├── .github ├── ISSUE_TEMPLATE │ ├── bug_report.md │ └── feature_request.md └── PULL_REQUEST_TEMPLATE.md ├── .gitignore ├── README.md ├── assets ├── config.png ├── gazebo.png └── rqt_yolov8.png ├── contributing.md ├── detector_plugin.md ├── object_detection ├── config │ └── params.yaml ├── launch │ └── object_detection.launch.py ├── object_detection │ ├── DetectorBase.py │ ├── Detectors │ │ ├── EfficientDet.py │ │ ├── RetinaNet.py │ │ ├── YOLOv5.py │ │ ├── YOLOv8.py │ │ └── __init__.py │ ├── ObjectDetection.py │ └── __init__.py ├── package.xml ├── requirements.txt ├── resource │ └── object_detection ├── setup.cfg ├── setup.py └── test │ ├── test_copyright.py │ ├── test_flake8.py │ └── test_pep257.py └── perception_bringup ├── CMakeLists.txt ├── config └── bridge.yaml ├── env_hooks └── perception_bringup.sh.in ├── launch └── playground.launch.py ├── models ├── adhesive │ ├── meshes │ │ └── adhesive.dae │ ├── model.config │ └── model.sdf ├── first_aid │ ├── model.config │ └── model.sdf ├── medicine │ ├── materials │ │ └── textures │ │ │ └── texture.png │ ├── meshes │ │ ├── model.mtl │ │ └── model.obj │ ├── model.config │ ├── model.sdf │ └── thumbnails │ │ ├── 0.jpg │ │ ├── 1.jpg │ │ ├── 2.jpg │ │ ├── 3.jpg │ │ └── 4.jpg ├── plastic_cup │ ├── meshes │ │ └── plastic_cup.dae │ ├── model.config │ ├── model.sdf │ └── thumbnails │ │ ├── 1.png │ │ ├── 2.png │ │ ├── 3.png │ │ ├── 4.png │ │ └── 5.png └── table │ ├── Table_Diffuse.jpg │ ├── model-1_2.sdf │ ├── model-1_3.sdf │ ├── model-1_4.sdf │ ├── model.config │ ├── model.sdf │ └── thumbnails │ ├── 1.png │ ├── 2.png │ ├── 3.png │ ├── 4.png │ └── 5.png ├── package.xml └── worlds └── playground.sdf /.github/ISSUE_TEMPLATE/bug_report.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Bug report 3 | about: Create a report to help us improve 4 | title: '' 5 | labels: '' 6 | assignees: '' 7 | 8 | --- 9 | 10 | --- 11 | name: Bug report 12 | about: Report a bug 13 | labels: bug 14 | --- 15 | 16 | ## Environment 🖥️ 17 | * OS Version: 18 | 19 | * Python Version: 20 | 21 | * Do you have a dedicated GPU ? 22 | 23 | * NVIDIA Driver Version (if applicable): 24 | 25 | * CUDA Version (if applicable): 26 | 27 | * Are you running this project natively or using Docker ? 28 | 29 | * Is the issue present in the main branch or any of the development branch ? 30 | 31 |
32 | 33 | ``` 34 | # paste log here 35 | ``` 36 | 37 |
38 | 39 | ## Description 📖 40 | * Expected behavior: 41 | * Actual behavior: 42 | 43 | ## Steps to reproduce 👀 44 | 45 | 46 | 1. 47 | 2. 48 | 3. 49 | 50 | ## Output 💥 51 | 53 | -------------------------------------------------------------------------------- /.github/ISSUE_TEMPLATE/feature_request.md: -------------------------------------------------------------------------------- 1 | --- 2 | name: Feature Request Template 3 | about: "For feature requests. Please search for existing issues first. Also see CONTRIBUTING." 4 | 5 | --- 6 | 7 | **Please Describe The Problem To Be Solved** 8 | (Replace This Text: Please present a concise description of the problem to be addressed by this feature request. Please be clear what parts of the problem are considered to be in-scope and out-of-scope.) 9 | 10 | **(Optional): Suggest A Solution** 11 | (Replace This Text: A concise description of your preferred solution. Things to address include: 12 | * Details of the technical implementation 13 | * Tradeoffs made in design decisions 14 | * Caveats and considerations for the future 15 | 16 | If there are multiple solutions, please present each one separately. Save comparisons for the very end.) -------------------------------------------------------------------------------- /.github/PULL_REQUEST_TEMPLATE.md: -------------------------------------------------------------------------------- 1 | --- 2 | Name: Pull Request template 3 | About: Mention details about your Pull Request 4 | Title: '' 5 | Labels: '' 6 | Assignees: '' 7 | 8 | --- 9 | 10 | # Description 📖 11 | 12 | Please include a summary of the changes and the related issue. Please also include relevant motivation and context. List any dependencies that are required for this change. 13 | 14 | Fixes # (issue) 15 | 16 | ## Type of change 📜 17 | 18 | Please delete options that are not relevant. 19 | 20 | - [ ] Bug fix (non-breaking change which fixes an issue) 21 | - [ ] New feature (non-breaking change which adds functionality) 22 | - [ ] Breaking change (fix or feature that would cause existing functionality to not work as expected) 23 | - [ ] This change requires a documentation update 24 | - [ ] This change requires testing before it can be merged into the main/development branch 25 | 26 | # How Has This Been Tested? 👀 27 | 28 | Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. Please also list any relevant details for your test configuration 29 | 30 | - [ ] Test A 31 | - [ ] Test B 32 | 33 | **Test Configuration** 🖥️ 34 | * OS version: 35 | * Hardware: 36 | * NVIDIA Driver: 37 | * CUDA Version: 38 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | #Ignore the test node 2 | /object_detection/object_detection/test_node.py 3 | 4 | #Ignore pycache dirs 5 | object_detection/object_detection/Detectors/__pycache__/ 6 | object_detection/object_detection/__pycache__/ 7 | 8 | #Ignore .vscode dir 9 | .vscode -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 10 | 11 | 12 | 13 | 20 | [![Contributors][contributors-shield]][contributors-url] 21 | [![Forks][forks-shield]][forks-url] 22 | [![Stargazers][stars-shield]][stars-url] 23 | [![Issues][issues-shield]][issues-url] 24 | [![LinkedIn][linkedin-shield]][linkedin-url] 25 | 26 | 27 | 28 | 29 |
30 |
31 | 32 | Logo 33 | 34 | 35 |

ROS-Perception-Pipeline

36 | 37 |

38 | This repo is focused on developing a ROS 2 based perception-pipeline. 39 | If you’re interested in helping to improve our Project, find out how to contribute. 40 |
41 |
42 |
43 | Report Bug 44 | · 45 | Request Feature 46 |

47 |
48 | 49 | 50 | 51 | 52 |
53 | Table of Contents 54 |
    55 |
  1. 56 | About The Project 57 | 60 |
    2. 61 |
  2. 62 | Getting Started 63 | 67 |
    3. 68 |
  3. Usage 69 | 72 |
    4. 73 | 74 |
  4. Contributing
    5. 75 |
  5. License
    6. 76 |
  6. Contact
    7. 77 |
  7. Acknowledgments
    8. 78 |
79 |
80 | 81 | 82 | 83 | 84 | ## About The Project 85 | 86 | Our aim is to build a one-stop solution to all the problems related to Robotics-Perception. We are creating a plug-and-play ROS 2 based perception-pipeline which can be customized 87 | for user-specific custom tasks in the blink of an eye. We are in the process of creating different components for tasks like Object Detection, Image Pre-Processing, Image Segmentation etc. 88 | These components can be stitched together to make a custom pipeline for any use-case, just like how we play with LEGO bricks. 89 | 90 |

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91 | 92 | ### Built With 93 | 94 | * [![ROS 2](https://img.shields.io/badge/ros-%230A0FF9.svg?style=for-the-badge&logo=ros&logoColor=white)](https://www.sphinx-docs.org) 95 | * [![OpenCV](https://img.shields.io/badge/opencv-%23white.svg?style=for-the-badge&logo=opencv&logoColor=white)](https://opencv.org/) 96 | * [![Ubuntu](https://img.shields.io/badge/Ubuntu-E95420?style=for-the-badge&logo=ubuntu&logoColor=white)](https://ubuntu.com/) 97 | * [![Python](https://img.shields.io/badge/Python-3776AB?style=for-the-badge&logo=python&logoColor=white)](https://www.python.org/) 98 | 99 |

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100 | 101 | 102 | ## Getting Started 103 | 104 | Follow these steps to setup this project on your systm 105 | 106 | ### Prerequisites 107 | 108 | Follow these steps to install ROS Humble and OpenCV 109 | * ROS Humble 110 | Refer to the official [ROS 2 installation guide](https://docs.ros.org/en/humble/Installation.html) 111 | 112 | * OpenCV 113 | ```bash 114 | pip install opencv-contrib-python 115 | ``` 116 | 117 | ### Installation 118 | 119 | 1. Make a new workspace 120 | ```bash 121 | mkdir -p percep_ws/src 122 | ``` 123 | 124 | 2. Clone the ROS-Perception-Pipeline repository 125 | 126 | Now go ahead and clone this repository inside the "src" folder of the workspace you just created. 127 | 128 | ```bash 129 | cd percep_ws/src 130 | git clone git@github.com:atom-robotics-lab/ros-perception-pipeline.git 131 | ``` 132 | 133 | 3. Compile the package 134 | 135 | Follow this execution to compile your ROS 2 package 136 | 137 | ```bash 138 | colcon build --symlink-install 139 | ``` 140 | 141 | 4. Source your workspace 142 | ```bash 143 | source install/local_setup.bash 144 | ``` 145 | 146 | 147 | 148 | 149 | ## Usage 150 | 151 |
152 | 153 | ### 1. Launch the Playground simulation 154 | We have made a demo playground world to test our pipeline. To launch this world, follow the steps 155 | given below 156 | 157 | ```bash 158 | ros2 launch perception_bringup playground.launch.py 159 | ``` 160 | The above command will launch the playground world as shown below : 161 | 162 | 163 |
164 | 165 | Don't forget to click on the **play** button on the bottom left corner of the Ignition Gazebo window 166 | 167 |
168 | 169 | ### 2. Launch the Object Detection node 170 |
171 | 172 | Use the pip install command as shown below to install the required packages. 173 | ```bash 174 | pip install -r src/ros-perception-pipeline/object_detection/requirements.txt 175 | ``` 176 | 177 | Use the command given below to run the ObjectDetection node. Remember to change the path of the object_detection.yaml 178 | file according to your present working directory 179 | 180 | ```bash 181 | ros2 run object_detection ObjectDetection --ros-args --params-file src/ros-perception-pipeline/object_detection/config/object_detection.yaml 182 | ``` 183 | 184 | ### 3. Changing the Detector 185 | 186 | To change the object detector being used, you can change the parameters inside the object_detection.yaml file location inside 187 | the **config** folder. 188 | 189 | 190 |
191 | 192 | ## Testing 193 | 194 | Now to see the inference results, open a new terminal and enter the given command 195 | 196 | ```bash 197 | ros2 run rqt_image_view rqt_image_view 198 | ``` 199 | 200 | 201 |
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203 | 204 | 205 | 219 | 220 | 221 | 222 | 223 | ## Contributing 224 | 225 | Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are **greatly appreciated**. 226 | 227 | 1. Fork the Project 228 | 2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`) 229 | 3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`) 230 | 4. Push to the Branch (`git push origin feature/AmazingFeature`) 231 | 5. Open a Pull Request 232 | 233 |

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234 | 235 | 236 | ## License 237 | 238 | Distributed under the MIT License. See `LICENSE` for more information. 239 | 240 |
241 | 242 | 243 | ## Contact 244 | 245 | Our Socials - [Linktree](https://linktr.ee/atomlabs) - atom@inventati.org 246 | 247 |

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248 | 249 | 250 | ## Acknowledgments 251 | 252 | * [Our wiki](https://atom-robotics-lab.github.io/wiki) 253 | * [ROS Official Documentation](http://wiki.ros.org/Documentation) 254 | * [Opencv Official Documentation](https://docs.opencv.org/4.x/) 255 | * [Rviz Documentation](http://wiki.ros.org/rviz) 256 | * [Gazebo Tutorials](https://classic.gazebosim.org/tutorials) 257 | * [Ubuntu Installation guide](https://ubuntu.com/tutorials/install-ubuntu-desktop#1-overview) 258 | 259 |

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260 | 261 | 262 | 263 | 264 | 265 | [contributors-shield]: https://img.shields.io/github/contributors/atom-robotics-lab/ros-perception-pipeline.svg?style=for-the-badge 266 | [contributors-url]: https://github.com/atom-robotics-lab/ros-perception-pipeline/graphs/contributors 267 | [forks-shield]: https://img.shields.io/github/forks/atom-robotics-lab/ros-perception-pipeline.svg?style=for-the-badge 268 | [forks-url]: https://github.com/atom-robotics-lab/ros-perception-pipeline/network/members 269 | [stars-shield]: https://img.shields.io/github/stars/atom-robotics-lab/ros-perception-pipeline.svg?style=for-the-badge 270 | [stars-url]: https://github.com/atom-robotics-lab/ros-perception-pipeline/stargazers 271 | [issues-shield]: https://img.shields.io/github/issues/atom-robotics-lab/ros-perception-pipeline.svg?style=for-the-badge 272 | [issues-url]: https://github.com/atom-robotics-lab/ros-perception-pipeline/issues 273 | [linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555 274 | [linkedin-url]: https://www.linkedin.com/company/a-t-o-m-robotics-lab/ -------------------------------------------------------------------------------- /assets/config.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/atom-robotics-lab/ros-perception-pipeline/fcf6b6d92d50b33a6cabad83f7bfb4628810c38c/assets/config.png -------------------------------------------------------------------------------- /assets/gazebo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/atom-robotics-lab/ros-perception-pipeline/fcf6b6d92d50b33a6cabad83f7bfb4628810c38c/assets/gazebo.png -------------------------------------------------------------------------------- /assets/rqt_yolov8.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/atom-robotics-lab/ros-perception-pipeline/fcf6b6d92d50b33a6cabad83f7bfb4628810c38c/assets/rqt_yolov8.png -------------------------------------------------------------------------------- /contributing.md: -------------------------------------------------------------------------------- 1 | ## Contributing 2 | 3 | Hi there! We're thrilled that you'd like to contribute to Hacktoberfest. Your help is essential for keeping it great. 4 | 5 | 6 | ## Opening an issue 7 | 8 | Thank you for taking the time to open an issue, your feedback helps make Hacktoberfest better. 9 | Before opening an issue, please be sure that your issue hasn't already been asked by using [GitHub search](https://help.github.com/articles/searching-issues/) 10 | 11 | Here are a few things that will help us help resolve your issues: 12 | 13 | - A descriptive title that gives an idea of what your issue refers to 14 | - A thorough description of the issue, (one word descriptions are very hard to understand) 15 | - Screenshots (if appropriate) 16 | - Links (if appropriate) 17 | 18 | ## Submitting a pull request 19 | 20 | * Clone the repository using : 21 | ```bash 22 | git clone git@github.com:atom-robotics-lab/ros-perception-pipeline.git 23 | ``` 24 | * Our wiki uses an open-source framework called ROS. Use this [link](http://wiki.ros.org/) to install and use ROS 25 | 26 | * Refer to the section below if your task requires you to make changes to Repository. 27 | 28 | * Fork the repo and Create a new branch: 29 | ```bash 30 | git checkout -b my-branch-name 31 | ``` 32 | * Make changes, commit and Push to your branch. [Submit a Pull Request](https://github.com/atom-robotics-lab/ros-perception-pipeline/pulls) 33 | 34 | * Wait for your pull request to be reviewed and merged! 35 | 36 | ## Resources 37 | 38 | - [Contributing to Open Source on GitHub](https://guides.github.com/activities/contributing-to-open-source/) 39 | - [Using Pull Requests](https://help.github.com/articles/using-pull-requests/) 40 | - [GitHub Help](https://help.github.com) 41 | - [ros](http://wiki.ros.org/) -------------------------------------------------------------------------------- /detector_plugin.md: -------------------------------------------------------------------------------- 1 | # Detector Plugin Architecture 2 | 3 | The `object_detection` package follows a plugin-based architecture for allowing the use of different object detection models. These can be loaded at launch time by setting the [`detector_type`](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/2c8152d6a5ae5b5f6e3541648ae97d9ba79ac6a9/object_detection/config/params.yaml#L7P) 4 | param in the `config/params.yaml` file of the package. Currently the package supports the following detectors out of the box: 5 | * YOLOv5 6 | * YOLOv8 7 | * RetinaNET 8 | * EdgeDET 9 | 10 | The package provides a [`DetectorBase`](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/detector_plugin_architecture/object_detection/object_detection/DetectorBase.py) class which is an abstract class. 11 | It uses the python's in-built `abc.ABC` to define the abstract class and `abc.abstractmethod` decorator to define the blueprint for different class methods that the plugin should implement. 12 | All the detector plugin classes are stored in the [Detectors](https://github.com/atom-robotics-lab/ros-perception-pipeline/tree/detector_plugin_architecture/object_detection/object_detection/Detectors) directory of the 13 | `object_detection` package. 14 | 15 | ## Creating Your own Detector Plugin 16 | To create your own detector plugin, follow the steps below: 17 | * Create a file for your Detector class inside the [Detectors](https://github.com/atom-robotics-lab/ros-perception-pipeline/tree/detector_plugin_architecture/object_detection/object_detection/Detectors) directory. 18 | * The file should import the `DetectorBase` class from the [`DetectorBase.py`](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/detector_plugin_architecture/object_detection/object_detection/DetectorBase.py) module. You can create a class for your Detector in this file which should inherit the `DetectorBase` abstract class. 19 | 20 | > **Note:** The name of the file and class should be the same. This is required in order to allow the [`object_detection node`](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/071c63aa4bc71913d4bf5a4c7f9b4fd03b136338/object_detection/object_detection/ObjectDetection.py#L79) to load the module and its class using the value of the `detector_type` in the param. 21 | * Inside the class constructor, make sure to call the constructor of the `DetectorBase` class using `super().__init__()`. This initializes an empty `predictions` list that would be used to store the predictions later. (explained below) 22 | 23 | * After this, the Detector plugin class needs to implement the abstract methods listed below. These are defined in the `DetectorBase` class and provide a signature for the function's implementations. These abstract methods act as a standard API between the Detector plugins and the ObjectDetection node. The plugins only need to match the function signature (parameter and return types) to allow ObjectDetection node to use them. 24 | * [`build_model()`](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/071c63aa4bc71913d4bf5a4c7f9b4fd03b136338/object_detection/object_detection/DetectorBase.py#L21): It takes 2 strings as parameters: `model_dir_path` and `weight_file_name`. `model_dir_path` is the path which contains the model file and class file. The `weight_file_name` is the name of the weights file (like `.onxx` in case of Yolov5 models). This function should return no parameters and is used by the ObjectDetection node [here](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/071c63aa4bc71913d4bf5a4c7f9b4fd03b136338/object_detection/object_detection/ObjectDetection.py#L83). While creating the plugin, you need not worry about the parameters as they are provided by the node through the ROS 2 params. You just need to use their values inside the functions according to your Detector's requirements. 25 | 26 | * [`load_classes()`](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/071c63aa4bc71913d4bf5a4c7f9b4fd03b136338/object_detection/object_detection/DetectorBase.py#L25): This is similar to the `build_model()` function. It should load the classes file as per the requirement using the provided `model_dir_path` parameter. 27 | 28 | * [`get_predictions()`](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/071c63aa4bc71913d4bf5a4c7f9b4fd03b136338/object_detection/object_detection/DetectorBase.py#L29): This function is [used by the ObjectDetection node](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/071c63aa4bc71913d4bf5a4c7f9b4fd03b136338/object_detection/object_detection/ObjectDetection.py#L92) in the subscriber callback to get the predictions for each frame passed. This function should take an opencv image (which is essentially a numpy array) as a parameter and return a list of dictionaries that contain the predictions. This function can implement any kind of checks, formatting, preprocessing, etc. on the frame (see [this](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/071c63aa4bc71913d4bf5a4c7f9b4fd03b136338/object_detection/object_detection/Detectors/YOLOv5.py#L131) for example). It only strictly needs to follow the signature described by the abstract method definition in `DetectorBase`. To create the predictions list, the function should call the [`create_predictions_list()`](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/071c63aa4bc71913d4bf5a4c7f9b4fd03b136338/object_detection/object_detection/DetectorBase.py#L10) function from the `DetectorBase` class like [this](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/071c63aa4bc71913d4bf5a4c7f9b4fd03b136338/object_detection/object_detection/Detectors/YOLOv5.py#L144). Using the `create_predictions_list()` function is necessary as it arranges the prediction data in a standard format that the `ObjectDetection` node expects. 29 | 30 | > **Note:** For reference you can through the [YOLOv5 Plugin class](https://github.com/atom-robotics-lab/ros-perception-pipeline/blob/detector_plugin_architecture/object_detection/object_detection/Detectors/YOLOv5.py#L131) and how it implements all the abstract methods. -------------------------------------------------------------------------------- /object_detection/config/params.yaml: -------------------------------------------------------------------------------- 1 | object_detection: 2 | ros__parameters: 3 | input_img_topic: color_camera/image_raw 4 | output_bb_topic: object_detection/img_bb 5 | output_img_topic: object_detection/img 6 | model_params: 7 | detector_type: YOLOv5 8 | model_dir_path: models/ 9 | weight_file_name: auto_final.onnx 10 | confidence_threshold : 0.7 11 | show_fps : 1 -------------------------------------------------------------------------------- /object_detection/launch/object_detection.launch.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018 Intel Corporation 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import os 16 | import sys 17 | 18 | from ament_index_python.packages import get_package_share_directory 19 | 20 | from launch import LaunchDescription 21 | from launch.actions import IncludeLaunchDescription, DeclareLaunchArgument 22 | from launch.substitutions import LaunchConfiguration 23 | from launch.launch_description_sources import PythonLaunchDescriptionSource 24 | from launch_ros.actions import Node 25 | 26 | 27 | def generate_launch_description(): 28 | pkg_object_detection = get_package_share_directory("object_detection") 29 | 30 | params = os.path.join( 31 | pkg_object_detection, 32 | 'config', 33 | 'params.yaml' 34 | ) 35 | 36 | node=Node( 37 | package = 'object_detection', 38 | name = 'object_detection', 39 | executable = 'ObjectDetection', 40 | parameters = [params], 41 | output="screen" 42 | ) 43 | 44 | 45 | return LaunchDescription([node]) 46 | -------------------------------------------------------------------------------- /object_detection/object_detection/DetectorBase.py: -------------------------------------------------------------------------------- 1 | from abc import ABC, abstractmethod 2 | import numpy as np 3 | 4 | 5 | class DetectorBase(ABC): 6 | 7 | def __init__(self) -> None: 8 | self.predictions = [] 9 | 10 | def create_predictions_list(self, class_ids, confidences, boxes): 11 | for i in range(len(class_ids)): 12 | obj_dict = { 13 | "class_id": class_ids[i], 14 | "confidence": confidences[i], 15 | "box": boxes[i] 16 | } 17 | 18 | self.predictions.append(obj_dict) 19 | 20 | @abstractmethod 21 | def build_model(self, model_dir_path: str, weight_file_name: str) -> None: 22 | pass 23 | 24 | @abstractmethod 25 | def load_classes(self, model_dir_path: str) -> None: 26 | pass 27 | 28 | @abstractmethod 29 | def get_predictions(self, cv_image: np.ndarray) -> list[dict]: 30 | pass -------------------------------------------------------------------------------- /object_detection/object_detection/Detectors/EfficientDet.py: -------------------------------------------------------------------------------- 1 | import tensorflow_hub as hub 2 | import cv2 3 | import numpy 4 | import pandas as pd 5 | import tensorflow as tf 6 | import matplotlib.pyplot as plt 7 | 8 | import tempfile 9 | 10 | 11 | # For drawing onto the image. 12 | import numpy as np 13 | from PIL import Image 14 | from PIL import ImageColor 15 | from PIL import ImageDraw 16 | from PIL import ImageFont 17 | from PIL import ImageOps 18 | 19 | # For measuring the inference time. 20 | import time 21 | 22 | class EfficientDet: 23 | def __init__(self, model_dir_path, weight_file_name, conf_threshold = 0.7, score_threshold = 0.25, nms_threshold = 0.4): 24 | 25 | self.frame_count = 0 26 | self.total_frames = 0 27 | self.fps = -1 28 | self.start = time.time_ns() 29 | self.frame = None 30 | 31 | self.model_dir_path = model_dir_path 32 | self.weight_file_name = weight_file_name 33 | self.conf=conf_threshold 34 | 35 | # Resizing image 36 | self.img_height=800 37 | self.img_width=800 38 | self.predictions=[] 39 | 40 | self.build_model() 41 | self.load_classes() 42 | 43 | def build_model(self) : 44 | module_handle="https://tfhub.dev/tensorflow/efficientdet/d0/1" 45 | # Loading model directly from TensorFlow Hub 46 | self.detector = hub.load(module_handle) 47 | 48 | 49 | def load_classes(self): 50 | self.labels = [] 51 | with open(self.model_dir_path + "/classes.txt", "r") as f: 52 | self.labels = [cname.strip() for cname in f.readlines()] 53 | return self.labels 54 | 55 | def display_image(self,image): 56 | cv2.imshow("result", image) 57 | cv2.waitKey(0) 58 | cv2.destroyAllWindows() 59 | 60 | def draw_bounding_box_on_image(self,image,ymin,xmin,ymax,xmax,color,font,thickness=4,display_str_list=()): 61 | """Adds a bounding box to an image.""" 62 | draw = ImageDraw.Draw(image) 63 | im_width, im_height = image.size 64 | (left, right, top, bottom) = (xmin * im_width, xmax * im_width, 65 | ymin * im_height, ymax * im_height) 66 | draw.line([(left, top), (left, bottom), (right, bottom), (right, top), 67 | (left, top)], 68 | width=thickness, 69 | fill=color) 70 | # If the total height of the display strings added to the top of the bounding 71 | # box exceeds the top of the image, stack the strings below the bounding box 72 | # instead of above. 73 | display_str_heights = [font.getsize(ds)[1] for ds in display_str_list] 74 | # Each display_str has a top and bottom margin of 0.05x. 75 | total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights) 76 | 77 | if top > total_display_str_height: 78 | text_bottom = top 79 | else: 80 | text_bottom = top + total_display_str_height 81 | # Reverse list and print from bottom to top. 82 | for display_str in display_str_list[::-1]: 83 | text_width, text_height = font.getsize(display_str) 84 | margin = np.ceil(0.05 * text_height) 85 | draw.rectangle([(left, text_bottom - text_height - 2 * margin), 86 | (left + text_width, text_bottom)], 87 | fill=color) 88 | draw.text((left + margin, text_bottom - text_height - margin), 89 | display_str, 90 | fill="black", 91 | font=font) 92 | text_bottom -= text_height - 2 * margin 93 | 94 | # create list of dictionary containing predictions 95 | def create_predictions_list(self, class_ids, confidences, boxes): 96 | for i in range(len(class_ids)): 97 | obj_dict = { 98 | "class_id": class_ids[i], 99 | "confidence": confidences[i], 100 | "box": boxes[i] 101 | } 102 | self.predictions.append(obj_dict) 103 | 104 | def draw_boxes(self,image,boxes,class_ids,confidences,max_boxes=10): 105 | """Overlay labeled boxes on an image with formatted scores and label names.""" 106 | colors = list(ImageColor.colormap.values()) 107 | 108 | try: 109 | font = ImageFont.truetype("/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf", 110 | 25) 111 | except IOError: 112 | print("Font not found, using default font.") 113 | font = ImageFont.load_default() 114 | 115 | for i in range(min(boxes.shape[0], max_boxes)): 116 | if confidences[i] >= self.conf: 117 | ymin, xmin, ymax, xmax = tuple(boxes[i]) 118 | display_str = "{}: {}%".format(self.labels[class_ids[i]], int(100 * confidences[i])) 119 | color = colors[hash(class_ids[i]) % len(colors)] 120 | image_pil = Image.fromarray(np.uint8(image)).convert("RGB") 121 | self.draw_bounding_box_on_image(image_pil,ymin,xmin,ymax,xmax,color,font,display_str_list=[display_str]) 122 | np.copyto(image, np.array(image_pil)) 123 | return image 124 | 125 | def load_img(self,path): 126 | img = tf.io.read_file(path) 127 | img = tf.image.decode_jpeg(img, channels=3) 128 | return img 129 | 130 | def get_predictions(self,cv_image): 131 | 132 | if cv_image is None: 133 | # TODO: show warning message (different color, maybe) 134 | return None,None 135 | 136 | else : 137 | #Convert img to RGB 138 | self.frame = cv_image 139 | 140 | self.frame_count += 1 141 | self.total_frames += 1 142 | 143 | rgb = cv2.cvtColor(self.frame, cv2.COLOR_BGR2RGB) 144 | 145 | # COnverting to uint8 146 | rgb_tensor = tf.convert_to_tensor(rgb, dtype=tf.uint8) 147 | 148 | #Add dims to rgb_tensor 149 | rgb_tensor = tf.expand_dims(rgb_tensor , 0) 150 | 151 | 152 | result = self.detector(rgb_tensor) 153 | result = {key:value.numpy() for key,value in result.items()} 154 | 155 | self.create_predictions_list(result["detection_boxes"][0],result["detection_classes"][0], result["detection_scores"][0]) 156 | image_with_boxes = self.draw_boxes(cv_image,result["detection_boxes"][0],result["detection_classes"][0], result["detection_scores"][0]) 157 | 158 | # fps 159 | if self.frame_count >= 30: 160 | self.end = time.time_ns() 161 | self.fps = 1000000000 * self.frame_count / (self.end - self.start) 162 | self.frame_count = 0 163 | self.start = time.time_ns() 164 | 165 | if self.fps > 0: 166 | self.fps_label = "FPS: %.2f" % self.fps 167 | cv2.putText(self.frame, self.fps_label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) 168 | 169 | return [self.predictions, image_with_boxes] 170 | 171 | 172 | def detect_img(self,image_url): 173 | start_time = time.time() 174 | self.run_detector(self.detector, image_url)#Convert img to RGB 175 | rgb = cv2.cvtColor(cv_image, cv2.COLOR_BGR2RGB) 176 | # COnverting to uint8 177 | rgb_tensor = tf.convert_to_tensor(rgb, dtype=tf.uint8) 178 | #Add dims to rgb_tensor 179 | rgb_tensor = tf.expand_dims(rgb_tensor , 0) 180 | start_time = time.time() 181 | result = self.detector(rgb_tensor) 182 | end_time = time.time() 183 | result = {key:value.numpy() for key,value in result.items()} 184 | print("Found %d objects." % len(result["detection_scores"])) 185 | print("Inference time: ", end_time-start_time) 186 | self.create_predictions_list(cv_image,result["detection_boxes"][0],result["detection_classes"][0], result["detection_scores"][0]) 187 | image_with_boxes = self.draw_boxes(cv_image,result["detection_boxes"][0],result["detection_classes"][0], result["detection_scores"][0]) 188 | self.display_image(self.predictions,image_with_boxes) 189 | 190 | end_time = time.time() 191 | print("Inference time:",end_time-start_time) 192 | 193 | if __name__=='__main__': 194 | # Load model 195 | det = EfficientDet() 196 | det.detect_img("/home/sanchay/yolo_catkin/src/yolov8_test/scripts/dog_cat.jpg") -------------------------------------------------------------------------------- /object_detection/object_detection/Detectors/RetinaNet.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | 3 | from tensorflow import keras 4 | from keras_retinanet import models 5 | from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image 6 | from keras_retinanet.utils.visualization import draw_box, draw_caption 7 | from keras_retinanet.utils.colors import label_color 8 | import matplotlib.pyplot as plt 9 | import cv2 10 | import os 11 | import numpy as np 12 | import time 13 | import matplotlib.pyplot as plt 14 | 15 | 16 | class RetinaNet: 17 | def __init__(self, model_dir_path, weight_file_name, conf_threshold = 0.7, 18 | score_threshold = 0.4, nms_threshold = 0.25, is_cuda = 0, show_fps = 1): 19 | 20 | self.model_dir_path = model_dir_path 21 | self.weight_file_name = weight_file_name 22 | 23 | self.predictions = [] 24 | self.conf_threshold = conf_threshold 25 | self.show_fps = show_fps 26 | self.is_cuda = is_cuda 27 | 28 | if self.show_fps : 29 | self.frame_count = 0 30 | self.total_frames = 0 31 | self.fps = -1 32 | self.start = time.time_ns() 33 | 34 | self.labels_to_names = self.load_classes() 35 | self.build_model() 36 | 37 | def build_model(self) : 38 | 39 | try : 40 | self.model_path = os.path.join(self.model_dir_path, self.weight_file_name) 41 | self.model = models.load_model(self.model_path, backbone_name='resnet50') 42 | 43 | except : 44 | raise Exception("Error loading given model from path: {}. Maybe the file doesn't exist?".format(self.model_path)) 45 | 46 | 47 | def load_classes(self): 48 | self.class_list = [] 49 | 50 | with open(self.model_dir_path + "/classes.txt", "r") as f: 51 | self.class_list = [cname.strip() for cname in f.readlines()] 52 | 53 | return self.class_list 54 | 55 | def create_predictions_list(self, class_ids, confidences, boxes): 56 | for i in range(len(class_ids)): 57 | obj_dict = { 58 | "class_id": class_ids[i], 59 | "confidence": confidences[i], 60 | "box": boxes[i] 61 | } 62 | 63 | self.predictions.append(obj_dict) 64 | 65 | 66 | def get_predictions(self, cv_image): 67 | 68 | if cv_image is None: 69 | # TODO: show warning message (different color, maybe) 70 | return None,None 71 | 72 | else : 73 | 74 | # copy to draw on 75 | self.frame = cv_image.copy() 76 | # preprocess image for network 77 | input = preprocess_image(self.frame) 78 | input, scale = resize_image(input) 79 | 80 | self.frame_count += 1 81 | self.total_frames += 1 82 | 83 | # process image 84 | start = time.time() 85 | boxes, scores, labels = self.model.predict_on_batch(np.expand_dims(input, axis=0)) 86 | #print("processing time: ", time.time() - start) 87 | 88 | # correct for image scale 89 | boxes /= scale 90 | 91 | self.create_predictions_list(labels, scores, boxes) 92 | 93 | # visualize detections 94 | for box, score, label in zip(boxes[0], scores[0], labels[0]): 95 | # scores are sorted so we can break 96 | if score < self.conf_threshold: 97 | break 98 | 99 | color = label_color(label) 100 | 101 | b = box.astype(int) 102 | draw_box(self.frame, b, color=color) 103 | 104 | caption = "{} {:.3f}".format(self.labels_to_names[label], score) 105 | #print(self.labels_to_names[label]) 106 | draw_caption(self.frame, b, caption) 107 | 108 | if self.show_fps : 109 | if self.frame_count >= 30: 110 | self.end = time.time_ns() 111 | self.fps = 1000000000 * self.frame_count / (self.end - self.start) 112 | self.frame_count = 0 113 | self.start = time.time_ns() 114 | 115 | if self.fps > 0: 116 | self.fps_label = "FPS: %.2f" % self.fps 117 | cv2.putText(self.frame, self.fps_label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) 118 | 119 | return (self.predictions, self.frame) 120 | 121 | 122 | -------------------------------------------------------------------------------- /object_detection/object_detection/Detectors/YOLOv5.py: -------------------------------------------------------------------------------- 1 | import time 2 | import os 3 | import cv2 4 | import numpy as np 5 | 6 | from ..DetectorBase import DetectorBase 7 | 8 | 9 | class YOLOv5(DetectorBase): 10 | def __init__(self, conf_threshold = 0.7, score_threshold = 0.4, nms_threshold = 0.25, is_cuda = 0): 11 | 12 | super().__init__() 13 | 14 | # opencv img input 15 | self.frame = None 16 | self.net = None 17 | self.INPUT_WIDTH = 640 18 | self.INPUT_HEIGHT = 640 19 | self.CONFIDENCE_THRESHOLD = conf_threshold 20 | 21 | self.is_cuda = is_cuda 22 | 23 | 24 | # load model and prepare its backend to either run on GPU or CPU, see if it can be added in constructor 25 | def build_model(self, model_dir_path, weight_file_name): 26 | model_path = os.path.join(model_dir_path, weight_file_name) 27 | 28 | try: 29 | self.net = cv2.dnn.readNet(model_path) 30 | except: 31 | raise Exception("Error loading given model from path: {}. Maybe the file doesn't exist?".format(model_path)) 32 | 33 | if self.is_cuda: 34 | print("is_cuda was set to True. Attempting to use CUDA") 35 | self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA) 36 | self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA_FP16) 37 | else: 38 | print("is_cuda was set to False. Running on CPU") 39 | self.net.setPreferableBackend(cv2.dnn.DNN_BACKEND_OPENCV) 40 | self.net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU) 41 | 42 | 43 | # load classes.txt that contains mapping of model with labels 44 | # TODO: add try/except to raise exception that tells the use to check the name if it is classes.txt 45 | def load_classes(self, model_dir_path): 46 | self.class_list = [] 47 | with open(model_dir_path + "/classes.txt", "r") as f: 48 | self.class_list = [cname.strip() for cname in f.readlines()] 49 | return self.class_list 50 | 51 | 52 | def detect(self, image): 53 | # convert image to 640x640 54 | blob = cv2.dnn.blobFromImage(image, 1/255.0, (self.INPUT_WIDTH, self.INPUT_HEIGHT), swapRB=True, crop=False) 55 | self.net.setInput(blob) 56 | preds = self.net.forward() 57 | return preds 58 | 59 | 60 | # extract bounding box, class IDs and confidences of detected objects 61 | # YOLOv5 returns a 3D tensor of dimension 25200*(5 + n_classes) 62 | def wrap_detection(self, input_image, output_data): 63 | class_ids = [] 64 | confidences = [] 65 | boxes = [] 66 | 67 | rows = output_data.shape[0] 68 | 69 | image_width, image_height, _ = input_image.shape 70 | 71 | x_factor = image_width / self.INPUT_WIDTH 72 | y_factor = image_height / self.INPUT_HEIGHT 73 | 74 | # Iterate through all the 25200 vectors 75 | for r in range(rows): 76 | row = output_data[r] 77 | 78 | # Continue only if Pc > conf_threshold 79 | confidence = row[4] 80 | if confidence >= self.CONFIDENCE_THRESHOLD: 81 | 82 | # One-hot encoded vector representing class of object 83 | classes_scores = row[5:] 84 | 85 | # Returns min and max values in a array alongwith their indices 86 | _, _, _, max_indx = cv2.minMaxLoc(classes_scores) 87 | 88 | # Extract the column index of the maximum values in classes_scores 89 | class_id = max_indx[1] 90 | 91 | # Continue of the class score is greater than a threshold 92 | # class_score represents the probability of an object belonging to that class 93 | if (classes_scores[class_id] > .25): 94 | 95 | confidences.append(confidence) 96 | 97 | class_ids.append(class_id) 98 | 99 | x, y, w, h = row[0].item(), row[1].item(), row[2].item(), row[3].item() 100 | left = int((x - 0.5 * w) * x_factor) 101 | top = int((y - 0.5 * h) * y_factor) 102 | width = int(w * x_factor) 103 | height = int(h * y_factor) 104 | box = np.array([left, top, width, height]) 105 | boxes.append(box) 106 | 107 | # removing intersecting bounding boxes 108 | indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.25, 0.45) 109 | 110 | result_class_ids = [] 111 | result_confidences = [] 112 | result_boxes = [] 113 | 114 | for i in indexes: 115 | result_confidences.append(confidences[i]) 116 | result_class_ids.append(class_ids[i]) 117 | result_boxes.append(boxes[i]) 118 | 119 | return result_class_ids, result_confidences, result_boxes 120 | 121 | 122 | # makes image square with dimension max(h, w) 123 | def format_yolov5(self): 124 | row, col, _ = self.frame.shape 125 | _max = max(col, row) 126 | result = np.zeros((_max, _max, 3), np.uint8) 127 | result[0:row, 0:col] = self.frame 128 | return result 129 | 130 | 131 | def get_predictions(self, cv_image): 132 | #Clear list 133 | self.predictions = [] 134 | 135 | if cv_image is None: 136 | # TODO: show warning message (different color, maybe) 137 | return None,None 138 | else: 139 | self.frame = cv_image 140 | 141 | # make image square 142 | inputImage = self.format_yolov5() 143 | 144 | outs = self.detect(inputImage) 145 | class_ids, confidences, boxes = self.wrap_detection(inputImage, outs[0]) 146 | 147 | super().create_predictions_list(class_ids, confidences, boxes) 148 | 149 | print("Detected ids: ", class_ids) 150 | 151 | return self.predictions -------------------------------------------------------------------------------- /object_detection/object_detection/Detectors/YOLOv8.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | from ultralytics import YOLO 3 | import os 4 | import time 5 | 6 | class YOLOv8: 7 | def __init__(self, model_dir_path, weight_file_name, conf_threshold = 0.7, 8 | score_threshold = 0.4, nms_threshold = 0.25, 9 | show_fps = 1, is_cuda = 0): 10 | 11 | self.model_dir_path = model_dir_path 12 | self.weight_file_name = weight_file_name 13 | 14 | 15 | self.conf_threshold = conf_threshold 16 | self.show_fps = show_fps 17 | self.is_cuda = is_cuda 18 | 19 | #FPS 20 | if self.show_fps : 21 | self.frame_count = 0 22 | self.total_frames = 0 23 | self.fps = -1 24 | self.start = time.time_ns() 25 | self.frame = None 26 | 27 | 28 | self.predictions = [] 29 | self.build_model() 30 | self.load_classes() 31 | 32 | 33 | def build_model(self) : 34 | 35 | try : 36 | model_path = os.path.join(self.model_dir_path, self.weight_file_name) 37 | self.model = YOLO(model_path) 38 | 39 | except : 40 | raise Exception("Error loading given model from path: {}. Maybe the file doesn't exist?".format(model_path)) 41 | 42 | def load_classes(self): 43 | 44 | self.class_list = [] 45 | 46 | with open(self.model_dir_path + "/classes.txt", "r") as f: 47 | self.class_list = [cname.strip() for cname in f.readlines()] 48 | 49 | return self.class_list 50 | 51 | # create list of dictionary containing predictions 52 | def create_predictions_list(self, class_ids, confidences, boxes): 53 | 54 | for i in range(len(class_ids)): 55 | obj_dict = { 56 | "class_id": class_ids[i], 57 | "confidence": confidences[i], 58 | "box": boxes[i] 59 | } 60 | self.predictions.append(obj_dict) 61 | 62 | def get_predictions(self, cv_image): 63 | 64 | if cv_image is None: 65 | # TODO: show warning message (different color, maybe) 66 | return None,None 67 | 68 | else : 69 | self.frame = cv_image 70 | self.frame_count += 1 71 | self.total_frames += 1 72 | 73 | class_id = [] 74 | confidence = [] 75 | bb = [] 76 | result = self.model.predict(self.frame, conf = self.conf_threshold) # Perform object detection on image 77 | row = result[0].boxes 78 | 79 | for box in row: 80 | class_id.append(box.cls) 81 | confidence.append(box.conf) 82 | bb.append(box.xyxy) 83 | 84 | self.create_predictions_list(class_id,confidence,bb) 85 | result = self.model.predict(self.frame, conf = self.conf_threshold) 86 | output_frame = result[0].plot() # Frame with bounding boxes 87 | 88 | print("frame_count : ", self.frame_count) 89 | 90 | 91 | if self.show_fps : 92 | if self.frame_count >= 30: 93 | self.end = time.time_ns() 94 | self.fps = 1000000000 * self.frame_count / (self.end - self.start) 95 | self.frame_count = 0 96 | self.start = time.time_ns() 97 | 98 | if self.fps > 0: 99 | self.fps_label = "FPS: %.2f" % self.fps 100 | cv2.putText(output_frame, self.fps_label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2) 101 | 102 | return self.predictions, output_frame 103 | -------------------------------------------------------------------------------- /object_detection/object_detection/Detectors/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/atom-robotics-lab/ros-perception-pipeline/fcf6b6d92d50b33a6cabad83f7bfb4628810c38c/object_detection/object_detection/Detectors/__init__.py -------------------------------------------------------------------------------- /object_detection/object_detection/ObjectDetection.py: -------------------------------------------------------------------------------- 1 | #! /usr/bin/env python3 2 | 3 | import os 4 | import importlib 5 | 6 | import rclpy 7 | from rclpy.node import Node 8 | 9 | from sensor_msgs.msg import Image 10 | #from vision_msgs.msg import BoundingBox2D 11 | 12 | from cv_bridge import CvBridge 13 | import cv2 14 | 15 | 16 | class ObjectDetection(Node): 17 | def __init__(self): 18 | super().__init__('object_detection') 19 | 20 | # create an empty list that will hold the names of all available detector 21 | self.available_detectors = [] 22 | 23 | # fill available_detectors with the detectors from Detectors dir 24 | self.discover_detectors() 25 | 26 | self.declare_parameters( 27 | namespace='', 28 | parameters=[ 29 | 30 | ('input_img_topic', ""), 31 | ('output_bb_topic', ""), 32 | ('output_img_topic', ""), 33 | ('model_params.detector_type', ""), 34 | ('model_params.model_dir_path', ""), 35 | ('model_params.weight_file_name', ""), 36 | ('model_params.confidence_threshold', 0.7), 37 | ('model_params.show_fps', 1), 38 | ] 39 | ) 40 | 41 | # node params 42 | self.input_img_topic = self.get_parameter('input_img_topic').value 43 | self.output_bb_topic = self.get_parameter('output_bb_topic').value 44 | self.output_img_topic = self.get_parameter('output_img_topic').value 45 | 46 | # model params 47 | self.detector_type = self.get_parameter('model_params.detector_type').value 48 | self.model_dir_path = self.get_parameter('model_params.model_dir_path').value 49 | self.weight_file_name = self.get_parameter('model_params.weight_file_name').value 50 | self.confidence_threshold = self.get_parameter('model_params.confidence_threshold').value 51 | self.show_fps = self.get_parameter('model_params.show_fps').value 52 | 53 | # raise an exception if specified detector was not found 54 | if self.detector_type not in self.available_detectors: 55 | raise ModuleNotFoundError(self.detector_type + " Detector specified in config was not found. " + 56 | "Check the Detectors dir for available detectors.") 57 | else: 58 | self.load_detector() 59 | 60 | 61 | self.img_pub = self.create_publisher(Image, self.output_img_topic, 10) 62 | self.bb_pub = None 63 | self.img_sub = self.create_subscription(Image, self.input_img_topic, self.detection_cb, 10) 64 | 65 | self.bridge = CvBridge() 66 | 67 | 68 | def discover_detectors(self): 69 | curr_dir = os.path.dirname(__file__) 70 | dir_contents = os.listdir(curr_dir + "/Detectors") 71 | 72 | for entity in dir_contents: 73 | if entity.endswith('.py'): 74 | self.available_detectors.append(entity[:-3]) 75 | 76 | self.available_detectors.remove('__init__') 77 | 78 | 79 | def load_detector(self): 80 | detector_mod = importlib.import_module(".Detectors." + self.detector_type, "object_detection") 81 | detector_class = getattr(detector_mod, self.detector_type) 82 | self.detector = detector_class() 83 | 84 | self.detector.build_model(self.model_dir_path, self.weight_file_name) 85 | self.detector.load_classes(self.model_dir_path) 86 | 87 | print("Your detector : {} has been loaded !".format(self.detector_type)) 88 | 89 | 90 | def detection_cb(self, img_msg): 91 | cv_image = self.bridge.imgmsg_to_cv2(img_msg, "bgr8") 92 | 93 | predictions = self.detector.get_predictions(cv_image=cv_image) 94 | 95 | if predictions == None : 96 | print("Image input from topic : {} is empty".format(self.input_img_topic)) 97 | else : 98 | for prediction in predictions: 99 | left, top, width, height = prediction['box'] 100 | right = left + width 101 | bottom = top + height 102 | 103 | #Draw the bounding box 104 | cv_image = cv2.rectangle(cv_image,(left,top),(right, bottom),(0,255,0),1) 105 | 106 | output = self.bridge.cv2_to_imgmsg(cv_image, "bgr8") 107 | self.img_pub.publish(output) 108 | print(predictions) 109 | 110 | 111 | def main(): 112 | rclpy.init() 113 | od = ObjectDetection() 114 | try : 115 | rclpy.spin(od) 116 | 117 | except Exception as e: 118 | print(e) 119 | 120 | 121 | if __name__=="__main__" : 122 | main() 123 | 124 | 125 | 126 | -------------------------------------------------------------------------------- /object_detection/object_detection/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/atom-robotics-lab/ros-perception-pipeline/fcf6b6d92d50b33a6cabad83f7bfb4628810c38c/object_detection/object_detection/__init__.py -------------------------------------------------------------------------------- /object_detection/package.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | object_detection 5 | 0.0.0 6 | TODO: Package description 7 | singh 8 | TODO: License declaration 9 | 10 | ament_copyright 11 | ament_flake8 12 | ament_pep257 13 | python3-pytest 14 | 15 | 16 | ament_python 17 | 18 | 19 | -------------------------------------------------------------------------------- /object_detection/requirements.txt: -------------------------------------------------------------------------------- 1 | keras-retinanet==1.0.0 2 | matplotlib==3.5.4 3 | numpy==1.25.0 4 | opencv-python==4.7.0.72 5 | pandas==2.0.3 6 | pillow==9.5.0 7 | tensorflow==2.12.0 8 | tensorflow-hub==0.13.0 9 | ultralytics==8.0.124 -------------------------------------------------------------------------------- /object_detection/resource/object_detection: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/atom-robotics-lab/ros-perception-pipeline/fcf6b6d92d50b33a6cabad83f7bfb4628810c38c/object_detection/resource/object_detection -------------------------------------------------------------------------------- /object_detection/setup.cfg: -------------------------------------------------------------------------------- 1 | [develop] 2 | script_dir=$base/lib/object_detection 3 | [install] 4 | install_scripts=$base/lib/object_detection 5 | -------------------------------------------------------------------------------- /object_detection/setup.py: -------------------------------------------------------------------------------- 1 | from setuptools import setup 2 | import os 3 | from glob import glob 4 | 5 | package_name = 'object_detection' 6 | 7 | setup( 8 | name=package_name, 9 | version='0.0.0', 10 | packages=[package_name], 11 | data_files=[ 12 | ('share/ament_index/resource_index/packages', 13 | ['resource/' + package_name]), 14 | ('share/' + package_name, ['package.xml']), 15 | (os.path.join('share', package_name, 'config'), glob('config/*.yaml')), 16 | (os.path.join('share', package_name, 'launch'), glob('launch/*.launch.py')), 17 | 18 | ], 19 | install_requires=['setuptools'], 20 | zip_safe=True, 21 | maintainer='singh', 22 | maintainer_email='jasmeet0915@gmail.com', 23 | description='TODO: Package description', 24 | license='TODO: License declaration', 25 | tests_require=['pytest'], 26 | entry_points={ 27 | 'console_scripts': [ 28 | 'ObjectDetection = object_detection.ObjectDetection:main', 29 | ], 30 | }, 31 | ) 32 | -------------------------------------------------------------------------------- /object_detection/test/test_copyright.py: -------------------------------------------------------------------------------- 1 | # Copyright 2015 Open Source Robotics Foundation, Inc. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from ament_copyright.main import main 16 | import pytest 17 | 18 | 19 | # Remove the `skip` decorator once the source file(s) have a copyright header 20 | @pytest.mark.skip(reason='No copyright header has been placed in the generated source file.') 21 | @pytest.mark.copyright 22 | @pytest.mark.linter 23 | def test_copyright(): 24 | rc = main(argv=['.', 'test']) 25 | assert rc == 0, 'Found errors' 26 | -------------------------------------------------------------------------------- /object_detection/test/test_flake8.py: -------------------------------------------------------------------------------- 1 | # Copyright 2017 Open Source Robotics Foundation, Inc. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from ament_flake8.main import main_with_errors 16 | import pytest 17 | 18 | 19 | @pytest.mark.flake8 20 | @pytest.mark.linter 21 | def test_flake8(): 22 | rc, errors = main_with_errors(argv=[]) 23 | assert rc == 0, \ 24 | 'Found %d code style errors / warnings:\n' % len(errors) + \ 25 | '\n'.join(errors) 26 | -------------------------------------------------------------------------------- /object_detection/test/test_pep257.py: -------------------------------------------------------------------------------- 1 | # Copyright 2015 Open Source Robotics Foundation, Inc. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from ament_pep257.main import main 16 | import pytest 17 | 18 | 19 | @pytest.mark.linter 20 | @pytest.mark.pep257 21 | def test_pep257(): 22 | rc = main(argv=['.', 'test']) 23 | assert rc == 0, 'Found code style errors / warnings' 24 | -------------------------------------------------------------------------------- /perception_bringup/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.8) 2 | project(perception_bringup) 3 | 4 | if(CMAKE_COMPILER_IS_GNUCXX OR CMAKE_CXX_COMPILER_ID MATCHES "Clang") 5 | add_compile_options(-Wall -Wextra -Wpedantic) 6 | endif() 7 | 8 | # find dependencies 9 | find_package(ament_cmake REQUIRED) 10 | # uncomment the following section in order to fill in 11 | # further dependencies manually. 12 | # find_package( REQUIRED) 13 | 14 | if(BUILD_TESTING) 15 | find_package(ament_lint_auto REQUIRED) 16 | # the following line skips the linter which checks for copyrights 17 | # comment the line when a copyright and license is added to all source files 18 | set(ament_cmake_copyright_FOUND TRUE) 19 | # the following line skips cpplint (only works in a git repo) 20 | # comment the line when this package is in a git repo and when 21 | # a copyright and license is added to all source files 22 | set(ament_cmake_cpplint_FOUND TRUE) 23 | ament_lint_auto_find_test_dependencies() 24 | endif() 25 | 26 | install( 27 | DIRECTORY launch models worlds config 28 | DESTINATION share/${PROJECT_NAME} 29 | ) 30 | 31 | ament_environment_hooks("${CMAKE_CURRENT_SOURCE_DIR}/env_hooks/${PROJECT_NAME}.sh.in") 32 | 33 | ament_package() 34 | -------------------------------------------------------------------------------- /perception_bringup/config/bridge.yaml: -------------------------------------------------------------------------------- 1 | - gz_topic_name: "/color_camera/image_raw" 2 | ros_topic_name: "/color_camera/image_raw" 3 | ros_type_name: "sensor_msgs/msg/Image" 4 | gz_type_name: "gz.msgs.Image" 5 | direction: GZ_TO_ROS -------------------------------------------------------------------------------- /perception_bringup/env_hooks/perception_bringup.sh.in: -------------------------------------------------------------------------------- 1 | ament_prepend_unique_value GZ_SIM_RESOURCE_PATH "@CMAKE_CURRENT_SOURCE_DIR@/models:@CMAKE_CURRENT_SOURCE_DIR@/worlds" -------------------------------------------------------------------------------- /perception_bringup/launch/playground.launch.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018 Intel Corporation 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | import os 16 | import sys 17 | 18 | from ament_index_python.packages import get_package_share_directory 19 | 20 | from launch import LaunchDescription 21 | from launch.actions import IncludeLaunchDescription, DeclareLaunchArgument 22 | from launch.substitutions import LaunchConfiguration 23 | from launch.launch_description_sources import PythonLaunchDescriptionSource 24 | from launch_ros.actions import Node 25 | 26 | 27 | def generate_launch_description(): 28 | 29 | pkg_perception_bringup = get_package_share_directory("perception_bringup") 30 | #pkg_ros_gz_sim = get_package_share_directory("ros_gz_sim") 31 | 32 | world_name = "playground" 33 | 34 | for arg in sys.argv: 35 | if arg.startswith("world:="): 36 | world_name = arg.split(":=")[1] 37 | 38 | world_sdf = pkg_perception_bringup + "/worlds/" + world_name + ".sdf" 39 | 40 | '''gz_sim = IncludeLaunchDescription( 41 | PythonLaunchDescriptionSource( 42 | os.path.join(pkg_ros_gz_sim, 'launch', 'gz_sim.launch.py')), 43 | )''' 44 | 45 | gz_sim_share = get_package_share_directory("ros_gz_sim") 46 | gz_sim = IncludeLaunchDescription( 47 | PythonLaunchDescriptionSource(os.path.join(gz_sim_share, "launch", "gz_sim.launch.py")), 48 | launch_arguments={ 49 | "gz_args" : world_sdf 50 | }.items() 51 | ) 52 | 53 | parameter_bridge = Node(package="ros_gz_bridge", executable="parameter_bridge", 54 | parameters = [ 55 | {'config_file' : os.path.join(pkg_perception_bringup, "config", "bridge.yaml")} 56 | ] 57 | ) 58 | 59 | arg_gz_sim = DeclareLaunchArgument('gz_args', default_value=world_sdf) 60 | 61 | arg_world_name = DeclareLaunchArgument('world', default_value='playground_world' ) 62 | 63 | launch = [ 64 | gz_sim, 65 | parameter_bridge 66 | ] 67 | 68 | args = [ 69 | arg_gz_sim, 70 | arg_world_name 71 | ] 72 | 73 | return LaunchDescription(args + launch) 74 | -------------------------------------------------------------------------------- /perception_bringup/models/adhesive/model.config: -------------------------------------------------------------------------------- 1 | 2 | 3 | adhesive 4 | 1.0 5 | model.sdf 6 | 7 | atom 8 | atom@atom 9 | 10 | 11 | 12 | -------------------------------------------------------------------------------- /perception_bringup/models/adhesive/model.sdf: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 0 0 0 0 0 0 9 | 0.08 10 | 11 | 12 | 13 | 0 0 0 0 0 0 14 | 15 | 16 | model://adhesive/meshes/adhesive.dae 17 | 18 | 19 | 20 | 21 | 22 | true 23 | 0 0 0 0 0 0 24 | 25 | 26 | model://adhesive/meshes/adhesive.dae 27 | 28 | 29 | 30 | 0 0.5 0.5 1 31 | 0 0.5 0.5 1 32 | 0 0.5 0.5 1 33 | 34 | 35 | 36 | 37 | 38 | 39 | -------------------------------------------------------------------------------- /perception_bringup/models/first_aid/model.config: -------------------------------------------------------------------------------- 1 | 9 | 10 | first aid 11 | 12 | atom 13 | atom@atom 14 | 15 | 1.0 16 | model.sdf 17 | First Aid Box 18 | 19 | -------------------------------------------------------------------------------- /perception_bringup/models/first_aid/model.sdf: -------------------------------------------------------------------------------- 1 | 2 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 0.2 0.1 0.20 17 | 18 | 19 | 20 | 1 1 1 1 21 | 1 1 1 1 22 | 0 0 0 1 23 | 0 0 0 1 24 | 25 | 26 | 27 | 28 | 29 | 0.2 0.1 0.20 30 | 31 | 32 | 33 | 34 | 35 | 36 | -------------------------------------------------------------------------------- /perception_bringup/models/medicine/materials/textures/texture.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/atom-robotics-lab/ros-perception-pipeline/fcf6b6d92d50b33a6cabad83f7bfb4628810c38c/perception_bringup/models/medicine/materials/textures/texture.png -------------------------------------------------------------------------------- /perception_bringup/models/medicine/meshes/model.mtl: -------------------------------------------------------------------------------- 1 | # Copyright 2020 Google LLC. 2 | # 3 | # This work is licensed under the Creative Commons Attribution 4.0 4 | # International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by/4.0/ or send a letter 6 | # to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | newmtl material_0 8 | # shader_type beckmann 9 | map_Kd texture.png 10 | 11 | # Kd: Diffuse reflectivity. 12 | Kd 1.000000 1.000000 1.000000 13 | -------------------------------------------------------------------------------- /perception_bringup/models/medicine/model.config: -------------------------------------------------------------------------------- 1 | 9 | 10 | Weston_No_22_Cajun_Jerky_Tonic_12_fl_oz_nLj64ZnGwDh 11 | 12 | Google 13 | scanned-objects@google.com 14 | 15 | 1.0 16 | model.sdf 17 | Weston No. 22 Cajun Jerky Tonic - 12 fl oz 18 | Weston Premium Jerky Tonic - the cure for the tired tastebud! Our bottles let you use only what you need. 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-------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | Table 5 | 1.0 6 | model-1_2.sdf 7 | model-1_3.sdf 8 | model-1_4.sdf 9 | model.sdf 10 | 11 | 12 | Nate Koenig 13 | nate@osrfoundation.org 14 | 15 | 16 | 17 | A wooden table. 18 | 19 | 20 | -------------------------------------------------------------------------------- /perception_bringup/models/table/model.sdf: -------------------------------------------------------------------------------- 1 | 2 | 3 | true 4 | 5 | 6 | 0 0 1.0 0 0 0 7 | 8 | 9 | 1.5 0.8 0.03 10 | 11 | 12 | 13 | 14 | 15 | 0.6 16 | 0.6 17 | 18 | 19 | 20 | 21 | 22 | 0 0 1.0 0 0 0 23 | 24 | 25 | 1.5 0.8 0.03 26 | 27 | 28 | 29 | 1.0 1.0 1.0 30 | 34 | 35 | 36 | 37 | model://table/Table_Diffuse.jpg 38 | 39 | 40 | 41 | 42 | 43 | 0.68 0.38 0.5 0 0 0 44 | 45 | 46 | 0.02 47 | 1.0 48 | 49 | 50 | 51 | 52 | 0.68 0.38 0.5 0 0 0 53 | 54 | 55 | 0.02 56 | 1.0 57 | 58 | 59 | 60 | 0.5 0.5 0.5 61 | 65 | 66 | 67 | 68 | 0.68 -0.38 0.5 0 0 0 69 | 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-------------------------------------------------------------------------------- /perception_bringup/worlds/playground.sdf: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 3D View 8 | false 9 | docked 10 | 11 | ogre2 12 | scene 13 | 0.4 0.4 0.4 14 | 0.8 0.8 0.8 15 | -6 0 6 0 0.5 0 16 | 17 | 18 | 19 | 20 | 21 | floating 22 | 5 23 | 5 24 | false 25 | 26 | 27 | 28 | 29 | 30 | false 31 | 5 32 | 5 33 | floating 34 | false 35 | 36 | 37 | 38 | 39 | 40 | false 41 | 5 42 | 5 43 | floating 44 | false 45 | 46 | 47 | 48 | 49 | 50 | false 51 | 5 52 | 5 53 | floating 54 | false 55 | 56 | 57 | 58 | 59 | 60 | false 61 | 5 62 | 5 63 | floating 64 | false 65 | 66 | 67 | 68 | 69 | 70 | false 71 | 5 72 | 5 73 | floating 74 | false 75 | 76 | 77 | 78 | 79 | 80 | false 81 | 5 82 | 5 83 | floating 84 | false 85 | 86 | 87 | 88 | 89 | 90 | false 91 | 5 92 | 5 93 | floating 94 | false 95 | 96 | 97 | 98 | 99 | 100 | 101 | World control 102 | false 103 | 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