def _do_infer_annotate(self, img: np.ndarray, ann: Annotation) -> Annotation:
result_ann = ann.clone()
all_pixelwise_scores_labels = []
for roi in self._sliding_windows.get(ann.img_size):
raw_roi_ann = _get_annotation_for_bbox(img, roi, self._model)
all_pixelwise_scores_labels.extend(raw_roi_ann.pixelwise_scores_labels)
model_img_level_tags = make_renamed_tags(raw_roi_ann.img_tags, self._model_img_tag_meta_mapper,
make_renamed_tags)
result_ann = result_ann.add_labels(
_maybe_make_bbox_label(roi, self._intermediate_bbox_class, tags=model_img_level_tags))
model_class_name_to_id = {name: idx
for idx, name in enumerate(set(label.obj_class.name
for label in all_pixelwise_scores_labels))}
id_to_class_obj = {idx: self._model.model_out_meta.obj_classes.get(name)
for name, idx in model_class_name_to_id.items()}
summed_scores = np.zeros(ann.img_size + tuple([len(model_class_name_to_id)]))
for label in all_pixelwise_scores_labels:
class_idx = model_class_name_to_id[label.obj_class.name]
label_matching_summer_scores = label.geometry.to_bbox().get_cropped_numpy_slice(summed_scores)
label_matching_summer_scores[:, :, class_idx, np.newaxis] += label.geometry.data
# TODO consider instead filtering pixels by all-zero scores.
if np.sum(summed_scores, axis=2).min() == 0:
raise RuntimeError('Wrong sliding window moving, implementation error.')
aggregated_model_labels = raw_to_labels.segmentation_array_to_sly_bitmaps(id_to_class_obj,
np.argmax(summed_scores, axis=2))
result_ann = result_ann.add_labels(
replace_labels_classes(aggregated_model_labels, self._model_class_mapper, skip_missing=True))
return result_ann
def _do_infer_annotate(self, img: np.ndarray,
ann: Annotation) -> Annotation:
result_ann = ann.clone()
model_labels = []
for roi in self._sliding_windows.get(ann.img_size):
raw_roi_ann = _get_annotation_for_bbox(img, roi, self._model)
all_rectangle_labels = [
label for label in raw_roi_ann.labels
if isinstance(label.geometry, Rectangle)
]
model_labels.extend(
_replace_labels_classes(all_rectangle_labels,
self._model_class_mapper,
self._model_tag_meta_mapper,
skip_missing=True))
model_img_level_tags = make_renamed_tags(
raw_roi_ann.img_tags,
self._model_tag_meta_mapper,
skip_missing=True)
result_ann = result_ann.add_labels(
_maybe_make_bbox_label(roi,
self._intermediate_bbox_class,
tags=model_img_level_tags))
nms_conf = self._config.get(NMS_AFTER, {ENABLE: False})
if nms_conf[ENABLE]:
result_ann = result_ann.add_labels(
self._general_nms(labels=model_labels,
iou_thresh=nms_conf[IOU_THRESHOLD],
confidence_tag_name=nms_conf.get(
CONFIDENCE_TAG_NAME, CONFIDENCE)))
else:
result_ann = result_ann.add_labels(model_labels)
return result_ann
def _do_infer_annotate(self, img: np.ndarray, ann: Annotation) -> Annotation:
result_ann = ann.clone()
model_labels = []
roi_bbox_labels = []
for roi in self._sliding_windows.get(ann.img_size):
raw_roi_ann = _get_annotation_for_bbox(img, roi, self._model)
# Accumulate all the labels across the sliding windows to potentially run non-max suppression over them.
# Only retain the classes that will be eventually saved to avoid running NMS on objects we will
# throw away anyway.
model_labels.extend([
label for label in raw_roi_ann.labels
if isinstance(label.geometry, Rectangle) and self._model_class_mapper.map(label.obj_class) is not None])
model_img_level_tags = make_renamed_tags(
raw_roi_ann.img_tags, self._model_tag_meta_mapper, skip_missing=True)
roi_bbox_labels.extend(
_maybe_make_bbox_label(roi, self._intermediate_bbox_class, tags=model_img_level_tags))
nms_conf = self._config.get(NMS_AFTER, {ENABLE: False})
if nms_conf[ENABLE]:
confidence_tag_name = nms_conf.get(CONFIDENCE_TAG_NAME, CONFIDENCE)
model_labels = self._general_nms(
labels=model_labels, iou_thresh=nms_conf[IOU_THRESHOLD], confidence_tag_name=confidence_tag_name)
model_labels_renamed = _replace_or_drop_labels_classes(
model_labels, self._model_class_mapper, self._model_tag_meta_mapper)
result_ann = result_ann.add_labels(roi_bbox_labels + model_labels_renamed)
return result_ann
def _do_infer_annotate(self, img: np.ndarray,
ann: Annotation) -> Annotation:
result_labels = []
for src_label, roi in self._all_filtered_bbox_rois(
ann, self._config[FROM_CLASSES], self._config[PADDING]):
if roi is None:
result_labels.append(src_label)
else:
roi_ann = _get_annotation_for_bbox(img, roi, self._model)
result_labels.extend(
_replace_labels_classes(roi_ann.labels,
self._model_class_mapper,
self._model_tag_meta_mapper,
skip_missing=True))
model_img_level_tags = make_renamed_tags(
roi_ann.img_tags,
self._model_tag_meta_mapper,
skip_missing=True)
if self._config[SAVE]:
result_labels.append(
Label(geometry=roi,
obj_class=self._intermediate_class_mapper.map(
src_label.obj_class),
tags=model_img_level_tags))
# Regardless of whether we need to save intermediate bounding boxes, also put the inference result tags
# onto the original source object from which we created a bounding box.
# This is necessary for e.g. classification models to work, so that they put the classification results
# onto the original object.
result_labels.append(src_label.add_tags(model_img_level_tags))
return ann.clone(labels=result_labels)
def _replace_or_drop_labels_classes(labels, obj_class_mapper: ObjClassMapper, tag_meta_mapper: TagMetaMapper) -> list:
result = []
for label in labels:
dest_obj_class = obj_class_mapper.map(label.obj_class)
if dest_obj_class is not None:
renamed_tags = make_renamed_tags(tags=label.tags, tag_meta_mapper=tag_meta_mapper, skip_missing=True)
result.append(label.clone(obj_class=dest_obj_class, tags=renamed_tags))
return result
def _do_infer_annotate(self, img: np.ndarray, ann: Annotation) -> Annotation:
result_ann = ann.clone()
inference_result_ann = self._model.inference(img, ann)
result_ann = result_ann.add_labels(
replace_labels_classes(inference_result_ann.labels, self._model_class_mapper, skip_missing=True))
renamed_tags = make_renamed_tags(inference_result_ann.img_tags,
self._model_img_tag_meta_mapper,
skip_missing=True)
result_ann = result_ann.add_tags(renamed_tags)
return result_ann
def _do_infer_annotate(self, img: np.ndarray, ann: Annotation) -> Annotation:
result_ann = ann.clone()
roi = _make_cropped_rectangle(ann.img_size, self._config[BOUNDS])
roi_ann = _get_annotation_for_bbox(img, roi, self._model)
result_ann = result_ann.add_labels(
replace_labels_classes(roi_ann.labels, self._model_class_mapper, skip_missing=True))
img_level_tags = make_renamed_tags(roi_ann.img_tags, self._model_img_tag_meta_mapper, skip_missing=True)
result_ann = result_ann.add_labels(
_maybe_make_bbox_label(roi, self._intermediate_bbox_class, tags=roi_ann.img_tags))
result_ann = result_ann.add_tags(img_level_tags)
return result_ann
def _do_infer_annotate_generic(self, inference_fn, img, ann: Annotation):
result_ann = ann.clone()
inference_result_ann = inference_fn(img, ann)
result_ann = result_ann.add_labels(
_replace_or_drop_labels_classes(
inference_result_ann.labels, self._model_class_mapper, self._model_tag_meta_mapper))
renamed_tags = make_renamed_tags(inference_result_ann.img_tags,
self._model_tag_meta_mapper,
skip_missing=True)
result_ann = result_ann.add_tags(renamed_tags)
return result_ann
def _do_infer_annotate(self, img: np.ndarray, ann: Annotation) -> Annotation:
result_labels = []
for src_label, roi in self._all_filtered_bbox_rois(ann, self._config[FROM_CLASSES], self._config[PADDING]):
if roi is None:
result_labels.append(src_label)
else:
roi_ann = _get_annotation_for_bbox(img, roi, self._model)
result_labels.extend(replace_labels_classes(
roi_ann.labels, self._model_class_mapper, skip_missing=True))
model_img_level_tags = make_renamed_tags(roi_ann.img_tags, self._model_img_tag_meta_mapper,
skip_missing=True)
if self._config[SAVE]:
result_labels.append(
Label(geometry=roi, obj_class=self._intermediate_class_mapper.map(src_label.obj_class),
tags=model_img_level_tags))
result_labels.append(src_label.add_tags(model_img_level_tags))
return ann.clone(labels=result_labels)
def _do_infer_annotate(self, img: np.ndarray, ann: Annotation) -> Annotation:
result_ann = ann.clone()
roi = _make_cropped_rectangle(ann.img_size, self._config[BOUNDS])
roi_ann = _get_annotation_for_bbox(img, roi, self._model)
result_ann = result_ann.add_labels(
_replace_or_drop_labels_classes(roi_ann.labels, self._model_class_mapper, self._model_tag_meta_mapper))
img_level_tags = make_renamed_tags(roi_ann.img_tags, self._model_tag_meta_mapper, skip_missing=True)
result_ann = result_ann.add_labels(
_maybe_make_bbox_label(roi, self._intermediate_bbox_class, tags=img_level_tags))
result_ann = result_ann.add_tags(img_level_tags)
if self._config.get(SAVE_PROBABILITIES, False) is True:
result_problabels = _replace_or_drop_labels_classes(
roi_ann.pixelwise_scores_labels, self._model_class_mapper, self._model_tag_meta_mapper)
result_ann = result_ann.add_pixelwise_score_labels(result_problabels)
return result_ann
def _do_infer_annotate_generic(self, inference_fn, img, ann: Annotation):
result_ann = ann.clone()
inference_ann = inference_fn(img, ann)
result_labels = _replace_or_drop_labels_classes(
inference_ann.labels, self._model_class_mapper, self._model_tag_meta_mapper)
result_ann = result_ann.add_labels(result_labels)
renamed_tags = make_renamed_tags(inference_ann.img_tags, self._model_tag_meta_mapper, skip_missing=True)
result_ann = result_ann.add_tags(renamed_tags)
if self._config.get(SAVE_PROBABILITIES, False) is True:
result_problabels = _replace_or_drop_labels_classes(
inference_ann.pixelwise_scores_labels, self._model_class_mapper, self._model_tag_meta_mapper)
result_ann = result_ann.add_pixelwise_score_labels(result_problabels)
return result_ann
def _do_infer_annotate(self, img: np.ndarray, ann: Annotation) -> Annotation:
result_ann = ann.clone()
all_pixelwise_scores_labels = []
for roi in self._sliding_windows.get(ann.img_size):
raw_roi_ann = _get_annotation_for_bbox(img, roi, self._model)
all_pixelwise_scores_labels.extend(raw_roi_ann.pixelwise_scores_labels)
model_img_level_tags = make_renamed_tags(raw_roi_ann.img_tags, self._model_tag_meta_mapper,
make_renamed_tags)
result_ann = result_ann.add_labels(
_maybe_make_bbox_label(roi, self._intermediate_bbox_class, tags=model_img_level_tags))
model_class_name_to_id = {name: idx
for idx, name in enumerate(set(label.obj_class.name
for label in all_pixelwise_scores_labels))}
id_to_class_obj = {idx: self._model.model_out_meta.obj_classes.get(name)
for name, idx in model_class_name_to_id.items()}
summed_scores = np.zeros(ann.img_size + tuple([len(model_class_name_to_id)]))
summed_divisor = np.zeros_like(summed_scores)
for label in all_pixelwise_scores_labels:
class_idx = model_class_name_to_id[label.obj_class.name]
geom_bbox = label.geometry.to_bbox()
label_matching_summer_scores = geom_bbox.get_cropped_numpy_slice(summed_scores)
label_matching_summer_scores[:, :, class_idx, np.newaxis] += label.geometry.data
divisor_slice = geom_bbox.get_cropped_numpy_slice(summed_divisor)
divisor_slice[:, :, class_idx, np.newaxis] += 1.
# TODO consider instead filtering pixels by all-zero scores.
if np.sum(summed_scores, axis=2).min() == 0:
raise RuntimeError('Wrong sliding window moving, implementation error.')
aggregated_model_labels = raw_to_labels.segmentation_array_to_sly_bitmaps(id_to_class_obj,
np.argmax(summed_scores, axis=2))
result_ann = result_ann.add_labels(
_replace_or_drop_labels_classes(
aggregated_model_labels, self._model_class_mapper, self._model_tag_meta_mapper))
if self._config.get(SAVE_PROBABILITIES, False) is True:
# copied fom unet's inference.py
mean_scores = summed_scores / summed_divisor
accumulated_pixelwise_scores_labels = raw_to_labels.segmentation_scores_to_per_class_labels(
id_to_class_obj, mean_scores)
result_problabels = _replace_or_drop_labels_classes(
accumulated_pixelwise_scores_labels, self._model_class_mapper, self._model_tag_meta_mapper)
result_ann = result_ann.add_pixelwise_score_labels(result_problabels)
return result_ann
