add all files

Author: System Administrator

Diff for: README.md

@@ -0,0 +1,166 @@
+import numpy as np
+import tensorflow as tf
+import os
+from shutil import copyfile, rmtree
+#数据打包处理，将原始图像数据集转换成Keras要求的格式：
+#每一个子文件夹代表一类，其中有该类所有的图像数据
+'''
+data
+    customs folder
+        classA
+            image1
+            image2
+            ...
+        classB
+            image1
+            image2
+            ...
+        classC
+            ...
+        ...
+
+    research
+        test
+            0
+                image1
+                image2
+                ...
+            1
+                image1
+                image2
+                ...
+            2
+                image1
+                image2
+                ...
+        train
+            0
+                image1
+                image2
+                ...
+            1
+                image1
+                image2
+                ...
+            2
+                image1
+                image2
+                ...
+        validation
+            0
+                image1
+                image2
+                ...
+            1
+                image1
+                image2
+                ...
+            ...
+'''
+#图像原始数据文件夹
+source_data_folder = "F://ai_data/camelyon17/train_data"
+#新的文件夹
+research_data_folder = "F://ai_data/camelyon17/research_data"
+#类名文本
+label_text_file = source_data_folder + "//labels.txt"
+
+train_num = 210000      #用于训练的图像数目
+val_num   = 719      #用于训练测试的图像数目
+test_num  = 4000      #用于最终测试的图像数目
+
+def convert_class_data():
+    np.random.seed(0)    #使用统一的Seed，保证每次随机的结果都相同
+    #打开已经生成的标签文件
+    label_file = open(label_text_file)
+    #按行读取标签文件中的文本信息
+    labels = label_file.readlines()
+    #随机打乱标签文本信息的顺序
+    np.random.shuffle(labels)
+    current_i = 0
+
+    current_i = save_images(current_i=current_i, phase="train", d_size=train_num, labels=labels)
+    current_i = save_images(current_i=current_i, phase="test", d_size=test_num, labels=labels)
+    current_i = save_images(current_i=current_i, phase="validation", d_size=val_num, labels=labels)
+
+
+def save_images(current_i, phase, d_size, labels):
+    if phase == "train":        #选择存储训练集数据
+        dst_folder = research_data_folder + "\\train\\"
+    elif phase == "test":       #选择存储测试集数据
+        dst_folder = research_data_folder + "\\test\\"
+    elif phase == "validation": #选择存储训练测试集数据
+        dst_folder = research_data_folder + "\\validation\\"
+    else:
+        print("phase error : {0}".format(phase))
+        exit()
+    #打开新的标签文本文件，准备录入不同数据集的标签信息，以作备用
+    label_file = open(research_data_folder+"\\"+phase+"_label.txt", mode="w")
+    for i in range(current_i, current_i+d_size):
+        #获取被打乱顺序的标签
+        item = labels[i]
+        #根据空格分割文件名称和类别名称 
+        r = item.split(" ")
+        #获取文件名称
+        img_source_path = r[0]
+        #获取类别名称，注意需要把最后的换行符去掉
+        img_class_name  = r[1].split("\n")[0]
+        #创建新的路径，以拷贝图像文件
+        img_dst_path = dst_folder + img_class_name + "\\" + os.path.basename(img_source_path)
+        #如果新的路径不存在，则新建文件夹
+        if not os.path.exists(os.path.dirname(img_dst_path)):
+            os.makedirs(os.path.dirname(img_dst_path))
+        #将文件拷贝到新的路径中
+        copyfile(img_source_path, img_dst_path)
+        print("{0} copied".format(img_dst_path))
+        #顺手完成标签文本文件，以作备用
+        label_text = img_dst_path + " " + img_class_name + "\n"
+        #标签写入新的文本文件
+        label_file.write(label_text)
+        current_i = i
+    label_file.close()
+    return current_i    
+
+def image_labeling():
+    #数据目录
+    directories = []
+    #类别名称
+    class_names = []
+    #图像文件列表
+    image_filenames = []
+
+    #在数据根目录下寻找文件夹
+    for filename in os.listdir(source_data_folder):
+        #定位当前文件夹
+        path = os.path.join(source_data_folder, filename)
+        #如果路径为path的是文件夹
+        if os.path.isdir(path):
+            directories.append(path)        #录入数据目录
+    
+    #循环数据目录文件夹
+    for i, directory in enumerate(directories):
+        #在数据目录文件夹中遍历图像文件
+        for filename in os.listdir(directory):
+            path = os.path.join(directory, filename)
+            #加入所有图像文件名
+            image_filenames.append(path)
+            #加入图像所对应的标签编号
+            class_names.append(str(i))
+
+    #打开标签文本文件，准备录入标签数据
+    label_file = open(label_text_file, mode="w")
+    for idx, item in enumerate(image_filenames):
+        text = item + " " + class_names[idx] + "\n"
+        print(text)
+        label_file.write(text)
+    label_file.close()
+
+def main():
+    print("Start to convert data")
+    image_labeling()
+    convert_class_data()
+
+
+if __name__ == '__main__':
+    main()
+if __name__ == '__main__':
+    main()