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| import numpy as np | |
| import torch | |
| from PIL import Image | |
| from models.mtcnn.mtcnn_pytorch.src.get_nets import PNet, RNet, ONet | |
| from models.mtcnn.mtcnn_pytorch.src.box_utils import nms, calibrate_box, get_image_boxes, convert_to_square | |
| from models.mtcnn.mtcnn_pytorch.src.first_stage import run_first_stage | |
| from models.mtcnn.mtcnn_pytorch.src.align_trans import get_reference_facial_points, warp_and_crop_face | |
| device = 'cuda:0' | |
| class MTCNN(): | |
| def __init__(self): | |
| print(device) | |
| self.pnet = PNet().to(device) | |
| self.rnet = RNet().to(device) | |
| self.onet = ONet().to(device) | |
| self.pnet.eval() | |
| self.rnet.eval() | |
| self.onet.eval() | |
| self.refrence = get_reference_facial_points(default_square=True) | |
| def align(self, img): | |
| _, landmarks = self.detect_faces(img) | |
| if len(landmarks) == 0: | |
| return None, None | |
| facial5points = [[landmarks[0][j], landmarks[0][j + 5]] for j in range(5)] | |
| warped_face, tfm = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112)) | |
| return Image.fromarray(warped_face), tfm | |
| def align_multi(self, img, limit=None, min_face_size=30.0): | |
| boxes, landmarks = self.detect_faces(img, min_face_size) | |
| if limit: | |
| boxes = boxes[:limit] | |
| landmarks = landmarks[:limit] | |
| faces = [] | |
| tfms = [] | |
| for landmark in landmarks: | |
| facial5points = [[landmark[j], landmark[j + 5]] for j in range(5)] | |
| warped_face, tfm = warp_and_crop_face(np.array(img), facial5points, self.refrence, crop_size=(112, 112)) | |
| faces.append(Image.fromarray(warped_face)) | |
| tfms.append(tfm) | |
| return boxes, faces, tfms | |
| def detect_faces(self, image, min_face_size=20.0, | |
| thresholds=[0.15, 0.25, 0.35], | |
| nms_thresholds=[0.7, 0.7, 0.7]): | |
| """ | |
| Arguments: | |
| image: an instance of PIL.Image. | |
| min_face_size: a float number. | |
| thresholds: a list of length 3. | |
| nms_thresholds: a list of length 3. | |
| Returns: | |
| two float numpy arrays of shapes [n_boxes, 4] and [n_boxes, 10], | |
| bounding boxes and facial landmarks. | |
| """ | |
| # BUILD AN IMAGE PYRAMID | |
| width, height = image.size | |
| min_length = min(height, width) | |
| min_detection_size = 12 | |
| factor = 0.707 # sqrt(0.5) | |
| # scales for scaling the image | |
| scales = [] | |
| # scales the image so that | |
| # minimum size that we can detect equals to | |
| # minimum face size that we want to detect | |
| m = min_detection_size / min_face_size | |
| min_length *= m | |
| factor_count = 0 | |
| while min_length > min_detection_size: | |
| scales.append(m * factor ** factor_count) | |
| min_length *= factor | |
| factor_count += 1 | |
| # STAGE 1 | |
| # it will be returned | |
| bounding_boxes = [] | |
| with torch.no_grad(): | |
| # run P-Net on different scales | |
| for s in scales: | |
| boxes = run_first_stage(image, self.pnet, scale=s, threshold=thresholds[0]) | |
| bounding_boxes.append(boxes) | |
| # collect boxes (and offsets, and scores) from different scales | |
| bounding_boxes = [i for i in bounding_boxes if i is not None] | |
| bounding_boxes = np.vstack(bounding_boxes) | |
| keep = nms(bounding_boxes[:, 0:5], nms_thresholds[0]) | |
| bounding_boxes = bounding_boxes[keep] | |
| # use offsets predicted by pnet to transform bounding boxes | |
| bounding_boxes = calibrate_box(bounding_boxes[:, 0:5], bounding_boxes[:, 5:]) | |
| # shape [n_boxes, 5] | |
| bounding_boxes = convert_to_square(bounding_boxes) | |
| bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4]) | |
| # STAGE 2 | |
| img_boxes = get_image_boxes(bounding_boxes, image, size=24) | |
| img_boxes = torch.FloatTensor(img_boxes).to(device) | |
| output = self.rnet(img_boxes) | |
| offsets = output[0].cpu().data.numpy() # shape [n_boxes, 4] | |
| probs = output[1].cpu().data.numpy() # shape [n_boxes, 2] | |
| keep = np.where(probs[:, 1] > thresholds[1])[0] | |
| bounding_boxes = bounding_boxes[keep] | |
| bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,)) | |
| offsets = offsets[keep] | |
| keep = nms(bounding_boxes, nms_thresholds[1]) | |
| bounding_boxes = bounding_boxes[keep] | |
| bounding_boxes = calibrate_box(bounding_boxes, offsets[keep]) | |
| bounding_boxes = convert_to_square(bounding_boxes) | |
| bounding_boxes[:, 0:4] = np.round(bounding_boxes[:, 0:4]) | |
| # STAGE 3 | |
| img_boxes = get_image_boxes(bounding_boxes, image, size=48) | |
| if len(img_boxes) == 0: | |
| return [], [] | |
| img_boxes = torch.FloatTensor(img_boxes).to(device) | |
| output = self.onet(img_boxes) | |
| landmarks = output[0].cpu().data.numpy() # shape [n_boxes, 10] | |
| offsets = output[1].cpu().data.numpy() # shape [n_boxes, 4] | |
| probs = output[2].cpu().data.numpy() # shape [n_boxes, 2] | |
| keep = np.where(probs[:, 1] > thresholds[2])[0] | |
| bounding_boxes = bounding_boxes[keep] | |
| bounding_boxes[:, 4] = probs[keep, 1].reshape((-1,)) | |
| offsets = offsets[keep] | |
| landmarks = landmarks[keep] | |
| # compute landmark points | |
| width = bounding_boxes[:, 2] - bounding_boxes[:, 0] + 1.0 | |
| height = bounding_boxes[:, 3] - bounding_boxes[:, 1] + 1.0 | |
| xmin, ymin = bounding_boxes[:, 0], bounding_boxes[:, 1] | |
| landmarks[:, 0:5] = np.expand_dims(xmin, 1) + np.expand_dims(width, 1) * landmarks[:, 0:5] | |
| landmarks[:, 5:10] = np.expand_dims(ymin, 1) + np.expand_dims(height, 1) * landmarks[:, 5:10] | |
| bounding_boxes = calibrate_box(bounding_boxes, offsets) | |
| keep = nms(bounding_boxes, nms_thresholds[2], mode='min') | |
| bounding_boxes = bounding_boxes[keep] | |
| landmarks = landmarks[keep] | |
| return bounding_boxes, landmarks | |