get aligned shape targets

+import pandas as pd
+import os
+from natsort import natsorted
+
+csv = 'ce_train_targets.csv'
+data_path = 'ce_train'
+
+df = pd.read_csv(csv)
+
+#idx = df.index[df.iloc[:,0]=='BraTS19_CBICA_BHB_1_seg_flair_8.png'].tolist()
+
+# df = df.loc[df.iloc[:,0]=='BraTS19_CBICA_BHB_1_seg_flair_8.png']
+# print(df.iloc[0, 1])
+
+
+imgs = natsorted(os.listdir(data_path)) # img file list
+targets = []
+
+for fname in imgs:
+    row = df.loc[df['filename'] == fname]
+    targets.append(row.iloc[0, 1])
+
+#print(targets, len(targets))
+
+#BraTS19_2013_10_1_seg_flair_0.png -> class 0
+#BraTS19_CBICA_AYU_1_seg_flair_2.png -> class 1
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+data_path = '..\data\MICCAI_BraTS_2019_Data_Training\HGG_seg_flair\BraTS19_CBICA_AVG_1_seg_flair.nii';
+data = niftiread(data_path);
+
+cp_data = flipud(rot90(mat2gray(double(data(:,:,82)))));
+
+type = '.png';
+filename = strcat('BraTS19_CBICA_AVG_1_seg_flair_8', type); % BraTS19_2013_2_1_seg_flair_c.png
+outpath = strcat('..\data\MICCAI_BraTS_2019_Data_Training\zero to valid\', filename);
+imwrite(cp_data, outpath);
+
+
+% st, end 10
+
+%% (circle 0, ellipse 0)
+% BraTS19_CBICA_AOP_1_seg_flair_0 
+% BraTS19_CBICA_ASF_1_seg_flair_9 -> 8
+% BraTS19_CBICA_ASR_1_seg_flair_8
+% BraTS19_CBICA_ASR_1_seg_flair_9
+% BraTS19_CBICA_AVG_1_seg_flair_9 -> 8
+%
+%
+%
+%
+%
+%
+%
+%
+%
+%
+%
+%
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+getname_path = '..\data\MICCAI_BraTS_2019_Data_Training\zero2\';
+inputheader = '..\data\MICCAI_BraTS_2019_Data_Training\HGG_seg_flair\';
+outfolder = '..\data\MICCAI_BraTS_2019_Data_Training\zero to valid2\';
+
+files = dir(getname_path);
+id = {files.name};
+%  files + dir  file 
+flag = ~strcmp(id, '.') & ~strcmp(id, '..'); 
+files = files(flag);
+
+% get filenames in getname_path folder
+for i = 1 : length(files)
+    %id = split(files(i).name, '.nii');
+    fname = files(i).name;
+    id = reverse(extractBetween(reverse(fname), 7, strlength(fname)));
+    
+    data_path = convertCharsToStrings(strcat(inputheader,'\', id, '.nii'));
+    data = niftiread(data_path); 
+    
+    fprintf('ID #%d = %s\n', i, data_path);
+    
+    idx = 1;
+    frames = zeros(1,1);
+    for j = 1 : z 
+         n_nonblack = numel(find(data(:,:,j) > 0));
+         if(n_nonblack > 70)
+              frames(idx,1) = j;
+              idx = idx + 1;
+         end
+    end
+    
+%     for j = z : -1 : 1 
+%          n_nonblack = numel(find(data(:,:,j) > 0));
+%          if(n_nonblack > 50)
+%              fprintf('%s: %d\n ','number: ', n_nonblack);
+%              en = j;
+%              break;
+%          end
+%     end
+    
+
+    c = 0;
+    [nrow, ncol] = size(frames);
+    step = round(nrow/11);
+%     fprintf('%s: %d \n', 'nrow', nrow);
+%     fprintf('%s: %d\n ','st: ', frames(1, 1));
+%     fprintf('%s: %d\n ','end: ', frames(nrow, 1));
+    %disp(frames);
+
+    for k = 1 : step : step*10
+        %fprintf('%d ', k);
+        fprintf('%d ', frames(k, 1));
+        type = '.png';
+        filename = strcat(id, '_', int2str(c), type); % BraTS19_2013_2_1_seg_flair_c.png
+        outpath = convertCharsToStrings(strcat(outfolder, filename));
+        % typecase int16 to double, range[0, 1], rotate 90 and filp updown
+        % range [0, 1]
+        cp_data = flipud(rot90(mat2gray(double(data(:,:,frames(k, 1))))));
+%         M = max(cp_data(:));
+%         disp(M);
+        imwrite(cp_data, outpath);
+        
+        c = c+ 1; 
+    end
+
+    
+end
+

+import pandas as pd
+import os
+import csv
+
+class0_path = "../../data/MICCAI_BraTS_2019_Data_Training/circle_val"
+class1_path = "../../data/MICCAI_BraTS_2019_Data_Training/ellipse_val"
+target_csv = "../../data/MICCAI_BraTS_2019_Data_Training/ce_valid_targets.csv"
+
+f=open(target_csv, 'w', newline='')
+w=csv.writer(f)
+
+for path, dirs, files in os.walk(class0_path):
+    for filename in files:
+        print(filename)
+        n_class = 0
+        w.writerow([filename, n_class])
+
+for path, dirs, files in os.walk(class1_path):
+    for filename in files:
+        print(filename)
+        n_class = 1
+        w.writerow([filename, n_class])
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