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| import os | |
| import glob | |
| import numpy as np | |
| import torch | |
| import torch.utils.data as data | |
| import sys | |
| sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', '..')) | |
| from configs.anipose_data_info import COMPLETE_DATA_INFO | |
| from stacked_hourglass.utils.imutils import load_image | |
| from stacked_hourglass.utils.transforms import crop, color_normalize | |
| from stacked_hourglass.utils.pilutil import imresize | |
| from stacked_hourglass.utils.imutils import im_to_torch | |
| from configs.dataset_path_configs import TEST_IMAGE_CROP_ROOT_DIR | |
| from configs.data_info import COMPLETE_DATA_INFO_24 | |
| class ImgCrops(data.Dataset): | |
| DATA_INFO = COMPLETE_DATA_INFO_24 | |
| ACC_JOINTS = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 16] | |
| def __init__(self, img_crop_folder='default', image_path=None, is_train=False, inp_res=256, out_res=64, sigma=1, | |
| scale_factor=0.25, rot_factor=30, label_type='Gaussian', | |
| do_augment='default', shorten_dataset_to=None, dataset_mode='keyp_only'): | |
| assert is_train == False | |
| assert do_augment == 'default' or do_augment == False | |
| self.inp_res = inp_res | |
| if img_crop_folder == 'default': | |
| self.folder_imgs = os.path.join(os.path.dirname(__file__), '..', '..', '..', 'datasets', 'test_image_crops') | |
| else: | |
| self.folder_imgs = img_crop_folder | |
| name_list = glob.glob(os.path.join(self.folder_imgs, '*.png')) + glob.glob(os.path.join(self.folder_imgs, '*.jpg')) + glob.glob(os.path.join(self.folder_imgs, '*.jpeg')) | |
| name_list = sorted(name_list) | |
| self.test_name_list = [name.split('/')[-1] for name in name_list] | |
| print('len(dataset): ' + str(self.__len__())) | |
| def __getitem__(self, index): | |
| img_name = self.test_name_list[index] | |
| # load image | |
| img_path = os.path.join(self.folder_imgs, img_name) | |
| img = load_image(img_path) # CxHxW | |
| # prepare image (cropping and color) | |
| img_max = max(img.shape[1], img.shape[2]) | |
| img_padded = torch.zeros((img.shape[0], img_max, img_max)) | |
| if img_max == img.shape[2]: | |
| start = (img_max-img.shape[1])//2 | |
| img_padded[:, start:start+img.shape[1], :] = img | |
| else: | |
| start = (img_max-img.shape[2])//2 | |
| img_padded[:, :, start:start+img.shape[2]] = img | |
| img = img_padded | |
| img_prep = im_to_torch(imresize(img, [self.inp_res, self.inp_res], interp='bilinear')) | |
| inp = color_normalize(img_prep, self.DATA_INFO.rgb_mean, self.DATA_INFO.rgb_stddev) | |
| # add the following fields to make it compatible with stanext, most of them are fake | |
| target_dict = {'index': index, 'center' : -2, 'scale' : -2, | |
| 'breed_index': -2, 'sim_breed_index': -2, | |
| 'ind_dataset': 1} | |
| target_dict['pts'] = np.zeros((self.DATA_INFO.n_keyp, 3)) | |
| target_dict['tpts'] = np.zeros((self.DATA_INFO.n_keyp, 3)) | |
| target_dict['target_weight'] = np.zeros((self.DATA_INFO.n_keyp, 1)) | |
| target_dict['silh'] = np.zeros((self.inp_res, self.inp_res)) | |
| return inp, target_dict | |
| def __len__(self): | |
| return len(self.test_name_list) | |