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| import cv2 | |
| import numpy as np | |
| import dlib | |
| dnn_net = cv2.dnn.readNetFromCaffe("deploy.prototxt", "res10_300x300_ssd_iter_140000.caffemodel") | |
| # Initialize dlib's facial landmark predictor | |
| predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") | |
| dnn_net = cv2.dnn.readNetFromCaffe("deploy.prototxt", "res10_300x300_ssd_iter_140000.caffemodel") | |
| # Initialize dlib's facial landmark predictor | |
| predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat") | |
| def get_face(image, net=dnn_net, predictor=predictor): | |
| # Prepare the image for DNN face detection | |
| (h, w) = image.shape[:2] | |
| blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0)) | |
| net.setInput(blob) | |
| detections = net.forward() | |
| # Loop over the detections | |
| for i in range(0, detections.shape[2]): | |
| confidence = detections[0, 0, i, 2] | |
| # Filter out weak detections | |
| if confidence > 0.5: | |
| box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) | |
| (startX, startY, endX, endY) = box.astype("int") | |
| # Convert bounding box to dlib rectangle format | |
| dlib_rect = dlib.rectangle(int(startX), int(startY), int(endX), int(endY)) | |
| gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) | |
| landmarks = predictor(gray, dlib_rect) | |
| # Visualize landmarks | |
| for p in landmarks.parts(): | |
| cv2.circle(image, (p.x, p.y), 2, (0, 255, 0), -1) | |
| # Get the bounding box for the face based on landmarks | |
| landmarks_np = np.array([[p.x, p.y] for p in landmarks.parts()]) | |
| x, y, w, h = cv2.boundingRect(landmarks_np) | |
| x -= 25 | |
| y -= 25 | |
| w += 50 | |
| h += 50 | |
| x = max(0, x) | |
| y = max(0, y) | |
| w = min(w, image.shape[1] - x) | |
| h = min(h, image.shape[0] - y) | |
| # Crop and resize the face | |
| face_crop = image[y-h//2:y+h, x:x+w] | |
| face_crop = cv2.resize(face_crop, (224, 224)) | |
| return face_crop | |
| return None | |