def compute_hmean(submit_file_path, epoch):
print('EAST <==> Evaluation <==> Compute Hmean <==> Begin')
basename = os.path.basename(submit_file_path)
assert basename == 'submit.zip', 'There is no submit.zip'
dirname = '/home/binchengxiong/ssd_fcn_multitask_text_detection_pytorch1.0'
gt_file_path = os.path.join(dirname, 'gt_train.zip')
print('gt_file_path:', gt_file_path)
assert os.path.isfile(gt_file_path), 'There is no gt.zip'
tt = 'train_log_step_hmean_' + str(min_epoch) + '.txt'
log_file_path = os.path.join(
'/home/binchengxiong/ssd_fcn_multitask_text_detection_pytorch1.0/', tt)
# log_file_path = os.path.join(dirname, 'log_epoch_hmean_modify_loss.txt')
# log_file_path = os.path.join(dirname, 'log_epoch_hmean_cross_entropy_loss.txt')
if not os.path.isfile(log_file_path):
os.mknod(log_file_path)
result_dir_path = os.path.join(dirname, 'result' + str(min_epoch))
try:
shutil.rmtree(result_dir_path)
except:
pass
if not os.path.exists(result_dir_path):
os.makedirs(result_dir_path)
print('gt_file_path:', gt_file_path)
print('submit_file_path:', submit_file_path)
resDict = rrc_evaluation_funcs.main_evaluation(
{
'g': gt_file_path,
's': submit_file_path,
'o': result_dir_path
}, TL_iou.default_evaluation_params, TL_iou.validate_data,
TL_iou.evaluate_method)
print(resDict)
if resDict['method'] == '{}':
with open(log_file_path, 'a') as f:
f.write(
'step:{} ssd_fcn_multitask <==> Evaluation <==> Precision:{:.2f} Recall:{:.2f} Hmean{:.2f} <==> Done\n'
.format(epoch, -1, -1, -1))
return -1
else:
recall = resDict['method']['recall']
precision = resDict['method']['precision']
hmean = resDict['method']['hmean']
print(
'ssd_fcn_multitask <==> Evaluation <==> Precision:{:.2f} Recall:{:.2f} Hmean{:.2f} <==> Done'
.format(precision, recall, hmean))
with open(log_file_path, 'a') as f:
f.write(
'step:{} ssd_fcn_multitask <==> Evaluation <==> Precision:{:.4f} Recall:{:.4f} Hmean{:.4f} <==> Done\n'
.format(epoch, precision, recall, hmean))
return hmean
def compute_hmean(submit_file_path):
print('EAST <==> Evaluation <==> Compute Hmean <==> Begin')
basename = os.path.basename(submit_file_path)
assert basename == 'submit.zip', 'There is no submit.zip'
dirname = os.path.dirname(submit_file_path)
gt_file_path = os.path.join(dirname, 'gt.zip')
print(gt_file_path)
assert os.path.isfile(gt_file_path), 'There is no gt.zip'
log_file_path = os.path.join(dirname, 'log_epoch_hmean.txt')
if not os.path.isfile(log_file_path):
os.mknod(log_file_path)
result_dir_path = os.path.join(dirname, 'result')
try:
shutil.rmtree(result_dir_path)
except:
pass
os.mkdir(result_dir_path)
resDict = rrc_evaluation_funcs.main_evaluation(
{
'g': gt_file_path,
's': submit_file_path,
'o': result_dir_path
}, TL_iou.default_evaluation_params, TL_iou.validate_data,
TL_iou.evaluate_method)
print(resDict)
recall = resDict['method']['recall']
precision = resDict['method']['precision']
hmean = resDict['method']['hmean']
print(
'EAST <==> Evaluation <==> Precision:{:.2f} Recall:{:.2f} Hmean{:.2f} <==> Done'
.format(precision, recall, hmean))
with open(log_file_path, 'a') as f:
f.write(
'EAST <==> Evaluation <==> Precision:{:.2f} Recall:{:.2f} Hmean{:.2f} <==> Done\n'
.format(precision, recall, hmean))
return hmean
'AP':sampleAP,
'iouMat':[] if len(detPols)>100 else iouMat.tolist(),
'gtPolPoints':gtPolPoints,
'detPolPoints':detPolPoints,
'gtDontCare':gtDontCarePolsNum,
'detDontCare':detDontCarePolsNum,
'evaluationParams': evaluationParams,
'evaluationLog': evaluationLog
}
# Compute MAP and MAR
AP = 0
if evaluationParams['CONFIDENCES']:
AP = compute_ap(arrGlobalConfidences, arrGlobalMatches, numGlobalCareGt)
methodRecall = 0 if numGlobalCareGt == 0 else float(matchedSum)/numGlobalCareGt
methodPrecision = 0 if numGlobalCareDet == 0 else float(matchedSum)/numGlobalCareDet
methodHmean = 0 if methodRecall + methodPrecision==0 else 2* methodRecall * methodPrecision / (methodRecall + methodPrecision)
methodMetrics = {'precision':methodPrecision, 'recall':methodRecall,'hmean': methodHmean, 'AP': AP }
resDict = {'calculated':True,'Message':'','method': methodMetrics,'per_sample': perSampleMetrics}
return resDict;
if __name__=='__main__':
rrc_evaluation_funcs.main_evaluation(None,default_evaluation_params,validate_data,evaluate_method)