minor updates

Author: hankuipeng

README.md

@@ -16,7 +16,7 @@ conda create -n DAL-HERS python=3.8.5 anaconda
 
 conda activate DAL-HERS 
 ```
-Then the user may need to install a few other Python packages, such as pytorch, torchvision, opencv, etc. These packages can be installed with pip in terminal. 
+The user may need to install a few other Python packages, such as pytorch, torchvision, opencv, etc. These packages can be installed with pip in terminal. 
 
 
 ## Model set-up

analysis_multi_nC.py

@@ -39,7 +39,7 @@
 parser.add_argument('--input_dir', default='./sample_imgs/input/', help='path to images folder')
 parser.add_argument('--output_dir', default='./sample_imgs/output/', help='path to output folder')
 parser.add_argument('--output_suff', default='', help='suffix to the output file')
-parser.add_argument('--edge', default=True, help='whether to incorporate edge information')
+parser.add_argument('--edge', default=False, help='whether to incorporate edge information')
 parser.add_argument('--device', default=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"), help='default device (CPU / GPU)')
 args = parser.parse_args()
 
@@ -125,4 +125,4 @@ def main():
 
 if __name__ == '__main__':
 
-    main()
+    main()

analysis_single_nC.py

@@ -36,11 +36,11 @@
 parser = argparse.ArgumentParser(description='DAL-HERS Superpixel Segmentation on a folder of images',
                                  formatter_class=argparse.ArgumentDefaultsHelpFormatter)
 parser.add_argument('--nC', default=200, type=int, help='the number of desired superpixels')
-parser.add_argument('--pretrained', default='./pretrained/DGSS_loss=bce-rgb_date=23Feb2021.tar', help='path to the pretrained model')
+parser.add_argument('--pretrained', default='./pretrained/DAL_loss=bce-rgb_date=23Feb2021.tar', help='path to the pretrained model')
 parser.add_argument('--input_dir', default='./sample_imgs/input/', help='path to images folder')
 parser.add_argument('--output_dir', default='./sample_imgs/output/', help='path to output folder')
 parser.add_argument('--output_suff', default='', help='suffix to the output file')
-parser.add_argument('--edge', default=True, help='whether to incorporate edge information')
+parser.add_argument('--edge', default=False, help='whether to incorporate edge information')
 parser.add_argument('--device', default=torch.device("cuda:0" if torch.cuda.is_available() else "cpu"), help='default device (CPU / GPU)')
 args = parser.parse_args()
 
@@ -120,4 +120,4 @@ def main():
 
 if __name__ == '__main__':
 
-    main()
+    main()

pybuild/boruvka_superpixel.cpp

@@ -557,7 +557,8 @@ void BoruvkaSuperpixel::build_hierarchy()
             int label1 = label_[edge->v1];
 
             double dist_feat = calc_dist(edge, boruvka_iter);
-            double dist = edge->gain_ / dist_feat;
+            double dist = edge->gain_ / dist_feat; // entropy rate and RGB info
+            //double dist = edge->gain_; // entropy rate only 
 
             if (dist > min_pair[label0].value) {
                 min_pair[label0].value = dist;

pybuild/boruvka_superpixel.h

@@ -22,7 +22,7 @@
 }
 
 #define PENALTY   1e-8
-#define MIN_VALUE 1e-20
+#define MIN_VALUE 1e-50
 
 struct Edge {
     int v0;                             // vertex 0

pybuild/boruvka_superpixel_wrap.cpp

@@ -557,7 +557,8 @@ void BoruvkaSuperpixel::build_hierarchy()
             int label1 = label_[edge->v1];
 
             double dist_feat = calc_dist(edge, boruvka_iter);
-            double dist = edge->gain_ / dist_feat;
+            double dist = edge->gain_ / dist_feat; // entropy rate and RGB info
+            //double dist = edge->gain_; // entropy rate only 
 
             if (dist > min_pair[label0].value) {
                 min_pair[label0].value = dist;

pybuild/build/temp.linux-x86_64-3.8/boruvka_superpixel.o

@@ -22,7 +22,7 @@
 }
 
 #define PENALTY   1e-8
-#define MIN_VALUE 1e-20
+#define MIN_VALUE 1e-50
 
 struct Edge {
     int v0;                             // vertex 0