gives an association of lists of bounding boxes for each identified category of objects in image.
gives a list of bounding boxes for subimages identified as an instance of the specified category.
Details and Options
- ImageBoundingBoxes attempts to find instances of an object category present in an image.
- For each category, the result is given as a list of Rectangle objects.
- Coordinates are assumed to be in the standard image coordinate system.
- Possible forms for category include:
"concept" named concept, as used in "Concept" entities "word" English word, as used in WordData wordspec word sense specification, as used in WordData Entity[…] any appropriate entity category1|category2|… any of the categoryi
- The following options can be given:
AcceptanceThreshold Automatic identification acceptance threshold MaxFeatures Automatic maximum number of subimages to return MaxOverlapFraction Automatic maximum bounding box overlap TargetDevice "CPU" the target device on which to compute
- ImageBoundingBoxes uses machine learning. Its methods, training sets and biases included therein may change and yield varied results in different versions of the Wolfram Language.
- ImageBoundingBoxes may download resources that will be stored in your local object store at $LocalBase, and can be listed using LocalObjects and removed using ResourceRemove.
Examplesopen allclose all
Basic Examples (1)
Use the TargetDevice option to specify a different device:
Properties & Relations (1)
Wolfram Research (2019), ImageBoundingBoxes, Wolfram Language function, https://reference.wolfram.com/language/ref/ImageBoundingBoxes.html.
Wolfram Language. 2019. "ImageBoundingBoxes." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/ImageBoundingBoxes.html.
Wolfram Language. (2019). ImageBoundingBoxes. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ImageBoundingBoxes.html