吴裕雄 python 神经网络——TensorFlow图片预处理调整图片

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

def distort_color(image, color_ordering=0):
    '''
    随机调整图片的色彩，定义两种处理顺序。
    '''
    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 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)
    bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
        tf.shape(image), bounding_boxes=bbox)
    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

def pre_main(img,bbox=None):
    if bbox is None:
        bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
    with tf.gfile.FastGFile(img, "rb") as f:
        image_raw_data = f.read()
    with tf.Session() as sess:
        img_data = tf.image.decode_jpeg(image_raw_data)
        for i in range(9):
            result = preprocess_for_train(img_data, 299, 299, bbox)

            plt.imshow(result.eval())
            plt.axis('off')
            plt.savefig("E:\myresource\代号{}".format(i))


pre_main("E:\myresource\moutance.jpg",bbox=None)
exit()