change on FCN.py

BatchDatsetReader.py

@@ -1,10 +1,11 @@
+#!/usr/bin/env python
+# encoding: utf-8
 """
 Code ideas from https://github.com/Newmu/dcgan and tensorflow mnist dataset reader
 """
 import numpy as np
 import scipy.misc as misc
 
-
 class BatchDatset:
     files = []
     images = []
@@ -26,58 +27,91 @@ def __init__(self, records_list, image_options={}):
         """
         print("Initializing Batch Dataset Reader...")
         print(image_options)
-        self.files = records_list
+        self.files = records_list    # 文件列表
         self.image_options = image_options
         self._read_images()
 
     def _read_images(self):
         self.__channels = True
+        # 扫描files字典中所有image 图片全路径
         self.images = np.array([self._transform(filename['image']) for filename in self.files])
         self.__channels = False
+        # 扫描files字典中所有annotation 图片全路径, 根据文件全路径读取图像，并将annotation从三个维度扩充为四维度，第四个维度值为1
         self.annotations = np.array(
             [np.expand_dims(self._transform(filename['annotation']), axis=3) for filename in self.files])
         print (self.images.shape)
         print (self.annotations.shape)
 
     def _transform(self, filename):
+        # 使用模块scipy.misc读取文件图片
         image = misc.imread(filename)
+        # 如果是训练图片并且是单通道的，即维度为(h,w)，就重复三次变成3通道
         if self.__channels and len(image.shape) < 3:  # make sure images are of shape(h,w,3)
             image = np.array([image for i in range(3)])
-
+        # False 是从字典获取resize值属性不存在返回的默认值
         if self.image_options.get("resize", False) and self.image_options["resize"]:
+            # 获取需要调整的图片大小值
             resize_size = int(self.image_options["resize_size"])
+            # misc.imresize调整图片大小，使用最近邻插值法resize图片
             resize_image = misc.imresize(image,
                                          [resize_size, resize_size], interp='nearest')
         else:
             resize_image = image
 
-        return np.array(resize_image)
+        return np.array(resize_image)  # 返回已经resize的图片
 
     def get_records(self):
+        """
+        :return:图片和标签
+        """
         return self.images, self.annotations
 
     def reset_batch_offset(self, offset=0):
+        '''
+
+        :param offset:
+        :return:
+        '''
         self.batch_offset = offset
 
     def next_batch(self, batch_size):
+        # 将batch偏移量赋值给start
         start = self.batch_offset
+        # next_batch()方法每调用一次，batch_offset的值偏移一个batch_size大小
         self.batch_offset += batch_size
+        # 如果出现总偏移量大于训练集数量
         if self.batch_offset > self.images.shape[0]:
             # Finished epoch
             self.epochs_completed += 1
             print("****************** Epochs completed: " + str(self.epochs_completed) + "******************")
-            # Shuffle the data
+            # Shuffle the data--接下来的每次迭代都先生成一个索引序列，按照这个序列重排数据集，相当于数据清洗
             perm = np.arange(self.images.shape[0])
             np.random.shuffle(perm)
             self.images = self.images[perm]
             self.annotations = self.annotations[perm]
             # Start next epoch
             start = 0
             self.batch_offset = batch_size
-
+        # 结束位置索引
         end = self.batch_offset
+        # 按照索引得到训练图片及对应标签
         return self.images[start:end], self.annotations[start:end]
 
+    def next_batch_with_name(self, batch_size):
+        # 将batch偏移量赋值给start
+        start = self.batch_offset
+        # next_batch()方法每调用一次，batch_offset的值偏移一个batch_size大小
+        self.batch_offset += batch_size
+        # 结束位置索引
+        end = self.batch_offset
+        # 按照索引得到训练图片及对应标签
+        return self.images[start:end], self.annotations[start:end], self.files[start:end]
+
     def get_random_batch(self, batch_size):
+        '''
+        # 按照一个batch_size一个块  进行对所有图片总数进行随机操作， 相当于洗牌工作
+        :param batch_size: 指定的batch_size块大小
+        :return:batch_size个大小的图片和对应标签
+        '''
         indexes = np.random.randint(0, self.images.shape[0], size=[batch_size]).tolist()
         return self.images[indexes], self.annotations[indexes]