Output the detection result into a single json file to be evaluated with the ground truth Args: image: float numpy array with shape (img_height,

Wrapper of json.dump that allows specifying the float precision used. Args: obj: The object to dump. fid: The file id to write to. floa

Wrapper of json.dumps that allows specifying the float precision used. Args: obj: The object to dump. float_digits: The number of digits of

Returns parameters for use with Dump and Dumps to output pretty json. Example usage: ```json_str = json_utils.Dumps(obj, **json_utils.PrettyPar

Visualize bounding boxes

Constructs box collection. Args: data: a numpy array of shape [N, 4] representing box coordinates Raises: ValueError: if bbox da

Constructor. Args: matching_iou_threshold: IOU threshold to use for matching groundtruth boxes to detection boxes.

Initialized PerImageVRDEvaluation by evaluation parameters. Args: matching_iou_threshold: A ratio of area intersection to union, which is

Initialized PerImageEvaluation by evaluation parameters. Args: num_groundtruth_classes: Number of ground truth object classes matchin

Constructs box collection. Args: box_data: a numpy array of shape [N, 4] representing box coordinates mask_data: a numpy array of sha

Constructor. Args: categories: A list of dicts, each of which has the following keys - 'id': (required) an integer id uniquely iden

Constructor. Args: categories: A list of dicts, each of which has the following keys - 'id': (required) an integer id uniquely iden

Constructor. Args: categories: A list of dicts, each of which has the following keys - 'id': (required) an integer id uniquely iden

Visualize bounding boxes

Creates and loads pretrained deeplab model.

Class constructor. Args: image_format: Image format. Only 'jpeg', 'jpg', or 'png' are supported. channels: Image channels.

Constructor to set default values. Args: outputs_to_num_classes: A dictionary from output type to the number of classes. For exampl

Builds a clone of DeepLab. Args: inputs_queue: A prefetch queue for images and labels. outputs_to_num_classes: A map from output type to th

Auxiliary function to add support for 'reuse' to mobilenet_v2. Args: net: Input tensor of shape [batch_size, height, width, channels]. dept

Subtract Imagenet mean RGB value.

Map image values from [0, 255] to [-1, 1].

Pre-processes boxes before adding them to the VRDDetectionEvaluation. Phrase detection and Relation detection subclasses re-implement this method

Pre-processes boxes before adding them to the VRDDetectionEvaluation. In case of phrase evaluation task, evaluation expects exactly one bounding

Pre-processes boxes before adding them to the VRDDetectionEvaluation. Args: groundtruth_box_tuples: A numpy array of structures with the sh

Pre-processes boxes before adding them to the VRDDetectionEvaluation. In case of phrase evaluation task, evaluation expects exactly one bounding

Adds a tf.summary.image for a CDF plot of the values. Normalizes `values` such that they sum to 1, plots the cumulative distribution function and

Adds a tf.summary.image for a histogram plot of the values. Plots the histogram of values and creates a tf image summary. Args: values: a 1-

Adds detections for a single image to be used for evaluation. Args: image_key: A unique string/integer identifier for the image. dete

Adds detections for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. detec

Adds detections for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. detec

Adds detections for a single image to be used for evaluation. Args: image_key: A unique string/integer identifier for the image. dete

Adds groundtruth for a single image to be used for evaluation. Args: image_key: A unique string/integer identifier for the image. gro

Adds groundtruth for a single image to be used for evaluation. Args: image_id: A unique string/integer identifier for the image. grou