def log_total_metrics(self):
def exp_one(arg):
return str(arg).center(20)
def exp_arg(args):
return [exp_one(arg) for arg in args]
log_line()
log_head(' Result metrics values for {} IoU threshold '.format(
self._iou_threshold))
log_head(' Confusion matrix ')
sorted_gt_names = sorted(self._class_mapping.keys())
pred_names = [
self._class_mapping[gt_name] for gt_name in sorted_gt_names
]
logger.info(''.join(exp_arg([''] + pred_names + ['False Negatives'])))
for gt_name in sorted_gt_names:
logger.info(''.join([exp_one(gt_name)] + exp_arg([
self._confusion_matrix[gt_name, pred_name]
for pred_name in pred_names
]) + [exp_one(self._unmatched_gt[gt_name])]))
log_line()
logger.info(''.join([exp_one('False Positives')] + exp_arg(
[self._unmatched_pred[pred_name]
for pred_name in pred_names]) + [exp_one('0')]))
log_line()
def log_total_metrics(self):
log_line()
log_head(' Result metrics values for {} IoU threshold '.format(self._iou_threshold))
classes_values = self.get_metrics()
for i, (cls_gt, pair_values) in enumerate(classes_values.items()):
average_precision = pair_values[AP]
log_line()
log_head(' Results for pair of classes <<{} <-> {}>> '.format(cls_gt,
self._gt_to_pred_class_mapping[cls_gt]))
logger.info('Average Precision (AP): {}'.format(average_precision))
log_line()
log_head(' Mean metrics values ')
logger.info('Mean Average Precision (mAP): {}'.format(self.average_per_class_avg_precision(classes_values)))
log_line()
def log_total_metrics(self):
log_line()
log_head(' Result metrics values for {} IoU threshold '.format(self._iou_threshold))
for i, (gt_class, values) in enumerate(self.get_metrics().items()):
log_line()
log_head(' Results for pair of classes <<{} <-> {}>> '.format(gt_class,
self._gt_to_pred_class_mapping[gt_class]))
logger.info('Precision: {}'.format(values[PRECISION]))
logger.info('Recall: {}'.format(values[RECALL]))
log_line()
log_head(' Total metrics values ')
total_values = self.get_total_metrics()
logger.info('Precision: {}'.format(total_values[PRECISION]))
logger.info('Recall: {}'.format(total_values[RECALL]))
log_line()
def log_total_metrics(self):
common_info = """
P = condition positive (the number of real positive cases in the data)
N = condition negative (the number of real negative cases in the data)
TP = True Positive prediction
TN = True Negative prediction
FP = False Positive prediction (Type I error)
FN = False Negative prediction (Type II error)
Accuracy = (TP + TN)/(TP + TN + FP + FN) = TRUE/TOTAL
Precision = TP / (TP + FP)
Recall = TP / (TP + FN)
F1-Measure = (2 * TP) / (2 * TP + FP + FN)
"""
log_line()
log_line(c='*')
for line in common_info.split('\n'):
line = line.strip()
if len(line) > 0:
logger.info(line.ljust(80))
log_line(c='*')
log_line()
def print_evaluation_values(tag_pair_metrics):
labels = [
ACCURACY, PRECISION, RECALL, F1_MEASURE, TRUE_POSITIVE,
TRUE_NEGATIVE, FALSE_POSITIVE, FALSE_NEGATIVE
]
for label in labels:
logger.info(' {0}: {1:2.4f}'.format(
label.ljust(16), tag_pair_metrics[label]))
for i, (tag_name_gt,
tag_metrics) in enumerate(self.get_metrics().items(), start=1):
logger.info('{}) {} <--> {}:'.format(
i, tag_name_gt, self._tags_mapping[tag_name_gt]))
print_evaluation_values(tag_metrics)
log_line()
logger.info('Total values:')
total_values = self.get_total_metrics()
print_evaluation_values(total_values)
log_line()
log_line(c='*')
