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data_loader.py
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data_loader.py
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"""
description: DataLoader
@author: Xiaoxu Meng (xiaoxumeng1993@gmail.com)
@author: QZheng
"""
from __future__ import division
import os
import numpy as np
import scipy.misc
import math
import PIL.Image
import array
import tensorflow as tf
from image_utils import load_exr
class dataLoader(object):
def __init__(self,
data_dir,
subset,
image_start_idx,
img_per_scene,
scene_list,
patch_per_img=50,
patch_width=128,
patch_height=128):
"""
- data_dir is location
- subset use train|test
- batch_size is int
"""
self.data_dir = data_dir
self.subset = subset
self.patch_width = patch_width
self.patch_height = patch_height
self.scene_list = scene_list
self.patch_per_img = patch_per_img
self.image_start_idx = image_start_idx
self.img_per_scene = img_per_scene
self.dataset_name = self.get_dataset_name()
self.load_dataset(subset)
def get_dataset_name(self):
dataset_name = 'bw_data_' + str(
self.patch_height) + 'x' + str(self.patch_width) + '_' + str(
len(self.scene_list)) + 'scenes_' + str(self.img_per_scene) + 'ips_' + str(
self.patch_per_img) + 'ppi_' + self.subset + '.tfrecords'
return os.path.join(dataset_name)
def load_dataset(self, subset):
if os.path.exists(self.dataset_name):
print(self.dataset_name, ' exisits.') # all is good
else:
self.encode_to_tfrecords(subset)
def encode_to_tfrecords(self, subset):
writer = tf.python_io.TFRecordWriter(self.dataset_name)
print(self.subset, 'Data_dir ', self.data_dir)
if subset == 'train' or subset == 'valid':
for scene_name in self.scene_list:
print('Processing scene ', scene_name)
data_subdir = os.path.join(self.data_dir, scene_name)
print('Visit data subdir ', data_subdir)
for idx in range(self.image_start_idx, self.img_per_scene + self.image_start_idx):
print(" " + str(idx))
exr_name = str(idx) + '.exr'
albedo_path = os.path.join(data_subdir, 'inputs', 'albedo' + exr_name)
normal_path = os.path.join(data_subdir, 'inputs', 'shading_normal' + exr_name)
depth_path = os.path.join(data_subdir, 'depth_normalized', str(idx) + '.png')
noisy_path = os.path.join(data_subdir, 'radiance_accum', 'accum_color_noabd' + exr_name)
GT_path = os.path.join(data_subdir, 'inputs', 'reference' + exr_name)
# original albedo ranges between [0,1] ==> [0,1]
albedo = load_exr(albedo_path, datatype=np.float32)
# original normal ranges between [-1,1] ==> [0,1]
normal = (load_exr(normal_path, datatype=np.float32) + 1.0) * 0.5
# original depth ranges between [0,1] ==> [0,1]
depth = np.expand_dims(np.asarray(PIL.Image.open(depth_path)), axis=2)/255
# original noisy ranges between [0, infty] ==> [0,1] (tonempping)
noisy = load_exr(noisy_path, datatype=np.float16)
# original GT ranges between [0, infty] ==> [0,1] (tonempping)
GT_full_image = load_exr(GT_path, datatype=np.float32)
noisy_full_image = np.concatenate(
(noisy, albedo, normal, depth), axis=2)
noisy_full_image = noisy_full_image[:, :, 0:10]
GT_full_image = GT_full_image.astype(np.float16)
noisy_full_image = noisy_full_image.astype(np.float16)
# crop
for _ in range(self.patch_per_img):
noisy_one, target_one = self.random_crop(
noisy_full_image, GT_full_image)
aug_idx = np.random.randint(0, 8)
target_one = self.aug_input(target_one, aug_idx)
noisy_one = self.aug_input(noisy_one, aug_idx)
feature = {
'target': tf.train.Feature(
bytes_list = tf.train.BytesList(
value=[target_one.tostring()])),
'input': tf.train.Feature(
bytes_list = tf.train.BytesList(
value=[noisy_one.tostring()]))}
example = tf.train.Example(
features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
else: # subset == 'test'
for scene_name in self.scene_list:
print('Processing scene ', scene_name)
data_subdir = os.path.join(self.data_dir, scene_name)
print('Visit test data subdir ', data_subdir)
padding_w = 0
padding_h = 0
for idx in range(self.image_start_idx, self.img_per_scene + self.image_start_idx):
print(" " + str(idx))
exr_name = str(idx) + '.exr'
albedo_path = os.path.join(data_subdir, 'inputs', 'albedo' + exr_name)
normal_path = os.path.join(data_subdir, 'inputs', 'shading_normal' + exr_name)
depth_path = os.path.join(data_subdir, 'depth_normalized', str(idx) + '.png')
noisy_path = os.path.join(data_subdir, 'radiance_accum', 'accum_color_noabd' + exr_name)
GT_path = os.path.join(data_subdir, 'inputs', 'reference' + exr_name)
# original albedo ranges between [0,1] ==> [0,1]
albedo = load_exr(albedo_path, datatype=np.float32)
# original normal ranges between [-1,1] ==> [0,1]
normal = (load_exr(normal_path, datatype=np.float32) + 1.0) * 0.5
# original depth ranges between [0,1] ==> [0,1]
depth = np.expand_dims(np.asarray(PIL.Image.open(depth_path)), axis=2)/255
# original noisy ranges between [0, infty] ==> [0,1] (tonempping)
noisy = load_exr(noisy_path, datatype=np.float16)
# original GT ranges between [0, infty] ==> [0,1] (tonempping)
GT_full_image = load_exr(GT_path, datatype=np.float32)
noisy_full_image = np.concatenate(
(noisy, albedo, normal, depth), axis=2)
noisy_full_image = noisy_full_image[:, :, 0:10]
resolution = noisy_full_image.shape
noisy_one = np.zeros((resolution[0] + padding_h,
resolution[1] + padding_w, 10),
dtype = np.float16)
noisy_one[0:resolution[0], 0:resolution[1],:] = \
noisy_full_image
target_one = np.zeros((resolution[0] + padding_h,
resolution[1] + padding_w, 3),
dtype=np.float16)
target_one[0:resolution[0], 0:resolution[1],:] = \
GT_full_image
feature = {
'target': tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[target_one.tostring()])),
'input': tf.train.Feature(
bytes_list=tf.train.BytesList(
value=[noisy_one.tostring()]))}
example = tf.train.Example(
features=tf.train.Features(feature=feature))
writer.write(example.SerializeToString())
writer.close()
print(self.subset, ' data preprocess finished.')
def with_offset_crop(self, x, y, offseth, offsetw, size=(256, 256)):
cropped_x = x[offseth:offseth + size[0], offsetw:offsetw + size[1], :]
cropped_y = y[offseth:offseth + size[0], offsetw:offsetw + size[1], :]
cropped_x = cropped_x
cropped_y = cropped_y
return cropped_x, cropped_y
def random_crop(self, x, y):
cropped_x, cropped_y = self.random_crop_np(x, y, size=(
self.patch_height, self.patch_width))
return cropped_x, cropped_y
def random_crop_np(self, x, y, size=(256, 256)):
assert x.shape[0] >= size[0]
assert x.shape[1] >= size[1]
offseth, offsetw = self.generate_offset(
shape=[x.shape[0], x.shape[1], x.shape[2]], size=size)
cropped_x = x[offseth:offseth + size[0], offsetw:offsetw + size[1], :]
cropped_y = y[offseth:offseth + size[0], offsetw:offsetw + size[1], :]
return cropped_x, cropped_y
def generate_offset(self, shape, size=(256, 256)):
h, w, ch = shape
range_h = h - size[0]
range_w = w - size[1]
offseth = 0 if range_h == 0 else np.random.randint(range_h)
if range_w == 0:
offsetw = 0
else:
my_rand = np.random.randint(range_w)
offsetw = 1 if my_rand == 0 else int(np.log2(my_rand) / np.log2(
range_w) * range_w)
return offseth, offsetw
def aug_input(self, img, idx=0):
if idx == 0:
return img
elif idx == 1:
return np.rot90(img)
elif idx == 2:
return np.rot90(img, k=2) # 180
elif idx == 3:
return np.rot90(img, k=3) # 270
elif idx == 4:
return np.flipud(img)
elif idx == 5:
return np.flipud(np.rot90(img))
elif idx == 6:
return np.flipud(np.rot90(img, k=2))
elif idx == 7:
return np.flipud(np.rot90(img, k=3))