Figures corrections

figure_4.py

@@ -2,6 +2,7 @@ import matplotlib.pyplot as plt
 import matplotlib.image as mpimg
 from scipy.io import loadmat
 import os
+import numpy as np
 
 base_dir = os.path.abspath('')
 # ---------------- FIRST ROW ------------------------
@@ -60,6 +61,7 @@ plt.imshow(original_small_img)
 plt.subplot(4, 4, 8)
 #original_sa_fcn_img = loadmat(r'C:\Users\lutobi\Documents\testA_44_post.mat',variable_names='testI',appendmat=True).get('testI')
 original_sa_fcn_img = loadmat(base_dir + '/sa_fcn/post_processing/original/1/predicted_pp/testA_44_post.mat',variable_names='testI',appendmat=True).get('testI')
+original_sa_fcn_img = np.square(original_sa_fcn_img)
 # /dtu-compute/s162377/mlsp2019_software_package/sa_fcn/post_processing/original/1/predicted_pp
 plt.title("FINAL PREDICTION")
 plt.axis('off')
@@ -94,6 +96,7 @@ plt.imshow(dcan_fill_img)
 plt.subplot(4, 4, 12)
 #dcan_img = loadmat(r'C:\Users\lutobi\Documents\testA_44_post.mat',variable_names='testI',appendmat=True).get('testI')
 dcan_img = loadmat(base_dir + '/sa_fcn/post_processing/dcan/1/predicted_pp/testA_44_post.mat',variable_names='testI',appendmat=True).get('testI')
+dcan_img = np.square(dcan_img)
 # /dtu-compute/s162377/mlsp2019_software_package/sa_fcn/post_processing/dcan/1/predicted_pp
 plt.title("FINAL PREDICTION")
 plt.axis('off')
@@ -129,10 +132,10 @@ plt.imshow(our_dilated_img)
 plt.subplot(4, 4, 16)
 #our_img = loadmat(r'C:\Users\lutobi\Documents\testA_44_post.mat',variable_names='testI',appendmat=True).get('testI')
 our_img = loadmat(base_dir + '/sa_fcn/post_processing/our/1/predicted_pp/testA_44_post.mat',variable_names='testI',appendmat=True).get('testI')
+our_img = np.square(our_img)
 # /dtu-compute/s162377/mlsp2019_software_package/sa_fcn/post_processing/our/1/predicted_pp
 plt.title("FINAL PREDICTION")
 plt.axis('off')
 plt.imshow(our_img)
 
-plt.savefig('figure_4.png')
 plt.savefig('figure_4.pdf')

figure_6.py

 import matplotlib.pyplot as plt
 import matplotlib.image as mpimg
 from scipy.io import loadmat
+import numpy as np
 import os
 
 
@@ -37,6 +38,7 @@ plt.imshow(mask_binary_img)
 plt.subplot(2, 5, 4)
 #mask_post_img = loadmat(r'C:\Users\lutobi\Documents\figures_mlsp_reporducibility_package\testA_56_post_mask.mat',variable_names='testI',appendmat=True).get('testI')
 mask_post_img = loadmat(base_dir + '/mask_rcnn/post_processing/1/predicted_pp/testA_56_post.mat',variable_names='testI',appendmat=True).get('testI')
+mask_post_img = np.square(mask_post_img)
 # /dtu-compute/s162377/mlsp2019_software_package/mask_rcnn/post_processing/1/predicted_pp
 plt.title("AFTER POST-PROCESSING")
 plt.axis('off')
@@ -77,6 +79,8 @@ plt.imshow(safcn_binary_img)
 plt.subplot(2, 5, 9)
 #safcn_post_img = loadmat(r'C:\Users\lutobi\Documents\figures_mlsp_reporducibility_package\testA_56_post_safcn.mat',variable_names='testI',appendmat=True).get('testI')
 safcn_post_img = loadmat(base_dir + '/sa_fcn/post_processing/our/1/predicted_pp/testA_56_post.mat',variable_names='testI',appendmat=True).get('testI')
+safcn_post_img = np.square(safcn_post_img)
+
 # /dtu-compute/s162377/mlsp2019_software_package/sa_fcn/post_processing/our/1/predicted_pp
 plt.axis('off')
 plt.imshow(safcn_post_img)

mask_rcnn/run_maskrcnn.sh

@@ -6,10 +6,10 @@ echo Welcome to Mask RCNN!
 echo ============================================================================================== &&
 echo Lets start with some training! &&
 cd samples/glands/ &&
-for i in {1..5}
-do 
-	python3 -W ignore glands.py train --dataset=/dtu-compute/s162377/mask_rcnn_git/samples/glands/glands/ --subset=train --weights=coco
-done || exit 1
+#for i in {1..5}
+#do 
+#	python3 -W ignore glands.py train --dataset=/dtu-compute/s162377/mask_rcnn_git/samples/glands/glands/ --subset=train --weights=coco
+#done || exit 1
 echo Successfully finished training! &&
 echo ============================================================================================== &&
 

run_all.sh

@@ -5,7 +5,7 @@ echo Welcome to MLSP 2019 Software Package! &&
 echo ============================================================================================== &&
 echo Lets start with Mask R-CNN!  &&
 cd mask_rcnn &&
-#./run_maskrcnn.sh &&
+./run_maskrcnn.sh &&
 echo Successfully finished Mask R-CNN! &&
 echo ============================================================================================== &&
 cd ../sa_fcn &&
@@ -28,6 +28,14 @@ echo ===========================================================================
 echo Lets generate Figure 4! &&
 python figure_4.py &&
 echo Successfully finished generating Figure 4! &&
+echo ==============================================================================================
+echo Lets generate Figure 5! &&
+python figure_5.py &&
+echo Successfully finished generating Figure 5! &&
+echo ==============================================================================================
+echo Lets generate Figure 6! &&
+python figure_6.py &&
+echo Successfully finished generating Figure 6! &&
 echo ============================================================================================== &&
 echo We are done here! Thank you. &&
 echo ==============================================================================================

sa_fcn/post_processing/pp_original_automatic_x5.m

@@ -6,6 +6,19 @@ currentFolder = pwd;
 idcs   = strfind(currentFolder,'/');
 baseDir = currentFolder(1:idcs(end)-1);
 
+figure;
+h    = [];
+h(1) = subplot(3,3,1);
+h(2) = subplot(3,3,2);
+h(3) = subplot(3,3,3);
+h(4) = subplot(3,3,4);
+h(5) = subplot(3,3,5);
+h(6) = subplot(3,3,6);
+h(7) = subplot(3,3,7);
+h(8) = subplot(3,3,8);
+h(9) = subplot(3,3,9);
+
+
 
 
 for exp_number = 1:5 %experiment number
@@ -121,11 +134,18 @@ for exp_number = 1:5 %experiment number
 
             write_name_post = strcat('test', ds, '_',  int2str(i), '_post.mat'); % generate file name for post processed prediction    
             write_name_gt = strcat('test', ds, '_',  int2str(i), '_truth.mat'); % generate file name for ground truth
+            write_name_post_bmp = strcat('test', ds, '_',  int2str(i), '_post.bmp'); % generate file name for post processed prediction    
+
+            image(testI,'CDataMapping','scaled','Parent',h(1));
+
             
             % save 
             save(strcat(write_dir_post, write_name_post), 'testI');
             save(strcat(write_dir_gt, write_name_gt), 'gtI');
             
+            imwrite(testI, strcat(write_dir_post, write_name_post_bmp));
+
+
             bigtestI=cat(2,bigtestI,testI);     % Concatenate arrays along specified dimension, 2 means it gona be one next to another, 1 would mean it would be one on top of another
             biggtI=cat(2,biggtI,gtI);           %   Instead of calculating each image seperately, they are creating one huge and calculate the metrices at once
 

sa_fcn/run_safcn.sh

@@ -4,10 +4,10 @@
 echo Welcome to SA-FCN! &&
 echo ============================================================================================== &&
 echo Lets start with some training!  &&
-for i in {1..5} 
-do 
-	python3 -W ignore python/train.py Gland; 
-done || exit 1
+#for i in {1..5} 
+#do 
+#	python3 -W ignore python/train.py Gland; 
+#done || exit 1
 echo Successfully finished training! &&
 echo ============================================================================================== &&
 
@@ -16,9 +16,19 @@ python3 -W ignore python/predict_x5.py &&
 echo Successfully finished predicting!  &&
 echo ============================================================================================== &&
 
-echo Lets do some post processing!  &&
+echo Lets do some our post processing!  &&
 /opt/apps/matlab/r2019a/bin/matlab -nodisplay -nosplash -nodesktop -r "run('post_processing/pp_our_automatic_x5.m');exit;" && 
-echo Successfully finished post processing &&
+echo Successfully finished our post processing &&
+echo ============================================================================================== &&
+
+echo Lets do some dcan post processing!  &&
+/opt/apps/matlab/r2019a/bin/matlab -nodisplay -nosplash -nodesktop -r "run('post_processing/pp_dcan_automatic_x5.m');exit;" && 
+echo Successfully finished dcan post processing &&
+echo ============================================================================================== &&
+
+echo Lets do some original post processing!  &&
+/opt/apps/matlab/r2019a/bin/matlab -nodisplay -nosplash -nodesktop -r "run('post_processing/pp_original_automatic_x5.m');exit;" && 
+echo Successfully finished original post processing &&
 echo ============================================================================================== &&
 
 echo Lets do some pdf!  &&