import numpy as npimport tensorflow as tfimport matplotlib.pyplot as pltdef distort_color(image, color_ordering=0): 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 bbox is None: bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]) 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, min_object_covered=0.4) bbox_begin, bbox_size, _ = tf.image.sample_distorted_bounding_box(tf.shape(image), bounding_boxes=bbox, min_object_covered=0.4) 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_imageimage_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) boxes = tf.constant([[[0.05, 0.05, 0.9, 0.7], [0.35, 0.47, 0.5, 0.56]]]) for i in range(9): result = preprocess_for_train(img_data, 299, 299, boxes) plt.imshow(result.eval()) plt.show() 
