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calculate_f1.py
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calculate_f1.py
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from sklearn.metrics import precision_recall_fscore_support, classification_report,accuracy_score
import cv2
import os
import numpy as np
from tqdm import tqdm
targetmasks='../other/targetmasks/'
outputmasks='../other/masks/'
outputfiles=os.listdir(outputmasks)
count=len(outputfiles)
f1_sum=0
p_sum=0
r_sum=0
acc=0
for i,imgname in tqdm(enumerate(outputfiles)):
output=cv2.imread(os.path.join(outputmasks,imgname),0)
output[output==255]=0
output[output!=0]=255
width,height=output.shape
target=cv2.imread(os.path.join(targetmasks,imgname),0)
target=cv2.resize(target,(height,width))
output=output.flatten()
target=target.flatten()
# cv2.imshow('Figure',output)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#
# cv2.imshow('Figure', target)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
results=precision_recall_fscore_support(target, output, average='micro')
f1_sum +=float(results[2])
p_sum+=float(results[0])
r_sum+=float(results[1])
acc+=accuracy_score(target, output)
#print(classification_report(target,output))
print('F1: ',f1_sum/count)