Created
March 10, 2021 23:34
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generate binary mask from grayscale images
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| def adjustData(img,mask,flag_multi_class,n_class): | |
| if(flag_multi_class): | |
| img /= 255 | |
| mask = mask[:,:,:,0] if(len(mask.shape) == 4) else mask[:,:,0] | |
| new_mask = np.zeros(mask.shape + (n_class,)) | |
| for i in range(n_class): | |
| new_mask[mask == i,i] = 1 | |
| new_mask = np.reshape(new_mask,(new_mask.shape[0],new_mask.shape[1]*new_mask.shape[2],new_mask.shape[3])) if flag_multi_class else np.reshape(new_mask,(new_mask.shape[0]*new_mask.shape[1],new_mask.shape[2])) | |
| mask = new_mask | |
| elif(np.max(img)>1): | |
| img = img / 255 | |
| mask = mask /255 | |
| mask[mask > 0.1] = 1 | |
| mask[mask <= 0.1] = 0 | |
| #print(np.shape(mask),np.shape(img)) | |
| return (img,mask) |
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