Hand Landmark Detection OpenCV Python With Source Code
The Hand Landmark Detection OpenCV Python was developed using Python OpenCV, In this Python Project With Source Code we are going to learn how to obtain hand landmarks from an web cam, using Python, MediaPipe and OpenCV.
What is OpenCV?
OpenCV is short for Open Source Computer Vision. Intuitively by the name, it is an open-source Computer Vision and Machine Learning library.
This library is capable of processing real-time image and video while also boasting analytical capabilities. It supports the Deep Learning frameworks.
A OpenCV Python Hand Landmark Detection, We will be using OpenCV to read the image and displaying it and MediaPipe to perform the hand detection and landmark estimation.
In short, MediaPipe is a free and open-source framework that offers cross-platform, customizable Machine Learning solutions for live and streaming media.
In This Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.
By the way if you are new to python programming and you don’t know what would be the the Python IDE to use, I have here a list of Best Python IDE for Windows, Linux, Mac OS that will suit for you. I also have here How to Download and Install Latest Version of Python on Windows.
To start executing Hand Landmark Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm in your computer.
Hand Landmark Detection OpenCV Python With Source Code : Steps on how to run the project
Time needed: 5 minutes
These are the steps on how to run Hand Landmark Detection OpenCV Python With Source Code
- Step 1: Download the given source code below.
First, download the given source code below and unzip the source code.
- Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve download to your PyCharm IDE.
- Step 3: Run the project.
last, run the project with the command “py main.py”
Installed Libraries
import cv2 import mediapipe as mp
Complete Source Code
import cv2
import mediapipe as mp
cap = cv2.VideoCapture(0)
mpHands = mp.solutions.hands
hands = mpHands.Hands()
mpDraw = mp.solutions.drawing_utils
while True:
success,img = cap.read()
imgRGB = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
results = hands.process(imgRGB)
if results.multi_hand_landmarks:
for handlandmark in results.multi_hand_landmarks:
for id,lm in enumerate(handlandmark.landmark):
print(id,lm) # lm produce output in ratio format
h,w,_ = img.shape
cx,cy = int(lm.x*w),int(lm.y*h)
cv2.circle(img,(cx,cy),4,(0,0,255),cv2.FILLED)
mpDraw.draw_landmarks(img,handlandmark,mpHands.HAND_CONNECTIONS)
cv2.imshow('Image',img)
if cv2.waitKey(1) & 0xff==ord('q'):
breakOutput
Download Source Code below
Anyway, if you want to level up your programming knowledge, especially Python OpenCV, try this new article I’ve made for you Best OpenCV Projects With Source Code For Beginners 2021.
Summary
Hand detection is the process of finding the joints on the fingers as well as the finger-tips in a given image. It is similar to finding key points on Face or Body.
But, different from Hand Detection since in that case, we treat the whole hand as one object.
In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free.
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Inquiries
If you have any questions or suggestions about Hand Landmark Detection OpenCV Python With Source Code, please feel free to leave a comment below.
Frequently Asked Questions
How does hand landmark detection work?
This project uses Google’s MediaPipe library (pip install mediapipe). MediaPipe’s Hands solution returns 21 3D landmark points per detected hand (knuckles, fingertips, palm center). OpenCV captures frames, MediaPipe processes each frame, then OpenCV draws the landmark connections back on the original frame. Real-time on a CPU at 30 FPS.
What OpenCV version do I need to run this project?
Use OpenCV 4.5 or newer. Install with pip install opencv-python (the standard build for desktop projects). Some projects also need opencv-contrib-python which adds extra modules (SIFT, SURF, advanced trackers). The pip install command auto-downloads pre-built wheels so no compilation is needed on Windows, Mac, or Linux.
How do I install OpenCV and the dependencies for this project?
Open a terminal, then: pip install opencv-python numpy. Most projects also need one of these: mediapipe (for face / hand / pose detection), pyzbar (for barcode and QR), pytesseract (for OCR), Pillow (for image manipulation), pyautogui (for screen capture). Pin Python version to 3.10, 3.11, or 3.12 for maximum library compatibility.
Can I use this OpenCV project for a BSIT or CSE capstone?
Yes, but extend it. A single OpenCV demo (face detection alone, lane detection alone) is too narrow for full capstone scope. Combine it with a real domain (attendance system using face recognition, traffic monitoring system using lane detection, fitness coach app using pose detection), add a database to log results, build a simple Tkinter or Streamlit UI, and document the whole pipeline in Chapter 3.
Why am I getting AttributeError or ImportError when running this code?
Three most common causes: (1) You installed opencv-python but the code needs opencv-contrib-python (extra modules like xfeatures2d). Reinstall with pip install opencv-contrib-python. (2) You are on Python 3.13 but some wheels (mediapipe) lag behind, downgrade to Python 3.11 or 3.12. (3) NumPy version mismatch, pin numpy to a version your other libraries support.
Where do I find more OpenCV and Machine Learning project ideas?
Browse our Machine Learning Projects hub for 23+ OpenCV demos with source code. For capstone-scale AI ideas (RAG, NLP, recommendation systems), see 100+ AI Capstone Project Ideas. For broader Python project ideas, our Python Projects library has 250+ working capstones.
Angel Jude Suarez
Full-Stack Developer at PIES IT Solution
Focuses on Python development, machine learning, and AI integration. Has built production AI systems including OpenAI Whisper integration for medical transcription and GPT-4o-powered diagnosis assistance. Strong background in pandas, scikit-learn, and TensorFlow.
Expertise: Python · PHP · Java · VB.NET · ASP.NET · Machine Learning · AI Integration · OpenCV · Django · CodeIgniter · View all posts by Angel Jude Suarez →