TensorFlow：实战Google深度学习框架，数据增强 - 好文

在“TensorFlow：实战Google深度学习框架”一书中，发现一段很好的增强图像数据样本数量的方法，和大家分享下。

import matplotlib.pyplot as plt def distort_color(image, color_ordering=0): if
color_ordering == 0: image = tf.image.random_brightness(image, max_delta=32. /
255.)#亮度 image = tf.image.random_saturation(image, lower=0.5, upper=1.5)#饱和度
image = tf.image.random_hue(image, max_delta=0.2)#色相 image =
tf.image.random_contrast(image, lower=0.5, upper=1.5)#对比度 if color_ordering ==
1: 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) image =
tf.image.random_brightness(image, max_delta=32. / 255.) if color_ordering == 2:
image = tf.image.random_hue(image, max_delta=0.2) image =
tf.image.random_contrast(image, lower=0.5, upper=1.5) image =
tf.image.random_brightness(image, max_delta=32. / 255.) image =
tf.image.random_saturation(image, lower=0.5, upper=1.5) if color_ordering == 3:
image = tf.image.random_contrast(image, lower=0.5, upper=1.5) 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) return tf.clip_by_value(image, 0.0,
1.0) def preprocess_for_train(image, height, width, bbox): if bbox is None:
bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]) if
image.dytpe != tf.float32: image = tf.image.convert_image_dtype(image,
dtype=tf.float32) 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) 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(4)) return distorted_image
image_raw_data = tf.gfile.FastGFile("").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]]]) result =
preprocess_for_train(img_data, 299, 299, boxes)
最后，我也是在持续学习中，如果，哪里有不正确的，或者建议，希望能指出。

祝近安！

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