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| import cv2 | |
| import time | |
| import numpy as np | |
| import torch | |
| from utils.models import PNet,RNet,ONet | |
| import utils.tool as utils | |
| import utils.dataloader as image_tools | |
| def create_mtcnn_net(p_model_path=None, r_model_path=None, o_model_path=None, use_cuda=True): | |
| pnet, rnet, onet = None, None, None | |
| if p_model_path is not None: | |
| pnet = PNet(use_cuda=use_cuda) | |
| if(use_cuda): | |
| print('p_model_path:{0}'.format(p_model_path)) | |
| pnet.load_state_dict(torch.load(p_model_path)) | |
| pnet.cuda() | |
| else: | |
| # forcing all GPU tensors to be in CPU while loading | |
| #pnet.load_state_dict(torch.load(p_model_path, map_location=lambda storage, loc: storage)) | |
| pnet.load_state_dict(torch.load(p_model_path, map_location='cpu')) | |
| pnet.eval() | |
| if r_model_path is not None: | |
| rnet = RNet(use_cuda=use_cuda) | |
| if (use_cuda): | |
| print('r_model_path:{0}'.format(r_model_path)) | |
| rnet.load_state_dict(torch.load(r_model_path)) | |
| rnet.cuda() | |
| else: | |
| rnet.load_state_dict(torch.load(r_model_path, map_location=lambda storage, loc: storage)) | |
| rnet.eval() | |
| if o_model_path is not None: | |
| onet = ONet(use_cuda=use_cuda) | |
| if (use_cuda): | |
| print('o_model_path:{0}'.format(o_model_path)) | |
| onet.load_state_dict(torch.load(o_model_path)) | |
| onet.cuda() | |
| else: | |
| onet.load_state_dict(torch.load(o_model_path, map_location=lambda storage, loc: storage)) | |
| onet.eval() | |
| return pnet,rnet,onet | |
| class MtcnnDetector(object): | |
| """ | |
| P,R,O net face detection and landmarks align | |
| """ | |
| def __init__(self, | |
| pnet = None, | |
| rnet = None, | |
| onet = None, | |
| min_face_size=12, | |
| stride=2, | |
| threshold=[0.6, 0.7, 0.7], | |
| #threshold=[0.1, 0.1, 0.1], | |
| scale_factor=0.709, | |
| ): | |
| self.pnet_detector = pnet | |
| self.rnet_detector = rnet | |
| self.onet_detector = onet | |
| self.min_face_size = min_face_size | |
| self.stride=stride | |
| self.thresh = threshold | |
| self.scale_factor = scale_factor | |
| def unique_image_format(self,im): | |
| if not isinstance(im,np.ndarray): | |
| if im.mode == 'I': | |
| im = np.array(im, np.int32, copy=False) | |
| elif im.mode == 'I;16': | |
| im = np.array(im, np.int16, copy=False) | |
| else: | |
| im = np.asarray(im) | |
| return im | |
| def square_bbox(self, bbox): | |
| """ | |
| convert bbox to square | |
| Parameters: | |
| ---------- | |
| bbox: numpy array , shape n x m | |
| input bbox | |
| Returns: | |
| ------- | |
| a square bbox | |
| """ | |
| square_bbox = bbox.copy() | |
| # x2 - x1 | |
| # y2 - y1 | |
| h = bbox[:, 3] - bbox[:, 1] + 1 | |
| w = bbox[:, 2] - bbox[:, 0] + 1 | |
| l = np.maximum(h,w) | |
| # x1 = x1 + w*0.5 - l*0.5 | |
| # y1 = y1 + h*0.5 - l*0.5 | |
| square_bbox[:, 0] = bbox[:, 0] + w*0.5 - l*0.5 | |
| square_bbox[:, 1] = bbox[:, 1] + h*0.5 - l*0.5 | |
| # x2 = x1 + l - 1 | |
| # y2 = y1 + l - 1 | |
| square_bbox[:, 2] = square_bbox[:, 0] + l - 1 | |
| square_bbox[:, 3] = square_bbox[:, 1] + l - 1 | |
| return square_bbox | |
| def generate_bounding_box(self, map, reg, scale, threshold): | |
| """ | |
| generate bbox from feature map | |
| Parameters: | |
| ---------- | |
| map: numpy array , n x m x 1 | |
| detect score for each position | |
| reg: numpy array , n x m x 4 | |
| bbox | |
| scale: float number | |
| scale of this detection | |
| threshold: float number | |
| detect threshold | |
| Returns: | |
| ------- | |
| bbox array | |
| """ | |
| stride = 2 | |
| cellsize = 12 # receptive field | |
| t_index = np.where(map[:,:,0] > threshold) | |
| # print('shape of t_index:{0}'.format(len(t_index))) | |
| # print('t_index{0}'.format(t_index)) | |
| # time.sleep(5) | |
| # find nothing | |
| if t_index[0].size == 0: | |
| return np.array([]) | |
| # reg = (1, n, m, 4) | |
| # choose bounding box whose socre are larger than threshold | |
| dx1, dy1, dx2, dy2 = [reg[0, t_index[0], t_index[1], i] for i in range(4)] | |
| #print(dx1.shape) | |
| #exit() | |
| # time.sleep(5) | |
| reg = np.array([dx1, dy1, dx2, dy2]) | |
| #print('shape of reg{0}'.format(reg.shape)) | |
| #exit() | |
| # lefteye_dx, lefteye_dy, righteye_dx, righteye_dy, nose_dx, nose_dy, \ | |
| # leftmouth_dx, leftmouth_dy, rightmouth_dx, rightmouth_dy = [landmarks[0, t_index[0], t_index[1], i] for i in range(10)] | |
| # | |
| # landmarks = np.array([lefteye_dx, lefteye_dy, righteye_dx, righteye_dy, nose_dx, nose_dy, leftmouth_dx, leftmouth_dy, rightmouth_dx, rightmouth_dy]) | |
| # abtain score of classification which larger than threshold | |
| # t_index[0]: choose the first column of t_index | |
| # t_index[1]: choose the second column of t_index | |
| score = map[t_index[0], t_index[1], 0] | |
| # hence t_index[1] means column, t_index[1] is the value of x | |
| # hence t_index[0] means row, t_index[0] is the value of y | |
| boundingbox = np.vstack([np.round((stride * t_index[1]) / scale), # x1 of prediction box in original image | |
| np.round((stride * t_index[0]) / scale), # y1 of prediction box in original image | |
| np.round((stride * t_index[1] + cellsize) / scale), # x2 of prediction box in original image | |
| np.round((stride * t_index[0] + cellsize) / scale), # y2 of prediction box in original image | |
| # reconstruct the box in original image | |
| score, | |
| reg, | |
| # landmarks | |
| ]) | |
| return boundingbox.T | |
| def resize_image(self, img, scale): | |
| """ | |
| resize image and transform dimention to [batchsize, channel, height, width] | |
| Parameters: | |
| ---------- | |
| img: numpy array , height x width x channel | |
| input image, channels in BGR order here | |
| scale: float number | |
| scale factor of resize operation | |
| Returns: | |
| ------- | |
| transformed image tensor , 1 x channel x height x width | |
| """ | |
| height, width, channels = img.shape | |
| new_height = int(height * scale) # resized new height | |
| new_width = int(width * scale) # resized new width | |
| new_dim = (new_width, new_height) | |
| img_resized = cv2.resize(img, new_dim, interpolation=cv2.INTER_LINEAR) # resized image | |
| return img_resized | |
| def pad(self, bboxes, w, h): | |
| """ | |
| pad the the boxes | |
| Parameters: | |
| ---------- | |
| bboxes: numpy array, n x 5 | |
| input bboxes | |
| w: float number | |
| width of the input image | |
| h: float number | |
| height of the input image | |
| Returns : | |
| ------ | |
| dy, dx : numpy array, n x 1 | |
| start point of the bbox in target image | |
| edy, edx : numpy array, n x 1 | |
| end point of the bbox in target image | |
| y, x : numpy array, n x 1 | |
| start point of the bbox in original image | |
| ex, ex : numpy array, n x 1 | |
| end point of the bbox in original image | |
| tmph, tmpw: numpy array, n x 1 | |
| height and width of the bbox | |
| """ | |
| # width and height | |
| tmpw = (bboxes[:, 2] - bboxes[:, 0] + 1).astype(np.int32) | |
| tmph = (bboxes[:, 3] - bboxes[:, 1] + 1).astype(np.int32) | |
| numbox = bboxes.shape[0] | |
| dx = np.zeros((numbox, )) | |
| dy = np.zeros((numbox, )) | |
| edx, edy = tmpw.copy()-1, tmph.copy()-1 | |
| # x, y: start point of the bbox in original image | |
| # ex, ey: end point of the bbox in original image | |
| x, y, ex, ey = bboxes[:, 0], bboxes[:, 1], bboxes[:, 2], bboxes[:, 3] | |
| tmp_index = np.where(ex > w-1) | |
| edx[tmp_index] = tmpw[tmp_index] + w - 2 - ex[tmp_index] | |
| ex[tmp_index] = w - 1 | |
| tmp_index = np.where(ey > h-1) | |
| edy[tmp_index] = tmph[tmp_index] + h - 2 - ey[tmp_index] | |
| ey[tmp_index] = h - 1 | |
| tmp_index = np.where(x < 0) | |
| dx[tmp_index] = 0 - x[tmp_index] | |
| x[tmp_index] = 0 | |
| tmp_index = np.where(y < 0) | |
| dy[tmp_index] = 0 - y[tmp_index] | |
| y[tmp_index] = 0 | |
| return_list = [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] | |
| return_list = [item.astype(np.int32) for item in return_list] | |
| return return_list | |
| def detect_pnet(self, im): | |
| """Get face candidates through pnet | |
| Parameters: | |
| ---------- | |
| im: numpy array | |
| input image array | |
| one batch | |
| Returns: | |
| ------- | |
| boxes: numpy array | |
| detected boxes before calibration | |
| boxes_align: numpy array | |
| boxes after calibration | |
| """ | |
| # im = self.unique_image_format(im) | |
| # original wider face data | |
| h, w, c = im.shape | |
| net_size = 12 | |
| current_scale = float(net_size) / self.min_face_size # find initial scale | |
| #print('imgshape:{0}, current_scale:{1}'.format(im.shape, current_scale)) | |
| im_resized = self.resize_image(im, current_scale) # scale = 1.0 | |
| current_height, current_width, _ = im_resized.shape | |
| # fcn | |
| all_boxes = list() | |
| while min(current_height, current_width) > net_size: | |
| #print('current:',current_height, current_width) | |
| feed_imgs = [] | |
| image_tensor = image_tools.convert_image_to_tensor(im_resized) | |
| feed_imgs.append(image_tensor) | |
| feed_imgs = torch.stack(feed_imgs) | |
| feed_imgs.requires_grad = True | |
| if self.pnet_detector.use_cuda: | |
| feed_imgs = feed_imgs.cuda() | |
| # self.pnet_detector is a trained pnet torch model | |
| # receptive field is 12×12 | |
| # 12×12 --> score | |
| # 12×12 --> bounding box | |
| cls_map, reg = self.pnet_detector(feed_imgs) | |
| cls_map_np = image_tools.convert_chwTensor_to_hwcNumpy(cls_map.cpu()) | |
| reg_np = image_tools.convert_chwTensor_to_hwcNumpy(reg.cpu()) | |
| # print(cls_map_np.shape, reg_np.shape) # cls_map_np = (1, n, m, 1) reg_np.shape = (1, n, m 4) | |
| # time.sleep(5) | |
| # landmark_np = image_tools.convert_chwTensor_to_hwcNumpy(landmark.cpu()) | |
| # self.threshold[0] = 0.6 | |
| # print(cls_map_np[0,:,:].shape) | |
| # time.sleep(4) | |
| # boxes = [x1, y1, x2, y2, score, reg] | |
| boxes = self.generate_bounding_box(cls_map_np[ 0, :, :], reg_np, current_scale, self.thresh[0]) | |
| #cv2.rectangle(im,(300,100),(400,200),color=(0,0,0)) | |
| #cv2.rectangle(im,(400,200),(500,300),color=(0,0,0)) | |
| # generate pyramid images | |
| current_scale *= self.scale_factor # self.scale_factor = 0.709 | |
| im_resized = self.resize_image(im, current_scale) | |
| current_height, current_width, _ = im_resized.shape | |
| if boxes.size == 0: | |
| continue | |
| # non-maximum suppresion | |
| keep = utils.nms(boxes[:, :5], 0.5, 'Union') | |
| boxes = boxes[keep] | |
| all_boxes.append(boxes) | |
| """ img = im.copy() | |
| bw = boxes[:,2]-boxes[:,0] | |
| bh = boxes[:,3]-boxes[:,1] | |
| for i in range(boxes.shape[0]): | |
| p1=(int(boxes[i][0]+boxes[i][5]*bw[i]),int(boxes[i][1]+boxes[i][6]*bh[i])) | |
| p2=(int(boxes[i][2]+boxes[i][7]*bw[i]),int(boxes[i][3]+boxes[i][8]*bh[i])) | |
| cv2.rectangle(img,p1,p2,color=(0,0,0)) | |
| cv2.imshow('ss',img) | |
| cv2.waitKey(0) | |
| #ii+=1 | |
| exit() """ | |
| if len(all_boxes) == 0: | |
| return None, None | |
| all_boxes = np.vstack(all_boxes) | |
| # print("shape of all boxes {0}".format(all_boxes.shape)) | |
| # time.sleep(5) | |
| # merge the detection from first stage | |
| keep = utils.nms(all_boxes[:, 0:5], 0.7, 'Union') | |
| all_boxes = all_boxes[keep] | |
| # boxes = all_boxes[:, :5] | |
| # x2 - x1 | |
| # y2 - y1 | |
| bw = all_boxes[:, 2] - all_boxes[:, 0] + 1 | |
| bh = all_boxes[:, 3] - all_boxes[:, 1] + 1 | |
| # landmark_keep = all_boxes[:, 9:].reshape((5,2)) | |
| boxes = np.vstack([all_boxes[:,0], | |
| all_boxes[:,1], | |
| all_boxes[:,2], | |
| all_boxes[:,3], | |
| all_boxes[:,4], | |
| # all_boxes[:, 0] + all_boxes[:, 9] * bw, | |
| # all_boxes[:, 1] + all_boxes[:,10] * bh, | |
| # all_boxes[:, 0] + all_boxes[:, 11] * bw, | |
| # all_boxes[:, 1] + all_boxes[:, 12] * bh, | |
| # all_boxes[:, 0] + all_boxes[:, 13] * bw, | |
| # all_boxes[:, 1] + all_boxes[:, 14] * bh, | |
| # all_boxes[:, 0] + all_boxes[:, 15] * bw, | |
| # all_boxes[:, 1] + all_boxes[:, 16] * bh, | |
| # all_boxes[:, 0] + all_boxes[:, 17] * bw, | |
| # all_boxes[:, 1] + all_boxes[:, 18] * bh | |
| ]) | |
| boxes = boxes.T | |
| # boxes = boxes = [x1, y1, x2, y2, score, reg] reg= [px1, py1, px2, py2] (in prediction) | |
| align_topx = all_boxes[:, 0] + all_boxes[:, 5] * bw | |
| align_topy = all_boxes[:, 1] + all_boxes[:, 6] * bh | |
| align_bottomx = all_boxes[:, 2] + all_boxes[:, 7] * bw | |
| align_bottomy = all_boxes[:, 3] + all_boxes[:, 8] * bh | |
| # refine the boxes | |
| boxes_align = np.vstack([ align_topx, | |
| align_topy, | |
| align_bottomx, | |
| align_bottomy, | |
| all_boxes[:, 4], | |
| # align_topx + all_boxes[:,9] * bw, | |
| # align_topy + all_boxes[:,10] * bh, | |
| # align_topx + all_boxes[:,11] * bw, | |
| # align_topy + all_boxes[:,12] * bh, | |
| # align_topx + all_boxes[:,13] * bw, | |
| # align_topy + all_boxes[:,14] * bh, | |
| # align_topx + all_boxes[:,15] * bw, | |
| # align_topy + all_boxes[:,16] * bh, | |
| # align_topx + all_boxes[:,17] * bw, | |
| # align_topy + all_boxes[:,18] * bh, | |
| ]) | |
| boxes_align = boxes_align.T | |
| #remove invalid box | |
| valindex = [True for _ in range(boxes_align.shape[0])] | |
| for i in range(boxes_align.shape[0]): | |
| if boxes_align[i][2]-boxes_align[i][0]<=3 or boxes_align[i][3]-boxes_align[i][1]<=3: | |
| valindex[i]=False | |
| #print('pnet has one smaller than 3') | |
| else: | |
| if boxes_align[i][2]<1 or boxes_align[i][0]>w-2 or boxes_align[i][3]<1 or boxes_align[i][1]>h-2: | |
| valindex[i]=False | |
| #print('pnet has one out') | |
| boxes_align=boxes_align[valindex,:] | |
| boxes = boxes[valindex,:] | |
| return boxes, boxes_align | |
| def detect_rnet(self, im, dets): | |
| """Get face candidates using rnet | |
| Parameters: | |
| ---------- | |
| im: numpy array | |
| input image array | |
| dets: numpy array | |
| detection results of pnet | |
| Returns: | |
| ------- | |
| boxes: numpy array | |
| detected boxes before calibration | |
| boxes_align: numpy array | |
| boxes after calibration | |
| """ | |
| # im: an input image | |
| h, w, c = im.shape | |
| if dets is None: | |
| return None,None | |
| if dets.shape[0]==0: | |
| return None, None | |
| # (705, 5) = [x1, y1, x2, y2, score, reg] | |
| # print("pnet detection {0}".format(dets.shape)) | |
| # time.sleep(5) | |
| detss = dets | |
| # return square boxes | |
| dets = self.square_bbox(dets) | |
| detsss = dets | |
| # rounds | |
| dets[:, 0:4] = np.round(dets[:, 0:4]) | |
| [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self.pad(dets, w, h) | |
| num_boxes = dets.shape[0] | |
| ''' | |
| # helper for setting RNet batch size | |
| batch_size = self.rnet_detector.batch_size | |
| ratio = float(num_boxes) / batch_size | |
| if ratio > 3 or ratio < 0.3: | |
| print "You may need to reset RNet batch size if this info appears frequently, \ | |
| face candidates:%d, current batch_size:%d"%(num_boxes, batch_size) | |
| ''' | |
| # cropped_ims_tensors = np.zeros((num_boxes, 3, 24, 24), dtype=np.float32) | |
| cropped_ims_tensors = [] | |
| for i in range(num_boxes): | |
| try: | |
| tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8) | |
| tmp[dy[i]:edy[i]+1, dx[i]:edx[i]+1, :] = im[y[i]:ey[i]+1, x[i]:ex[i]+1, :] | |
| except: | |
| print(dy[i],edy[i],dx[i],edx[i],y[i],ey[i],x[i],ex[i],tmpw[i],tmph[i]) | |
| print(dets[i]) | |
| print(detss[i]) | |
| print(detsss[i]) | |
| print(h,w) | |
| exit() | |
| crop_im = cv2.resize(tmp, (24, 24)) | |
| crop_im_tensor = image_tools.convert_image_to_tensor(crop_im) | |
| # cropped_ims_tensors[i, :, :, :] = crop_im_tensor | |
| cropped_ims_tensors.append(crop_im_tensor) | |
| feed_imgs = torch.stack(cropped_ims_tensors) | |
| feed_imgs.requires_grad = True | |
| if self.rnet_detector.use_cuda: | |
| feed_imgs = feed_imgs.cuda() | |
| cls_map, reg = self.rnet_detector(feed_imgs) | |
| cls_map = cls_map.cpu().data.numpy() | |
| reg = reg.cpu().data.numpy() | |
| # landmark = landmark.cpu().data.numpy() | |
| keep_inds = np.where(cls_map > self.thresh[1])[0] | |
| if len(keep_inds) > 0: | |
| boxes = dets[keep_inds] | |
| cls = cls_map[keep_inds] | |
| reg = reg[keep_inds] | |
| # landmark = landmark[keep_inds] | |
| else: | |
| return None, None | |
| keep = utils.nms(boxes, 0.7) | |
| if len(keep) == 0: | |
| return None, None | |
| keep_cls = cls[keep] | |
| keep_boxes = boxes[keep] | |
| keep_reg = reg[keep] | |
| # keep_landmark = landmark[keep] | |
| bw = keep_boxes[:, 2] - keep_boxes[:, 0] + 1 | |
| bh = keep_boxes[:, 3] - keep_boxes[:, 1] + 1 | |
| boxes = np.vstack([ keep_boxes[:,0], | |
| keep_boxes[:,1], | |
| keep_boxes[:,2], | |
| keep_boxes[:,3], | |
| keep_cls[:,0], | |
| # keep_boxes[:,0] + keep_landmark[:, 0] * bw, | |
| # keep_boxes[:,1] + keep_landmark[:, 1] * bh, | |
| # keep_boxes[:,0] + keep_landmark[:, 2] * bw, | |
| # keep_boxes[:,1] + keep_landmark[:, 3] * bh, | |
| # keep_boxes[:,0] + keep_landmark[:, 4] * bw, | |
| # keep_boxes[:,1] + keep_landmark[:, 5] * bh, | |
| # keep_boxes[:,0] + keep_landmark[:, 6] * bw, | |
| # keep_boxes[:,1] + keep_landmark[:, 7] * bh, | |
| # keep_boxes[:,0] + keep_landmark[:, 8] * bw, | |
| # keep_boxes[:,1] + keep_landmark[:, 9] * bh, | |
| ]) | |
| align_topx = keep_boxes[:,0] + keep_reg[:,0] * bw | |
| align_topy = keep_boxes[:,1] + keep_reg[:,1] * bh | |
| align_bottomx = keep_boxes[:,2] + keep_reg[:,2] * bw | |
| align_bottomy = keep_boxes[:,3] + keep_reg[:,3] * bh | |
| boxes_align = np.vstack([align_topx, | |
| align_topy, | |
| align_bottomx, | |
| align_bottomy, | |
| keep_cls[:, 0], | |
| # align_topx + keep_landmark[:, 0] * bw, | |
| # align_topy + keep_landmark[:, 1] * bh, | |
| # align_topx + keep_landmark[:, 2] * bw, | |
| # align_topy + keep_landmark[:, 3] * bh, | |
| # align_topx + keep_landmark[:, 4] * bw, | |
| # align_topy + keep_landmark[:, 5] * bh, | |
| # align_topx + keep_landmark[:, 6] * bw, | |
| # align_topy + keep_landmark[:, 7] * bh, | |
| # align_topx + keep_landmark[:, 8] * bw, | |
| # align_topy + keep_landmark[:, 9] * bh, | |
| ]) | |
| boxes = boxes.T | |
| boxes_align = boxes_align.T | |
| #remove invalid box | |
| valindex = [True for _ in range(boxes_align.shape[0])] | |
| for i in range(boxes_align.shape[0]): | |
| if boxes_align[i][2]-boxes_align[i][0]<=3 or boxes_align[i][3]-boxes_align[i][1]<=3: | |
| valindex[i]=False | |
| print('rnet has one smaller than 3') | |
| else: | |
| if boxes_align[i][2]<1 or boxes_align[i][0]>w-2 or boxes_align[i][3]<1 or boxes_align[i][1]>h-2: | |
| valindex[i]=False | |
| print('rnet has one out') | |
| boxes_align=boxes_align[valindex,:] | |
| boxes = boxes[valindex,:] | |
| """ img = im.copy() | |
| for i in range(boxes_align.shape[0]): | |
| p1=(int(boxes_align[i,0]),int(boxes_align[i,1])) | |
| p2=(int(boxes_align[i,2]),int(boxes_align[i,3])) | |
| cv2.rectangle(img,p1,p2,color=(0,0,0)) | |
| cv2.imshow('ss',img) | |
| cv2.waitKey(0) | |
| exit() """ | |
| return boxes, boxes_align | |
| def detect_onet(self, im, dets): | |
| """Get face candidates using onet | |
| Parameters: | |
| ---------- | |
| im: numpy array | |
| input image array | |
| dets: numpy array | |
| detection results of rnet | |
| Returns: | |
| ------- | |
| boxes_align: numpy array | |
| boxes after calibration | |
| landmarks_align: numpy array | |
| landmarks after calibration | |
| """ | |
| h, w, c = im.shape | |
| if dets is None: | |
| return None, None | |
| if dets.shape[0]==0: | |
| return None, None | |
| detss = dets | |
| dets = self.square_bbox(dets) | |
| dets[:, 0:4] = np.round(dets[:, 0:4]) | |
| [dy, edy, dx, edx, y, ey, x, ex, tmpw, tmph] = self.pad(dets, w, h) | |
| num_boxes = dets.shape[0] | |
| # cropped_ims_tensors = np.zeros((num_boxes, 3, 24, 24), dtype=np.float32) | |
| cropped_ims_tensors = [] | |
| for i in range(num_boxes): | |
| try: | |
| tmp = np.zeros((tmph[i], tmpw[i], 3), dtype=np.uint8) | |
| # crop input image | |
| tmp[dy[i]:edy[i] + 1, dx[i]:edx[i] + 1, :] = im[y[i]:ey[i] + 1, x[i]:ex[i] + 1, :] | |
| except: | |
| print(dy[i],edy[i],dx[i],edx[i],y[i],ey[i],x[i],ex[i],tmpw[i],tmph[i]) | |
| print(dets[i]) | |
| print(detss[i]) | |
| print(h,w) | |
| crop_im = cv2.resize(tmp, (48, 48)) | |
| crop_im_tensor = image_tools.convert_image_to_tensor(crop_im) | |
| # cropped_ims_tensors[i, :, :, :] = crop_im_tensor | |
| cropped_ims_tensors.append(crop_im_tensor) | |
| feed_imgs = torch.stack(cropped_ims_tensors) | |
| feed_imgs.requires_grad = True | |
| if self.rnet_detector.use_cuda: | |
| feed_imgs = feed_imgs.cuda() | |
| cls_map, reg, landmark = self.onet_detector(feed_imgs) | |
| cls_map = cls_map.cpu().data.numpy() | |
| reg = reg.cpu().data.numpy() | |
| landmark = landmark.cpu().data.numpy() | |
| keep_inds = np.where(cls_map > self.thresh[2])[0] | |
| if len(keep_inds) > 0: | |
| boxes = dets[keep_inds] | |
| cls = cls_map[keep_inds] | |
| reg = reg[keep_inds] | |
| landmark = landmark[keep_inds] | |
| else: | |
| return None, None | |
| keep = utils.nms(boxes, 0.7, mode="Minimum") | |
| if len(keep) == 0: | |
| return None, None | |
| keep_cls = cls[keep] | |
| keep_boxes = boxes[keep] | |
| keep_reg = reg[keep] | |
| keep_landmark = landmark[keep] | |
| bw = keep_boxes[:, 2] - keep_boxes[:, 0] + 1 | |
| bh = keep_boxes[:, 3] - keep_boxes[:, 1] + 1 | |
| align_topx = keep_boxes[:, 0] + keep_reg[:, 0] * bw | |
| align_topy = keep_boxes[:, 1] + keep_reg[:, 1] * bh | |
| align_bottomx = keep_boxes[:, 2] + keep_reg[:, 2] * bw | |
| align_bottomy = keep_boxes[:, 3] + keep_reg[:, 3] * bh | |
| align_landmark_topx = keep_boxes[:, 0] | |
| align_landmark_topy = keep_boxes[:, 1] | |
| boxes_align = np.vstack([align_topx, | |
| align_topy, | |
| align_bottomx, | |
| align_bottomy, | |
| keep_cls[:, 0], | |
| # align_topx + keep_landmark[:, 0] * bw, | |
| # align_topy + keep_landmark[:, 1] * bh, | |
| # align_topx + keep_landmark[:, 2] * bw, | |
| # align_topy + keep_landmark[:, 3] * bh, | |
| # align_topx + keep_landmark[:, 4] * bw, | |
| # align_topy + keep_landmark[:, 5] * bh, | |
| # align_topx + keep_landmark[:, 6] * bw, | |
| # align_topy + keep_landmark[:, 7] * bh, | |
| # align_topx + keep_landmark[:, 8] * bw, | |
| # align_topy + keep_landmark[:, 9] * bh, | |
| ]) | |
| boxes_align = boxes_align.T | |
| landmark = np.vstack([ | |
| align_landmark_topx + keep_landmark[:, 0] * bw, | |
| align_landmark_topy + keep_landmark[:, 1] * bh, | |
| align_landmark_topx + keep_landmark[:, 2] * bw, | |
| align_landmark_topy + keep_landmark[:, 3] * bh, | |
| align_landmark_topx + keep_landmark[:, 4] * bw, | |
| align_landmark_topy + keep_landmark[:, 5] * bh, | |
| align_landmark_topx + keep_landmark[:, 6] * bw, | |
| align_landmark_topy + keep_landmark[:, 7] * bh, | |
| align_landmark_topx + keep_landmark[:, 8] * bw, | |
| align_landmark_topy + keep_landmark[:, 9] * bh, | |
| ]) | |
| landmark_align = landmark.T | |
| return boxes_align, landmark_align | |
| def detect_face(self,img): | |
| """Detect face over image | |
| """ | |
| boxes_align = np.array([]) | |
| landmark_align =np.array([]) | |
| t = time.time() | |
| # pnet | |
| if self.pnet_detector: | |
| p_boxes, boxes_align = self.detect_pnet(img) | |
| if boxes_align is None: | |
| return np.array([]), np.array([]) | |
| t1 = time.time() - t | |
| t = time.time() | |
| # rnet | |
| if self.rnet_detector: | |
| r_boxes, boxes_align = self.detect_rnet(img, boxes_align) | |
| if boxes_align is None: | |
| return np.array([]), np.array([]) | |
| t2 = time.time() - t | |
| t = time.time() | |
| # onet | |
| if self.onet_detector: | |
| boxes_align, landmark_align = self.detect_onet(img, boxes_align) | |
| if boxes_align is None: | |
| return np.array([]), np.array([]) | |
| t3 = time.time() - t | |
| t = time.time() | |
| print("time cost " + '{:.3f}'.format(t1+t2+t3) + ' pnet {:.3f} rnet {:.3f} onet {:.3f}'.format(t1, t2, t3)) | |
| return p_boxes,r_boxes,boxes_align, landmark_align | |