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# 输入图片处理
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import os
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import math
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import numpy as np
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import tensorflow as tf
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import matplotlib.pyplot as plt
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# -----------------生成图片路径和标签的List------------------------------------
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# 生成图片的存储位置 /Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/
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train_dir = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'
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one = []
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label_one = []
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two = []
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label_two = []
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three = []
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label_three = []
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four = []
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label_four = []
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five = []
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label_five = []
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six = []
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label_six = []
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seven = []
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label_seven = []
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eight = []
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label_eight = []
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nine = []
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label_nine = []
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# step1：获取'/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train'下所有的图片路径名，存放到
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# 对应的列表中，同时贴上标签，存放到label列表中。
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# ratio是测试集的比例
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def get_files(file_dir, ratio):
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for file in os.listdir(file_dir + '/one'):
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one.append(file_dir + '/one' + '/' + file)
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label_one.append(0)
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for file in os.listdir(file_dir + '/two'):
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two.append(file_dir + '/two' + '/' + file)
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label_two.append(1)
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for file in os.listdir(file_dir + '/three'):
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three.append(file_dir + '/three' + '/' + file)
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label_three.append(2)
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for file in os.listdir(file_dir + '/four'):
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four.append(file_dir + '/four' + '/' + file)
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label_four.append(3)
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for file in os.listdir(file_dir + '/five'):
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five.append(file_dir + '/five' + '/' + file)
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label_five.append(5)
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for file in os.listdir(file_dir + '/six'):
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six.append(file_dir + '/six' + '/' + file)
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label_six.append(6)
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for file in os.listdir(file_dir + '/seven'):
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seven.append(file_dir + '/seven' + '/' + file)
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label_seven.append(7)
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for file in os.listdir(file_dir + '/eight'):
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eight.append(file_dir + '/eight' + '/' + file)
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label_eight.append(8)
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for file in os.listdir(file_dir + '/nine'):
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nine.append(file_dir + '/nine' + '/' + file)
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label_nine.append(9)
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# step2：对生成的图片路径和标签List做打乱处理把所有的数据合起来组成一个list（img和lab）
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# np.hstack水平（按列）按顺序堆叠数组。
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# >>> a = np.array((1,2,3))
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# >>> b = np.array((2,3,4))
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# >>> np.hstack((a,b))
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# array([1, 2, 3, 2, 3, 4])
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image_list = np.hstack((one, two, three, four, five, six, seven, eight, nine))
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label_list = np.hstack((label_one, label_two, label_three, label_four, label_five, label_six, label_seven, label_eight, label_nine))
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# 利用shuffle打乱顺序
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temp = np.array([image_list, label_list])
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temp = temp.transpose()
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np.random.shuffle(temp)
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# 从打乱的temp中再取出list（img和lab）
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# image_list = list(temp[:, 0])
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# label_list = list(temp[:, 1])
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# label_list = [int(i) for i in label_list]
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# return image_list, label_list
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# 将所有的img和lab转换成list
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all_image_list = list(temp[:, 0])
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all_label_list = list(temp[:, 1])
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# 将所得List分为两部分，一部分用来训练tra，一部分用来测试val
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# ratio是测试集的比例
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# n_sample全部样本数
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n_sample = len(all_label_list)
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n_val = int(math.ceil(n_sample * ratio)) # 测试样本数
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n_train = n_sample - n_val # 训练样本数
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# 训练的图片和标签
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tra_images = all_image_list[0:n_train]
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tra_labels = all_label_list[0:n_train]
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tra_labels = [int(float(i)) for i in tra_labels]
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# 测试图片和标签
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val_images = all_image_list[n_train:-1]
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val_labels = all_label_list[n_train:-1]
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val_labels = [int(float(i)) for i in val_labels]
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return tra_images, tra_labels, val_images, val_labels
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# --------------------生成Batch----------------------------------------------
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# step1：将上面生成的List传入get_batch() ，转换类型，产生一个输入队列queue，因为img和lab
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# 是分开的，所以使用tf.train.slice_input_producer()，然后用tf.read_file()从队列中读取图像
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# image_W, image_H, ：设置好固定的图像高度和宽度
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# 设置batch_size：每个batch要放多少张图片
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# capacity：一个队列最大多少
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def get_batch(image, label, image_W, image_H, batch_size, capacity):
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# 转换类型
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image = tf.cast(image, tf.string)
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label = tf.cast(label, tf.int32)
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# make an input queue
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# tf.train.slice_input_producer是一个tensor生成器，作用是按照设定，
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# 每次从一个tensor列表中按顺序或者随机抽取出一个tensor放入文件名队列。
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input_queue = tf.compat.v1.train.slice_input_producer([image, label])
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label = input_queue[1]
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image_contents = tf.io.read_file(input_queue[0]) # read img from a queue
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# step2：将图像解码，不同类型的图像不能混在一起，要么只用jpeg，要么只用png等。
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image = tf.image.decode_jpeg(image_contents, channels=3)
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# step3：数据预处理，对图像进行旋转、缩放、裁剪、归一化等操作，让计算出的模型更健壮。
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image = tf.image.resize_image_with_crop_or_pad(image, image_W, image_H)
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image = tf.image.per_image_standardization(image)
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# step4：生成batch
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# image_batch: 4D tensor [batch_size, width, height, 3],dtype=tf.float32
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# label_batch: 1D tensor [batch_size], dtype=tf.int32
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image_batch, label_batch = tf.compat.v1.train.batch([image, label],
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batch_size=batch_size,
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num_threads=32,
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capacity=capacity)
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# 重新排列label，行数为[batch_size]
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label_batch = tf.reshape(label_batch, [batch_size])
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image_batch = tf.cast(image_batch, tf.float32)
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return image_batch, label_batch
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