Spaces:
Running
on
Zero
Running
on
Zero
| # ---------------------------------------------------------------------------- | |
| # - TanksAndTemples Website Toolbox - | |
| # - http://www.tanksandtemples.org - | |
| # ---------------------------------------------------------------------------- | |
| # The MIT License (MIT) | |
| # | |
| # Copyright (c) 2017 | |
| # Arno Knapitsch <[email protected] > | |
| # Jaesik Park <[email protected]> | |
| # Qian-Yi Zhou <[email protected]> | |
| # Vladlen Koltun <[email protected]> | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # | |
| # The above copyright notice and this permission notice shall be included in | |
| # all copies or substantial portions of the Software. | |
| # | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN | |
| # THE SOFTWARE. | |
| # ---------------------------------------------------------------------------- | |
| # | |
| # This python script is for downloading dataset from www.tanksandtemples.org | |
| # The dataset has a different license, please refer to | |
| # https://tanksandtemples.org/license/ | |
| import json | |
| import copy | |
| import os | |
| import numpy as np | |
| import open3d as o3d | |
| import matplotlib.pyplot as plt | |
| def read_alignment_transformation(filename): | |
| with open(filename) as data_file: | |
| data = json.load(data_file) | |
| return np.asarray(data["transformation"]).reshape((4, 4)).transpose() | |
| def write_color_distances(path, pcd, distances, max_distance): | |
| o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug) | |
| # cmap = plt.get_cmap("afmhot") | |
| cmap = plt.get_cmap("hot_r") | |
| distances = np.array(distances) | |
| colors = cmap(np.minimum(distances, max_distance) / max_distance)[:, :3] | |
| pcd.colors = o3d.utility.Vector3dVector(colors) | |
| o3d.io.write_point_cloud(path, pcd) | |
| def EvaluateHisto( | |
| source, | |
| target, | |
| trans, | |
| crop_volume, | |
| voxel_size, | |
| threshold, | |
| filename_mvs, | |
| plot_stretch, | |
| scene_name, | |
| view_crop, | |
| verbose=True, | |
| ): | |
| print("[EvaluateHisto]") | |
| o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Debug) | |
| s = copy.deepcopy(source) | |
| s.transform(trans) | |
| if crop_volume is not None: | |
| s = crop_volume.crop_point_cloud(s) | |
| if view_crop: | |
| o3d.visualization.draw_geometries([s, ]) | |
| else: | |
| print("No bounding box provided to crop estimated point cloud, leaving it as the loaded version!!") | |
| s = s.voxel_down_sample(voxel_size) | |
| s.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamKNN(knn=20)) | |
| print(filename_mvs + "/" + scene_name + ".precision.ply") | |
| t = copy.deepcopy(target) | |
| if crop_volume is not None: | |
| t = crop_volume.crop_point_cloud(t) | |
| else: | |
| print("No bounding box provided to crop groundtruth point cloud, leaving it as the loaded version!!") | |
| t = t.voxel_down_sample(voxel_size) | |
| t.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamKNN(knn=20)) | |
| print("[compute_point_cloud_to_point_cloud_distance]") | |
| distance1 = s.compute_point_cloud_distance(t) | |
| print("[compute_point_cloud_to_point_cloud_distance]") | |
| distance2 = t.compute_point_cloud_distance(s) | |
| # write the distances to bin files | |
| # np.array(distance1).astype("float64").tofile( | |
| # filename_mvs + "/" + scene_name + ".precision.bin" | |
| # ) | |
| # np.array(distance2).astype("float64").tofile( | |
| # filename_mvs + "/" + scene_name + ".recall.bin" | |
| # ) | |
| # Colorize the poincloud files prith the precision and recall values | |
| # o3d.io.write_point_cloud( | |
| # filename_mvs + "/" + scene_name + ".precision.ply", s | |
| # ) | |
| # o3d.io.write_point_cloud( | |
| # filename_mvs + "/" + scene_name + ".precision.ncb.ply", s | |
| # ) | |
| # o3d.io.write_point_cloud(filename_mvs + "/" + scene_name + ".recall.ply", t) | |
| source_n_fn = filename_mvs + "/" + scene_name + ".precision.ply" | |
| target_n_fn = filename_mvs + "/" + scene_name + ".recall.ply" | |
| print("[ViewDistances] Add color coding to visualize error") | |
| # eval_str_viewDT = ( | |
| # OPEN3D_EXPERIMENTAL_BIN_PATH | |
| # + "ViewDistances " | |
| # + source_n_fn | |
| # + " --max_distance " | |
| # + str(threshold * 3) | |
| # + " --write_color_back --without_gui" | |
| # ) | |
| # os.system(eval_str_viewDT) | |
| write_color_distances(source_n_fn, s, distance1, 3 * threshold) | |
| print("[ViewDistances] Add color coding to visualize error") | |
| # eval_str_viewDT = ( | |
| # OPEN3D_EXPERIMENTAL_BIN_PATH | |
| # + "ViewDistances " | |
| # + target_n_fn | |
| # + " --max_distance " | |
| # + str(threshold * 3) | |
| # + " --write_color_back --without_gui" | |
| # ) | |
| # os.system(eval_str_viewDT) | |
| write_color_distances(target_n_fn, t, distance2, 3 * threshold) | |
| # get histogram and f-score | |
| [ | |
| precision, | |
| recall, | |
| fscore, | |
| edges_source, | |
| cum_source, | |
| edges_target, | |
| cum_target, | |
| ] = get_f1_score_histo2(threshold, filename_mvs, plot_stretch, distance1, | |
| distance2) | |
| np.savetxt(filename_mvs + "/" + scene_name + ".recall.txt", cum_target) | |
| np.savetxt(filename_mvs + "/" + scene_name + ".precision.txt", cum_source) | |
| np.savetxt( | |
| filename_mvs + "/" + scene_name + ".prf_tau_plotstr.txt", | |
| np.array([precision, recall, fscore, threshold, plot_stretch]), | |
| ) | |
| return [ | |
| precision, | |
| recall, | |
| fscore, | |
| edges_source, | |
| cum_source, | |
| edges_target, | |
| cum_target, | |
| ] | |
| def get_f1_score_histo2(threshold, | |
| filename_mvs, | |
| plot_stretch, | |
| distance1, | |
| distance2, | |
| verbose=True): | |
| print("[get_f1_score_histo2]") | |
| dist_threshold = threshold | |
| if len(distance1) and len(distance2): | |
| recall = float(sum(d < threshold for d in distance2)) / float( | |
| len(distance2)) | |
| precision = float(sum(d < threshold for d in distance1)) / float( | |
| len(distance1)) | |
| fscore = 2 * recall * precision / (recall + precision) | |
| num = len(distance1) | |
| bins = np.arange(0, dist_threshold * plot_stretch, dist_threshold / 100) | |
| hist, edges_source = np.histogram(distance1, bins) | |
| cum_source = np.cumsum(hist).astype(float) / num | |
| num = len(distance2) | |
| bins = np.arange(0, dist_threshold * plot_stretch, dist_threshold / 100) | |
| hist, edges_target = np.histogram(distance2, bins) | |
| cum_target = np.cumsum(hist).astype(float) / num | |
| else: | |
| precision = 0 | |
| recall = 0 | |
| fscore = 0 | |
| edges_source = np.array([0]) | |
| cum_source = np.array([0]) | |
| edges_target = np.array([0]) | |
| cum_target = np.array([0]) | |
| return [ | |
| precision, | |
| recall, | |
| fscore, | |
| edges_source, | |
| cum_source, | |
| edges_target, | |
| cum_target, | |
| ] | |