# detectron2.structures package¶

class detectron2.structures.Boxes(tensor: torch.Tensor)[source]

Bases: object

This structure stores a list of boxes as a Nx4 torch.Tensor. It supports some common methods about boxes (area, clip, nonempty, etc), and also behaves like a Tensor (support indexing, to(device), .device, and iteration over all boxes)

tensor

float matrix of Nx4.

BoxSizeType = typing.Union[typing.List[int], typing.Tuple[int, int]]
__init__(tensor: torch.Tensor)[source]
Parameters: tensor (Tensor[float]) – a Nx4 matrix. Each row is (x1, y1, x2, y2).
clone() → detectron2.structures.boxes.Boxes[source]

Clone the Boxes.

Returns: Boxes
to(device: str) → detectron2.structures.boxes.Boxes[source]
area() → torch.Tensor[source]

Computes the area of all the boxes.

Returns: torch.Tensor – a vector with areas of each box.
clip(box_size: Union[List[int], Tuple[int, int]]) → None[source]

Clip (in place) the boxes by limiting x coordinates to the range [0, width] and y coordinates to the range [0, height].

Parameters: box_size (height, width) – The clipping box’s size.
nonempty(threshold: int = 0) → torch.Tensor[source]

Find boxes that are non-empty. A box is considered empty, if either of its side is no larger than threshold.

Returns: Tensor – a binary vector which represents whether each box is empty (False) or non-empty (True).
__getitem__(item: Union[int, slice, torch.BoolTensor]) → detectron2.structures.boxes.Boxes[source]
Returns: Boxes – Create a new Boxes by indexing.

The following usage are allowed: 1. new_boxes = boxes[3]: return a Boxes which contains only one box. 2. new_boxes = boxes[2:10]: return a slice of boxes. 3. new_boxes = boxes[vector], where vector is a torch.BoolTensor

with length = len(boxes). Nonzero elements in the vector will be selected.

Note that the returned Boxes might share storage with this Boxes, subject to Pytorch’s indexing semantics.

inside_box(box_size: Union[List[int], Tuple[int, int]], boundary_threshold: int = 0) → torch.Tensor[source]
Parameters: box_size (height, width) – Size of the reference box. boundary_threshold (int) – Boxes that extend beyond the reference box boundary by more than boundary_threshold are considered “outside”. a binary vector, indicating whether each box is inside the reference box.
get_centers() → torch.Tensor[source]
Returns: The box centers in a Nx2 array of (x, y).
scale(scale_x: float, scale_y: float) → None[source]

Scale the box with horizontal and vertical scaling factors

static cat(boxes_list: List[Boxes]) → detectron2.structures.boxes.Boxes[source]

Concatenates a list of Boxes into a single Boxes

Parameters: boxes_list (list[Boxes]) – Boxes – the concatenated Boxes
device
__iter__() → Iterator[torch.Tensor][source]

Yield a box as a Tensor of shape (4,) at a time.

class detectron2.structures.BoxMode[source]

Bases: enum.Enum

Enum of different ways to represent a box.

XYXY_ABS

(x0, y0, x1, y1) in absolute floating points coordinates. The coordinates in range [0, width or height].

XYWH_ABS

(x0, y0, w, h) in absolute floating points coordinates.

XYXY_REL

(x0, y0, x1, y1) in range [0, 1]. They are relative to the size of the image.

XYWH_REL

(x0, y0, w, h) in range [0, 1]. They are relative to the size of the image.

XYXY_ABS = 0
XYWH_ABS = 1
XYXY_REL = 2
XYWH_REL = 3
convert = <function BoxMode.convert>[source]
detectron2.structures.pairwise_iou(boxes1: detectron2.structures.boxes.Boxes, boxes2: detectron2.structures.boxes.Boxes) → torch.Tensor[source]

Given two lists of boxes of size N and M, compute the IoU (intersection over union) between __all__ N x M pairs of boxes. The box order must be (xmin, ymin, xmax, ymax).

Parameters: boxes1,boxes2 (Boxes) – two Boxes. Contains N & M boxes, respectively. Tensor – IoU, sized [N,M].
class detectron2.structures.ImageList(tensor: torch.Tensor, image_sizes: List[Tuple[int, int]])[source]

Bases: object

Structure that holds a list of images (of possibly varying sizes) as a single tensor. This works by padding the images to the same size, and storing in a field the original sizes of each image

image_sizes

each tuple is (h, w)

Type: list[tuple[int, int]]
__init__(tensor: torch.Tensor, image_sizes: List[Tuple[int, int]])[source]
Parameters: tensor (Tensor) – of shape (N, H, W) or (N, C_1, …, C_K, H, W) where K >= 1 image_sizes (list[tuple[int, int]]) – Each tuple is (h, w).
__getitem__(idx: Union[int, slice]) → torch.Tensor[source]

Access the individual image in its original size.

Returns: Tensor – an image of shape (H, W) or (C_1, …, C_K, H, W) where K >= 1
to(*args, **kwargs) → detectron2.structures.image_list.ImageList[source]
device
static from_tensors(tensors: Sequence[torch.Tensor], size_divisibility: int = 0, pad_value: float = 0.0) → detectron2.structures.image_list.ImageList[source]
Parameters: tensors – a tuple or list of torch.Tensors, each of shape (Hi, Wi) or (C_1, …, C_K, Hi, Wi) where K >= 1. The Tensors will be padded with pad_value so that they will have the same shape. size_divisibility (int) – If size_divisibility > 0, also adds padding to ensure the common height and width is divisible by size_divisibility pad_value (float) – value to pad an ImageList.
class detectron2.structures.Instances(image_size: Tuple[int, int], **kwargs)[source]

Bases: object

This class represents a list of instances in an image. It stores the attributes of instances (e.g., boxes, masks, labels, scores) as “fields”. All fields must have the same __len__ which is the number of instances.

All other (non-field) attributes of this class are considered private: they must start with ‘_’ and are not modifiable by a user.

Some basic usage:

2. len(instances) returns the number of instances
3. Indexing: instances[indices] will apply the indexing on all the fields and returns a new Instances. Typically, indices is a binary vector of length num_instances, or a vector of integer indices.
__init__(image_size: Tuple[int, int], **kwargs)[source]
Parameters: image_size (height, width) – the spatial size of the image. kwargs – fields to add to this Instances.
image_size

tuple: height, width

Type: Returns
set(name: str, value: Any) → None[source]

Set the field named name to value. The length of value must be the number of instances, and must agree with other existing fields in this object.

has(name: str) → bool[source]
Returns: bool – whether the field called name exists.
remove(name: str) → None[source]

Remove the field called name.

get(name: str) → Any[source]

Returns the field called name.

get_fields() → Dict[str, Any][source]
Returns: dict – a dict which maps names (str) to data of the fields

Modifying the returned dict will modify this instance.

to(device: str) → detectron2.structures.instances.Instances[source]
Returns: Instances – all fields are called with a to(device), if the field has this method.
__getitem__(item: Union[int, slice, torch.BoolTensor]) → detectron2.structures.instances.Instances[source]
Parameters: item – an index-like object and will be used to index all the fields. If item is a string, return the data in the corresponding field. Otherwise, returns an Instances where all fields are indexed by item.
static cat(instance_lists: List[Instances]) → detectron2.structures.instances.Instances[source]
Parameters: instance_lists (list[Instances]) – Instances
class detectron2.structures.Keypoints(keypoints: Union[torch.Tensor, numpy.ndarray, List[List[float]]])[source]

Bases: object

Stores keypoint annotation data. GT Instances have a gt_keypoints property containing the x,y location and visibility flag of each keypoint. This tensor has shape (N, K, 3) where N is the number of instances and K is the number of keypoints per instance.

The visibility flag follows the COCO format and must be one of three integers: * v=0: not labeled (in which case x=y=0) * v=1: labeled but not visible * v=2: labeled and visible

__init__(keypoints: Union[torch.Tensor, numpy.ndarray, List[List[float]]])[source]
Parameters: keypoints – A Tensor, numpy array, or list of the x, y, and visibility of each keypoint. The shape should be (N, K, 3) where N is the number of instances, and K is the number of keypoints per instance.
to(*args, **kwargs) → detectron2.structures.keypoints.Keypoints[source]
device
to_heatmap(boxes: torch.Tensor, heatmap_size: int) → torch.Tensor[source]
Parameters: boxes – Nx4 tensor, the boxes to draw the keypoints to heatmaps – A tensor of shape (N, K) containing an integer spatial label in the range [0, heatmap_size**2 - 1] for each keypoint in the input. valid: A tensor of shape (N, K) containing whether each keypoint is in the roi or not.
__getitem__(item: Union[int, slice, torch.BoolTensor]) → detectron2.structures.keypoints.Keypoints[source]

Create a new Keypoints by indexing on this Keypoints.

The following usage are allowed:

1. new_kpts = kpts[3]: return a Keypoints which contains only one instance.
2. new_kpts = kpts[2:10]: return a slice of key points.
3. new_kpts = kpts[vector], where vector is a torch.ByteTensor with length = len(kpts). Nonzero elements in the vector will be selected.

Note that the returned Keypoints might share storage with this Keypoints, subject to Pytorch’s indexing semantics.

detectron2.structures.heatmaps_to_keypoints(maps: torch.Tensor, rois: torch.Tensor) → torch.Tensor[source]
Parameters: maps (Tensor) – (#ROIs, #keypoints, POOL_H, POOL_W) rois (Tensor) – (#ROIs, 4)

Extract predicted keypoint locations from heatmaps. Output has shape (#rois, #keypoints, 4) with the last dimension corresponding to (x, y, logit, prob) for each keypoint.

Converts a discrete image coordinate in an NxN image to a continuous keypoint coordinate. We maintain consistency with keypoints_to_heatmap by using the conversion from Heckbert 1990: c = d + 0.5, where d is a discrete coordinate and c is a continuous coordinate.

class detectron2.structures.BitMasks(tensor: Union[torch.Tensor, numpy.ndarray])[source]

Bases: object

This class stores the segmentation masks for all objects in one image, in the form of bitmaps.

tensor

bool Tensor of N,H,W, representing N instances in the image.

__init__(tensor: Union[torch.Tensor, numpy.ndarray])[source]
Parameters: tensor – bool Tensor of N,H,W, representing N instances in the image.
to(device: str) → detectron2.structures.masks.BitMasks[source]
device
__getitem__(item: Union[int, slice, torch.BoolTensor]) → detectron2.structures.masks.BitMasks[source]
Returns: BitMasks – Create a new BitMasks by indexing.

The following usage are allowed:

3. new_masks = masks[vector], where vector is a torch.BoolTensor with length = len(masks). Nonzero elements in the vector will be selected.

Note that the returned object might share storage with this object, subject to Pytorch’s indexing semantics.

nonempty() → torch.Tensor[source]

Returns: Tensor – a BoolTensor which represents whether each mask is empty (False) or non-empty (True).
static from_polygon_masks(polygon_masks: Union[PolygonMasks, List[List[numpy.ndarray]]], height: int, width: int) → detectron2.structures.masks.BitMasks[source]
crop_and_resize(boxes: torch.Tensor, mask_size: int) → torch.Tensor[source]

Crop each bitmask by the given box, and resize results to (mask_size, mask_size). This can be used to prepare training targets for Mask R-CNN. It has less reconstruction error compared to rasterization with polygons. However we observe no difference in accuracy, but BitMasks requires more memory to store all the masks.

Parameters: boxes (Tensor) – Nx4 tensor storing the boxes for each mask mask_size (int) – the size of the rasterized mask. Tensor – A bool tensor of shape (N, mask_size, mask_size), where N is the number of predicted boxes for this image.
get_bounding_boxes() → None[source]
static cat(bitmasks_list: List[BitMasks]) → detectron2.structures.masks.BitMasks[source]

class detectron2.structures.PolygonMasks(polygons: List[List[Union[torch.Tensor, numpy.ndarray]]])[source]

Bases: object

This class stores the segmentation masks for all objects in one image, in the form of polygons.

polygons

list[list[ndarray]]. Each ndarray is a float64 vector representing a polygon.

__init__(polygons: List[List[Union[torch.Tensor, numpy.ndarray]]])[source]
Parameters: polygons (list[list[Tensor[float]]]) – The first level of the list correspond to individual instances, the second level to all the polygons that compose the instance, and the third level to the polygon coordinates. The third level Tensor should have the format of torch.Tensor([x0, y0, x1, y1, …, xn, yn]) (n >= 3).
to(*args, **kwargs) → detectron2.structures.masks.PolygonMasks[source]
device
get_bounding_boxes() → detectron2.structures.boxes.Boxes[source]
Returns: Boxes – tight bounding boxes around polygon masks.
nonempty() → torch.Tensor[source]

Returns: Tensor – a BoolTensor which represents whether each mask is empty (False) or not (True).
__getitem__(item: Union[int, slice, List[int], torch.BoolTensor]) → detectron2.structures.masks.PolygonMasks[source]

Support indexing over the instances and return a PolygonMasks object. item can be:

1. An integer. It will return an object with only one instance.
2. A slice. It will return an object with the selected instances.
3. A list[int]. It will return an object with the selected instances, correpsonding to the indices in the list.
4. A vector mask of type BoolTensor, whose length is num_instances. It will return an object with the instances whose mask is nonzero.
__iter__() → Iterator[List[torch.Tensor]][source]
Yields: list[ndarray] – the polygons for one instance. Each Tensor is a float64 vector representing a polygon.
crop_and_resize(boxes: torch.Tensor, mask_size: int) → torch.Tensor[source]

Crop each mask by the given box, and resize results to (mask_size, mask_size). This can be used to prepare training targets for Mask R-CNN.

Parameters: boxes (Tensor) – Nx4 tensor storing the boxes for each mask mask_size (int) – the size of the rasterized mask. Tensor – A bool tensor of shape (N, mask_size, mask_size), where N is the number of predicted boxes for this image.
area()[source]

Computes area of the mask. Only works with Polygons, using the shoelace formula: https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates

Returns: Tensor – a vector, area for each instance
static cat(polymasks_list: List[PolygonMasks]) → detectron2.structures.masks.PolygonMasks[source]

detectron2.structures.rasterize_polygons_within_box(polygons: List[numpy.ndarray], box: numpy.ndarray, mask_size: int) → torch.Tensor[source]

This function is used when generating training targets for mask head in Mask R-CNN. Given original ground-truth masks for an image, new ground-truth mask training targets in the size of mask_size x mask_size must be provided for each predicted box. This function will be called to produce such targets.

Parameters: polygons (list[ndarray[float]]) – a list of polygons, which represents an instance. box – 4-element numpy array mask_size (int) – Tensor – BoolTensor of shape (mask_size, mask_size)
class detectron2.structures.RotatedBoxes(tensor: torch.Tensor)[source]

Bases: detectron2.structures.boxes.Boxes

This structure stores a list of rotated boxes as a Nx5 torch.Tensor. It supports some common methods about boxes (area, clip, nonempty, etc), and also behaves like a Tensor (support indexing, to(device), .device, and iteration over all boxes)

__init__(tensor: torch.Tensor)[source]
Parameters: tensor (Tensor[float]) – a Nx5 matrix. Each row is (x_center, y_center, width, height, angle), in which angle is represented in degrees. While there’s no strict range restriction for it, the recommended principal range is between [-180, 180) degrees.

Assume we have a horizontal box B = (x_center, y_center, width, height), where width is along the x-axis and height is along the y-axis. The rotated box B_rot (x_center, y_center, width, height, angle) can be seen as:

1. When angle == 0: B_rot == B
2. When angle > 0: B_rot is obtained by rotating B w.r.t its center by $$|angle|$$ degrees CCW;
3. When angle < 0: B_rot is obtained by rotating B w.r.t its center by $$|angle|$$ degrees CW.

Mathematically, since the right-handed coordinate system for image space is (y, x), where y is top->down and x is left->right, the 4 vertices of the rotated rectangle $$(yr_i, xr_i)$$ (i = 1, 2, 3, 4) can be obtained from the vertices of the horizontal rectangle (y_i, x_i) (i = 1, 2, 3, 4) in the following way ($$\theta = angle*\pi/180$$ is the angle in radians, (y_c, x_c) is the center of the rectangle):

\begin{align}\begin{aligned}yr_i = \cos(\theta) (y_i - y_c) - \sin(\theta) (x_i - x_c) + y_c,\\xr_i = \sin(\theta) (y_i - y_c) + \cos(\theta) (x_i - x_c) + x_c,\end{aligned}\end{align}

which is the standard rigid-body rotation transformation.

Intuitively, the angle is (1) the rotation angle from y-axis in image space to the height vector (top->down in the box’s local coordinate system) of the box in CCW, and (2) the rotation angle from x-axis in image space to the width vector (left->right in the box’s local coordinate system) of the box in CCW.

More intuitively, consider the following horizontal box ABCD represented in (x1, y1, x2, y2): (3, 2, 7, 4), covering the [3, 7] x [2, 4] region of the continuous coordinate system which looks like this:

Note that each capital letter represents one 0-dimensional geometric point instead of a ‘square pixel’ here.

In the example above, using (x, y) to represent a point we have:

$O = (0, 0), A = (3, 2), B = (7, 2), C = (7, 4), D = (3, 4)$

We name vector AB = vector DC as the width vector in box’s local coordinate system, and vector AD = vector BC as the height vector in box’s local coordinate system. Initially, when angle = 0 degree, they’re aligned with the positive directions of x-axis and y-axis in the image space, respectively.

For better illustration, we denote the center of the box as E,

where the center E = ((3+7)/2, (2+4)/2) = (5, 3).

Also,

$width = |AB| = |CD| = 7 - 3 = 4, height = |AD| = |BC| = 4 - 2 = 2.$

Therefore, the corresponding representation for the same shape in rotated box in (x_center, y_center, width, height, angle) format is:

(5, 3, 4, 2, 0),

Now, let’s consider (5, 3, 4, 2, 90), which is rotated by 90 degrees CCW (counter-clockwise) by definition. It looks like this:

The center E is still located at the same point (5, 3), while the vertices ABCD are rotated by 90 degrees CCW with regard to E: A = (4, 5), B = (4, 1), C = (6, 1), D = (6, 5)

Here, 90 degrees can be seen as the CCW angle to rotate from y-axis to vector AD or vector BC (the top->down height vector in box’s local coordinate system), or the CCW angle to rotate from x-axis to vector AB or vector DC (the left->right width vector in box’s local coordinate system).

$width = |AB| = |CD| = 5 - 1 = 4, height = |AD| = |BC| = 6 - 4 = 2.$

Next, how about (5, 3, 4, 2, -90), which is rotated by 90 degrees CW (clockwise) by definition? It looks like this:

The center E is still located at the same point (5, 3), while the vertices ABCD are rotated by 90 degrees CW with regard to E: A = (6, 1), B = (6, 5), C = (4, 5), D = (4, 1)

$width = |AB| = |CD| = 5 - 1 = 4, height = |AD| = |BC| = 6 - 4 = 2.$

This covers exactly the same region as (5, 3, 4, 2, 90) does, and their IoU will be 1. However, these two will generate different RoI Pooling results and should not be treated as an identical box.

On the other hand, it’s easy to see that (X, Y, W, H, A) is identical to (X, Y, W, H, A+360N), for any integer N. For example (5, 3, 4, 2, 270) would be identical to (5, 3, 4, 2, -90), because rotating the shape 270 degrees CCW is equivalent to rotating the same shape 90 degrees CW.

We could rotate further to get (5, 3, 4, 2, 180), or (5, 3, 4, 2, -180):

\begin{align}\begin{aligned}A = (7, 4), B = (3, 4), C = (3, 2), D = (7, 2),\\width = |AB| = |CD| = 7 - 3 = 4, height = |AD| = |BC| = 4 - 2 = 2.\end{aligned}\end{align}

Finally, this is a very inaccurate (heavily quantized) illustration of how (5, 3, 4, 2, 60) looks like in case anyone wonders:

It’s still a rectangle with center of (5, 3), width of 4 and height of 2, but its angle (and thus orientation) is somewhere between (5, 3, 4, 2, 0) and (5, 3, 4, 2, 90).

clone() → detectron2.structures.rotated_boxes.RotatedBoxes[source]

Clone the RotatedBoxes.

Returns: RotatedBoxes
to(device: str) → detectron2.structures.rotated_boxes.RotatedBoxes[source]
area() → torch.Tensor[source]

Computes the area of all the boxes.

Returns: torch.Tensor – a vector with areas of each box.
normalize_angles() → None[source]

Restrict angles to the range of [-180, 180) degrees

clip(box_size: Union[List[int], Tuple[int, int]], clip_angle_threshold: float = 1.0) → None[source]

Clip (in place) the boxes by limiting x coordinates to the range [0, width] and y coordinates to the range [0, height].

For RRPN: Only clip boxes that are almost horizontal with a tolerance of clip_angle_threshold to maintain backward compatibility.

Rotated boxes beyond this threshold are not clipped for two reasons:

1. There are potentially multiple ways to clip a rotated box to make it fit within the image.
2. It’s tricky to make the entire rectangular box fit within the image and still be able to not leave out pixels of interest.

Therefore we rely on ops like RoIAlignRotated to safely handle this.

Parameters: box_size (height, width) – The clipping box’s size. clip_angle_threshold – Iff. abs(normalized(angle)) <= clip_angle_threshold (in degrees), we do the clipping as horizontal boxes.
nonempty(threshold: int = 0) → torch.Tensor[source]

Find boxes that are non-empty. A box is considered empty, if either of its side is no larger than threshold.

Returns: Tensor – a binary vector which represents whether each box is empty (False) or non-empty (True).
__getitem__(item: Union[int, slice, torch.BoolTensor]) → detectron2.structures.rotated_boxes.RotatedBoxes[source]
Returns: RotatedBoxes – Create a new RotatedBoxes by indexing.

The following usage are allowed:

1. new_boxes = boxes[3]: return a RotatedBoxes which contains only one box.
2. new_boxes = boxes[2:10]: return a slice of boxes.
3. new_boxes = boxes[vector], where vector is a torch.ByteTensor with length = len(boxes). Nonzero elements in the vector will be selected.

Note that the returned RotatedBoxes might share storage with this RotatedBoxes, subject to Pytorch’s indexing semantics.

inside_box(box_size: Union[List[int], Tuple[int, int]], boundary_threshold: int = 0) → torch.Tensor[source]
Parameters: box_size (height, width) – Size of the reference box covering [0, width] x [0, height] boundary_threshold (int) – Boxes that extend beyond the reference box boundary by more than boundary_threshold are considered “outside”.

For RRPN, it might not be necessary to call this function since it’s common for rotated box to extend to outside of the image boundaries (the clip function only clips the near-horizontal boxes)

Returns: a binary vector, indicating whether each box is inside the reference box.
get_centers() → torch.Tensor[source]
Returns: The box centers in a Nx2 array of (x, y).
scale(scale_x: float, scale_y: float) → None[source]

Scale the rotated box with horizontal and vertical scaling factors Note: when scale_factor_x != scale_factor_y, the rotated box does not preserve the rectangular shape when the angle is not a multiple of 90 degrees under resize transformation. Instead, the shape is a parallelogram (that has skew) Here we make an approximation by fitting a rotated rectangle to the parallelogram.

static cat(boxes_list: List[RotatedBoxes]) → detectron2.structures.rotated_boxes.RotatedBoxes[source]

Concatenates a list of RotatedBoxes into a single RotatedBoxes

Parameters: boxes_list (list[RotatedBoxes]) – RotatedBoxes – the concatenated RotatedBoxes
device
__iter__() → Iterator[torch.Tensor][source]

Yield a box as a Tensor of shape (5,) at a time.

detectron2.structures.pairwise_iou_rotated(boxes1: detectron2.structures.rotated_boxes.RotatedBoxes, boxes2: detectron2.structures.rotated_boxes.RotatedBoxes) → None

Given two lists of rotated boxes of size N and M, compute the IoU (intersection over union) between __all__ N x M pairs of boxes. The box order must be (x_center, y_center, width, height, angle).

Parameters: boxes2 (boxes1,) – two RotatedBoxes. Contains N & M rotated boxes, respectively. Tensor – IoU, sized [N,M].