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