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eval_with_pngs.py
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eval_with_pngs.py
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# Copyright (C) 2019 Jin Han Lee
#
# This file is a part of BTS.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>
from __future__ import absolute_import, division, print_function
import os
import argparse
import fnmatch
import cv2
import numpy as np
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
def convert_arg_line_to_args(arg_line):
for arg in arg_line.split():
if not arg.strip():
continue
yield arg
parser = argparse.ArgumentParser(description='BTS TensorFlow implementation.', fromfile_prefix_chars='@')
parser.convert_arg_line_to_args = convert_arg_line_to_args
parser.add_argument('--pred_path', type=str, help='path to the prediction results in png', required=True)
parser.add_argument('--gt_path', type=str, help='root path to the groundtruth data', required=False)
parser.add_argument('--dataset', type=str, help='dataset to test on, nyu or kitti', default='nyu')
parser.add_argument('--eigen_crop', help='if set, crops according to Eigen NIPS14', action='store_true')
parser.add_argument('--garg_crop', help='if set, crops according to Garg ECCV16', action='store_true')
parser.add_argument('--min_depth_eval', type=float, help='minimum depth for evaluation', default=1e-3)
parser.add_argument('--max_depth_eval', type=float, help='maximum depth for evaluation', default=80)
parser.add_argument('--do_kb_crop', help='if set, crop input images as kitti benchmark images', action='store_true')
args = parser.parse_args()
def compute_errors(gt, pred):
thresh = np.maximum((gt / pred), (pred / gt))
d1 = (thresh < 1.25 ).mean()
d2 = (thresh < 1.25 ** 2).mean()
d3 = (thresh < 1.25 ** 3).mean()
rmse = (gt - pred) ** 2
rmse = np.sqrt(rmse.mean())
rmse_log = (np.log(gt) - np.log(pred)) ** 2
rmse_log = np.sqrt(rmse_log.mean())
abs_rel = np.mean(np.abs(gt - pred) / gt)
sq_rel = np.mean(((gt - pred)**2) / gt)
err = np.log(pred) - np.log(gt)
silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100
err = np.abs(np.log10(pred) - np.log10(gt))
log10 = np.mean(err)
return silog, log10, abs_rel, sq_rel, rmse, rmse_log, d1, d2, d3
def test():
global gt_depths, missing_ids, pred_filenames
gt_depths = []
missing_ids = set()
pred_filenames = []
for root, dirnames, filenames in os.walk(args.pred_path):
for pred_filename in fnmatch.filter(filenames, '*.png'):
if 'cmap' in pred_filename or 'gt' in pred_filename:
continue
dirname = root.replace(args.pred_path, '')
pred_filenames.append(os.path.join(dirname, pred_filename))
num_test_samples = len(pred_filenames)
pred_depths = []
for i in range(num_test_samples):
pred_depth_path = os.path.join(args.pred_path, pred_filenames[i])
pred_depth = cv2.imread(pred_depth_path, -1)
if pred_depth is None:
print('Missing: %s ' % pred_depth_path)
missing_ids.add(i)
continue
if args.dataset == 'nyu':
pred_depth = pred_depth.astype(np.float32) / 1000.0
elif args.dataset = 'rili':
pred_depth = pred_depth.astype(np.float32) / 1000.0
else:
pred_depth = pred_depth.astype(np.float32) / 256.0
pred_depths.append(pred_depth)
print('Raw png files reading done')
print('Evaluating {} files'.format(len(pred_depths)))
if args.dataset == 'kitti':
for t_id in range(num_test_samples):
file_dir = pred_filenames[t_id].split('.')[0]
filename = file_dir.split('_')[-1]
directory = file_dir.replace('_' + filename, '')
gt_depth_path = os.path.join(args.gt_path, directory, 'proj_depth/groundtruth/image_02', filename + '.png')
depth = cv2.imread(gt_depth_path, -1)
if depth is None:
print('Missing: %s ' % gt_depth_path)
missing_ids.add(t_id)
continue
depth = depth.astype(np.float32) / 256.0
gt_depths.append(depth)
elif args.dataset == 'nyu':
for t_id in range(num_test_samples):
file_dir = pred_filenames[t_id].split('.')[0]
filename = file_dir.split('_')[-1]
directory = file_dir.replace('_rgb_'+file_dir.split('_')[-1], '')
gt_depth_path = os.path.join(args.gt_path, directory, 'sync_depth_' + filename + '.png')
depth = cv2.imread(gt_depth_path, -1)
if depth is None:
print('Missing: %s ' % gt_depth_path)
missing_ids.add(t_id)
continue
depth = depth.astype(np.float32) / 1000.0
gt_depths.append(depth)
print('GT files reading done')
print('{} GT files missing'.format(len(missing_ids)))
print('Computing errors')
eval(pred_depths)
print('Done.')
def eval(pred_depths):
num_samples = len(pred_depths)
pred_depths_valid = []
i = 0
for t_id in range(num_samples):
if t_id in missing_ids:
continue
pred_depths_valid.append(pred_depths[t_id])
num_samples = num_samples - len(missing_ids)
silog = np.zeros(num_samples, np.float32)
log10 = np.zeros(num_samples, np.float32)
rms = np.zeros(num_samples, np.float32)
log_rms = np.zeros(num_samples, np.float32)
abs_rel = np.zeros(num_samples, np.float32)
sq_rel = np.zeros(num_samples, np.float32)
d1 = np.zeros(num_samples, np.float32)
d2 = np.zeros(num_samples, np.float32)
d3 = np.zeros(num_samples, np.float32)
for i in range(num_samples):
gt_depth = gt_depths[i]
pred_depth = pred_depths_valid[i]
pred_depth[pred_depth < args.min_depth_eval] = args.min_depth_eval
pred_depth[pred_depth > args.max_depth_eval] = args.max_depth_eval
pred_depth[np.isinf(pred_depth)] = args.max_depth_eval
gt_depth[np.isinf(gt_depth)] = 0
gt_depth[np.isnan(gt_depth)] = 0
valid_mask = np.logical_and(gt_depth > args.min_depth_eval, gt_depth < args.max_depth_eval)
if args.do_kb_crop:
height, width = gt_depth.shape
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
pred_depth_uncropped = np.zeros((height, width), dtype=np.float32)
pred_depth_uncropped[top_margin:top_margin + 352, left_margin:left_margin + 1216] = pred_depth
pred_depth = pred_depth_uncropped
if args.garg_crop or args.eigen_crop:
gt_height, gt_width = gt_depth.shape
eval_mask = np.zeros(valid_mask.shape)
if args.garg_crop:
eval_mask[int(0.40810811 * gt_height):int(0.99189189 * gt_height), int(0.03594771 * gt_width):int(0.96405229 * gt_width)] = 1
elif args.eigen_crop:
eval_mask[int(0.3324324 * gt_height):int(0.91351351 * gt_height), int(0.0359477 * gt_width):int(0.96405229 * gt_width)] = 1
valid_mask = np.logical_and(valid_mask, eval_mask)
silog[i], log10[i], abs_rel[i], sq_rel[i], rms[i], log_rms[i], d1[i], d2[i], d3[i] = compute_errors(gt_depth[valid_mask], pred_depth[valid_mask])
print("{:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}".format(
'd1', 'd2', 'd3', 'AbsRel', 'SqRel', 'RMSE', 'RMSElog', 'SILog', 'log10'))
print("{:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}".format(
d1.mean(), d2.mean(), d3.mean(),
abs_rel.mean(), sq_rel.mean(), rms.mean(), log_rms.mean(), silog.mean(), log10.mean()))
return silog, log10, abs_rel, sq_rel, rms, log_rms, d1, d2, d3
def main():
test()
if __name__ == '__main__':
main()