# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Evaluate Object Detection result on a single image. Annotate each detected result as true positives or false positive according to a predefined IOU ratio. Non Maximum Supression is used by default. Multi class detection is supported by default. Based on the settings, per image evaluation is either performed on boxes or on object masks. """ import numpy as np from object_detection.utils import np_box_list from object_detection.utils import np_box_list_ops from object_detection.utils import np_box_mask_list from object_detection.utils import np_box_mask_list_ops class PerImageEvaluation(object): """Evaluate detection result of a single image.""" def __init__(self, num_groundtruth_classes, matching_iou_threshold=0.5, nms_iou_threshold=0.3, nms_max_output_boxes=50, group_of_weight=0.0): """Initialized PerImageEvaluation by evaluation parameters. Args: num_groundtruth_classes: Number of ground truth object classes matching_iou_threshold: A ratio of area intersection to union, which is the threshold to consider whether a detection is true positive or not nms_iou_threshold: IOU threshold used in Non Maximum Suppression. nms_max_output_boxes: Number of maximum output boxes in NMS. group_of_weight: Weight of the group-of boxes. """ self.matching_iou_threshold = matching_iou_threshold self.nms_iou_threshold = nms_iou_threshold self.nms_max_output_boxes = nms_max_output_boxes self.num_groundtruth_classes = num_groundtruth_classes self.group_of_weight = group_of_weight def compute_object_detection_metrics( self, detected_boxes, detected_scores, detected_class_labels, groundtruth_boxes, groundtruth_class_labels, groundtruth_is_difficult_list, groundtruth_is_group_of_list, detected_masks=None, groundtruth_masks=None): """Evaluates detections as being tp, fp or weighted from a single image. The evaluation is done in two stages: 1. All detections are matched to non group-of boxes; true positives are determined and detections matched to difficult boxes are ignored. 2. Detections that are determined as false positives are matched against group-of boxes and weighted if matched. Args: detected_boxes: A float numpy array of shape [N, 4], representing N regions of detected object regions. Each row is of the format [y_min, x_min, y_max, x_max] detected_scores: A float numpy array of shape [N, 1], representing the confidence scores of the detected N object instances. detected_class_labels: A integer numpy array of shape [N, 1], repreneting the class labels of the detected N object instances. groundtruth_boxes: A float numpy array of shape [M, 4], representing M regions of object instances in ground truth groundtruth_class_labels: An integer numpy array of shape [M, 1], representing M class labels of object instances in ground truth groundtruth_is_difficult_list: A boolean numpy array of length M denoting whether a ground truth box is a difficult instance or not groundtruth_is_group_of_list: A boolean numpy array of length M denoting whether a ground truth box has group-of tag detected_masks: (optional) A uint8 numpy array of shape [N, height, width]. If not None, the metrics will be computed based on masks. groundtruth_masks: (optional) A uint8 numpy array of shape [M, height, width]. Returns: scores: A list of C float numpy arrays. Each numpy array is of shape [K, 1], representing K scores detected with object class label c tp_fp_labels: A list of C boolean numpy arrays. Each numpy array is of shape [K, 1], representing K True/False positive label of object instances detected with class label c is_class_correctly_detected_in_image: a numpy integer array of shape [C, 1], indicating whether the correponding class has a least one instance being correctly detected in the image """ detected_boxes, detected_scores, detected_class_labels, detected_masks = ( self._remove_invalid_boxes(detected_boxes, detected_scores, detected_class_labels, detected_masks)) scores, tp_fp_labels = self._compute_tp_fp( detected_boxes=detected_boxes, detected_scores=detected_scores, detected_class_labels=detected_class_labels, groundtruth_boxes=groundtruth_boxes, groundtruth_class_labels=groundtruth_class_labels, groundtruth_is_difficult_list=groundtruth_is_difficult_list, groundtruth_is_group_of_list=groundtruth_is_group_of_list, detected_masks=detected_masks, groundtruth_masks=groundtruth_masks) is_class_correctly_detected_in_image = self._compute_cor_loc( detected_boxes=detected_boxes, detected_scores=detected_scores, detected_class_labels=detected_class_labels, groundtruth_boxes=groundtruth_boxes, groundtruth_class_labels=groundtruth_class_labels, detected_masks=detected_masks, groundtruth_masks=groundtruth_masks) return scores, tp_fp_labels, is_class_correctly_detected_in_image def _compute_cor_loc(self, detected_boxes, detected_scores, detected_class_labels, groundtruth_boxes, groundtruth_class_labels, detected_masks=None, groundtruth_masks=None): """Compute CorLoc score for object detection result. Args: detected_boxes: A float numpy array of shape [N, 4], representing N regions of detected object regions. Each row is of the format [y_min, x_min, y_max, x_max] detected_scores: A float numpy array of shape [N, 1], representing the confidence scores of the detected N object instances. detected_class_labels: A integer numpy array of shape [N, 1], repreneting the class labels of the detected N object instances. groundtruth_boxes: A float numpy array of shape [M, 4], representing M regions of object instances in ground truth groundtruth_class_labels: An integer numpy array of shape [M, 1], representing M class labels of object instances in ground truth detected_masks: (optional) A uint8 numpy array of shape [N, height, width]. If not None, the scores will be computed based on masks. groundtruth_masks: (optional) A uint8 numpy array of shape [M, height, width]. Returns: is_class_correctly_detected_in_image: a numpy integer array of shape [C, 1], indicating whether the correponding class has a least one instance being correctly detected in the image Raises: ValueError: If detected masks is not None but groundtruth masks are None, or the other way around. """ if (detected_masks is not None and groundtruth_masks is None) or (detected_masks is None and groundtruth_masks is not None): raise ValueError( 'If `detected_masks` is provided, then `groundtruth_masks` should ' 'also be provided.' ) is_class_correctly_detected_in_image = np.zeros( self.num_groundtruth_classes, dtype=int) for i in range(self.num_groundtruth_classes): (gt_boxes_at_ith_class, gt_masks_at_ith_class, detected_boxes_at_ith_class, detected_scores_at_ith_class, detected_masks_at_ith_class) = self._get_ith_class_arrays( detected_boxes, detected_scores, detected_masks, detected_class_labels, groundtruth_boxes, groundtruth_masks, groundtruth_class_labels, i) is_class_correctly_detected_in_image[i] = ( self._compute_is_class_correctly_detected_in_image( detected_boxes=detected_boxes_at_ith_class, detected_scores=detected_scores_at_ith_class, groundtruth_boxes=gt_boxes_at_ith_class, detected_masks=detected_masks_at_ith_class, groundtruth_masks=gt_masks_at_ith_class)) return is_class_correctly_detected_in_image def _compute_is_class_correctly_detected_in_image( self, detected_boxes, detected_scores, groundtruth_boxes, detected_masks=None, groundtruth_masks=None): """Compute CorLoc score for a single class. Args: detected_boxes: A numpy array of shape [N, 4] representing detected box coordinates detected_scores: A 1-d numpy array of length N representing classification score groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth box coordinates detected_masks: (optional) A np.uint8 numpy array of shape [N, height, width]. If not None, the scores will be computed based on masks. groundtruth_masks: (optional) A np.uint8 numpy array of shape [M, height, width]. Returns: is_class_correctly_detected_in_image: An integer 1 or 0 denoting whether a class is correctly detected in the image or not """ if detected_boxes.size > 0: if groundtruth_boxes.size > 0: max_score_id = np.argmax(detected_scores) mask_mode = False if detected_masks is not None and groundtruth_masks is not None: mask_mode = True if mask_mode: detected_boxlist = np_box_mask_list.BoxMaskList( box_data=np.expand_dims(detected_boxes[max_score_id], axis=0), mask_data=np.expand_dims(detected_masks[max_score_id], axis=0)) gt_boxlist = np_box_mask_list.BoxMaskList( box_data=groundtruth_boxes, mask_data=groundtruth_masks) iou = np_box_mask_list_ops.iou(detected_boxlist, gt_boxlist) else: detected_boxlist = np_box_list.BoxList( np.expand_dims(detected_boxes[max_score_id, :], axis=0)) gt_boxlist = np_box_list.BoxList(groundtruth_boxes) iou = np_box_list_ops.iou(detected_boxlist, gt_boxlist) if np.max(iou) >= self.matching_iou_threshold: return 1 return 0 def _compute_tp_fp(self, detected_boxes, detected_scores, detected_class_labels, groundtruth_boxes, groundtruth_class_labels, groundtruth_is_difficult_list, groundtruth_is_group_of_list, detected_masks=None, groundtruth_masks=None): """Labels true/false positives of detections of an image across all classes. Args: detected_boxes: A float numpy array of shape [N, 4], representing N regions of detected object regions. Each row is of the format [y_min, x_min, y_max, x_max] detected_scores: A float numpy array of shape [N, 1], representing the confidence scores of the detected N object instances. detected_class_labels: A integer numpy array of shape [N, 1], repreneting the class labels of the detected N object instances. groundtruth_boxes: A float numpy array of shape [M, 4], representing M regions of object instances in ground truth groundtruth_class_labels: An integer numpy array of shape [M, 1], representing M class labels of object instances in ground truth groundtruth_is_difficult_list: A boolean numpy array of length M denoting whether a ground truth box is a difficult instance or not groundtruth_is_group_of_list: A boolean numpy array of length M denoting whether a ground truth box has group-of tag detected_masks: (optional) A np.uint8 numpy array of shape [N, height, width]. If not None, the scores will be computed based on masks. groundtruth_masks: (optional) A np.uint8 numpy array of shape [M, height, width]. Returns: result_scores: A list of float numpy arrays. Each numpy array is of shape [K, 1], representing K scores detected with object class label c result_tp_fp_labels: A list of boolean numpy array. Each numpy array is of shape [K, 1], representing K True/False positive label of object instances detected with class label c Raises: ValueError: If detected masks is not None but groundtruth masks are None, or the other way around. """ if detected_masks is not None and groundtruth_masks is None: raise ValueError( 'Detected masks is available but groundtruth masks is not.') if detected_masks is None and groundtruth_masks is not None: raise ValueError( 'Groundtruth masks is available but detected masks is not.') result_scores = [] result_tp_fp_labels = [] for i in range(self.num_groundtruth_classes): groundtruth_is_difficult_list_at_ith_class = ( groundtruth_is_difficult_list[groundtruth_class_labels == i]) groundtruth_is_group_of_list_at_ith_class = ( groundtruth_is_group_of_list[groundtruth_class_labels == i]) (gt_boxes_at_ith_class, gt_masks_at_ith_class, detected_boxes_at_ith_class, detected_scores_at_ith_class, detected_masks_at_ith_class) = self._get_ith_class_arrays( detected_boxes, detected_scores, detected_masks, detected_class_labels, groundtruth_boxes, groundtruth_masks, groundtruth_class_labels, i) scores, tp_fp_labels = self._compute_tp_fp_for_single_class( detected_boxes=detected_boxes_at_ith_class, detected_scores=detected_scores_at_ith_class, groundtruth_boxes=gt_boxes_at_ith_class, groundtruth_is_difficult_list= groundtruth_is_difficult_list_at_ith_class, groundtruth_is_group_of_list= groundtruth_is_group_of_list_at_ith_class, detected_masks=detected_masks_at_ith_class, groundtruth_masks=gt_masks_at_ith_class) result_scores.append(scores) result_tp_fp_labels.append(tp_fp_labels) return result_scores, result_tp_fp_labels def _get_overlaps_and_scores_mask_mode( self, detected_boxes, detected_scores, detected_masks, groundtruth_boxes, groundtruth_masks, groundtruth_is_group_of_list): """Computes overlaps and scores between detected and groudntruth masks. Args: detected_boxes: A numpy array of shape [N, 4] representing detected box coordinates detected_scores: A 1-d numpy array of length N representing classification score detected_masks: A uint8 numpy array of shape [N, height, width]. If not None, the scores will be computed based on masks. groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth box coordinates groundtruth_masks: A uint8 numpy array of shape [M, height, width]. groundtruth_is_group_of_list: A boolean numpy array of length M denoting whether a ground truth box has group-of tag. If a groundtruth box is group-of box, every detection matching this box is ignored. Returns: iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If gt_non_group_of_boxlist.num_boxes() == 0 it will be None. ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If gt_group_of_boxlist.num_boxes() == 0 it will be None. scores: The score of the detected boxlist. num_boxes: Number of non-maximum suppressed detected boxes. """ detected_boxlist = np_box_mask_list.BoxMaskList( box_data=detected_boxes, mask_data=detected_masks) detected_boxlist.add_field('scores', detected_scores) detected_boxlist = np_box_mask_list_ops.non_max_suppression( detected_boxlist, self.nms_max_output_boxes, self.nms_iou_threshold) gt_non_group_of_boxlist = np_box_mask_list.BoxMaskList( box_data=groundtruth_boxes[~groundtruth_is_group_of_list], mask_data=groundtruth_masks[~groundtruth_is_group_of_list]) gt_group_of_boxlist = np_box_mask_list.BoxMaskList( box_data=groundtruth_boxes[groundtruth_is_group_of_list], mask_data=groundtruth_masks[groundtruth_is_group_of_list]) iou = np_box_mask_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist) ioa = np.transpose( np_box_mask_list_ops.ioa(gt_group_of_boxlist, detected_boxlist)) scores = detected_boxlist.get_field('scores') num_boxes = detected_boxlist.num_boxes() return iou, ioa, scores, num_boxes def _get_overlaps_and_scores_box_mode( self, detected_boxes, detected_scores, groundtruth_boxes, groundtruth_is_group_of_list): """Computes overlaps and scores between detected and groudntruth boxes. Args: detected_boxes: A numpy array of shape [N, 4] representing detected box coordinates detected_scores: A 1-d numpy array of length N representing classification score groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth box coordinates groundtruth_is_group_of_list: A boolean numpy array of length M denoting whether a ground truth box has group-of tag. If a groundtruth box is group-of box, every detection matching this box is ignored. Returns: iou: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If gt_non_group_of_boxlist.num_boxes() == 0 it will be None. ioa: A float numpy array of size [num_detected_boxes, num_gt_boxes]. If gt_group_of_boxlist.num_boxes() == 0 it will be None. scores: The score of the detected boxlist. num_boxes: Number of non-maximum suppressed detected boxes. """ detected_boxlist = np_box_list.BoxList(detected_boxes) detected_boxlist.add_field('scores', detected_scores) detected_boxlist = np_box_list_ops.non_max_suppression( detected_boxlist, self.nms_max_output_boxes, self.nms_iou_threshold) gt_non_group_of_boxlist = np_box_list.BoxList( groundtruth_boxes[~groundtruth_is_group_of_list]) gt_group_of_boxlist = np_box_list.BoxList( groundtruth_boxes[groundtruth_is_group_of_list]) iou = np_box_list_ops.iou(detected_boxlist, gt_non_group_of_boxlist) ioa = np.transpose( np_box_list_ops.ioa(gt_group_of_boxlist, detected_boxlist)) scores = detected_boxlist.get_field('scores') num_boxes = detected_boxlist.num_boxes() return iou, ioa, scores, num_boxes def _compute_tp_fp_for_single_class( self, detected_boxes, detected_scores, groundtruth_boxes, groundtruth_is_difficult_list, groundtruth_is_group_of_list, detected_masks=None, groundtruth_masks=None): """Labels boxes detected with the same class from the same image as tp/fp. Args: detected_boxes: A numpy array of shape [N, 4] representing detected box coordinates detected_scores: A 1-d numpy array of length N representing classification score groundtruth_boxes: A numpy array of shape [M, 4] representing ground truth box coordinates groundtruth_is_difficult_list: A boolean numpy array of length M denoting whether a ground truth box is a difficult instance or not. If a groundtruth box is difficult, every detection matching this box is ignored. groundtruth_is_group_of_list: A boolean numpy array of length M denoting whether a ground truth box has group-of tag. If a groundtruth box is group-of box, every detection matching this box is ignored. detected_masks: (optional) A uint8 numpy array of shape [N, height, width]. If not None, the scores will be computed based on masks. groundtruth_masks: (optional) A uint8 numpy array of shape [M, height, width]. Returns: Two arrays of the same size, containing all boxes that were evaluated as being true positives or false positives; if a box matched to a difficult box or to a group-of box, it is ignored. scores: A numpy array representing the detection scores. tp_fp_labels: a boolean numpy array indicating whether a detection is a true positive. """ if detected_boxes.size == 0: return np.array([], dtype=float), np.array([], dtype=bool) mask_mode = False if detected_masks is not None and groundtruth_masks is not None: mask_mode = True if mask_mode: (iou, ioa, scores, num_detected_boxes) = self._get_overlaps_and_scores_mask_mode( detected_boxes=detected_boxes, detected_scores=detected_scores, detected_masks=detected_masks, groundtruth_boxes=groundtruth_boxes, groundtruth_masks=groundtruth_masks, groundtruth_is_group_of_list=groundtruth_is_group_of_list) else: (iou, ioa, scores, num_detected_boxes) = self._get_overlaps_and_scores_box_mode( detected_boxes=detected_boxes, detected_scores=detected_scores, groundtruth_boxes=groundtruth_boxes, groundtruth_is_group_of_list=groundtruth_is_group_of_list) if groundtruth_boxes.size == 0: return scores, np.zeros(num_detected_boxes, dtype=bool) tp_fp_labels = np.zeros(num_detected_boxes, dtype=bool) is_matched_to_difficult_box = np.zeros(num_detected_boxes, dtype=bool) is_matched_to_group_of_box = np.zeros(num_detected_boxes, dtype=bool) # The evaluation is done in two stages: # 1. All detections are matched to non group-of boxes; true positives are # determined and detections matched to difficult boxes are ignored. # 2. Detections that are determined as false positives are matched against # group-of boxes and scored with weight w per ground truth box is # matched. # Tp-fp evaluation for non-group of boxes (if any). if iou.shape[1] > 0: groundtruth_nongroup_of_is_difficult_list = groundtruth_is_difficult_list[ ~groundtruth_is_group_of_list] max_overlap_gt_ids = np.argmax(iou, axis=1) is_gt_box_detected = np.zeros(iou.shape[1], dtype=bool) for i in range(num_detected_boxes): gt_id = max_overlap_gt_ids[i] if iou[i, gt_id] >= self.matching_iou_threshold: if not groundtruth_nongroup_of_is_difficult_list[gt_id]: if not is_gt_box_detected[gt_id]: tp_fp_labels[i] = True is_gt_box_detected[gt_id] = True else: is_matched_to_difficult_box[i] = True scores_group_of = np.zeros(ioa.shape[1], dtype=float) tp_fp_labels_group_of = self.group_of_weight * np.ones( ioa.shape[1], dtype=float) # Tp-fp evaluation for group of boxes. if ioa.shape[1] > 0: max_overlap_group_of_gt_ids = np.argmax(ioa, axis=1) for i in range(num_detected_boxes): gt_id = max_overlap_group_of_gt_ids[i] if (not tp_fp_labels[i] and not is_matched_to_difficult_box[i] and ioa[i, gt_id] >= self.matching_iou_threshold): is_matched_to_group_of_box[i] = True scores_group_of[gt_id] = max(scores_group_of[gt_id], scores[i]) selector = np.where((scores_group_of > 0) & (tp_fp_labels_group_of > 0)) scores_group_of = scores_group_of[selector] tp_fp_labels_group_of = tp_fp_labels_group_of[selector] return np.concatenate( (scores[~is_matched_to_difficult_box & ~is_matched_to_group_of_box], scores_group_of)), np.concatenate( (tp_fp_labels[~is_matched_to_difficult_box & ~is_matched_to_group_of_box].astype(float), tp_fp_labels_group_of)) def _get_ith_class_arrays(self, detected_boxes, detected_scores, detected_masks, detected_class_labels, groundtruth_boxes, groundtruth_masks, groundtruth_class_labels, class_index): """Returns numpy arrays belonging to class with index `class_index`. Args: detected_boxes: A numpy array containing detected boxes. detected_scores: A numpy array containing detected scores. detected_masks: A numpy array containing detected masks. detected_class_labels: A numpy array containing detected class labels. groundtruth_boxes: A numpy array containing groundtruth boxes. groundtruth_masks: A numpy array containing groundtruth masks. groundtruth_class_labels: A numpy array containing groundtruth class labels. class_index: An integer index. Returns: gt_boxes_at_ith_class: A numpy array containing groundtruth boxes labeled as ith class. gt_masks_at_ith_class: A numpy array containing groundtruth masks labeled as ith class. detected_boxes_at_ith_class: A numpy array containing detected boxes corresponding to the ith class. detected_scores_at_ith_class: A numpy array containing detected scores corresponding to the ith class. detected_masks_at_ith_class: A numpy array containing detected masks corresponding to the ith class. """ selected_groundtruth = (groundtruth_class_labels == class_index) gt_boxes_at_ith_class = groundtruth_boxes[selected_groundtruth] if groundtruth_masks is not None: gt_masks_at_ith_class = groundtruth_masks[selected_groundtruth] else: gt_masks_at_ith_class = None selected_detections = (detected_class_labels == class_index) detected_boxes_at_ith_class = detected_boxes[selected_detections] detected_scores_at_ith_class = detected_scores[selected_detections] if detected_masks is not None: detected_masks_at_ith_class = detected_masks[selected_detections] else: detected_masks_at_ith_class = None return (gt_boxes_at_ith_class, gt_masks_at_ith_class, detected_boxes_at_ith_class, detected_scores_at_ith_class, detected_masks_at_ith_class) def _remove_invalid_boxes(self, detected_boxes, detected_scores, detected_class_labels, detected_masks=None): """Removes entries with invalid boxes. A box is invalid if either its xmax is smaller than its xmin, or its ymax is smaller than its ymin. Args: detected_boxes: A float numpy array of size [num_boxes, 4] containing box coordinates in [ymin, xmin, ymax, xmax] format. detected_scores: A float numpy array of size [num_boxes]. detected_class_labels: A int32 numpy array of size [num_boxes]. detected_masks: A uint8 numpy array of size [num_boxes, height, width]. Returns: valid_detected_boxes: A float numpy array of size [num_valid_boxes, 4] containing box coordinates in [ymin, xmin, ymax, xmax] format. valid_detected_scores: A float numpy array of size [num_valid_boxes]. valid_detected_class_labels: A int32 numpy array of size [num_valid_boxes]. valid_detected_masks: A uint8 numpy array of size [num_valid_boxes, height, width]. """ valid_indices = np.logical_and(detected_boxes[:, 0] < detected_boxes[:, 2], detected_boxes[:, 1] < detected_boxes[:, 3]) detected_boxes = detected_boxes[valid_indices] detected_scores = detected_scores[valid_indices] detected_class_labels = detected_class_labels[valid_indices] if detected_masks is not None: detected_masks = detected_masks[valid_indices] return [ detected_boxes, detected_scores, detected_class_labels, detected_masks ]