easycv.core package

Submodules

easycv.core.standard_fields module

Contains classes specifying naming conventions used for object detection.

Specifies:

InputDataFields: standard fields used by reader/preprocessor/batcher. DetectionResultFields: standard fields returned by object detector. BoxListFields: standard field used by BoxList TfExampleFields: standard fields for tf-example data format (go/tf-example).

class easycv.core.standard_fields.InputDataFields[source]

Bases: object

Names for the input tensors.

Holds the standard data field names to use for identifying input tensors. This should be used by the decoder to identify keys for the returned tensor_dict containing input tensors. And it should be used by the model to identify the tensors it needs.

image

image.

original_image

image in the original input size.

key

unique key corresponding to image.

source_id

source of the original image.

filename

original filename of the dataset (without common path).

groundtruth_image_classes

image-level class labels.

groundtruth_boxes

coordinates of the ground truth boxes in the image.

groundtruth_classes

box-level class labels.

groundtruth_label_types

box-level label types (e.g. explicit negative).

groundtruth_is_crowd

[DEPRECATED, use groundtruth_group_of instead] is the groundtruth a single object or a crowd.

groundtruth_area

area of a groundtruth segment.

groundtruth_difficult

is a difficult object

groundtruth_group_of

is a group_of objects, e.g. multiple objects of the same class, forming a connected group, where instances are heavily occluding each other.

proposal_boxes

coordinates of object proposal boxes.

proposal_objectness

objectness score of each proposal.

groundtruth_instance_masks

ground truth instance masks.

groundtruth_instance_boundaries

ground truth instance boundaries.

groundtruth_instance_classes

instance mask-level class labels.

groundtruth_keypoints

ground truth keypoints.

groundtruth_keypoint_visibilities

ground truth keypoint visibilities.

groundtruth_label_scores

groundtruth label scores.

groundtruth_weights

groundtruth weight factor for bounding boxes.

num_groundtruth_boxes

number of groundtruth boxes.

true_image_shapes

true shapes of images in the resized images, as resized images can be padded with zeros.

image = 'image'
mask = 'mask'
width = 'width'
height = 'height'
original_image = 'original_image'
optical_flow = 'optical_flow'
key = 'key'
source_id = 'source_id'
filename = 'filename'
dataset_name = 'dataset_name'
groundtruth_image_classes = 'groundtruth_image_classes'
groundtruth_image_classes_num = 'groundtruth_image_classes_num'
groundtruth_boxes = 'groundtruth_boxes'
groundtruth_classes = 'groundtruth_classes'
groundtruth_label_types = 'groundtruth_label_types'
groundtruth_is_crowd = 'groundtruth_is_crowd'
groundtruth_area = 'groundtruth_area'
groundtruth_difficult = 'groundtruth_difficult'
groundtruth_group_of = 'groundtruth_group_of'
proposal_boxes = 'proposal_boxes'
proposal_objectness = 'proposal_objectness'
groundtruth_instance_masks = 'groundtruth_instance_masks'
groundtruth_instance_boundaries = 'groundtruth_instance_boundaries'
groundtruth_instance_classes = 'groundtruth_instance_classes'
groundtruth_keypoints = 'groundtruth_keypoints'
groundtruth_keypoint_visibilities = 'groundtruth_keypoint_visibilities'
groundtruth_label_scores = 'groundtruth_label_scores'
groundtruth_weights = 'groundtruth_weights'
num_groundtruth_boxes = 'num_groundtruth_boxes'
true_image_shape = 'true_image_shape'
original_image_shape = 'original_image_shape'
original_instance_masks = 'original_instance_masks'
groundtruth_boxes_absolute = 'groundtruth_boxes_absolute'
groundtruth_keypoints_absolute = 'groundtruth_keypoints_absolute'
label_map = 'label_map'
char_dict = 'char_dict'
class easycv.core.standard_fields.DetectionResultFields[source]

Bases: object

Naming conventions for storing the output of the detector.

source_id

source of the original image.

key

unique key corresponding to image.

detection_boxes

coordinates of the detection boxes in the image.

detection_scores

detection scores for the detection boxes in the image.

detection_classes

detection-level class labels.

detection_masks

contains a segmentation mask for each detection box.

detection_boundaries

contains an object boundary for each detection box.

detection_keypoints

contains detection keypoints for each detection box.

num_detections

number of detections in the batch.

source_id = 'source_id'
key = 'key'
detection_boxes = 'detection_boxes'
detection_scores = 'detection_scores'
detection_classes = 'detection_classes'
detection_masks = 'detection_masks'
detection_boundaries = 'detection_boundaries'
detection_keypoints = 'detection_keypoints'
num_detections = 'num_detections'
class easycv.core.standard_fields.TfExampleFields[source]

Bases: object

TF-example proto feature names for object detection.

Holds the standard feature names to load from an Example proto for object detection.

image_encoded

JPEG encoded string

image_format

image format, e.g. “JPEG”

filename

filename

channels

number of channels of image

colorspace

colorspace, e.g. “RGB”

height

height of image in pixels, e.g. 462

width

width of image in pixels, e.g. 581

source_id

original source of the image

object_class_text

labels in text format, e.g. [“person”, “cat”]

object_class_label

labels in numbers, e.g. [16, 8]

object_bbox_xmin

xmin coordinates of groundtruth box, e.g. 10, 30

object_bbox_xmax

xmax coordinates of groundtruth box, e.g. 50, 40

object_bbox_ymin

ymin coordinates of groundtruth box, e.g. 40, 50

object_bbox_ymax

ymax coordinates of groundtruth box, e.g. 80, 70

object_view

viewpoint of object, e.g. [“frontal”, “left”]

object_truncated

is object truncated, e.g. [true, false]

object_occluded

is object occluded, e.g. [true, false]

object_difficult

is object difficult, e.g. [true, false]

object_group_of

is object a single object or a group of objects

object_depiction

is object a depiction

object_is_crowd

[DEPRECATED, use object_group_of instead] is the object a single object or a crowd

object_segment_area

the area of the segment.

object_weight

a weight factor for the object’s bounding box.

instance_masks

instance segmentation masks.

instance_boundaries

instance boundaries.

instance_classes

Classes for each instance segmentation mask.

detection_class_label

class label in numbers.

detection_bbox_ymin

ymin coordinates of a detection box.

detection_bbox_xmin

xmin coordinates of a detection box.

detection_bbox_ymax

ymax coordinates of a detection box.

detection_bbox_xmax

xmax coordinates of a detection box.

detection_score

detection score for the class label and box.

image_encoded = 'image/encoded'
image_format = 'image/format'
filename = 'image/filename'
channels = 'image/channels'
colorspace = 'image/colorspace'
height = 'image/height'
width = 'image/width'
source_id = 'image/source_id'
object_class_text = 'image/object/class/text'
object_class_label = 'image/object/class/label'
object_bbox_ymin = 'image/object/bbox/ymin'
object_bbox_xmin = 'image/object/bbox/xmin'
object_bbox_ymax = 'image/object/bbox/ymax'
object_bbox_xmax = 'image/object/bbox/xmax'
object_view = 'image/object/view'
object_truncated = 'image/object/truncated'
object_occluded = 'image/object/occluded'
object_difficult = 'image/object/difficult'
object_group_of = 'image/object/group_of'
object_depiction = 'image/object/depiction'
object_is_crowd = 'image/object/is_crowd'
object_segment_area = 'image/object/segment/area'
object_weight = 'image/object/weight'
instance_masks = 'image/segmentation/object'
instance_boundaries = 'image/boundaries/object'
instance_classes = 'image/segmentation/object/class'
detection_class_label = 'image/detection/label'
detection_bbox_ymin = 'image/detection/bbox/ymin'
detection_bbox_xmin = 'image/detection/bbox/xmin'
detection_bbox_ymax = 'image/detection/bbox/ymax'
detection_bbox_xmax = 'image/detection/bbox/xmax'
detection_score = 'image/detection/score'