ImageBoundingBoxes

ImageBoundingBoxes[image]

gives an association of lists of bounding boxes for each identified category of objects in image.

ImageBoundingBoxes[image,category]

gives a list of bounding boxes for subimages identified as an instance of the specified category.

ImageBoundingBoxes[video,…]

gives a time series of detected bounding boxes in frames of video.

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
    wordspecword sense specification, as used in WordData
    Entity[…]any appropriate entity
    category1|category2|…any of the categoryi
  • The following options can be given:
  • AcceptanceThreshold Automaticidentification acceptance threshold
    MaxFeatures Automaticmaximum number of subimages to return
    MaxOverlapFraction Automaticmaximum 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.

Examples

open allclose all

Basic Examples  (1)

Find the bounding boxes around identified birds in an image:

Highlight the identified bounding boxes:

Scope  (4)

Data  (2)

Detect all objects in an image:

Highlight detected objects:

Detect instances of a specific object:

Highlight detected instances:

Detect objects in frames of video:

Highlight detected objects on the video frames:

Categories  (2)

Find all the detectable objects in an image:

Find all birds in the image:

Options  (4)

AcceptanceThreshold  (1)

Objects with low probability are not returned:

Allowing a lower probability may result in more objects being recognized:

MaxFeatures  (1)

By default, all the detected objects are returned:

Specify a maximum number of results:

MaxOverlapFraction  (1)

The detected bounding boxes may overlap each other:

Find only non-intersecting objects:

TargetDevice  (1)

By default, the function is evaluated on CPU:

Use the TargetDevice option to specify a different device:

Properties & Relations  (1)

ImageBoundingBoxes is equivalent to ImageCases[image, All -> "BoundingBox"]:

Interactive Examples  (1)

Dynamically locate an object in an image:

Wolfram Research (2019), ImageBoundingBoxes, Wolfram Language function, https://reference.wolfram.com/language/ref/ImageBoundingBoxes.html (updated 2025).

Text

Wolfram Research (2019), ImageBoundingBoxes, Wolfram Language function, https://reference.wolfram.com/language/ref/ImageBoundingBoxes.html (updated 2025).

CMS

Wolfram Language. 2019. "ImageBoundingBoxes." Wolfram Language & System Documentation Center. Wolfram Research. Last Modified 2025. https://reference.wolfram.com/language/ref/ImageBoundingBoxes.html.

APA

Wolfram Language. (2019). ImageBoundingBoxes. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/ImageBoundingBoxes.html

BibTeX

@misc{reference.wolfram_2024_imageboundingboxes, author="Wolfram Research", title="{ImageBoundingBoxes}", year="2025", howpublished="\url{https://reference.wolfram.com/language/ref/ImageBoundingBoxes.html}", note=[Accessed: 20-January-2025 ]}

BibLaTeX

@online{reference.wolfram_2024_imageboundingboxes, organization={Wolfram Research}, title={ImageBoundingBoxes}, year={2025}, url={https://reference.wolfram.com/language/ref/ImageBoundingBoxes.html}, note=[Accessed: 20-January-2025 ]}