import numpy as np

import tensorflow as tf

import matplotlib.pyplot as plt

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)

    # 输出解码之后的三维矩阵。

    print(img_data.eval())

    img_data.set_shape([1797, 2673, 3])

    print(img_data.get_shape())

with tf.Session() as sess:

    plt.imshow(img_data.eval())

    plt.show()

with tf.Session() as sess:

    # 如果直接以0-255范围的整数数据输入resize_images，那么输出将是0-255之间的实数，

    # 不利于后续处理。本书建议在调整图片大小前，先将图片转为0-1范围的实数。

    image_float = tf.image.convert_image_dtype(img_data, tf.float32)

    resized = tf.image.resize_images(image_float, [300, 300], method=0)

    plt.imshow(resized.eval())

    plt.show()

with tf.Session() as sess:

    croped = tf.image.resize_image_with_crop_or_pad(img_data, 1000, 1000)

    padded = tf.image.resize_image_with_crop_or_pad(img_data, 3000, 3000)

    plt.imshow(croped.eval())

    plt.show()

    plt.imshow(padded.eval())

    plt.show()

with tf.Session() as sess:

    central_cropped = tf.image.central_crop(img_data, 0.5)

    plt.imshow(central_cropped.eval())

    plt.show()

with tf.Session() as sess:

    # 上下翻转

    #flipped1 = tf.image.flip_up_down(img_data)

    # 左右翻转

    #flipped2 = tf.image.flip_left_right(img_data)

    #对角线翻转

    transposed = tf.image.transpose_image(img_data)

    plt.imshow(transposed.eval())

    plt.show()

    # 以一定概率上下翻转图片。

    #flipped = tf.image.random_flip_up_down(img_data)

    # 以一定概率左右翻转图片。

    #flipped = tf.image.random_flip_left_right(img_data)