https://github.com/ros2/openrobotics_darknet_ros
ROS 2 interface to darknet, an open source neural network library.
https://github.com/ros2/openrobotics_darknet_ros
Last synced: about 1 year ago
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ROS 2 interface to darknet, an open source neural network library.
- Host: GitHub
- URL: https://github.com/ros2/openrobotics_darknet_ros
- Owner: ros2
- License: apache-2.0
- Created: 2019-10-04T00:29:11.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-10-28T20:57:43.000Z (over 1 year ago)
- Last Synced: 2024-10-28T21:44:13.200Z (over 1 year ago)
- Language: C++
- Size: 754 KB
- Stars: 21
- Watchers: 3
- Forks: 16
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Open Robotics Darknet ROS
This is a **ROS 2 wrapper around [darknet](https://pjreddie.com/darknet)**, an open source neural network framework.
## DetectorNode
This node can run **object detectors** like [YOLO v3](https://pjreddie.com/darknet/yolo/) or [YOLO v7](https://github.com/WongKinYiu/yolov7) on images and video streams.
### 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
## Dependencies
This package depends on [darknet](https://github.com/AlexeyAB/darknet). If you can't use CUDA but want to use your CPU instead make sure to build it with the flag `-DENABLE_CUDA=OFF` and potentially also disabling multi-threading with `-DCMAKE_DISABLE_FIND_PACKAGE_OpenMP=TRUE`.
### Launching
Compiling this package with
```bash
$ colcon build --cmake-args -DDOWNLOAD_YOLO_CONFIG=ON
```
will automatically download the pretrained YOLO v3, v4 and v7 configuration files.
You can then launch the detector node with
```bash
$ ros2 launch openrobotics_darknet_ros detector_launch.py rgb_image:=/topic
```
optionally supplying a desired parameter file `detector_parameters:=path/to/detector_node_params.yaml`.
You can also train YOLO to detect custom objects like described [here](https://github.com/AlexeyAB/darknet#how-to-train-tiny-yolo-to-detect-your-custom-objects) and create the following as `detector_node_params.yaml`:
```yaml
/**:
ros__parameters:
network:
config: "./your-yolo-config.cfg"
weights: "./your-yolo-weights.weights"
class_names: "./your-cocos.names"
detection:
threshold: 0.25
nms_threshold: 0.45
```
Finally you can run the detector node with
```bash
$ ros2 run openrobotics_darknet_ros detector_node --ros-args --params-file path/to/detector_node_params.yaml
```
and publish images on `~/images` to get the node to detect objects. You can also manually remap an external topic to the `~/images` topic with:
```bash
$ ros2 run openrobotics_darknet_ros detector_node --ros-args --params-file path/to/detector_node_params.yaml -r '~/images:=/your/camera/topic'
```