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| ''' | |
| Manually passing scale to COTR, skip the scale difference estimation. | |
| ''' | |
| import argparse | |
| import os | |
| import time | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import imageio | |
| from scipy.spatial import distance_matrix | |
| 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.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)['model_state_dict'] | |
| utils.safe_load_weights(model, weights) | |
| model = model.eval() | |
| img_a = imageio.imread('./sample_data/imgs/petrzin_01.png') | |
| img_b = imageio.imread('./sample_data/imgs/petrzin_02.png') | |
| img_a_area = 1.0 | |
| img_b_area = 1.0 | |
| gt_corrs = np.loadtxt('./sample_data/petrzin_pts.txt') | |
| kp_a = gt_corrs[:, :2] | |
| kp_b = gt_corrs[:, 2:] | |
| engine = SparseEngine(model, 32, mode='tile') | |
| t0 = time.time() | |
| corrs = engine.cotr_corr_multiscale(img_a, img_b, np.linspace(0.75, 0.1, 4), 1, max_corrs=kp_a.shape[0], queries_a=kp_a, force=True, areas=[img_a_area, img_b_area]) | |
| t1 = time.time() | |
| print(f'COTR spent {t1-t0} seconds.') | |
| utils.visualize_corrs(img_a, img_b, corrs) | |
| plt.imshow(img_b) | |
| plt.scatter(kp_b[:,0], kp_b[:,1]) | |
| plt.scatter(corrs[:,2], corrs[:,3]) | |
| plt.plot(np.stack([kp_b[:,0], corrs[:,2]], axis=1).T, np.stack([kp_b[:,1], corrs[:,3]], axis=1).T, color=[1,0,0]) | |
| 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') | |
| 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) | |