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| import cv2 | |
| import mediapipe as mp | |
| import os | |
| from gradio_client import Client | |
| # from test_image_fusion import Test | |
| # from test_image_fusion import Test | |
| from test_image import Test | |
| import numpy as np | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| # client = Client("https://tbvl-real-and-fake-face-detection.hf.space/--replicas/40d41jxhhx/") | |
| data = 'faceswap' | |
| dct = 'fft' | |
| # testet = Test(model_paths = [f"weights/{data}-hh-best_model.pth", | |
| # f"weights/{data}-fft-best_model.pth"], | |
| # multi_modal = ['hh', 'fft']) | |
| testet = Test(model_path =f"weights/{data}-hh-best_model.pth", | |
| multi_modal ='hh') | |
| # Initialize MediaPipe Face Detection | |
| mp_face_detection = mp.solutions.face_detection | |
| mp_drawing = mp.solutions.drawing_utils | |
| face_detection = mp_face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.35) | |
| # Create a directory to save the cropped face images if it does not exist | |
| save_dir = "cropped_faces" | |
| os.makedirs(save_dir, exist_ok=True) | |
| # def detect_and_label_faces(image_path): | |
| # Function to crop faces from a video and save them as images | |
| # def crop_faces_from_video(video_path): | |
| # # Read the video | |
| # cap = cv2.VideoCapture(video_path) | |
| # frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) | |
| # frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) | |
| # fps = int(cap.get(cv2.CAP_PROP_FPS)) | |
| # total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) | |
| # # Define the codec and create VideoWriter object | |
| # out = cv2.VideoWriter(f'output_{real}_{data}_fusion.avi', cv2.VideoWriter_fourcc('M','J','P','G'), fps, (frame_width, frame_height)) | |
| # if not cap.isOpened(): | |
| # print("Error: Could not open video.") | |
| # return | |
| # Convert PIL Image to NumPy array for OpenCV | |
| def pil_to_opencv(pil_image): | |
| open_cv_image = np.array(pil_image) | |
| # Convert RGB to BGR for OpenCV | |
| open_cv_image = open_cv_image[:, :, ::-1].copy() | |
| return open_cv_image | |
| # Convert OpenCV NumPy array to PIL Image | |
| def opencv_to_pil(opencv_image): | |
| # Convert BGR to RGB | |
| pil_image = Image.fromarray(opencv_image[:, :, ::-1]) | |
| return pil_image | |
| def detect_and_label_faces(frame): | |
| frame = pil_to_opencv(frame) | |
| print(type(frame)) | |
| # Convert the frame to RGB | |
| frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| # Perform face detection | |
| results = face_detection.process(frame_rgb) | |
| # If faces are detected, crop and save each face as an image | |
| if results.detections: | |
| for face_count,detection in enumerate(results.detections): | |
| bboxC = detection.location_data.relative_bounding_box | |
| ih, iw, _ = frame.shape | |
| x, y, w, h = int(bboxC.xmin * iw), int(bboxC.ymin * ih), int(bboxC.width * iw), int(bboxC.height * ih) | |
| # Crop the face region and make sure the bounding box is within the frame dimensions | |
| crop_img = frame[max(0, y):min(ih, y+h), max(0, x):min(iw, x+w)] | |
| if crop_img.size > 0: | |
| face_filename = os.path.join(save_dir, f'face_{face_count}.jpg') | |
| cv2.imwrite(face_filename, crop_img) | |
| label = testet.testimage(face_filename) | |
| if os.path.exists(face_filename): | |
| os.remove(face_filename) | |
| color = (0, 0, 255) if label == 'fake' else (0, 255, 0) | |
| cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2) | |
| cv2.putText(frame, label, (x, y + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, color, 2) | |
| return opencv_to_pil(frame) | |