def add_single_detected_image_info(self, image_id, detections_dict): """Adds detections for a single image to be used for evaluation. If a detection has already been added for this image id, a warning is logged, and the detection is skipped. Args: image_id: A unique string/integer identifier for the image. detections_dict: A dictionary containing - 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. DetectionResultFields.detection_scores: float32 numpy array of shape [num_boxes] containing detection scores for the boxes. DetectionResultFields.detection_classes: integer numpy array of shape [num_boxes] containing 1-indexed detection classes for the boxes. DetectionResultFields.detection_masks: optional uint8 numpy array of shape [num_boxes, image_height, image_width] containing instance masks for the boxes. Raises: ValueError: If groundtruth for the image_id is not available. """ if image_id not in self._image_ids: raise ValueError('Missing groundtruth for image id: {}'.format(image_id)) if self._image_ids[image_id]: tf.logging.warning('Ignoring detection with image id %s since it was ' 'previously added', image_id) return self._detection_boxes_list.extend( coco_tools.ExportSingleImageDetectionBoxesToCoco( image_id=image_id, category_id_set=self._category_id_set, detection_boxes=detections_dict[standard_fields. DetectionResultFields .detection_boxes], detection_scores=detections_dict[standard_fields. DetectionResultFields. detection_scores], detection_classes=detections_dict[standard_fields. DetectionResultFields. detection_classes])) self._image_ids[image_id] = True
def testSingleImageDetectionBoxesExport(self): boxes = np.array([[0, 0, 1, 1], [0, 0, .5, .5], [.5, .5, 1, 1]], dtype=np.float32) classes = np.array([1, 2, 3], dtype=np.int32) scores = np.array([0.8, 0.2, 0.7], dtype=np.float32) coco_boxes = np.array([[0, 0, 1, 1], [0, 0, .5, .5], [.5, .5, .5, .5]], dtype=np.float32) coco_annotations = coco_tools.ExportSingleImageDetectionBoxesToCoco( image_id='first_image', category_id_set=set([1, 2, 3]), detection_boxes=boxes, detection_classes=classes, detection_scores=scores) for i, annotation in enumerate(coco_annotations): self.assertEqual(annotation['image_id'], 'first_image') self.assertEqual(annotation['category_id'], classes[i]) self.assertAlmostEqual(annotation['score'], scores[i]) self.assertTrue( np.all(np.isclose(annotation['bbox'], coco_boxes[i])))