Wu Yuxiong - Natural born pythonTensorFlow graphic data: the complete image preprocessing Sample

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

#随机调整图片的色彩，定义两种顺序。
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)

# Of image preprocessing, the image data is converted into the input layer of the neural network. 
DEF preprocess_for_train (Image, height, width, BBOX):
     # see if the callout box. 
    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) 
        
    # randomly taken a picture block. 
    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

#读取图片。
image_raw_data = tf.gfile.FastGFile("F:\\TensorFlowGoogle\\201806-github\\datasets\\cat.jpg", "rb").read()
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()