def _make_final_ann(self, result_ann):
frontend_compatible_labels = _remove_backend_only_labels(
result_ann.labels)
return Annotation(
img_size=result_ann.img_size,
labels=frontend_compatible_labels,
img_tags=result_ann.img_tags,
img_description=result_ann.img_description,
pixelwise_scores_labels=result_ann.pixelwise_scores_labels)
def _get_annotation_for_bbox(img: np.ndarray, roi: Rectangle,
model) -> Annotation:
"""Runs inference within the given roi; moves resulting figures to global reference frame."""
img_cropped = roi.get_cropped_numpy_slice(img)
# TODO pass through image and parent figure tags via roi_ann.
roi_ann = Annotation(img_size=(roi.height, roi.width))
raw_result_ann = model.inference(img_cropped, roi_ann)
return Annotation(img_size=img.shape[:2],
labels=[
label.translate(drow=roi.top, dcol=roi.left)
for label in raw_result_ann.labels
],
img_tags=raw_result_ann.img_tags,
img_description=raw_result_ann.img_description,
pixelwise_scores_labels=[
label.translate(drow=roi.top, dcol=roi.left)
for label in raw_result_ann.pixelwise_scores_labels
])
def _set_item(self,
name,
title,
image_url,
ann: Union[Annotation, dict] = None):
setattr(self, f"_{name}_title", title)
setattr(self, f"_{name}_image_url", image_url)
res_ann = Annotation((1, 1))
if ann is not None:
if type(ann) is dict:
res_ann = Annotation.from_json(ann, self._project_meta)
else:
res_ann = ann.clone()
setattr(self, f"_{name}_ann", res_ann)
def _do_single_img_inference(self, img, in_msg):
in_project_meta = self._in_project_meta_from_msg(in_msg)
ann_json = in_msg.get('annotation')
if ann_json is not None:
if in_project_meta is None:
raise ValueError('In order to perform inference with annotation you must specify the appropriate'
' project meta.')
ann = Annotation.from_json(ann_json, in_project_meta)
else:
in_project_meta = in_project_meta or ProjectMeta()
ann = Annotation(img.shape[:2])
inference_mode = self._make_inference_mode(in_msg.get(MODE, {}), in_project_meta)
inference_result = inference_mode.infer_annotate(img, ann)
return inference_result.to_json()
def add_item(self,
title,
image_url,
ann: Union[Annotation, dict] = None,
col_index=None,
custom_info: dict = None,
zoom_to_figure=None,
title_url=None):
if col_index is not None:
if col_index <= 0 or col_index > self.col_number:
raise ValueError(
"Column number is not correct, check your input data")
res_ann = Annotation((1, 1))
if ann is not None:
if type(ann) is dict:
res_ann = Annotation.from_json(ann, self._project_meta)
else:
res_ann = ann.clone()
self._data[title] = {
"image_url": image_url,
"ann": res_ann,
"col_index": col_index
}
if zoom_to_figure is not None:
self._data[title]["zoom_to_figure"] = zoom_to_figure
self._need_zoom = True
if title_url is not None:
self.preview_info = True
self._with_title_url = True
self._data[title]["labelingUrl"] = title_url
if self.preview_info:
if custom_info is not None:
self._data[title]["info"] = custom_info
else:
self._data[title]["info"] = None
def test_with_matches(self):
ann = Annotation(
img_size=[100, 100],
labels=[self._gt_obj_1, self._gt_obj_2, self._gt_obj_3,
self._pred_obj_1, self._pred_obj_2, self._pred_obj_3] + self._pred_objs_fp)
self._metric_calculator.add_pair(ann, ann)
# Sorted matches by confidence:
# 0.75 - recall 0 precision 0
# 0.7 + recall 1/3 precision 1/2
# 0.65 - recall 1/3 precision 1/3
# 0.6 - recall 1/3 precision 1/4
# 0.55 - recall 1/3 precision 1/5
# 0.45 - recall 1/3 precision 1/6
# 0.35 - recall 1/3 precision 1/7
# 0.25 - recall 1/3 precision 1/8
# 0.15 - recall 2/3 precision 1/9
# 0.1 + recall 2/3 precision 2/10
# Recalls 0.7, 0.8, 0.9, 1.0 -> max precision 0.
# Recalls 0.6, 0.5, 0.4 -> max precision 2/10
# Recalls 0.3, 0.2, 0.1, 0.0 -> max precision 1/2
expected_map = (4 * 0.0 + 3 * (2/10) + 4 * 1/2) / 11
self.assertEqual(self._metric_calculator.get_total_metrics()[AP], expected_map)
def test_empty_predictions(self):
ann = Annotation(
img_size=[100, 100], labels=[self._gt_obj_1, self._gt_obj_2, self._gt_obj_3])
self._metric_calculator.add_pair(ann, ann)
self.assertEqual(self._metric_calculator.get_total_metrics()[AP], 0)