from abc import ABCMeta from abc import abstractmethod #import collections import logging import unicodedata import numpy as np from .fields import InputDataFields, DetectionResultFields from .object_detection_evaluation import ObjectDetectionEvaluation def create_category_index(categories): """Creates dictionary of COCO compatible categories keyed by category id. Args: categories: a list of dicts, each of which has the following keys: 'id': (required) an integer id uniquely identifying this category. 'name': (required) string representing category name e.g., 'cat', 'dog', 'pizza'. Returns: category_index: a dict containing the same entries as categories, but keyed by the 'id' field of each category. """ category_index = {} for cat in categories: category_index[cat['id']] = cat return category_index class DetectionEvaluator(metaclass=ABCMeta): """Interface for object detection evalution classes. Example usage of the Evaluator: ------------------------------ evaluator = DetectionEvaluator(categories) # Detections and groundtruth for image 1. evaluator.add_single_gt_image_info(...) evaluator.add_single_detected_image_info(...) # Detections and groundtruth for image 2. evaluator.add_single_gt_image_info(...) evaluator.add_single_detected_image_info(...) metrics_dict = evaluator.evaluation() """ def __init__(self, categories): """Constructor. Args: categories: A list of dicts, each of which has the following keys - 'id': (required) an integer id uniquely identifying this category. 'name': (required) string representing category name e.g., 'cat', 'dog'. """ self._categories = categories def observe_result_dict_for_single_example(self, eval_dict): """Observes an evaluation result dict for a single example. When executing eagerly, once all observations have been observed by this method you can use `.evaluation()` to get the final metrics. When using `tf.estimator.Estimator` for evaluation this function is used by `get_estimator_eval_metric_ops()` to construct the metric update op. Args: eval_dict: A dictionary that holds tensors for evaluating an object detection model, returned from eval_util.result_dict_for_single_example(). Returns: None when executing eagerly, or an update_op that can be used to update the eval metrics in `tf.estimator.EstimatorSpec`. """ raise NotImplementedError('Not implemented for this evaluator!') @abstractmethod def add_single_ground_truth_image_info(self, image_id, gt_dict): """Adds groundtruth for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. gt_dict: A dictionary of groundtruth numpy arrays required for evaluations. """ pass @abstractmethod def add_single_detected_image_info(self, image_id, detections_dict): """Adds detections for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. detections_dict: A dictionary of detection numpy arrays required for evaluation. """ pass @abstractmethod def evaluate(self): """Evaluates detections and returns a dictionary of metrics.""" pass @abstractmethod def clear(self): """Clears the state to prepare for a fresh evaluation.""" pass class ObjectDetectionEvaluator(DetectionEvaluator): """A class to evaluation detections.""" def __init__(self, categories, matching_iou_threshold=0.5, recall_lower_bound=0.0, recall_upper_bound=1.0, evaluate_corlocs=False, evaluate_precision_recall=False, metric_prefix=None, use_weighted_mean_ap=False, evaluate_masks=False, group_of_weight=0.0): """Constructor. Args: categories: A list of dicts, each of which has the following keys - 'id': (required) an integer id uniquely identifying this category. 'name': (required) string representing category name e.g., 'cat', 'dog'. matching_iou_threshold: IOU threshold to use for matching groundtruth boxes to detection boxes. recall_lower_bound: lower bound of recall operating area. recall_upper_bound: upper bound of recall operating area. evaluate_corlocs: (optional) boolean which determines if corloc scores are to be returned or not. evaluate_precision_recall: (optional) boolean which determines if precision and recall values are to be returned or not. metric_prefix: (optional) string prefix for metric name; if None, no prefix is used. use_weighted_mean_ap: (optional) boolean which determines if the mean average precision is computed directly from the scores and tp_fp_labels of all classes. evaluate_masks: If False, evaluation will be performed based on boxes. If True, mask evaluation will be performed instead. group_of_weight: Weight of group-of boxes.If set to 0, detections of the correct class within a group-of box are ignored. If weight is > 0, then if at least one detection falls within a group-of box with matching_iou_threshold, weight group_of_weight is added to true positives. Consequently, if no detection falls within a group-of box, weight group_of_weight is added to false negatives. Raises: ValueError: If the category ids are not 1-indexed. """ super(ObjectDetectionEvaluator, self).__init__(categories) self._num_classes = max([cat['id'] for cat in categories]) if min(cat['id'] for cat in categories) < 1: raise ValueError('Classes should be 1-indexed.') self._matching_iou_threshold = matching_iou_threshold self._recall_lower_bound = recall_lower_bound self._recall_upper_bound = recall_upper_bound self._use_weighted_mean_ap = use_weighted_mean_ap self._label_id_offset = 1 self._evaluate_masks = evaluate_masks self._group_of_weight = group_of_weight self._evaluation = ObjectDetectionEvaluation( num_gt_classes=self._num_classes, matching_iou_threshold=self._matching_iou_threshold, recall_lower_bound=self._recall_lower_bound, recall_upper_bound=self._recall_upper_bound, use_weighted_mean_ap=self._use_weighted_mean_ap, label_id_offset=self._label_id_offset, group_of_weight=self._group_of_weight) self._image_ids = set([]) self._evaluate_corlocs = evaluate_corlocs self._evaluate_precision_recall = evaluate_precision_recall self._metric_prefix = (metric_prefix + '_') if metric_prefix else '' self._build_metric_names() def _build_metric_names(self): """Builds a list with metric names.""" if self._recall_lower_bound > 0.0 or self._recall_upper_bound < 1.0: self._metric_names = [ self._metric_prefix + 'Precision/mAP@{}IOU@[{:.1f},{:.1f}]Recall'.format( self._matching_iou_threshold, self._recall_lower_bound, self._recall_upper_bound) ] else: self._metric_names = [ self._metric_prefix + 'Precision/mAP@{}IOU'.format(self._matching_iou_threshold) ] if self._evaluate_corlocs: self._metric_names.append( self._metric_prefix + 'Precision/meanCorLoc@{}IOU'.format(self._matching_iou_threshold)) category_index = create_category_index(self._categories) for idx in range(self._num_classes): if idx + self._label_id_offset in category_index: category_name = category_index[idx + self._label_id_offset]['name'] category_name = unicodedata.normalize('NFKD', category_name) self._metric_names.append( self._metric_prefix + 'PerformanceByCategory/AP@{}IOU/{}'.format( self._matching_iou_threshold, category_name)) if self._evaluate_corlocs: self._metric_names.append( self._metric_prefix + 'PerformanceByCategory/CorLoc@{}IOU/{}'.format( self._matching_iou_threshold, category_name)) def add_single_ground_truth_image_info(self, image_id, gt_dict): """Adds groundtruth for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. gt_dict: A dictionary containing - InputDataFields.gt_boxes: float32 numpy array of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of the format [ymin, xmin, ymax, xmax] in absolute image coordinates. InputDataFields.gt_classes: integer numpy array of shape [num_boxes] containing 1-indexed groundtruth classes for the boxes. InputDataFields.gt_difficult: Optional length M numpy boolean array denoting whether a ground truth box is a difficult instance or not. This field is optional to support the case that no boxes are difficult. InputDataFields.gt_instance_masks: Optional numpy array of shape [num_boxes, height, width] with values in {0, 1}. Raises: ValueError: On adding groundtruth for an image more than once. Will also raise error if instance masks are not in groundtruth dictionary. """ if image_id in self._image_ids: return gt_classes = gt_dict[InputDataFields.gt_classes] - self._label_id_offset # If the key is not present in the gt_dict or the array is empty # (unless there are no annotations for the groundtruth on this image) # use values from the dictionary or insert None otherwise. if (InputDataFields.gt_difficult in gt_dict and (gt_dict[InputDataFields.gt_difficult].size or not gt_classes.size)): gt_difficult = gt_dict[InputDataFields.gt_difficult] else: gt_difficult = None # FIXME disable difficult flag warning, will support flag eventually # if not len(self._image_ids) % 1000: # logging.warning('image %s does not have groundtruth difficult flag specified', image_id) gt_masks = None if self._evaluate_masks: if InputDataFields.gt_instance_masks not in gt_dict: raise ValueError('Instance masks not in groundtruth dictionary.') gt_masks = gt_dict[InputDataFields.gt_instance_masks] self._evaluation.add_single_ground_truth_image_info( image_key=image_id, gt_boxes=gt_dict[InputDataFields.gt_boxes], gt_class_labels=gt_classes, gt_is_difficult_list=gt_difficult, gt_masks=gt_masks) self._image_ids.update([image_id]) def add_single_detected_image_info(self, image_id, detections_dict): """Adds detections for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. detections_dict: A dictionary containing - DetectionResultFields.detection_boxes: float32 numpy array of shape [num_boxes, 4] containing `num_boxes` detection boxes of the format [ymin, xmin, ymax, xmax] in absolute image coordinates. DetectionResultFields.detection_scores: float32 numpy array of shape [num_boxes] containing detection scores for the boxes. DetectionResultFields.detection_classes: integer numpy array of shape [num_boxes] containing 1-indexed detection classes for the boxes. DetectionResultFields.detection_masks: uint8 numpy array of shape [num_boxes, height, width] containing `num_boxes` masks of values ranging between 0 and 1. Raises: ValueError: If detection masks are not in detections dictionary. """ detection_classes = detections_dict[DetectionResultFields.detection_classes] - self._label_id_offset detection_masks = None if self._evaluate_masks: if DetectionResultFields.detection_masks not in detections_dict: raise ValueError('Detection masks not in detections dictionary.') detection_masks = detections_dict[DetectionResultFields.detection_masks] self._evaluation.add_single_detected_image_info( image_key=image_id, detected_boxes=detections_dict[DetectionResultFields.detection_boxes], detected_scores=detections_dict[DetectionResultFields.detection_scores], detected_class_labels=detection_classes, detected_masks=detection_masks) def evaluate(self): """Compute evaluation result. Returns: A dictionary of metrics with the following fields - 1. summary_metrics: '_Precision/mAP@IOU': mean average precision at the specified IOU threshold. 2. per_category_ap: category specific results with keys of the form '_PerformanceByCategory/ mAP@IOU/category'. """ metrics = self._evaluation.evaluate() pascal_metrics = {self._metric_names[0]: metrics['mean_ap']} if self._evaluate_corlocs: pascal_metrics[self._metric_names[1]] = metrics['mean_corloc'] category_index = create_category_index(self._categories) for idx in range(metrics['per_class_ap'].size): if idx + self._label_id_offset in category_index: category_name = category_index[idx + self._label_id_offset]['name'] category_name = unicodedata.normalize('NFKD', category_name) display_name = self._metric_prefix + 'PerformanceByCategory/AP@{}IOU/{}'.format( self._matching_iou_threshold, category_name) pascal_metrics[display_name] = metrics['per_class_ap'][idx] # Optionally add precision and recall values if self._evaluate_precision_recall: display_name = self._metric_prefix + 'PerformanceByCategory/Precision@{}IOU/{}'.format( self._matching_iou_threshold, category_name) pascal_metrics[display_name] = metrics['per_class_precision'][idx] display_name = self._metric_prefix + 'PerformanceByCategory/Recall@{}IOU/{}'.format( self._matching_iou_threshold, category_name) pascal_metrics[display_name] = metrics['per_class_recall'][idx] # Optionally add CorLoc metrics.classes if self._evaluate_corlocs: display_name = self._metric_prefix + 'PerformanceByCategory/CorLoc@{}IOU/{}'.format( self._matching_iou_threshold, category_name) pascal_metrics[display_name] = metrics['per_class_corloc'][idx] return pascal_metrics def clear(self): """Clears the state to prepare for a fresh evaluation.""" self._evaluation = ObjectDetectionEvaluation( num_gt_classes=self._num_classes, matching_iou_threshold=self._matching_iou_threshold, use_weighted_mean_ap=self._use_weighted_mean_ap, label_id_offset=self._label_id_offset) self._image_ids.clear() class PascalDetectionEvaluator(ObjectDetectionEvaluator): """A class to evaluation detections using PASCAL metrics.""" def __init__(self, categories, matching_iou_threshold=0.5): super(PascalDetectionEvaluator, self).__init__( categories, matching_iou_threshold=matching_iou_threshold, evaluate_corlocs=False, metric_prefix='PascalBoxes', use_weighted_mean_ap=False) class WeightedPascalDetectionEvaluator(ObjectDetectionEvaluator): """A class to evaluation detections using weighted PASCAL metrics. Weighted PASCAL metrics computes the mean average precision as the average precision given the scores and tp_fp_labels of all classes. In comparison, PASCAL metrics computes the mean average precision as the mean of the per-class average precisions. This definition is very similar to the mean of the per-class average precisions weighted by class frequency. However, they are typically not the same as the average precision is not a linear function of the scores and tp_fp_labels. """ def __init__(self, categories, matching_iou_threshold=0.5): super(WeightedPascalDetectionEvaluator, self).__init__( categories, matching_iou_threshold=matching_iou_threshold, evaluate_corlocs=False, metric_prefix='WeightedPascalBoxes', use_weighted_mean_ap=True) class PrecisionAtRecallDetectionEvaluator(ObjectDetectionEvaluator): """A class to evaluation detections using precision@recall metrics.""" def __init__(self, categories, matching_iou_threshold=0.5, recall_lower_bound=0., recall_upper_bound=1.0): super(PrecisionAtRecallDetectionEvaluator, self).__init__( categories, matching_iou_threshold=matching_iou_threshold, recall_lower_bound=recall_lower_bound, recall_upper_bound=recall_upper_bound, evaluate_corlocs=False, metric_prefix='PrecisionAtRecallBoxes', use_weighted_mean_ap=False) class OpenImagesDetectionEvaluator(ObjectDetectionEvaluator): """A class to evaluation detections using Open Images V2 metrics. Open Images V2 introduce group_of type of bounding boxes and this metric handles those boxes appropriately. """ def __init__(self, categories, matching_iou_threshold=0.5, evaluate_masks=False, evaluate_corlocs=False, metric_prefix='OpenImagesV5', group_of_weight=0.0): """Constructor. Args: categories: A list of dicts, each of which has the following keys - 'id': (required) an integer id uniquely identifying this category. 'name': (required) string representing category name e.g., 'cat', 'dog'. matching_iou_threshold: IOU threshold to use for matching groundtruth boxes to detection boxes. evaluate_masks: if True, evaluator evaluates masks. evaluate_corlocs: if True, additionally evaluates and returns CorLoc. metric_prefix: Prefix name of the metric. group_of_weight: Weight of the group-of bounding box. If set to 0 (default for Open Images V2 detection protocol), detections of the correct class within a group-of box are ignored. If weight is > 0, then if at least one detection falls within a group-of box with matching_iou_threshold, weight group_of_weight is added to true positives. Consequently, if no detection falls within a group-of box, weight group_of_weight is added to false negatives. """ super(OpenImagesDetectionEvaluator, self).__init__( categories, matching_iou_threshold, evaluate_corlocs, metric_prefix=metric_prefix, group_of_weight=group_of_weight, evaluate_masks=evaluate_masks) def add_single_ground_truth_image_info(self, image_id, gt_dict): """Adds groundtruth for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. gt_dict: A dictionary containing - InputDataFields.gt_boxes: float32 numpy array of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of the format [ymin, xmin, ymax, xmax] in absolute image coordinates. InputDataFields.gt_classes: integer numpy array of shape [num_boxes] containing 1-indexed groundtruth classes for the boxes. InputDataFields.gt_group_of: Optional length M numpy boolean array denoting whether a groundtruth box contains a group of instances. Raises: ValueError: On adding groundtruth for an image more than once. """ if image_id in self._image_ids: return gt_classes = (gt_dict[InputDataFields.gt_classes] - self._label_id_offset) # If the key is not present in the gt_dict or the array is empty # (unless there are no annotations for the groundtruth on this image) # use values from the dictionary or insert None otherwise. if (InputDataFields.gt_group_of in gt_dict and (gt_dict[InputDataFields.gt_group_of].size or not gt_classes.size)): gt_group_of = gt_dict[InputDataFields.gt_group_of] else: gt_group_of = None # FIXME disable warning for now, will add group_of flag eventually # if not len(self._image_ids) % 1000: # logging.warning('image %s does not have groundtruth group_of flag specified', image_id) if self._evaluate_masks: gt_masks = gt_dict[InputDataFields.gt_instance_masks] else: gt_masks = None self._evaluation.add_single_ground_truth_image_info( image_id, gt_dict[InputDataFields.gt_boxes], gt_classes, gt_is_difficult_list=None, gt_is_group_of_list=gt_group_of, gt_masks=gt_masks) self._image_ids.update([image_id]) class OpenImagesChallengeEvaluator(OpenImagesDetectionEvaluator): """A class implements Open Images Challenge metrics. Both Detection and Instance Segmentation evaluation metrics are implemented. Open Images Challenge Detection metric has two major changes in comparison with Open Images V2 detection metric: - a custom weight might be specified for detecting an object contained in a group-of box. - verified image-level labels should be explicitly provided for evaluation: in case an image has neither positive nor negative image level label of class c, all detections of this class on this image will be ignored. Open Images Challenge Instance Segmentation metric allows to measure performance of models in case of incomplete annotations: some instances are annotations only on box level and some - on image-level. In addition, image-level labels are taken into account as in detection metric. Open Images Challenge Detection metric default parameters: evaluate_masks = False group_of_weight = 1.0 Open Images Challenge Instance Segmentation metric default parameters: evaluate_masks = True (group_of_weight will not matter) """ def __init__( self, categories, evaluate_masks=False, matching_iou_threshold=0.5, evaluate_corlocs=False, group_of_weight=1.0): """Constructor. Args: categories: A list of dicts, each of which has the following keys - 'id': (required) an integer id uniquely identifying this category. 'name': (required) string representing category name e.g., 'cat', 'dog'. evaluate_masks: set to true for instance segmentation metric and to false for detection metric. matching_iou_threshold: IOU threshold to use for matching groundtruth boxes to detection boxes. evaluate_corlocs: if True, additionally evaluates and returns CorLoc. group_of_weight: Weight of group-of boxes. If set to 0, detections of the correct class within a group-of box are ignored. If weight is > 0, then if at least one detection falls within a group-of box with matching_iou_threshold, weight group_of_weight is added to true positives. Consequently, if no detection falls within a group-of box, weight group_of_weight is added to false negatives. """ if not evaluate_masks: metrics_prefix = 'OpenImagesDetectionChallenge' else: metrics_prefix = 'OpenImagesInstanceSegmentationChallenge' super(OpenImagesChallengeEvaluator, self).__init__( categories, matching_iou_threshold, evaluate_masks=evaluate_masks, evaluate_corlocs=evaluate_corlocs, group_of_weight=group_of_weight, metric_prefix=metrics_prefix) self._evaluatable_labels = {} def add_single_ground_truth_image_info(self, image_id, gt_dict): """Adds groundtruth for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. gt_dict: A dictionary containing - InputDataFields.gt_boxes: float32 numpy array of shape [num_boxes, 4] containing `num_boxes` groundtruth boxes of the format [ymin, xmin, ymax, xmax] in absolute image coordinates. InputDataFields.gt_classes: integer numpy array of shape [num_boxes] containing 1-indexed groundtruth classes for the boxes. InputDataFields.gt_image_classes: integer 1D numpy array containing all classes for which labels are verified. InputDataFields.gt_group_of: Optional length M numpy boolean array denoting whether a groundtruth box contains a group of instances. Raises: ValueError: On adding groundtruth for an image more than once. """ super(OpenImagesChallengeEvaluator, self).add_single_ground_truth_image_info(image_id, gt_dict) input_fields = InputDataFields gt_classes = gt_dict[input_fields.gt_classes] - self._label_id_offset image_classes = np.array([], dtype=int) if input_fields.gt_image_classes in gt_dict: image_classes = gt_dict[input_fields.gt_image_classes] elif input_fields.gt_labeled_classes in gt_dict: image_classes = gt_dict[input_fields.gt_labeled_classes] image_classes -= self._label_id_offset self._evaluatable_labels[image_id] = np.unique( np.concatenate((image_classes, gt_classes))) def add_single_detected_image_info(self, image_id, detections_dict): """Adds detections for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. detections_dict: A dictionary containing - DetectionResultFields.detection_boxes: float32 numpy array of shape [num_boxes, 4] containing `num_boxes` detection boxes of the format [ymin, xmin, ymax, xmax] in absolute image coordinates. DetectionResultFields.detection_scores: float32 numpy array of shape [num_boxes] containing detection scores for the boxes. DetectionResultFields.detection_classes: integer numpy array of shape [num_boxes] containing 1-indexed detection classes for the boxes. Raises: ValueError: If detection masks are not in detections dictionary. """ if image_id not in self._image_ids: # Since for the correct work of evaluator it is assumed that groundtruth # is inserted first we make sure to break the code if is it not the case. self._image_ids.update([image_id]) self._evaluatable_labels[image_id] = np.array([]) detection_classes = detections_dict[DetectionResultFields.detection_classes] - self._label_id_offset allowed_classes = np.where(np.isin(detection_classes, self._evaluatable_labels[image_id])) detection_classes = detection_classes[allowed_classes] detected_boxes = detections_dict[DetectionResultFields.detection_boxes][allowed_classes] detected_scores = detections_dict[DetectionResultFields.detection_scores][allowed_classes] if self._evaluate_masks: detection_masks = detections_dict[DetectionResultFields.detection_masks][allowed_classes] else: detection_masks = None self._evaluation.add_single_detected_image_info( image_key=image_id, detected_boxes=detected_boxes, detected_scores=detected_scores, detected_class_labels=detection_classes, detected_masks=detection_masks) def clear(self): """Clears stored data.""" super(OpenImagesChallengeEvaluator, self).clear() self._evaluatable_labels.clear()