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#ARDUINO CAMERA OPENCV SERIAL#
#ARDUINO CAMERA OPENCV PRO#

D PYTHON_EXECUTABLE= \~/.virtualenvs/cv/bin/python \ D OPENCV_EXTRA_MODULES_PATH= \~/opencv_contrib/modules \ I used the following cmake command, as opposed to that given in step 4: During my setup, I deviated from the above setup in the following ways and results differed in the following ways: If using Ubuntu, or similar, this guide presents a more than sufficient set of instructions for compiling and installing OpenCV.

#ARDUINO CAMERA OPENCV PRO#

I installed XUbuntu 19.04 on an external hard drive connected to my Microsoft Surface Pro 3, then executed installation of OpenCV and other software requisites. Specifics are highly variable, depending on your rig.

(Project used 1/2 inch square dowel rods and relevant fasteners.

Sufficient motor linkages and connections.

Electrical project basics, such as jump wires and breadboards.

(Project used a DC power supply providing 24 volts.).

12 to 48 volt power source for driving motors and motor controller.

(Project used two L298N dual H-bridge motor controllers.).

Two stepper motor controllers capable of interfacing with the Arduino Stepper.h library.

(Project used a Logitech C920 HD webcam.).

An Arduino microcontroller with at least 8 available digital output pins and a USB cable.

(Project developed using OpenCV 4.1.0 on 64-bit XUbuntu 19.04 running Python 3.7.).

#ARDUINO CAMERA OPENCV SERIAL#

Computer requires a USB port for camera input and a USB port for serial communication with Arduino.

A computer with Python 3, NumPy, OpenCV, and imutils installed.

Additionally shown is the mask of the camera view containing the object to track after computer vision selection techniques have been applied (bottom). A yellow circle is imposed around the contour of the detected object and a red dot is imposed indicating the calculated centroid of the contour. View from the camera being articulated to track a pink Post-It (top). This repository contains the Python and Arduino code written to execute this project. The communication between the computer (Python) and the Arduino would occur over a serial connection using mutually known flag characters, ASCII integers 1 through 4, sent from Python, over USB, to indicate which motor to move and and which direction. I opted for two stepper motors controlled by an Arduino and L298N dual H-bridge motor controllers. I opted to use the OpenCV computer vision library and Python 3 for computer vision. Utilizing a webcam and computer vision techniques, I sought to track an object in 3D space utilizing a two degree of freedom motorized mechanism. A longer video of the mechanism in action can be found here: Theory and Methods Camera is tracking a suspended pink Post-It swinging behind the filming camera.

View of the tracking mechanism in action.