463 lines
24 KiB
Python
Executable File
463 lines
24 KiB
Python
Executable File
#!/usr/bin/env python3
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# -*- coding: utf-8 -*
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import os,time,sys,threading, colorsys, argparse
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import asyncio, cv2, multiprocessing
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from PIL import Image
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import numpy as np
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def getFileList(dir,Filelist=[], ext=None, Max_layer=1, layer=0, Donot_Search=['1_颜色预处理', '2_灰度化', '3_边缘化']):
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"""
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获取文件夹及其子文件夹中文件列表
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输入 dir：文件夹根目录
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输入 ext: 扩展名
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返回： 文件路径列表
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"""
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newDir = dir
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if os.path.isfile(dir):
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if ext is None:
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Filelist.append(dir)
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else:
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if ext in dir[-3:]:
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Filelist.append(dir)
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elif os.path.isdir(dir):
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file_name = os.path.basename(dir)
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# 判断是否在禁搜名单中
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if file_name in Donot_Search:
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return Filelist
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for s in os.listdir(dir):
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newDir=os.path.join(dir,s)
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if layer <= Max_layer:
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getFileList(newDir, Filelist, ext, Max_layer, layer+1)
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return Filelist
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class Deal_image():
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def __init__(self, Annotate_CLASSES = ('肝脏','胆囊'), Annotate_PALETTE = [[255,91,0],[255,234,0]], src_label_fold = "./Label", save_pro_label_fold = "./LABEL_PNG_new", save_GT_label_fold = "./Label_Generate", GT_channel = 1, pro_append_name="_label", GT_append_name="_gtFine_labelTrainIds", ori_img_folder="./ORI_PNG", res_label_folder="./Result_label", save_merge_pic_folder="./Result_merge", back_gnd_color=0, first_class_color=1, pic_type="png", Max_width = 10000, Label_Max_Search_layer=1000, save_process_pics=False):
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self.src_CLASSES = ('肝脏','胆囊','分离钳','止血海绵','肝总管','胆总管','吸引器','剪刀','止血纱布','生物夹','无损伤钳','喷洒','胆囊管','胆囊动脉','电凝','标本袋','引流管','纱布','金属钛夹','背景')
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self.src_PALETTE = np.array([[255,91,0],[255,234,0],[85, 107, 179],[181, 227, 14],[72, 0, 255],[0, 155, 33],[255,0,255],[29, 32, 136],[160, 15, 95],[0,160,233],[52,184,178],[90,120,41],[255,0,0],[177,0,0],[167,24,233],[112,113,150],[0,255,0],[255,255,255],[0,255,255],[0,0,0]])
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self.src_CLASSES_NUM = np.shape(self.src_CLASSES)[0]
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self.Annotate_CLASSES = Annotate_CLASSES # 待分类的类
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self.Annotate_PALETTE = np.array(Annotate_PALETTE) # 每一类的像素直
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self.Annotate_CLASSES_NUM = np.shape(Annotate_CLASSES)[0] # 类数量
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self.save_process_pics = save_process_pics # 保存中间过程图片
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self.src_label_fold = src_label_fold # 原始标签图片 保存位置
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self.save_pro_label_fold = save_pro_label_fold # 优化后标签图片 保存位置
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self.save_GT_label_fold = save_GT_label_fold # GT标签图片 保存位置
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self.ori_img_folder = ori_img_folder # 最原始手术图片 保存位置
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self.res_label_folder = res_label_folder # 训练出来的label 保存位置
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self.save_merge_pic_folder = save_merge_pic_folder # 融合图像保存位置
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self.pro_append_name = pro_append_name # 优化后标签图片后缀
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self.GT_append_name = GT_append_name # GT标签图片后缀
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self.GT_channel = GT_channel # GT标签图片通道数
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self.Max_width = Max_width # 最大图片宽度（匹配时候用）
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self.pic_type = pic_type # 图片类型
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self.back_gnd_color = back_gnd_color # 背景颜色
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self.first_class_color = first_class_color # 第一类上的颜色
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self.Label_Max_Search_layer=Label_Max_Search_layer # 文件夹最大搜索深度
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try:
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self.labellist_src = getFileList(src_label_fold, [], pic_type, self.Label_Max_Search_layer)
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print('本次执行检索到ori_label图片 '+str(len(self.labellist_src))+' 张图像')
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except:
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self.labellist_src = None
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print("没有ori_label相关文件")
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try:
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# print(save_pro_label_fold)
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self.labellist_pro = getFileList(save_pro_label_fold, [], pic_type, self.Label_Max_Search_layer)
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print('本次执行检索到pro_label图片 '+str(len(self.labellist_pro))+' 张图像')
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except:
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self.labellist_pro = None
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print("没有pro_label相关文件")
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try:
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self.imglist_src = getFileList(ori_img_folder, [], pic_type, self.Label_Max_Search_layer)
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self.reslist_src = getFileList(res_label_folder, [], pic_type, self.Label_Max_Search_layer)
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print('本次执行检索到ori原始图片 '+str(len(self.imglist_src))+' 张图像')
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print('本次执行检索到训练train_result图片 '+str(len(self.reslist_src))+' 张图像')
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except:
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self.imglist_src = None
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self.reslist_src = None
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print("没有train_result和原始图片相关文件")
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# 获取单张图片各个通路信息
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def get_single_pic_rgb(self, imgpath):
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print(imgpath)
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image = Image.open(imgpath).convert('RGB') # 转为RGB图片
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# 将 RGB 色值分离
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image.load()
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r, g, b = image.split()
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r = np.array(r)
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g = np.array(g)
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b = np.array(b)
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return image, r, g, b
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# 将单个pro图片变成GT图片
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def Conver_pro_label_pic_2_GT_pic(self, imgpath, imgname):
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time_start=time.time() # 记录开始时间
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# 获取单张图片各个通路信息
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image, r,g,b = self.get_single_pic_rgb(imgpath)
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result_gt = np.ones(np.shape(image))*self.back_gnd_color # 初始化填充内容为back_gnd_color
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gt_number = self.first_class_color # 第一类上色颜色确定
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# 遍历所有待识别颜色
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for [Annotate_PALETTE_r, Annotate_PALETTE_g, Annotate_PALETTE_b] in self.Annotate_PALETTE:
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# 查找三原色匹配位置
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locate_r = np.where( r == Annotate_PALETTE_r )
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locate_g = np.where( g == Annotate_PALETTE_g )
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locate_b = np.where( b == Annotate_PALETTE_b )
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# 查找都匹配位置（交集）
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# 将矩阵换一种表示形式
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locate_r = np.array(locate_r[0]) * Max_width + np.array(locate_r[1])
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locate_g = np.array(locate_g[0]) * Max_width + np.array(locate_g[1])
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locate_b = np.array(locate_b[0]) * Max_width + np.array(locate_b[1])
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# 用自带函数寻找匹配项
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matched = np.intersect1d(np.intersect1d(locate_r, locate_g), locate_b)
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matched = np.concatenate(([matched // self.Max_width], [np.mod(matched, self.Max_width)]), 0)
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result_gt[matched[0],matched[1], :] = gt_number
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gt_number = gt_number + 1
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# 输出GT图片
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if(int(self.GT_channel) == 1):
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result_gt = result_gt[:,:,0]
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elif(int(self.GT_channel) == 3):
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result_gt = cv2.cvtColor(np.float32(result_gt), cv2.COLOR_RGB2BGR) # rgb颜色互换
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else:
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print("GT_channel 必须为1或3")
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quit
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try: # 新建文件夹
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os.mkdir(self.save_GT_label_fold)
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except:
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print("已有"+self.save_GT_label_fold)
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save_dir = os.path.join(self.save_GT_label_fold, os.path.splitext(imgname)[0]+self.GT_append_name+'.'+self.pic_type)
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cv2.imwrite(save_dir, result_gt)
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print("GT图片已保存", save_dir)
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time_end=time.time() # 输出结束时间
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print('time cost',time_end-time_start,'s')
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# 将处理好的图片转化为GT图片
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def Conver_pro_label_pic_2_GT_pic_all(self):
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print("\033[33m**** 进行转换将Pro_label_pic转换为GT_label_pic ****\033[0m")
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print("\033[33mPro_label_pic存储位置为：\033[0m", self.save_pro_label_fold)
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print("\033[33mGT_label_pic生成位置为：\033[0m", self.save_GT_label_fold)
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try:
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# print(save_pro_label_fold)
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self.labellist_pro = getFileList(save_pro_label_fold, [], pic_type, self.Label_Max_Search_layer)
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print('本次执行检索到pro_label图片 '+str(len(self.labellist_pro))+' 张图像')
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except:
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self.labellist_pro = None
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print("没有pro_label相关文件")
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try:
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os.mkdir(self.save_GT_label_fold) # 新建存储文件夹
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except:
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print("已有"+self.save_GT_label_fold)
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# 指定最大进程数为 3
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max_processes = 20
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# 创建Pool对象
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pool = multiprocessing.Pool(processes=max_processes)
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# 创建并启动进程
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args_list1 = []
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args_list2 = []
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# 遍历整个文件夹
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for imgpath in self.labellist_pro:
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imgname= os.path.splitext(os.path.basename(imgpath))[0].replace(self.pro_append_name,"")
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args_list1.append(imgpath)
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args_list2.append(imgname)
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args_list = zip(args_list1, args_list2)
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# 使用进程池并行执行任务
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pool.starmap(self.Conver_pro_label_pic_2_GT_pic, args_list)
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# 关闭进程池
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pool.close()
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pool.join()
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def Conver_ori_label_pic_2_pro_pic(self, imgpath, imgname):
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time_start=time.time() # 记录开始时间
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# 获取单张图片各个通路信息
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image, r, g, b = self.get_single_pic_rgb(imgpath)
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result_pro = np.zeros(np.shape(image)) # pro图片初始化，默认图片背景为0.0.0
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# 生成距离初始矩阵 width*height*num_of_classes
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Dis_mat = np.zeros((np.shape(image)[0], np.shape(image)[1], self.src_CLASSES_NUM))
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# 遍历所有类
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i = 0 # 顺序指示物
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# 遍历寻找最短距离是第几个内容
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for palette in self.src_PALETTE:
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Dis_mat[:,:,i] = np.abs((r - palette[0])) + np.abs((g - palette[1])) + np.abs((b - palette[2]))
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i = i + 1
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Min_Dis_mat = np.argmin(Dis_mat, axis = 2)
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# 给图片上色
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for number_class in range(self.src_CLASSES_NUM):
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# 查找图片种类
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loc_of_class_pic = np.where(Min_Dis_mat == number_class)
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# 否则给图片上对应颜色
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result_pro[loc_of_class_pic[0], loc_of_class_pic[1], :] = self.src_PALETTE[number_class]
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result_pro = cv2.cvtColor(np.float32(result_pro), cv2.COLOR_RGB2BGR) # rgb颜色互换
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# 如果需要存储中间态图片
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if(self.save_process_pics == True):
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save_dir = os.path.join(self.save_pro_label_fold, '1_颜色预处理', os.path.splitext(imgname)[0]+self.pro_append_name+'_preproc'+'.'+self.pic_type)
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cv2.imwrite(save_dir, result_pro)
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print("中间态-颜色图片已保存", save_dir)
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time_end=time.time() # 输出结束时间
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print('time cost',time_end-time_start,'s')
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# 边界区域大小
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half_region_len=(5-1)//2
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pic_height = result_pro.shape[0]
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pic_width = result_pro.shape[1]
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# 2_灰度化
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gray = cv2.cvtColor(result_pro, cv2.COLOR_BGR2GRAY)
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gray = (gray*255).astype(np.uint8) # cv2对图片格式要求较为严格
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# 如果需要存储中间态图片
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if(self.save_process_pics == True):
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save_dir = os.path.join(self.save_pro_label_fold, '2_灰度化', os.path.splitext(imgname)[0]+self.pro_append_name+'_gray'+'.'+self.pic_type)
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cv2.imwrite(save_dir, gray)
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print("中间态-灰度图片已保存", save_dir)
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time_end=time.time() # 输出结束时间
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print('time cost',time_end-time_start,'s')
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# 边缘检测
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edges = cv2.Canny(gray, 50, 150)
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# 定义膨胀核
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kernel = np.ones((3, 3), np.uint8)
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# 对Canny输出图像进行膨胀
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edges = cv2.dilate(edges, kernel, iterations=1)
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# 如果需要存储中间态图片
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if(self.save_process_pics == True):
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save_dir = os.path.join(self.save_pro_label_fold, '3_边缘化', os.path.splitext(imgname)[0]+self.pro_append_name+'_edge'+'.'+self.pic_type)
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cv2.imwrite(save_dir, edges)
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print("中间态-边缘图片已保存", save_dir)
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time_end=time.time() # 输出结束时间
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print('time cost',time_end-time_start,'s')
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# 查找边缘
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nonzero_idx = np.array(np.nonzero(edges)).T
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# nonzero_idx, counts = np.unique(nonzero_idx, axis=0, return_counts=True)
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print("边缘个数：",np.shape(nonzero_idx)[0])
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for k in range(len(nonzero_idx)):
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i = nonzero_idx[k][0]
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j = nonzero_idx[k][1]
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# 遍历周围3*3的区域
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# 找到与当前像素值附近5*5*3(channel)的像素，并将矩阵变为25(5*5)*3(channel)
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if i <= half_region_len:
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i = half_region_len
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elif i >= pic_height-half_region_len:
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i = pic_height-half_region_len
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if j <= half_region_len:
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j = half_region_len
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elif j >= pic_width-half_region_len:
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j = pic_width-half_region_len
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region_x = i
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region_y = j
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region = np.array(result_pro[region_x-half_region_len:region_x+half_region_len, region_y-half_region_len:region_y+half_region_len,:]).reshape(-1, 3)
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# 计算唯一列及其出现次数
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# print(region_x-half_region_len, region_x+half_region_len, region_y-half_region_len, region_y+half_region_len)
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unique_columns, counts = np.unique(region, axis=0, return_counts=True)
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# 找出出现次数最多的列的索引
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max_count_index = np.argmax(counts)
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# 将其赋值为出现次数最多的列
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result_pro[i, j, :] = unique_columns[max_count_index]
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# result_pro[i, j, :] = [0,0,0]
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save_dir = os.path.join(self.save_pro_label_fold, os.path.splitext(imgname)[0]+self.pro_append_name+'.'+self.pic_type)
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print("Pro图片已保存", save_dir)
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cv2.imwrite(save_dir, result_pro)
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time_end=time.time() # 输出结束时间
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print('time cost',time_end-time_start,'s')
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# 将原始src图片转化为处理好的pro图片
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def Conver_ori_label_pic_2_pro_pic_all(self):
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print("\033[33m**** 进行转换将Ori_label_pic转换为Pro_label_pic ****\033[0m")
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print("\033[33mOri_label_pic存储位置为：\033[0m", self.src_label_fold)
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print("\033[33mPro_label_pic生成位置为：\033[0m", self.save_pro_label_fold)
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# 输出颜色预处理图片
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try:
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os.mkdir(self.save_pro_label_fold) # 新建存储文件夹
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except:
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print("已有"+self.save_pro_label_fold)
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if(self.save_process_pics == True):
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try:
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os.mkdir(os.path.join(self.save_pro_label_fold, '1_颜色预处理')) # 新建存储1_颜色预处理文件夹
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except:
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print("已有"+os.path.join(self.save_pro_label_fold, '1_颜色预处理'))
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try:
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os.mkdir(os.path.join(self.save_pro_label_fold, '2_灰度化')) # 新建存储2_灰度化文件夹
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except:
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print("已有"+os.path.join(self.save_pro_label_fold, '2_灰度化'))
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try:
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os.mkdir(os.path.join(self.save_pro_label_fold, '3_边缘化')) # 新建存储1_颜色预处理文件夹
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except:
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print("已有"+os.path.join(self.save_pro_label_fold, '3_边缘化'))
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# 指定最大进程数为 20，多参数函数并行
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max_processes = 20
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# 创建Pool对象
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pool = multiprocessing.Pool(processes=max_processes)
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# 创建并启动进程
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args_list1 = []
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args_list2 = []
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# 遍历整个文件夹
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for imgpath in self.labellist_src:
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try:
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imgname= os.path.splitext(os.path.basename(imgpath))[0].replace(self.pro_append_name,"")
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print("Processing: ", imgname, "...")
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# self.Conver_ori_label_pic_2_pro_pic(imgpath, imgname)s
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# args_list.append({'imgpath': imgpath, 'imgname': imgname})
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args_list1.append(imgpath)
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args_list2.append(imgname)
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except:
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os.system("echo "+imgname+" >> error_1.txt")
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args_list = zip(args_list1, args_list2)
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# 使用进程池并行执行任务
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pool.starmap(self.Conver_ori_label_pic_2_pro_pic, args_list) # 使用starmap进行多参数并行
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# 关闭进程池
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pool.close()
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pool.join()
|
||
|
||
# 图片堆叠
|
||
def Merge_ori_pic_and_label_pic(self, res_img_path, res_imgname):
|
||
time_start=time.time() # 记录开始时间
|
||
# 获取单张图片各个通路信息
|
||
ori_img_path = os.path.join(self.ori_img_folder, res_imgname+'.'+self.pic_type)
|
||
if not os.path.exists(ori_img_path):
|
||
print("****照片不存在：****", ori_img_path)
|
||
return -1
|
||
ori_image, ori_r, ori_g, ori_b = self.get_single_pic_rgb(ori_img_path)
|
||
res_image, res_r, res_g, res_b = self.get_single_pic_rgb(res_img_path)
|
||
|
||
merge_img = np.array(ori_image) # merge图片初始化，默认图片背景为0.0.0
|
||
|
||
# 遍历所有待识别颜色
|
||
for [Annotate_PALETTE_r, Annotate_PALETTE_g, Annotate_PALETTE_b] in self.Annotate_PALETTE:
|
||
# 查找三原色匹配位置
|
||
locate_r = np.where( res_r == Annotate_PALETTE_r )
|
||
locate_g = np.where( res_g == Annotate_PALETTE_g )
|
||
locate_b = np.where( res_b == Annotate_PALETTE_b )
|
||
|
||
# 查找都匹配位置（交集）
|
||
# 将矩阵换一种表示形式
|
||
locate_r = np.array(locate_r[0]) * self.Max_width + np.array(locate_r[1])
|
||
locate_g = np.array(locate_g[0]) * self.Max_width + np.array(locate_g[1])
|
||
locate_b = np.array(locate_b[0]) * self.Max_width + np.array(locate_b[1])
|
||
|
||
# 用自带函数寻找匹配项
|
||
matched = np.intersect1d(np.intersect1d(locate_r, locate_g), locate_b)
|
||
matched = np.concatenate(([matched // self.Max_width], [np.mod(matched, self.Max_width)]), 0)
|
||
merge_img[matched[0],matched[1], 0] = Annotate_PALETTE_r
|
||
merge_img[matched[0],matched[1], 1] = Annotate_PALETTE_g
|
||
merge_img[matched[0],matched[1], 2] = Annotate_PALETTE_b
|
||
|
||
# 转成cv2形式
|
||
merge_img = cv2.cvtColor(np.float32(merge_img), cv2.COLOR_RGB2BGR)
|
||
|
||
try: # 新建文件夹
|
||
os.mkdir(self.save_merge_pic_folder)
|
||
except:
|
||
print("已有"+self.save_merge_pic_folder)
|
||
save_dir = os.path.join(self.save_merge_pic_folder, os.path.splitext(res_imgname)[0]+'.'+self.pic_type)
|
||
cv2.imwrite(save_dir, merge_img)
|
||
print("Merge图片已保存", save_dir)
|
||
time_end=time.time() # 输出结束时间
|
||
print('time cost',time_end-time_start,'s')
|
||
|
||
# 将label图片与原图片重合
|
||
def Merge_ori_pic_and_label_pic_all(self):
|
||
# 遍历整个文件夹
|
||
for res_img_path in self.reslist_src:
|
||
res_imgname = os.path.splitext(os.path.basename(res_img_path))[0].replace(self.pro_append_name,"")
|
||
print("Processing: ", res_imgname, "...")
|
||
self.Merge_ori_pic_and_label_pic(res_img_path, res_imgname)
|
||
|
||
if __name__ == "__main__":
|
||
|
||
Annotate_CLASSES = ('肝脏','胆囊','分离钳','止血海绵','肝总管','胆总管','吸引器','剪刀','止血纱布','生物夹','无损伤钳','喷洒','胆囊管','胆囊动脉','电凝','标本袋','引流管','纱布','金属钛夹') # 待分类的类
|
||
Annotate_PALETTE = [[255,91,0],[255,234,0],[85, 107, 179],[181, 227, 14],[72, 0, 255],[0, 155, 33],[255,0,255],[29, 32, 136],[160, 15, 95],[0,160,233],[52,184,178],[90,120,41],[255,0,0],[177,0,0],[167,24,233],[112,113,150],[0,255,0],[255,255,255],[0,255,255]]# [[255,91,0],[255,234,0]] # 每一类的像素直
|
||
|
||
# 创建参数解析器
|
||
parser = argparse.ArgumentParser(description='Process some files.')
|
||
# 添加参数选项
|
||
parser.add_argument('-src_fold', dest='src_label_fold', default='', help='source label folder')
|
||
parser.add_argument('-save_pro_fold', dest='save_pro_label_fold', default='./save_pro_label_fold', help='processed label folder')
|
||
parser.add_argument('-save_GT_fold', dest='save_GT_label_fold', default='./save_GT_label_fold', help='ground truth folder')
|
||
parser.add_argument('-fold_search_depth', dest='Label_Max_Search_layer', default='1000', type=int, help='Folder Search Depth')
|
||
parser.add_argument('-pro_suffix_name', dest='pro_append_name', default='_label', help='Pro file suffix')
|
||
parser.add_argument('-GT_suffix_name', dest='GT_append_name', default='_gtFine_labelTrainIds', help='GT file suffix')
|
||
parser.add_argument('-GT_channel', dest='GT_channel', default='1', type=int, help='GT file channel(1 or 3)')
|
||
parser.add_argument('-back_gnd_color', dest='back_gnd_color', default='0', type=int, help='Color of "Back ground"(0 or 255)')
|
||
parser.add_argument('-first_class_color', dest='first_class_color', default='1', type=int, help='Color of "First Class"')
|
||
parser.add_argument('-pic_type', dest='pic_type', default='png', help='type of picture(Do not add ".")')
|
||
parser.add_argument('-Max_width', dest='Max_width', default='10000', type=int, help='Max width of picture')
|
||
parser.add_argument('-Rebuild_from', dest='Rebuild_from', default='label', help='Source to Rebuild Labels(label/pro)')
|
||
parser.add_argument('-Rebuild_to', dest='Rebuild_to', default='GT', help='Destination of Rebuild Labels(pro/GT)')
|
||
parser.add_argument('-save_process_pics', dest='save_process_pics', default='False', help='Save the processed pics(e.g.Gray_pics,Color_pics) in generating pro_pics')
|
||
|
||
# 解析命令行参数
|
||
args = parser.parse_args()
|
||
|
||
src_label_fold = args.src_label_fold
|
||
save_pro_label_fold = args.save_pro_label_fold
|
||
save_GT_label_fold = args.save_GT_label_fold
|
||
Label_Max_Search_layer = args.Label_Max_Search_layer
|
||
pro_append_name = args.pro_append_name
|
||
GT_append_name = args.GT_append_name
|
||
GT_channel = args.GT_channel
|
||
back_gnd_color = args.back_gnd_color
|
||
first_class_color = args.first_class_color
|
||
pic_type = args.pic_type
|
||
Max_width = args.Max_width
|
||
Rebuild_from = args.Rebuild_from
|
||
Rebuild_to = args.Rebuild_to
|
||
save_process_pics = args.save_process_pics
|
||
|
||
|
||
try: # 遍历文件深度，最小为1
|
||
Label_Max_Search_layer=int(Label_Max_Search_layer)
|
||
except:
|
||
Label_Max_Search_layer=1000
|
||
try: # GT标签图片通道数
|
||
GT_channel=int(GT_channel)
|
||
except:
|
||
GT_channel=1
|
||
try: # 背景颜色（背景选择0或255)
|
||
back_gnd_color=int(back_gnd_color)
|
||
except:
|
||
back_gnd_color=0
|
||
try: # 第一类上的颜色(如果背景为0,选择1；)
|
||
first_class_color=int(first_class_color)
|
||
except:
|
||
first_class_color=1
|
||
try: # 最大图片宽度（匹配时候用）
|
||
Max_width=int(Max_width)
|
||
except:
|
||
Max_width=10000
|
||
if(save_process_pics.lower() == 'false'):
|
||
save_process_pics = False
|
||
elif(save_process_pics.lower() == 'true'):
|
||
save_process_pics = True
|
||
else:
|
||
save_process_pics = False
|
||
|
||
D = Deal_image(Annotate_CLASSES=Annotate_CLASSES, Annotate_PALETTE=Annotate_PALETTE, src_label_fold=src_label_fold, save_pro_label_fold=save_pro_label_fold, save_GT_label_fold=save_GT_label_fold, GT_channel=GT_channel, pro_append_name=pro_append_name, GT_append_name=GT_append_name, back_gnd_color=back_gnd_color, first_class_color=first_class_color, pic_type=pic_type, Max_width=Max_width, Label_Max_Search_layer=Label_Max_Search_layer, save_process_pics=save_process_pics)
|
||
# print(D.src_CLASSES_NUM)
|
||
if Rebuild_from == 'label':
|
||
# 1.先将所有原始图片转为pro图片
|
||
D.Conver_ori_label_pic_2_pro_pic_all()
|
||
pass
|
||
if Rebuild_to == 'GT':
|
||
# 2.再将pro图片转为GT图片
|
||
D.Conver_pro_label_pic_2_GT_pic_all()
|
||
pass |