Computer Vision Projects with Arduino | 2 Projects

Install Python on your computer by downloading the official installer, choosing default or customized options, and completing installation; verify by running Python and print hello, world.

Install PyCharm community edition, set up the Python 3.9 interpreter, create a new project, write a simple Python file, and run the code to verify the IDE setup.

Install the OpenCV Python package via the terminal, create a Python file, import cv2, and print cv2.__version__ to verify a successful setup.

Install the Mediapart module to detect facial landmarks and track lip movements using facial coordinates. Run pip install mediapart and proceed to the next lesson.

Install the Arduino IDE by downloading from the official site, selecting Windows seven and newer, accepting the license, and installing all components.

Explore the Arduino IDE interface, including void setup and the looping function, and learn how to verify, upload, and run motor control and lamp commands on the board.

Learn how to control a 16x2 LCD screen with minimal wiring, using two power lines and two I2C signal pins, and understand basic data transfer for two-row, sixteen-column displays.

Explore i2c protocol, the two-line bidirectional communication between a master Arduino and a slave LCD. Learn how devices use addresses like 0x27 and eight-bit data transfers to prevent data collisions.

Download the necessary library zip from GitHub, install the library in the Arduino IDE, include the lcd i2c and liquid crystal libraries, and verify the code compiles successfully.

Learn the connection diagram to control an lcd screen with an Arduino Uno, wiring the five volt power line and i2c lines, including sd to a4 and sl to a5.

Write and display text on an LCD using the Liquid Crystal library. Set up the I2C address, position the cursor, and print hello world on the 16x2 screen.

Learn to clear an lcd screen with the liquid crystal library, set i2c address 0x27, define 16 columns, and print messages with delays.

Learn to create a custom character on an Arduino LCD, selecting pixels, defining a custom char, and displaying it with set cursor and write functions.

Apply the deep learning module and the major pipe facial landmarks, developed by Google, to detect facial features. Analyze lip-area expressions and print facial expressions on a display.

Capture images from a computer webcam by creating a video capture object, reading frames in a loop, and displaying them in a window named result, exiting when you press Q.

Learn to detect and draw facial landmarks using the mediapart face mesh on live webcam video, configuring detection and tracking confidence and rendering landmarks on frames.

Explore how to customize the appearance of facial landmark overlays by adjusting thickness, circle radius, and color using the drawing function in the arduino computer vision project.

Get an overview of two computer vision projects using Arduino, introducing the goals and structure of these two projects.

Install the serial module to enable Arduino–Python communication, then verify by importing serial and printing its version to confirm a successful setup.

Create a Python project file, import media and serial modules, set up webcam capture, use face mesh for landmarks, and configure serial to Arduino on port 5 at 9600 baud.

Develop a Python workflow to detect facial landmarks from a live webcam stream using face mesh, converting frames to rgb and applying detection and tracking confidences.

Identify lip corner landmark indices and extract their x and y coordinates, using points 306 and 61 to map lip geometry and facial expressions.

Learn to locate facial landmarks by index (such as 306 and 61), compute x and y coordinates, convert to integers, and draw red circles at lip corners to analyze expressions.

Learn the distance formula between two points using x1, y1, x2, y2, including squaring, summing, and taking the square root to analyze lip corner positions for facial expressions.

Implement a Python distance formula using the math module, with sqrt and pow, to compute the distance between two points, print the result, and signal an Arduino board.

Learn how to send data from Python to an Arduino board by evaluating lip-corner distance and triggering serial signals using letters to control Arduino actions.

Learn to set up an Arduino lcd display with the liquid crystal library, define a 16x2 screen, read data from Python, and display with cursor and lcd.print for A or B.

Test and fix an Arduino-based project by debugging the code, clearing the screen before set cursor calls, uploading to the board, and running the Python code to verify the result.

Discover what a servo motor is as a motion control mechanism and how to connect and control it with an Arduino using brown ground, red 5V, and orange signal lines.

Learn to control a servo motor with Arduino Uno by including the servo library, creating a servo object, attaching to pin nine, and using the write function to set angles.

Explore two computer vision projects with Arduino, outlining the scope and aims of hands-on work.

Create a two-segment arm using image processing and computer vision to move with a servo motor, based on shoulder-wrist distance, with arm parts measuring 25 cm and 27 cm.

Cut and assemble cardboard arm parts from elbow to wrist, using scissors, then stick the cut pieces onto cardboard; download and print the project sources from the source section.

Construct and paint a cardboard arm, cut and glue parts, applying cream color. Demonstrate wrist movement toward and away from the shoulder to show arm control for the project.

Construct a cardboard robotic arm with a shoulder and elbow, driven by a motor and a servo for opening and closing, reinforced with blue paper on the cardboard.

Attach a servo motor to the elbow with hot silicone, align at the elbow midpoint, and fix it for 180–90 degree control, then proceed with Arduino and image processing.

Develop and run a Python project that imports OpenCV, MediaPipe (MP), and the serial module, captures webcam video, detects arm landmarks with MP, calculates wrist–shoulder distance, and communicates with Arduino.

Write a python script to build a pose model with media pipe, using static_image_mode and 0.5 detection and 0.5 tracking confidence, process frames with pose.process to detect arms and radius.

Detect wrist, elbow, and shoulder landmarks using pose landmarks indices, extract corresponding x and y coordinates scaled to the video frame, and draw circles to visualize the arm regions.

Test and visualize arm detection using OpenCV in Python, display frames from a webcam, and draw lines between the wrist, elbow, and shoulder to illustrate arm pose estimation.

Calculate the distance between the shoulder and the wrist using the distance formula between two points, and use the result to send a signal to an Arduino servo motor.

Send signals from Python to an Arduino servo motor by mapping a distance value to three commands (A, B, C) for closing, halfway, and open positions.

Learn to program an Arduino servo with Servo library, attach it to pin 9, serial at 9600, and map A, B, and C signals to 45, 90, 180 degrees.