Created
March 10, 2021 23:29
-
-
Save sohiniroych/bf757b580c85025b52819e0a68564e1d to your computer and use it in GitHub Desktop.
Preparing 3 class data
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| def prepare_multi_class_GT(GT_PATH, class_names, savepath, target_size=(512,512), n_class=3): | |
| f_names = os.listdir(GT_PATH+class_names[0]) | |
| for files in f_names: | |
| GT_im=np.zeros(np.concatenate((target_size,n_class),axis=None)) #This creates a zero array of size (512,512,3) | |
| FG=np.zeros(target_size) | |
| for idx,cn in enumerate(class_names): | |
| lab=io.imread(GT_PATH+cn+files, as_gray=True) | |
| lab = trans.resize(lab,target_size) | |
| if(np.max(lab)>1): | |
| lab=lab/255 | |
| lab[lab>=0.1]=1 #threshold at 0.1. Change this value based on your requirement | |
| lab[lab<0.1]=0 | |
| if (idx<2): #Red Lesions | |
| GT_im[:,:,0]=((GT_im[:,:,0]+lab)>0).astype(int) | |
| FG=((FG+lab)>0).astype(int) | |
| else:#Bright Lesions | |
| GT_im[:,:,1]=((GT_im[:,:,1]+lab-FG)>0).astype(int) | |
| io.imsave(savepath+files,GT_im) |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment