所以，我一直在尝试使用TensorFlow数据集API加载图像和分割掩模（用于语义分割项目），我希望能够生成批次的图像和掩模，每个图像都经过任何预处理函数的随机组合，例如亮度变化、对比度变化、裁剪、饱和度变化等。因此，我的批次中的第一张图像可能没有预处理，第二张可能有饱和度变化，第三张可能有亮度和饱和度等。
我尝试了以下方法：
import tensorflow as tf
from tensorflow.contrib.data import Dataset, Iterator
import random
def _resize_image(image, mask):
image = tf.image.resize_bicubic(image, [480, 640], True)
mask = tf.image.resize_bicubic(mask, [480, 640], True)
return image, mask
def _corrupt_contrast(image, mask):
image = tf.image.random_contrast(image, 0, 5)
return image, mask
def _corrupt_saturation(image, mask):
image = tf.image.random_saturation(image, 0, 5)
return image, mask
def _corrupt_brightness(image, mask):
image = tf.image.random_brightness(image, 5)
return image, mask
def _random_crop(image, mask):
seed = random.random()
image = tf.random_crop(image, [240, 320, 3], seed=seed)
mask = tf.random_crop(mask, [240, 320, 1], seed=seed)
return image, mask
def _flip_image_horizontally(image, mask):
seed = random.random()
image = tf.image.random_flip_left_right(image, seed=seed)
mask = tf.image.random_flip_left_right(mask, seed=seed)
return image, mask
def _flip_image_vertically(image, mask):
seed = random.random()
image = tf.image.random_flip_up_down(image, seed=seed)
mask = tf.image.random_flip_up_down(mask, seed=seed)
return image, mask
def _normalize_data(image, mask):
image = tf.cast(image, tf.float32)
image = image / 255.0
mask = tf.cast(mask, tf.float32)
mask = mask / 255.0
return image, mask
def _parse_data(image_paths, mask_paths):
image_content = tf.read_file(image_paths)
mask_content = tf.read_file(mask_paths)
images = tf.image.decode_png(image_content, channels=3)
masks = tf.image.decode_png(mask_content, channels=1)
return images, masks
def data_batch(image_paths, mask_paths, params, batch_size=4, num_threads=2):
# Convert lists of paths to tensors for tensorflow
images_name_tensor = tf.constant(image_paths)
mask_name_tensor = tf.constant(mask_paths)
# Create dataset out of the 2 files:
data = Dataset.from_tensor_slices(
(images_name_tensor, mask_name_tensor))
# Parse images and labels
data = data.map(
_parse_data, num_threads=num_threads, output_buffer_size=6 * batch_size)
# Normalize images and masks for vals. between 0 and 1
data = data.map(_normalize_data, num_threads=num_threads, output_buffer_size=6 * batch_size)
if params['crop'] and not random.randint(0, 1):
data = data.map(_random_crop, num_threads=num_threads,
output_buffer_size=6 * batch_size)
if params['brightness'] and not random.randint(0, 1):
data = data.map(_corrupt_brightness, num_threads=num_threads,
output_buffer_size=6 * batch_size)
if params['contrast'] and not random.randint(0, 1):
data = data.map(_corrupt_contrast, num_threads=num_threads,
output_buffer_size=6 * batch_size)
if params['saturation'] and not random.randint(0, 1):
data = data.map(_corrupt_saturation, num_threads=num_threads,
output_buffer_size=6 * batch_size)
if params['flip_horizontally'] and not random.randint(0, 1):
data = data.map(_flip_image_horizontally,
num_threads=num_threads, output_buffer_size=6 * batch_size)
if params['flip_vertically'] and not random.randint(0, 1):
data = data.map(_flip_image_vertically, num_threads=num_threads,
output_buffer_size=6 * batch_size)
# Shuffle the data queue
data = data.shuffle(len(image_paths))
# Create a batch of data
data = data.batch(batch_size)
data = data.map(_resize_image, num_threads=num_threads,
output_buffer_size=6 * batch_size)
# Create iterator
iterator = Iterator.from_structure(data.output_types, data.output_shapes)
# Next element Op
next_element = iterator.get_next()
# Data set init. op
init_op = iterator.make_initializer(data)
return next_element, init_op
random_crop的seed参数是否保证以相同（一致）的方式裁剪image和mask？ - Rohan Saxena