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| import cv2 | |
| import numpy as np | |
| from tqdm import tqdm | |
| def detect_faces(frames, min_face_size=30): | |
| """ | |
| Detect faces in frames using OpenCV's Haar Cascade classifier instead of dlib | |
| Args: | |
| frames: List of frames to detect faces in | |
| min_face_size: Minimum face size to detect | |
| Returns: | |
| Tuple of (face_frames, count) where face_frames is a numpy array of detected faces | |
| and count is the number of faces detected | |
| """ | |
| # Initialize face detector | |
| face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') | |
| # Initialize array to store faces | |
| temp_face = np.zeros((len(frames), 224, 224, 3), dtype=np.uint8) | |
| count = 0 | |
| # Process each frame | |
| for _, frame in tqdm(enumerate(frames), total=len(frames)): | |
| # Convert to grayscale for face detection | |
| gray = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) | |
| gray = cv2.cvtColor(gray, cv2.COLOR_BGR2GRAY) | |
| # Detect faces | |
| faces = face_cascade.detectMultiScale( | |
| gray, | |
| scaleFactor=1.1, | |
| minNeighbors=5, | |
| minSize=(min_face_size, min_face_size) | |
| ) | |
| # Process each detected face | |
| for (x, y, w, h) in faces: | |
| if count < len(frames): | |
| # Extract and resize face | |
| face_image = frame[y:y+h, x:x+w] | |
| face_image = cv2.resize(face_image, (224, 224), interpolation=cv2.INTER_AREA) | |
| # Store face | |
| temp_face[count] = face_image | |
| count += 1 | |
| else: | |
| break | |
| return ([], 0) if count == 0 else (temp_face[:count], count) |