def annotations_to_instances(annos, image_size, mask_format="polygon"):
"""
Create an :class:`Instances` object used by the models,
from instance annotations in the dataset dict.
Args:
annos (list[dict]): a list of instance annotations in one image, each
element for one instance.
image_size (tuple): height, width
Returns:
Instances:
It will contain fields "gt_boxes", "gt_classes",
"gt_masks", "gt_keypoints", if they can be obtained from `annos`.
This is the format that builtin models expect.
"""
boxes = [
BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS)
for obj in annos
]
target = Instances(image_size)
boxes = target.gt_boxes = Boxes(boxes)
boxes.clip(image_size)
classes = [obj["category_id"] for obj in annos]
classes = torch.tensor(classes, dtype=torch.int64)
target.gt_classes = classes
if len(annos) and "viewpoint" in annos[0]:
viewpoints = np.array([obj["viewpoint"] for obj in annos])
viewpoints_class = torch.tensor(viewpoints[:, 0], dtype=torch.int64)
target.gt_viewpoint = viewpoints_class
if len(annos[0]["viewpoint"]) == 2:
viewpoints_rads = torch.tensor(viewpoints[:, 1],
dtype=torch.float32)
target.gt_viewpoint_rads = viewpoints_rads
if len(annos) and "bbox3D" in annos[0]:
bbox3D = [obj["bbox3D"] for obj in annos]
bbox3D = torch.tensor(bbox3D, dtype=torch.float)
target.gt_bbox3D = bbox3D
if len(annos) and "height" in annos[0]:
height = [obj["height"] for obj in annos]
height = torch.tensor(height, dtype=torch.float)
target.gt_height = height
if len(annos) and "segmentation" in annos[0]:
polygons = [obj["segmentation"] for obj in annos]
if mask_format == "polygon":
masks = PolygonMasks(polygons)
else:
assert mask_format == "bitmask", mask_format
masks = BitMasks.from_polygon_masks(polygons, *image_size)
target.gt_masks = masks
if len(annos) and "keypoints" in annos[0]:
kpts = [obj.get("keypoints", []) for obj in annos]
target.gt_keypoints = Keypoints(kpts)
return target
def annotations_to_instances(annos, image_size, mask_format="polygon"):
"""
Create an :class:`Instances` object used by the models,
from instance annotations in the dataset dict.
Args:
annos (list[dict]): a list of instance annotations in one image, each
element for one instance.
image_size (tuple): height, width
Returns:
Instances:
It will contain fields "gt_boxes", "gt_classes",
"gt_masks", "gt_keypoints", if they can be obtained from `annos`.
This is the format that builtin models expect.
"""
boxes = [BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos]
target = Instances(image_size)
boxes = target.gt_boxes = Boxes(boxes)
if 'light' in annos[0].keys():
light = [BoxMode.convert(obj['light'],obj["bbox_mode"], BoxMode.XYXY_ABS) for obj in annos]
light = target.gt_light = Boxes(light)
light.clip(image_size)
boxes.clip(image_size)
classes = [obj["category_id"] for obj in annos]
classes = torch.tensor(classes, dtype=torch.int64)
target.gt_classes = classes
if len(annos) and "segmentation" in annos[0]:
polygons = [obj["segmentation"] for obj in annos]
if mask_format == "polygon":
masks = PolygonMasks(polygons)
else:
assert mask_format == "bitmask", mask_format
masks = BitMasks.from_polygon_masks(polygons, *image_size)
target.gt_masks = masks
if len(annos) and "keypoints" in annos[0]:
kpts = [obj.get("keypoints", []) for obj in annos]
target.gt_keypoints = Keypoints(kpts)
return target
def annotations_to_instances(annos, image_size, mask_format="polygon"):
"""
Create an :class:`Instances` object used by the models,
from instance annotations in the dataset dict.
Args:
annos (list[dict]): a list of instance annotations in one image, each
element for one instance.
image_size (tuple): height, width
Returns:
Instances:
It will contain fields "gt_boxes", "gt_classes",
"gt_masks", "gt_keypoints", if they can be obtained from `annos`.
This is the format that builtin models expect.
"""
boxes = [
BoxMode.convert(obj["bbox"], obj["bbox_mode"], BoxMode.XYXY_ABS)
for obj in annos
]
target = Instances(image_size)
boxes = target.gt_boxes = Boxes(boxes)
boxes.clip(image_size)
classes = [obj["category_id"] for obj in annos]
classes = torch.tensor(classes, dtype=torch.int64)
target.gt_classes = classes
if len(annos) and "segmentation" in annos[0]:
segm = [obj["segmentation"]
for obj in annos] # it may be bitmask instead of polygon
visible_segm = [obj["visible_mask"] for obj in annos
] # it may be bitmask instead of polygon
if mask_format == "polygon":
masks = PolygonMasks(segm)
if not isinstance(visible_segm[0], list):
visible_masks = visible_segm
visible_masks = BitMasks(
torch.stack([torch.from_numpy(x) for x in visible_masks]))
else:
# visible_masks = BitMasks.from_polygon_masks(visible_polygons, *image_size)
visible_masks = PolygonMasks(visible_segm)
else:
assert mask_format == "bitmask", mask_format
if not isinstance(segm[0], list):
masks = BitMasks(
torch.stack([torch.from_numpy(x) for x in segm]))
# visible_masks = visible_polygons
# visible_masks = BitMasks(torch.stack([torch.from_numpy(x) for x in visible_masks]))
else:
masks = BitMasks.from_polygon_masks(segm, *image_size)
# visible_masks = BitMasks.from_polygon_masks(visible_polygons, *image_size)
# print('masks:{}'.format(polygons))
if not isinstance(visible_segm[0], list):
visible_masks = visible_segm
visible_masks = BitMasks(
torch.stack([torch.from_numpy(x) for x in visible_masks]))
else:
# print('visible_masks:{}'.format(visible_polygons))
visible_masks = BitMasks.from_polygon_masks(
visible_segm, *image_size)
target.gt_masks = masks
target.gt_visible_masks = visible_masks
if len(annos) and "keypoints" in annos[0]:
kpts = [obj.get("keypoints", []) for obj in annos]
target.gt_keypoints = Keypoints(kpts)
return target
