text: """
  Copyright (c) 2020-present NAVER Corp.
  
  Permission is hereby granted, free of charge, to any person obtaining a copy of
  this software and associated documentation files (the "Software"), to deal in
  the Software without restriction, including without limitation the rights to
  use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
  the Software, and to permit persons to whom the Software is furnished to do so,
  subject to the following conditions:
  
  The above copyright notice and this permission notice shall be included in all
  copies or substantial portions of the Software.
  
  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
  FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
  COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
  IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
  CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
  """
  
  import numpy as np
  import os
  import unittest
  
  from data_loaders import configure_metadata
  from data_loaders import get_image_ids
  from evaluation import BoxEvaluator
  from evaluation import calculate_multiple_iou
  from evaluation import compute_bboxes_from_scoremaps
  from evaluation import get_mask
  from evaluation import MaskEvaluator
  from evaluation import resize_bbox
  
  
  class EvalUtilTest(unittest.TestCase):
      def test_calculate_multiple_iou_shape_type_degenerate_zero(self):
          box_a = np.zeros((3, 4), dtype=np.int)
          box_b = np.zeros((5, 4), dtype=np.int)
          ious = calculate_multiple_iou(box_a, box_b)
          self.assertIsInstance(ious, np.ndarray)
          self.assertEqual(ious.dtype, np.float)
          self.assertEqual(ious.shape, (3, 5))
          for i in range(ious.shape[0]):
              for j in range(ious.shape[1]):
                  self.assertEqual(ious[i, j], 1.0)
  
      def test_calculate_multiple_iou_box_convention(self):
          box_a = np.array([
              [1, 1, 0, 0],
          ], dtype=np.int)
          box_b = np.array([
              [1, 1, 2, 2],
          ], dtype=np.int)
          self.assertRaises(RuntimeError, calculate_multiple_iou, box_a, box_b)
  
      def test_calculate_multiple_iou_eye_boxes(self):
          box_a = np.array([
              [1, 1, 3, 3],
              [4, 4, 5, 5],
          ], dtype=np.int)
          box_b = np.array([
              [1, 1, 3, 3],
              [4, 4, 5, 5],
          ], dtype=np.int)
          ious = calculate_multiple_iou(box_a, box_b)
          self.assertTrue((ious == np.eye(2)).all())
  
      def test_calculate_multiple_iou_realistic_values(self):
          box_a = np.array([
              [1, 2, 5, 6],
          ], dtype=np.int)
          box_b = np.array([
              [3, 1, 4, 5],
          ], dtype=np.int)
          ious = calculate_multiple_iou(box_a, box_b)
          self.assertAlmostEqual(ious[0, 0], 8. / 27, delta=1e-6)
  
      def test_resize_bbox_case(self):
          box = 1, 2, 4, 3
          image_size = 10, 20
          resize_size = 35, 10
          resized_box = resize_bbox(box, image_size, resize_size)
          self.assertEqual(resized_box, (3, 1, 14, 1))
  
      def test_resize_bbox_degenerate(self):
          box = 1, 2, 1, 2
          image_size = 3, 3
          resize_size = 3, 3
          resized_box = resize_bbox(box, image_size, resize_size)
          self.assertEqual(resized_box, (1, 2, 1, 2))
  
      def test_compute_bboxes_from_scoremaps_degenerate(self):
          scoremap = np.zeros([3, 3], dtype=np.float)
          scoremap_threshold_list = np.arange(0, 1, 0.2)
          boxes = compute_bboxes_from_scoremaps(scoremap, scoremap_threshold_list)
          self.assertListEqual(boxes, [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0],
                                       [0, 0, 0, 0], [0, 0, 0, 0]])
  
      def test_compute_bboxes_from_scoremaps_unimodal(self):
          # array has shape array[y][x]
          scoremap = np.array([[0.0, 0.0, 0.0, 0.0, 0.0],
                               [0.0, 0.4, 0.4, 0.4, 0.6],
                               [0.0, 0.4, 1.0, 0.8, 0.6],
                               [0.0, 0.4, 0.4, 0.4, 0.6]],
                              dtype=np.float)
          scoremap_threshold_list = np.arange(0, 1, 0.2)
          boxes = compute_bboxes_from_scoremaps(scoremap, scoremap_threshold_list)
          self.assertListEqual(boxes, [[1, 1, 4, 3],
                                       [1, 1, 4, 3],
                                       [2, 1, 4, 3],
                                       [2, 2, 4, 3],
                                       [2, 2, 3, 3]])
  
      def test_compute_bboxes_from_scoremaps_multimodal(self):
          # array has shape array[y][x]
          scoremap = np.array([[0.4, 0.0, 0.2, 0.2, 0.2],
                               [0.4, 0.4, 0.0, 0.4, 0.0],
                               [0.0, 0.0, 0.0, 0.4, 0.0],
                               [1.0, 0.6, 0.8, 0.2, 0.2]],
                              dtype=np.float)
          scoremap_threshold_list = np.arange(0, 1, 0.2)
          boxes = compute_bboxes_from_scoremaps(scoremap, scoremap_threshold_list)
          self.assertListEqual(boxes, [[0, 0, 4, 3],
                                       [0, 0, 2, 2],
                                       [0, 3, 3, 3],
                                       [2, 3, 3, 3],
                                       [0, 3, 1, 3]])
  
      def test_compute_bboxes_from_scoremaps_nan(self):
          # array has shape array[y][x]
          scoremap = np.full([3, 3], np.nan)
          scoremap_threshold_list = np.arange(0, 1, 0.2)
          self.assertRaises(ValueError, compute_bboxes_from_scoremaps,
                            scoremap, scoremap_threshold_list)
  
  
  def set_metadata(dataset_name, split):
      metadata_root = os.path.join('metadata', dataset_name, split)
      metadata = configure_metadata(metadata_root)
      return metadata
  
  
  def load_evaluator(Evaluator, dataset_name, split, cam_curve_interval,
                     mask_root=None):
      metadata = set_metadata(dataset_name, split)
      threshold_list = list(np.arange(0, 1, cam_curve_interval))
      evaluator = Evaluator(metadata=metadata,
                            dataset_name=dataset_name,
                            split=split,
                            threshold_list=threshold_list,
                            mask_root=mask_root)
      return evaluator
  
  
  class BoxEvaluatorTest(unittest.TestCase):
      _DATASET_NAMES = ('CUB', 'ILSVRC')
      _SPLITS = ('val', 'test')
      _CAM_CURVE_INTERVAL = 0.01
      _BOX_VERIFICATION = {
          'CUB': {
              'val': [
                  [(176, 136, 459, 291)],
                  [(44, 183, 426, 254)],
                  [(437, 42, 834, 422)],
              ],
              'test': [
                  [(60, 27, 385, 331)],
                  [(14, 112, 402, 298)],
                  [(33, 53, 284, 448)],
              ],
          },
          'ILSVRC': {
              'val': [
                  [(156, 163, 318, 230)],
                  [(143, 142, 394, 248)],
                  [(28, 94, 485, 303)],
              ],
              'test': [
                  [(111, 108, 441, 193)],
                  [(45, 49, 499, 162),
                   (2, 69, 437, 207)],
                  [(38, 19, 385, 373)],
              ],
          }
      }
      _SIZE_VERIFICATION = {
          'CUB': {
              'val': [
                  (560, 419),
                  (576, 432),
                  (999, 587),
              ],
              'test': [
                  (500, 335),
                  (500, 347),
                  (500, 470),
              ],
          },
          'ILSVRC': {
              'val': [
                  (500, 375),
                  (500, 375),
                  (500, 375),
              ],
              'test': [
                  (500, 375),
                  (500, 375),
                  (500, 375),
              ],
          }
      }
  
      def _check_box_sizes(self, image_ids, evaluator):
          for image_id in image_ids:
              boxes_in_image = evaluator.original_bboxes[image_id]
              size = evaluator.image_sizes[image_id]
              print(image_id)
              for box in boxes_in_image:
                  x0, y0, x1, y1 = box
                  width, height = size
                  self.assertLessEqual(x0, width - 1)
                  self.assertLessEqual(x1, width - 1)
                  self.assertGreaterEqual(x0, 0)
                  self.assertGreaterEqual(x1, 0)
                  self.assertGreaterEqual(x1, x0)
                  self.assertLessEqual(y0, height - 1)
                  self.assertLessEqual(y1, height - 1)
                  self.assertGreaterEqual(y0, 0)
                  self.assertGreaterEqual(y1, 0)
                  self.assertGreaterEqual(y1, y0)
  
      def test_box_evaluator_boxes_and_sizes(self):
          for dataset_name in self._DATASET_NAMES:
              for split in self._SPLITS:
                  metadata = set_metadata(
                      dataset_name=dataset_name,
                      split=split)
                  evaluator = load_evaluator(
                      BoxEvaluator,
                      dataset_name=dataset_name,
                      split=split,
                      cam_curve_interval=self._CAM_CURVE_INTERVAL)
                  image_ids = get_image_ids(metadata)
  
                  test_boxes = [evaluator.original_bboxes[image_id]
                                for image_id in image_ids[:3]]
                  test_sizes = [evaluator.image_sizes[image_id]
                                for image_id in image_ids[:3]]
                  self.assertEqual(
                      test_boxes, self._BOX_VERIFICATION[dataset_name][split])
                  self.assertEqual(
                      test_sizes, self._SIZE_VERIFICATION[dataset_name][split])
                  self._check_box_sizes(image_ids, evaluator)
  
  
  class MaskEvaluatorTest(unittest.TestCase):
      _DATASET_NAME = 'OpenImages'
      _SPLIT = 'val'
      _TEST_IMAGE_IDS = (
          'val/0bt_c3/1cd9ac0169ec7df0.jpg',
          'val/0bt_c3/3a29f0b33894d4a6.jpg',
          'val/0bt_c3/3b3b9931325d65c9.jpg',
      )
      _TEST_CONSTANT_GT_IMAGE_IDS = (
          'val/0by6g/490365f63135f3e0.jpg',
          'val/0by6g/b4d953e7105da1d8.jpg',
      )
      _IGNORE_PATHS = {
          'val/0bt_c3/1cd9ac0169ec7df0.jpg':
              'val/0bt_c3/1cd9ac0169ec7df0_ignore.png',
          'val/0bt_c3/3a29f0b33894d4a6.jpg':
              'val/0bt_c3/3a29f0b33894d4a6_ignore.png',
          'val/0bt_c3/3b3b9931325d65c9.jpg':
              'val/0bt_c3/3b3b9931325d65c9_ignore.png',
          'val/0by6g/490365f63135f3e0.jpg':
              'val/0by6g/b4d953e7105da1d8_ignore.png',
          'val/0by6g/b4d953e7105da1d8.jpg':
              'val/0by6g/490365f63135f3e0_ignore.png',
      }
      _MASK_PATHS = {
          'val/0bt_c3/1cd9ac0169ec7df0.jpg':
              ['val/0bt_c3/1cd9ac0169ec7df0_m0bt_c3_6932e993.png'],
          'val/0bt_c3/3a29f0b33894d4a6.jpg':
              ['val/0bt_c3/3a29f0b33894d4a6_m0bt_c3_3581e3ae.png',
               'val/0bt_c3/3a29f0b33894d4a6_m0bt_c3_d2422806.png'],
          'val/0bt_c3/3b3b9931325d65c9.jpg':
              ['val/0bt_c3/3b3b9931325d65c9_m0bt_c3_d4e2e136.png'],
          'val/0by6g/490365f63135f3e0.jpg':
              ['val/0by6g/b4d953e7105da1d8_m0by6g_9f8621ff.png'],
          'val/0by6g/b4d953e7105da1d8.jpg':
              ['val/0by6g/490365f63135f3e0_m0by6g_4f9ded4f.png'],
      }
      _CAM_CURVE_INTERVAL = 0.01
      _MASK_ROOT = 'test_data/test_masks'
      _VALIDATION_SCORES = (0.0, 1.0)
  
      def _get_perfect_scoremap(self, image_id, ignore_score):
          mask = get_mask(self._MASK_ROOT, self._MASK_PATHS[image_id],
                          self._IGNORE_PATHS[image_id])
          ignore_region = mask == 255
          scoremap = mask.astype(np.float)
          scoremap[ignore_region] = ignore_score
          return scoremap
  
      def _get_constant_scoremap(self, image_id, value):
          mask = get_mask(self._MASK_ROOT, self._MASK_PATHS[image_id],
                          self._IGNORE_PATHS[image_id])
          scoremap = np.ones_like(mask).astype(np.float)
          return scoremap * value
  
      def _constant_scoremap_test_template(self, image_id, value):
          evaluator = load_evaluator(
              MaskEvaluator,
              dataset_name=self._DATASET_NAME,
              split=self._SPLIT,
              cam_curve_interval=self._CAM_CURVE_INTERVAL,
              mask_root=self._MASK_ROOT)
          scoremap = self._get_constant_scoremap(image_id, value)
          evaluator.accumulate(scoremap, image_id)
          auc = evaluator.compute()
          return auc
  
      def test_mask_evaluator_masks_perfect_scoremap(self):
          for image_id in self._TEST_IMAGE_IDS:
              for ignore_score in self._VALIDATION_SCORES:
                  evaluator = load_evaluator(
                      MaskEvaluator,
                      dataset_name=self._DATASET_NAME,
                      split=self._SPLIT,
                      cam_curve_interval=self._CAM_CURVE_INTERVAL,
                      mask_root=self._MASK_ROOT)
                  scoremap = self._get_perfect_scoremap(
                      image_id, ignore_score=ignore_score)
                  evaluator.accumulate(scoremap, image_id)
                  auc = evaluator.compute()
                  self.assertEqual(auc, 100.0)
  
      def test_mask_evaluator_masks_zero_scoremap_mask0(self):
          image_id = self._TEST_IMAGE_IDS[0]
          value = 0.0
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100. * 43031 / (43031 + 7145), delta=0.1)
  
      def test_mask_evaluator_masks_half_scoremap_mask0(self):
          image_id = self._TEST_IMAGE_IDS[0]
          value = 0.5
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100. * 43031 / (43031 + 7145), delta=0.1)
  
      def test_mask_evaluator_masks_one_scoremap_mask0(self):
          image_id = self._TEST_IMAGE_IDS[0]
          value = 1.0
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100. * 43031 / (43031 + 7145), delta=0.1)
  
      def test_mask_evaluator_masks_zero_scoremap_mask1(self):
          image_id = self._TEST_IMAGE_IDS[1]
          value = 0.0
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100. * 833 / (833 + 49342), delta=0.1)
  
      def test_mask_evaluator_masks_half_scoremap_mask1(self):
          image_id = self._TEST_IMAGE_IDS[1]
          value = 0.5
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100. * 833 / (833 + 49342), delta=0.1)
  
      def test_mask_evaluator_masks_one_scoremap_mask1(self):
          image_id = self._TEST_IMAGE_IDS[1]
          value = 1.0
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100. * 833 / (833 + 49342), delta=0.1)
  
      def test_mask_evaluator_masks_zero_scoremap_mask2(self):
          image_id = self._TEST_IMAGE_IDS[2]
          value = 0.0
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100. * 347 / (347 + 13909), delta=0.1)
  
      def test_mask_evaluator_masks_half_scoremap_mask2(self):
          image_id = self._TEST_IMAGE_IDS[2]
          value = 0.5
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100. * 347 / (347 + 13909), delta=0.1)
  
      def test_mask_evaluator_masks_one_scoremap_mask2(self):
          image_id = self._TEST_IMAGE_IDS[2]
          value = 1.0
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100. * 347 / (347 + 13909), delta=0.1)
  
      def test_mask_evaluator_masks_zero_scoremap_zero_gt(self):
          image_id = self._TEST_CONSTANT_GT_IMAGE_IDS[0]
          value = 0.0
          self.assertRaises(RuntimeError, self._constant_scoremap_test_template,
                            image_id, value)
  
      def test_mask_evaluator_masks_one_scoremap_zero_gt(self):
          image_id = self._TEST_CONSTANT_GT_IMAGE_IDS[0]
          value = 1.0
          self.assertRaises(RuntimeError, self._constant_scoremap_test_template,
                            image_id, value)
  
      def test_mask_evaluator_masks_zero_scoremap_one_gt(self):
          image_id = self._TEST_CONSTANT_GT_IMAGE_IDS[1]
          value = 0.0
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100.0, delta=0.1)
  
      def test_mask_evaluator_masks_one_scoremap_one_gt(self):
          image_id = self._TEST_CONSTANT_GT_IMAGE_IDS[1]
          value = 1.0
          auc = self._constant_scoremap_test_template(image_id, value)
          self.assertAlmostEqual(auc, 100.0, delta=0.1)
  
      def test_mask_evaluator_masks_nan_scoremap(self):
          image_id = self._TEST_CONSTANT_GT_IMAGE_IDS[1]
          value = np.full(1, np.nan)
          self.assertRaises(ValueError, self._constant_scoremap_test_template,
                            image_id, value)
  
  
  if __name__ == '__main__':
      unittest.main()
  