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bts_sequence.py
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bts_sequence.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
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'
import numpy as np
import argparse
import time
import glob
import cv2
import errno
import matplotlib.pyplot as plt
import sys
import tensorflow as tf
from bts_dataloader import *
parser = argparse.ArgumentParser(description='BTS TensorFlow implementation.')
parser.add_argument('--model_name', type=str, help='model name', default='bts_v0_0_1')
parser.add_argument('--encoder', type=str, help='type of encoder, densenet121_bts or densenet161_bts', default='densenet161_bts')
parser.add_argument('--dataset', type=str, help='dataset to test, kitti or nyu', default='')
parser.add_argument('--max_depth', type=float, help='maximum depth in estimation', default=80)
parser.add_argument('--focal', type=float, help='focal length in pixels', default=-1)
parser.add_argument('--image_path', type=str, help='image sequence path', required=True)
parser.add_argument('--out_path', type=str, help='output path', required=True)
parser.add_argument('--checkpoint_path', type=str, help='path to a checkpoint to load', 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)
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 test_sequence(params):
image_files = []
for filename in glob.glob(os.path.join(args.image_path, '*.png')):
image_files.append(filename)
image_files.sort()
num_test_samples = len(image_files)
if num_test_samples == 0:
print("No images found! Program abort.")
return
if args.dataset == 'nyu':
focal = 518.8579
elif args.dataset == 'kitti':
focal = 718.856 # Visualize purpose only
elif args.dataset == '' and args.focal == -1:
print('Custom dataset needs to specify focal length with --focal')
return
image = tf.placeholder(tf.float32, [1, args.input_height, args.input_width, 3])
focals = tf.constant([focal])
model = BtsModel(params, 'test', image, None, focal=focals, 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())
coordinator = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coordinator)
# SAVER
train_saver = tf.train.Saver()
with tf.device('/cpu:0'):
restore_path = args.checkpoint_path
# RESTORE
train_saver.restore(sess, restore_path)
print('now testing {} files for model {}'.format(num_test_samples, args.checkpoint_path))
print('Saving result pngs')
if not os.path.exists(os.path.dirname(args.out_path)):
try:
os.mkdir(args.out_path)
os.mkdir(args.out_path + '/depth')
os.mkdir(args.out_path + '/lpg2x2')
os.mkdir(args.out_path + '/lpg4x4')
os.mkdir(args.out_path + '/lpg8x8')
os.mkdir(args.out_path + '/rgb')
except OSError as e:
if e.errno != errno.EEXIST:
raise
start_time = time.time()
for s in range(num_test_samples):
input_image = cv2.imread(image_files[s])
if args.dataset == 'kitti':
height, width, ch = input_image.shape
top_margin = int(height - 352)
left_margin = int((width - 1216) / 2)
input_image = input_image[top_margin:top_margin + 352, left_margin:left_margin + 1216, :]
input_image_original = input_image
input_image = input_image.astype(np.float32)
# Normalize image
input_image[:, :, 0] = (input_image[:, :, 0] - 103.939) * 0.017
input_image[:, :, 1] = (input_image[:, :, 1] - 116.779) * 0.017
input_image[:, :, 2] = (input_image[:, :, 2] - 123.68) * 0.017
input_images = np.reshape(input_image, (1, args.input_height, args.input_width, 3))
depth, pred_8x8, pred_4x4, pred_2x2 = sess.run(
[model.depth_est, model.depth_8x8, model.depth_4x4, model.depth_2x2], feed_dict={image: input_images})
pred_depth = depth.squeeze()
pred_8x8 = pred_8x8.squeeze()
pred_4x4 = pred_4x4.squeeze()
pred_2x2 = pred_2x2.squeeze()
save_path = os.path.join(args.out_path, 'depth', image_files[s].split('/')[-1])
plt.imsave(save_path, np.log10(pred_depth), cmap='Greys')
save_path = os.path.join(args.out_path, 'rgb', image_files[s].split('/')[-1])
cv2.imwrite(save_path, input_image_original)
save_path = os.path.join(args.out_path, 'lpg2x2', image_files[s].split('/')[-1])
plt.imsave(save_path, np.log10(pred_2x2), cmap='Greys')
save_path = os.path.join(args.out_path, 'lpg4x4', image_files[s].split('/')[-1])
plt.imsave(save_path, np.log10(pred_4x4), cmap='Greys')
save_path = os.path.join(args.out_path, 'lpg8x8', image_files[s].split('/')[-1])
plt.imsave(save_path, np.log10(pred_8x8), cmap='Greys')
print('{}/{}'.format(s, num_test_samples))
elapsed_time = time.time() - start_time
print('Elapesed time: %s' % str(elapsed_time))
print('done.')
def main(_):
params = bts_parameters(
encoder=args.encoder,
height=args.input_height,
width=args.input_width,
batch_size=None,
dataset=None,
max_depth=args.max_depth,
num_gpus=None,
num_threads=None,
num_epochs=None,
)
test_sequence(params)
if __name__ == '__main__':
tf.app.run()