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data_loader.py
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data_loader.py
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from __future__ import division
import os
import random
import tensorflow as tf
class DataLoader(object):
def __init__(self,
dataset_dir=None,
batch_size=None,
img_height=None,
img_width=None,
num_source=None,
num_scales=None):
self.dataset_dir = dataset_dir
self.batch_size = batch_size
self.img_height = img_height
self.img_width = img_width
self.num_source = num_source
self.num_scales = num_scales
def load_train_batch(self):
"""Load a batch of training instances.
"""
seed = random.randint(0, 2**31 - 1)
# Load the list of training files into queues
file_list = self.format_file_list(self.dataset_dir, 'train')
image_paths_queue = tf.train.string_input_producer(
file_list['image_file_list'],
seed=seed,
shuffle=True)
cam_paths_queue = tf.train.string_input_producer(
file_list['cam_file_list'],
seed=seed,
shuffle=True)
self.steps_per_epoch = int(
len(file_list['image_file_list'])//self.batch_size)
# Load images
img_reader = tf.WholeFileReader()
_, image_contents = img_reader.read(image_paths_queue)
image_seq = tf.image.decode_jpeg(image_contents)
tgt_image, src_image_stack = \
self.unpack_image_sequence(
image_seq, self.img_height, self.img_width, self.num_source)
# Load camera intrinsics
cam_reader = tf.TextLineReader()
_, raw_cam_contents = cam_reader.read(cam_paths_queue)
rec_def = []
for i in range(9):
rec_def.append([1.])
raw_cam_vec = tf.decode_csv(raw_cam_contents,
record_defaults=rec_def)
raw_cam_vec = tf.stack(raw_cam_vec)
intrinsics = tf.reshape(raw_cam_vec, [3, 3])
# Form training batches
src_image_stack, tgt_image, intrinsics = \
tf.train.batch([src_image_stack, tgt_image, intrinsics],
batch_size=self.batch_size)
# Data augmentation
image_all = tf.concat([tgt_image, src_image_stack], axis=3)
image_all, intrinsics = self.data_augmentation(
image_all, intrinsics, self.img_height, self.img_width)
tgt_image = image_all[:, :, :, :3]
src_image_stack = image_all[:, :, :, 3:]
intrinsics = self.get_multi_scale_intrinsics(
intrinsics, self.num_scales)
return tgt_image, src_image_stack, intrinsics
def make_intrinsics_matrix(self, 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(self, 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 = self.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 = self.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)
im = tf.cast(im, dtype=tf.uint8)
return im, intrinsics
def format_file_list(self, data_root, split):
with open(data_root + '/%s.txt' % split, 'r') as f:
frames = f.readlines()
subfolders = [x.split(' ')[0] for x in frames]
frame_ids = [x.split(' ')[1][:-1] for x in frames]
image_file_list = [os.path.join(data_root, subfolders[i],
frame_ids[i] + '.jpg') for i in range(len(frames))]
cam_file_list = [os.path.join(data_root, subfolders[i],
frame_ids[i] + '_cam.txt') for i in range(len(frames))]
all_list = {}
all_list['image_file_list'] = image_file_list
all_list['cam_file_list'] = cam_file_list
return all_list
def unpack_image_sequence(self, image_seq, img_height, img_width, num_source):
# Assuming the center image is the target frame
tgt_start_idx = int(img_width * (num_source//2))
tgt_image = tf.slice(image_seq,
[0, tgt_start_idx, 0],
[-1, img_width, -1])
# Source frames before the target frame
src_image_1 = tf.slice(image_seq,
[0, 0, 0],
[-1, int(img_width * (num_source//2)), -1])
# Source frames after the target frame
src_image_2 = tf.slice(image_seq,
[0, int(tgt_start_idx + img_width), 0],
[-1, int(img_width * (num_source//2)), -1])
src_image_seq = tf.concat([src_image_1, src_image_2], axis=1)
# Stack source frames along the color channels (i.e. [H, W, N*3])
src_image_stack = tf.concat([tf.slice(src_image_seq,
[0, i*img_width, 0],
[-1, img_width, -1])
for i in range(num_source)], axis=2)
src_image_stack.set_shape([img_height,
img_width,
num_source * 3])
tgt_image.set_shape([img_height, img_width, 3])
return tgt_image, src_image_stack
def batch_unpack_image_sequence(self, image_seq, img_height, img_width, num_source):
# Assuming the center image is the target frame
tgt_start_idx = int(img_width * (num_source//2))
tgt_image = tf.slice(image_seq,
[0, 0, tgt_start_idx, 0],
[-1, -1, img_width, -1])
# Source frames before the target frame
src_image_1 = tf.slice(image_seq,
[0, 0, 0, 0],
[-1, -1, int(img_width * (num_source//2)), -1])
# Source frames after the target frame
src_image_2 = tf.slice(image_seq,
[0, 0, int(tgt_start_idx + img_width), 0],
[-1, -1, int(img_width * (num_source//2)), -1])
src_image_seq = tf.concat([src_image_1, src_image_2], axis=2)
# Stack source frames along the color channels (i.e. [B, H, W, N*3])
src_image_stack = tf.concat([tf.slice(src_image_seq,
[0, 0, i*img_width, 0],
[-1, -1, img_width, -1])
for i in range(num_source)], axis=3)
return tgt_image, src_image_stack
def get_multi_scale_intrinsics(self, intrinsics, num_scales):
intrinsics_mscale = []
# Scale the intrinsics accordingly for each scale
for s in range(num_scales):
fx = intrinsics[:,0,0]/(2 ** s)
fy = intrinsics[:,1,1]/(2 ** s)
cx = intrinsics[:,0,2]/(2 ** s)
cy = intrinsics[:,1,2]/(2 ** s)
intrinsics_mscale.append(
self.make_intrinsics_matrix(fx, fy, cx, cy))
intrinsics_mscale = tf.stack(intrinsics_mscale, axis=1)
return intrinsics_mscale