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import os
from os.path import join
import glob
from sklearn.metrics import roc_auc_score, roc_curve
import sys
def file_to_score(file):
try:
content = open(file, 'r').readlines()
st = 0
if len(content) == 0:
# Empty File Should Return []
return 0.
if content[0].split()[0].isalpha():
st = 1
return max([float(line.split()[st]) for line in content])
except FileNotFoundError:
print(f'No Corresponding Box Found for file {file}, using [] as preds')
return []
except Exception as e:
print('Some Error',e)
return []
# Create the image dict
def generate_image_dict(preds_folder_name='preds_42',
root_fol='/home/krithika_1/densebreeast_datasets/AIIMS_C1',
mal_path=None, ben_path=None, gt_path=None,
mal_img_path = None, ben_img_path = None
):
mal_path = join(root_fol, mal_path) if mal_path else join(
root_fol, 'mal', preds_folder_name)
ben_path = join(root_fol, ben_path) if ben_path else join(
root_fol, 'ben', preds_folder_name)
mal_img_path = join(root_fol, mal_img_path) if mal_img_path else join(
root_fol, 'mal', 'images')
ben_img_path = join(root_fol, ben_img_path) if ben_img_path else join(
root_fol, 'ben', 'images')
gt_path = join(root_fol, gt_path) if gt_path else join(
root_fol, 'mal', 'gt')
'''
image_dict structure:
'image_name(without txt/png)' : {'gt' : [[...]], 'preds' : score}
'''
image_dict = dict()
# GT Might be sightly different from images, therefore we will index gts based on
# the images folder instead.
for file in os.listdir(mal_img_path):
# for file in glob.glob(join(gt_path, '*.txt')):
if not file.endswith('.png'):
continue
file = file[:-4] + '.txt'
file = join(gt_path, file)
key = os.path.split(file)[-1][:-4]
image_dict[key] = dict()
image_dict[key]['gt'] = 1.
image_dict[key]['preds'] = 0.
for file in glob.glob(join(mal_path, '*.txt')):
key = os.path.split(file)[-1][:-4]
assert key in image_dict
image_dict[key]['preds'] = file_to_score(file)
for file in os.listdir(ben_img_path):
# for file in glob.glob(join(ben_path, '*.txt')):
if not file.endswith('.png'):
continue
file = file[:-4] + '.txt'
file = join(ben_path, file)
key = os.path.split(file)[-1][:-4]
# if key == 'Calc-Test_P_00353_LEFT_CC' or key == 'Calc-Training_P_00600_LEFT_CC':
# continue
if key in image_dict:
print(key)
print('SHIT')
continue
# assert key not in image_dict
image_dict[key] = dict()
image_dict[key]['preds'] = file_to_score(file)
image_dict[key]['gt'] = 0.
return image_dict
def get_auc_score_from_imdict(image_dict):
keys = list(image_dict.keys())
y = [image_dict[k]['gt']for k in keys]
preds = [image_dict[k]['preds']for k in keys]
return roc_auc_score(y, preds)
def get_accuracy_from_imdict(image_dict, thresh = 0.3):
keys = list(image_dict.keys())
ys = [image_dict[k]['gt']for k in keys]
preds = [image_dict[k]['preds']for k in keys]
acc = 0
for y,pred in zip(ys,preds):
if pred < thresh and y == 0.:
acc+=1
elif pred > thresh and y == 1.:
acc+=1
return acc/len(preds)
def get_auc_score(preds_image_folder, root_fol, retAcc = False, acc_thresh = 0.3):
im_dict = generate_image_dict(preds_image_folder, root_fol = root_fol)
if retAcc:
return get_auc_score_from_imdict(im_dict), get_accuracy_from_imdict(im_dict, acc_thresh)
else:
return get_auc_score_from_imdict(im_dict)
if __name__ == '__main__':
seed = '42' if len(sys.argv)== 1 else sys.argv[1]
root_fol = '../bilateral_new/MammoDatasets/AIIMS_highres_reliable/test'
auc_score = get_auc_score(f'preds_{seed}',root_fol)
print(f'ROC AUC Score: {auc_score}')
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