TensorFlow图像预处理-函数

更多的基本的API请参看TensorFlow中文社区：http://www.tensorfly.cn/tfdoc/api_docs/python/array_ops.html

下面是实验的代码，可以参考，对应的图片是输出的结果：

import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
%matplotlib inline
path = '/home/ubuntu-mm/TensorFlow/Learning/D-TestJupyter/images/Train/Dogs.jpg'
file_queue = tf.train.string_input_producer([path])
image_reader = tf.WholeFileReader()
_, image = image_reader.read(file_queue)
image = tf.image.decode_jpeg(image)
with tf.Session() as sess:  
    coord = tf.train.Coordinator() #协同启动的线程  
    threads = tf.train.start_queue_runners(sess=sess, coord=coord) #启动线程运行队列  
    sess.run(image)  
    coord.request_stop() #停止所有的线程  
    coord.join(threads)  
    image_uint8 = tf.image.convert_image_dtype(image, dtype = tf.uint8)
    plt.figure(1)
    plt.imshow(image_uint8.eval())
    print image_uint8.get_shape()
    resize_image1 = tf.image.resize_images(image_uint8,[400,300],method=tf.image.ResizeMethod.NEAREST_NEIGHBOR) #修改图片的尺寸
    resize_image2 = tf.image.resize_images(image_uint8,[400,300],method=1) #修改图片的尺寸 1代表的就是NEAREST_NEIGHBOR的方法
    central_crop = tf.image.central_crop(image_uint8, 0.6) #从图片中心开始裁剪图片，裁剪比例为60%
    bounding_crop = tf.image.crop_to_bounding_box(resize_image1, offset_height=100, offset_width=100, target_height=100, target_width=100) #设定裁剪的起始位置和终止位置进行裁剪
    pad = tf.image.pad_to_bounding_box(bounding_crop, offset_height=0, offset_width=0, target_height=105, target_width=105) #设定边缘对图像的边缘进行填充（填0）
    flip1 = tf.image.flip_left_right(resize_image1) #左右翻转图片
    flip2 = tf.image.flip_up_down(flip1) #上下翻转图片
    adjust_brightness = tf.image.adjust_brightness(resize_image1, 0.2) #调节图像的亮度为原来的20%
    adjust_saturation = tf.image.adjust_saturation(resize_image1, 0.4) #调节图像的饱和度为原来的40%
    adjust_hue = tf.image.adjust_hue(resize_image1, 0.7) #调节原来的H（灰度）为原来的70%
    image_float = tf.cast(resize_image1, dtype=tf.float32) 
    gray = tf.image.rgb_to_grayscale(image_float) #对图像的类型进行转换rgb-grayscale
    hsv = tf.image.rgb_to_hsv(image_float) #对图像进行hsv转换rgb-hsv
    imag_gray = tf.image.convert_image_dtype(gray, tf.uint8)
    imag_hsv = tf.image.convert_image_dtype(hsv, tf.uint8)
    sess.run([flip1, flip2])
    plt.figure(2)
    plt.imshow(resize_image1.eval())
    plt.figure(3)
    plt.imshow(resize_image2.eval())
    plt.figure(4)
    plt.imshow(central_crop.eval())
    plt.figure(5)
    plt.imshow(bounding_crop.eval())
    plt.figure(6)
    plt.imshow(pad.eval())
    plt.figure(7)
    plt.imshow(flip2.eval())
    plt.figure(8)
    plt.imshow(adjust_brightness.eval())
    plt.figure(9)
    plt.imshow(adjust_saturation.eval())
    plt.figure(10)
    plt.imshow(adjust_hue.eval())
    plt.figure(1)
    plt.imshow(imag_hsv.eval(), cmap=cm.hsv)