都行 1 import numpy as np
 2 import tensorflow as tf
 3 import matplotlib.pyplot as plt
 4 
 5 # 使用‘r‘会出错，无法解码，只能以2进制形式读取
 6 # img_raw = tf.gfile.FastGFile(‘./123.png‘,‘rb‘).read()
 7 img_raw = open(‘./123.png‘,‘rb‘).read()
 8 
 9 # 把二进制文件解码为uint8
10 img_0 = tf.image.decode_png(img_raw)
11 # 不太必要了，可以用np直接转换了
12 # img_1 = tf.image.convert_image_dtype(img_0,dtype=tf.uint8)
13 
14 sess = tf.Session()
15 print(sess.run(img_0).shape)
16 # plt.imshow(sess.run(img_0))
17 # plt.show()
18 
19 def show_pho(img,sess=sess):
20     ‘‘‘
21     TF处理过的图片自动转换了类型，需要调整回uint8才能正常显示
22     :param sess: 
23     :param img: 
24     :return: 
25     ‘‘‘
26     cat = np.asarray(sess.run(img),dtype=‘uint8‘)
27     print(cat.shape)
28     plt.imshow(cat)
29     plt.show()
30 
31 
32 ‘‘‘调整图像大小‘‘‘
33 # 插值尽量保存原图信息
34 img_1 = tf.image.resize_images(img_0,[500,500],method=3)
35 # show_pho(img_1)
36 
37 # 裁剪或填充
38 # 自动中央截取
39 img_2 = tf.image.resize_image_with_crop_or_pad(img_0,500,500)
40 # show_pho(img_2)
41 # 自动四周填充[0,0,0]
42 img_3 = tf.image.resize_image_with_crop_or_pad(img_0,2500,2500)
43 # show_pho(sess,img_3)
44 
45 # 比例中央裁剪
46 img_4 = tf.image.central_crop(img_0,0.5)
47 # show_pho(img_4)
48 
49 # 画框裁剪
50 # {起点高度，起点宽度，框高，框宽}
51 img_5 = tf.image.crop_to_bounding_box(img_0,700,300,500,500)
52 show_pho(img_5)

 1 import tensorflow as tf
 2 import numpy as np
 3 # import matplotlib.pyplot as plt
 4 
 5 def distort_color(image, color_ordering=0):
 6     ‘‘‘
 7     随机调整图片的色彩，定义两种处理顺序。
 8     注意，对3通道图像正常，4通道图像会出错，自行先reshape之
 9     :param image: 
10     :param color_ordering: 
11     :return: 
12     ‘‘‘
13     if color_ordering == 0:
14         image = tf.image.random_brightness(image, max_delta=32./255.)
15         image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
16         image = tf.image.random_hue(image, max_delta=0.2)
17         image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
18     else:
19         image = tf.image.random_saturation(image, lower=0.5, upper=1.5)
20         image = tf.image.random_brightness(image, max_delta=32./255.)
21         image = tf.image.random_contrast(image, lower=0.5, upper=1.5)
22         image = tf.image.random_hue(image, max_delta=0.2)
23 
24     return tf.clip_by_value(image, 0.0, 1.0)
25 
26 
27 def preprocess_for_train(image, height, width, bbox):
28     ‘‘‘
29     对图片进行预处理，将图片转化成神经网络的输入层数据。
30     :param image: 
31     :param height: 
32     :param width: 
33     :param bbox: 
34     :return: 
35     ‘‘‘
36     # 查看是否存在标注框。
37     if image.dtype != tf.float32:
38         image = tf.image.convert_image_dtype(image, dtype=tf.float32)
39 
40     # 随机的截取图片中一个块。
41     bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
42         tf.shape(image), bounding_boxes=bbox)
43     bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(
44         tf.shape(image), bounding_boxes=bbox)
45     distorted_image = tf.slice(image, bbox_begin, bbox_size)
46 
47     # 将随机截取的图片调整为神经网络输入层的大小。
48     distorted_image = tf.image.resize_images(distorted_image, [height, width], method=np.random.randint(4))
49     distorted_image = tf.image.random_flip_left_right(distorted_image)
50     distorted_image = distort_color(distorted_image, np.random.randint(2))
51     return distorted_image
52 
53 def pre_main(img,bbox=None):
54     if bbox is None:
55         bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4])
56     with tf.gfile.FastGFile(img, "rb") as f:
57         image_raw_data =http://www.mamicode.com/ f.read()
58     with tf.Session() as sess:
59         img_data =http://www.mamicode.com/ tf.image.decode_jpeg(image_raw_data)
60         for i in range(9):
61             result = preprocess_for_train(img_data, 299, 299, bbox)
62             # {wb打开文件{矩阵编码为jpeg{格式转换为uint8}}.eval()}
63             with tf.gfile.FastGFile(‘./代号{}.jpeg‘.format(i),‘wb‘) as f:
64                 f.write(sess.run(tf.image.encode_jpeg(tf.image.convert_image_dtype(result,dtype=tf.uint8))))
65             # plt.imshow(result.eval())
66             # plt.axis(‘off‘)
67             # plt.savefig(‘代号{}‘.format(i))
68 
69 
70 if __name__==‘__main__‘:
71     pre_main("./123123.jpeg",bbox=None)
72     exit()