research/object_detection/utils/per_image_evaluation_test.py
# 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.
# ==============================================================================
"""Tests for object_detection.utils.per_image_evaluation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import range
import tensorflow.compat.v1 as tf
from object_detection.utils import per_image_evaluation
class SingleClassTpFpWithDifficultBoxesTest(tf.test.TestCase):
def setUp(self):
num_groundtruth_classes = 1
matching_iou_threshold = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
self.eval = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
nms_max_output_boxes)
self.detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
dtype=float)
self.detected_scores = np.array([0.6, 0.8, 0.5], dtype=float)
detected_masks_0 = np.array([[0, 1, 1, 0],
[0, 0, 1, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_1 = np.array([[1, 0, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_2 = np.array([[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 0, 0]], dtype=np.uint8)
self.detected_masks = np.stack(
[detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
self.groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 10, 10]],
dtype=float)
groundtruth_masks_0 = np.array([[1, 1, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]], dtype=np.uint8)
groundtruth_masks_1 = np.array([[0, 0, 0, 1],
[0, 0, 0, 1],
[0, 0, 0, 1]], dtype=np.uint8)
self.groundtruth_masks = np.stack(
[groundtruth_masks_0, groundtruth_masks_1], axis=0)
def test_match_to_gt_box_0(self):
groundtruth_groundtruth_is_difficult_list = np.array([False, True],
dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, True, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_to_gt_mask_0(self):
groundtruth_groundtruth_is_difficult_list = np.array([False, True],
dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_match_to_gt_box_1(self):
groundtruth_groundtruth_is_difficult_list = np.array([True, False],
dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_to_gt_mask_1(self):
groundtruth_groundtruth_is_difficult_list = np.array([True, False],
dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
expected_scores = np.array([0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
class SingleClassTpFpWithGroupOfBoxesTest(tf.test.TestCase):
def setUp(self):
num_groundtruth_classes = 1
matching_iou_threshold = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
self.eval = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
nms_max_output_boxes)
self.detected_boxes = np.array(
[[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float)
self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
detected_masks_0 = np.array([[0, 1, 1, 0],
[0, 0, 1, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_1 = np.array([[1, 0, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_2 = np.array([[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 0, 0]], dtype=np.uint8)
self.detected_masks = np.stack(
[detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
self.groundtruth_boxes = np.array(
[[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float)
groundtruth_masks_0 = np.array([[1, 0, 0, 0],
[1, 0, 0, 0],
[1, 0, 0, 0]], dtype=np.uint8)
groundtruth_masks_1 = np.array([[0, 0, 1, 0],
[0, 0, 1, 0],
[0, 0, 1, 0]], dtype=np.uint8)
groundtruth_masks_2 = np.array([[0, 1, 0, 0],
[0, 1, 0, 0],
[0, 1, 0, 0]], dtype=np.uint8)
self.groundtruth_masks = np.stack(
[groundtruth_masks_0, groundtruth_masks_1, groundtruth_masks_2], axis=0)
def test_match_to_non_group_of_and_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, True, True], dtype=bool)
expected_scores = np.array([0.8], dtype=float)
expected_tp_fp_labels = np.array([True], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_to_non_group_of_and_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, True, True], dtype=bool)
expected_scores = np.array([0.6], dtype=float)
expected_tp_fp_labels = np.array([True], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_match_two_to_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[True, False, True], dtype=bool)
expected_scores = np.array([0.5], dtype=float)
expected_tp_fp_labels = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_two_to_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[True, False, True], dtype=bool)
expected_scores = np.array([0.8], dtype=float)
expected_tp_fp_labels = np.array([True], dtype=bool)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
class SingleClassTpFpWithGroupOfBoxesTestWeighted(tf.test.TestCase):
def setUp(self):
num_groundtruth_classes = 1
matching_iou_threshold = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
self.group_of_weight = 0.5
self.eval = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold, nms_iou_threshold,
nms_max_output_boxes, self.group_of_weight)
self.detected_boxes = np.array(
[[0, 0, 1, 1], [0, 0, 2, 1], [0, 0, 3, 1]], dtype=float)
self.detected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
detected_masks_0 = np.array(
[[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]], dtype=np.uint8)
detected_masks_1 = np.array(
[[1, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0]], dtype=np.uint8)
detected_masks_2 = np.array(
[[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 0, 0]], dtype=np.uint8)
self.detected_masks = np.stack(
[detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
self.groundtruth_boxes = np.array(
[[0, 0, 1, 1], [0, 0, 5, 5], [10, 10, 20, 20]], dtype=float)
groundtruth_masks_0 = np.array(
[[1, 0, 0, 0], [1, 0, 0, 0], [1, 0, 0, 0]], dtype=np.uint8)
groundtruth_masks_1 = np.array(
[[0, 0, 1, 0], [0, 0, 1, 0], [0, 0, 1, 0]], dtype=np.uint8)
groundtruth_masks_2 = np.array(
[[0, 1, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0]], dtype=np.uint8)
self.groundtruth_masks = np.stack(
[groundtruth_masks_0, groundtruth_masks_1, groundtruth_masks_2], axis=0)
def test_match_to_non_group_of_and_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, True, True], dtype=bool)
expected_scores = np.array([0.8, 0.6], dtype=float)
expected_tp_fp_labels = np.array([1.0, self.group_of_weight], dtype=float)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_to_non_group_of_and_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, True, True], dtype=bool)
expected_scores = np.array([0.6, 0.8, 0.5], dtype=float)
expected_tp_fp_labels = np.array(
[1.0, self.group_of_weight, self.group_of_weight], dtype=float)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
tf.logging.info(
"test_mask_match_to_non_group_of_and_group_of_box {} {}".format(
tp_fp_labels, expected_tp_fp_labels))
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_match_two_to_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[True, False, True], dtype=bool)
expected_scores = np.array([0.5, 0.8], dtype=float)
expected_tp_fp_labels = np.array([0.0, self.group_of_weight], dtype=float)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
tf.logging.info("test_match_two_to_group_of_box {} {}".format(
tp_fp_labels, expected_tp_fp_labels))
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_match_two_to_group_of_box(self):
groundtruth_groundtruth_is_difficult_list = np.array(
[False, False, False], dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[True, False, True], dtype=bool)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array(
[1.0, self.group_of_weight, self.group_of_weight], dtype=float)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
self.groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=self.groundtruth_masks)
tf.logging.info("test_mask_match_two_to_group_of_box {} {}".format(
tp_fp_labels, expected_tp_fp_labels))
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
class SingleClassTpFpNoDifficultBoxesTest(tf.test.TestCase):
def setUp(self):
num_groundtruth_classes = 1
matching_iou_threshold_high_iou = 0.5
matching_iou_threshold_low_iou = 0.1
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
self.eval_high_iou = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold_high_iou,
nms_iou_threshold, nms_max_output_boxes)
self.eval_low_iou = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold_low_iou,
nms_iou_threshold, nms_max_output_boxes)
self.detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
dtype=float)
self.detected_scores = np.array([0.6, 0.8, 0.5], dtype=float)
detected_masks_0 = np.array([[0, 1, 1, 0],
[0, 0, 1, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_1 = np.array([[1, 0, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]], dtype=np.uint8)
detected_masks_2 = np.array([[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 1, 0, 0]], dtype=np.uint8)
self.detected_masks = np.stack(
[detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
def test_no_true_positives(self):
groundtruth_boxes = np.array([[100, 100, 105, 105]], dtype=float)
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_no_true_positives(self):
groundtruth_boxes = np.array([[100, 100, 105, 105]], dtype=float)
groundtruth_masks_0 = np.array([[1, 1, 1, 1],
[1, 1, 1, 1],
[1, 1, 1, 1]], dtype=np.uint8)
groundtruth_masks = np.stack([groundtruth_masks_0], axis=0)
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=groundtruth_masks)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_one_true_positives_with_large_iou_threshold(self):
groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, True, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_one_true_positives_with_large_iou_threshold(self):
groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
groundtruth_masks_0 = np.array([[1, 0, 0, 0],
[1, 1, 0, 0],
[0, 0, 0, 0]], dtype=np.uint8)
groundtruth_masks = np.stack([groundtruth_masks_0], axis=0)
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
self.detected_boxes,
self.detected_scores,
groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=self.detected_masks,
groundtruth_masks=groundtruth_masks)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_one_true_positives_with_very_small_iou_threshold(self):
groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
groundtruth_groundtruth_is_difficult_list = np.zeros(1, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False], dtype=bool)
scores, tp_fp_labels = self.eval_low_iou._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_two_true_positives_with_large_iou_threshold(self):
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3.5, 3.5]], dtype=float)
groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels = self.eval_high_iou._compute_tp_fp_for_single_class(
self.detected_boxes, self.detected_scores, groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = np.array([0.8, 0.6, 0.5], dtype=float)
expected_tp_fp_labels = np.array([False, True, True], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
class SingleClassTpFpEmptyMaskAndBoxesTest(tf.test.TestCase):
def setUp(self):
num_groundtruth_classes = 1
matching_iou_threshold_iou = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
self.eval = per_image_evaluation.PerImageEvaluation(
num_groundtruth_classes, matching_iou_threshold_iou, nms_iou_threshold,
nms_max_output_boxes)
def test_mask_tp_and_ignore(self):
# GT: one box with mask, one without
# Det: One mask matches gt1, one matches box gt2 and is ignored
groundtruth_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 2]], dtype=float)
groundtruth_mask_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
dtype=np.uint8)
groundtruth_mask_1 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
dtype=np.uint8)
groundtruth_masks = np.stack([groundtruth_mask_0, groundtruth_mask_1],
axis=0)
groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False, False],
dtype=bool)
detected_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 2]], dtype=float)
detected_scores = np.array([0.6, 0.8], dtype=float)
detected_masks_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
dtype=np.uint8)
detected_masks_1 = np.array([[1, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0]],
dtype=np.uint8)
detected_masks = np.stack([detected_masks_0, detected_masks_1], axis=0)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
detected_boxes, detected_scores, groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list, detected_masks,
groundtruth_masks)
expected_scores = np.array([0.6], dtype=float)
expected_tp_fp_labels = np.array([True], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_mask_one_tp_one_fp(self):
# GT: one box with mask, one without
# Det: one mask matches gt1, one is fp (box does not match)
groundtruth_boxes = np.array([[0, 0, 2, 3], [2, 2, 4, 4]], dtype=float)
groundtruth_mask_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
dtype=np.uint8)
groundtruth_mask_1 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
dtype=np.uint8)
groundtruth_masks = np.stack([groundtruth_mask_0, groundtruth_mask_1],
axis=0)
groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False, False],
dtype=bool)
detected_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 2]], dtype=float)
detected_scores = np.array([0.6, 0.8], dtype=float)
detected_masks_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
dtype=np.uint8)
detected_masks_1 = np.array([[1, 0, 0, 0], [1, 1, 0, 0], [0, 0, 0, 0]],
dtype=np.uint8)
detected_masks = np.stack([detected_masks_0, detected_masks_1], axis=0)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
detected_boxes,
detected_scores,
groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=detected_masks,
groundtruth_masks=groundtruth_masks)
expected_scores = np.array([0.8, 0.6], dtype=float)
expected_tp_fp_labels = np.array([False, True], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_two_mask_one_gt_one_ignore(self):
# GT: one box with mask, one without.
# Det: two mask matches same gt, one is tp, one is passed down to box match
# and ignored.
groundtruth_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 3]], dtype=float)
groundtruth_mask_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
dtype=np.uint8)
groundtruth_mask_1 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
dtype=np.uint8)
groundtruth_masks = np.stack([groundtruth_mask_0, groundtruth_mask_1],
axis=0)
groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False, False],
dtype=bool)
detected_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 3]], dtype=float)
detected_scores = np.array([0.6, 0.8], dtype=float)
detected_masks_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
dtype=np.uint8)
detected_masks_1 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
dtype=np.uint8)
detected_masks = np.stack([detected_masks_0, detected_masks_1], axis=0)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
detected_boxes,
detected_scores,
groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=detected_masks,
groundtruth_masks=groundtruth_masks)
expected_scores = np.array([0.8], dtype=float)
expected_tp_fp_labels = np.array([True], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_two_mask_one_gt_one_fp(self):
# GT: one box with mask, one without.
# Det: two mask matches same gt, one is tp, one is passed down to box match
# and is fp.
groundtruth_boxes = np.array([[0, 0, 2, 3], [2, 3, 4, 6]], dtype=float)
groundtruth_mask_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
dtype=np.uint8)
groundtruth_mask_1 = np.array([[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]],
dtype=np.uint8)
groundtruth_masks = np.stack([groundtruth_mask_0, groundtruth_mask_1],
axis=0)
groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=bool)
groundtruth_groundtruth_is_group_of_list = np.array([False, False],
dtype=bool)
detected_boxes = np.array([[0, 0, 2, 3], [0, 0, 2, 3]], dtype=float)
detected_scores = np.array([0.6, 0.8], dtype=float)
detected_masks_0 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
dtype=np.uint8)
detected_masks_1 = np.array([[0, 1, 1, 0], [0, 0, 1, 0], [0, 0, 0, 0]],
dtype=np.uint8)
detected_masks = np.stack([detected_masks_0, detected_masks_1], axis=0)
scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
detected_boxes,
detected_scores,
groundtruth_boxes,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list,
detected_masks=detected_masks,
groundtruth_masks=groundtruth_masks)
expected_scores = np.array([0.8, 0.6], dtype=float)
expected_tp_fp_labels = np.array([True, False], dtype=bool)
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
class MultiClassesTpFpTest(tf.test.TestCase):
def test_tp_fp(self):
num_groundtruth_classes = 3
matching_iou_threshold = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes,
matching_iou_threshold,
nms_iou_threshold,
nms_max_output_boxes)
detected_boxes = np.array([[0, 0, 1, 1], [10, 10, 5, 5], [0, 0, 2, 2],
[5, 10, 10, 5], [10, 5, 5, 10], [0, 0, 3, 3]],
dtype=float)
detected_scores = np.array([0.8, 0.1, 0.8, 0.9, 0.7, 0.8], dtype=float)
detected_class_labels = np.array([0, 1, 1, 2, 0, 2], dtype=int)
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3.5, 3.5]], dtype=float)
groundtruth_class_labels = np.array([0, 2], dtype=int)
groundtruth_groundtruth_is_difficult_list = np.zeros(2, dtype=float)
groundtruth_groundtruth_is_group_of_list = np.array(
[False, False], dtype=bool)
scores, tp_fp_labels, _ = eval1.compute_object_detection_metrics(
detected_boxes, detected_scores, detected_class_labels,
groundtruth_boxes, groundtruth_class_labels,
groundtruth_groundtruth_is_difficult_list,
groundtruth_groundtruth_is_group_of_list)
expected_scores = [np.array([0.8], dtype=float)] * 3
expected_tp_fp_labels = [np.array([True]), np.array([False]), np.array([True
])]
for i in range(len(expected_scores)):
self.assertTrue(np.allclose(expected_scores[i], scores[i]))
self.assertTrue(np.array_equal(expected_tp_fp_labels[i], tp_fp_labels[i]))
class CorLocTest(tf.test.TestCase):
def test_compute_corloc_with_normal_iou_threshold(self):
num_groundtruth_classes = 3
matching_iou_threshold = 0.5
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes,
matching_iou_threshold,
nms_iou_threshold,
nms_max_output_boxes)
detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3],
[0, 0, 5, 5]], dtype=float)
detected_scores = np.array([0.9, 0.9, 0.1, 0.9], dtype=float)
detected_class_labels = np.array([0, 1, 0, 2], dtype=int)
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3, 3], [0, 0, 6, 6]],
dtype=float)
groundtruth_class_labels = np.array([0, 0, 2], dtype=int)
is_class_correctly_detected_in_image = eval1._compute_cor_loc(
detected_boxes, detected_scores, detected_class_labels,
groundtruth_boxes, groundtruth_class_labels)
expected_result = np.array([1, 0, 1], dtype=int)
self.assertTrue(np.array_equal(expected_result,
is_class_correctly_detected_in_image))
def test_compute_corloc_with_very_large_iou_threshold(self):
num_groundtruth_classes = 3
matching_iou_threshold = 0.9
nms_iou_threshold = 1.0
nms_max_output_boxes = 10000
eval1 = per_image_evaluation.PerImageEvaluation(num_groundtruth_classes,
matching_iou_threshold,
nms_iou_threshold,
nms_max_output_boxes)
detected_boxes = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3],
[0, 0, 5, 5]], dtype=float)
detected_scores = np.array([0.9, 0.9, 0.1, 0.9], dtype=float)
detected_class_labels = np.array([0, 1, 0, 2], dtype=int)
groundtruth_boxes = np.array([[0, 0, 1, 1], [0, 0, 3, 3], [0, 0, 6, 6]],
dtype=float)
groundtruth_class_labels = np.array([0, 0, 2], dtype=int)
is_class_correctly_detected_in_image = eval1._compute_cor_loc(
detected_boxes, detected_scores, detected_class_labels,
groundtruth_boxes, groundtruth_class_labels)
expected_result = np.array([1, 0, 0], dtype=int)
self.assertTrue(np.array_equal(expected_result,
is_class_correctly_detected_in_image))
if __name__ == "__main__":
tf.test.main()