utils_data.py 文件源码

def read_data(prefix, labels_dic, mixing, files_from_cl):
    image_list = sorted(map(lambda x: os.path.join(prefix, x),
                        filter(lambda x: x.endswith('JPEG'), files_from_cl)))
    prefix2 = []

    for file_i in image_list:
        tmp = file_i.split(prefix+'/')[1].split("_")[0]
        prefix2.append(tmp)

    prefix2     = np.array(prefix2)
    labels_list = np.array([mixing[labels_dic[i]] for i in prefix2])

    assert(len(image_list) == len(labels_list))
    images             = tf.convert_to_tensor(image_list, dtype=tf.string)
    labels             = tf.convert_to_tensor(labels_list, dtype=tf.int32)
    input_queue        = tf.train.slice_input_producer([images, labels], shuffle=True, capacity=2000)
    image_file_content = tf.read_file(input_queue[0])
    label              = input_queue[1]
    image              = tf.image.resize_images(tf.image.decode_jpeg(image_file_content, channels=3), [256, 256])
    image              = tf.random_crop(image, [224, 224, 3])
    image              = tf.image.random_flip_left_right(image)

    return image, label