Convenience function for accesssing box coordinates. Returns: a numpy array of shape [N, 4] representing box corners

Add data to a specified field. Args: field: a string parameter used to speficy a related field to be accessed. field_data: a numpy ar

Accesses data associated with the specified field in the box collection. Args: field: a string parameter used to speficy a related field to

Writes a config object to disk.

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

Return number of boxes held in collections.

Labels boxes detected with the same class from the same image as tp/fp. Args: detected_boxes: A numpy array of shape [N, 4] representing de

Gather boxes from BoxList according to indices and return new BoxList. By default, Gather returns boxes corresponding to the input index list, as

Return all non-box fields.

This function creates an image that can be used to test vis functions. It makes an image composed of four colored rectangles. Returns:

Evaluates detections and returns a dictionary of metrics.

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

Clears the state to prepare for a fresh evaluation.

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

Asserts successful updating of all learning rate schemes.

Compute CorLoc score for object detection result. Args: detected_boxes: A float numpy array of shape [N, 4], representing N regio

Returns a boolean indicating whether the input is a tensor. Args: t: the input to be tested. Returns: a boolean that indicates whether t

Sets the 0-th dimension of the input tensor. Args: t: the input tensor, assuming the rank is at least 1. d0: an integer indicating the 0-th

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

Computes area of boxes. Args: boxes: Numpy array with shape [N, 4] holding N boxes Returns: a numpy array with shape [N*1] representing

Retains groundtruth by valid indices. Args: tensor_dict: a dictionary of following groundtruth tensors - fields.InputDataFields.groundtru

Copies the extra fields of boxlist_to_copy_from to boxlist_to_copy_to. Args: boxlist_to_copy_to: BoxList to which extra fields are copied.

Returns the classification loss for a model.

Updates input configuration to reflect a new input path. The input_config object is updated in place, and hence not returned. Args: input_co

Evaluates detections as being tp, fp or ignored from a single image. The evaluation is done in two stages: 1. All detections are matched to

Adds a bounding box to an image. Each string in display_str_list is displayed on a separate line above the bounding box in black text on a rectan

Adds a bounding box to an image. Each string in display_str_list is displayed on a separate line above the bounding box in black text on a rectan

Inserts multiple ones into a shape vector. Inserts an all-1 vector of length num_dims at position start_dim into a shape. Can be combined with tf

Filter to keep only boxes with score exceeding a given threshold. This op keeps the collection of boxes whose corresponding scores are greater th

Filters out the variables matching the filter_regex. Filter out the variables whose name matches the any of the regular expressions in filter_reg

Get corner coordinates of boxes. Returns: a list of 4 1-d numpy arrays [y_min, x_min, y_max, x_max]

Returns the optimizer type for training. Args: train_config: A train_pb2.TrainConfig. Returns: The type of the optimizer

Compute pairwise intersection areas between boxes. Args: boxes1: a numpy array with shape [N, 4] holding N boxes boxes2: a numpy array with

Loads label map proto. Args: path: path to StringIntLabelMap proto text file. Returns: a StringIntLabelMapProto

Sort boxes and associated fields according to a scalar field. A common use case is reordering the boxes according to descending scores. Args:

Compute CorLoc score for a single class. Args: detected_boxes: A numpy array of shape [N, 4] representing detected box coordinate

Labels true/false positives of detections of an image across all classes. Args: detected_boxes: A float numpy array of shape [N, 4], repres

Check whether data fullfills the format of N*[ymin, xmin, ymax, xmin]. Args: data: a numpy array of shape [N, 4] representing box coordinat

Updates `configs` to reflect the new training batch size. The configs dictionary is updated in place, and hence not returned. Args: configs:

Updates the classification/localization weight loss ratio. Detection models usually define a loss weight for both classification and objectness.

Updates `configs` to reflect new number of eval steps per evaluation.

Updates the alpha value for a sigmoid focal loss. The configs dictionary is updated in place, and hence not returned. Args: configs: Diction

Updates the gamma value for a sigmoid focal loss. The configs dictionary is updated in place, and hence not returned. Args: configs: Diction

Update grouth truth statitistics. 1. Difficult boxes are ignored when counting the number of ground truth instances as done in Pascal VOC dev

Updates `configs` to reflect the new initial learning rate. The configs dictionary is updated in place, and hence not returned. Args: config

Updates the label map path for both train and eval input readers. The configs dictionary is updated in place, and hence not returned. Args:

Updates `configs` to reflect the new momentum value. Momentum is only supported for RMSPropOptimizer and MomentumOptimizer. For any other optimiz

Adds a new constant learning rate.

Adds a new exponential decay learning rate.

Adds a learning rate schedule.

Updates `configs` to reflect new number of training steps.

Checks if a label map is valid. Args: label_map: StringIntLabelMap to validate. Raises: ValueError: if label map is invalid.

Computes area of boxes. Args: boxlist: BoxList holding N boxes Returns: a numpy array with shape [N*1] representing box areas

Clips the input tensor along the first dimension up to the length. Args: t: the input tensor, assuming the rank is at least 1. length: a te

Concatenate list of BoxLists. This op concatenates a list of input BoxLists into a larger BoxList. It also handles concatenation of BoxList fiel

Loads label map proto and returns categories list compatible with eval. This function loads a label map and returns a list of dicts, each of which

Creates dictionary of COCO compatible categories keyed by category id. Args: categories: a list of dicts, each of which has the following keys:

detect red and yellow :param img: :param Threshold: :return:

Detect traffic lights and draw bounding boxes around the traffic lights :param PATH_TO_TEST_IMAGES_DIR: testing image directory :param MO

Adds a bounding box to an image (numpy array). Args: image: a numpy array with shape [height, width, 3]. ymin: ymin of bounding box in norm

Adds a bounding box to an image (numpy array). Args: image: a numpy array with shape [height, width, 3]. ymin: ymin of bounding box in norm

Draws bounding boxes on image. Args: image: a PIL.Image object. boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).

Draws bounding boxes on image. Args: image: a PIL.Image object. boxes: a 2 dimensional numpy array of [N, 4]: (ymin, xmin, ymax, xmax).

Draws keypoints on an image. Args: image: a PIL.Image object. keypoints: a numpy array with shape [num_keypoints, 2]. color: color to d

Draws keypoints on an image. Args: image: a PIL.Image object. keypoints: a numpy array with shape [num_keypoints, 2]. color: color to d

Draws keypoints on an image (numpy array). Args: image: a numpy array with shape [height, width, 3]. keypoints: a numpy array with shape [n

Draws keypoints on an image (numpy array). Args: image: a numpy array with shape [height, width, 3]. keypoints: a numpy array with shape [n

Draws mask on an image. Args: image: uint8 numpy array with shape (img_height, img_height, 3) mask: a uint8 numpy array of shape (img_heigh

Draws mask on an image. Args: image: uint8 numpy array with shape (img_height, img_height, 3) mask: a uint8 numpy array of shape (img_heigh

Returns the learning rate type for training. Args: optimizer_config: An optimizer_pb2.Optimizer. Returns: The type of the learning rate.

Computes pairwise intersection-over-area between box collections. Intersection-over-area (ioa) between two boxes box1 and box2 is defined as thei

Non maximum suppression. This op greedily selects a subset of detection bounding boxes, pruning away boxes that have high IOU (intersection over

Pads the input tensor with 0s along the first dimension up to the length. Args: t: the input tensor, assuming the rank is at least 1. lengt

Scale box coordinates in x and y dimensions. Args: boxlist: BoxList holding N boxes y_scale: float x_scale: float Returns: boxli

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

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

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