吴裕雄 python 神经网络——TensorFlow 图像预处理完整样例

```import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

def distort_color(image, color_ordering=0):
if color_ordering == 0:
image = tf.image.random_brightness(image, max_delta=32./255.)
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
else:
image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
image = tf.image.random_brightness(image, max_delta=32./255.)
image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
image = tf.image.random_hue(image, max_delta=0.2)

return tf.clip_by_value(image, 0.0, 1.0)

def preprocess_for_train(image, height, width, bbox):
# 查看是否存在标注框。
if bbox is None:
bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
if image.dtype != tf.float32:
image = tf.image.convert_image_dtype(image, dtype=tf.float32)

# 随机的截取图片中一个块。
bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(tf.shape(image), bounding_boxes=bbox, min_object_covered=0.4)
bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(tf.shape(image), bounding_boxes=bbox, min_object_covered=0.4)
distorted_image = tf.slice(image, bbox_begin, bbox_size)

# 将随机截取的图片调整为神经网络输入层的大小。
distorted_image = tf.image.resize_images(distorted_image, [height, width], method=np.random.randint(4))
distorted_image = tf.image.random_flip_left_right(distorted_image)
distorted_image = distort_color(distorted_image, np.random.randint(2))
return distorted_image

with tf.Session() as sess:
img_data = tf.image.decode_jpeg(image_raw_data)
boxes = tf.constant([[[0.05, 0.05, 0.9, 0.7], [0.35, 0.47, 0.5, 0.56]]])
for i in range(9):
result = preprocess_for_train(img_data, 299, 299, boxes)
plt.imshow(result.eval())
plt.show()```