Python standard_fields.DetectionResultFields方法代码示例

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def testExceptionRaisedWithMissingGroundtruth(self):
    """Tests that exception is raised for detection with missing groundtruth."""
    categories = [{'id': 1, 'name': 'cat'},
                  {'id': 2, 'name': 'dog'},
                  {'id': 3, 'name': 'elephant'}]
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(categories)
    with self.assertRaises(ValueError):
      coco_evaluator.add_single_detected_image_info(
          image_id='image1',
          detections_dict={
              standard_fields.DetectionResultFields.detection_boxes:
                  np.array([[100., 100., 200., 200.]]),
              standard_fields.DetectionResultFields.detection_scores:
                  np.array([.8]),
              standard_fields.DetectionResultFields.detection_classes:
                  np.array([1])
          }) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def add_common_detected(self):
    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([1, 1, 3], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.wp_eval.add_single_detected_image_info(
        image_key, {
            standard_fields.DetectionResultFields.detection_boxes:
                detected_boxes,
            standard_fields.DetectionResultFields.detection_scores:
                detected_scores,
            standard_fields.DetectionResultFields.detection_classes:
                detected_class_labels
        }) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def testGetOneMAPWithMatchingGroundtruthAndDetectionsSkipCrowd(self):
    """Tests computing mAP with is_crowd GT boxes skipped."""
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
                np.array([[100., 100., 200., 200.], [99., 99., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes:
                np.array([1, 2]),
            standard_fields.InputDataFields.groundtruth_is_crowd:
                np.array([0, 1])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
                np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
                np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
                np.array([1])
        })
    metrics = coco_evaluator.evaluate()
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def testGetOneMAPWithMatchingGroundtruthAndDetectionsEmptyCrowd(self):
    """Tests computing mAP with empty is_crowd array passed in."""
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
                np.array([[100., 100., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes:
                np.array([1]),
            standard_fields.InputDataFields.groundtruth_is_crowd:
                np.array([])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
                np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
                np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
                np.array([1])
        })
    metrics = coco_evaluator.evaluate()
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def testRejectionOnDuplicateDetections(self):
    """Tests that detections cannot be added more than once for an image."""
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    #  Add groundtruth
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
            np.array([[99., 100., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes: np.array([1])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1])
        })
    detections_lists_len = len(coco_evaluator._detection_boxes_list)
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',  # Note that this image id was previously added.
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1])
        })
    self.assertEqual(detections_lists_len,
                     len(coco_evaluator._detection_boxes_list)) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def testExceptionRaisedWithMissingGroundtruth(self):
    """Tests that exception is raised for detection with missing groundtruth."""
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(
        _get_categories_list())
    with self.assertRaises(ValueError):
      coco_evaluator.add_single_detected_image_info(
          image_id='image1',
          detections_dict={
              standard_fields.DetectionResultFields.detection_boxes:
                  np.array([[100., 100., 200., 200.]]),
              standard_fields.DetectionResultFields.detection_scores:
                  np.array([.8]),
              standard_fields.DetectionResultFields.detection_classes:
                  np.array([1])
          }) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def _make_evaluation_dict(self):
    input_data_fields = fields.InputDataFields
    detection_fields = fields.DetectionResultFields

    image = tf.zeros(shape=[1, 20, 20, 3], dtype=tf.uint8)
    key = tf.constant('image1')
    detection_boxes = tf.constant([[[0., 0., 1., 1.]]])
    detection_scores = tf.constant([[0.8]])
    detection_classes = tf.constant([[0]])
    detection_masks = tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32)
    num_detections = tf.constant([1])
    groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
    groundtruth_classes = tf.constant([1])
    groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
    detections = {
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes,
        detection_fields.detection_masks: detection_masks,
        detection_fields.num_detections: num_detections
    }
    groundtruth = {
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
    }
    return eval_util.result_dict_for_single_example(image, key, detections,
                                                    groundtruth) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def testGetOneMAPWithMatchingGroundtruthAndDetectionsSkipCrowd(self):
    """Tests computing mAP with is_crowd GT boxes skipped."""
    category_list = [{
        'id': 0,
        'name': 'person'
    }, {
        'id': 1,
        'name': 'cat'
    }, {
        'id': 2,
        'name': 'dog'
    }]
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(category_list)
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
                np.array([[100., 100., 200., 200.], [99., 99., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes:
                np.array([1, 2]),
            standard_fields.InputDataFields.groundtruth_is_crowd:
                np.array([0, 1])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
                np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
                np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
                np.array([1])
        })
    metrics = coco_evaluator.evaluate()
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def testGetOneMAPWithMatchingGroundtruthAndDetectionsEmptyCrowd(self):
    """Tests computing mAP with empty is_crowd array passed in."""
    category_list = [{
        'id': 0,
        'name': 'person'
    }, {
        'id': 1,
        'name': 'cat'
    }, {
        'id': 2,
        'name': 'dog'
    }]
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(category_list)
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
                np.array([[100., 100., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes:
                np.array([1]),
            standard_fields.InputDataFields.groundtruth_is_crowd:
                np.array([])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
                np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
                np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
                np.array([1])
        })
    metrics = coco_evaluator.evaluate()
    self.assertAlmostEqual(metrics['DetectionBoxes_Precision/mAP'], 1.0) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def testRejectionOnDuplicateDetections(self):
    """Tests that detections cannot be added more than once for an image."""
    categories = [{'id': 1, 'name': 'cat'},
                  {'id': 2, 'name': 'dog'},
                  {'id': 3, 'name': 'elephant'}]
    #  Add groundtruth
    coco_evaluator = coco_evaluation.CocoDetectionEvaluator(categories)
    coco_evaluator.add_single_ground_truth_image_info(
        image_id='image1',
        groundtruth_dict={
            standard_fields.InputDataFields.groundtruth_boxes:
            np.array([[99., 100., 200., 200.]]),
            standard_fields.InputDataFields.groundtruth_classes: np.array([1])
        })
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1])
        })
    detections_lists_len = len(coco_evaluator._detection_boxes_list)
    coco_evaluator.add_single_detected_image_info(
        image_id='image1',  # Note that this image id was previously added.
        detections_dict={
            standard_fields.DetectionResultFields.detection_boxes:
            np.array([[100., 100., 200., 200.]]),
            standard_fields.DetectionResultFields.detection_scores:
            np.array([.8]),
            standard_fields.DetectionResultFields.detection_classes:
            np.array([1])
        })
    self.assertEqual(detections_lists_len,
                     len(coco_evaluator._detection_boxes_list)) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def _make_evaluation_dict(self, resized_groundtruth_masks=False):
    input_data_fields = fields.InputDataFields
    detection_fields = fields.DetectionResultFields

    image = tf.zeros(shape=[1, 20, 20, 3], dtype=tf.uint8)
    key = tf.constant('image1')
    detection_boxes = tf.constant([[[0., 0., 1., 1.]]])
    detection_scores = tf.constant([[0.8]])
    detection_classes = tf.constant([[0]])
    detection_masks = tf.ones(shape=[1, 1, 20, 20], dtype=tf.float32)
    num_detections = tf.constant([1])
    groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
    groundtruth_classes = tf.constant([1])
    groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
    if resized_groundtruth_masks:
      groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
    detections = {
        detection_fields.detection_boxes: detection_boxes,
        detection_fields.detection_scores: detection_scores,
        detection_fields.detection_classes: detection_classes,
        detection_fields.detection_masks: detection_masks,
        detection_fields.num_detections: num_detections
    }
    groundtruth = {
        input_data_fields.groundtruth_boxes: groundtruth_boxes,
        input_data_fields.groundtruth_classes: groundtruth_classes,
        input_data_fields.groundtruth_instance_masks: groundtruth_instance_masks
    }
    return eval_util.result_dict_for_single_example(image, key, detections,
                                                    groundtruth) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
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 -
        standard_fields.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.
        standard_fields.DetectionResultFields.detection_scores: float32 numpy
          array of shape [num_boxes] containing detection scores for the boxes.
        standard_fields.DetectionResultFields.detection_classes: integer numpy
          array of shape [num_boxes] containing 1-indexed detection classes for
          the boxes.
        standard_fields.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[standard_fields.DetectionResultFields.detection_classes]
        - self._label_id_offset)
    detection_masks = None
    if self._evaluate_masks:
      if (standard_fields.DetectionResultFields.detection_masks not in
          detections_dict):
        raise ValueError('Detection masks not in detections dictionary.')
      detection_masks = detections_dict[
          standard_fields.DetectionResultFields.detection_masks]
    self._evaluation.add_single_detected_image_info(
        image_key=image_id,
        detected_boxes=detections_dict[
            standard_fields.DetectionResultFields.detection_boxes],
        detected_scores=detections_dict[
            standard_fields.DetectionResultFields.detection_scores],
        detected_class_labels=detection_classes,
        detected_masks=detection_masks) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def add_common_detected(self):
    image_key = 'img2'
    detected_boxes = np.array(
        [[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
        dtype=float)
    detected_class_labels = np.array([1, 1, 3], dtype=int)
    detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
    self.wp_eval.add_single_detected_image_info(
        image_key,
        {standard_fields.DetectionResultFields.detection_boxes: detected_boxes,
         standard_fields.DetectionResultFields.detection_scores:
         detected_scores,
         standard_fields.DetectionResultFields.detection_classes:
         detected_class_labels}) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def test_export_saved_model_and_run_inference(
      self, input_type='image_tensor'):
    tmp_dir = self.get_temp_dir()
    self._save_checkpoint_from_mock_model(tmp_dir)
    with mock.patch.object(
        model_builder, 'build', autospec=True) as mock_builder:
      mock_builder.return_value = FakeModel()
      output_directory = os.path.join(tmp_dir, 'output')
      pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
      exporter_lib_v2.export_inference_graph(
          input_type=input_type,
          pipeline_config=pipeline_config,
          trained_checkpoint_dir=tmp_dir,
          output_directory=output_directory)

      saved_model_path = os.path.join(output_directory, 'saved_model')
      detect_fn = tf.saved_model.load(saved_model_path)
      image = self.get_dummy_input(input_type)
      detections = detect_fn(image)

      detection_fields = fields.DetectionResultFields
      self.assertAllClose(detections[detection_fields.detection_boxes],
                          [[[0.0, 0.0, 0.5, 0.5],
                            [0.5, 0.5, 0.8, 0.8]],
                           [[0.5, 0.5, 1.0, 1.0],
                            [0.0, 0.0, 0.0, 0.0]]])
      self.assertAllClose(detections[detection_fields.detection_scores],
                          [[0.7, 0.6], [0.9, 0.0]])
      self.assertAllClose(detections[detection_fields.detection_classes],
                          [[1, 2], [2, 1]])
      self.assertAllClose(detections[detection_fields.num_detections], [2, 1]) 

# 需要导入模块: from object_detection.core import standard_fields [as 别名]
# 或者: from object_detection.core.standard_fields import DetectionResultFields [as 别名]
def testGetECEWithUnmatchedGroundtruthAndDetections(self):
    """Tests that ECE is correctly calculated when boxes are unmatched."""
    calibration_evaluator = calibration_evaluation.CalibrationDetectionEvaluator(
        _get_categories_list(), iou_threshold=0.5)
    input_data_fields = standard_fields.InputDataFields
    detection_fields = standard_fields.DetectionResultFields
    # No gt and detection boxes match.
    eval_dict = {
        input_data_fields.key:
            tf.constant(['image_1', 'image_2', 'image_3']),
        input_data_fields.groundtruth_boxes:
            tf.constant([[[100., 100., 200., 200.]],
                         [[50., 50., 100., 100.]],
                         [[25., 25., 50., 50.]]],
                        dtype=tf.float32),
        detection_fields.detection_boxes:
            tf.constant([[[50., 50., 100., 100.]],
                         [[25., 25., 50., 50.]],
                         [[100., 100., 200., 200.]]],
                        dtype=tf.float32),
        input_data_fields.groundtruth_classes:
            tf.constant([[1], [2], [3]], dtype=tf.int64),
        detection_fields.detection_classes:
            tf.constant([[1], [1], [3]], dtype=tf.int64),
        # Detection scores of zero when boxes are unmatched = ECE of zero.
        detection_fields.detection_scores:
            tf.constant([[0.0], [0.0], [0.0]], dtype=tf.float32)
    }

    ece_op, update_op = calibration_evaluator.get_estimator_eval_metric_ops(
        eval_dict)['CalibrationError/ExpectedCalibrationError']
    ece = self._get_ece(ece_op, update_op)
    self.assertAlmostEqual(ece, 0.0)