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