def evaluate(self):
"""Evaluates the detection boxes and returns a dictionary of coco metrics.
Returns:
A dictionary holding -
1. summary_metrics:
'DetectionBoxes_Precision/mAP': mean average precision over classes
averaged over IOU thresholds ranging from .5 to .95 with .05
increments.
'DetectionBoxes_Precision/[email protected]': mean average precision at 50% IOU
'DetectionBoxes_Precision/[email protected]': mean average precision at 75% IOU
'DetectionBoxes_Precision/mAP (small)': mean average precision for small
objects (area < 32^2 pixels).
'DetectionBoxes_Precision/mAP (medium)': mean average precision for
medium sized objects (32^2 pixels < area < 96^2 pixels).
'DetectionBoxes_Precision/mAP (large)': mean average precision for large
objects (96^2 pixels < area < 10000^2 pixels).
'DetectionBoxes_Recall/AR@1': average recall with 1 detection.
'DetectionBoxes_Recall/AR@10': average recall with 10 detections.
'DetectionBoxes_Recall/AR@100': average recall with 100 detections.
'DetectionBoxes_Recall/AR@100 (small)': average recall for small objects
with 100.
'DetectionBoxes_Recall/AR@100 (medium)': average recall for medium objects
with 100.
'DetectionBoxes_Recall/AR@100 (large)': average recall for large objects
with 100 detections.
2. per_category_ap: if include_metrics_per_category is True, category
specific results with keys of the form:
'Precision mAP ByCategory/category' (without the supercategory part if
no supercategories exist). For backward compatibility
'PerformanceByCategory' is included in the output regardless of
all_metrics_per_category.
"""
groundtruth_dict = {
'annotations': self._groundtruth_list,
'images': [{
'id': image_id
} for image_id in self._image_ids],
'categories': self._categories
}
coco_wrapped_groundtruth = coco_tools.COCOWrapper(groundtruth_dict)
coco_wrapped_detections = coco_wrapped_groundtruth.LoadAnnotations(
self._detection_boxes_list)
box_evaluator = coco_tools.COCOEvalWrapper(coco_wrapped_groundtruth,
coco_wrapped_detections,
agnostic_mode=False)
box_metrics, box_per_category_ap = box_evaluator.ComputeMetrics(
include_metrics_per_category=self._include_metrics_per_category,
all_metrics_per_category=self._all_metrics_per_category)
box_metrics.update(box_per_category_ap)
box_metrics = {
'DetectionBoxes_' + key: value
for key, value in iter(box_metrics.items())
}
return box_metrics
def evaluate(self):
"""Evaluates the detection masks and returns a dictionary of coco metrics.
Returns:
A dictionary holding -
1. summary_metrics:
'Precision/mAP': mean average precision over classes averaged over IOU
thresholds ranging from .5 to .95 with .05 increments
'Precision/[email protected]': mean average precision at 50% IOU
'Precision/[email protected]': mean average precision at 75% IOU
'Precision/mAP (small)': mean average precision for small objects
(area < 32^2 pixels)
'Precision/mAP (medium)': mean average precision for medium sized
objects (32^2 pixels < area < 96^2 pixels)
'Precision/mAP (large)': mean average precision for large objects
(96^2 pixels < area < 10000^2 pixels)
'Recall/AR@1': average recall with 1 detection
'Recall/AR@10': average recall with 10 detections
'Recall/AR@100': average recall with 100 detections
'Recall/AR@100 (small)': average recall for small objects with 100
detections
'Recall/AR@100 (medium)': average recall for medium objects with 100
detections
'Recall/AR@100 (large)': average recall for large objects with 100
detections
2. per_category_ap: if include_metrics_per_category is True, category
specific results with keys of the form:
'Precision mAP ByCategory/category' (without the supercategory part if
no supercategories exist). For backward compatibility
'PerformanceByCategory' is included in the output regardless of
all_metrics_per_category.
"""
groundtruth_dict = {
'annotations': self._groundtruth_list,
'images': [{'id': image_id, 'height': shape[1], 'width': shape[2]}
for image_id, shape in self._image_id_to_mask_shape_map.
iteritems()],
'categories': self._categories
}
coco_wrapped_groundtruth = coco_tools.COCOWrapper(
groundtruth_dict, detection_type='segmentation')
coco_wrapped_detection_masks = coco_wrapped_groundtruth.LoadAnnotations(
self._detection_masks_list)
mask_evaluator = coco_tools.COCOEvalWrapper(
coco_wrapped_groundtruth, coco_wrapped_detection_masks,
agnostic_mode=False, iou_type='segm')
mask_metrics, mask_per_category_ap = mask_evaluator.ComputeMetrics(
include_metrics_per_category=self._include_metrics_per_category)
mask_metrics.update(mask_per_category_ap)
mask_metrics = {'DetectionMasks_'+ key: value
for key, value in mask_metrics.iteritems()}
return mask_metrics
def testCocoWrappers(self):
groundtruth = coco_tools.COCOWrapper(self._groundtruth_dict)
detections = groundtruth.LoadAnnotations(self._detections_list)
evaluator = coco_tools.COCOEvalWrapper(groundtruth, detections)
summary_metrics, _ = evaluator.ComputeMetrics()
self.assertAlmostEqual(1.0, summary_metrics['Precision/mAP'])