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Semantic Segmentation

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The focus of this example is to introduce user to renderer.SegMapRenderer module, which generates semantic segmentations of scenes.

Usage

Execute in the BlenderProc main directory:

blenderproc run examples/basics/semantic_segmentation/main.py examples/resources/camera_positions examples/basics/semantic_segmentation/scene.blend examples/basics/semantic_segmentation/output
  • examples/basics/semantic_segmentation/main.py: path to the python file.
  • examples/resources/camera_positions: text file with parameters of camera positions.
  • examples/basics/semantic_segmentation/scene.blend: path to the blend file with the basic scene.
  • examples/basics/semantic_segmentation/output: path to the output directory.

Steps

Blend loading

# load the objects into the scene
objs = bproc.loader.load_blend(args.scene)

This loads the .blend file, it extracts hereby only the mesh objects from the file, not all information stored in this .blend file.

Be aware that in the loaded .blend file all objects already have set custom properties for the attribute name "category_id". This can be done manually by:

obj.set_cp("category_id", 0)

SegMapRenderer

# Render segmentation masks (per class and per instance)
data.update(bproc.renderer.render_segmap(map_by=["class", "instance", "name"]))

This module can map any kind of object related information to an image or to a list of indices of the objects in the scene. So, if you want to map the custom property category_id to an image, you write map_by=["class"]. Then each pixel gets assigned the category_id of the object present in that pixel. If it is set to instance each pixel gets an id for the obj nr in the scene, these are consistent for several frames, which also means that not all ids must appear in each image. It can also be set to different custom properties or attributes of the object class like: "name", which returns the name of each object. This can not be saved in an image, so a csv file is generated, which is attached to the .hdf5 container in the end. Where it maps each instance nr to a name. If there are keys, which can not be stored in an image, it is necessary to also generate an instance image. Furthermore, if an instance image is used all other keys are stored in the .csv.

For example, it would also be possible to use the key: "location". This would access the location of each object and add it to the csv file. Be aware that if the background is visible this will raise an error, as the background has no location attribute. This can be avoided by providing a default value like: default_values= {"location: [0,0,0]}.