YOLOv5 ROS package

Table of Contents

1. About The Project
2. Getting Started
   • Prerequisites
   • Install dependencies
   • Installation
3. Usage
4. Contributing
5. License
6. Contact
7. Acknowledgments

About The Project

This project was created for the 2nd Smart Cities Robotics Challenge (SciRoc) 2021. One of our team's tasks was detecting people and objects via a mounted camera on the robot. Thus creating a ros package that utilizes YOLOv5.

Getting Started

In order to use this package follow the next steps.

Prerequisites

Yolov5 uses python 3.8. Check your python version using

python -V

To install python 3.8 go here. The installation of the dependencied is done using anaconda. Installing dependencies with pip is also tested and working.

Install dependencies

The installation of the dependencies is done using anaconda. Installing dependencies with pip is also tested and working.

Install package dependencies (anaconda)

1. Create a new virtual environment named venv

conda create -n venv python=3.8 jupyter

2. Activate new virtual environment

conda activate venv

3. Install pytorch. Insert your specifications for os (windows/linux), your package manager (e.g. conda/pip etc) and Compute Platform (cpu/gpu etc). Copy the installation command next to Run this command to your terminal and run it.
4. Install other dependencies

sudo apt-get install python3-pip python3-yaml
pip3 install rospkg catkin_pkg
cd /path/to/ros_yolov5
pip3 install -r requirements.txt

Choose python interpreter

Modify the shebang at the top of ros_yolov5.py file at ros_yolov5/scripts/ros_yolov5.py and ros_yolov5/scripts/process_detections.py

Example

Default:

#!/path/to/venv/bin/python3

Modified:

#!/home/ronnie_coleman/venv/bin/python

or

#!/home/ronnie_coleman/miniconda3/envs/venv/bin/python

Installation

Clone the repository to your workspace.

mkdir -p "yolo_ws/src" && cd $_
git clone https://github.com/uniwa-gravastars/yolov5-rospackage.git
cd yolov5-rospackage
git submodule init
git submodule update

Then compile the package and source the workspace

cd ..
catkin_make -DPYTHON_EXECUTABLE=/usr/bin/python3 -DPYTHON_INCLUDE_DIR=/usr/include/python3

Usage

To change the weights that Yolov5 uses, open ros_yolov5.launch on ros_yolov5/launch/ros_yolov5.launch and change the value of the parameter weights_path to point to your pt file. Moreover change the source_topic to the correct camera topic.

The YOLOv5 ros package is now ready. Open a terminal, source the workspace and run:

roslaunch ros_yolov5 ros_yolov5.launch

Usual errors

Resize error

!!ATTENTION!!

If you come across this error:

[ERROR] [1661788501.044783]: bad callback: <bound method ObjectDetector.image_callback of <__main__.ObjectDetector object at 0x7f011fe2bdc0>>
Traceback (most recent call last):
  File "/opt/ros/melodic/lib/python2.7/dist-packages/rospy/topics.py", line 750, in _invoke_callback
    cb(msg)
  File "/home/user/catkin_ws/src/yolov5-rospackage/ros_yolov5/scripts/ros_yolov5.py", line 46, in image_callback
    results = self.model(img, size=640)
  File "/home/user/miniconda3/envs/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/miniconda3/envs/venv/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/user/catkin_ws/src/yolov5-rospackage/ros_yolov5/yolov5/models/common.py", line 278, in forward
    y = self.model(x, augment, profile)[0]  # forward
  File "/home/user/miniconda3/envs/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/catkin_ws/src/yolov5-rospackage/ros_yolov5/yolov5/models/yolo.py", line 122, in forward
    return self.forward_once(x, profile, visualize)  # single-scale inference, train
  File "/home/user/catkin_ws/src/yolov5-rospackage/ros_yolov5/yolov5/models/yolo.py", line 153, in forward_once
    x = m(x)  # run
  File "/home/user/miniconda3/envs/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1110, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/user/miniconda3/envs/venv/lib/python3.8/site-packages/torch/nn/modules/upsampling.py", line 154, in forward
    recompute_scale_factor=self.recompute_scale_factor)
  File "/home/user/miniconda3/envs/venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1185, in __getattr__
    raise AttributeError("'{}' object has no attribute '{}'".format(
AttributeError: 'Upsample' object has no attribute 'recompute_scale_factor'

This error is from the internal code of pytorch. The "forward" function is not working properly.

To fix this, replace in miniconda3/envs/venv/lib/python3.8/site-packages/torch/nn/modules/upsampling.py:

def forward(self, input: Tensor) -> Tensor:
        return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners,
                         recompute_scale_factor=self.recompute_scale_factor)

with:

def forward(self, input: Tensor) -> Tensor:
        return F.interpolate(input, self.size, self.scale_factor, self.mode, self.align_corners)

Find fix here.

Contributing

Any contributions you make are greatly appreciated.

If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with an appropriate tag.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE.txt for more information.

Acknowledgments

Special thanks to the original author of this work Kaloterakis Evangelos.

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