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| import streamlit as st | |
| from transformers import AutoImageProcessor, AutoModelForImageClassification | |
| import cv2 | |
| import torch | |
| import numpy as np | |
| import tempfile | |
| image_processor = AutoImageProcessor.from_pretrained( | |
| 'ashish-001/deepfake-detection-using-ViT') | |
| model = AutoModelForImageClassification.from_pretrained( | |
| 'ashish-001/deepfake-detection-using-ViT') | |
| def classify_frame(frame): | |
| inputs = image_processor(images=frame, return_tensors="pt") | |
| outputs = model(**inputs) | |
| logits = outputs.logits | |
| probs = torch.nn.functional.sigmoid(logits) | |
| pred = torch.argmax(logits, dim=1).item() | |
| lab = 'Real' if pred == 1 else 'Fake' | |
| confidence, _ = torch.max(probs, dim=1) | |
| return f"{lab}::{format(confidence.item(), '.2f')}" | |
| st.title("Deepfake detector") | |
| uploaded_file = st.file_uploader( | |
| "Upload an image or video", | |
| type=["jpg", "jpeg", "png", "mp4", "avi", "mov", "mkv"] | |
| ) | |
| placeholder = st.empty() | |
| if st.button('Detect'): | |
| if uploaded_file is not None: | |
| clf = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') | |
| mime_type = uploaded_file.type | |
| if mime_type.startswith("image"): | |
| file_bytes = uploaded_file.read() | |
| np_arr = np.frombuffer(file_bytes, np.uint8) | |
| image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR) | |
| image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| faces = clf.detectMultiScale( | |
| gray, scaleFactor=1.3, minNeighbors=5) | |
| for (x, y, w, h) in faces: | |
| cv2.rectangle(image_rgb, (x, y), (x+w, y+h), (0, 0, 255), 2) | |
| face = image_rgb[y:y + h, x:x + w] | |
| img = cv2.cvtColor(face, cv2.COLOR_BGR2RGB) | |
| label = classify_frame(img) | |
| new_frame = cv2.putText( | |
| image_rgb, label, (x, y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) | |
| st.image(new_frame) | |
| elif mime_type.startswith('video'): | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file: | |
| temp_file.write(uploaded_file.read()) | |
| temp_video_path = temp_file.name | |
| cap = cv2.VideoCapture(temp_video_path) | |
| if not cap.isOpened(): | |
| st.error("Error: Cannot open video file.") | |
| else: | |
| while True: | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| faces = clf.detectMultiScale( | |
| gray, scaleFactor=1.3, minNeighbors=5) | |
| for (x, y, w, h) in faces: | |
| cv2.rectangle( | |
| frame, (x, y), (x+w, y+h), (0, 0, 255), 2) | |
| face = frame[y:y + h, x:x + w] | |
| img = cv2.cvtColor(face, cv2.COLOR_BGR2RGB) | |
| label = classify_frame(img) | |
| frame = cv2.putText( | |
| frame, label, (x, y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) | |
| placeholder.image(frame) | |
| cap.release() | |
| else: | |
| st.write("Please upload an image or video") | |
| if st.button('Use Example Video'): | |
| clf = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') | |
| cap = cv2.VideoCapture("Sample.mp4") | |
| if not cap.isOpened(): | |
| st.error("Error: Cannot open video file.") | |
| else: | |
| st.write(f"Video credits: 'Deep Fakes' Are Becoming More Realistic Thanks To New Technology. Link:https://www.youtube.com/watch?v=CDMVaQOvtxU") | |
| while True: | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) | |
| faces = clf.detectMultiScale(gray, scaleFactor=1.3, minNeighbors=5) | |
| for (x, y, w, h) in faces: | |
| cv2.rectangle( | |
| frame, (x, y), (x+w, y+h), (0, 0, 255), 2) | |
| face = frame[y:y + h, x:x + w] | |
| img = cv2.cvtColor(face, cv2.COLOR_BGR2RGB) | |
| label = classify_frame(img) | |
| frame = cv2.putText( | |
| frame, label, (x, y+h+20), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2) | |
| placeholder.image(frame) | |
| cap.release() | |