def trajectory_inference():
"""Generates trajectories from the KITTI odometry sets and a checkpoint."""
# Note that the struct2depth code only works at batch_size=1, because it uses
# the training mode of batchnorm at inference.
inference_model = model.Model(is_training=False,
batch_size=1,
img_height=FLAGS.img_height,
img_width=FLAGS.img_width)
saver = tf.train.Saver()
sess = tf.Session()
def infer_egomotion(image1, image2):
return inference_model.inference_egomotion(image1, image2, sess)
saver.restore(sess, FLAGS.checkpoint_path)
if not tf.gfile.Exists(FLAGS.output_dir):
tf.gfile.MakeDirs(FLAGS.output_dir)
for odo_set in ODOMETRY_SETS:
logging.info('Evaluating odometry on %s', odo_set)
test_file_dir = os.path.join(FLAGS.odometry_test_set_dir, odo_set)
output_file = os.path.join(FLAGS.output_dir,
'odometry_%s.txt' % odo_set)
odometry_inference(test_file_dir, output_file, infer_egomotion)
def main(_):
# Fixed seed for repeatability
seed = FLAGS.seed
tf.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
if not gfile.Exists(FLAGS.checkpoint_dir):
gfile.MakeDirs(FLAGS.checkpoint_dir)
train_model = model.Model(
boxify=FLAGS.boxify,
data_dir=FLAGS.data_dir,
file_extension=FLAGS.file_extension,
is_training=True,
foreground_dilation=FLAGS.foreground_dilation,
learn_intrinsics=FLAGS.learn_intrinsics,
learning_rate=FLAGS.learning_rate,
reconstr_weight=FLAGS.reconstr_weight,
smooth_weight=FLAGS.smooth_weight,
ssim_weight=FLAGS.ssim_weight,
translation_consistency_weight=FLAGS.translation_consistency_weight,
rotation_consistency_weight=FLAGS.rotation_consistency_weight,
batch_size=FLAGS.batch_size,
img_height=FLAGS.img_height,
img_width=FLAGS.img_width,
weight_reg=FLAGS.weight_reg,
depth_consistency_loss_weight=FLAGS.depth_consistency_loss_weight,
queue_size=FLAGS.queue_size,
input_file=FLAGS.input_file)
_train(train_model, FLAGS.checkpoint_dir, FLAGS.train_steps,
FLAGS.summary_freq)
if FLAGS.debug:
_print_losses(os.path.join(FLAGS.checkpoint_dir, 'debug'))
def _run_inference(output_dir=None,
file_extension='png',
depth=True,
egomotion=False,
model_ckpt=None,
input_dir=None,
input_list_file=None,
batch_size=1,
img_height=128,
img_width=416,
seq_length=3,
architecture=nets.RESNET,
imagenet_norm=True,
use_skip=True,
joint_encoder=True,
shuffle=False,
flip_for_depth=False,
inference_mode=INFERENCE_MODE_SINGLE,
inference_crop=INFERENCE_CROP_NONE,
use_masks=False):
"""Runs inference. Refer to flags in inference.py for details."""
inference_model = model.Model(is_training=False,
batch_size=1,
img_height=128,
img_width=416)
vars_to_restore = util.get_vars_to_save_and_restore(model_ckpt)
saver = tf.train.Saver(vars_to_restore)
sv = tf.train.Supervisor(logdir='/tmp/', saver=None)
with sv.managed_session() as sess:
saver.restore(sess, model_ckpt)
if not gfile.Exists(output_dir):
gfile.MakeDirs(output_dir)
logging.info('Predictions will be saved in %s.', output_dir)
# Collect all images to run inference on.
im_files, basepath_in = collect_input_images(input_dir,
input_list_file,
file_extension)
if shuffle:
logging.info('Shuffling data...')
np.random.shuffle(im_files)
logging.info('Running inference on %d files.', len(im_files))
# Create missing output folders and pre-compute target directories.
output_dirs = create_output_dirs(im_files, basepath_in, output_dir)
# Run depth prediction network.
if depth:
im_batch = []
npys = []
for i in range(len(im_files)):
if i % 100 == 0:
logging.info('%s of %s files processed.', i, len(im_files))
# Read image and run inference.
if inference_mode == INFERENCE_MODE_SINGLE:
if inference_crop == INFERENCE_CROP_NONE:
im = util.load_image(im_files[i],
resize=(img_width, img_height))
elif inference_crop == INFERENCE_CROP_CITYSCAPES:
im = util.crop_cityscapes(util.load_image(im_files[i]),
resize=(img_width,
img_height))
elif inference_mode == INFERENCE_MODE_TRIPLETS:
im = util.load_image(im_files[i],
resize=(img_width * 3, img_height))
im = im[:, img_width:img_width * 2]
if flip_for_depth:
im = np.flip(im, axis=1)
im_batch.append(im)
if len(im_batch) == batch_size or i == len(im_files) - 1:
# Call inference on batch.
for _ in range(batch_size -
len(im_batch)): # Fill up batch.
im_batch.append(
np.zeros(shape=(img_height, img_width, 3),
dtype=np.float32))
im_batch = np.stack(im_batch, axis=0)
est_depth = inference_model.inference_depth(im_batch, sess)
if flip_for_depth:
est_depth = np.flip(est_depth, axis=2)
im_batch = np.flip(im_batch, axis=2)
for j in range(len(im_batch)):
color_map = util.normalize_depth_for_display(
np.squeeze(est_depth[j]))
visualization = np.concatenate(
(im_batch[j], color_map), axis=0)
# Save raw prediction and color visualization. Extract filename
# without extension from full path: e.g. path/to/input_dir/folder1/
# file1.png -> file1
k = i - len(im_batch) + 1 + j
filename_root = os.path.splitext(
os.path.basename(im_files[k]))[0]
pref = '_flip' if flip_for_depth else ''
output_raw = os.path.join(
output_dirs[k], filename_root + pref + '.npy')
output_vis = os.path.join(
output_dirs[k], filename_root + pref + '.png')
with gfile.Open(output_raw, 'wb') as f:
np.save(f, est_depth[j])
npys.append(est_depth[j])
util.save_image(output_vis, visualization,
file_extension)
im_batch = []
with gfile.Open(output_dir + "result.npy", 'wb') as f:
np.save(f, npys)
from matplotlib import pyplot as plt
import cv2 # used for resize. if you dont have it, use anything else
import numpy as np
import tensorflow as tf
import sys
from PIL import Image
from depth_from_video_in_the_wild import depth_prediction_net
from depth_from_video_in_the_wild import model
if __name__ == "__main__":
# Load images
img = np.asarray(Image.open("data_example/erfurt_93/0000000002.png"))
img1 = img[:, :416]
img1 = np.expand_dims(img1, axis=0)
img2 = img[:, 416:832]
img2 = np.expand_dims(img2, axis=0)
inference_model = model.Model(is_training=False,
batch_size=1,
img_height=128,
img_width=416)
saver = tf.train.Saver()
sess = tf.Session()
saver.restore(sess, "example_model/model-413174")
test = inference_model.inference_egomotion(img1, img2, sess)
print(test)
def _run_inference(output_dir=None,
file_extension='png',
depth=True,
egomotion=False,
model_ckpt=None,
input_dir=None,
input_list_file=None,
batch_size=1,
img_height=128,
img_width=416,
imagenet_norm=True,
shuffle=False,
flip_for_depth=False,
inference_mode=INFERENCE_MODE_SINGLE,
inference_crop=INFERENCE_CROP_NONE,
use_masks=False):
"""Runs inference. Refer to flags in inference.py for details."""
inference_model = model.Model(is_training=False,
batch_size=batch_size,
img_height=img_height,
img_width=img_width,
imagenet_norm=imagenet_norm)
vars_to_restore = util.get_vars_to_save_and_restore(model_ckpt)
saver = tf.train.Saver(vars_to_restore)
sv = tf.train.Supervisor(logdir='/tmp/', saver=None)
with sv.managed_session() as sess:
saver.restore(sess, model_ckpt)
if not gfile.Exists(output_dir):
gfile.MakeDirs(output_dir)
logging.info('Predictions will be saved in %s.', output_dir)
# Collect all images to run inference on.
im_files, basepath_in = collect_input_images(input_dir,
input_list_file,
file_extension)
if shuffle:
logging.info('Shuffling data...')
np.random.shuffle(im_files)
logging.info('Running inference on %d files.', len(im_files))
# Create missing output folders and pre-compute target directories.
output_dirs = create_output_dirs(im_files, basepath_in, output_dir)
# Run depth prediction network.
if depth:
im_batch = []
for i in range(len(im_files)):
if i % 100 == 0:
logging.info('%s of %s files processed.', i, len(im_files))
# Read image and run inference.
if inference_mode == INFERENCE_MODE_SINGLE:
if inference_crop == INFERENCE_CROP_NONE:
im = util.load_image(im_files[i],
resize=(img_width, img_height))
elif inference_crop == INFERENCE_CROP_CITYSCAPES:
im = util.crop_cityscapes(util.load_image(im_files[i]),
resize=(img_width,
img_height))
elif inference_mode == INFERENCE_MODE_TRIPLETS:
im = util.load_image(im_files[i],
resize=(img_width * 3, img_height))
im = im[:, img_width:img_width * 2]
if flip_for_depth:
im = np.flip(im, axis=1)
im_batch.append(im)
if len(im_batch) == batch_size or i == len(im_files) - 1:
# Call inference on batch.
for _ in range(batch_size -
len(im_batch)): # Fill up batch.
im_batch.append(
np.zeros(shape=(img_height, img_width, 3),
dtype=np.float32))
im_batch = np.stack(im_batch, axis=0)
est_depth = inference_model.inference_depth(im_batch, sess)
if flip_for_depth:
est_depth = np.flip(est_depth, axis=2)
im_batch = np.flip(im_batch, axis=2)
for j in range(len(im_batch)):
color_map = util.normalize_depth_for_display(
np.squeeze(est_depth[j]))
visualization = np.concatenate(
(im_batch[j], color_map), axis=0)
# Save raw prediction and color visualization. Extract filename
# without extension from full path: e.g. path/to/input_dir/folder1/
# file1.png -> file1
k = i - len(im_batch) + 1 + j
filename_root = os.path.splitext(
os.path.basename(im_files[k]))[0]
pref = '_flip' if flip_for_depth else ''
output_raw = os.path.join(
output_dirs[k], filename_root + pref + '.npy')
output_vis = os.path.join(
output_dirs[k], filename_root + pref + '.png')
with gfile.Open(output_raw, 'wb') as f:
np.save(f, est_depth[j])
util.save_image(output_vis, visualization,
file_extension)
im_batch = []
# Run egomotion network.
if egomotion:
if inference_mode == INFERENCE_MODE_SINGLE:
# Run regular egomotion inference loop.
input_image_seq = []
input_seg_seq = []
current_sequence_dir = None
current_output_handle = None
for i in range(len(im_files)):
sequence_dir = os.path.dirname(im_files[i])
if sequence_dir != current_sequence_dir:
# Assume start of a new sequence, since this image lies in a
# different directory than the previous ones.
# Clear egomotion input buffer.
output_filepath = os.path.join(output_dirs[i],
'egomotion.txt')
if current_output_handle is not None:
current_output_handle.close()
current_sequence_dir = sequence_dir
logging.info('Writing egomotion sequence to %s.',
output_filepath)
current_output_handle = gfile.Open(
output_filepath, 'w')
input_image_seq = []
im = util.load_image(im_files[i],
resize=(img_width, img_height))
input_image_seq.append(im)
if use_masks:
im_seg_path = im_files[i].replace(
'.%s' % file_extension, '-seg.%s' % file_extension)
if not gfile.Exists(im_seg_path):
raise ValueError(
'No segmentation mask %s has been found for '
'image %s. If none are available, disable '
'use_masks.' % (im_seg_path, im_files[i]))
input_seg_seq.append(
util.load_image(im_seg_path,
resize=(img_width, img_height),
interpolation='nn'))
if len(input_image_seq
) < seq_length: # Buffer not filled yet.
continue
if len(input_image_seq
) > seq_length: # Remove oldest entry.
del input_image_seq[0]
if use_masks:
del input_seg_seq[0]
input_image_stack = np.concatenate(input_image_seq, axis=2)
input_image_stack = np.expand_dims(input_image_stack,
axis=0)
if use_masks:
input_image_stack = mask_image_stack(
input_image_stack, input_seg_seq)
est_egomotion = np.squeeze(
inference_model.inference_egomotion(
input_image_stack, sess))
egomotion_str = []
for j in range(seq_length - 1):
egomotion_str.append(','.join(
[str(d) for d in est_egomotion[j]]))
current_output_handle.write(
str(i) + ' ' + ' '.join(egomotion_str) + '\n')
if current_output_handle is not None:
current_output_handle.close()
elif inference_mode == INFERENCE_MODE_TRIPLETS:
written_before = []
for i in range(len(im_files)):
im = util.load_image(im_files[i],
resize=(img_width * 3, img_height))
input_image_stack = np.concatenate([
im[:, :img_width], im[:, img_width:img_width * 2],
im[:, img_width * 2:]
],
axis=2)
input_image_stack = np.expand_dims(input_image_stack,
axis=0)
if use_masks:
im_seg_path = im_files[i].replace(
'.%s' % file_extension, '-seg.%s' % file_extension)
if not gfile.Exists(im_seg_path):
raise ValueError(
'No segmentation mask %s has been found for '
'image %s. If none are available, disable '
'use_masks.' % (im_seg_path, im_files[i]))
seg = util.load_image(im_seg_path,
resize=(img_width * 3,
img_height),
interpolation='nn')
input_seg_seq = [
seg[:, :img_width], seg[:,
img_width:img_width * 2],
seg[:, img_width * 2:]
]
input_image_stack = mask_image_stack(
input_image_stack, input_seg_seq)
est_egomotion = inference_model.inference_egomotion(
input_image_stack, sess)
est_egomotion = np.squeeze(est_egomotion)
egomotion_1_2 = ','.join(
[str(d) for d in est_egomotion[0]])
egomotion_2_3 = ','.join(
[str(d) for d in est_egomotion[1]])
output_filepath = os.path.join(output_dirs[i],
'egomotion.txt')
file_mode = 'w' if output_filepath not in written_before else 'a'
with gfile.Open(output_filepath,
file_mode) as current_output_handle:
current_output_handle.write(
str(i) + ' ' + egomotion_1_2 + ' ' +
egomotion_2_3 + '\n')
written_before.append(output_filepath)
logging.info('Done.')
