Update 'README.md'

git-subtree-dir: ros2_ws/src/perception_pcl
git-subtree-mainline: 0c9504b343
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README.md

@@ -1,3 +1,244 @@
 # greenhouse
 
-This repository contains code for detecting heat pipes in the greenhouse as well as estimating the pose of the pipes
+This repository contains code for detecting heat pipes in the greenhouse as well as estimating the pose of the pipes.
+
+Platform: ROS 2, Humble, Ubuntu 22.04
+
+## How to install dependencies??
+- Install `git`
+```
+sudo apt install git
+```
+- Install `ROS 2 Humble` on `Ubuntu 22.04` by following https://docs.ros.org/en/foxy/Installation/Ubuntu-Install-Debians.html
+- Install `colcon build` by following https://docs.ros.org/en/foxy/Tutorials/Beginner-CLI-Tools/Configuring-ROS2-Environment.html
+```
+sudo apt install python3-colcon-common-extensions
+```
+- Instal `pip` for python packages
+```
+sudo apt install python3-pip
+```
+- Now clone the repository
+```
+git clone https://tea.der-space.de/apoorva/greenhouse.git
+```
+- Install `ultralytics` for yolov3 package
+```
+pip install ultralytics
+```
+- For `yolov3_ros`, there are a bunch of other requirements. Go to `yolov3` folder and install using following commands:
+```
+cd ~/greenhouse/yolov3
+pip install -r requirements.txt
+```
+- Go to `ros2_ws` inside `greenhouse`. Make sure you only have `src` folder.
+```
+cd ~/greenhouse/ros2_ws
+ls
+src
+```
+- Inside `ros2_ws` folder, start building individual packages in the below sequence to avoid errors.
+```
+colcon build --packages-select pipe_msgs
+colcon build --packages-select pcl_ros
+colcon build --allow-overriding pcl_ros
+colcon build --packages-select pcl_conversions
+colcon build --allow-overriding pcl_conversions
+colcon build --packages-select find-pose
+colcon build --packages-select yolov3_ros
+. install/setup.bash
+```
+- The code should be ready to launch as explained in [How to run Live Detection?](#how-to-run-live-detection)
+This section explains what each module is responsible for.
+## perception_pcl
+This module is responsible for providing `pcl_conversions` and `pcl_ros` modules in `ros 2`.
+To build, run the following command:
+```
+cd ros2_ws/
+colcon build --packages-select perception_pcl
+. install/setup.bash
+```
+
+## pipe_msgs
+This module contains ros msgs for storing information about the detected object's bounding box.
+```
+cd ros2_ws/
+colcon build --packages-select pipe_msgs
+. install/setup.bash
+```
+To check if msgs are built properly, run following command
+```
+ros2 interface show pipe_msgs/msg/BoundingBox
+```
+The output will be:
+float64 probability
+int64 xmin
+int64 ymin
+int64 xmax
+int64 ymax
+int16 id
+string class_id
+
+
+## find-pose
+This ROS module is responsible for determining the position of the detected objects.
+The following input/ros topics are needed:
+- /rgb_img: RGB Image topic
+- /camera_info: Camera calibration parameters topic
+- /depth_img: Aligned depth image topic (aligned with rgb image)
+- /bboxes: Bounding box of each detected object. (Comes from yolov3 detection module)
+Output:
+- TF: Transform between camera_link and detected_object frame.
+How to build and run?
+This package is dependent on custom `pcl_conversion` and `pcl_ros` module. Make sure you have built those before building this package.
+```
+colcon build --packages-select find-pose
+. install/setup.bash
+ros2 launch find-pose find-pose-node.launch.py
+```
+All the topics can be remapped in the launch file.
+
+## yolov3_ros
+This ROS module is responsible for detecting the pipes from rgb image topic and also syncing the depth, rgb and camera_info topics.
+The following input/ros topics are needed:
+- /camera/color/image_raw: RGB Image topic
+- /camera/aligned_depth_to_color/image_raw: Aligned depth image topic (aligned with rgb image)
+- /camera/color/camera_info: Camera calibration parameters topic
+
+ROS Paramater Input:
+- best_weights: String that is the path to the best weights file of yolov3 detection
+Defaults: `'src/pipe_weights.pt'` inside `ros2_ws` folder
+
+The following are the output topics:
+- /detection_image: The RGB Image topic with bounding box drawn on it for visualization and debugging purpose
+- /bboxes: Bounding box of each detected object.
+- /rgb_img: Time Synced RGB Image topic
+- /camera_info: Time Synced Camera calibration parameters topic
+- /depth_img: Time Synced Aligned depth image topic (aligned with rgb image)
+
+How to build and run?
+```
+colcon build --packages-select yolov3_ros
+. install/setup.bash
+cd greenhouse/ros2_ws/
+ros2 launch yolov3_ros pipe_detection.launch.py
+```
+All the topics can be remapped in the launch file. The path to best_weights can also be changed inside the launch file.
+Launch file is stored in `yolov3_ros/launch/`.
+
+## yolov3
+This module contains code for yolov3. This method is being used to train the model to detect pipes in the greenhouse.
+- Ros bag was converted from ros 1 to ros 2 using rosbags module
+- Ros 2 bag was played, convert_2_img was used to subscribe to images and all the images were saved as .jpeg in a folder.
+- Images were labeled used labelImg.
+- Create a `custom-yolov3.yaml` file that has information about number of classes, location for labels,images.
+
+- Used google colab to run the yolov3 training.
+- Upload the label and images to drive and Mount the google drive in python notebook
+```
+from google.colab import drive
+drive.mount("/content/gdrive")
+```
+- Upload the yolov3 code and cd into the location of code
+```
+cd /content/gdrive/MyDrive/yolov3
+```
+- Run the training script
+```
+!python train.py --img 1280 --batch 16 --epochs 300 --data data/custom-yolov3.yaml --weights '' --cfg yolov3.yaml
+```
+- Important to note that we are not using any pre-trained weights.
+- Once training is finished, a `run` folder is created with exp number that stores the best weights (a .pt file).
+- This file is then used by detection to node to detect on new data. Update the image path and weights path to run detection.
+```
+!python detect.py --img 1280 --source ../pipe-dataset/validate/images/stereo_image103.jpeg --weights runs/train/exp14/weights/best.pt
+```
+- Once detect.py is finished, it create a new folder called `detect` inside `runs` folder that store the image with bounding box of detected object.
+More details: https://github.com/ultralytics/yolov3
+
+## convert_2_img
+This ROS module converts the rosbag data to images for yolo training purpose etc.
+- Make sure you have this module inside a ros workspace.
+- Create folder called `stereo` inside convert_2_img module.
+- Run following command to launch the node. Currently, this node listens for `/camera/color/image_raw` topic.
+```
+cd ros2_ws/src/convert_2_img
+python3 convert_2_img/convert_to_img.py
+```
+- Play the rosbag in another terminal
+```
+ros2 bag play bag/bag.db
+```
+- Once bag has finished playing, the images will be stored inside `stereo` folder.
+
+## labelImg
+This module is used to label images for yolo. The pre-defined custom classes file was changed to use new labels. This file is stored in `cd labelImg/data/predefined_classes.txt`
+To launch the gui, run
+```
+cd labelImg
+python3 labelImg.py 
+```
+More details: https://github.com/heartexlabs/labelImg
+
+## rosbags
+This module convert rosbags from ros 1 to ros 2. 
+```
+git clone https://gitlab.com/ternaris/rosbags.git
+cd rosbags
+python -m venv venv
+. venv/bin/activate
+
+pip install -r requirements-dev.txt
+pip install -e .
+
+rosbags-convert ../single_depth_color_640x480.bag
+```
+
+## flann_based
+Module that uses 3D model of the pipe to estimate pose. This method was not successful.
+
+## yolov7
+This module contains code for yolov7. This method was too heavy and didn't produce great results for detection.
+
+## darknet_ros2
+This module was provides interface to run neural network as rosnodes. The purpose was to use yolo model as ros node but this method was not successful
+
+## darknet_vendor
+This module was needed to build darknet_ros2.
+
+# How to run live detection?
+Once you have succefully built the ROS Modules specifically, `yolov3_ros` and `find-pose` along with dependencies like `pipe_msgs` and `perception_ros`, `perception_pcl`,you can do the following: 
+Assumption:
+You have the following required data in forms of ros topic:
+- /camera/color/image_raw: RGB Image topic
+- /camera/aligned_depth_to_color/image_raw: Aligned depth image topic (aligned with rgb image)
+- /camera/color/camera_info: Camera calibration parameters topic
+How do you get this data?
+- This data can come from a pre recorded Rosbag. If this is the case, do following:
+```
+ros2 bag play bag-folder/bag-name.db3
+```
+- This data can come directly from camera's (D455/ZED2I) ROS Node: Launch your node in a terminal. 
+- If your camera topics have different names than the default topic names mentioned above, update/remap ONLY the `pipe_detection.launch.py` script stored in `ros2_ws/src/yolov3_ros/launch` folder.
+- Once you have updated the launch file, build the code again using `colcon build --packages-select yolov3_ros` and proceedas mentioned below.
+
+Steps to run:
+- Open a terminal.
+- Run `$ cd greenhouse/ros2_ws/` and `$ . install/setup.bash`.
+- Launch node for object detection:
+```
+ros2 launch yolov3_ros pipe_detection.launch.py
+```
+- This node will output two topics: `/bboxes` and `/detection_image`.
+- Make sure that you run this launch file from `greenhouse/ros2_ws/` folder since the path of weights is relative (`/src/pipe_weights.pt`) inside the launch file.
+- Launch node for pose estimation:
+```
+ros2 launch find-pose find-pose-node.launch.py
+```
+- This node will output TF topics between `/camera_link` and `/${detected_object_name}`.
+- Open RVIZ by running `$ rviz2`. Change `fixed frame` from `map` to `camera_link`.
+    - Go to `Add` button. Under `By Display type`, select `tf`. Once tf is added, select required frames like `camera_link` and `l_trail` to see the tfs.
+    - To see the current image with detected object, Go to `Add` Button. Under `By Topic`, select topci called `detection_image`.
+    -You can add other topics as per the need and topic names. 
+- You can open launch files to update/remap topic name if different camera is being used. 
+- You can also update ros parameter from launch file. Currently, the `pipe_weights.pt` file is the one used. This file can be changed and you can update the parameter name `best_weights` inside the `pipe_detection.launch.py` file.

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flann_based/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/CsvReader.cpp

@@ -45,10 +45,14 @@ void CsvReader::readPLY(vector<Point3f> &list_vertex, vector<vector<int> > &list
              getline(liness, z);
 
              cv::Point3f tmp_p;
-             tmp_p.x = (float)StringToInt(x);
-             tmp_p.y = (float)StringToInt(y);
-             tmp_p.z = (float)StringToInt(z);
+            //  tmp_p.x = (float)StringToInt(x);
+            //  tmp_p.y = (float)StringToInt(y);
+            //  tmp_p.z = (float)StringToInt(z);
+             tmp_p.x = std::stod(x);
+             tmp_p.y = std::stod(y);
+             tmp_p.z = std::stod(z);
              list_vertex.push_back(tmp_p);
+             std::cout << "x: " << tmp_p.x << " y: " << tmp_p.y << " z: " << tmp_p.z << std::endl;
 
              count++;
              if(count == num_vertex)
@@ -70,6 +74,7 @@ void CsvReader::readPLY(vector<Point3f> &list_vertex, vector<vector<int> > &list
              tmp_triangle[0] = StringToInt(id0);
              tmp_triangle[1] = StringToInt(id1);
              tmp_triangle[2] = StringToInt(id2);
+             std::cout << "id0: " << tmp_triangle[0] << " id1: " << tmp_triangle[1] << " id2: " << tmp_triangle[2] << std::endl;
              list_triangles.push_back(tmp_triangle);
 
              count++;

flann_based/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/Mesh.cpp

@@ -75,4 +75,5 @@ void Mesh::load(const std::string& path)
     // Update mesh attributes
     num_vertices_ = (int)list_vertex_.size();
     num_triangles_ = (int)list_triangles_.size();
+    std::cout << "num_vertices_: " << num_vertices_ << std::endl;
 }

flann_based/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/Utils.cpp

@@ -19,6 +19,9 @@
 #include <opencv2/xfeatures2d.hpp>
 #endif
 
+#include <opencv2/line_descriptor.hpp>
+using namespace cv::line_descriptor;
+
 // For text
 const int fontFace = cv::FONT_ITALIC;
 const double fontScale = 0.75;
@@ -31,9 +34,9 @@ const int radius = 4;
 // Draw a text with the question point
 void drawQuestion(cv::Mat image, cv::Point3f point, cv::Scalar color)
 {
-    std::string x = IntToString((int)point.x);
-    std::string y = IntToString((int)point.y);
-    std::string z = IntToString((int)point.z);
+    std::string x = std::to_string(point.x);
+    std::string y = std::to_string(point.y);
+    std::string z = std::to_string(point.z);
 
     std::string text = " Where is point (" + x + ","  + y + "," + z + ") ?";
     cv::putText(image, text, cv::Point(25,50), fontFace, fontScale, color, thickness_font, 8);
@@ -71,7 +74,8 @@ void drawCounter(cv::Mat image, int n, int n_max, cv::Scalar color)
 {
     std::string n_str = IntToString(n);
     std::string n_max_str = IntToString(n_max);
-    std::string text = n_str + " of " + n_max_str + " points";
+    // std::string text = n_str + " of " + n_max_str + " points";
+    std::string text = "";
     cv::putText(image, text, cv::Point(500,50), fontFace, fontScale, color, thickness_font, 8);
 }
 

flann_based/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/main_detection.cpp

@@ -47,7 +47,7 @@ int main(int argc, char *argv[])
             "{inliers in        |30    | minimum inliers for Kalman update                                  }"
             "{method  pnp       |0     | PnP method: (0) ITERATIVE - (1) EPNP - (2) P3P - (3) DLS - (5) AP3P}"
             "{fast f            |true  | use of robust fast match                                           }"
-            "{feature           |ORB   | feature name (ORB, KAZE, AKAZE, BRISK, SIFT, SURF, BINBOOST, VGG)  }"
+            "{feature           |ORB   | feature name (Line, ORB, KAZE, AKAZE, BRISK, SIFT, SURF, BINBOOST, VGG)  }"
             "{FLANN             |false | use FLANN library for descriptors matching                         }"
             "{save              |      | path to the directory where to save the image results              }"
             "{displayFiltered   |false | display filtered pose (from Kalman filter)                         }"
@@ -62,14 +62,23 @@ int main(int argc, char *argv[])
     // samples::findFile("samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/Data/box.ply");         // mesh
 
     // Intrinsic camera parameters: UVC WEBCAM
-    double f = 55;                           // focal length in mm
-    double sx = 22.3, sy = 14.9;             // sensor size
-    double width = 640, height = 480;        // image size
+    // double f = 55;                           // focal length in mm
+    // double sx = 22.3, sy = 14.9;             // sensor size
+    // double width = 640, height = 480;        // image size
 
-    double params_WEBCAM[] = { width*f/sx,   // fx
-                               height*f/sy,  // fy
-                               width/2,      // cx
-                               height/2};    // cy
+    // double params_WEBCAM[] = { width*f/sx,   // fx
+    //                            height*f/sy,  // fy
+    //                            width/2,      // cx
+    //                            height/2};    // cy
+
+    // Intrinsic camera parameters: Greenhouse Cameras
+    const double f = 45; // focal length in mm
+    const double sx = 22.3, sy = 14.9;
+    const double width = 1280, height = 720;
+    const double params_WEBCAM[] = { 643.008 ,   // fx
+                                642.201,  // fy
+                                648.812 ,      // cx
+                                360.262};    // cy
 
     // Some basic colors
     Scalar red(0, 0, 255);
@@ -92,7 +101,7 @@ int main(int argc, char *argv[])
 
     // PnP parameters
     int pnpMethod = SOLVEPNP_ITERATIVE;
-    string featureName = "ORB";
+    string featureName = "Line";
     bool useFLANN = false;
 
     // Save results
@@ -293,6 +302,7 @@ int main(int argc, char *argv[])
 
             // -- Step 6: Set estimated projection matrix
             pnp_detection_est.set_P_matrix(rotation_estimated, translation_estimated);
+            std::cout << "Current Pose!! x: " << translation_estimated.at<double>(0,0) << " y: " << translation_estimated.at<double>(1,0) << " z: " << translation_estimated.at<double>(2,0) << std::endl;
         }
 
         // -- Step X: Draw pose and coordinate frame

flann_based/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/main_registration.cpp

@@ -13,15 +13,25 @@
 #include "ModelRegistration.h"
 #include "Utils.h"
 
+#include <opencv2/line_descriptor.hpp>
+
 using namespace cv;
 using namespace std;
-
+ using namespace cv::line_descriptor;
 /**  GLOBAL VARIABLES  **/
 
 // Boolean the know if the registration it's done
 bool end_registration = false;
-
 // Intrinsic camera parameters: UVC WEBCAM
+// const double f = 45; // focal length in mm
+// const double sx = 22.3, sy = 14.9;
+// const double width = 2592, height = 1944;
+// const double params_CANON[] = { width*f/sx,   // fx
+//                                 height*f/sy,  // fy
+//                                 width/2,      // cx
+//                                 height/2};    // cy
+
+// Intrinsic camera parameters: Greenhouse Cameras
 const double f = 45; // focal length in mm
 const double sx = 22.3, sy = 14.9;
 const double width = 1280, height = 720;
@@ -30,10 +40,16 @@ const double params_CANON[] = { 643.008 ,   // fx
                                 648.812 ,      // cx
                                 360.262};    // cy
 
+
 // Setup the points to register in the image
 // In the order of the *.ply file and starting at 1
-const int n = 8;
-const int pts[] = {1, 2, 3, 4, 5, 6, 7, 8}; // 3 -> 4
+// const int n = 8;
+// const int pts[] = {1, 2, 3, 4, 5, 6, 7, 8}; // 3 -> 4
+
+// Setup the points to register in the image
+// In the order of the *.ply file and starting at 1
+const int n = 16;
+const int pts[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; // 3 -> 4
 
 /*
  * CREATE MODEL REGISTRATION OBJECT
@@ -89,7 +105,7 @@ int main(int argc, char *argv[])
             "{model         |      | path to output yml model                                           }"
             "{mesh          |      | path to ply mesh                                                   }"
             "{keypoints k   |2000  | number of keypoints to detect (only for ORB)                       }"
-            "{feature       |ORB   | feature name (ORB, KAZE, AKAZE, BRISK, SIFT, SURF, BINBOOST, VGG)  }"
+            "{feature       |ORB   | feature name (Line, ORB, KAZE, AKAZE, BRISK, SIFT, SURF, BINBOOST, VGG)  }"
             ;
     CommandLineParser parser(argc, argv, keys);
 
@@ -129,7 +145,15 @@ int main(int argc, char *argv[])
     //Instantiate robust matcher: detector, extractor, matcher
     RobustMatcher rmatcher;
     Ptr<Feature2D> detector, descriptor;
-    createFeatures(featureName, numKeyPoints, detector, descriptor);
+    Ptr<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
+    if(featureName == "Line")
+    {
+        bd = BinaryDescriptor::createBinaryDescriptor();
+    }
+    else
+    {
+        createFeatures(featureName, numKeyPoints, detector, descriptor);
+    }
     rmatcher.setFeatureDetector(detector);
     rmatcher.setDescriptorExtractor(descriptor);
 
@@ -235,26 +259,110 @@ int main(int argc, char *argv[])
     vector<KeyPoint> keypoints_model;
     Mat descriptors;
 
-    // Compute keypoints and descriptors
-    rmatcher.computeKeyPoints(img_in, keypoints_model);
-    rmatcher.computeDescriptors(img_in, keypoints_model, descriptors);
     
-    // Check if keypoints are on the surface of the registration image and add to the model
-    for (unsigned int i = 0; i < keypoints_model.size(); ++i) {
-        Point2f point2d(keypoints_model[i].pt);
-        Point3f point3d;
-        bool on_surface = pnp_registration.backproject2DPoint(&mesh, point2d, point3d);
-        if (on_surface)
+    /* compute lines */
+    std::vector<KeyLine> keylines;
+
+    cv::Rect bbox(78, 152, 430, 550); // define the bounding box
+
+    cv::Mat roi = img_in(bbox); // extract the region of interest (ROI) inside the bounding box
+    imshow( "roi", roi );
+    waitKey();
+
+    if(featureName == "Line")
+    {
+        /* create a binary mask */
+        cv::Mat mask = Mat::ones( roi.size(), CV_8UC1 );
+        
+        bd->detect( roi, keylines, mask );
+        
+        bd->compute( roi, keylines, descriptors);
+    
+        cv::Mat output = img_in.clone();
+        /* draw lines extracted from octave 0 */
+        if( output.channels() == 1 )
+            cvtColor( output, output, COLOR_GRAY2BGR );
+        for ( size_t i = 0; i < keylines.size(); i++ )
         {
-            model.add_correspondence(point2d, point3d);
-            model.add_descriptor(descriptors.row(i));
-            model.add_keypoint(keypoints_model[i]);
+            KeyLine kl = keylines[i];
+            std::cout << "keyline length: " << kl.lineLength << std::endl;
+            if( kl.octave == 0 && kl.lineLength > 100)
+            {
+                /* get a random color */
+                int R = ( rand() % (int) ( 255 + 1 ) );
+                int G = ( rand() % (int) ( 255 + 1 ) );
+                int B = ( rand() % (int) ( 255 + 1 ) );
+            
+                /* get extremes of line */
+                Point pt1 = Point2f( kl.startPointX + bbox.x, kl.startPointY + bbox.y);
+                Point pt2 = Point2f( kl.endPointX + bbox.x, kl.endPointY + bbox.y);
+            
+                /* draw line */
+                line( output, pt1, pt2, Scalar( B, G, R ), 3 );
+            }
+        
         }
-        else
-        {
-            model.add_outlier(point2d);
+        
+        /* show lines on image */
+        imshow( "LSD lines", output );
+        imwrite( "lsd_lines.jpeg", output);
+        waitKey();
+        // Check if keypoints are on the surface of the registration image and add to the model
+        for (unsigned int i = 0; i < keylines.size(); ++i) {
+            KeyLine kl = keylines[i];
+            if( kl.octave == 0 && kl.lineLength > 100)
+            {
+                /* get extremes of line */
+                Point pt1 = Point2f( kl.startPointX + bbox.x, kl.startPointY + bbox.y);
+                Point pt2 = Point2f( kl.endPointX + bbox.x, kl.endPointY + bbox.y);
+                std::vector<Point2f> point2d;
+                // point2d.push_back(keylines[i].pt); // mid point of the line
+                point2d.push_back(pt1);
+                point2d.push_back(pt2);
+
+                for (unsigned int j = 0; j < point2d.size(); j++)
+                {
+                    Point3f point3d;
+                    bool on_surface = pnp_registration.backproject2DPoint(&mesh, point2d[j], point3d);
+                    if (on_surface)
+                    {
+                        model.add_correspondence(point2d[j], point3d);
+                        model.add_descriptor(descriptors.row(i));
+                        KeyPoint mid_point;
+                        mid_point.pt = point2d[j];
+                        model.add_keypoint(mid_point);
+                    }
+                    else
+                    {
+                        model.add_outlier(point2d[j]);
+                    }
+                }
+            }
         }
     }
+    else
+    {
+        // Compute keypoints and descriptors
+        rmatcher.computeKeyPoints(roi, keypoints_model);
+        rmatcher.computeDescriptors(roi, keypoints_model, descriptors);
+        // Check if keypoints are on the surface of the registration image and add to the model
+        for (unsigned int i = 0; i < keypoints_model.size(); ++i) {
+            Point2f point2d(keypoints_model[i].pt);
+            Point3f point3d;
+            bool on_surface = pnp_registration.backproject2DPoint(&mesh, point2d, point3d);
+            if (on_surface)
+            {
+                model.add_correspondence(point2d, point3d);
+                model.add_descriptor(descriptors.row(i));
+                model.add_keypoint(keypoints_model[i]);
+            }
+            else
+            {
+                model.add_outlier(point2d);
+            }
+        }
+
+    }
 
     model.set_trainingImagePath(img_path);
     // save the model into a *.yaml file

flann_based/sq_2.yml

@@ -0,0 +1,75 @@
+%YAML:1.0
+---
+points_3d: !!opencv-matrix
+   rows: 9
+   cols: 1
+   dt: "3f"
+   data: [ 5.56015968e-03, 1.42522305e-02, -2.98023224e-08,
+       3.35794687e-03, 1.43547952e-02, -2.98023224e-08, 4.15065885e-03,
+       1.42303258e-02, 0., 0., 4.48415509e-44, 0., 1.32452548e-02,
+       1.17547214e-02, 9.76203918e-01, -1.85193717e-02, 5.06480634e-01,
+       3.08125377e-01, 1.08953416e-02, 1.41046643e-02, 9.83510494e-01,
+       -2.08262205e-02, 5.04173756e-01, 3.06661725e-01, -2.39754319e-02,
+       5.01024604e-01, 1.64968729e-01 ]
+points_2d: !!opencv-matrix
+   rows: 9
+   cols: 1
+   dt: "2f"
+   data: [ 288., 6.45119995e+02, 2.88576019e+02, 6.48000061e+02,
+       2.88230438e+02, 6.46963318e+02, 8.65935547e+02, 4.77757568e+02,
+       5.25035645e+02, 3.18505035e+02, 8.68921570e+02, 4.77757568e+02,
+       5.26727722e+02, 3.18903198e+02, 8.67130005e+02, 4.80146362e+02,
+       9.17294556e+02, 5.51809998e+02 ]
+keypoints:
+   - [ 288., 6.4511999511718750e+02, 4.4640003204345703e+01,
+       2.0814511108398438e+02, 2.2853157133795321e-04, 2, -1 ]
+   - [ 2.8857601928710938e+02, 6.4800006103515625e+02,
+       5.3568004608154297e+01, 2.0699720764160156e+02,
+       2.5694066425785422e-04, 3, -1 ]
+   - [ 2.8823043823242188e+02, 6.4696331787109375e+02,
+       6.4281608581542969e+01, 2.0898962402343750e+02,
+       2.3757420422043651e-04, 4, -1 ]
+   - [ 8.6593554687500000e+02, 4.7775756835937500e+02,
+       7.7137939453125000e+01, 3.2710388183593750e+02,
+       2.0695610146503896e-04, 5, -1 ]
+   - [ 5.2503564453125000e+02, 3.1850503540039062e+02,
+       7.7137939453125000e+01, 1.6490226745605469e+01,
+       1.4561964781023562e-04, 5, -1 ]
+   - [ 8.6892156982421875e+02, 4.7775756835937500e+02,
+       9.2565528869628906e+01, 3.3250491333007812e+02,
+       1.7157701950054616e-04, 6, -1 ]
+   - [ 5.2672772216796875e+02, 3.1890319824218750e+02,
+       1.1107863616943359e+02, 4.8507041931152344e+01,
+       1.9299793348181993e-04, 7, -1 ]
+   - [ 8.6713000488281250e+02, 4.8014636230468750e+02,
+       1.1107863616943359e+02, 3.3898977661132812e+02,
+       1.3654734357260168e-04, 7, -1 ]
+   - [ 9.1729455566406250e+02, 5.5180999755859375e+02,
+       1.1107863616943359e+02, 3.0569372558593750e+02,
+       1.0311038204235956e-04, 7, -1 ]
+descriptors: !!opencv-matrix
+   rows: 9
+   cols: 32
+   dt: u
+   data: [ 59, 123, 112, 225, 229, 191, 248, 87, 15, 161, 190, 15, 143,
+       11, 86, 136, 238, 189, 223, 29, 156, 186, 81, 200, 215, 221, 154,
+       179, 143, 220, 219, 159, 186, 255, 112, 227, 245, 157, 254, 247,
+       13, 45, 254, 15, 175, 15, 22, 132, 238, 187, 159, 221, 222, 184,
+       80, 230, 255, 217, 142, 243, 39, 156, 216, 207, 42, 253, 114, 243,
+       53, 157, 252, 182, 173, 12, 190, 15, 174, 27, 118, 133, 238, 163,
+       223, 21, 238, 188, 80, 239, 255, 217, 158, 211, 6, 12, 217, 207,
+       9, 151, 175, 129, 244, 118, 189, 87, 60, 77, 222, 231, 156, 54,
+       162, 236, 109, 158, 191, 156, 149, 153, 49, 192, 255, 103, 190,
+       192, 138, 76, 165, 249, 90, 35, 19, 168, 59, 105, 25, 105, 173,
+       172, 139, 169, 206, 214, 104, 84, 246, 21, 237, 32, 16, 3, 163,
+       212, 144, 244, 43, 255, 34, 6, 37, 119, 19, 155, 167, 241, 246,
+       190, 60, 87, 53, 89, 222, 102, 185, 63, 180, 109, 230, 31, 63,
+       156, 147, 217, 33, 196, 127, 251, 246, 194, 166, 204, 181, 255,
+       67, 26, 205, 189, 114, 136, 105, 19, 62, 73, 82, 245, 189, 7, 146,
+       88, 6, 45, 63, 157, 135, 156, 197, 69, 196, 58, 186, 192, 44, 132,
+       1, 89, 3, 147, 63, 249, 182, 140, 104, 55, 63, 25, 82, 114, 176,
+       31, 180, 97, 230, 27, 63, 145, 211, 216, 67, 213, 12, 58, 254,
+       194, 148, 128, 241, 173, 92, 205, 106, 206, 157, 210, 254, 232,
+       196, 128, 207, 2, 107, 233, 61, 96, 120, 254, 139, 234, 72, 163,
+       28, 253, 251, 237, 143, 73, 51, 59, 124, 42 ]
+training_image_path: "/media/psf/freelancing/greenhouse/flann_based/stereo_image782.jpeg"

flann_based/sq_2_try.ply

@@ -0,0 +1,38 @@
+ply
+format ascii 1.0
+comment Created by Blender 3.4.1 - www.blender.org
+element vertex 16
+property float x
+property float y
+property float z
+element face 12
+property list uchar uint vertex_indices
+end_header
+0.000000 0.025000 0.000000
+0.000000 0.025000 1.000000
+0.025000 0.000000 1.000000
+0.025000 0.000000 0.000000
+0.000000 -0.025000 1.000000
+0.000000 -0.025000 0.000000
+-0.025000 0.000000 1.000000
+-0.025000 0.000000 0.000000
+0.000000 0.525000 0.000000
+0.000000 0.525000 1.000000
+0.025000 0.500000 1.000000
+0.025000 0.500000 0.000000
+0.000000 0.475000 1.000000
+0.000000 0.475000 0.000000
+-0.025000 0.500000 1.000000
+-0.025000 0.500000 0.000000
+4 0 1 2 3
+4 3 2 4 5
+4 2 1 6 4
+4 5 4 6 7
+4 7 6 1 0
+4 0 3 5 7
+4 8 9 10 11
+4 11 10 12 13
+4 10 9 14 12
+4 13 12 14 15
+4 15 14 9 8
+4 8 11 13 15

ros2_ws/src/convert_2_img/convert_2_img/convert_to_img.py

@@ -1,25 +1,61 @@
-import rclpy
-from sensor_msgs.msg import Image
+#!/usr/bin/env python
+import os
 import cv2
-import cv_bridge
+from cv_bridge import CvBridge
+from sensor_msgs.msg import Image
+import rclpy
+from rclpy.node import Node
+import threading
 
-count = 0
-def image_callback(data):
-    bridge = cv_bridge.CvBridge()
-    try:
-        cv_image = bridge.imgmsg_to_cv2(data, "bgr8")
-    except cv_bridge.CvBridgeError as e:
-        print(e)
+class ImageSaver(Node):
+    def __init__(self):
+        super().__init__('image_saver')
+        self.depth_sub = self.create_subscription(Image, '/camera/aligned_depth_to_color/image_raw', self.depth_callback, 1)
+        self.image_sub = self.create_subscription(Image, '/camera/color/image_raw', self.image_callback, 1)
+        self.cv_bridge = CvBridge()
+        self.depth_image = None
+        self.color_image = None
+        self.count_stereo = 0
+        self.count_depth = 0
 
-    global count
-    cv2.imwrite('stereo_image' + str(count)+ '.jpeg', cv_image)   
-    count = count + 1
+    def depth_callback(self, data):
+        print("depth cb")
+        self.depth_image = self.cv_bridge.imgmsg_to_cv2(data, desired_encoding="passthrough")
+        self.save_images('d')
 
-def main(args=None):
-    rclpy.init(args=args)
-    node = rclpy.create_node('stereo_image_subscriber')
-    sub = node.create_subscription(Image, '/camera/color/image_raw', image_callback, False)
-    rclpy.spin(node)
+
+    def image_callback(self, data):
+        print("image cb")
+        
+        self.color_image = self.cv_bridge.imgmsg_to_cv2(data)
+        self.save_images('s')
+
+
+    def save_images(self, img_type):
+        print('save images')
+
+        if img_type == 's':
+            filename_stereo = "/stereo/stereo_{}.jpg".format(self.count_stereo)
+            # import pdb; pdb.set_trace()
+            path_stereo = os.getcwd() + filename_stereo
+            cv2.imwrite(path_stereo, self.color_image)
+            self.get_logger().info("Saved image to {}".format(path_stereo))
+            self.count_stereo += 1
+
+        if img_type == 'd':
+            filename_depth = "/depth/depth_{}.jpg".format(self.count_depth)
+            path_depth = os.getcwd() + filename_depth
+            cv2.imwrite(path_depth, self.depth_image)
+            self.get_logger().info("Saved image to {}".format(path_depth))
+            self.count_depth += 1
 
 if __name__ == '__main__':
-    main()
+    rclpy.init()
+    image_saver = ImageSaver()
+    # rate = image_saver.create_rate(1)  # 10 Hz
+    rclpy.spin(image_saver)
+    # while rclpy.ok():
+    #     image_saver.save_images()
+    #     # rate.sleep()
+    # image_saver.destroy_node()
+    # rclpy.shutdown()

ros2_ws/src/darknet_ros2/CMakeLists.txt

@@ -0,0 +1,75 @@
+cmake_minimum_required(VERSION 3.10)
+project(openrobotics_darknet_ros)
+
+# Default to C++14
+if(NOT CMAKE_CXX_STANDARD)
+  set(CMAKE_CXX_STANDARD 14)
+endif()
+
+if(NOT WIN32)
+  add_compile_options(-Wall -Wextra -Wpedantic)
+endif()
+
+find_package(ament_cmake REQUIRED)
+find_package(cv_bridge REQUIRED)
+find_package(darknet_vendor REQUIRED)
+find_package(rclcpp REQUIRED)
+find_package(rclcpp_components REQUIRED)
+find_package(sensor_msgs REQUIRED)
+find_package(vision_msgs REQUIRED)
+
+add_library(openrobotics_darknet_ros SHARED
+  src/detector_network.cpp
+  src/parse.cpp)
+target_include_directories(openrobotics_darknet_ros PUBLIC include)
+target_compile_definitions(openrobotics_darknet_ros PRIVATE "DARKNET_ROS_BUILDING_DLL")
+ament_target_dependencies(openrobotics_darknet_ros
+  cv_bridge
+  darknet_vendor
+  sensor_msgs
+  vision_msgs)
+
+add_library(detector_node SHARED
+  src/detector_node.cpp)
+target_compile_definitions(detector_node PRIVATE "DARKNET_ROS_NODE_BUILDING_DLL")
+ament_target_dependencies(detector_node PUBLIC
+  "rclcpp"
+  "rclcpp_components")
+target_link_libraries(detector_node PUBLIC openrobotics_darknet_ros)
+rclcpp_components_register_nodes(detector_node "openrobotics::darknet_ros::DetectorNode")
+
+add_executable(detector_node_main
+  src/detector_node_main.cpp)
+target_link_libraries(detector_node_main detector_node)
+set_target_properties(detector_node_main PROPERTIES OUTPUT_NAME "detector_node")
+
+if(BUILD_TESTING)
+  find_package(ament_lint_auto REQUIRED)
+  ament_lint_auto_find_test_dependencies()
+
+  ament_add_gtest(test_parser test/test_parser.cpp
+    WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/test/")
+  target_link_libraries(test_parser openrobotics_darknet_ros)
+
+  ament_add_gtest(test_detector_network test/test_detector_network.cpp)
+  target_link_libraries(test_detector_network openrobotics_darknet_ros)
+endif()
+
+ament_export_libraries(openrobotics_darknet_ros)
+
+install(TARGETS openrobotics_darknet_ros
+  ARCHIVE DESTINATION lib
+  LIBRARY DESTINATION lib
+  RUNTIME DESTINATION bin)
+
+install(TARGETS detector_node
+  ARCHIVE DESTINATION lib
+  LIBRARY DESTINATION lib
+  RUNTIME DESTINATION bin)
+
+install(TARGETS detector_node_main
+  DESTINATION lib/${PROJECT_NAME})
+
+install(DIRECTORY include/ DESTINATION include)
+
+ament_package()

ros2_ws/src/darknet_ros2/CONTRIBUTING.md

@@ -0,0 +1,18 @@
+Any contribution that you make to this repository will
+be under the Apache 2 License, as dictated by that
+[license](http://www.apache.org/licenses/LICENSE-2.0.html):
+
+~~~
+5. Submission of Contributions. Unless You explicitly state otherwise,
+   any Contribution intentionally submitted for inclusion in the Work
+   by You to the Licensor shall be under the terms and conditions of
+   this License, without any additional terms or conditions.
+   Notwithstanding the above, nothing herein shall supersede or modify
+   the terms of any separate license agreement you may have executed
+   with Licensor regarding such Contributions.
+~~~
+
+Contributors must sign-off each commit by adding a `Signed-off-by: ...`
+line to commit messages to certify that they have the right to submit
+the code they are contributing to the project according to the
+[Developer Certificate of Origin (DCO)](https://developercertificate.org/).

ros2_ws/src/darknet_ros2/LICENSE

@@ -0,0 +1,202 @@
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
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+
+      "Legal Entity" shall mean the union of the acting entity and all
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+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
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+   6. Trademarks. This License does not grant permission to use the trade
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+   limitations under the License.

ros2_ws/src/darknet_ros2/README.md

@@ -0,0 +1,58 @@
+# Open Robotics Darknet ROS
+
+This is a ROS 2 wrapper around [darknet](https://pjreddie.com/darknet), an open source neural network framework.
+
+![Example image with bounding boxes created using darknet and the yolov3-tiny network](doc/example_darknet_yolov3-tiny.png)
+
+## DetectorNode
+
+This node can run object detectors like [YOLOv3](https://pjreddie.com/darknet/yolo/) on images.
+
+### Subscribers
+
+* `~/images` (type `sensor_msgs/msg/Image`) - Input mages to feed to the detector
+
+### Publishers
+
+* `~/detections` (type `vision_msgs/msg/Detection2DArray`) - Objects detected in an image (if any)
+
+### Parameters
+
+* `network.config` - a path to a file describing a darknet detector network
+* `network.weights` - a path to a file with weights for the given network
+* `network.class_names` - a path to a file with names of classes the network can detect (1 per line)
+* `detection.threshold` - Minimum probability of a detection to be published
+* `detection.nms_threshold` - Non-maximal Suppression threshold - controls filtering of overlapping boxes
+
+### Example
+
+Download `YOLOv3-tiny`.
+
+```
+wget https://raw.githubusercontent.com/pjreddie/darknet/f86901f6177dfc6116360a13cc06ab680e0c86b0/cfg/yolov3-tiny.cfg
+wget https://pjreddie.com/media/files/yolov3-tiny.weights
+wget https://raw.githubusercontent.com/pjreddie/darknet/c6afc7ff1499fbbe64069e1843d7929bd7ae2eaa/data/coco.names
+```
+
+Save the following as `detector_node_params.yaml`
+
+```yaml
+/**:
+  ros__parameters:
+    network:
+      config: "./yolov3-tiny.cfg"
+      weights: "./yolov3-tiny.weights"
+      class_names: "./coco.names"
+    detection:
+      threshold: 0.25
+      nms_threshold: 0.45
+```
+
+Then run the node.
+
+```
+ros2 run openrobotics_darknet_ros detector_node __params:=detector_node_params.yaml
+```
+
+The node is now running.
+Publish images on `~/images` to get the node to detect objects.

ros2_ws/src/darknet_ros2/include/openrobotics_darknet_ros/detector_network.hpp

@@ -0,0 +1,72 @@
+// Copyright 2019 Open Source Robotics Foundation, Inc.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef OPENROBOTICS_DARKNET_ROS__DETECTOR_NETWORK_HPP_
+#define OPENROBOTICS_DARKNET_ROS__DETECTOR_NETWORK_HPP_
+
+#include <memory>
+#include <string>
+#include <vector>
+
+#include "openrobotics_darknet_ros/visibility.hpp"
+#include "sensor_msgs/msg/image.hpp"
+#include "vision_msgs/msg/detection2_d_array.hpp"
+#include "vision_msgs/msg/object_hypothesis_with_pose.hpp"
+
+namespace openrobotics
+{
+namespace darknet_ros
+{
+// Forward declaration
+class DetectorNetworkPrivate;
+
+class DetectorNetwork
+{
+public:
+  /// \brief load a network from disk
+  /// \param[in] config_file Path to a file describing the network
+  /// \param[in] weights_file Path to a file containing the network's weights
+  /// \param[in] classes Ordered list of class names the network can predict
+  DARKNET_ROS_PUBLIC
+  DetectorNetwork(
+    const std::string & config_file,
+    const std::string & weights_file,
+    const std::vector<std::string> & classes);
+
+  DARKNET_ROS_PUBLIC
+  ~DetectorNetwork();
+
+  /// \brief Detect objects in image
+  /// \param[in] image An image to analyze
+  /// \param[in] threshold How confident the network must be to detect something [0.0, 1.0]
+  /// \param[in] nms_threshold Non-Maximal Suppression threhsold [0.0, 1.0].
+  ///   When the intersection over union (iou) of two bounding boxes is greater than nms_threshold,
+  ///   the box with the lower objectness score is discarded.
+  /// \param[out] output_detections Things detected in the image (does not set source_img)
+  /// \return number of objects detected
+  DARKNET_ROS_PUBLIC
+  size_t
+  detect(
+    const sensor_msgs::msg::Image & image,
+    double threshold,
+    double nms_threshold,
+    vision_msgs::msg::Detection2DArray * output_detections);
+
+private:
+  std::unique_ptr<DetectorNetworkPrivate> impl_;
+};
+}  // namespace darknet_ros
+}  // namespace openrobotics
+
+#endif  // OPENROBOTICS_DARKNET_ROS__DETECTOR_NETWORK_HPP_

ros2_ws/src/darknet_ros2/include/openrobotics_darknet_ros/detector_node.hpp

@@ -0,0 +1,49 @@
+// Copyright 2019 Open Source Robotics Foundation, Inc.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef OPENROBOTICS_DARKNET_ROS__DETECTOR_NODE_HPP_
+#define OPENROBOTICS_DARKNET_ROS__DETECTOR_NODE_HPP_
+
+#include <memory>
+#include <string>
+
+#include "rclcpp/node.hpp"
+#include "openrobotics_darknet_ros/visibility_node.hpp"
+
+
+namespace openrobotics
+{
+namespace darknet_ros
+{
+// Forward declaration
+class DetectorNodePrivate;
+
+class DetectorNode : public rclcpp::Node
+{
+public:
+  /// \brief Create a node that uses ROS parameters to get the network
+  DARKNET_ROS_NODE_PUBLIC
+  explicit DetectorNode(rclcpp::NodeOptions options);
+
+  DARKNET_ROS_NODE_PUBLIC
+  virtual ~DetectorNode();
+
+private:
+  std::unique_ptr<DetectorNodePrivate> impl_;
+  OnSetParametersCallbackHandle::SharedPtr callback_handle_;
+};
+}  // namespace darknet_ros
+}  // namespace openrobotics
+
+#endif  // OPENROBOTICS_DARKNET_ROS__DETECTOR_NODE_HPP_

ros2_ws/src/darknet_ros2/include/openrobotics_darknet_ros/parse.hpp

@@ -0,0 +1,36 @@
+// Copyright 2019 Open Source Robotics Foundation, Inc.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#ifndef OPENROBOTICS_DARKNET_ROS__PARSE_HPP_
+#define OPENROBOTICS_DARKNET_ROS__PARSE_HPP_
+
+#include <string>
+#include <vector>
+
+#include "openrobotics_darknet_ros/visibility.hpp"
+
+namespace openrobotics
+{
+namespace darknet_ros
+{
+/// \brief Read file containing class names, one per line
+/// \param[in] filename a path to a file containing classes a network can detect
+/// \return a container with all of the class names detected
+DARKNET_ROS_PUBLIC
+std::vector<std::string>
+parse_class_names(const std::string & filename);
+}  // namespace darknet_ros
+}  // namespace openrobotics
+
+#endif  // OPENROBOTICS_DARKNET_ROS__PARSE_HPP_

ros2_ws/src/darknet_ros2/include/openrobotics_darknet_ros/visibility.hpp

@@ -0,0 +1,56 @@
+// Copyright 2019 Open Source Robotics Foundation, Inc.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/* This header must be included by all rclcpp headers which declare symbols
+ * which are defined in the rclcpp library. When not building the rclcpp
+ * library, i.e. when using the headers in other package's code, the contents
+ * of this header change the visibility of certain symbols which the rclcpp
+ * library cannot have, but the consuming code must have inorder to link.
+ */
+
+#ifndef OPENROBOTICS_DARKNET_ROS__VISIBILITY_HPP_
+#define OPENROBOTICS_DARKNET_ROS__VISIBILITY_HPP_
+
+// This logic was borrowed (then namespaced) from the examples on the gcc wiki:
+//     https://gcc.gnu.org/wiki/Visibility
+
+#if defined _WIN32 || defined __CYGWIN__
+  #ifdef __GNUC__
+    #define DARKNET_ROS_EXPORT __attribute__ ((dllexport))
+    #define DARKNET_ROS_IMPORT __attribute__ ((dllimport))
+  #else
+    #define DARKNET_ROS_EXPORT __declspec(dllexport)
+    #define DARKNET_ROS_IMPORT __declspec(dllimport)
+  #endif
+  #ifdef DARKNET_ROS_BUILDING_LIBRARY
+    #define DARKNET_ROS_PUBLIC DARKNET_ROS_EXPORT
+  #else
+    #define DARKNET_ROS_PUBLIC DARKNET_ROS_IMPORT
+  #endif
+  #define DARKNET_ROS_PUBLIC_TYPE DARKNET_ROS_PUBLIC
+  #define DARKNET_ROS_LOCAL
+#else
+  #define DARKNET_ROS_EXPORT __attribute__ ((visibility("default")))
+  #define DARKNET_ROS_IMPORT
+  #if __GNUC__ >= 4
+    #define DARKNET_ROS_PUBLIC __attribute__ ((visibility("default")))
+    #define DARKNET_ROS_LOCAL  __attribute__ ((visibility("hidden")))
+  #else
+    #define DARKNET_ROS_PUBLIC
+    #define DARKNET_ROS_LOCAL
+  #endif
+  #define DARKNET_ROS_PUBLIC_TYPE
+#endif
+
+#endif  // OPENROBOTICS_DARKNET_ROS__VISIBILITY_HPP_

ros2_ws/src/darknet_ros2/include/openrobotics_darknet_ros/visibility_node.hpp

@@ -0,0 +1,56 @@
+// Copyright 2019 Open Source Robotics Foundation, Inc.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+/* This header must be included by all rclcpp headers which declare symbols
+ * which are defined in the rclcpp library. When not building the rclcpp
+ * library, i.e. when using the headers in other package's code, the contents
+ * of this header change the visibility of certain symbols which the rclcpp
+ * library cannot have, but the consuming code must have inorder to link.
+ */
+
+#ifndef OPENROBOTICS_DARKNET_ROS__VISIBILITY_NODE_HPP_
+#define OPENROBOTICS_DARKNET_ROS__VISIBILITY_NODE_HPP_
+
+// This logic was borrowed (then namespaced) from the examples on the gcc wiki:
+//     https://gcc.gnu.org/wiki/Visibility
+
+#if defined _WIN32 || defined __CYGWIN__
+  #ifdef __GNUC__
+    #define DARKNET_ROS_NODE_EXPORT __attribute__ ((dllexport))
+    #define DARKNET_ROS_NODE_IMPORT __attribute__ ((dllimport))
+  #else
+    #define DARKNET_ROS_NODE_EXPORT __declspec(dllexport)
+    #define DARKNET_ROS_NODE_IMPORT __declspec(dllimport)
+  #endif
+  #ifdef DARKNET_ROS_NODE_BUILDING_LIBRARY
+    #define DARKNET_ROS_NODE_PUBLIC DARKNET_ROS_NODE_EXPORT
+  #else
+    #define DARKNET_ROS_NODE_PUBLIC DARKNET_ROS_NODE_IMPORT
+  #endif
+  #define DARKNET_ROS_NODE_PUBLIC_TYPE DARKNET_ROS_NODE_PUBLIC
+  #define DARKNET_ROS_NODE_LOCAL
+#else
+  #define DARKNET_ROS_NODE_EXPORT __attribute__ ((visibility("default")))
+  #define DARKNET_ROS_NODE_IMPORT
+  #if __GNUC__ >= 4
+    #define DARKNET_ROS_NODE_PUBLIC __attribute__ ((visibility("default")))
+    #define DARKNET_ROS_NODE_LOCAL  __attribute__ ((visibility("hidden")))
+  #else
+    #define DARKNET_ROS_NODE_PUBLIC
+    #define DARKNET_ROS_NODE_LOCAL
+  #endif
+  #define DARKNET_ROS_NODE_PUBLIC_TYPE
+#endif
+
+#endif  // OPENROBOTICS_DARKNET_ROS__VISIBILITY_NODE_HPP_

ros2_ws/src/darknet_ros2/package.xml

@@ -0,0 +1,29 @@
+<?xml version="1.0"?>
+<package format="2">
+  <name>openrobotics_darknet_ros</name>
+  <version>0.1.0</version>
+  <description>ROS wrapper around darknet, an open source neural network framework.</description>
+
+  <author email="sloretz@openrobotics.org">Shane Loretz</author>
+  <maintainer email="sloretz@openrobotics.org">Shane Loretz</maintainer>
+
+  <license>Apache License 2.0</license>
+
+  <buildtool_depend>ament_cmake</buildtool_depend>
+
+  <depend>cv_bridge</depend>
+  <depend>darknet_vendor</depend>
+  <depend>rclcpp</depend>
+  <depend>rclcpp_components</depend>
+  <depend>sensor_msgs</depend>
+  <depend>vision_msgs</depend>
+
+  <test_depend>ament_cmake_gtest</test_depend>
+  <test_depend>ament_lint_auto</test_depend>
+  <test_depend>ament_lint_common</test_depend>
+
+  <export>
+    <build_type>ament_cmake</build_type>
+  </export>
+</package>
+

ros2_ws/src/darknet_ros2/src/darknet_detections.hpp

@@ -0,0 +1,44 @@
+// Copyright 2019 Open Source Robotics Foundation, Inc.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+//
+#ifndef DARKNET_DETECTIONS_HPP_
+#define DARKNET_DETECTIONS_HPP_
+
+#include <darknet_vendor/darknet_vendor.h>
+
+namespace openrobotics
+{
+namespace darknet_ros
+{
+/// \brief RAII wrapper around darknet type `detections`
+class DarknetDetections
+{
+public:
+  /// \brief Steal ownership of detections
+  DarknetDetections(detection * darknet_detections, size_t num_detections)
+  : detections_(darknet_detections), num_detections_(num_detections)
+  {
+  }
+
+  ~DarknetDetections()
+  {
+    free_detections(detections_, num_detections_);
+  }
+
+  detection * detections_;
+  const size_t num_detections_ = 0;
+};
+}  // namespace darknet_ros
+}  // namespace openrobotics
+#endif  // DARKNET_DETECTIONS_HPP_