| OpenCV 5.0.0alpha Open Source Computer Vision

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Point cloud visualisation

Original author | Dmitrii Klepikov
Compatibility | OpenCV >= 5.0

# Goal

In this tutorial you will:

- Load and save point cloud data
- Visualise your data

# Requirements

For visualisations you need to compile OpenCV library with OpenGL support. For this you should set WITH_OPENGL flag ON in CMake while building OpenCV from source.

# Practice

Loading and saving of point cloud can be done using `cv::loadPointCloud` and `cv::savePointCloud` accordingly.

Currently supported formats are:

- .OBJ (supported keys are v(which is responsible for point position), vn(normal coordinates) and f(faces of a mesh), other keys are ignored)
- .PLY (all encoding types(ascii and byte) are supported with limitation to only float type for data)

vertices, normals = cv2.loadPointCloud("teapot.obj")

Function `cv::loadPointCloud` returns vector of points of float (`cv::Point3f`) and vector of their normals(if specified in source file). To visualize it you can use functions from viz3d module and it is needed to reinterpret data into another format

vertices = np.squeeze(vertices, axis=1)
color = [1.0, 1.0, 0.0]
colors = np.tile(color, (vertices.shape[0], 1))
obj_pts = np.concatenate((vertices, colors), axis=1).astype(np.float32)
cv2.viz3d.showPoints("Window", "Points", obj_pts)
cv2.waitKey(0)

In presented code sample we add a colour attribute to every point Result will be:

For additional info grid can be added

vertices, normals = cv2.loadPointCloud("teapot.obj")

Other possible way to draw 3d objects can be a mesh. For that we use special functions to load mesh data and display it. Here for now only .OBJ files are supported and they should be triangulated before processing (triangulation - process of breaking faces into triangles).

vertices, indices = cv2.loadMesh("../data/teapot.obj")
vertices = np.squeeze(vertices, axis=1)
cv2.viz3d.showMesh("window", "mesh", vertices, indices)