# 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.
# ==============================================================================
"""object_detection_evaluation module.
ObjectDetectionEvaluation is a class which manages ground truth information of a
object detection dataset, and computes frequently used detection metrics such as
Precision, Recall, CorLoc of the provided detection results.
It supports the following operations:
1) Add ground truth information of images sequentially.
2) Add detection result of images sequentially.
3) Evaluate detection metrics on already inserted detection results.
4) Write evaluation result into a pickle file for future processing or
visualization.
Note: This module operates on numpy boxes and box lists.
"""
import logging
import numpy as np
from object_detection.utils import metrics
from object_detection.utils import per_image_evaluation
class ObjectDetectionEvaluation(object):
"""Evaluate Object Detection Result."""
def __init__(self,
num_groundtruth_classes,
matching_iou_threshold=0.5,
nms_iou_threshold=1.0,
nms_max_output_boxes=10000):
self.per_image_eval = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
nms_max_output_boxes)
self.num_class = num_groundtruth_classes
self.groundtruth_boxes = {}
self.groundtruth_class_labels = {}
self.groundtruth_is_difficult_list = {}
self.num_gt_instances_per_class = np.zeros(self.num_class, dtype=int)
self.num_gt_imgs_per_class = np.zeros(self.num_class, dtype=int)
self.detection_keys = set()
self.scores_per_class = [[] for _ in range(self.num_class)]
self.tp_fp_labels_per_class = [[] for _ in range(self.num_class)]
self.num_images_correctly_detected_per_class = np.zeros(self.num_class)
self.average_precision_per_class = np.empty(self.num_class, dtype=float)
self.average_precision_per_class.fill(np.nan)
self.precisions_per_class = []
self.recalls_per_class = []
self.corloc_per_class = np.ones(self.num_class, dtype=float)
def clear_detections(self):
self.detection_keys = {}
self.scores_per_class = [[] for _ in range(self.num_class)]
self.tp_fp_labels_per_class = [[] for _ in range(self.num_class)]
self.num_images_correctly_detected_per_class = np.zeros(self.num_class)
self.average_precision_per_class = np.zeros(self.num_class, dtype=float)
self.precisions_per_class = []
self.recalls_per_class = []
self.corloc_per_class = np.ones(self.num_class, dtype=float)
def add_single_ground_truth_image_info(self,
image_key,
groundtruth_boxes,
groundtruth_class_labels,
groundtruth_is_difficult_list=None):
"""Add ground truth info of a single image into the evaluation database.
Args:
image_key: sha256 key of image content
groundtruth_boxes: A numpy array of shape [M, 4] representing object box
coordinates[y_min, x_min, y_max, x_max]
groundtruth_class_labels: A 1-d numpy array of length M representing class
labels
groundtruth_is_difficult_list: A length M numpy boolean array denoting
whether a ground truth box is a difficult instance or not. To support
the case that no boxes are difficult, it is by default set as None.
"""
if image_key in self.groundtruth_boxes:
logging.warn(
'image %s has already been added to the ground truth database.',
image_key)
return
self.groundtruth_boxes[image_key] = groundtruth_boxes
self.groundtruth_class_labels[image_key] = groundtruth_class_labels
if groundtruth_is_difficult_list is None:
num_boxes = groundtruth_boxes.shape[0]
groundtruth_is_difficult_list = np.zeros(num_boxes, dtype=bool)
self.groundtruth_is_difficult_list[
image_key] = groundtruth_is_difficult_list.astype(dtype=bool)
self._update_ground_truth_statistics(groundtruth_class_labels,
groundtruth_is_difficult_list)
def add_single_detected_image_info(self, image_key, detected_boxes,
detected_scores, detected_class_labels):
"""Add detected result of a single image into the evaluation database.
Args:
image_key: sha256 key of image content
detected_boxes: A numpy array of shape [N, 4] representing detected box
coordinates[y_min, x_min, y_max, x_max]
detected_scores: A 1-d numpy array of length N representing classification
score
detected_class_labels: A 1-d numpy array of length N representing class
labels
Raises:
ValueError: if detected_boxes, detected_scores and detected_class_labels
do not have the same length.
"""
if (len(detected_boxes) != len(detected_scores) or
len(detected_boxes) != len(detected_class_labels)):
raise ValueError('detected_boxes, detected_scores and '
'detected_class_labels should all have same lengths. Got'
'[%d, %d, %d]' % len(detected_boxes),
len(detected_scores), len(detected_class_labels))
if image_key in self.detection_keys:
logging.warn(
'image %s has already been added to the detection result database',
image_key)
return
self.detection_keys.add(image_key)
if image_key in self.groundtruth_boxes:
groundtruth_boxes = self.groundtruth_boxes[image_key]
groundtruth_class_labels = self.groundtruth_class_labels[image_key]
groundtruth_is_difficult_list = self.groundtruth_is_difficult_list[
image_key]
else:
groundtruth_boxes = np.empty(shape=[0, 4], dtype=float)
groundtruth_class_labels = np.array([], dtype=int)
groundtruth_is_difficult_list = np.array([], dtype=bool)
scores, tp_fp_labels, is_class_correctly_detected_in_image = (
self.per_image_eval.compute_object_detection_metrics(
detected_boxes, detected_scores, detected_class_labels,
groundtruth_boxes, groundtruth_class_labels,
groundtruth_is_difficult_list))
for i in range(self.num_class):
self.scores_per_class[i].append(scores[i])
self.tp_fp_labels_per_class[i].append(tp_fp_labels[i])
(self.num_images_correctly_detected_per_class
) += is_class_correctly_detected_in_image
def _update_ground_truth_statistics(self, groundtruth_class_labels,
groundtruth_is_difficult_list):
"""Update grouth truth statitistics.
1. Difficult boxes are ignored when counting the number of ground truth
instances as done in Pascal VOC devkit.
2. Difficult boxes are treated as normal boxes when computing CorLoc related
statitistics.
Args:
groundtruth_class_labels: An integer numpy array of length M,
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
"""
for class_index in range(self.num_class):
num_gt_instances = np.sum(groundtruth_class_labels[
~groundtruth_is_difficult_list] == class_index)
self.num_gt_instances_per_class[class_index] += num_gt_instances
if np.any(groundtruth_class_labels == class_index):
self.num_gt_imgs_per_class[class_index] += 1
def evaluate(self):
"""Compute evaluation result.
Returns:
average_precision_per_class: float numpy array of average precision for
each class.
mean_ap: mean average precision of all classes, float scalar
precisions_per_class: List of precisions, each precision is a float numpy
array
recalls_per_class: List of recalls, each recall is a float numpy array
corloc_per_class: numpy float array
mean_corloc: Mean CorLoc score for each class, float scalar
"""
if (self.num_gt_instances_per_class == 0).any():
logging.warn(
'The following classes have no ground truth examples: %s',
np.squeeze(np.argwhere(self.num_gt_instances_per_class == 0)))
for class_index in range(self.num_class):
if self.num_gt_instances_per_class[class_index] == 0:
continue
scores = np.concatenate(self.scores_per_class[class_index])
tp_fp_labels = np.concatenate(self.tp_fp_labels_per_class[class_index])
precision, recall = metrics.compute_precision_recall(
scores, tp_fp_labels, self.num_gt_instances_per_class[class_index])
self.precisions_per_class.append(precision)
self.recalls_per_class.append(recall)
average_precision = metrics.compute_average_precision(precision, recall)
self.average_precision_per_class[class_index] = average_precision
self.corloc_per_class = metrics.compute_cor_loc(
self.num_gt_imgs_per_class,
self.num_images_correctly_detected_per_class)
mean_ap = np.nanmean(self.average_precision_per_class)
mean_corloc = np.nanmean(self.corloc_per_class)
return (self.average_precision_per_class, mean_ap,
self.precisions_per_class, self.recalls_per_class,
self.corloc_per_class, mean_corloc)
def get_eval_result(self):
return EvalResult(self.average_precision_per_class,
self.precisions_per_class, self.recalls_per_class,
self.corloc_per_class)
class EvalResult(object):
def __init__(self, average_precisions, precisions, recalls, all_corloc):
self.precisions = precisions
self.recalls = recalls
self.all_corloc = all_corloc
self.average_precisions = average_precisions
