--- a
+++ b/dataprocess/get_dicom_info.py
@@ -0,0 +1,192 @@
+'''
+Created by Wang Qiu Li
+7/3/2018
+
+get dicom info according to malignancy.csv and ld_scan.txt
+'''
+
+import csvTools
+import os
+import pandas as pd
+import pydicom
+import scipy.misc
+import cv2
+import numpy as np
+import glob
+
+import xmlopt
+
+basedir = '/home/wangqiuli/Data/LIDC/DOI/'
+three_dir = 'three_channel/'
+imagedir = 'ori_images/'
+maskdir = 'ori_masks/'
+png_dir = 'image_1/'
+
+noduleinfo = csvTools.readCSV('files/malignancy.csv')
+idscaninfo = csvTools.readCSV('files/id_scan.txt')
+maskinfo = glob.glob(maskdir)
+
+def get_pixels_hu(ds):
+    image = ds.pixel_array
+    image = np.array(image , dtype = np.float32)
+    intercept = ds.RescaleIntercept
+    slope = ds.RescaleSlope
+    image = image * slope
+    image += intercept
+    return image
+
+def getThreeChannel(pixhu):
+    lungwindow = truncate_hu(pixhu, 800, -1000)
+    highattenuation = truncate_hu(pixhu, 240, -160)
+    lowattenuation = truncate_hu(pixhu, -950, -1400)
+    pngfile = [lungwindow, highattenuation, lowattenuation]
+    pngfile = np.array(pngfile).transpose(1,2,0)
+    return  pngfile  
+
+def truncate_hu(image_array, max, min):
+    image = image_array.copy()
+    image[image > max] = max
+    image[image < min] = min
+    image = normalazation(image)
+    return image
+    
+# LUNA2016 data prepare ,second step: normalzation the HU
+def normalazation(image_array):
+    max = image_array.max()
+    min = image_array.min()
+    image_array = (image_array-min)/(max-min)  # float cannot apply the compute,or array error will occur
+    avg = image_array.mean()
+    image_array = image_array-avg
+    return image_array   # a bug here, a array must be returned,directly appling function did't work
+
+
+def cutTheImage(x, y, pix):
+    temp = 25
+    x1 = x - temp
+    x2 = x + temp
+    y1 = y - temp
+    y2 = y + temp
+    img_cut = pix[x1:x2, y1:y2]
+    return img_cut
+
+def caseid_to_scanid(caseid):
+    returnstr = ''
+    if caseid < 10:
+        returnstr = '000' + str(caseid)
+    elif caseid < 100:
+        returnstr = '00' + str(caseid)
+    elif caseid < 1000:
+        returnstr = '0' + str(caseid)
+    else:
+        returnstr = str(caseid)
+    return 'LIDC-IDRI-' + returnstr
+
+def reverse(inputarray):
+    shape = inputarray.shape
+    nparray = np.ones(shape)
+    for i in range(shape[0]):
+        for j in range(shape[1]):
+            if inputarray[i][j] == 0:
+                nparray[i][j] = 1
+            else:
+                nparray[i][j] = 0
+    return nparray
+
+
+f = open('errlist.txt', 'w')
+count1 = 0
+count2 = 0
+count3 = 0
+count4 = 0
+count5 = 0
+
+errorcount = 0
+
+tempsign = 0
+
+import tqdm
+
+for onenodule in tqdm.tqdm(noduleinfo[:10]):
+    xml = ''
+    # try:
+    scanid = onenodule[1]
+    scanid = caseid_to_scanid(int(scanid))
+    noduleid = onenodule[3]
+    scan_list_id = onenodule[2]
+    # if scanid != 'LIDC-IDRI-0195':
+    #     continue
+
+    # if int(noduleid) != 2:
+    #     continue
+    scanpaths = []
+    for idscan in idscaninfo:
+        if scanid in idscan[0]:
+            scanpaths.append(idscan[0])
+#        print('len of paths: ', len(scanpaths))
+
+    noduleld_list = []
+    for i in range(10, 14):
+        if str(onenodule[i]).strip() != '':
+            noduleld_list.append(onenodule[i])
+    # print('id list: ', noduleld_list)
+
+    for scanpath in scanpaths:
+        try:    
+            filelist1 = os.listdir(basedir + scanpath)
+            filelist2 = []
+
+            xmlfiles = []
+            for onefile in filelist1:
+                if '.dcm' in onefile:
+                    filelist2.append(onefile)
+                elif '.xml' in onefile:
+                    xmlfiles.append(onefile)
+
+            xmlfile = basedir + scanpath + '/' + xmlfiles[0]
+            xml = xmlfile
+            slices = [pydicom.dcmread(basedir + scanpath + '/' + s) for s in filelist2]
+
+            slices.sort(key = lambda x : float(x.ImagePositionPatient[2]),reverse=True)
+            x_loc = int(onenodule[6])
+            y_loc = int(onenodule[7])
+            z_loc = int(onenodule[8])
+            ds = slices[z_loc]
+            if (str(ds.SeriesNumber) == onenodule[2]) or (str(onenodule[2]) == str(0)):
+                slice_location = ds.ImagePositionPatient[2]
+                # print('slice location: ', slice_location)
+                # print('noduleld_list: ', noduleld_list)
+                mask_image, signtemp = xmlopt.getEdgeMap(xmlfile, slice_location, noduleld_list)
+                # # print(signtemp)
+                # if signtemp == True:
+                #     zzz = 1
+                # else:
+
+                ori_hu = get_pixels_hu(ds)
+                pix = getThreeChannel(ori_hu)
+                
+                if (x_loc < 25 or x_loc > (512 - 25)) or (y_loc < 25 or y_loc > (512 - 25)):
+                    print('out of size:', scanid, noduleid)
+                else:
+                    cut_img = cutTheImage(y_loc, x_loc, pix)
+                    cut_mask = cutTheImage(y_loc, x_loc, mask_image)
+                    # cut_hu = cutTheImage(y_loc, x_loc, ori_hu)
+                    
+                    #cut_img = cv2.resize(cut_img,(128, 128))
+                    #cut_mask = cv2.resize(cut_mask,(128, 128))
+                    #cut_hu = cv2.resize(cut_hu,(128, 128))
+                    
+
+                    # reverse_cut_mask = reverse(cut_mask)
+                    # np.save(three_dir + str(scanid) + '_' + str(noduleid) + '_' + str(scan_list_id) + '_5', cut_hu)
+                    # np.save(maskdir + str(scanid) + '_' + str(noduleid) + '_' + str(scan_list_id), cut_mask)
+                    # np.save(three_dir + str(scanid) + '_' + str(noduleid) + '_' + str(scan_list_id), cut_img)
+
+
+                    scipy.misc.imsave(png_dir + str(scanid) + '_' + str(noduleid) + '_' + str(scan_list_id) + '2.png', cut_img)
+                    scipy.misc.imsave(png_dir + str(scanid) + '_' + str(noduleid) + '_' + str(scan_list_id) + '_mask2.png', cut_mask)
+            else:
+                print(scanid)
+                print('not equal')
+        except:
+            print(scanid)           
+            print('Error')