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| from argparse import Namespace | |
| import os, sys | |
| import torch | |
| import cv2 | |
| from pathlib import Path | |
| from .base import Viz | |
| from src.utils.metrics import compute_symmetrical_epipolar_errors, compute_pose_errors | |
| patch2pix_path = Path(__file__).parent / '../../third_party/patch2pix' | |
| sys.path.append(str(patch2pix_path)) | |
| from third_party.patch2pix.utils.eval.model_helper import load_model, estimate_matches | |
| class VizPatch2Pix(Viz): | |
| def __init__(self, args): | |
| super().__init__() | |
| if type(args) == dict: | |
| args = Namespace(**args) | |
| self.imsize = args.imsize | |
| self.match_threshold = args.match_threshold | |
| self.ksize = args.ksize | |
| self.model = load_model(args.ckpt, method='patch2pix') | |
| self.name = 'Patch2Pix' | |
| print(f'Initialize {self.name} with image size {self.imsize}') | |
| def match_and_draw(self, data_dict, root_dir=None, ground_truth=False, measure_time=False, viz_matches=True): | |
| img_name0, img_name1 = list(zip(*data_dict['pair_names']))[0] | |
| path_img0 = os.path.join(root_dir, img_name0) | |
| path_img1 = os.path.join(root_dir, img_name1) | |
| img0, img1 = cv2.imread(path_img0), cv2.imread(path_img1) | |
| return_m_upscale = True | |
| if str(data_dict["dataset_name"][0]).lower() == 'scannet': | |
| # self.imsize = 640 | |
| img0 = cv2.resize(img0, tuple(self.imsize)) # (640, 480)) | |
| img1 = cv2.resize(img1, tuple(self.imsize)) # (640, 480)) | |
| return_m_upscale = False | |
| outputs = estimate_matches(self.model, path_img0, path_img1, | |
| ksize=self.ksize, io_thres=self.match_threshold, | |
| eval_type='fine', imsize=self.imsize, | |
| return_upscale=return_m_upscale, measure_time=measure_time) | |
| if measure_time: | |
| self.time_stats.append(outputs[-1]) | |
| matches, mconf = outputs[0], outputs[1] | |
| kpts0 = matches[:, :2] | |
| kpts1 = matches[:, 2:4] | |
| if viz_matches: | |
| saved_name = "_".join([img_name0.split('/')[-1].split('.')[0], img_name1.split('/')[-1].split('.')[0]]) | |
| folder_matches = os.path.join(root_dir, "{}_viz_matches".format(self.name)) | |
| if not os.path.exists(folder_matches): | |
| os.makedirs(folder_matches) | |
| path_to_save_matches = os.path.join(folder_matches, "{}.png".format(saved_name)) | |
| if ground_truth: | |
| data_dict["mkpts0_f"] = torch.from_numpy(matches[:, :2]).float().to(self.device) | |
| data_dict["mkpts1_f"] = torch.from_numpy(matches[:, 2:4]).float().to(self.device) | |
| data_dict["m_bids"] = torch.zeros(matches.shape[0], device=self.device, dtype=torch.float32) | |
| compute_symmetrical_epipolar_errors(data_dict) # compute epi_errs for each match | |
| compute_pose_errors(data_dict) # compute R_errs, t_errs, pose_errs for each pair | |
| epi_errors = data_dict['epi_errs'].cpu().numpy() | |
| R_errors, t_errors = data_dict['R_errs'][0], data_dict['t_errs'][0] | |
| self.draw_matches(kpts0, kpts1, img0, img1, epi_errors, path=path_to_save_matches, | |
| R_errs=R_errors, t_errs=t_errors) | |
| rel_pair_names = list(zip(*data_dict['pair_names'])) | |
| bs = data_dict['image0'].size(0) | |
| metrics = { | |
| # to filter duplicate pairs caused by DistributedSampler | |
| 'identifiers': ['#'.join(rel_pair_names[b]) for b in range(bs)], | |
| 'epi_errs': [data_dict['epi_errs'][data_dict['m_bids'] == b].cpu().numpy() for b in range(bs)], | |
| 'R_errs': data_dict['R_errs'], | |
| 't_errs': data_dict['t_errs'], | |
| 'inliers': data_dict['inliers']} | |
| self.eval_stats.append({'metrics': metrics}) | |
| else: | |
| m_conf = 1 - mconf | |
| self.draw_matches(kpts0, kpts1, img0, img1, m_conf, path=path_to_save_matches, conf_thr=0.4) | |