def verify_data(orig_ann: Annotation, classes_matching: dict, res_project_meta: ProjectMeta) -> Annotation:
ann = orig_ann.clone()
imsize = ann.img_size
for first_class, second_class in classes_matching.items():
mask1 = np.zeros(imsize, dtype=np.bool)
mask2 = np.zeros(imsize, dtype=np.bool)
for label in ann.labels:
if label.obj_class.name == first_class:
label.geometry.draw(mask1, True)
elif label.obj_class.name == second_class:
label.geometry.draw(mask2, True)
iou_value = _compute_masks_iou(mask1, mask2)
tag_meta = res_project_meta.img_tag_metas.get(make_iou_tag_name(first_class))
tag = Tag(tag_meta, iou_value)
ann.add_tag(tag)
fp_mask = _create_fp_mask(mask1, mask2)
if fp_mask.sum() != 0:
fp_object_cls = res_project_meta.obj_classes.get(make_false_positive_name(first_class))
fp_geom = Bitmap(data=fp_mask)
fp_label = Label(fp_geom, fp_object_cls)
ann.add_label(fp_label)
fn_mask = _create_fn_mask(mask1, mask2)
if fn_mask.sum() != 0:
fn_object_cls = res_project_meta.obj_classes.get(make_false_negative_name(first_class))
fn_geom = Bitmap(data=fn_mask)
fn_label = Label(fn_geom, fn_object_cls)
ann.add_label(fn_label)
return ann
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 test_to_contours(self):
bitmap = Bitmap(data=np.array([[1, 1, 0, 1, 1, 1],
[1, 1, 0, 1, 0, 1],
[0, 0, 0, 1, 1, 1],
[1, 0, 0, 1, 0, 1],
[1, 0, 0, 1, 1, 1],
[1, 0, 0, 0, 0, 0],
[1, 0, 0, 1, 1, 1]], dtype=np.bool),
origin=PointLocation(10, 110))
polygons = bitmap.to_contours()
exteriors_points = [np.array([[10, 113], [14, 113], [14, 115], [10, 115]]),
np.array([[10, 110], [11, 110], [11, 111], [10, 111]])]
interiors_points = [[],
[np.array([[13, 113], [12, 114], [13, 115], [14, 114]]),
np.array([[11, 113], [10, 114], [11, 115], [12, 114]])],
[]]
self.assertEqual(len(polygons), 2)
for polygon, target_exterior, target_interiors in zip(polygons, exteriors_points, interiors_points):
self.assertTrue(np.equal(polygon.exterior_np, target_exterior).all())
self.assertTrue(all(np.equal(p_inter, t_inter)
for p_inter, t_inter in zip(polygon.interior_np, target_interiors)))
json.dumps(polygon.to_json())
self.assertIsInstance(polygon, Polygon)
def setUp(self):
self.origin = PointLocation(0, 4)
self.mask = np.array([[0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0]], dtype=np.bool)
self.mask_no_margins = self.mask[:, 1:-1]
self.bitmap = Bitmap(data=self.mask, origin=self.origin)
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 infer_on_image(image,
graph,
model,
idx_to_class_title,
project_meta,
confidence_tag_meta,
min_confidence=0):
with graph.as_default():
[results] = model.detect([image], verbose=0)
res_labels = []
for mask_idx, class_id in enumerate(results['class_ids']):
confidence = results['scores'][mask_idx]
if confidence < min_confidence:
continue
bool_mask = results['masks'][:, :, mask_idx] != 0
class_geometry = Bitmap(data=bool_mask)
cls_title = idx_to_class_title[class_id]
label = Label(geometry=class_geometry,
obj_class=project_meta.get_obj_class(cls_title))
confidence_tag = Tag(confidence_tag_meta,
value=round(float(confidence), 4))
label = label.add_tag(confidence_tag)
res_labels.append(label)
return res_labels
def extract_labels_from_mask(mask: np.ndarray, color_id_to_obj_class: collections.Mapping) -> list:
"""
Extract multiclass instances from grayscale mask and save it to labels list.
Args:
mask: multiclass grayscale mask
color_id_to_obj_class: dict of objects classes assigned to color id (e.g. {1: ObjClass('cat), ...})
Returns:
list of labels with bitmap geometry
"""
zero_offset = 1 if 0 in color_id_to_obj_class else 0
if zero_offset > 0:
mask = mask + zero_offset
labeled, labels_count = measure.label(mask, connectivity=1, return_num=True)
objects_slices = ndimage.find_objects(labeled)
labels = []
for object_index, slices in enumerate(objects_slices, start=1):
crop = mask[slices]
sub_mask = crop * (labeled[slices] == object_index).astype(np.int)
class_index = np.max(sub_mask) - zero_offset
if class_index in color_id_to_obj_class:
bitmap = Bitmap(data=sub_mask.astype(np.bool), origin=PointLocation(slices[0].start, slices[1].start))
label = Label(geometry=bitmap, obj_class=color_id_to_obj_class.get(class_index))
labels.append(label)
return labels
def test_from_json(self):
packed_obj = {
BITMAP: {
DATA: 'eJzrDPBz5+WS4mJgYOD19HAJAtLsIMzIDCT/zTk6AUixBfiEuALp////L705/y6QxVgS5BfM4PDsRhqQI+j'
'p4hhSMSdZIGFDAkeiQIMDA7sVw125xatvACUZPF39XNY5JTQBADRqHJQ=',
ORIGIN: [0, 1]
}
}
bitmap = Bitmap.from_json(packed_obj)
self.assertBitmapEquals(bitmap, 1, 1, self.mask_no_margins)
def to_segmentation_task(self):
class_mask = {}
for label in self.labels:
if label.obj_class not in class_mask:
class_mask[label.obj_class] = np.zeros(self.img_size, np.uint8)
label.draw(class_mask[label.obj_class], color=255)
new_labels = []
for obj_class, white_mask in class_mask.items():
mask = white_mask == 255
bitmap = Bitmap(data=mask)
new_labels.append(Label(geometry=bitmap, obj_class=obj_class))
return self.clone(labels=new_labels)
def to_nonoverlapping_masks(self, mapping):
common_img = np.zeros(self.img_size, np.int32) # size is (h, w)
for idx, lbl in enumerate(self.labels, start=1):
if mapping[lbl.obj_class] is not None:
lbl.draw(common_img, color=idx)
(unique, counts) = np.unique(common_img, return_counts=True)
new_labels = []
for idx, lbl in enumerate(self.labels, start=1):
dest_class = mapping[lbl.obj_class]
if dest_class is None:
continue # skip labels
mask = common_img == idx
if np.any(mask): # figure may be entirely covered by others
g = lbl.geometry
new_mask = Bitmap(data=mask)
new_lbl = lbl.clone(geometry=new_mask, obj_class=dest_class)
new_labels.append(new_lbl)
new_ann = self.clone(labels=new_labels)
return new_ann
def segmentation_array_to_sly_bitmaps(idx_to_class: dict,
pred: np.ndarray,
origin: PointLocation = None) -> list:
"""
Converts array with segmentation results to Labels with Bitmap geometry according to idx_to_class mapping.
Args:
idx_to_class: Dict matching values in prediction array with appropriate ObjClass.
pred: Array containing raw segmentation results.
origin: Origin point for all output Bitmaps.
return:
A list containing result labels.
"""
labels = []
for cls_idx, cls_obj in idx_to_class.items():
predicted_class_pixels = (pred == cls_idx)
if np.any(predicted_class_pixels):
class_geometry = Bitmap(data=predicted_class_pixels, origin=origin)
labels.append(Label(geometry=class_geometry, obj_class=cls_obj))
return labels
def from_imgaug(cls,
img,
ia_boxes=None,
ia_masks=None,
index_to_class=None,
meta: ProjectMeta = None):
if ((ia_boxes is not None) or (ia_masks is not None)) and meta is None:
raise ValueError("Project meta has to be provided")
labels = []
if ia_boxes is not None:
for ia_box in ia_boxes:
obj_class = meta.get_obj_class(ia_box.label)
if obj_class is None:
raise KeyError(
"Class {!r} not found in project meta".format(
ia_box.label))
lbl = Label(
Rectangle(top=ia_box.y1,
left=ia_box.x1,
bottom=ia_box.y2,
right=ia_box.x2), obj_class)
labels.append(lbl)
if ia_masks is not None:
if index_to_class is None:
raise ValueError(
"mapping from index to class name is needed to transform masks to SLY format"
)
class_mask = ia_masks.get_arr()
# mask = white_mask == 255
(unique, counts) = np.unique(class_mask, return_counts=True)
for index, count in zip(unique, counts):
if index == 0:
continue
mask = class_mask == index
bitmap = Bitmap(data=mask[:, :, 0])
restore_class = meta.get_obj_class(index_to_class[index])
labels.append(Label(geometry=bitmap, obj_class=restore_class))
return cls(img_size=img.shape[:2], labels=labels)
def get_effective_nonoverlapping_masks(geometries, img_size=None):
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
class BitmapTest(unittest.TestCase):
def setUp(self):
self.origin = PointLocation(0, 4)
self.mask = np.array([[0, 0, 0, 1, 0, 0, 0],
[0, 0, 1, 1, 1, 0, 0],
[0, 1, 0, 1, 0, 1, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 1, 0, 0, 0]], dtype=np.bool)
self.mask_no_margins = self.mask[:, 1:-1]
self.bitmap = Bitmap(data=self.mask, origin=self.origin)
def assertBitmapEquals(self, bitmap, origin_row, origin_col, mask):
self.assertIsInstance(bitmap, Bitmap),
self.assertEqual(bitmap.origin.row, origin_row)
self.assertEqual(bitmap.origin.col, origin_col)
self.assertListEqual(bitmap.data.tolist(), mask.tolist())
def test_rotate(self):
in_size = (15, 15)
rotator = ImageRotator(imsize=in_size, angle_degrees_ccw=90)
res_bitmap = self.bitmap.rotate(rotator)
expected_mask = np.array([[0, 0, 1, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 1, 1],
[0, 1, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0]], dtype=np.bool)
self.assertListEqual(res_bitmap.data.tolist(), expected_mask.tolist())
def test_empty_crop(self):
crop_rect = Rectangle(0, 0, 4, 4)
res_geoms = self.bitmap.crop(crop_rect)
self.assertEqual(len(res_geoms), 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)
def test_translate(self):
drows = 10
dcols = 7
res_bitmap = self.bitmap.translate(drows, dcols)
self.assertBitmapEquals(res_bitmap, 10, 12, self.mask_no_margins)
def test_area(self):
area = self.bitmap.area
self.assertIsInstance(area, float)
self.assertEqual(area, 11)
def test_to_bbox(self):
res_rect = self.bitmap.to_bbox()
self.assertIsInstance(res_rect, Rectangle)
self.assertEqual(res_rect.top, 0)
self.assertEqual(res_rect.left, 5)
self.assertEqual(res_rect.right, 9)
self.assertEqual(res_rect.bottom, 6)
def test_from_json(self):
packed_obj = {
BITMAP: {
DATA: 'eJzrDPBz5+WS4mJgYOD19HAJAtLsIMzIDCT/zTk6AUixBfiEuALp////L705/y6QxVgS5BfM4PDsRhqQI+j'
'p4hhSMSdZIGFDAkeiQIMDA7sVw125xatvACUZPF39XNY5JTQBADRqHJQ=',
ORIGIN: [0, 1]
}
}
bitmap = Bitmap.from_json(packed_obj)
self.assertBitmapEquals(bitmap, 1, 1, self.mask_no_margins)
def test_resize(self):
in_size = (20, 20)
out_size = (40, 40)
res_bitmap = self.bitmap.resize(in_size, out_size)
self.assertIsInstance(res_bitmap, Bitmap)
def test_flipud(self):
im_size = (20, 20)
res_bitmap = self.bitmap.flipud(im_size)
expected_mask = np.array([[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[0, 0, 1, 0, 0],
[1, 0, 1, 0, 1],
[0, 1, 1, 1, 0],
[0, 0, 1, 0, 0]], dtype=np.bool)
self.assertBitmapEquals(res_bitmap, 13, 5, expected_mask)
def test_to_json(self):
obj = self.bitmap.to_json()
expected_dict = {
BITMAP: {
DATA: 'eJzrDPBz5+WS4mJgYOD19HAJAtKsQMzOyAwkf2WKrgVSbAE+Ia5A+v///0tvzr8LZDGWBPkFMzg8u5EG5Ah'
'6ujiGVMxJDkjQSIg4uIChkYEvjXHncvfAUqAkg6ern8s6p4QmAAVvHAE=',
ORIGIN: [5, 0]
}
}
self.assertDictEqual(obj, expected_dict)
