提出钙化簇

import glob
import os,sys
import shutil
import numpy as np
import cv2
import matplotlib.pyplot as plt



import os, random, shutil,cv2

# labelDir = 'F:/project/Breast/InBreast/INBreast/Unet/data/Inbreast/yes/test/label/'
# imageDir = 'F:/project/Breast/InBreast/INBreast/Unet/data/Inbreast/yes/test/image/'
# labelDir1 = 'F:/project/Breast/InBreast/INBreast/Unet/data/Inbreast/no/test/label/'
# imageDir1 = 'F:/project/Breast/InBreast/INBreast/Unet/data/Inbreast/no/test/image/'
import time
import os
import math
import sys
import os,os.path,shutil
import numpy as np
import re

txtPath = 'F:/project/Breast/InBreast/INBreast/outimgpatch/allouttxtpatch/'
imagePath = 'F:/project/Breast/InBreast/INBreast/outimgpatch/allcalcification/'
labelPath = 'F:/project/Breast/InBreast/INBreast/outimgpatch/allcalcificationimglabel/'
noimagePath = 'F:/project/Breast/InBreast/INBreast/outimgpatch/allnocalcification/'
nolabelPath = 'F:/project/Breast/InBreast/INBreast/outimgpatch/allnocalcificationlabel/'

imagePath1 = 'F:/project/Breast/InBreast/INBreast/outimgpatch/newcalcification/images/'
labelPath1 = 'F:/project/Breast/InBreast/INBreast/outimgpatch/newcalcification/labels/'
noimagePath1 = 'F:/project/Breast/InBreast/INBreast/outimgpatch/newcalcification/noimages/'
nolabelPath1 = 'F:/project/Breast/InBreast/INBreast/outimgpatch/newcalcification/nolabels/'

imagePath2 = 'F:/project/Breast/InBreast/INBreast/outimgpatch/newcalcification/others/images/'
labelPath2 = 'F:/project/Breast/InBreast/INBreast/outimgpatch/newcalcification/others/labels/'
noimagePath2 = 'F:/project/Breast/InBreast/INBreast/outimgpatch/newcalcification/others/noimages/'
nolabelPath2 = 'F:/project/Breast/InBreast/INBreast/outimgpatch/newcalcification/others/nolabels/'

txtType = 'txt'
txtLists = os.listdir(txtPath) #列出文件夹下所有的目录与文件
print(txtLists)

# Read the points(before 11:30,712)
# Convert points to digital form(before 2:30,712)
# Obtain the right batch(before 5.00, 712)
for filename in txtLists:
    print(filename)
    name = filename[:-4] + '.png'
    print(name)
    with open(txtPath + filename, 'r') as file:
        lines = file.readlines()
        dataset = [[] for i in range(len(lines))]
        for i in range(len(dataset)):
            dataset[i][:] = (item for item in lines[i].strip().split(','))  # 逐行读取数据
        print(i)

        if os.path.exists(imagePath + name):
            if i > 3:
                print("yes")
                shutil.copy(imagePath + name, imagePath2 + name)
                shutil.copy(labelPath + name, labelPath2 + name)
                shutil.copy(noimagePath + name, noimagePath2 + name)
                shutil.copy(nolabelPath + name, nolabelPath2 + name)
            if i <= 3:
                print("no")
                shutil.copy(imagePath + name, imagePath1 + name)
                shutil.copy(labelPath + name, labelPath1 + name)
                shutil.copy(noimagePath + name, noimagePath1 + name)
                shutil.copy(nolabelPath + name, nolabelPath1 + name)




        # print("dateset:", dataset)
        # # print(type(dataset[0][0]))
        # # print(dataset.__sizeof__())
        # u = np.array(dataset)
        # for i in range(u.shape[0]):
        #     # print(u[i,0][0])
        #     findNumber = u[i,0].find(" ")
        #     # print(findNumber)
        #     x = round(float(u[i, 0][0:findNumber]))
        #     findNumber1 = u[i, 0][findNumber+1:].find(" ")
        #     y = round(float(u[i, 0][findNumber+1: findNumber + findNumber1]))
        #     print(x,y)









        # name = '0_'+str(i)+'_predict.png'
        # i =i + 2
        # print(name)
        # shutil.copy(fileDir + filename, tarDir + name)
        # if filename.startswith('yes'):
        #     filename1 = filename[4:]
        #     print(filename1)
        #     filename2 = os.path.join(tarDir, filename1.split('.')[0] + '_yes.png')
        #     print(filename2)
        # elif filename.startswith('no'):
        #     filename1 = filename[3:]
        #     print(filename1)
        #     filename2 = os.path.join(tarDir, filename1.split('.')[0] + '_no.png')#filename1 + '_no'
        #     print(filename2)
        #
        # img = cv2.imread(fileDir + filename)
        # image = img[:,:,0]
        # cv2.imwrite(filename2,image)
        # name = 'yes_' + filename#'yes_0_'+ str(i) + '_predict.png'
        # name1 = 'no_' + filename#'yes_0_'+ str(i) + '_predict.png'
        # i = i + 1
        # print(filename,name)
        # shutil.copy(fileDir + filename, tarDir + name)
        # shutil.copy(fileDir2 + filename, tarDir + name1)
        # shutil.copy(fileDir1 + filename, tarDir1 + name)
        # shutil.copy(fileDir22 + filename, tarDir1 + name1)
        # image1 = cv2.imread(fileDir + filename)
        # label1 = cv2.imread(fileDir1 + filename)
        # image2 = cv2.resize(image1,(448,448))
        # label2 = cv2.resize(label1,(448,448))
        # cv2.imwrite(fileDir + filename, image2)
        # cv2.imwrite(fileDir1 + filename,label2)
#         # source = fileDir + filename
#         # print(source)

        # shutil.copy(fileDir2 + filename, tarDir2 + name)
        # shutil.copy(fileDir3 + filename, tarDir3 + name)

 

posted on 2019-08-20 02:19 Hebye 阅读(...) 评论(...) 编辑 收藏

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