There are multiple graphical tools out there that you can use to remove everything that surrounds an object in an image. However, doing this automatically it's quite difficult to do, normally an user will always need to interact with the tool to remove it so good as possible. What if I told you that there's an awesome open source project that aims to remove the background of an image automatically using machine learning?
In this article, I will explain to you how to easily install and configure the Rembg library for Python to remove the background from an image.
1. Install PyTorch
If you are using anaconda, you can install Pytorch with the following command (you can learn how to install Anaconda following this tutorial just in case that you don't have it installed and want to use it):
conda install pytorch torchvision torchaudio cpuonly -c pytorchAlternatively, you may install Pytorch using pip with the following command:
pip install torch==1.7.1+cpu torchvision==0.8.2+cpu -f https://download.pytorch.org/whl/torch_stable.htmlNote: for this tutorial and explained as well in the github repository, we are using Pytorch for CPU. If you have a graphic card available in the server, you may install Pytorch with support for CUDA. For more information about Pytorch, please visit the official website here.
2. Install RemBG
RemBG is a tool to remove the background of any image. This library uses the U2-Net under the hood for pattern recognition ("U^2-Net: Going Deeper with Nested U-Structure for Salient Object Detection."). To install this package in your system, use pip:
pip install rembgFor more information about this awesome open source tool, please visit the official repository at Github here.
3. Using the library
After installing the Rembg library, you will be able to choose either directly from the command line or inside a simple python script. Note that when running for first time, the model will be downloaded so it will take a while, once it finishes, the background removal doesn't take that much for every image. Rembg will be available globally in your environment.
Background removal through the CLI
The fastest way to test if the library is working is to simply use a remote image, the following command will use a Wikipedia image of Julian Casablancas:
curl -s https://upload.wikimedia.org/wikipedia/commons/thumb/e/ec/Julian_Casablancas_%40_Lollapalooza_2014_%2813657827913%29.jpg/440px-Julian_Casablancas_%40_Lollapalooza_2014_%2813657827913%29.jpg | rembg > output.pngThe generated output.png will look like this (considering the original image):
Really awesome isn't? In some servers, specially the virtualized ones (avx nor avx2 instructions), you may get the following warning:
[W NNPACK.cpp:79] Could not initialize NNPACK! Reason: Unsupported hardware.But you can ignore it as the output image hasn't background anymore. If you need to remove the mentioned warning, you may need to compile Pytorch from source and disable NNPack support. You can of course do the same with local images:
rembg -o ./output-image.png ./input-image.jpgUsing the library inside a Python script
You can as well use the library along with your pipeline embedding it into your scripts. For example, the following script will do the same as the previous example, take a local file and create the version without background:
# example.py
from rembg.bg import remove
import numpy as np
import io
from PIL import Image
# Uncomment the following lines if working with trucated image formats (ex. JPEG / JPG)
# In my case I do give JPEG images as input, so i'll leave it uncommented
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True
input_image = 'input-image.jpg'
output_image = 'output-image.png'
f = np.fromfile(input_image)
result = remove(f)
img = Image.open(io.BytesIO(result)).convert("RGBA")
img.save(output_image)Give it a try and start removing the background of your images automatically using this wonderful tool!
In case of error when using rembg
In my case, when using rembg directly from the command line in production after the installation, it throwed the following distutils.errors.CompileError:
Failed to import ahead-of-time-compiled modules. This is expected on first import.
Compiling modules and trying again (this might take a minute).
Traceback (most recent call last):
File "/usr/local/bin/rembg", line 5, in <module>
from rembg.cli import main
File "/usr/local/lib/python3.9/dist-packages/rembg/cli.py", line 9, in <module>
from .bg import remove
File "/usr/local/lib/python3.9/dist-packages/rembg/bg.py", line 6, in <module>
from pymatting.alpha.estimate_alpha_cf import estimate_alpha_cf
File "/usr/local/lib/python3.9/dist-packages/pymatting/__init__.py", line 2, in <module>
import pymatting_aot.cc
File "/usr/local/lib/python3.9/dist-packages/pymatting_aot/cc.py", line 28, in <module>
compile_modules()
File "/usr/local/lib/python3.9/dist-packages/pymatting_aot/cc.py", line 16, in compile_modules
cc.compile()
File "/usr/local/lib/python3.9/dist-packages/numba/core/compiler_lock.py", line 35, in _acquire_compile_lock
return func(*args, **kwargs)
File "/usr/local/lib/python3.9/dist-packages/numba/pycc/cc.py", line 217, in compile
objects += self._compile_mixins(build_dir)
File "/usr/local/lib/python3.9/dist-packages/numba/pycc/cc.py", line 187, in _compile_mixins
objects = self._toolchain.compile_objects(sources, build_dir,
File "/usr/local/lib/python3.9/dist-packages/numba/pycc/platform.py", line 137, in compile_objects
objects = self._compiler.compile(sources,
File "/usr/local/lib/python3.9/dist-packages/numpy/distutils/ccompiler.py", line 89, in <lambda>
m = lambda self, *args, **kw: func(self, *args, **kw)
File "/usr/local/lib/python3.9/dist-packages/numpy/distutils/ccompiler.py", line 360, in CCompiler_compile
pool.map(single_compile, build_items)
File "/usr/lib/python3.9/multiprocessing/pool.py", line 364, in map
return self._map_async(func, iterable, mapstar, chunksize).get()
File "/usr/lib/python3.9/multiprocessing/pool.py", line 771, in get
raise self._value
File "/usr/lib/python3.9/multiprocessing/pool.py", line 125, in worker
result = (True, func(*args, **kwds))
File "/usr/lib/python3.9/multiprocessing/pool.py", line 48, in mapstar
return list(map(*args))
File "/usr/local/lib/python3.9/dist-packages/numpy/distutils/ccompiler.py", line 325, in single_compile
self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)
File "/usr/local/lib/python3.9/dist-packages/numpy/distutils/ccompiler.py", line 89, in <lambda>
m = lambda self, *args, **kw: func(self, *args, **kw)
File "/usr/local/lib/python3.9/dist-packages/numpy/distutils/unixccompiler.py", line 58, in UnixCCompiler__compile
raise CompileError(msg) from None
distutils.errors.CompileError: Command "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fstack-protector-strong -Wformat -Werror=format-security -fPIC -DPYCC_MODULE_NAME=aot -DPYCC_USE_NRT=1 -I/usr/include/python3.9 -I/usr/local/lib/python3.9/dist-packages/numpy/core/include -c /usr/local/lib/python3.9/dist-packages/numba/pycc/modulemixin.c -o /tmp/pycc-build-aot-2b86dl9a/usr/local/lib/python3.9/dist-packages/numba/pycc/modulemixin.o" failed with exit status 1To solve this problem, I simply had to install the python3-dev package (in my case Python 3.9):
sudo apt-get install python3.9-devHappy coding ❤️!
