Sets the model log directory and epoch counter. model_path: If None, or a format different from what this code uses then set a ne

It's common to have tensors larger than the available data and pad with zeros. This function removes rows that are all zeros. x: [rows, colum

It's common to have tensors larger than the available data and pad with zeros. This function removes rows that are all zeros. x: [rows, colum

It's common to have tensors larger than the available data and pad with zeros. This function removes rows that are all zeros. x: [rows, colum

Often boxes are represented with matricies of shape [N, 4] and are padded with zeros. This removes zero boxes. boxes: [N, 4] matrix of boxes.

Often boxes are represented with matrices of shape [N, 4] and are padded with zeros. This removes zero boxes. boxes: [N, 4] matrix of boxes.

Often boxes are represented with matrices of shape [N, 4] and are padded with zeros. This removes zero boxes. boxes: [N, 4] matrix of boxes.

Reformats the detections of one image from the format of the neural network output to a format suitable for use in the rest of the app

Reformats the detections of one image from the format of the neural network output to a format suitable for use in the rest of the app

Constructs tensorflow detection graph and returns output tensors. Args: model: model to perform predictions with. create_input_dict_fn: fun

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

Convert annotation which can be polygons, uncompressed RLE, or RLE to binary mask. :return: binary mask (numpy 2D array)

Convert annotation which can be polygons, uncompressed RLE to RLE. :return: binary mask (numpy 2D array)

Download the COCO dataset/annotations if requested. dataDir: The root directory of the COCO dataset. dataType: What to load (train, va

Picks different number of values from each row in x depending on the values in counts.

Picks different number of values from each row in x depending on the values in counts.

Picks different number of values from each row in x depending on the values in counts.

Build Mask R-CNN architecture. input_shape: The shape of the input image. mode: Either "training" or "inference". The inputs a

Build Mask R-CNN architecture. input_shape: The shape of the input image. mode: Either "training" or "inference". The inputs a

Arrange resutls to match COCO specs in http://cocodataset.org/#format

Generate targets for training Stage 2 classifier and mask heads. This is not used in normal training. It's useful for debugging or to train th

Generate targets for training Stage 2 classifier and mask heads. This is not used in normal training. It's useful for debugging or to train th

Generate targets for training Stage 2 classifier and mask heads. This is not used in normal training. It's useful for debugging or to train th

Builds a Keras model of the Region Proposal Network. It wraps the RPN graph so it can be used multiple times with shared weights. anchors

Builds a Keras model of the Region Proposal Network. It wraps the RPN graph so it can be used multiple times with shared weights. anchors

Builds a Keras model of the Region Proposal Network. It wraps the RPN graph so it can be used multiple times with shared weights. anchors

Given the anchors and GT boxes, compute overlaps and identify positive anchors and deltas to refine them to match their corresponding GT boxes.

Given the anchors and GT boxes, compute overlaps and identify positive anchors and deltas to refine them to match their corresponding GT boxes.

Given the anchors and GT boxes, compute overlaps and identify positive anchors and deltas to refine them to match their corresponding GT boxes.

Compute Average Precision at a set IoU threshold (default 0.5). Returns: mAP: Mean Average Precision precisions: List of precisions at di

Compute Average Precision at a set IoU threshold (default 0.5). Returns: mAP: Mean Average Precision precisions: List of precisions at di

Finds matches between prediction and ground truth instances. Returns: gt_match: 1-D array. For each GT box it has the index of the matche

Finds matches between prediction and ground truth instances. Returns: gt_match: 1-D array. For each GT box it has the index of the matche

Computes IoU overlaps between two sets of boxes. boxes1, boxes2: [N, (y1, x1, y2, x2)]. For better performance, pass the largest set first an

Computes IoU overlaps between two sets of boxes. boxes1, boxes2: [N, (y1, x1, y2, x2)]. For better performance, pass the largest set first an

Computes IoU overlaps between two sets of boxes. boxes1, boxes2: [N, (y1, x1, y2, x2)]. For better performance, pass the largest set first an

Computes IoU overlaps between two sets of masks. masks1, masks2: [Height, Width, instances]

Computes IoU overlaps between two sets of masks. masks1, masks2: [Height, Width, instances]

Computes IoU overlaps between two sets of masks. masks1, masks2: [Height, Width, instances]

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:

Sets up reader, prefetcher and returns input queue. Args: batch_size_per_clone: batch size to use per clone. create_tensor_dict_fn: functio

Generates detection targets for one image. Subsamples proposals and generates target class IDs, bounding box deltas, and masks for each. Inpu

Generates detection targets for one image. Subsamples proposals and generates target class IDs, bounding box deltas, and masks for each. Inpu

Generates detection targets for one image. Subsamples proposals and generates target class IDs, bounding box deltas, and masks for each. Inpu

Display the given set of images, optionally with titles. images: list or array of image tensors in HWC format. titles: optional. A list of tit

Display the given set of images, optionally with titles. images: list or array of image tensors in HWC format. titles: optional. A list of tit

Display the given set of images, optionally with titles. images: list or array of image tensors in HWC format. titles: optional. A list of tit

Display the given set of images, optionally with titles. images: list or array of image tensors in HWC format. titles: optional. A list of tit

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]

Display values in a table format. table: an iterable of rows, and each row is an iterable of values.

Display values in a table format. table: an iterable of rows, and each row is an iterable of values.

Display values in a table format. table: an iterable of rows, and each row is an iterable of values.

Display values in a table format. table: an iterable of rows, and each row is an iterable of values.

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

Runs official COCO evaluation. dataset: A Dataset object with valiadtion data eval_type: "bbox" or "segm" for bounding box or segmentation eva

Finds the last checkpoint file of the last trained model in the model directory. Returns: log_dir: The directory where eve

Finds the last checkpoint file of the last trained model in the model directory. Returns: The path of the last checkpoint

Finds the last checkpoint file of the last trained model in the model directory. Returns: The path of the last checkpoint

If a layer is encapsulated by another layer, this function digs through the encapsulation and returns the layer that holds the weights

If a layer is encapsulated by another layer, this function digs through the encapsulation and returns the layer that holds the weights

If a layer is encapsulated by another layer, this function digs through the encapsulation and returns the layer that holds the weights

scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128] ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2]

scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128] ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2]

scales: 1D array of anchor sizes in pixels. Example: [32, 64, 128] ratios: 1D array of anchor ratios of width/height. Example: [0.5, 1, 2]

Generates ROI proposals similar to what a region proposal network would generate. image_shape: [Height, Width, Depth] count: Number of RO

Generates ROI proposals similar to what a region proposal network would generate. image_shape: [Height, Width, Depth] count: Number of RO

Generates ROI proposals similar to what a region proposal network would generate. image_shape: [Height, Width, Depth] count: Number of RO

Returns the evaluator class according to eval_config, valid for categories. Args: eval_config: evaluation configurations. categories: a lis

Downloads ImageNet trained weights from Keras. Returns path to weights file.

Downloads ImageNet trained weights from Keras. Returns path to weights file.

Downloads ImageNet trained weights from Keras. Returns path to weights file.

Dequeues batch and constructs inputs to object detection model. Args: input_queue: BatchQueue object holding enqueued tensor_dicts. num_cla

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]

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