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Merge pull request #17 from vortexntnu/Thomas_refactor
Thomas refactor
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‎.gitignore‎

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logs/
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build/
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bin/
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lib/
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msg_gen/
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srv_gen/
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msg/*Action.msg
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msg/*ActionFeedback.msg
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msg/*ActionGoal.msg
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msg/*ActionResult.msg
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msg/*Feedback.msg
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msg/*Goal.msg
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msg/*Result.msg
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msg/_*.py
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build_isolated/
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devel_isolated/
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*/launch/__pycache__
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# Generated by dynamic reconfigure
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*.cfgc
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/cfg/cpp/
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/cfg/*.py
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# Ignore generated docs
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*.dox
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*.wikidoc
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# eclipse stuff
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.project
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.cproject
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# qcreator stuff
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CMakeLists.txt.user
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srv/_*.py
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*.pcd
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*.pyc
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qtcreator-*
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*.user
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/planning/cfg
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/planning/docs
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/planning/src
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*~
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# Emacs
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.#*
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# Catkin custom files
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CATKIN_IGNORE
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.vscode/*
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image-filtering/.vscode/*devel/
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logs/
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build/
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bin/
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msg_gen/
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srv_gen/
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msg/*Action.msg
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.vscode/
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log/
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image-filtering/.vscode/*

‎README.md‎

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# Image Filtering Node
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[![Industrial CI](https://github.com/vortexntnu/vortex-image-filtering/actions/workflows/industrial-ci.yml/badge.svg)](https://github.com/vortexntnu/vortex-image-filtering/actions/workflows/industrial-ci.yml)
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[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/vortexntnu/vortex-image-filtering/main.svg)](https://results.pre-commit.ci/latest/github/vortexntnu/vortex-image-filtering/main)
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[![codecov](https://codecov.io/github/vortexntnu/vortex-image-filtering/graph/badge.svg?token=6XHprkpUsR)](https://codecov.io/github/vortexntnu/vortex-image-filtering)
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The `image_filtering_node` is a ROS 2 node developed in the `vortex::image_filters` namespace. It is designed to subscribe to image topics, apply various image filters using OpenCV, and publish the filtered images back to ROS.
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## Implementing New Filters
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To extend the functionality of the `image_filtering_node` by adding new filters, follow these steps to ensure compatibility and integration with the existing codebase:
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To extend the functionality of the `image_filtering_node` by adding new filters, follow these steps to ensure compatibility and integration with the existing codebase. There should be //TODO(New filter) comments where you add your filter:
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### Step 1: Define Filter Parameters
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### Step 1: Filter Enum
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Each filter should have its own set of parameters encapsulated in a structure. Define this structure within the `vortex::image_filters` namespace.
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You should define your filtertype in the filtertype enum in [typedef.hpp](image-filtering/include/lib/typedef.hpp)
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```cpp
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struct YourFilterParams {
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// Add necessary parameters here
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int example_param;
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enum class FilterType {
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NoFilter,
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Flip,
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Unsharpening,
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Erosion,
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Dilation,
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...
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// Add your filter here
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};
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```
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### Step 2: Add to FilterParams Structure
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Integrate your new filter parameters structure into the existing `FilterParams` structure. This allows the `apply_filter` function to access the parameters specific to your filter.
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To access the filter through the yaml file we need to access it through a string. In the same file you need to add it as an item in the kFilterMap.
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```cpp
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struct FilterParams {
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std::string filter_type;
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UnsharpeningFilterParams unsharpening;
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ErodingFilterParams eroding;
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DilatingFilterParams dilating;
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WhiteBalancingFilterParams white_balancing;
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EbusFilterParams ebus;
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YourFilterParams your_filter; // Add your filter params here
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static constexpr std::pair<std::string_view, FilterType> kFilterMap[] = {
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{"no_filter", FilterType::NoFilter},
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{"flip", FilterType::Flip},
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{"unsharpening", FilterType::Unsharpening},
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...
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// Add your filter here
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{"example", FilterType::Example},
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{"unknown", FilterType::Unknown}
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};
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```
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### Step 3: Create the Filter Function
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### Step 2: Make the filter header
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Each filter should have its own headerfile asosiated with it. You can add this in the [filters](image-filtering/include/lib/filters), and name it the same as your filter (your_filter.hpp). In this file you start with adding these lines (swapping out example with your filter):
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Implement your filter function. This function should take the `cv::Mat` objects for the input and output images and a `const FilterParams&` which includes your specific filter parameters. Make sure to use your parameter structure within this function.
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```cpp
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#ifndef LIB__FILTERS__EXAMPLE_HPP_
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#define LIB__FILTERS__EXAMPLE_HPP_
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#include "abstract_filter_class.hpp"
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// Insert code here ...
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#endif // LIB__FILTERS__EXAMPLE_HPP_
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```
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This new file needs to be added to [all_filters.hpp](image-filtering/include/lib/filters/all_filters.hpp).
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```cpp
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void your_filter_function(const cv::Mat &original, cv::Mat &filtered, const FilterParams& filter_params) {
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// Access your filter-specific parameters like this:
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int example_param = filter_params.your_filter.example_param;
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#ifndef LIB__FILTERS__EXAMPLE_HPP_
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#define LIB__FILTERS__EXAMPLE_HPP_
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// Implement your filtering logic here
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}
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// Add your filter to the top of this file:
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#include "lib/filters/your_filter.hpp"
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#include "lib/filters/example.hpp"
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#include "lib/filters/no_filter.hpp"
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#endif // LIB__FILTERS__EXAMPLE_HPP_
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```
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### Step 4: Register the Filter Function
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Add an entry to the `filter_functions` map for your new filter. This step is crucial as it links the filter name (as a string) to the corresponding filter function pointer.
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### Step 3: Define Filter Parameters
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Each filter should have its own set of parameters encapsulated in a structure. Define this structure within your_filter.hpp.
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```cpp
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std::map<std::string, FilterFunction> filter_functions = {
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{"no_filter", no_filter},
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{"sharpening", sharpening_filter},
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{"unsharpening", unsharpening_filter},
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{"eroding", eroding_filter},
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{"dilating", dilating_filter},
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{"white_balancing", white_balance_filter},
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{"ebus", ebus_filter},
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{"your_filter", your_filter_function} // Add your filter here
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struct ExampleParams{
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// Add necessary filter parameters here
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int example_int;
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std::string example_string;
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};
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```
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### Step 5: Declare and Assign Parameters
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### Step 4: Add filter class
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Below the filter parameters add a Class for your filter inheriting from the Filter class, with the same structure as shown below.
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```cpp
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class Example: public Filter{
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public:
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explicit Example(ExampleParams params): filter_params(params) {}
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void apply_filter(const cv::Mat& original, cv::Mat& filtered) const override; // This is the filter itself
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private:
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ExampleParams filter_params;
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};
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```
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Here you can add other filter specific stuff like storing variables that need to change between runs and so on.
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Declare the new filter parameters in the ROS 2 node constructor and assign these parameters to the `FilterParams` structure within the `set_filter_params` function.
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#### In the Node Constructor
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### Step 5: Define the filter function
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In the constructor of your ROS 2 node, declare each of the new filter parameters using the `declare_parameter` function. This sets the default values and prepares the node to accept these parameters at runtime through command line or a YAML configuration file.
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You can do this in two different ways. If your filter is big you can add a cpp file for your filter, explained in the [helperfunctions](#helper-functions) section of this page. Otherwise you can add the function definition just below the class definition like this.
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```cpp
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ImageFilteringNode::ImageFilteringNode() : Node("image_filtering_node")
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{
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this->declare_parameter<std::string>("filter_params.your_filter.example_param", "default_value");
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...
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// Other parameters declarations
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inline void Example::apply_filter(const cv::Mat& original, cv::Mat& filtered) const{
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std::string example_str = this->filter_params.example_string;
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int example_int = this->filter_params.example_int;
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DoExample(original,filtered, example_str, example_int);
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}
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```
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#### In the set_filter_params Function
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### Step 6: Add to config file
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In the set_filter_params function, retrieve and assign the parameters to the corresponding fields in the FilterParams structure. Ensure to handle cases where the parameter might not be set or provided.
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In the [image_filtering_params.yaml](image-filtering/config/image_filtering_params.yaml) file you add your filter and filterparameters for easily interfacing with the filters:
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```cpp
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```yaml
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filter_params:
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filter_type: "example"
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void ImageFilteringNode::set_filter_params(){
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FilterParams params = filter_params_; // assuming filter_params_ is already defined in your class
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flip:
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flip_code: 1
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...
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# Add your filter type here
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example:
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example_int: 5
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example_string: "This is an example"
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```
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params.your_filter.example_param = this->get_parameter("filter_params.your_filter.example_param").as_string();
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### Step 7: Declare and Assign Parameters
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Now we need to use the right filter and set the variables for your filter. We do that by making a new case in `set_filter_params`, in [image_filtering_ros.cpp](image-filtering/src/ros/image_filtering_ros.cpp), for your filter.
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```cpp
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void ImageFilteringNode::set_filter_params() {
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// Retrieve other parameters and handle cases where parameters might not be provided
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filter_params_ = params; // Update the filter parameters structure
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RCLCPP_INFO(this->get_logger(), "Filter parameters updated for your_filter.");
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switch (filter_type){
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...
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case FilterType::Example: {
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ExampleParams params;
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params.example_int =
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declare_and_get<int>("filter_params.example.example_int");
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params.example_string =
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declare_and_get<std::string>(
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"filter_params.example.example_string");
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filter_ptr = std::make_unique<Example>(params);
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break;
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}
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}
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}
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```
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### Helper functions
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#### Define the apply filter function
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If the YourFilter::apply_filter function gets to big you can add a cpp file named the same as your hpp file (your_filter.cpp) in the [filters](image-filtering/src/lib/filters) folder. There you add #include "lib/filters/your_filter.hpp" at the top. Then you can define your filter there. Whenever you make a new c++ file, add the path to [CMakeList.txt](image-filtering/CMakeLists.txt), to the `add_library(${CORE_LIB} SHARED` part.
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#### Make and use helperfunctions
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If you are making a function that is useful for many different filters, then you can add the declaration to [utilities.hpp](image-filtering/include/lib/utilities.hpp), and the definition to [utilities.cpp](image-filtering/src/lib/utilities.cpp). Make a description off the function above the declaration. To use this in the filter add `#include "image-filtering/include/lib/utilities.hpp"` to your_filter.hpp

‎image-filtering/CMakeLists.txt‎

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find_package(spdlog REQUIRED)
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find_package(fmt REQUIRED)
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include_directories(include)
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set(LIB_NAME "${PROJECT_NAME}_component")
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add_library(${LIB_NAME} SHARED
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src/image_processing.cpp
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src/image_filtering_ros.cpp
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# ---- Library (no ROS) ----
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set(LIB "${PROJECT_NAME}")
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add_library(${LIB} SHARED
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src/lib/utilities.cpp
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)
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target_link_libraries(${LIB_NAME} PUBLIC
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target_link_libraries(${LIB} PUBLIC
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${OpenCV_LIBS}
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)
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target_include_directories(${LIB_NAME} PUBLIC
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target_include_directories(${LIB} PUBLIC
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$<INSTALL_INTERFACE:include>
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ament_target_dependencies(${LIB_NAME} PUBLIC
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ament_target_dependencies(${LIB} PUBLIC
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spdlog
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fmt
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)
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# ---- ROS library ----
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set(ROS_LIB "${PROJECT_NAME}_component")
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add_library(${ROS_LIB} SHARED
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src/ros/image_filtering_ros.cpp
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src/ros/image_filtering_ros_utils.cpp
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)
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target_link_libraries(${ROS_LIB} PUBLIC
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${LIB}
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)
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target_include_directories(${ROS_LIB} PUBLIC
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$<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/include>
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$<INSTALL_INTERFACE:include>
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)
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ament_target_dependencies(${ROS_LIB} PUBLIC
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rclcpp
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rclcpp_components
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cv_bridge
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rclcpp_components_register_node(
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${LIB_NAME}
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PLUGIN "ImageFilteringNode"
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EXECUTABLE ${PROJECT_NAME}_node
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${ROS_LIB}
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PLUGIN "ImageFilteringNode"
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EXECUTABLE ${PROJECT_NAME}_node
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)
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# Export the target for other packages to use
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ament_export_targets(export_${LIB_NAME})
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install(TARGETS ${LIB_NAME}
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EXPORT export_${LIB_NAME}
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# Export BOTH targets
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ament_export_targets(export_${LIB} HAS_LIBRARY_TARGET)
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ament_export_targets(export_${ROS_LIB} HAS_LIBRARY_TARGET)
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ament_export_include_directories(include)
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ament_export_dependencies(OpenCV spdlog fmt)
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ament_export_dependencies(rclcpp rclcpp_components cv_bridge sensor_msgs)
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install(TARGETS ${LIB}
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EXPORT export_${LIB}
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ARCHIVE DESTINATION lib
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LIBRARY DESTINATION lib
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RUNTIME DESTINATION bin
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)
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install(TARGETS ${ROS_LIB}
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EXPORT export_${ROS_LIB}
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ARCHIVE DESTINATION lib
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LIBRARY DESTINATION lib
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RUNTIME DESTINATION bin
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)
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install(
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DIRECTORY include/
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DESTINATION include

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