def main(parsed_args):
all_box_annotations = pd.read_csv(parsed_args.input_annotations_boxes)
all_label_annotations = pd.read_csv(parsed_args.input_annotations_labels)
all_label_annotations.rename(
columns={'Confidence': 'ConfidenceImageLabel'}, inplace=True)
all_annotations = pd.concat([all_box_annotations, all_label_annotations])
class_label_map, categories = _load_labelmap(
parsed_args.input_class_labelmap)
challenge_evaluator = (object_detection_evaluation.
OpenImagesDetectionChallengeEvaluator(categories))
for _, groundtruth in enumerate(all_annotations.groupby('ImageID')):
image_id, image_groundtruth = groundtruth
groundtruth_dictionary = utils.build_groundtruth_boxes_dictionary(
image_groundtruth, class_label_map)
challenge_evaluator.add_single_ground_truth_image_info(
image_id, groundtruth_dictionary)
all_predictions = pd.read_csv(parsed_args.input_predictions)
for _, prediction_data in enumerate(all_predictions.groupby('ImageID')):
image_id, image_predictions = prediction_data
prediction_dictionary = utils.build_predictions_dictionary(
image_predictions, class_label_map)
challenge_evaluator.add_single_detected_image_info(
image_id, prediction_dictionary)
metrics = challenge_evaluator.evaluate()
with open(parsed_args.output_metrics, 'w') as fid:
io_utils.write_csv(fid, metrics)
def main(parsed_args):
all_box_annotations = pd.read_csv(parsed_args.input_annotations_boxes)
all_label_annotations = pd.read_csv(parsed_args.input_annotations_labels)
all_label_annotations.rename(
columns={'Confidence': 'ConfidenceImageLabel'}, inplace=True)
all_annotations = pd.concat([all_box_annotations, all_label_annotations])
class_label_map, categories = _load_labelmap(parsed_args.input_class_labelmap)
challenge_evaluator = (
object_detection_evaluation.OpenImagesDetectionChallengeEvaluator(
categories))
for _, groundtruth in enumerate(all_annotations.groupby('ImageID')):
image_id, image_groundtruth = groundtruth
groundtruth_dictionary = utils.build_groundtruth_boxes_dictionary(
image_groundtruth, class_label_map)
challenge_evaluator.add_single_ground_truth_image_info(
image_id, groundtruth_dictionary)
all_predictions = pd.read_csv(parsed_args.input_predictions)
for _, prediction_data in enumerate(all_predictions.groupby('ImageID')):
image_id, image_predictions = prediction_data
prediction_dictionary = utils.build_predictions_dictionary(
image_predictions, class_label_map)
challenge_evaluator.add_single_detected_image_info(image_id,
prediction_dictionary)
metrics = challenge_evaluator.evaluate()
with open(parsed_args.output_metrics, 'w') as fid:
io_utils.write_csv(fid, metrics)
def main(unused_argv):
flags.mark_flag_as_required('input_annotations_boxes')
flags.mark_flag_as_required('input_annotations_labels')
flags.mark_flag_as_required('input_predictions')
flags.mark_flag_as_required('input_class_labelmap')
flags.mark_flag_as_required('output_metrics')
all_location_annotations = pd.read_csv(FLAGS.input_annotations_boxes)
all_label_annotations = pd.read_csv(FLAGS.input_annotations_labels)
all_label_annotations.rename(
columns={'Confidence': 'ConfidenceImageLabel'}, inplace=True)
is_instance_segmentation_eval = False
if FLAGS.input_annotations_segm:
is_instance_segmentation_eval = True
all_segm_annotations = pd.read_csv(FLAGS.input_annotations_segm)
# Note: this part is unstable as it requires the float point numbers in both
# csvs are exactly the same;
# Will be replaced by more stable solution: merge on LabelName and ImageID
# and filter down by IoU.
all_location_annotations = utils.merge_boxes_and_masks(
all_location_annotations, all_segm_annotations)
all_annotations = pd.concat(
[all_location_annotations, all_label_annotations])
class_label_map, categories = _load_labelmap(FLAGS.input_class_labelmap)
challenge_evaluator = (
object_detection_evaluation.OpenImagesChallengeEvaluator(
categories, evaluate_masks=is_instance_segmentation_eval))
all_predictions = pd.read_csv(FLAGS.input_predictions)
images_processed = 0
for _, groundtruth in enumerate(all_annotations.groupby('ImageID')):
logging.info('Processing image %d', images_processed)
image_id, image_groundtruth = groundtruth
groundtruth_dictionary = utils.build_groundtruth_dictionary(
image_groundtruth, class_label_map)
challenge_evaluator.add_single_ground_truth_image_info(
image_id, groundtruth_dictionary)
prediction_dictionary = utils.build_predictions_dictionary_kaggle(
all_predictions.loc[all_predictions['ImageID'] == image_id],
class_label_map)
challenge_evaluator.add_single_detected_image_info(
image_id, prediction_dictionary)
images_processed += 1
metrics = challenge_evaluator.evaluate()
with open(FLAGS.output_metrics, 'w') as fid:
io_utils.write_csv(fid, metrics)
def main(parsed_args):
all_box_annotations = pd.read_csv(parsed_args.input_annotations_boxes)
all_label_annotations = pd.read_csv(parsed_args.input_annotations_labels)
all_annotations = pd.concat([all_box_annotations, all_label_annotations])
class_label_map = _load_labelmap(parsed_args.input_class_labelmap)
relationship_label_map = _load_labelmap(
parsed_args.input_relationship_labelmap)
relation_evaluator = vrd_evaluation.VRDRelationDetectionEvaluator()
phrase_evaluator = vrd_evaluation.VRDPhraseDetectionEvaluator()
for _, groundtruth in enumerate(all_annotations.groupby('ImageID')):
image_id, image_groundtruth = groundtruth
groundtruth_dictionary = utils.build_groundtruth_vrd_dictionary(
image_groundtruth, class_label_map, relationship_label_map)
relation_evaluator.add_single_ground_truth_image_info(
image_id, groundtruth_dictionary)
phrase_evaluator.add_single_ground_truth_image_info(
image_id, groundtruth_dictionary)
all_predictions = pd.read_csv(parsed_args.input_predictions)
for _, prediction_data in enumerate(all_predictions.groupby('ImageID')):
image_id, image_predictions = prediction_data
prediction_dictionary = utils.build_predictions_vrd_dictionary(
image_predictions, class_label_map, relationship_label_map)
relation_evaluator.add_single_detected_image_info(
image_id, prediction_dictionary)
phrase_evaluator.add_single_detected_image_info(
image_id, prediction_dictionary)
relation_metrics = relation_evaluator.evaluate(
relationships=_swap_labelmap_dict(relationship_label_map))
phrase_metrics = phrase_evaluator.evaluate(
relationships=_swap_labelmap_dict(relationship_label_map))
with open(parsed_args.output_metrics, 'w') as fid:
io_utils.write_csv(fid, relation_metrics)
io_utils.write_csv(fid, phrase_metrics)