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Calculate the undersegmentation error of superpixels compare to GT.
'''
import json
import numpy as np
from glob import glob
import argparse
from tqdm import tqdm
import ipdb
def ue_func(mask, gt_mask):
over_segment_area = 0
K = np.unique(mask) # numbers of superpixels
for i in K:
if ((mask == i) & (gt_mask == 1)).any():
over_segment_area += np.sum(mask==i)
gt_area = np.sum(gt_mask == 1)
ue = (over_segment_area - gt_area) * 1.0 / gt_area
return ue
def dice_func(pred, gt, type='fg'):
smooth = 1e-8
if type == 'fg':
pred = pred > 0.5
label = gt > 0
else:
pred = pred < 0.5
label = gt == 0
inter_size = np.sum(((pred * label) > 0))
sum_size = (np.sum(pred) + np.sum(label))
dice = (2 * inter_size + smooth) / (sum_size + smooth)
return dice
def dice_func(mask, gt_mask):
smooth = 1e-8
segment_mask = np.zeros_like(gt_mask)
K = np.unique(mask) # numbers of superpixels
for i in K:
if np.sum((mask == i) & (gt_mask == 1)) >= 1/2 * np.sum(mask == i):
segment_mask[(mask == i)] = 1
inter_size = np.sum(((segment_mask * gt_mask) > 0))
sum_size = (np.sum(segment_mask) + np.sum(gt_mask))
dice = (2 * inter_size + smooth) / (sum_size + smooth)
return dice
def stat():
stat = {'ue': [], 'dice':[]}
super_paths = sorted(glob(Params['super_dir'] + '*.json'))
gt_paths = sorted(glob(Params['clean_dir'] + '*.json'))
for ith in tqdm(range(len(gt_paths))):
super_path, gt_path = super_paths[ith], gt_paths[ith]
with open(super_path, 'rb') as f:
mask = json.load(f)
mask = np.array(mask)
with open(gt_path, 'rb') as f:
gt_mask = json.load(f)[Params['class']]
gt_mask = np.array(gt_mask)
stat['ue'].append(ue_func(mask, gt_mask))
stat['dice'].append(dice_func(mask, gt_mask))
mean_ue = np.array(stat['ue']).mean()
mean_dice = np.array(stat['dice']).mean()
print('Average undersegmentation error: %.4f' % (mean_ue))
print('Average dice: %.4f' % (mean_dice))
if __name__ == '__main__':
ap = argparse.ArgumentParser()
ap.add_argument("--sup_id", default='superpixel', help="superpixel id")
ap.add_argument("--subdir", default='ISIC_noise', help="[ISIC_noise, JSRT_noise]")
ap.add_argument("--class_name", default='lesion', help= "['lesion', 'lung', 'heart', 'clavicle']")
args = ap.parse_args()
Params = {
'super_dir': '/group/gaozht/Dataset/%s/train/%s/' % (args.subdir, args.sup_id),
'clean_dir': '/group/gaozht/Dataset/%s/train/label/' % args.subdir,
'class': args.class_name
}
stat()
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