from data import create_dataset from models import create_model from util.util import save_images import numpy as np from util.util import mkdir import argparse from PIL import Image import torchvision.transforms as transforms def transform(): transform_list = [] transform_list += [transforms.ToTensor()] transform_list += [transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] return transforms.Compose(transform_list) def val(opt): image_1_path = opt.image1_path image_2_path = opt.image2_path A_img = Image.open(image_1_path).convert('RGB') B_img = Image.open(image_2_path).convert('RGB') trans = transform() A = trans(A_img).unsqueeze(0) B = trans(B_img).unsqueeze(0) # dataset = create_dataset(opt) # create a dataset given opt.dataset_mode and other options model = create_model(opt) # create a model given opt.model and other options model.setup(opt) # regular setup: load and print networks; create schedulers save_path = opt.results_dir mkdir(save_path) model.eval() data = {} data['A']= A data['B'] = B data['A_paths'] = [image_1_path] model.set_input(data) # unpack data from data loader pred = model.test(val=False) # run inference return pred img_path = [image_1_path] # get image paths save_images(pred, save_path, img_path) if __name__ == '__main__': # 从外界调用方式： # python test.py --image1_path [path-to-img1] --image2_path [path-to-img2] --results_dir [path-to-result_dir] parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--image1_path', type=str, default='./samples/A/test_2_0000_0000.png', help='path to images A') parser.add_argument('--image2_path', type=str, default='./samples/B/test_2_0000_0000.png', help='path to images B') parser.add_argument('--results_dir', type=str, default='./samples/output/', help='saves results here.') parser.add_argument('--name', type=str, default='pam', help='name of the experiment. It decides where to store samples and models') parser.add_argument('--gpu_ids', type=str, default='0', help='gpu ids: e.g. 0 0,1,2, 0,2. use -1 for CPU') parser.add_argument('--checkpoints_dir', type=str, default='./checkpoints', help='models are saved here') # model parameters parser.add_argument('--model', type=str, default='CDFA', help='chooses which model to use. [CDF0 | CDFA]') parser.add_argument('--input_nc', type=int, default=3, help='# of input image channels: 3 for RGB ') parser.add_argument('--output_nc', type=int, default=3, help='# of output image channels: 3 for RGB') parser.add_argument('--arch', type=str, default='mynet3', help='feature extractor architecture | mynet3') parser.add_argument('--f_c', type=int, default=64, help='feature extractor channel num') parser.add_argument('--n_class', type=int, default=2, help='# of output pred channels: 2 for num of classes') parser.add_argument('--SA_mode', type=str, default='PAM', help='choose self attention mode for change detection, | ori |1 | 2 |pyramid, ...') # dataset parameters parser.add_argument('--dataset_mode', type=str, default='changedetection', help='chooses how datasets are loaded. [changedetection | json]') parser.add_argument('--val_dataset_mode', type=str, default='changedetection', help='chooses how datasets are loaded. [changedetection | json]') parser.add_argument('--split', type=str, default='train', help='chooses wihch list-file to open when use listDataset. [train | val | test]') parser.add_argument('--ds', type=int, default='1', help='self attention module downsample rate') parser.add_argument('--angle', type=int, default=0, help='rotate angle') parser.add_argument('--istest', type=bool, default=False, help='True for the case without label') parser.add_argument('--serial_batches', action='store_true', help='if true, takes images in order to make batches, otherwise takes them randomly') parser.add_argument('--num_threads', default=0, type=int, help='# threads for loading data') parser.add_argument('--batch_size', type=int, default=1, help='input batch size') parser.add_argument('--load_size', type=int, default=256, help='scale images to this size') parser.add_argument('--crop_size', type=int, default=256, help='then crop to this size') parser.add_argument('--max_dataset_size', type=int, default=float("inf"), help='Maximum number of samples allowed per dataset. If the dataset directory contains more than max_dataset_size, only a subset is loaded.') parser.add_argument('--preprocess', type=str, default='resize_and_crop', help='scaling and cropping of images at load time [resize_and_crop | none]') parser.add_argument('--no_flip', type=bool, default=True, help='if specified, do not flip(left-right) the images for data augmentation') parser.add_argument('--display_winsize', type=int, default=256, help='display window size for both visdom and HTML') parser.add_argument('--epoch', type=str, default='pam', help='which epoch to load? set to latest to use latest cached model') parser.add_argument('--load_iter', type=int, default='0', help='which iteration to load? if load_iter > 0, the code will load models by iter_[load_iter]; otherwise, the code will load models by [epoch]') parser.add_argument('--verbose', action='store_true', help='if specified, print more debugging information') parser.add_argument('--phase', type=str, default='test', help='train, val, test, etc') parser.add_argument('--isTrain', type=bool, default=False, help='is or not') parser.add_argument('--num_test', type=int, default=np.inf, help='how many test images to run') opt = parser.parse_args() val(opt)