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| import cv2, os | |
| import sys | |
| sys.path.insert(0, 'FaceBoxesV2') | |
| sys.path.insert(0, '..') | |
| import numpy as np | |
| import pickle | |
| import importlib | |
| from math import floor | |
| from faceboxes_detector import * | |
| import time | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.parallel | |
| import torch.optim as optim | |
| import torch.utils.data | |
| import torch.nn.functional as F | |
| import torchvision.transforms as transforms | |
| import torchvision.datasets as datasets | |
| import torchvision.models as models | |
| from networks import * | |
| import data_utils | |
| from functions import * | |
| from mobilenetv3 import mobilenetv3_large | |
| if not len(sys.argv) == 3: | |
| print('Format:') | |
| print('python lib/demo.py config_file image_file') | |
| exit(0) | |
| experiment_name = sys.argv[1].split('/')[-1][:-3] | |
| data_name = sys.argv[1].split('/')[-2] | |
| config_path = '.experiments.{}.{}'.format(data_name, experiment_name) | |
| image_file = sys.argv[2] | |
| my_config = importlib.import_module(config_path, package='PIPNet') | |
| Config = getattr(my_config, 'Config') | |
| cfg = Config() | |
| cfg.experiment_name = experiment_name | |
| cfg.data_name = data_name | |
| save_dir = os.path.join('./snapshots', cfg.data_name, cfg.experiment_name) | |
| meanface_indices, reverse_index1, reverse_index2, max_len = get_meanface(os.path.join('data', cfg.data_name, 'meanface.txt'), cfg.num_nb) | |
| if cfg.backbone == 'resnet18': | |
| resnet18 = models.resnet18(pretrained=cfg.pretrained) | |
| net = Pip_resnet18(resnet18, cfg.num_nb, num_lms=cfg.num_lms, input_size=cfg.input_size, net_stride=cfg.net_stride) | |
| elif cfg.backbone == 'resnet50': | |
| resnet50 = models.resnet50(pretrained=cfg.pretrained) | |
| net = Pip_resnet50(resnet50, cfg.num_nb, num_lms=cfg.num_lms, input_size=cfg.input_size, net_stride=cfg.net_stride) | |
| elif cfg.backbone == 'resnet101': | |
| resnet101 = models.resnet101(pretrained=cfg.pretrained) | |
| net = Pip_resnet101(resnet101, cfg.num_nb, num_lms=cfg.num_lms, input_size=cfg.input_size, net_stride=cfg.net_stride) | |
| elif cfg.backbone == 'mobilenet_v2': | |
| mbnet = models.mobilenet_v2(pretrained=cfg.pretrained) | |
| net = Pip_mbnetv2(mbnet, cfg.num_nb, num_lms=cfg.num_lms, input_size=cfg.input_size, net_stride=cfg.net_stride) | |
| elif cfg.backbone == 'mobilenet_v3': | |
| mbnet = mobilenetv3_large() | |
| if cfg.pretrained: | |
| mbnet.load_state_dict(torch.load('lib/mobilenetv3-large-1cd25616.pth')) | |
| net = Pip_mbnetv3(mbnet, cfg.num_nb, num_lms=cfg.num_lms, input_size=cfg.input_size, net_stride=cfg.net_stride) | |
| else: | |
| print('No such backbone!') | |
| exit(0) | |
| if cfg.use_gpu: | |
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | |
| else: | |
| device = torch.device("cpu") | |
| net = net.to(device) | |
| weight_file = os.path.join(save_dir, 'epoch%d.pth' % (cfg.num_epochs-1)) | |
| state_dict = torch.load(weight_file, map_location=device) | |
| net.load_state_dict(state_dict) | |
| normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], | |
| std=[0.229, 0.224, 0.225]) | |
| preprocess = transforms.Compose([transforms.Resize((cfg.input_size, cfg.input_size)), transforms.ToTensor(), normalize]) | |
| def demo_image(image_file, net, preprocess, input_size, net_stride, num_nb, use_gpu, device): | |
| detector = FaceBoxesDetector('FaceBoxes', 'FaceBoxesV2/weights/FaceBoxesV2.pth', use_gpu, device) | |
| my_thresh = 0.6 | |
| det_box_scale = 1.2 | |
| net.eval() | |
| image = cv2.imread(image_file) | |
| image_height, image_width, _ = image.shape | |
| detections, _ = detector.detect(image, my_thresh, 1) | |
| for i in range(len(detections)): | |
| det_xmin = detections[i][2] | |
| det_ymin = detections[i][3] | |
| det_width = detections[i][4] | |
| det_height = detections[i][5] | |
| det_xmax = det_xmin + det_width - 1 | |
| det_ymax = det_ymin + det_height - 1 | |
| det_xmin -= int(det_width * (det_box_scale-1)/2) | |
| # remove a part of top area for alignment, see paper for details | |
| det_ymin += int(det_height * (det_box_scale-1)/2) | |
| det_xmax += int(det_width * (det_box_scale-1)/2) | |
| det_ymax += int(det_height * (det_box_scale-1)/2) | |
| det_xmin = max(det_xmin, 0) | |
| det_ymin = max(det_ymin, 0) | |
| det_xmax = min(det_xmax, image_width-1) | |
| det_ymax = min(det_ymax, image_height-1) | |
| det_width = det_xmax - det_xmin + 1 | |
| det_height = det_ymax - det_ymin + 1 | |
| cv2.rectangle(image, (det_xmin, det_ymin), (det_xmax, det_ymax), (0, 0, 255), 2) | |
| det_crop = image[det_ymin:det_ymax, det_xmin:det_xmax, :] | |
| det_crop = cv2.resize(det_crop, (input_size, input_size)) | |
| inputs = Image.fromarray(det_crop[:,:,::-1].astype('uint8'), 'RGB') | |
| inputs = preprocess(inputs).unsqueeze(0) | |
| inputs = inputs.to(device) | |
| lms_pred_x, lms_pred_y, lms_pred_nb_x, lms_pred_nb_y, outputs_cls, max_cls = forward_pip(net, inputs, preprocess, input_size, net_stride, num_nb) | |
| lms_pred = torch.cat((lms_pred_x, lms_pred_y), dim=1).flatten() | |
| tmp_nb_x = lms_pred_nb_x[reverse_index1, reverse_index2].view(cfg.num_lms, max_len) | |
| tmp_nb_y = lms_pred_nb_y[reverse_index1, reverse_index2].view(cfg.num_lms, max_len) | |
| tmp_x = torch.mean(torch.cat((lms_pred_x, tmp_nb_x), dim=1), dim=1).view(-1,1) | |
| tmp_y = torch.mean(torch.cat((lms_pred_y, tmp_nb_y), dim=1), dim=1).view(-1,1) | |
| lms_pred_merge = torch.cat((tmp_x, tmp_y), dim=1).flatten() | |
| lms_pred = lms_pred.cpu().numpy() | |
| lms_pred_merge = lms_pred_merge.cpu().numpy() | |
| for i in range(cfg.num_lms): | |
| x_pred = lms_pred_merge[i*2] * det_width | |
| y_pred = lms_pred_merge[i*2+1] * det_height | |
| cv2.circle(image, (int(x_pred)+det_xmin, int(y_pred)+det_ymin), 1, (0, 0, 255), 2) | |
| #cv2.imwrite('images/1_out.jpg', image) | |
| cv2.imshow('1', image) | |
| cv2.waitKey(0) | |
| demo_image(image_file, net, preprocess, cfg.input_size, cfg.net_stride, cfg.num_nb, cfg.use_gpu, device) | |