mindpose.data

mindpose.data.create_dataset(image_root, annotation_file=None, dataset_format='coco_topdown', is_train=True, device_num=None, rank_id=None, num_workers=1, config=None, **kwargs)

Create dataset for training or evaluation.

Parameters:

• image_root (str) – The path of the directory storing images

• annotation_file (Optional[str]) – The path of the annotation file. Default: None

• dataset_format (str) – The dataset format. Different format yield different final output. Default: coco_topdown

• is_train (bool) – Wether this dataset is used for training/testing: Default: True

• device_num (Optional[int]) – Number of devices (e.g. GPU). Default: None

• rank_id (Optional[int]) – Current process’s rank id. Default: None

• num_workers (int) – Number of workers in reading data. Default: 1

• config (Optional[Dict[str, Any]]) – Dataset-specific configuration

• use_gt_bbox_for_val – Use GT bbox instead of detection result during evaluation. Default: False

• detection_file – Path of the detection result. Default: None

Return type:

GeneratorDataset

Returns:

Dataset for training or evaluation

mindpose.data.create_pipeline(dataset, transforms, method='topdown', batch_size=1, is_train=True, normalize=True, normalize_mean=[0.485, 0.456, 0.406], normalize_std=[0.229, 0.224, 0.255], hwc_to_chw=True, num_workers=1, config=None)

Create dataset tranform pipeline. The returned datatset is transformed sequentially based on the given list of transforms.

Parameters:

• dataset (Dataset) – Dataset to perform transformations

• transforms (List[Union[str, Dict[str, Any]]]) – List of transformations

• method (str) – The method to use. Default: “topdown”

• batch_size (int) – Batch size. Default: 1

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• normalize (bool) – Perform normalization. Default: True

• normalize_mean (List[float]) – Mean of the normalization: Default: [0.485, 0.456, 0.406]

• normalize_std (List[float]) – Std of the normalization: Default: [0.229, 0.224, 0.255]

• hwc_to_chw (bool) – Wwap height x width x channel to channel x height x width. Default: True

• num_workers (int) – Number of workers in processing data. Default: 1

• config (Optional[Dict[str, Any]]) – Transform-specific configuration

Return type:

Dataset

Returns:

The transformed dataset

mindpose.data.dataset

class mindpose.data.dataset.BottomUpDataset(image_root, annotation_file=None, is_train=False, num_joints=17, config=None)

Bases: object

Create an iterator for ButtomUp dataset, return the tuple with (image, boxes, keypoints, target, mask, tag_ind) for training; return the tuple with (image, mask, center, scale, image_file, image_shape) for evaluation.

Parameters:

• image_root (str) – The path of the directory storing images

• annotation_file (Optional[str]) – The path of the annotation file. Default: None

• is_train (bool) – Wether this dataset is used for training/testing. Default: False

• num_joints (int) – Number of joints in the dataset. Default: 17

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

Items in iterator:

image: Encoded data for image file

keypoints: Keypoints in (x, y, visibility)

mask: Mask of the image showing the valid annotations

target: A placeholder for later pipline using

tag_ind: A placeholder of later pipline using

image_file: Path of the image file

boxes: Bounding box coordinate (x0, y0), (x1, y1)

Note

This is an abstract class, child class must implement load_dataset_cfg and load_dataset method.

load_dataset()

Loading the dataset, where the returned record should contain the following key

Keys:

image_file: Path of the image file.

keypoints (For training only): Keypoints in (x, y, visibility).

boxes (For training only): Bounding box coordinate (x0, y0), (x1, y1).

mask_info (For training only): The mask info of crowed or zero keypoints instances.

Return type:

List[Dict[str, Any]]

Returns:

A list of records of groundtruth or predictions

load_dataset_cfg()

Loading the dataset config, where the returned config must be a dictionary which stores the configuration of the dataset, such as the image_size, etc.

Return type:

Dict[str, Any]

Returns:

Dataset configurations

class mindpose.data.dataset.COCOBottomUpDataset(image_root, annotation_file=None, is_train=False, num_joints=17, config=None)

Bases: BottomUpDataset

Create an iterator for ButtomUp dataset, return the tuple with (image, boxes, keypoints, mask, target, tag_ind) for training; return the tuple with (image, mask, center, scale, image_file, image_shape) for evaluation.

Parameters:

• image_root (str) – The path of the directory storing images

• annotation_file (Optional[str]) – The path of the annotation file. Default: None

• is_train (bool) – Wether this dataset is used for training/testing. Default: False

• num_joints (int) – Number of joints in the dataset. Default: 17

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

Items in iterator:

image: Encoded data for image file

keypoints: Keypoints in (x, y, visibility)

mask: Mask of the image showing the valid annotations

target: A placeholder for later pipline using

keypoints_coordinate: A placeholder of later pipline using

image_file: Path of the image file

boxes: Bounding box coordinate (x0, y0), (x1, y1)

load_dataset()

Loading the dataset, where the returned record should contain the following key

Keys:

image_file: Path of the image file.

keypoints (For training only): Keypoints in (x, y, visibility).

boxes (For training only): Bounding box coordinate (x0, y0), (x1, y1).

mask_info (For training only): The mask info of crowed or zero keypoints instances.

Return type:

List[Dict[str, Any]]

Returns:

A list of records of groundtruth or predictions

load_dataset_cfg()

Loading the dataset config, where the returned config must be a dictionary which stores the configuration of the dataset, such as the image_size, etc.

Return type:

Dict[str, Any]

Returns:

Dataset configurations

class mindpose.data.dataset.COCOTopDownDataset(image_root, annotation_file=None, is_train=False, num_joints=17, use_gt_bbox_for_val=False, detection_file=None, config=None)

Bases: TopDownDataset

Create an iterator for TopDown dataset based COCO annotation format. return the tuple with (image, center, scale, keypoints, rotation, target, target_weight) for training; return the tuple with (image, center, scale, rotation, image_file, boxes, bbox_ids, bbox_score) for evaluation.

Parameters:

• image_root (str) – The path of the directory storing images

• annotation_file (Optional[str]) – The path of the annotation file. Default: None

• is_train (bool) – Wether this dataset is used for training/testing. Default: False

• num_joints (int) – Number of joints in the dataset. Default: 17

• use_gt_bbox_for_val (bool) – Use GT bbox instead of detection result during evaluation. Default: False

• detection_file (Optional[str]) – Path of the detection result. Defaul: None

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

Item in iterator:

image: Encoded data for image file

center: A placeholder for later pipline using

scale: A placeholder of later pipline using

keypoints: Keypoints in (x, y, visibility)

rotation: Rotatated degree

target: A placeholder for later pipline using

target_weight: A placeholder of later pipline using

image_file: Path of the image file

boxes: Bounding box coordinate (x, y, w, h)

bbox_id: Bounding box id for each single image

bbox_score: Bounding box score, 1 for ground truth

load_dataset()

Loading the dataset, where the returned record should contain the following key

Keys:

image_file: Path of the image file

bbox: Bounding box coordinate (x, y, w, h)

keypoints: Keypoints in [K, 3(x, y, visibility)]

bbox_score: Bounding box score, 1 for ground truth

bbox_id: Bounding box id for each single image

Return type:

List[Dict[str, Any]]

Returns:

A list of records of groundtruth or predictions

load_dataset_cfg()

Loading the dataset config, where the returned config must be a dictionary which stores the configuration of the dataset, such as the image_size, etc.

Return type:

Dict[str, Any]

Returns:

Dataset configurations

class mindpose.data.dataset.ImageFolderBottomUpDataset(image_root, annotation_file=None, is_train=False, num_joints=17, config=None)

Bases: BottomUpDataset

Create an iterator for ButtomUp dataset based on image folder. It is usually used for demo usage. Return the tuple with (image, mask, center, scale, image_file, image_shape)

Parameters:

• image_root (str) – The path of the directory storing images

• annotation_file (Optional[str]) – The path of the annotation file. Default: None

• is_train (bool) – Wether this dataset is used for training/testing. Default: False

• num_joints (int) – Number of joints in the dataset. Default: 17

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

load_dataset()

Loading the dataset, where the returned record should contain the following key

Keys:

image_file: Path of the image file.

Return type:

List[Dict[str, Any]]

Returns:

A list of records of groundtruth or predictions

load_dataset_cfg()

Loading the dataset config, where the returned config must be a dictionary which stores the configuration of the dataset, such as the image_size, etc.

Return type:

Dict[str, Any]

Returns:

Dataset configurations

class mindpose.data.dataset.TopDownDataset(image_root, annotation_file=None, is_train=False, num_joints=17, use_gt_bbox_for_val=False, detection_file=None, config=None)

Bases: object

Create an iterator for TopDown dataset, return the tuple with (image, center, scale, keypoints, rotation, target, target_weight) for training; return the tuple with (image, center, scale, rotation, image_file, boxes, bbox_ids, bbox_score) for evaluation.

Parameters:

• image_root (str) – The path of the directory storing images

• annotation_file (Optional[str]) – The path of the annotation file. Default: None

• is_train (bool) – Wether this dataset is used for training/testing. Default: False

• num_joints (int) – Number of joints in the dataset. Default: 17

• use_gt_bbox_for_val (bool) – Use GT bbox instead of detection result during evaluation. Default: False

• detection_file (Optional[str]) – Path of the detection result. Default: None

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

Item in iterator:

image: Encoded data for image file

center: A placeholder for later pipline using

scale: A placeholder of later pipline using

keypoints: Keypoints in [K, 3(x, y, visibility)]

rotation: Rotatated degree

target: A placeholder for later pipline using

target_weight: A placeholder of later pipline using

image_file: Path of the image file

bbox: Bounding box coordinate (x, y, w, h)

bbox_id: Bounding box id for each single image

bbox_score: Bounding box score, 1 for ground truth

Note

This is an abstract class, child class must implement load_dataset_cfg and load_dataset method.

load_dataset()

Loading the dataset, where the returned record should contain the following key

Keys:

image_file: Path of the image file

bbox: Bounding box coordinate (x, y, w, h)

keypoints: Keypoints in [K, 3(x, y, visibility)]

bbox_score: Bounding box score, 1 for ground truth

bbox_id: Bounding box id for each single image

Return type:

List[Dict[str, Any]]

Returns:

A list of records of groundtruth or predictions

load_dataset_cfg()

Loading the dataset config, where the returned config must be a dictionary which stores the configuration of the dataset, such as the image_size, etc.

Return type:

Dict[str, Any]

Returns:

Dataset configurations

mindpose.data.transform

class mindpose.data.transform.BottomUpGenerateTarget(is_train=True, config=None, sigma=2.0, max_num=30)

Bases: BottomUpTransform

Generate heatmap with the keypoint coordinatess with multiple scales.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• sigma (float) – The sigmal size of gausian distribution. Default: 2.0

• max_num (int) – Maximum number of instances within the image. Default: 30

Inputs:

data: Data tuples need to be transformed

Outputs:

result: Transformed data tuples

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: keypoints

Returned keys after transform: target, tag_ind

class mindpose.data.transform.BottomUpHorizontalRandomFlip(is_train=True, config=None, flip_prob=0.5)

Bases: BottomUpTransform

Perform randomly horizontal flip in bottomup approach.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• flip_prob (float) – Probability of performing a horizontal flip. Default: 0.5

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: image, mask, keypoints

Returned keys after transform: image, mask, keypoints

class mindpose.data.transform.BottomUpPad(is_train=True, config=None)

Bases: BottomUpTransform

Padding the image to the max_image_size.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: image

Returned keys after transform: image, mask

class mindpose.data.transform.BottomUpRandomAffine(is_train=True, config=None, rot_factor=30.0, scale_factor=(0.75, 1.5), scale_type='short', trans_factor=40.0)

Bases: BottomUpTransform

Random affine transform the image. The mask and keypoints will be rescaled to the heatmap sizes after the transformation.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• rot_factor (float) – Randomly rotated in [-rotation_factor, rotation_factor]. Default: 30.

• scale_factor (Tuple[float, float]) – Randomly Randomly scaled in [scale_factor[0], scale_factor[1]]. Default: (0.75, 1.5)

• scale_type (str) – Scaling with the long / short length of the image. Default: short

• trans_factor (float) – Translation factor. Default: 40.

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: image, mask, keypoints

Returned keys after transform: image, mask, keypoints

class mindpose.data.transform.BottomUpRescale(is_train=True, config=None)

Bases: BottomUpTransform

Rescaling the image to the max_image_size without change the aspect ratio.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: image

Returned keys after transform: image, center, scale, image_shape

class mindpose.data.transform.BottomUpResize(is_train=True, config=None, size=512, base_length=64)

Bases: BottomUpTransform

Resize the image without change the aspect ratio. The length of the short side of the image will be equal to the input size.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• size (int) – The target size of the short side of the image. Default: 512

• base_length (int) – The minimum size the image. Default: 64

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: image

Returned keys after transform: image, mask, center, scale, image_shape

class mindpose.data.transform.BottomUpTransform(is_train=True, config=None)

Bases: Transform

Transform the input data into the output data based on bottom-up approach.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

Inputs:

data: Data tuples need to be transformed

Outputs:

result: Transformed data tuples

Note

This is an abstract class, child class must implement transform method.

load_transform_cfg()

Loading the transform config, where the returned the config must be a dictionary which stores the configuration of this transformation, such as the transformed image size, etc.

Return type:

Dict[str, Any]

Returns:

Transform configuration

setup_required_field()

Get the required columns names used for this transformation. The columns names will be later used with Minspore Dataset map func.

Return type:

List[str]

Returns:

The column names

class mindpose.data.transform.TopDownAffine(is_train=True, config=None, use_udp=False)

Bases: TopDownTransform

Affine transform the image, and the transform image will contain single instance only.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• use_udp (bool) – Use Unbiased Data Processing (UDP) affine transform. Default: False

Inputs:

data: Data tuples need to be transformed

Outputs:

result: Transformed data tuples

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: image, center, scale, rotation, keypoints (optional)

Returned keys after transform: image, keypoints (optional)

class mindpose.data.transform.TopDownBoxToCenterScale(is_train=True, config=None)

Bases: TopDownTransform

Convert the box coordinate to center and scale. If is_train is True, the center will be randomly shifted by a small amount.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

Inputs:

data: Data tuples need to be transformed

Outputs:

result: Transformed data tuples

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: boxes

Returned keys after transform: center, scale

class mindpose.data.transform.TopDownGenerateTarget(is_train=True, config=None, sigma=2.0, use_different_joint_weights=False, use_udp=False)

Bases: TopDownTransform

Generate heatmap from the coordinates.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• sigma (float) – The sigmal size of gausian distribution. Default: 2.0

• use_different_joint_weights (bool) – Use extra joint weight in target weight calculation. Default: False

• use_udp (bool) – Use Unbiased Data Processing (UDP) encoding. Default: False

Inputs:

data: Data tuples need to be transformed

Outputs:

result: Transformed data tuples

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: keypoints

Returned keys after transform: target, target_weight

class mindpose.data.transform.TopDownHalfBodyTransform(is_train=True, config=None, num_joints_half_body=8, prob_half_body=0.3, scale_padding=1.5)

Bases: TopDownTransform

Perform half-body transform. Keep only the upper body or the lower body at random.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• num_joints_half_body (int) – Threshold number of performing half-body transform. Default: 8

• prob_half_body (float) – Probability of performing half-body transform. Default: 0.3

• scale_padding (float) – Extra scale padding multiplier in generating the cropped images. Default: 1.5

Inputs:

data: Data tuples need to be transformed

Outputs:

result: Transformed data tuples

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: keypoints

Returned keys after transform: center, scale

class mindpose.data.transform.TopDownHorizontalRandomFlip(is_train=True, config=None, flip_prob=0.5)

Bases: TopDownTransform

Perform randomly horizontal flip in topdown approach.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• flip_prob (float) – Probability of performing a horizontal flip. Default: 0.5

Inputs:

data: Data tuples need to be transformed

Outputs:

result: Transformed data tuples

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: image, keypoints, center

Returned keys after transform: image, keypoints, center

class mindpose.data.transform.TopDownRandomScaleRotation(is_train=True, config=None, rot_factor=40.0, scale_factor=0.5, rot_prob=0.6)

Bases: TopDownTransform

Perform random scaling and rotation.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

• rot_factor (float) – Std of rotation degree. Default: 40.

• scale_factor (float) – Std of scaling value. Default: 0.5

• rot_prob (float) – Probability of performing rotation. Default: 0.6

Inputs:

data: Data tuples need to be transformed

Outputs:

result: Transformed data tuples

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation

Note

Required keys for transform: scale

Returned keys after transform: scale, rotation

class mindpose.data.transform.TopDownTransform(is_train=True, config=None)

Bases: Transform

Transform the input data into the output data based on top-down approach.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

Inputs:

data: Data tuples need to be transformed

Outputs:

result: Transformed data tuples

Note

This is an abstract class, child class must implement transform method.

load_transform_cfg()

Loading the transform config, where the returned the config must be a dictionary which stores the configuration of this transformation, such as the transformed image size, etc.

Return type:

Dict[str, Any]

Returns:

Transform configuration

setup_required_field()

Get the required columns names used for this transformation. The columns names will be later used with Minspore Dataset map func.

Return type:

List[str]

Returns:

The column names

class mindpose.data.transform.Transform(is_train=True, config=None)

Bases: object

Transform the input data into the output data.

Parameters:

• is_train (bool) – Whether the transformation is for training/testing. Default: True

• config (Optional[Dict[str, Any]]) – Method-specific configuration. Default: None

Inputs:

data: Data tuples need to be transformed

Outputs:

result: Transformed data tuples

Note

This is an abstract class, child class must implement load_transform_cfg, transform and setup_required_field method.

load_transform_cfg()

Loading the transform config, where the returned the config must be a dictionary which stores the configuration of this transformation, such as the transformed image size, etc.

Return type:

Dict[str, Any]

Returns:

Transform configuration

setup_required_field()

Get the required columns names used for this transformation. The columns names will be later used with Minspore Dataset map func.

Return type:

List[str]

Returns:

The column names

transform(state)

Transform the state into the transformed state. state is a dictionay storing the informaton of the image and labels, the returned states is the updated dictionary storing the updated image and labels.

Parameters:

state (Dict[str, Any]) – Stored information of image and labels

Return type:

Dict[str, Any]

Returns:

Updated inforamtion of image and labels based on the transformation