Place a solid-color object in front of the camera - for example, a red ball - and use the keyboard to modify these ranges: hue: sat: val:įor example, to increase the max hue, press e, to decrease the min hue press x, to decrease the range press s, to increase the range press d. The program will start out with HSV set to the following values: The filtered feed is a mask where white is the color you’re tracking, and black is not. The program will pop up two windows: “camera feed” and “filtered feed”. # e.g., if /dev/video1 is your camera device, then use -camera=1 # use -camera=N to set the index of your camera. To illustrate this concept to yourself, try running the color_detector.py script in the blob-tracker folder: Saturation and value will depend on the object’s texture and lighting conditions, and we can set those those values to a wider range to account for a wider range of texture and lighting conditions. The hue is what we care about - for example, red - and the range we’ll look for here will be fairly narrow. HSV stands for Hue, Saturation, and Value. That’s because something that’s “red” in one lighting condition might look like “dark red” in low light or “light red” in bright light.Īn alternative is the HSV color model. However, when it comes to trying to track an object of a certain “color”, the RGB space is not very useful. Most of the time, we think in terms of the RGB color model. (Note: If you’re lazy and just want to track a color without understanding how it works, you can skip this section and pass in –red, –green, or –blue to blob-tracker.py below)
#Arduino camera opencv code
The code is available on Github at Understanding color tracking
#Arduino camera opencv install
Anything greater than 2.2 should workĪlternatively, you can install ROS, which comes with OpenCV. # run apt-cache search opencv-core first, to see which version is available. If you’ve done things correctly, you should be able to view images from the camera on your computer (using any webcam software, like Skype or Google hangouts), and control the pan-tilt unit by plugging in your Arduino and sending commands over serial. Follow instructions there to assemble the unit, and upload the provided Arduino code to your Arduino. This tutorial builds off the Remotely Controlled Pan-Tilt Unit post. On the computer, a simple Python script takes in the camera images, processes them using OpenCV, and sends back commands to the Arduino to move the pan-tilt servos and track the desired color.
The camera and Arduino are hooked up to a computer via USB.
The setup consists of a camera mounted on a pan-tilt unit that’s wired to an Arduino.
The Blob Tracker is a simple demo that shows how you can track a certain color in OpenCV.
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