |
| 'face_detection_threshold': 0.997, | |
| 'deformation_detection_threshold': 0.6, | |
| 'deepfake_detection_threshold': 0.65, | |
| 'frame_sampling_interval': st.slider('frame_sampling_interval', 0, 300, 60, step=5), | |
| 'frame_sampling_max': st.slider('frame_sampling_max', 0, 120, 50), | |
| } | |
| st.title("Face Fake Detection") | |
| setup() | |
| main() |