OpenCV Projects
Face Detection
ActiveReal-time face detection using OpenCV and Python. Implements Haar Cascade classifiers for accurate facial recognition.
Object Detection
ActiveYOLO-based object detection system capable of identifying multiple objects in real-time with high accuracy.
Image Segmentation
ActiveAdvanced image segmentation using deep learning models to separate objects from backgrounds.
Pose Estimation
In ProgressReal-time human pose estimation using OpenCV and MediaPipe for tracking body movements.
Text Recognition
ActiveOCR system using Tesseract and OpenCV for extracting text from images and documents.
Motion Detection
ActiveReal-time motion detection system with background subtraction and object tracking.
OpenCV Project Documentation
Getting Started with OpenCV Projects
Prerequisites
- Python 3.7 or higher
- OpenCV 4.x
- NumPy
- Basic understanding of image processing
Quick Installation
pip install opencv-python
pip install numpy
Basic Usage Example
import cv2
import numpy as np
# Read an image
img = cv2.imread('image.jpg')
# Convert to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Display the image
cv2.imshow('Image', gray)
cv2.waitKey(0)
Image Processing
Learn about basic image processing operations including filtering, transformations, and color space conversions.
Feature Detection
Explore methods for detecting corners, edges, and other image features using OpenCV.
Next Steps
Ready to dive deeper? Check out our detailed tutorials section for step-by-step guides on implementing various computer vision projects.
Getting Started with Face Detection
Learn the basics of implementing face detection using OpenCV and Haar Cascades.
Real-time Object Detection
Implement YOLO object detection for real-time applications using OpenCV.
Advanced Image Processing
Master advanced image processing techniques using OpenCV filters and transformations.
Motion Tracking Basics
Learn to implement basic motion tracking using OpenCV's optical flow.
OpenCV Interactive Playground
Test and experiment with OpenCV functions in real-time
Code Editor
import cv2
import numpy as np
# Your OpenCV code here
img = cv2.imread('input.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Apply some filters
blur = cv2.GaussianBlur(gray, (5,5), 0)
edges = cv2.Canny(blur, 100, 200)
# Display result
cv2.imshow('Output', edges)
cv2.waitKey(0)
Quick Templates
Output Preview
Parameters
Console Output
[INFO] Loading image...
[INFO] Applying filters...
[INFO] Processing complete.
Pre-trained Models
Choose from our collection of optimized OpenCV models
Face Detection Model
Production ReadyHaar Cascade based face detection model with high accuracy and performance.
YOLO Object Detection
BetaYOLOv4 model optimized for real-time object detection with multiple classes.
Pose Estimation
Production ReadyHuman pose estimation model with support for multiple people tracking.
Model Comparison
| Model Name | Accuracy | Size | FPS | Status |
|---|---|---|---|---|
| Face Detection | 95.8% | 2.3 MB | 60 | Production |
| YOLO Object Detection | 91.2% | 245 MB | 30 | Beta |
| Pose Estimation | 93.5% | 180 MB | 45 | Production |
Settings
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John Developer
Computer Vision Engineer
About
Computer Vision Engineer specializing in OpenCV and Python development. Passionate about creating efficient and innovative solutions.
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Recent Activity
Successfully implemented real-time face detection using OpenCV.
Initiated object tracking project using OpenCV and Python.
Added comprehensive documentation for image processing module.
Project Results
View and analyze your OpenCV project results
Face Detection Results
Latest processing results
Faces Detected
5
Processing Time
0.3s
Object Detection Results
Latest processing results
Objects Detected
8
Processing Time
0.5s
Processing History
| Timestamp | Project | Status | Results | Actions |
|---|---|---|---|---|
| 2024-01-20 15:30 | Face Detection | Success | 5 faces detected | |
| 2024-01-20 15:25 | Object Detection | Success | 8 objects detected | |
| 2024-01-20 15:20 | Image Processing | Processing | In progress |