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)

#对图片进行预处理，将图片转化成神经网络的输入层数据。

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

#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()