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BoofCV Documentation

repository·SNAPSHOT·Indexed 22 days ago

https://github.com/lessthanoptimal/boofcv

An open-source real-time computer vision library written in Java. BoofCV provides functionality for low-level image processing, camera calibration, feature tracking, and recognition. It includes specialized modules such as boofcv-geo for geometry algorithms, sfm for Structure from Motion, and integration libraries for Android (Camera2 API) and JavaFX. The library is modular, with boofcv-core containing the primary libraries and additional modules for FFmpeg, JavaCV, and Kotlin extensions.

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What's inside BoofCV

  1. Overview of the sfm submodule

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    The sfm (Structure from Motion) submodule provides geometric algorithms designed for 3D reconstruction and pose estimation.

    Key characteristics include:

    • Focus: Primarily geometric algorithms, though it may include image processing and feature-based approaches.
    • Complexity: It uses more simplifying assumptions compared to the reconstruction submodule and avoids the advanced techniques found in the recognition submodule.
    • Use Case: Ideal for tasks requiring 3D reconstruction and estimating the pose of a camera from image sequences.

  2. Overview of the reconstruction submodule

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    The reconstruction submodule provides tools for 3D scene reconstruction. It utilizes techniques from both the sfm (Structure from Motion) and recognition submodules. Unlike standard sfm approaches, the reconstruction module is designed to be more graph-centric, allowing it to process larger datasets while making fewer underlying assumptions about the scene.

  3. What is the boofcv-geo module?

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    The boofcv-geo module (short for geometry) is dedicated to pure geometry algorithms. It provides low-level operations for 2D, 3D, and perspective geometry.

    Key constraints and characteristics:

    • No Image Processing: This module does not allow or include image processing algorithms.
    • Low-Level Focus: Algorithms focus on individual geometric operations and do not explicitly combine information from multiple frames.
    • Calibration: While most algorithms are frame-independent, calibration algorithms are included here (though they are noted as a potential exception to the rule of not combining multi-frame information).

  6. BoofCV Project Directory Structure

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    Understanding the repository layout:

    DirectoryDescription
    applications/Helpful standalone applications
    data/Optional data used by applets and examples
    demonstrations/Code for real-time parameter experimentation
    examples/Easy-to-read example code
    integration/3rd party library integrations (primarily video I/O)
    main/The core BoofCV source code

  7. Understand the scope of the recognition module

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    The recognition module provides algorithms for identifying objects within an image. It focuses on information derived directly from pixels and supports machine learning approaches.

    Key constraints and relationships:

    • Focus: Pixel-based information and machine learning.
    • Exclusions: It does not emphasize geometry. 3D Structure from Motion (SFM) based recognition approaches are not included here (use the sfm module instead).
    • Dependencies: It depends on the geo module, but direct use of 3D geometry from geo is discouraged within this module.

  10. Understanding BoofCV code generation architecture

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    BoofCV uses a multi-stage generation process to manage dependencies between data structures (e.g., converting F64 to F32 or generating concurrent _MT versions).

    Key Components

    • boofcv/main/autocode: Contains the core code generation logic and defines the autogenerate Gradle command.
    • boofcv/main/boofcv-types: Contains data types used to specify image types for generation.
    • sourceSet.noauto: A special source set used in boofcv-types to prevent cyclical dependencies. Auto-generated code is generally prohibited here.
    • sourceSet.generate: The designated location where individual sub-projects store their auto-generated code.

    Generation Order

    When running ./gradlew autogenerate, tasks are executed in this specific order to maintain dependency integrity:

    1. autogenerateSource tasks: Invoked for projects referenced by the autogenerateDep task in autocode.
    2. autogenerateDerived task: Creates _F32 classes from F64, and then _MT (concurrent) classes from labeled code.

  11. How to implement custom code generation

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    For any other types of auto-generated code, follow these steps to integrate with the BoofCV build system:

    1. Place generated code: All custom auto-generated code must be placed in the sub-project's src/generate/java directory.
    2. Implement the generator: Create a new class that extends CodeGeneratorBase. Place this class in the package where you want the generated code to reside.
    3. Register the Gradle task: In the sub-project's build.gradle file, define a Gradle task named autogenerateSource.
    4. Link to the main build: Add a reference to your new autogenerateSource task in boofcv/main/autocode/build.gradle so the central autogenerate command can invoke it.

  12. Build and run all examples using the application launcher

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    To build all available examples and launch the interactive example application launcher on Linux or Unix systems, navigate to the examples directory and use Gradle to build the jar, then run it with Java.

    cd boofcv/examples
gradle examples
java -jar examples.jar