Added rembg

wiki/rembg.md

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+# rembg
+
+[rembg](https://github.com/danielgatis/rembg) is a [command line](/wiki/linux/shell.md) tool and
+Python library for removing image backgrounds using pretrained
+[neural networks](/wiki/neural_network.md) executed through ONNX Runtime.
+
+## Setup
+
+rembg can be installed from source as described on the
+[official GitHub repository](https://github.com/danielgatis/rembg).
+
+Alternatively a [Python package manager](/wiki/programming_language/python.md#package-management)
+can be used to install rembg.
+For the backend one must select CPU or GPU usage.
+For command line usage an extra package has to be installed in addition to the inference backend.
+Depending on that choice the name of the package will be `"rembg[cpu,cli]"` or `rembg[gpu,cli]`.
+
+## Usage
+
+This section addresses the usage of rembg.
+
+### Remove Background
+
+The following command removes the background from an image using a specific `<model>`.
+In this case `<input>` is a placeholder for the input image path and `<output>` for the output image
+path.
+
+```sh
+rembg i -m <model> <input> <output>
+```
+
+Depending on the use-case other models may also be used.
+`u2net` is a general purpose model and most of the time a good default.
+`u2net_human_seg` is optimized for human subjects.
+`isnet-general-use` is an alternative general-purpose model.
+
+The quality depends on the selected model and the input image.
+For portraits with complex backgrounds such as bushes or trees, `u2net` often produces the best
+overall results, while other models may perform better on different types of images.
+
+Additionally, alpha matting can improve difficult edges such as hair.
+
+```sh
+rembg i -m <model> -a <input> <output>
+```