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| ''' | |
| COTR demo for a single image pair | |
| ''' | |
| import argparse | |
| import os | |
| import time | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import imageio | |
| import matplotlib.pyplot as plt | |
| from COTR.utils import utils, debug_utils | |
| from COTR.models import build_model | |
| from COTR.options.options import * | |
| from COTR.options.options_utils import * | |
| from COTR.inference.inference_helper import triangulate_corr | |
| from COTR.inference.sparse_engine import SparseEngine | |
| utils.fix_randomness(0) | |
| torch.set_grad_enabled(False) | |
| def main(opt): | |
| model = build_model(opt) | |
| model = model.cuda() | |
| weights = torch.load(opt.load_weights_path, map_location='cpu')['model_state_dict'] | |
| utils.safe_load_weights(model, weights) | |
| model = model.eval() | |
| img_a = imageio.imread('./sample_data/imgs/cathedral_1.jpg', pilmode='RGB') | |
| img_b = imageio.imread('./sample_data/imgs/cathedral_2.jpg', pilmode='RGB') | |
| engine = SparseEngine(model, 32, mode='tile') | |
| t0 = time.time() | |
| corrs = engine.cotr_corr_multiscale_with_cycle_consistency(img_a, img_b, np.linspace(0.5, 0.0625, 4), 1, max_corrs=opt.max_corrs, queries_a=None) | |
| t1 = time.time() | |
| utils.visualize_corrs(img_a, img_b, corrs) | |
| print(f'spent {t1-t0} seconds for {opt.max_corrs} correspondences.') | |
| dense = triangulate_corr(corrs, img_a.shape, img_b.shape) | |
| warped = cv2.remap(img_b, dense[..., 0].astype(np.float32), dense[..., 1].astype(np.float32), interpolation=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT) | |
| plt.imshow(warped / 255 * 0.5 + img_a / 255 * 0.5) | |
| plt.show() | |
| if __name__ == "__main__": | |
| parser = argparse.ArgumentParser() | |
| set_COTR_arguments(parser) | |
| parser.add_argument('--out_dir', type=str, default=general_config['out'], help='out directory') | |
| parser.add_argument('--load_weights', type=str, default=None, help='load a pretrained set of weights, you need to provide the model id') | |
| parser.add_argument('--max_corrs', type=int, default=100, help='number of correspondences') | |
| opt = parser.parse_args() | |
| opt.command = ' '.join(sys.argv) | |
| layer_2_channels = {'layer1': 256, | |
| 'layer2': 512, | |
| 'layer3': 1024, | |
| 'layer4': 2048, } | |
| opt.dim_feedforward = layer_2_channels[opt.layer] | |
| if opt.load_weights: | |
| opt.load_weights_path = os.path.join(opt.out_dir, opt.load_weights, 'checkpoint.pth.tar') | |
| print_opt(opt) | |
| main(opt) | |