def geometry_to_bitmap(geometry,
radius: int = 0,
crop_image_shape: tuple = None) -> list:
"""
Args:
geometry: Geometry type which implemented 'draw', 'translate' and 'to_bbox` methods
radius: half of thickness of drawed vector elements
crop_image_shape: if not None - crop bitmap object by this shape (HxW)
Returns:
Bitmap (geometry) object
"""
thickness = radius + 1
bbox = geometry.to_bbox()
extended_bbox = Rectangle(top=bbox.top - radius,
left=bbox.left - radius,
bottom=bbox.bottom + radius,
right=bbox.right + radius)
bitmap_data = np.full(shape=(extended_bbox.height, extended_bbox.width),
fill_value=False)
geometry = geometry.translate(-extended_bbox.top, -extended_bbox.left)
geometry.draw(bitmap_data, color=True, thickness=thickness)
origin = PointLocation(extended_bbox.top, extended_bbox.left)
bitmap_geometry = Bitmap(data=bitmap_data, origin=origin)
if crop_image_shape is not None:
crop_rect = Rectangle.from_size(*crop_image_shape)
return bitmap_geometry.crop(crop_rect)
return [bitmap_geometry]
def crop(img: np.ndarray, rect: Rectangle) -> np.ndarray:
'''
The function crop cut out part of the image with rectangle size. If rectangle for crop is out of image area it
generates exception error(ValueError).
:param img: image(numpy matrix) to be cropped
:param rect: class object Rectangle of a certain size
:return: cropped image
'''
img_rect = Rectangle.from_array(img)
if not img_rect.contains(rect):
raise ValueError('Rectangle for crop out of image area!')
return rect.get_cropped_numpy_slice(img)
def get_add_padding(self, source_shape):
source_rect = Rectangle.from_size(source_shape)
window_rect = Rectangle.from_size(self.window_shape)
if not source_rect.contains(window_rect):
raise RuntimeError(
'Sliding window: window is larger than source (image).')
hw_limit = tuple(source_shape[i] - self.window_shape[i]
for i in (0, 1))
for wind_top in range(0, hw_limit[0] + self.stride[0], self.stride[0]):
for wind_left in range(0, hw_limit[1] + self.stride[1],
self.stride[1]):
roi = window_rect.translate(drow=wind_top, dcol=wind_left)
yield roi
def to_bbox(self):
'''
The function to_bbox create Rectangle class object from current Bitmap class object
:return: Rectangle class object
'''
return Rectangle.from_array(self._data).translate(
drow=self._origin.row, dcol=self._origin.col)
def to_bbox(self):
exterior_np = self.exterior_np
rows, cols = exterior_np[:, 0], exterior_np[:, 1]
return Rectangle(top=round(min(rows).item()),
left=round(min(cols).item()),
bottom=round(max(rows).item()),
right=round(max(cols).item()))
def detection_preds_to_sly_rects(
idx_to_class, network_prediction: DetectionNetworkPrediction,
img_shape, min_score_threshold, score_tag_meta) -> list:
"""
Converts network detection results to Supervisely Labels with Rectangle geometry.
Args:
idx_to_class: Dict matching predicted boxes with appropriate ObjClass.
network_prediction: Network predictions packed into DetectionNetworkPrediction instance.
img_shape: Size(height, width) of image that was used for inference.
min_score_threshold: All detections with less scores will be dropped.
score_tag_meta: TagMeta instance for score tags.
Returns:
A list containing labels with detection rectangles.
"""
labels = []
thr_mask = np.squeeze(network_prediction.scores) > min_score_threshold
for box, class_id, score in zip(
np.squeeze(network_prediction.boxes)[thr_mask],
np.squeeze(network_prediction.classes)[thr_mask],
np.squeeze(network_prediction.scores)[thr_mask]):
xmin = round(float(box[1] * img_shape[1]))
ymin = round(float(box[0] * img_shape[0]))
xmax = round(float(box[3] * img_shape[1]))
ymax = round(float(box[2] * img_shape[0]))
rect = Rectangle(top=ymin, left=xmin, bottom=ymax, right=xmax)
class_obj = idx_to_class[int(class_id)]
label = Label(geometry=rect, obj_class=class_obj)
score_tag = Tag(score_tag_meta, value=round(float(score), 4))
label = label.add_tag(score_tag)
labels.append(label)
return labels
def to_bbox(self):
'''
The function to_bbox create Rectangle class object from current GraphNodes class object
:return: Rectangle class object
'''
return Rectangle.from_geometries_list(
[Point.from_point_location(node.location) for node in self._nodes.values()])
def get_effective_nonoverlapping_masks(geometries, img_size=None):
'''
Find nonoverlapping objects from given list of geometries
:param geometries: list of geometry type objects(Point, Polygon, PolyLine, Bitmap etc.)
:param img_size: tuple or list of integers
:return: list of bitmaps, numpy array
'''
if img_size is None:
if len(geometries) > 0:
common_bbox = Rectangle.from_geometries_list(geometries)
img_size = (common_bbox.bottom + 1, common_bbox.right + 1)
else:
img_size = (0, 0)
canvas = np.full(shape=img_size,
fill_value=len(geometries),
dtype=np.int32)
for idx, geometry in enumerate(geometries):
geometry.draw(canvas, color=idx)
result_masks = []
for idx, geometry in enumerate(geometries):
effective_indicator = (canvas == idx)
if np.any(effective_indicator):
result_masks.append(Bitmap(effective_indicator))
else:
result_masks.append(None)
return result_masks, canvas
def test_crop(self):
crop_rect = Rectangle(top=1, left=0, bottom=10, right=4)
res_geoms = self.bitmap.crop(crop_rect)
self.assertEqual(len(res_geoms), 1)
res_bitmap = res_geoms[0]
res_data = np.array([[[0.6, 0.7]]], dtype=np.float64)
self.assertMultichannelBitmapEquals(res_bitmap, 1, 4, res_data)
def _rect_from_bounds(padding_config: dict, img_h: int,
img_w: int) -> Rectangle:
def get_padding_pixels(raw_side, dim_name):
side_padding_config = padding_config.get(dim_name)
if side_padding_config is None:
padding_pixels = 0
elif side_padding_config.endswith('px'):
padding_pixels = int(side_padding_config[:-len('px')])
elif side_padding_config.endswith('%'):
padding_fraction = float(side_padding_config[:-len('%')])
padding_pixels = int(raw_side * padding_fraction / 100.0)
else:
raise ValueError(
'Unknown padding size format: {}. Expected absolute values as "5px" or relative as "5%"'
.format(side_padding_config))
return padding_pixels
def get_padded_side(raw_side, l_name, r_name):
l_bound = -get_padding_pixels(raw_side, l_name)
r_bound = raw_side + get_padding_pixels(raw_side, r_name)
return l_bound, r_bound
left, right = get_padded_side(img_w, 'left', 'right')
top, bottom = get_padded_side(img_h, 'top', 'bottom')
return Rectangle(top=top, left=left, bottom=bottom, right=right)
def crop(img: np.ndarray,
ann: Annotation,
top_pad: int = 0,
left_pad: int = 0,
bottom_pad: int = 0,
right_pad: int = 0) -> (np.ndarray, Annotation):
"""
Crops the given image array and annotation from all sides with the given values.
Args:
img: Input image array.
ann: Input annotation.
top_pad: The size in pixels of the piece of picture that will be cut from the top side.
left_pad: The size in pixels of the piece of picture that will be cut from the left side.
bottom_pad: The size in pixels of the piece of picture that will be cut from the bottom side.
right_pad: The size in pixels of the piece of picture that will be cut from the right side.
Returns:
A tuple containing cropped image array and annotation.
"""
_validate_image_annotation_shape(img, ann)
height, width = img.shape[:2]
crop_rect = Rectangle(top_pad, left_pad, height - bottom_pad - 1,
width - right_pad - 1)
res_img = sly_image.crop(img, crop_rect)
res_ann = ann.relative_crop(crop_rect)
return res_img, res_ann
def to_bbox(self):
rows = [keypoint.location.row for keypoint in self.points]
cols = [keypoint.location.col for keypoint in self.points]
return Rectangle(top=round(min(rows)),
left=round(min(cols)),
bottom=round(max(rows)),
right=round(max(cols)))
def _rectangle_from_cropping_or_padding_bounds(img_shape, crop_config,
do_crop: bool):
def get_crop_pixels(raw_side, dim_name):
side_crop_config = crop_config.get(dim_name)
if side_crop_config is None:
crop_pixels = 0
elif side_crop_config.endswith(PX):
crop_pixels = int(side_crop_config[:-len(PX)])
elif side_crop_config.endswith(PERCENT):
padding_fraction = float(side_crop_config[:-len(PERCENT)])
crop_pixels = int(raw_side * padding_fraction / 100.0)
else:
raise ValueError(
'Unknown padding size format: {}. Expected absolute values as "5px" or relative as "5%"'
.format(side_crop_config))
if not do_crop:
crop_pixels *= -1 # Pad instead of crop.
return crop_pixels
# TODO more informative error message.
return Rectangle(
top=get_crop_pixels(img_shape[0], TOP),
left=get_crop_pixels(img_shape[1], LEFT),
bottom=img_shape[0] - get_crop_pixels(img_shape[0], BOTTOM) - 1,
right=img_shape[1] - get_crop_pixels(img_shape[1], RIGHT) - 1)
def test_crop(self):
crop_rect = Rectangle(25, 0, 200, 200)
res_geoms = self.poly.crop(crop_rect)
self.assertEqual(len(res_geoms), 1)
crop = res_geoms[0]
self.assertPolyEquals(crop,
[[10, 25], [20, 25], [20, 30], [30, 30], [30, 25], [35, 25], [30, 40], [10, 30]],
[])
def to_bbox(self):
points_np = np.array([[self._points[p].row, self._points[p].col]
for face in self._faces for p in face.tolist()])
rows, cols = points_np[:, 0], points_np[:, 1]
return Rectangle(top=round(min(rows).item()),
left=round(min(cols).item()),
bottom=round(max(rows).item()),
right=round(max(cols).item()))
def crop(self, rect: Rectangle):
'''
The function "crop" return list containing graph if all nodes of graph located in given rectangle and an empty list otherwise
:param rect: Rectangle class object
:return: list containing GraphNodes class object or empty list
'''
is_all_nodes_inside = all(rect.contains_point_location(node.location) for node in self._nodes.values())
return [self] if is_all_nodes_inside else []
def _find_mask_tight_bbox(raw_mask: np.ndarray) -> Rectangle:
rows = list(np.any(raw_mask, axis=1).tolist()) # Redundant conversion to list to help PyCharm static analysis.
cols = list(np.any(raw_mask, axis=0).tolist())
top_margin = rows.index(True)
bottom_margin = rows[::-1].index(True)
left_margin = cols.index(True)
right_margin = cols[::-1].index(True)
return Rectangle(top=top_margin, left=left_margin, bottom=len(rows) - 1 - bottom_margin,
right=len(cols) - 1 - right_margin)
def to_bbox(self):
'''
The function to_bbox create Rectangle class object from current VectorGeometry class object
:return: Rectangle class object
'''
exterior_np = self.exterior_np
rows, cols = exterior_np[:, 0], exterior_np[:, 1]
return Rectangle(top=round(min(rows).item()), left=round(min(cols).item()), bottom=round(max(rows).item()),
right=round(max(cols).item()))
def test_crop_by_border(self):
exterior = [[10, 10], [40, 10], [40, 40], [10, 40]]
interiors = [[[11, 11], [11, 20], [20, 11]], [[20, 20], [21, 20], [20, 21]]]
poly = Polygon(exterior=row_col_list_to_points(exterior, flip_row_col_order=True),
interior=[row_col_list_to_points(interior, flip_row_col_order=True) for interior in interiors])
crop_rect = Rectangle(0, 0, 100, 10)
res_geoms = poly.crop(crop_rect)
self.assertEqual(len(res_geoms), 0)
def to_bbox(self):
'''
The function to_bbox create Rectangle class object from Point class object
:return: Rectangle class object
'''
return Rectangle(top=self.row,
left=self.col,
bottom=self.row,
right=self.col)
def to_bbox(self):
'''
The function to_bbox create Rectangle class object from current Cuboid class object
:return: Rectangle class object
'''
points_np = np.array([[self._points[p].row, self._points[p].col]
for face in self._faces for p in face.tolist()])
rows, cols = points_np[:, 0], points_np[:, 1]
return Rectangle(top=round(min(rows).item()), left=round(min(cols).item()), bottom=round(max(rows).item()),
right=round(max(cols).item()))
def validate_bounds(self, img_size, _auto_correct=False):
canvas_rect = Rectangle.from_size(img_size)
if canvas_rect.contains(self.geometry.to_bbox()) is False:
raise OutOfImageBoundsExtension("Figure is out of image bounds")
if _auto_correct is True:
geometries_after_crop = [cropped_geometry for cropped_geometry in self.geometry.crop(canvas_rect)]
if len(geometries_after_crop) != 1:
raise OutOfImageBoundsExtension("Several geometries after crop")
self._set_geometry_inplace(geometries_after_crop[0])
def test_from_json(self):
packed_obj = {
'some_stuff': 'aaa',
POINTS: {
EXTERIOR: [[17, 3], [34, 45]],
INTERIOR: []
}
}
res_rect = Rectangle.from_json(packed_obj)
self.assertRectEquals(res_rect, 3, 17, 45, 34)
def get_change_size(self, source_shape):
source_rect = Rectangle.from_size(source_shape)
window_rect = Rectangle.from_size(self.window_shape)
if not source_rect.contains(window_rect):
raise RuntimeError(
'Sliding window: window is larger than source (image).')
hw_limit = tuple(source_shape[i] - self.window_shape[i]
for i in (0, 1))
for wind_top in range(0, hw_limit[0] + self.stride[0], self.stride[0]):
for wind_left in range(0, hw_limit[1] + self.stride[1],
self.stride[1]):
wind_bottom = min(wind_top + self.stride[0], source_shape[0])
wind_right = min(wind_left + self.stride[1], source_shape[1])
roi = Rectangle(wind_top, wind_left, wind_bottom - 1,
wind_right - 1)
if not source_rect.contains(roi):
raise RuntimeError(
'Sliding window: result crop bounds are invalid.')
yield roi
def _add_labels_impl(self, dest, labels):
'''
The function _add_labels_impl extend list of the labels of the current Annotation object
:param dest: destination list of the Label class objects
:param labels: list of the Label class objects to be added to the destination list
:return: list of the Label class objects
'''
for label in labels:
# TODO Reconsider silent automatic normalization, reimplement
canvas_rect = Rectangle.from_size(self.img_size)
dest.extend(label.crop(canvas_rect))
def _all_filtered_bbox_rois(ann: Annotation, included_classes, crop_config: dict):
for src_label in ann.labels:
effective_roi = None
if is_name_included(src_label.obj_class.name, included_classes):
bbox = src_label.geometry.to_bbox()
roi = _make_padded_rectangle((bbox.height, bbox.width), crop_config)
maybe_effective_roi = roi.translate(drow=bbox.top, dcol=bbox.left).crop(
Rectangle.from_size(ann.img_size))
if len(maybe_effective_roi) > 0:
[effective_roi] = maybe_effective_roi
yield src_label, effective_roi
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 instance_crop(img: np.ndarray,
ann: Annotation,
class_title: str,
save_other_classes_in_crop: bool = True,
padding_config: dict = None) -> list:
"""
Crops objects of specified classes from image with configurable padding.
Args:
img: Input image array.
ann: Input annotation.
class_title: Name of class to crop.
save_other_classes_in_crop: save non-target classes in each cropped annotation.
padding_config: Dict with padding
Returns:
List of cropped [image, annotation] pairs.
"""
padding_config = take_with_default(padding_config, {})
_validate_image_annotation_shape(img, ann)
results = []
img_rect = Rectangle.from_size(img.shape[:2])
if save_other_classes_in_crop:
non_target_labels = [
label for label in ann.labels
if label.obj_class.name != class_title
]
else:
non_target_labels = []
ann_with_non_target_labels = ann.clone(labels=non_target_labels)
for label in ann.labels:
if label.obj_class.name == class_title:
src_fig_rect = label.geometry.to_bbox()
new_img_rect = _rect_from_bounds(padding_config,
img_w=src_fig_rect.width,
img_h=src_fig_rect.height)
rect_to_crop = new_img_rect.translate(src_fig_rect.top,
src_fig_rect.left)
crops = rect_to_crop.crop(img_rect)
if len(crops) == 0:
continue
rect_to_crop = crops[0]
image_crop = sly_image.crop(img, rect_to_crop)
cropped_ann = ann_with_non_target_labels.relative_crop(
rect_to_crop)
label_crops = label.relative_crop(rect_to_crop)
for label_crop in label_crops:
results.append((image_crop, cropped_ann.add_label(label_crop)))
return results
def _calc_inner_crop(self):
"""
Given a rectangle of self.src_imsize HxW that has been rotated by
self.angle_degrees_ccw (in degrees), computes the location of the
largest possible axis-aligned rectangle within the rotated rectangle.
"""
# TODO This needs significant streamlinig.
a_ccw = np.deg2rad(self.angle_degrees_ccw)
quadrant = math.floor(a_ccw / (math.pi / 2)) & 3
sign_alpha = a_ccw if ((quadrant & 1) == 0) else math.pi - a_ccw
alpha = (sign_alpha % math.pi + math.pi) % math.pi
h, w = self.src_imsize
bb_w = w * math.cos(alpha) + h * math.sin(alpha)
bb_h = w * math.sin(alpha) + h * math.cos(alpha)
gamma = math.atan2(bb_w, bb_w) if (w < h) else math.atan2(bb_w, bb_w)
delta = math.pi - alpha - gamma
length = h if (w < h) else w
d = length * math.cos(alpha)
a = d * math.sin(alpha) / math.sin(delta)
y = a * math.cos(gamma)
x = y * math.tan(gamma)
largest_w, largest_h = bb_w - 2 * x, bb_h - 2 * y
new_h, new_w = self.new_imsize
left = round((new_w - largest_w) * 0.5)
right = round((new_w + largest_w) * 0.5)
top = round((new_h - largest_h) * 0.5)
bottom = round((new_h + largest_h) * 0.5)
some_inner_crop = Rectangle(top, left, bottom, right)
new_img_bbox = Rectangle(0, 0, self.new_imsize[0] - 1,
self.new_imsize[1] - 1)
self.inner_crop = new_img_bbox.crop(some_inner_crop)[0]
def test_crop(self): # @TODO: mb delete compress while cropping
crop_rect = Rectangle(0, 0, 8, 8)
res_geoms = self.bitmap.crop(crop_rect)
self.assertEqual(len(res_geoms), 1)
res_bitmap = res_geoms[0]
res_mask = np.array([[0, 0, 1, 0],
[0, 1, 1, 1],
[1, 0, 1, 0],
[0, 0, 1, 0],
[0, 0, 1, 0],
[0, 0, 1, 0],
[0, 0, 1, 0]], dtype=np.bool)
self.assertBitmapEquals(res_bitmap, 0, 5, res_mask)
