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bts_test.py
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bts_test.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 numpy as np
import argparse
import time
import tensorflow as tf
import errno
import matplotlib.pyplot as plt
import cv2
import sys
import tqdm
from bts_dataloader import *
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('--model_name', type=str, help='model name', default='bts_nyu_v2')
parser.add_argument('--encoder', type=str, help='type of encoder, vgg or desenet121_bts or densenet161_bts', default='densenet161_bts')
parser.add_argument('--data_path', type=str, help='path to the data', required=True)
parser.add_argument('--filenames_file', type=str, help='path to the filenames text file', required=True)
parser.add_argument('--input_height', type=int, help='input height', default=480)
parser.add_argument('--input_width', type=int, help='input width', default=640)
parser.add_argument('--max_depth', type=float, help='maximum depth in estimation', default=80)
parser.add_argument('--checkpoint_path', type=str, help='path to a specific checkpoint to load', default='')
parser.add_argument('--dataset', type=str, help='dataset to train on, make3d or nyudepthv2', default='nyu')
parser.add_argument('--do_kb_crop', help='if set, crop input images as kitti benchmark images', action='store_true')
parser.add_argument('--save_lpg', help='if set, save outputs from lpg layers', action='store_true')
if sys.argv.__len__() == 2:
arg_filename_with_prefix = '@' + sys.argv[1]
args = parser.parse_args([arg_filename_with_prefix])
else:
args = parser.parse_args()
model_dir = os.path.dirname(args.checkpoint_path)
sys.path.append(model_dir)
for key, val in vars(__import__(args.model_name)).items():
if key.startswith('__') and key.endswith('__'):
continue
vars()[key] = val
def get_num_lines(file_path):
f = open(file_path, 'r')
lines = f.readlines()
f.close()
return len(lines)
def test(params):
"""Test function."""
dataloader = BtsDataloader(args.data_path, None, args.filenames_file, params, 'test', do_kb_crop=args.do_kb_crop)
dataloader_iter = dataloader.loader.make_initializable_iterator()
iter_init_op = dataloader_iter.initializer
image, focal = dataloader_iter.get_next()
model = BtsModel(params, 'test', image, None, focal=focal, bn_training=False)
# SESSION
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
# INIT
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
# SAVER
train_saver = tf.train.Saver()
with tf.device('/cpu:0'):
restore_path = args.checkpoint_path
# RESTORE
train_saver.restore(sess, restore_path)
num_test_samples = get_num_lines(args.filenames_file)
with open(args.filenames_file) as f:
lines = f.readlines()
print('Now testing {} files with {}'.format(num_test_samples, args.checkpoint_path))
sess.run(iter_init_op)
pred_depths = []
pred_8x8s = []
pred_4x4s = []
pred_2x2s = []
start_time = time.time()
print('Processing images..')
for s in tqdm(range(num_test_samples)):
depth, pred_8x8, pred_4x4, pred_2x2 = sess.run([model.depth_est, model.lpg8x8, model.lpg4x4, model.lpg2x2])
pred_depths.append(depth[0].squeeze())
pred_8x8s.append(pred_8x8[0].squeeze())
pred_4x4s.append(pred_4x4[0].squeeze())
pred_2x2s.append(pred_2x2[0].squeeze())
print('Done.')
save_name = 'result_' + args.model_name
print('Saving result pngs..')
if not os.path.exists(os.path.dirname(save_name)):
try:
os.mkdir(save_name)
os.mkdir(save_name + '/raw')
os.mkdir(save_name + '/cmap')
os.mkdir(save_name + '/rgb')
os.mkdir(save_name + '/gt')
except OSError as e:
if e.errno != errno.EEXIST:
raise
for s in tqdm(range(num_test_samples)):
if args.dataset == 'kitti':
date_drive = lines[s].split('/')[1]
filename_pred_png = save_name + '/raw/' + date_drive + '_' + lines[s].split()[0].split('/')[-1].replace('.jpg', '.png')
filename_cmap_png = save_name + '/cmap/' + date_drive + '_' + lines[s].split()[0].split('/')[-1].replace('.jpg', '.png')
filename_image_png = save_name + '/rgb/' + date_drive + '_' + lines[s].split()[0].split('/')[-1]
elif args.dataset == 'kitti_benchmark':
filename_pred_png = save_name + '/raw/' + lines[s].split()[0].split('/')[-1].replace('.jpg', '.png')
filename_cmap_png = save_name + '/cmap/' + lines[s].split()[0].split('/')[-1].replace('.jpg', '.png')
filename_image_png = save_name + '/rgb/' + lines[s].split()[0].split('/')[-1]
else:
scene_name = lines[s].split()[0].split('/')[0]
filename_pred_png = save_name + '/raw/' + scene_name + '_' + lines[s].split()[0].split('/')[1].replace('.jpg', '.png')
filename_cmap_png = save_name + '/cmap/' + scene_name + '_' + lines[s].split()[0].split('/')[1].replace('.jpg', '.png')
filename_gt_png = save_name + '/gt/' + scene_name + '_' + lines[s].split()[0].split('/')[1].replace('.jpg', '.png')
filename_image_png = save_name + '/rgb/' + scene_name + '_' + lines[s].split()[0].split('/')[1]
rgb_path = os.path.join(args.data_path, lines[s].split()[0])
image = cv2.imread(rgb_path)
if args.dataset == 'nyu':
gt_path = os.path.join(args.data_path, lines[s].split()[1])
gt = cv2.imread(gt_path, -1).astype(np.float32) / 1000.0 # Visualization purpose only
gt[gt == 0] = np.amax(gt)
pred_depth = pred_depths[s]
pred_8x8 = pred_8x8s[s]
pred_4x4 = pred_4x4s[s]
pred_2x2 = pred_2x2s[s]
if args.dataset == 'kitti' or args.dataset == 'kitti_benchmark':
pred_depth_scaled = pred_depth * 256.0
else:
pred_depth_scaled = pred_depth * 1000.0
pred_depth_scaled = pred_depth_scaled.astype(np.uint16)
cv2.imwrite(filename_pred_png, pred_depth_scaled, [cv2.IMWRITE_PNG_COMPRESSION, 0])
if args.save_lpg:
cv2.imwrite(filename_image_png, image[10:-1 - 9, 10:-1 - 9, :])
if args.dataset == 'nyu':
plt.imsave(filename_gt_png, np.log10(gt[10:-1 - 9, 10:-1 - 9]), cmap='Greys')
pred_depth_cropped = pred_depth[10:-1 - 9, 10:-1 - 9]
plt.imsave(filename_cmap_png, np.log10(pred_depth_cropped), cmap='Greys')
pred_8x8_cropped = pred_8x8[10:-1 - 9, 10:-1 - 9]
filename_lpg_cmap_png = filename_cmap_png.replace('.png', '_8x8.png')
plt.imsave(filename_lpg_cmap_png, np.log10(pred_8x8_cropped), cmap='Greys')
pred_4x4_cropped = pred_4x4[10:-1 - 9, 10:-1 - 9]
filename_lpg_cmap_png = filename_cmap_png.replace('.png', '_4x4.png')
plt.imsave(filename_lpg_cmap_png, np.log10(pred_4x4_cropped), cmap='Greys')
pred_2x2_cropped = pred_2x2[10:-1 - 9, 10:-1 - 9]
filename_lpg_cmap_png = filename_cmap_png.replace('.png', '_2x2.png')
plt.imsave(filename_lpg_cmap_png, np.log10(pred_2x2_cropped), cmap='Greys')
else:
plt.imsave(filename_cmap_png, np.log10(pred_depth), cmap='Greys')
filename_lpg_cmap_png = filename_cmap_png.replace('.png', '_8x8.png')
plt.imsave(filename_lpg_cmap_png, np.log10(pred_8x8), cmap='Greys')
filename_lpg_cmap_png = filename_cmap_png.replace('.png', '_4x4.png')
plt.imsave(filename_lpg_cmap_png, np.log10(pred_4x4), cmap='Greys')
filename_lpg_cmap_png = filename_cmap_png.replace('.png', '_2x2.png')
plt.imsave(filename_lpg_cmap_png, np.log10(pred_2x2), cmap='Greys')
return
def main(_):
params = bts_parameters(
encoder=args.encoder,
height=args.input_height,
width=args.input_width,
batch_size=None,
dataset=args.dataset,
max_depth=args.max_depth,
num_gpus=None,
num_threads=None,
num_epochs=None)
test(params)
if __name__ == '__main__':
tf.app.run()