Draw bounding boxes and segmentation masks with differnt customizations. boxes: [N, (y1, x1, y2, x2, class_id)] in image coordinates. ref

Draw bounding boxes and segmentation masks with different customizations. boxes: [N, (y1, x1, y2, x2, class_id)] in image coordinates. re

Draw bounding boxes and segmentation masks with different customizations. boxes: [N, (y1, x1, y2, x2, class_id)] in image coordinates. re

anchors: [n, (y1, x1, y2, x2)] list of anchors in image coordinates. proposals: [n, 4] the same anchors but refined to fit objects better.

anchors: [n, (y1, x1, y2, x2)] list of anchors in image coordinates. proposals: [n, 4] the same anchors but refined to fit objects better.

anchors: [n, (y1, x1, y2, x2)] list of anchors in image coordinates. proposals: [n, 4] the same anchors but refined to fit objects better.

anchors: [n, (y1, x1, y2, x2)] list of anchors in image coordinates. proposals: [n, 4] the same anchors but refined to fit objects better.

Encodes a numpy array into a PNG string. Args: image: a numpy array with shape [height, width, 3]. Returns: PNG encoded image string.

Evaluation function for detection models. Args: create_input_dict_fn: a function to create a tensor input dictionary. create_model_fn: a fu

Resizes mini masks back to image size. Reverses the change of minimize_mask(). See inspect_data.ipynb notebook for more details.

Resizes mini masks back to image size. Reverses the change of minimize_mask(). See inspect_data.ipynb notebook for more details.

Resizes mini masks back to image size. Reverses the change of minimize_mask(). See inspect_data.ipynb notebook for more details.

Compute bounding boxes from masks. mask: [height, width, num_instances]. Mask pixels are either 1 or 0. Returns: bbox array [num_instances, (

Compute bounding boxes from masks. mask: [height, width, num_instances]. Mask pixels are either 1 or 0. Returns: bbox array [num_instances, (

Compute bounding boxes from masks. mask: [height, width, num_instances]. Mask pixels are either 1 or 0. Returns: bbox array [num_instances, (

Extract images and targets from the input dict.

boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates. masks: [height, width, num_instances] class_ids: [num_instances]

boxes: [num_instance, (y1, x1, y2, x2, class_id)] in image coordinates. masks: [height, width, num_instances] class_ids: [num_instances]

Generate anchors at different levels of a feature pyramid. Each scale is associated with a level of the pyramid, but each ratio is used in all

Generate anchors at different levels of a feature pyramid. Each scale is associated with a level of the pyramid, but each ratio is used in all

Generate anchors at different levels of a feature pyramid. Each scale is associated with a level of the pyramid, but each ratio is used in all

A function to get test inputs. Returns an image with one box.

Reads a label map and returns a dictionary of label names to id. Args: label_map_path: path to label_map. use_display_name: whether to use

Map an internal class ID to the corresponding class ID in the source dataset.

Map an internal class ID to the corresponding class ID in the source dataset.

Determines which augmenters to apply to masks.

Determines which augmenters to apply to masks.

Return a link to the image in its source Website or details about the image that help looking it up or debugging it. Override for you

Return a link to the image in the COCO Website.

Return the path of the image.

Return a link to the image in its source Website or details about the image that help looking it up or debugging it. Override for you

Return the path of the image.

Return a link to the image in its source Website or details about the image that help looking it up or debugging it. Override for you

Load instance masks for the given image. Different datasets use different ways to store masks. This function converts the different m

Generate instance masks for an image. Returns: masks: A bool array of shape [height, width, instance count] with one mask p

Generate instance masks for an image. Returns: masks: A bool array of shape [height, width, instance count] with one mask p

Takes a source class ID and returns the int class ID assigned to it. For example: dataset.map_source_class_id("coco.12") -> 23

Takes a source class ID and returns the int class ID assigned to it. For example: dataset.map_source_class_id("coco.12") -> 23

Resize masks to a smaller version to cut memory load. Mini-masks can then resized back to image scale using expand_masks() See inspect_data.i

Resize masks to a smaller version to reduce memory load. Mini-masks can be resized back to image scale using expand_masks() See inspect_data.

Resize masks to a smaller version to reduce memory load. Mini-masks can be resized back to image scale using expand_masks() See inspect_data.

Apply Non-Maximum Suppression on ROIs of the given class.

Apply Non-Maximum Suppression on ROIs of the given class.

Apply Non-Maximum Suppression on ROIs of the given class.

Performs non-maximum supression and returns indicies of kept boxes. boxes: [N, (y1, x1, y2, x2)]. Notice that (y2, x2) lays outside the box. s

Performs non-maximum suppression and returns indices of kept boxes. boxes: [N, (y1, x1, y2, x2)]. Notice that (y2, x2) lays outside the box. s

Performs non-maximum suppression and returns indices of kept boxes. boxes: [N, (y1, x1, y2, x2)]. Notice that (y2, x2) lays outside the box. s

Converts boxes from pixel coordinates to normalized coordinates. boxes: [N, (y1, x1, y2, x2)] in pixel coordinates shape: [..., (height, width

Converts boxes from pixel coordinates to normalized coordinates. boxes: [N, (y1, x1, y2, x2)] in pixel coordinates shape: [..., (height, width

Parses an array that contains image attributes to its components. See compose_image_meta() for more details. meta: [batch, meta length] where

Parses an array that contains image attributes to its components. See compose_image_meta() for more details. meta: [batch, meta length] where

Draw a grid showing how ground truth objects are classified. gt_class_ids: [N] int. Ground truth class IDs pred_class_id: [N] int. Predicted c

Draw a grid showing how ground truth objects are classified. gt_class_ids: [N] int. Ground truth class IDs pred_class_id: [N] int. Predicted c

Draw a grid showing how ground truth objects are classified. gt_class_ids: [N] int. Ground truth class IDs pred_class_id: [N] int. Predicted c

Draw a grid showing how ground truth objects are classified. gt_class_ids: [N] int. Ground truth class IDs pred_class_id: [N] int. Predicted c

Draw the precision-recall curve. AP: Average precision at IoU >= 0.5 precisions: list of precision values recalls: list of recall values

Draw the precision-recall curve. AP: Average precision at IoU >= 0.5 precisions: list of precision values recalls: list of recall values

Draw the precision-recall curve. AP: Average precision at IoU >= 0.5 precisions: list of precision values recalls: list of recall values

Draw the precision-recall curve. AP: Average precision at IoU >= 0.5 precisions: list of precision values recalls: list of recall values

Returns a list of regularization losses for this model. Returns a list of regularization losses for this model that the estimator needs to us

Resizes an image keeping the aspect ratio. min_dim: if provided, resizes the image such that it's smaller dimension == min_dim m

Resizes an image keeping the aspect ratio unchanged. min_dim: if provided, resizes the image such that it's smaller dimension == min_dim

Resizes an image keeping the aspect ratio unchanged. min_dim: if provided, resizes the image such that it's smaller dimension == min_dim

Resizes a mask using the given scale and padding. Typically, you get the scale and padding from resize_image() to ensure both, the image and t

Resizes a mask using the given scale and padding. Typically, you get the scale and padding from resize_image() to ensure both, the image and t

Resizes a mask using the given scale and padding. Typically, you get the scale and padding from resize_image() to ensure both, the image and t

Runs a sub-set of the computation graph that computes the given outputs. outputs: List of tuples (name, tensor) to compute. The tenso

Runs a sub-set of the computation graph that computes the given outputs. image_metas: If provided, the images are assumed to be alrea

Runs a sub-set of the computation graph that computes the given outputs. image_metas: If provided, the images are assumed to be alrea

Saves an image (represented as a numpy array) to PNG. Args: image: a numpy array with shape [height, width, 3]. output_path: path to which

Returns the path or URL to the image. Override this to return a URL to the image if it's availble online for easy debugging.

Returns the path or URL to the image. Override this to return a URL to the image if it's available online for easy debugging.

Returns the path or URL to the image. Override this to return a URL to the image if it's available online for easy debugging.