libtorch-png

A ros package tested on ros-melodic for encoding/decoding png images with libtorch torch::Tensor.

This package is intended to be compiled with ros-melodic and catkin but you may as well just take the include/torch_png/Png.hpp and src/Png.cpp files and compile and link however you want.

Requirements

• c++17
• libtorch
• libpng

Optional

• ros-melodic

Add it to you ros workspace (example)

$ cd catkin_ws/src/external_libraries
$ git clone https://github.com/Palinody/libtorch-png

Compiling

If you are using ros:

$ catkin_make

Using the ros package library from another ros package (minimal example)

find_package(
  catkin REQUIRED COMPONENTS
  # <...>
  roscpp
  torch_png
)

target_link_libraries(
    ${PROJECT_NAME} 
    # <...>
    ${LIBPNG_LIBRARIES}
    ${TORCH_LIBRARIES}
)

Add the package to your package.xml

<!-- ... -->
<build_depend>torch_png</build_depend>
<build_export_depend>torch_png</build_export_depend>
<exec_depend>torch_png</exec_depend>
<!-- ... -->

Unit tests

$ catkin_make run_tests_torch_png

The test/PngTest.cpp file is ugly because its content aggregates the content of files from a greater project. It has been included in this repository just to show how the package is intended to be used and how it has been tested. The images are being stored in /tmp and immediately deleted upon creation.

Supported image formats

torch::kUInt/std::uint8_t type only.

• gray
• gray alpha
• rgb
• rgb alpha

How to use

#include "torch_png/Png.hpp"

int main(int argc, char** argv) {
  // returns a torch::UInt8 with dims {channels, height, width}
  const auto image = torch_png::decode("path/to/dir/file.png");
  // expects a torch::UInt8 with dims {channels, height, width}
  torch_png::encode("path/to/dir/file.png", image);
  // ...
  const auto batched_tensor = /*some batched tensor with TYPE torch::UInt8 and DIMS {batch, channels, height, width}*/
  /*
   * If your tensor is a batch of images, 
   * a sequence of png images will be written
   * Let batch = 3 and pathname = "path/to/dir/filename.png"
   * torch_png::encode_batch(...) will write batch files sequentially:
   *    "path/to/dir/filename_0.png"
   *    "path/to/dir/filename_1.png"
   *    "path/to/dir/filename_2.png"
   *
   * You may as well choose your own delimiter instead of "_", which is the default one.
   */
  // expects a torch::UInt8 with dims {batch, channels, height, width}
  torch_png::encode_batch("path/to/dir/filename.png", batched_tensor);
  return 0;
}