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monodepth_dataloader.py
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monodepth_dataloader.py
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# Copyright UCL Business plc 2017. Patent Pending. All rights reserved.
#
# The MonoDepth Software is licensed under the terms of the UCLB ACP-A licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
#
# For any other use of the software not covered by the UCLB ACP-A Licence,
# please contact [email protected]
"""
Adopted from https://github.com/mrharicot/monodepth
Please see LICENSE_monodepth for details
"""
from __future__ import absolute_import, division, print_function
import tensorflow as tf
def string_length_tf(t):
return tf.py_func(len, [t], [tf.int64])
def rescale_intrinsics(raw_cam_mat, opt, orig_height, orig_width):
fx = raw_cam_mat[0, 0]
fy = raw_cam_mat[1, 1]
cx = raw_cam_mat[0, 2]
cy = raw_cam_mat[1, 2]
r1 = tf.stack(
[fx * opt.img_width / orig_width, 0, cx * opt.img_width / orig_width])
r2 = tf.stack([
0, fy * opt.img_height / orig_height, cy * opt.img_height / orig_height
])
r3 = tf.constant([0., 0., 1.])
return tf.stack([r1, r2, r3])
def get_multi_scale_intrinsics(raw_cam_mat, num_scales):
proj_cam2pix = []
# Scale the intrinsics accordingly for each scale
for s in range(num_scales):
fx = raw_cam_mat[0, 0] / (2**s)
fy = raw_cam_mat[1, 1] / (2**s)
cx = raw_cam_mat[0, 2] / (2**s)
cy = raw_cam_mat[1, 2] / (2**s)
r1 = tf.stack([fx, 0, cx])
r2 = tf.stack([0, fy, cy])
r3 = tf.constant([0., 0., 1.])
proj_cam2pix.append(tf.stack([r1, r2, r3]))
proj_cam2pix = tf.stack(proj_cam2pix)
proj_pix2cam = tf.matrix_inverse(proj_cam2pix)
proj_cam2pix.set_shape([num_scales, 3, 3])
proj_pix2cam.set_shape([num_scales, 3, 3])
return proj_cam2pix, proj_pix2cam
def make_intrinsics_matrix(fx, fy, cx, cy):
# Assumes batch input
batch_size = fx.get_shape().as_list()[0]
zeros = tf.zeros_like(fx)
r1 = tf.stack([fx, zeros, cx], axis=1)
r2 = tf.stack([zeros, fy, cy], axis=1)
r3 = tf.constant([0., 0., 1.], shape=[1, 3])
r3 = tf.tile(r3, [batch_size, 1])
intrinsics = tf.stack([r1, r2, r3], axis=1)
return intrinsics
def data_augmentation(im, intrinsics, out_h, out_w):
# Random scaling
def random_scaling(im, intrinsics):
batch_size, in_h, in_w, _ = im.get_shape().as_list()
scaling = tf.random_uniform([2], 1, 1.15)
x_scaling = scaling[0]
y_scaling = scaling[1]
out_h = tf.cast(in_h * y_scaling, dtype=tf.int32)
out_w = tf.cast(in_w * x_scaling, dtype=tf.int32)
im = tf.image.resize_area(im, [out_h, out_w])
fx = intrinsics[:, 0, 0] * x_scaling
fy = intrinsics[:, 1, 1] * y_scaling
cx = intrinsics[:, 0, 2] * x_scaling
cy = intrinsics[:, 1, 2] * y_scaling
intrinsics = make_intrinsics_matrix(fx, fy, cx, cy)
return im, intrinsics
# Random cropping
def random_cropping(im, intrinsics, out_h, out_w):
# batch_size, in_h, in_w, _ = im.get_shape().as_list()
batch_size, in_h, in_w, _ = tf.unstack(tf.shape(im))
offset_y = tf.random_uniform(
[1], 0, in_h - out_h + 1, dtype=tf.int32)[0]
offset_x = tf.random_uniform(
[1], 0, in_w - out_w + 1, dtype=tf.int32)[0]
im = tf.image.crop_to_bounding_box(im, offset_y, offset_x, out_h,
out_w)
fx = intrinsics[:, 0, 0]
fy = intrinsics[:, 1, 1]
cx = intrinsics[:, 0, 2] - tf.cast(offset_x, dtype=tf.float32)
cy = intrinsics[:, 1, 2] - tf.cast(offset_y, dtype=tf.float32)
intrinsics = make_intrinsics_matrix(fx, fy, cx, cy)
return im, intrinsics
im, intrinsics = random_scaling(im, intrinsics)
im, intrinsics = random_cropping(im, intrinsics, out_h, out_w)
return im, intrinsics
class MonodepthDataloader(object):
"""monodepth dataloader"""
def __init__(self, opt):
self.data_path = opt.data_dir
self.opt = opt
filenames_file = opt.train_file
input_queue = tf.train.string_input_producer(
[filenames_file], shuffle=False)
line_reader = tf.TextLineReader()
_, line = line_reader.read(input_queue)
split_line = tf.string_split([line]).values
# we load only one image for test, except if we trained a stereo model
left_image_path = tf.string_join([self.data_path, split_line[0]])
right_image_path = tf.string_join([self.data_path, split_line[1]])
next_left_image_path = tf.string_join([self.data_path, split_line[2]])
next_right_image_path = tf.string_join([self.data_path, split_line[3]])
cam_intrinsic_path = tf.string_join([self.data_path, split_line[4]])
left_image_o, orig_height, orig_width = self.read_image(
left_image_path, get_shape=True)
right_image_o = self.read_image(right_image_path)
next_left_image_o = self.read_image(next_left_image_path)
next_right_image_o = self.read_image(next_right_image_path)
# randomly flip images
do_flip = tf.random_uniform([], 0, 1)
left_image = tf.cond(do_flip > 0.5,
lambda: tf.image.flip_left_right(right_image_o),
lambda: left_image_o)
right_image = tf.cond(do_flip > 0.5,
lambda: tf.image.flip_left_right(left_image_o),
lambda: right_image_o)
next_left_image = tf.cond(
do_flip > 0.5,
lambda: tf.image.flip_left_right(next_right_image_o),
lambda: next_left_image_o)
next_right_image = tf.cond(
do_flip > 0.5, lambda: tf.image.flip_left_right(next_left_image_o),
lambda: next_right_image_o)
do_flip_fb = tf.random_uniform([], 0, 1)
left_image, right_image, next_left_image, next_right_image = tf.cond(
do_flip_fb > 0.5,
lambda: (next_left_image, next_right_image, left_image, right_image),
lambda: (left_image, right_image, next_left_image, next_right_image)
)
# randomly augment images
# do_augment = tf.random_uniform([], 0, 0)
# image_list = [left_image, right_image, next_left_image, next_right_image]
# left_image, right_image, next_left_image, next_right_image = tf.cond(do_augment > 0.5,
# lambda: self.augment_image_list(image_list),
# lambda: image_list)
left_image.set_shape([None, None, 3])
right_image.set_shape([None, None, 3])
next_left_image.set_shape([None, None, 3])
next_right_image.set_shape([None, None, 3])
raw_cam_contents = tf.read_file(cam_intrinsic_path)
last_line = tf.string_split(
[raw_cam_contents], delimiter="\n").values[-1]
raw_cam_vec = tf.string_to_number(
tf.string_split([last_line]).values[1:])
raw_cam_mat = tf.reshape(raw_cam_vec, [3, 4])
raw_cam_mat = raw_cam_mat[0:3, 0:3]
raw_cam_mat = rescale_intrinsics(raw_cam_mat, opt, orig_height,
orig_width)
# Scale and crop augmentation
# im_batch = tf.concat([tf.expand_dims(left_image, 0),
# tf.expand_dims(right_image, 0),
# tf.expand_dims(next_left_image, 0),
# tf.expand_dims(next_right_image, 0)], axis=3)
# raw_cam_mat_batch = tf.expand_dims(raw_cam_mat, axis=0)
# im_batch, raw_cam_mat_batch = data_augmentation(im_batch, raw_cam_mat_batch, self.opt.img_height, self.opt.img_width)
# left_image, right_image, next_left_image, next_right_image = tf.split(im_batch[0,:,:,:], num_or_size_splits=4, axis=2)
# raw_cam_mat = raw_cam_mat_batch[0,:,:]
proj_cam2pix, proj_pix2cam = get_multi_scale_intrinsics(raw_cam_mat,
opt.num_scales)
# capacity = min_after_dequeue + (num_threads + a small safety margin) * batch_size
min_after_dequeue = 2048
capacity = min_after_dequeue + 4 * opt.batch_size
self.data_batch = tf.train.shuffle_batch([
left_image, right_image, next_left_image, next_right_image,
proj_cam2pix, proj_pix2cam
], opt.batch_size, capacity, min_after_dequeue, 10)
def augment_image_pair(self, left_image, right_image):
# randomly shift gamma
random_gamma = tf.random_uniform([], 0.8, 1.2)
left_image_aug = left_image**random_gamma
right_image_aug = right_image**random_gamma
# randomly shift brightness
random_brightness = tf.random_uniform([], 0.5, 2.0)
left_image_aug = left_image_aug * random_brightness
right_image_aug = right_image_aug * random_brightness
# randomly shift color
random_colors = tf.random_uniform([3], 0.8, 1.2)
white = tf.ones([tf.shape(left_image)[0], tf.shape(left_image)[1]])
color_image = tf.stack(
[white * random_colors[i] for i in range(3)], axis=2)
left_image_aug *= color_image
right_image_aug *= color_image
# saturate
left_image_aug = tf.clip_by_value(left_image_aug, 0, 1)
right_image_aug = tf.clip_by_value(right_image_aug, 0, 1)
return left_image_aug, right_image_aug
def augment_image_list(self, image_list):
# randomly shift gamma
random_gamma = tf.random_uniform([], 0.8, 1.2)
image_list = [img**random_gamma for img in image_list]
# randomly shift brightness
random_brightness = tf.random_uniform([], 0.5, 2.0)
image_list = [img * random_brightness for img in image_list]
# randomly shift color
random_colors = tf.random_uniform([3], 0.8, 1.2)
white = tf.ones(
[tf.shape(image_list[0])[0], tf.shape(image_list[0])[1]])
color_image = tf.stack(
[white * random_colors[i] for i in range(3)], axis=2)
image_list = [img * color_image for img in image_list]
# saturate
image_list = [tf.clip_by_value(img, 0, 1) for img in image_list]
return image_list
def read_image(self, image_path, get_shape=False):
# tf.decode_image does not return the image size, this is an ugly workaround to handle both jpeg and png
path_length = string_length_tf(image_path)[0]
file_extension = tf.substr(image_path, path_length - 3, 3)
file_cond = tf.equal(file_extension, 'jpg')
image = tf.cond(
file_cond, lambda: tf.image.decode_jpeg(tf.read_file(image_path)),
lambda: tf.image.decode_png(tf.read_file(image_path)))
orig_height = tf.cast(tf.shape(image)[0], "float32")
orig_width = tf.cast(tf.shape(image)[1], "float32")
image = tf.image.convert_image_dtype(image, tf.float32)
image = tf.image.resize_images(
image, [self.opt.img_height, self.opt.img_width],
tf.image.ResizeMethod.AREA)
if get_shape:
return image, orig_height, orig_width
else:
return image