This ROS node processes video feed from multiple cameras to detect the presence of specific colors: Red, Green, and Blue. Leveraging the power of OpenCV and the flexibility of the HSV color space.
git clone https://github.com/Team-Ausdroid-Unimelb/image_processing.git
Navigate to ../image_processing/config/ to configure the appropriate params for you. In the camera_config.yaml file, you can decalre which one or more cameras are used to detect. In Linux system, you can check available camera devices in command line using
ls /dev/video*
Sometimes, you may see multiple video devices for a single camera, check here for more details.
In the HSV_filter_param.yaml file, you can tune the thereshold for your application. Each color has a lower and an upper threshold, defining a range in the HSV space that represents that particular color.
Format:
<Color>_lower_threshold: [Hue_min, Saturation_min, Value_min]
<Color>_upper_threshold: [Hue_max, Saturation_max, Value_max]
Hue (H): Represents the type of color (e.g., red, green, or blue). It's measured in degrees on the color circle ranging from 0° to 360°. However, in OpenCV, it's scaled to 0 to 180 to fit into 8-bit value.
Saturation (S): Represents the richness of the color, ranging from 0 (grey) to 255 (vivid).
Value (V): Represents the brightness of the color, ranging from 0 (black) to 255 (bright).
For more details on HSV, please check the Wiki page and OpenCV page.
A sample launch file is provided in ../image_processing/launch/, the verbose_original and verbose_filter toggles whether to show the video output. They are deafult to False to save processing time. Set them to True if you would like to see the results.
Three publishers are included in this ros pack.
- detection_robot_r
- detection_robot_g
- detection_robot_b
The current algorithm evaluate all cameras as one big frame. If any of them detect the specific color, the results will be set to true. The publisher will only publish the messages once the state changes.