def precision_recall(gt, subm, threshold=None):
evalParams = polygon_evaluation_params()
p = {
'g': gt,
's': subm
}
resDict, precision, recall, confidences = rrc_evaluation_funcs.main_evaluation_notzip(p, evalParams, validate_data, evaluate_method,
show_result=False)
operating_point = None
if threshold is not None:
op_p_idx = np.argmin(np.abs(confidences - threshold))
operating_point = (float(precision[op_p_idx]), float(recall[op_p_idx]))
return precision, recall, confidences, operating_point
def count_pr_for_detectors():
evalParams = polygon_evaluation_params()
validate_data(gtFilePath, evalParams, isGT=True)
gt = load_zip_file(gtFilePath, evalParams['GT_SAMPLE_NAME_2_ID'])
subm_dict = {}
curves = {}
for key in using_detections:
validate_data(allDetFilePaths[key], evalParams, isGT=False)
subm_dict[key] = load_zip_file(allDetFilePaths[key], evalParams['DET_SAMPLE_NAME_2_ID'], True)
precision, recall, confidences, operating_point = precision_recall(gt, subm_dict[key], threshold=thresholds[key])
curves[key] = {'precision': list(precision.astype(np.float)),
'recall': list(recall.astype(np.float)),
'thresholds': list(confidences),
'operating_point': operating_point}
with open('./precision_recall/data/' + key + '.json', "w") as write_file:
json.dump({key: curves[key]}, write_file)
print()
return curves
def validate_TextALFA(test_index, detector_keys, subm_dict, cur_params):
text_alfa = TextALFA()
evalParams = polygon_evaluation_params()
joined_subm_dict = {}
for img_key in test_index:
joined_img_polygons = {}
joined_img_confs = {}
for j in range(len(detector_keys)):
detector_key = detector_keys[j]
detFile = subm_dict[detector_key][img_key]
pointsList, _, confidencesList, _ = get_tl_line_values_from_file_contents(detFile, evalParams,
False, evalParams['CONFIDENCES'])
detPols = []
for i in range(len(pointsList)):
points = pointsList[i]
detPol = polygon_from_points(points)
detPols.append(detPol)
joined_img_polygons[detector_keys[j]] = detPols
joined_img_confs[detector_keys[j]] = confidencesList
new_img_polygon, new_img_confs = text_alfa.TextALFA_result(detector_keys, joined_img_polygons,
joined_img_confs, tau=cur_params['tau'], gamma=0.5,
bounding_box_fusion_method=cur_params['bounding_box_fusion_method'],
scores_fusion_method=cur_params['scores_fusion_method'],
add_empty_detections=True,
empty_epsilon=cur_params['empty_epsilon'],
max_1_box_per_detector=True,
use_precision_instead_of_scores=cur_params['use_precision_instead_of_scores'])
polygons, confs = [], []
for i in range(len(new_img_polygon)):
if new_img_confs[i] < cur_params['threshold']:
continue
polygons.append(new_img_polygon[i])
confs.append(new_img_confs[i])
new_img_polygon = polygons
new_img_confs = confs
joined_subm_dict[img_key] = new_img_polygon, new_img_confs
return joined_subm_dict
av_hmean += best_result_per_fold[i]['hmean']
# print("AP/folds_count: ", ap_per_fold[i])
print()
av_ap /= folds_count
av_recall /= folds_count
av_precision /= folds_count
av_hmean /= folds_count
print("average precision: ", av_precision)
print("average recall: ", av_recall)
print("average hmean: ", av_hmean)
print("average ap: ", av_ap)
return best_result_per_fold, best_params_per_fold
if __name__ == '__main__':
evalParams = polygon_evaluation_params()
for key in using_detections:
validate_data(allDetFilePaths[key], evalParams, isGT=False)
subm_dict = {}
for key in using_detections:
new_key = key.split('_')[0]
subm_dict[new_key] = load_zip_file(allDetFilePaths[key],
evalParams['DET_SAMPLE_NAME_2_ID'],
True)
validate_data(gtFilePath, evalParams, isGT=True)
gt = load_zip_file(gtFilePath, evalParams['GT_SAMPLE_NAME_2_ID'])
detector_keys = list(subm_dict.keys())
if len(detector_keys) == 0:
exit()
img_keys = list(