Libtorch MobileSAM

elephant.mov

A C++ implementation of MobileSAM.

What does this do?

This can run Segment-Anything model without python. It is based on the TorchScript model.pt files (included).

Why is it useful?

MobileSAM project aims to "make SAM lightweight for mobile applications and beyond," yet it only offers Python code, which isn't ideal for running on mobile devices.

This project allows to actually run the model on mobile devices. It can be integrated into Flutter, native Android, or iOS apps, as these platforms support running C++ code.

Limitations

Cannot automatically segment images, you need to provide the input points and boxes. This could be implemented in the future.

How do I get started?

You need to build OpenCV (instructions below) and download libtorch.

Models

The models are included in the repo. If you really want you can export them with this script convert_pytorch_mobile.py.

Models

• cpu_mobilesam_predictor_mobile_optimized_lite_interpreter.ptl
• cpu_vit_image_embedding_mobile_optimized_lite_interpreter.ptl
• mobilesam_predictor.pt
• vit_image_embedding.pt

Model input size. Source: notebooks/onnx_model_example.ipynb

• `image_embeddings`: The image embedding from predictor.get_image_embedding(). Has a batch index of length 1.
• `point_coords`: Coordinates of sparse input prompts, corresponding to both point inputs and box inputs. Boxes are encoded using two points, one for the top-left corner and one for the bottom-right corner. Coordinates must already be transformed to long-side 1024. Has a batch index of length 1.
• `point_labels`: Labels for the sparse input prompts. 0 is a negative input point, 1 is a positive input point, 2 is a top-left box corner, 3 is a bottom-right box corner, and -1 is a padding point. If there is no box input, a single padding point with label -1 and coordinates (0.0, 0.0) should be concatenated.
• `mask_input`: A mask input to the model with shape 1x1x256x256. This must be supplied even if there is no mask input. In this case, it can just be zeros.
• `has_mask_input`: An indicator for the mask input. 1 indicates a mask input, 0 indicates no mask input.
• `orig_im_size`: The size of the input image in (H,W) format, before any transformation.

Dependencies

Linux:

 sudo apt install build-essential cmake git pkg-config libgtk-3-dev libavcodec-dev libavformat-dev libswscale-dev libv4l-dev  libxvidcore-dev libx264-dev libjpeg-dev libpng-dev libtiff-dev gfortran openexr libatlas-base-dev python3-dev python3-numpy  libtbb2 libtbb-dev libdc1394-22-dev

MacOS:

brew install cmake pkg-config jpeg libpng libtiff openexr eigen tbb

Note: Tested with Libtorch 1.13.0+ and OpenCV 4.5.4+

(Other versions might work, but have not been tested)

Libtorch dependency

The project expects libtorch/ in the top-level directory. I have not included this because its 727MB.

Linux

Download this version from pytorch.org, rename the folder to 'libtorch' and put it in the repository at top level.

git clone https://github.com/cyrillkuettel/Libtorch-MobileSAM-Example.git
cd Libtorch-MobileSAM-Example
wget https://download.pytorch.org/libtorch/cpu/libtorch-cxx11-abi-shared-with-deps-1.13.0%2Bcpu.zip
unzip libtorch-cxx11-abi-shared-with-deps-1.13.0+cpu.zip # important that it's the  `cxx11 ABI` version, works with OpenCV)

Mac M1 Chips

Pre-built binaries of pytorch for for Mac M2 can be found here libtorch-mac-m1/releases (no official builds at the point of writing this.) Rename the folder to 'libtorch' and put it in the top-level directory of the repository.

It should look like this:

.
├── README.md
├── example-app
└── libtorch

OpenCV dependency

Install OpenCV for your operating system.

Unix

I did it like this:

mkdir ~/opencv && cd ~/opencv
git clone https://github.com/opencv/opencv_contrib.git
git clone https://github.com/opencv/opencv.git
cd opencv
mkdir build 
cd build

cmake -D CMAKE_BUILD_TYPE=RELEASE \
	-D CMAKE_INSTALL_PREFIX=/usr/local \
	-D INSTALL_C_EXAMPLES=OFf \
	-D INSTALL_PYTHON_EXAMPLES=OFF \
	-D OPENCV_GENERATE_PKGCONFIG=ON \
	-D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib/modules \
        -D PYTHON_EXECUTABLE=/usr/bin/python3 \
	-D OPENCV_BUILD_3RDPARTY_LIBS=ON \
	-D BUILD_EXAMPLES=ON ..
  
make -j8
sudo make install

Run

cd example-app
./configure.sh
make
build/example_main  # run the main example demo

Run the tests

cd example-app
./configure.sh
make tests
build/UnitTests

The ./configure only needs to be called the first time.

Run in Clion

1. File -> Open -> example-app
2. Open build settings (should open automatically): File -> Settings -> Build, Execution, Deployment -> CMake
3. Delete contents of Cmake options, add this: -DCMAKE_BUILD_TYPE=Debug -DCMAKE_PREFIX_PATH=/absolute/path/to/libtorch

Todo

• Feature: Refactor to be object oriented
• Bug: Translate input coordinates to longest side for (1024x1024)
• Feature: Add ability to click with mouse
• Bug: Fix the drawPoints function.
• Feature: Implement automatic_mask_generator