def feed(self, img, ann, inference_cback):
src_objects, model_objects, interm_objects, img_wh = self._init_feed(
ann)
summed_probas = np.zeros(
(img_wh[1], img_wh[0], self._num_model_out_classes),
dtype=np.float64)
cls_mapping = None
for roi in self._sliding_windows.get(img_wh):
img_cropped = crop_image_with_rect(img, roi)
inference_result = inference_cback(img_cropped, None)
cls_mapping = inference_result.pixelwise_class_probas.class_title_to_idx_out_mapping
summed_probas[
roi.top:roi.bottom, roi.left:roi.
right, :] += inference_result.pixelwise_class_probas.probas
interm_objects.extend(
self._maybe_make_intermediate_bbox(img_wh, roi))
if np.sum(summed_probas, axis=2).min() == 0:
raise RuntimeError(
'Wrong sliding window moving, implementation error.')
model_objects_raw = prediction_to_sly_bitmaps(
cls_mapping, np.argmax(summed_probas, axis=2))
model_objects = rename_and_filter_figures(self._renamer_model,
model_objects_raw)
ann['objects'] = src_objects + interm_objects + model_objects
return ann
def get_objects_for_bbox(img, roi, inference_callback, renamer):
"""Runs inference within the given roi; renames the resulting figures and moves to global reference frame."""
img_cropped = crop_image_with_rect(img, roi)
inference_result = inference_callback(img_cropped, None)
for fig in inference_result.figures:
fig.shift((roi.left, roi.top))
return rename_and_filter_figures(renamer, inference_result.figures)
def process(self, data_el):
img_desc, ann_orig = data_el
img_wh = ann_orig.image_size_wh
angle_dct = self.settings['rotate_angles']
min_degrees, max_degrees = angle_dct['min_degrees'], angle_dct['max_degrees']
rotate_degrees = np.random.uniform(min_degrees, max_degrees)
rotator = ImageRotator(imsize_wh=img_wh, angle_degrees_ccw=rotate_degrees)
img_orig = img_desc.read_image()
img = rotator.rotate_img(img_orig, use_inter_nearest=False)
ann = deepcopy(ann_orig)
for fig in ann['objects']:
fig.rotate(rotator)
black_reg_mode = self.settings['black_regions']['mode']
if black_reg_mode == 'keep':
rect_to_crop = None
elif black_reg_mode == 'crop':
rect_to_crop = rotator.inner_crop
elif black_reg_mode == 'preserve_size':
rect_to_crop = rotator.source_rect
img, delta = expand_image_with_rect(img, rect_to_crop)
rect_to_crop = rect_to_crop.move(delta)
for fig in ann['objects']:
fig.shift(delta)
else:
raise NotImplementedError('Wrong black_regions mode.')
if rect_to_crop is not None:
if rect_to_crop.is_empty:
logger.warning('Rotate layer produced empty crop.')
return # no yield
if not Rect.from_arr(img).contains(rect_to_crop):
raise RuntimeError('Unable to crop image in Rotate layer.')
ann.apply_to_figures(lambda x: x.crop(rect_to_crop))
img = crop_image_with_rect(img, rect_to_crop)
delta = (-rect_to_crop.left, -rect_to_crop.top)
for fig in ann['objects']:
fig.shift(delta) # to new coords of image
ann.update_image_size(img)
new_img_desc = img_desc.clone_with_img(img)
yield new_img_desc, ann
def feed(self, img, ann, inference_cback):
img_wh = ann.image_size_wh
src_titles = self._mode_conf['from_classes']
for src_obj, roi in all_filtered_bbox_rois(ann, src_titles,
self._mode_conf['padding'],
img_wh):
img_cropped = crop_image_with_rect(img, roi)
_ = inference_cback(
img_cropped,
src_obj.data) # no need to crop & pass figures now
ann['objects'] = rename_and_filter_figures(self._renamer_existing,
ann['objects'])
return ann
def process(self, data_el):
img_desc, ann_orig = data_el
img_wh = ann_orig.image_size_wh
img_orig = img_desc.read_image()
for rect_to_crop in self.sliding_windows.get(img_wh):
img = crop_image_with_rect(img_orig, rect_to_crop)
new_img_desc = img_desc.clone_with_img(img)
ann = deepcopy(ann_orig)
ann.apply_to_figures(lambda x: x.crop(rect_to_crop))
delta = (-rect_to_crop.left, -rect_to_crop.top)
for fig in ann['objects']:
fig.shift(delta) # to new coords of image
ann.update_image_size(img)
yield new_img_desc, ann
def process(self, data_el):
img_desc, ann_orig = data_el
imsize_wh = ann_orig.image_size_wh
rect_img = Rect.from_size(imsize_wh)
padding_dct = self.settings['pad']['sides']
img_orig = None
for idx, src_fig in enumerate(ann_orig['objects']):
if src_fig.class_title not in self.classes_to_crop:
continue
src_fig_bbox = src_fig.get_bbox().round()
if src_fig_bbox.is_empty:
continue # tiny object
new_img_rect = rect_from_bounds(padding_dct,
img_w=src_fig_bbox.width,
img_h=src_fig_bbox.height,
shift_inside=False)
rect_to_crop = new_img_rect.move(src_fig_bbox.point0)
rect_to_crop = rect_to_crop.intersection(rect_img).round()
if rect_to_crop.is_empty:
continue
# let's crop
if img_orig is None:
img_orig = img_desc.read_image()
img = crop_image_with_rect(img_orig, rect_to_crop)
new_img_desc = img_desc.clone_with_img(img)
ann = deepcopy(ann_orig)
ann['objects'] = [
x for i, x in enumerate(ann['objects'])
if i == idx or x.class_title in self.classes_to_save
]
ann.apply_to_figures(lambda x: x.crop(rect_to_crop))
delta = (-rect_to_crop.left, -rect_to_crop.top)
for fig in ann['objects']:
fig.shift(delta) # to new coords of image
ann.update_image_size(img)
yield new_img_desc, ann