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import argparse |
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import os |
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import numpy as np |
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import h5py |
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import cv2 |
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from numpy.core.numeric import indices |
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import pyxis as px |
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from tqdm import trange |
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import sys |
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ROOT_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) |
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sys.path.insert(0, ROOT_DIR) |
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from utils import evaluation_utils, train_utils |
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parser = argparse.ArgumentParser(description="checking training data.") |
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parser.add_argument("--meta_dir", type=str, default="dataset/valid") |
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parser.add_argument("--dataset_dir", type=str, default="dataset") |
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parser.add_argument("--desc_dir", type=str, default="desc") |
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parser.add_argument("--raw_dir", type=str, default="raw_data") |
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parser.add_argument("--desc_suffix", type=str, default="_root_1000.hdf5") |
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parser.add_argument("--vis_folder", type=str, default=None) |
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args = parser.parse_args() |
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if __name__ == "__main__": |
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if args.vis_folder is not None and not os.path.exists(args.vis_folder): |
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os.mkdir(args.vis_folder) |
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pair_num_list = np.loadtxt(os.path.join(args.meta_dir, "pair_num.txt"), dtype=str) |
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pair_seq_list, accu_pair_list = train_utils.parse_pair_seq(pair_num_list) |
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total_pair = int(pair_num_list[0, 1]) |
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total_inlier_rate, total_corr_num, total_incorr_num = [], [], [] |
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pair_num_list = pair_num_list[1:] |
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for index in trange(total_pair): |
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seq = pair_seq_list[index] |
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index_within_seq = index - accu_pair_list[seq] |
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with h5py.File(os.path.join(args.dataset_dir, seq, "info.h5py"), "r") as data: |
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corr = data["corr"][str(index_within_seq)][()] |
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corr1, corr2 = corr[:, 0], corr[:, 1] |
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incorr1, incorr2 = ( |
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data["incorr1"][str(index_within_seq)][()], |
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data["incorr2"][str(index_within_seq)][()], |
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) |
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img_path1, img_path2 = ( |
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data["img_path1"][str(index_within_seq)][()][0].decode(), |
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data["img_path2"][str(index_within_seq)][()][0].decode(), |
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) |
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img_name1, img_name2 = img_path1.split("/")[-1], img_path2.split("/")[-1] |
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fea_path1, fea_path2 = os.path.join( |
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args.desc_dir, seq, img_name1 + args.desc_suffix |
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), os.path.join(args.desc_dir, seq, img_name2 + args.desc_suffix) |
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with h5py.File(fea_path1, "r") as fea1, h5py.File(fea_path2, "r") as fea2: |
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desc1, kpt1 = fea1["descriptors"][()], fea1["keypoints"][()][:, :2] |
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desc2, kpt2 = fea2["descriptors"][()], fea2["keypoints"][()][:, :2] |
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sim_mat = desc1 @ desc2.T |
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nn_index1, nn_index2 = np.argmax(sim_mat, axis=1), np.argmax( |
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sim_mat, axis=0 |
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) |
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mask_mutual = (nn_index2[nn_index1] == np.arange(len(nn_index1)))[corr1] |
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mask_inlier = nn_index1[corr1] == corr2 |
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mask_nn_correct = np.logical_and(mask_mutual, mask_inlier) |
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total_inlier_rate.append(mask_nn_correct.mean()) |
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total_corr_num.append(len(corr1)) |
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total_incorr_num.append((len(incorr1) + len(incorr2)) / 2) |
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if args.vis_folder is not None: |
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img1, img2 = cv2.imread( |
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os.path.join(args.raw_dir, img_path1) |
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), cv2.imread(os.path.join(args.raw_dir, img_path2)) |
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corr1_pos, corr2_pos = np.take_along_axis( |
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kpt1, corr1[:, np.newaxis], axis=0 |
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), np.take_along_axis(kpt2, corr2[:, np.newaxis], axis=0) |
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dis_corr = evaluation_utils.draw_match(img1, img2, corr1_pos, corr2_pos) |
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cv2.imwrite( |
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os.path.join(args.vis_folder, str(index) + ".png"), dis_corr |
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) |
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incorr1_pos, incorr2_pos = np.take_along_axis( |
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kpt1, incorr1[:, np.newaxis], axis=0 |
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), np.take_along_axis(kpt2, incorr2[:, np.newaxis], axis=0) |
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dis_incorr1, dis_incorr2 = evaluation_utils.draw_points( |
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img1, incorr1_pos |
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), evaluation_utils.draw_points(img2, incorr2_pos) |
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cv2.imwrite( |
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os.path.join(args.vis_folder, str(index) + "_incorr1.png"), |
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dis_incorr1, |
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) |
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cv2.imwrite( |
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os.path.join(args.vis_folder, str(index) + "_incorr2.png"), |
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dis_incorr2, |
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) |
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print("NN matching accuracy: ", np.asarray(total_inlier_rate).mean()) |
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print("mean corr number: ", np.asarray(total_corr_num).mean()) |
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print("mean incorr number: ", np.asarray(total_incorr_num).mean()) |
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