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code/python/preprocessing.py

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+#图片预处理
+
+# 将原始图片转换成需要的大小，并将其保存
+import os
+import tensorflow as tf
+from PIL import Image
+
+# 原始图片的存储位置 /Users/leixinhong/PycharmProjects/classification/teethimg/train-data/
+orig_picture = '/Users/leixinhong/PycharmProjects/classification/teethimg/train-data'
+
+# 生成图片的存储位置 /Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/
+gen_picture = '/Users/leixinhong/PycharmProjects/classification/teethimg/Re_train/'
+
+# 需要的识别类型
+classes = ['one', 'two', 'three', 'three', 'four', 'five', 'six', 'seven', 'eight', 'nine']
+
+# 样本总数
+num_samples = 90
+
+
+# 制作TFRecords数据
+def create_record():
+    writer = tf.compat.v1.python_io.TFRecordWriter("dishes_train.tfrecords")
+    for index, name in list(enumerate(classes)):
+        # /Users/leixinhong/PycharmProjects/classification/teethimg/train-data/one/
+        class_path = orig_picture + "/" + name + "/"
+        # print(index)
+        # print(name)
+        # print(class_path)
+        # print(os.listdir(class_path))
+        # os.listdir() 方法用于返回指定的文件夹包含的文件或文件夹的名字的列表。
+        for img_name in os.listdir(class_path):
+            img_path = class_path + img_name
+            #print(img_path)
+            img = Image.open(img_path)
+            #print(img)
+            img = img.resize((64, 64))  # 设置需要转换的图片大小
+            #print(img)
+            img_raw = img.tobytes()  # 将图片转化为原生bytes
+            print(index, img_raw)
+            example = tf.train.Example(
+                features=tf.train.Features(feature={
+                    "label": tf.train.Feature(int64_list=tf.train.Int64List(value=[index])),
+                    'img_raw': tf.train.Feature(bytes_list=tf.train.BytesList(value=[img_raw]))
+                }))
+            writer.write(example.SerializeToString())
+    writer.close()
+
+
+def read_and_decode(filename):
+    # 创建文件队列,不限读取的数量
+    filename_queue = tf.compat.v1.train.string_input_producer([filename])
+    # create a reader from file queue
+    reader = tf.compat.v1.TFRecordReader()
+    # reader从文件队列中读入一个序列化的样本
+    _, serialized_example = reader.read(filename_queue)
+    # get feature from serialized example
+    # 解析符号化的样本
+    features = tf.io.parse_single_example(
+        serialized_example,
+        features={
+            'label': tf.io.FixedLenFeature([], tf.int64),
+            'img_raw': tf.io.FixedLenFeature([], tf.string)
+        })
+    label = features['label']
+    img = features['img_raw']
+    img = tf.io.decode_raw(img, tf.uint8)
+    img = tf.reshape(img, [64, 64, 3])
+    # img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
+    label = tf.cast(label, tf.int32)
+    return img, label
+
+
+if __name__ == '__main__':  # 程序主入口
+    create_record()
+    batch = read_and_decode('dishes_train.tfrecords')
+    init_op = tf.group(tf.compat.v1.global_variables_initializer(), tf.compat.v1.local_variables_initializer())
+
+    with tf.compat.v1.Session() as sess:  # 开始一个会话
+        sess.run(init_op)
+        coord = tf.train.Coordinator()
+        threads = tf.compat.v1.train.start_queue_runners(coord=coord)
+
+        for i in range(num_samples):
+            example, lab = sess.run(batch)  # 在会话中取出image和label
+            img = Image.fromarray(example, 'RGB')  # 这里Image是之前提到的
+            img.save(gen_picture + '/' + str(i) + 'samples' + str(lab) + '.jpg')  # 存下图片;注意cwd后边加上‘/’
+            print(example, lab)
+        coord.request_stop()
+        coord.join(threads)
+        sess.close()