wehub-resource-sync
96 lines
4.0 KiB
Markdown
96 lines
4.0 KiB
Markdown
<!--[metadata]
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title = "Eigen and OpenCV C++ integration"
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source = "https://github.com/rerun-io/cpp-example-opencv-eigen"
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tags = ["2D", "3D", "C++", "Eigen", "OpenCV"]
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thumbnail = "https://static.rerun.io/eigen-and-opencv-c-integration/5d271725bb9215b55f53767c9dc0db980c73dade/480w.png"
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thumbnail_dimensions = [480, 480]
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-->
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<picture>
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<img src="https://static.rerun.io/cpp-example-opencv-eigen/2fc6355fd87fbb4d07cda384ee8805edb68b5e01/full.png" alt="">
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<source media="(max-width: 480px)" srcset="https://static.rerun.io/cpp-example-opencv-eigen/2fc6355fd87fbb4d07cda384ee8805edb68b5e01/480w.png">
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<source media="(max-width: 768px)" srcset="https://static.rerun.io/cpp-example-opencv-eigen/2fc6355fd87fbb4d07cda384ee8805edb68b5e01/768w.png">
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<source media="(max-width: 1024px)" srcset="https://static.rerun.io/cpp-example-opencv-eigen/2fc6355fd87fbb4d07cda384ee8805edb68b5e01/1024w.png">
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<source media="(max-width: 1200px)" srcset="https://static.rerun.io/cpp-example-opencv-eigen/2fc6355fd87fbb4d07cda384ee8805edb68b5e01/1200w.png">
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</picture>
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This is a minimal CMake project that shows how to use Rerun in your code in conjunction with [Eigen](https://gitlab.com/libeigen/eigen) and [OpenCV](https://opencv.org/).
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# Used Rerun types
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[`Image`](https://www.rerun.io/docs/reference/types/archetypes/image), [`Points3D`](https://www.rerun.io/docs/reference/types/archetypes/points3d), [`Pinhole`](https://www.rerun.io/docs/reference/types/archetypes/pinhole), [`Transform3D`](https://www.rerun.io/docs/reference/types/archetypes/transform3d)
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# Background
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This C++ example demonstrates the integration of the Rerun with Eigen and OpenCV libraries.
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Eigen handles 3D point calculations and camera orientations, while OpenCV assists with image processing tasks like reading and converting images.
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# Logging and visualizing with Rerun
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The visualizations in this example were created with the following Rerun code:
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## 3D points
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The positions of 3D points are logged to the "world/points_from_vector" and "world/points_from_matrix" entities using the [`Points3D`](https://www.rerun.io/docs/reference/types/archetypes/points3d) archetype.
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```cpp
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rec.log("world/points_from_vector", rerun::Points3D(points3d_vector));
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```
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```cpp
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rec.log("world/points_from_matrix", rerun::Points3D(points3d_matrix));
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```
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## Pinhole camera
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A pinhole camera is logged to "world/camera" using the [`Pinhole`](https://www.rerun.io/docs/reference/types/archetypes/pinhole) archetype.
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Additionally, the 3D transformation of the camera, including its position and orientation, is logged using the [`Transform3D`](https://www.rerun.io/docs/reference/types/archetypes/transform3d) archetype.
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```cpp
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rec.log(
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"world/camera",
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rerun::Pinhole::from_focal_length_and_resolution({500.0, 500.0}, {640.0, 480.0})
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);
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```
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```cpp
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rec.log(
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"world/camera",
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rerun::Transform3D(
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rerun::Vec3D(camera_position.data()),
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rerun::Mat3x3(camera_orientation.data())
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)
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);
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```
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## Images
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Images are logged using the [`Image`](https://www.rerun.io/docs/reference/types/archetypes/image) archetype. Two methods are demonstrated: logging images with a tensor buffer and logging images by passing a pointer to the image data.
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```cpp
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// Log image to rerun by borrowing binary data into a `Collection` from a pointer.
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rec.log(
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"image0",
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rerun::Image(
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rerun::borrow(img.data, img.total() * img.elemSize()),
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rerun::WidthHeight(
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static_cast<uint32_t>(img.cols),
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static_cast<uint32_t>(img.rows)
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),
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rerun::ColorModel::BGR
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)
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);
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// Or by passing a pointer to the image data.
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rec.log(
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"image1",
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rerun::Image(
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reinterpret_cast<const uint8_t*>(img.data),
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rerun::WidthHeight(
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static_cast<uint32_t>(img.cols),
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static_cast<uint32_t>(img.rows)
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),
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rerun::ColorModel::BGR
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)
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);
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```
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# Run the code
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You can find the build instructions here: [C++ Example with OpenCV and Eigen](https://github.com/rerun-io/cpp-example-opencv-eigen/blob/main/README.md)
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