tensorflow实战小项目——面部表情识别（2） - 好文

在之前的文章中，我们已经将fer2013这个数据集进行了分割以及图片的还原，接下来我们要开始构建训练所用的batch:
#构建训练模型时需要的batch import os import tensorflow as tf import matplotlib.pyplot as
plt import numpy as np anger_0 = [] anger_0_labels = [] disgust_1 = []
disgust_1_label = [] fear_2 = [] fear_2_label = [] happy_3 = [] happy_3_label =
[] sad_4 = [] sad_4_label = [] surprised_5 = [] surprised_5_label = [] normal_6
= [] normal_6_label = [] def get_file(file_dir): for file in
os.listdir(file_dir + '0'): anger_0.append(file_dir + '0' + '/' + file)
anger_0_labels.append(0) for file in os.listdir(file_dir + '1'):
disgust_1.append(file_dir + '1' + '/' + file) disgust_1_label.append(1) for
file in os.listdir(file_dir + '2'): fear_2.append(file_dir + '2' + '/' + file)
fear_2_label.append(2) for file in os.listdir(file_dir + '3'):
happy_3.append(file_dir + '3' + '/' + file) happy_3_label.append(3) for file in
os.listdir(file_dir + '4'): sad_4.append(file_dir + '4' + '/' + file)
sad_4_label.append(4) for file in os.listdir(file_dir + '5'):
surprised_5.append(file_dir + '5' + '/' + file) surprised_5_label.append(5) for
file in os.listdir(file_dir + '6'): normal_6.append(file_dir + '6' + '/' +
file) normal_6_label.append(6) image_list = np.hstack((anger_0, disgust_1,
fear_2, happy_3, sad_4, surprised_5, normal_6)) label_list =
np.hstack((anger_0_labels, disgust_1_label, fear_2_label, happy_3_label,
sad_4_label, surprised_5_label, normal_6_label)) temp = np.array([image_list,
label_list]) temp = temp.transpose() np.random.shuffle(temp) all_image_list =
list(temp[:, 0]) all_label_list = list(temp[:, 1]) all_label_list = [int(i) for
i in all_label_list] return all_image_list, all_label_list def get_batch(image,
label, image_W, image_H, batch_size, capacity): #改变数据类型 image = tf.cast(image,
tf.string) label = tf.cast(label, tf.int32) #生成一个输出的队列 input_queue =
tf.train.slice_input_producer([image, label]) label = input_queue[1]
image_contents = tf.read_file(input_queue[0]) # 通过该队列来读取图像 #将图像进行编码处理 image =
tf.image.decode_jpeg(image_contents, channels=1) #对目标图像进行预处理，改变 image =
tf.image.resize_image_with_crop_or_pad(image, image_W, image_H) image =
tf.image.per_image_standardization(image)#对图像进行标准化处理 #生成batch image_batch,
label_batch = tf.train.batch([image, label], batch_size=batch_size,
num_threads=32, capacity=capacity) label_batch = tf.reshape(label_batch,
[batch_size]) image_batch = tf.cast(image_batch, tf.float32) return
image_batch, label_batch这样通过调用get_batch()这个函数就可以生成相应的batch，这将对我们后边训练数据非常重要。

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