sleap.nn.data.instance_cropping¶

Transformers for cropping instances for topdown processing.

class sleap.nn.data.instance_cropping.InstanceCropper(crop_width: int, crop_height: int, keep_full_image: bool = False, mock_centroid_confidence: bool = False)[source]¶

Data transformer to crop and generate individual examples for instances.

This generates datasets that are instance cropped for topdown processing.

crop_width¶: Width of the crops in pixels.

crop_height¶: Height of the crops in pixels.

keep_full_image¶: If True, the output examples will contain the full images provided as input to the instance cropped. This can be useful for pipelines that use both full and cropped images, at the cost of increased memory requirements usage. Setting this to False can substantially improve performance of large pipelines if the full images are no longer required.

mock_centroid_confidence¶: If True, add confidence keys for compatibility with predicted instance cropping.

classmethod from_config(config: sleap.nn.config.data.InstanceCroppingConfig, crop_size: Optional[int] = None) → sleap.nn.data.instance_cropping.InstanceCropper [source]¶

Build an instance of this class from its configuration options.

Parameters

config – An InstanceCroppingConfig instance with the desired parameters.
crop_size – Integer specifying the crop height and width. This is only required and will only be used if the config.crop_size attribute does not specify an explicit integer crop size (e.g., it is set to None).

Returns

An instance of this class.

Raises

ValueError – If the crop_size is not specified in either the config attribute or function arguments.

property input_keys¶: Return the keys that incoming elements are expected to have.

property output_keys¶: Return the keys that outgoing elements will have.

transform_dataset(input_ds: tensorflow.python.data.ops.dataset_ops.DatasetV2) → tensorflow.python.data.ops.dataset_ops.DatasetV2[source]¶

Create a dataset that contains instance cropped data.

Parameters

ds_input –

A dataset with examples containing the following keys: “image”: The full image in a tensor of shape (height, width, channels). “instances”: Instance points in a tf.float32 tensor of shape

(n_instances, n_nodes, 2).

”centroids”: The computed centroid for each instance in a tf.float32: tensor of shape (n_instances, 2).

Any additional keys present will be replicated in each output.

Returns

A tf.data.Dataset with elements containing instance cropped data. Each instance will generate an example, so the total number of elements may change relative to the input dataset.

Each element in the output dataset will have the following keys:

”instance_image”: A cropped image of the same dtype as the input image: but with shape (crop_width, crop_height, channels) and will be centered on an instance.
”bbox”: The bounding box in absolute image coordinates in the format: (y1, x1, y2, x2) that resulted in the cropped image in “instance_image”. This will be a tf.float32 tensor of shape (4,).
”center_instance”: The points of the centered instance in image: coordinates in the “instance_image”. This will be a tf.float32 tensor of shape (n_nodes, 2). The absolute image coordinates can be recovered by adding (x1, y1) from the “bbox” key.
”center_instance_ind”: Scalar tf.int32 index of the centered instance: relative to all the instances in the frame. This can be used to index into additional keys that may contain data from all instances.
”all_instances”: The points of all instances in the frame in image: coordinates in the “instance_image”. This will be a tf.float32 tensor of shape (n_instances, n_nodes, 2). This is useful for multi- stage models that first predict all nodes and subsequently refine it to just the centered instance. The “center_instance_ind”-th row of this tensor is equal to “center_instance”.
”centroid”: The centroid coordinate that was used to generate this crop,: specified as a tf.float32 tensor of shape (2,) in absolute image coordinates.
”full_image_height”: The height of the full image from which the crop: was generated, specified as a scalar tf.int32 tensor.
”full_image_width”: The width of the full image from which the crop was: generated, specified as a scalar tf.int32 tensor.

If keep_full_image is True, examples will also have an “image” key containing the same image as the input.

if mock_centroid_confidence is True, examples will also have a “centroid_confidence” key with all ones. This is useful for evaluating models that use crops independently from centroid inference.

Additional keys will be replicated in each example under the same name.

sleap.nn.data.instance_cropping.crop_bboxes(image: tensorflow.python.framework.ops.Tensor, bboxes: tensorflow.python.framework.ops.Tensor) → tensorflow.python.framework.ops.Tensor[source]¶

Crop bounding boxes from an image.

This method serves as a convenience method for specifying the arguments of tf.image.crop_and_resize, becoming especially useful in the case of multiple bounding boxes with a single image and no resizing.

Parameters

image – Tensor of shape (height, width, channels) of a single image.
bboxes – Tensor of shape (n_bboxes, 4) and dtype tf.float32, where the last axis corresponds to (y1, x1, y2, x2) coordinates of the bounding boxes. This can be generated from centroids using make_centered_bboxes.

Returns

A tensor of shape (n_bboxes, crop_height, crop_width, channels) of the same dtype as the input image. The crop size is inferred from the bounding box coordinates.

Notes

This function expects bounding boxes with coordinates at the centers of the pixels in the box limits. Technically, the box will span (x1 - 0.5, x2 + 0.5) and (y1 - 0.5, y2 + 0.5).

For example, a 3x3 patch centered at (1, 1) would be specified by (y1, x1, y2, x2) = (0, 0, 2, 2). This would be exactly equivalent to indexing the image with image[0:3, 0:3].

sleap.nn.data.instance_cropping¶

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