🚀 A state-of-the-art Computer Vision repository featuring everything from fundamental image processing to cutting-edge deep learning and edge deployment solutions. Built with modern tools and designed for both learning and production use.
A comprehensive repository that takes you from pixel-level operations to deploying sophisticated computer vision models on edge devices. Perfect for beginners, researchers, and industry professionals.
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Image I/O Operations
- Image reading/writing
- Video processing
- Webcam integration
-
Basic Image Operations
- Resizing and scaling
- Cropping and rotation
- Pixel manipulation
- Color space conversions (RGB, HSV, etc.)
-
Image Enhancement
- Blurring techniques
- Thresholding (Global & Adaptive)
- Edge detection
- Contour detection and analysis
-
Color Detection System
- Real-time color identification
- Color tracking and segmentation
-
Face Anonymizer
- Face detection
- Privacy preservation techniques
- Real-time face blurring
-
Parking Lot Space Detection
- Space occupancy detection
- Vehicle counting
- Parking management system
-
Text Extraction
- OCR implementation
- Text detection and recognition
- Document analysis
-
YOLO Integration
- Object detection
- Instance segmentation
- Pose estimation
-
Segment Anything Model (SAM)
- Auto-annotation
- Instance segmentation
- Zero-shot segmentation
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Model Optimization
- Model quantization
- Pruning techniques
- Model compression
-
Edge Platforms Support
- Raspberry Pi deployment
- NVIDIA Jetson support
- Mobile device deployment
- Intel NCS2 integration
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Real-time Processing
- Optimization techniques
- Hardware acceleration
- Multi-threading implementation
Python 3.8+
CUDA (optional, for GPU acceleration)
OpenCV 4.x
PyTorch
Ultralytics# Create virtual environment
python -m venv venv
source venv/bin/activate # Linux/Mac
.\venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txt
graph TD
A[Computer-Vision] --> B[OpenCV]
A --> C[Ultralytics]
A --> D[Edge-Deployment]
A --> E[data]
B --> B1[Basic Operations]
B --> B2[ColorSpaces]
B --> B3[Edge Detection]
B --> B4[Projects]
C --> C1[Models]
C --> C2[Scripts]
D --> D1[Model-Optimization]
D --> D2[Platform-Specific]
B4 --> P1[Color Detection]
B4 --> P2[Face Anonymizer]
B4 --> P3[Parking Detection]
B4 --> P4[Text Extraction]
style A fill:#ff9900,stroke:#333,stroke-width:4px
style B fill:#00758f,stroke:#333,stroke-width:2px
style C fill:#00758f,stroke:#333,stroke-width:2px
style D fill:#00758f,stroke:#333,stroke-width:2px
style E fill:#00758f,stroke:#333,stroke-width:2px
📌 Each module is designed to be independent yet interconnectable, allowing for maximum flexibility and scalability.
🎯 Basic OpenCV Operations
import cv2
import numpy as np
def process_image(image_path):
# Read image
img = cv2.imread(image_path)
# Basic processing pipeline
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
edges = cv2.Canny(blur, 100, 200)
# Display results
cv2.imshow('Original', img)
cv2.imshow('Processed', edges)
cv2.waitKey(0)
return edges
# Example usage
process_image('path/to/image.jpg')🤖 Deep Learning with YOLO
from ultralytics import YOLO
import cv2
import numpy as np
def detect_objects(image_path):
# Load model
model = YOLO('yolov8n.pt')
# Perform inference
results = model(image_path)
# Process results
for r in results:
boxes = r.boxes
for box in boxes:
# Get box coordinates
x1, y1, x2, y2 = box.xyxy[0]
# Get class name
cls = int(box.cls[0])
# Get confidence score
conf = box.conf[0]
print(f"Detected {model.names[cls]} with confidence {conf:.2f}")
# Show results
img = cv2.imread(image_path)
annotated_frame = results[0].plot()
cv2.imshow("Detection Results", annotated_frame)
cv2.waitKey(0)
# Example usage
detect_objects('path/to/image.jpg')🎥 Real-time Video Processing
import cv2
import numpy as np
from ultralytics import YOLO
def process_video_stream():
# Initialize
cap = cv2.VideoCapture(0)
model = YOLO('yolov8n.pt')
while True:
ret, frame = cap.read()
if not ret:
break
# Process frame
results = model(frame)
# Draw results
annotated_frame = results[0].plot()
# Display
cv2.imshow('Real-time Detection', annotated_frame)
# Exit on 'q' press
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
# Start real-time processing
process_video_stream()💡 These examples demonstrate basic usage. Check individual module documentation for advanced features and customization options.
- Basic OpenCV implementations
- YOLO integration
- SAM model integration
- TensorRT optimization
- Custom hardware acceleration
- Cloud deployment architecture
- Real-time streaming pipeline
- Advanced AR applications
- 3D vision system
- Multi-camera support
- Automated CI/CD pipeline
- Comprehensive testing suite
- Model version control
- Performance monitoring
- Production documentation
- Custom neural architectures
- Federated learning support
- Edge AI optimization
- Cross-platform deployment
- Real-time 3D reconstruction
| Model | CPU | GPU | Edge Device |
|---|---|---|---|
| YOLOv8n | 25 | 120 | 15 |
| YOLOv8s | 20 | 100 | 12 |
| SAM | 10 | 45 | 5 |
pie title Model Size vs Accuracy
"YOLOv8n (7MB)" : 65
"YOLOv8s (20MB)" : 72
"YOLOv8m (50MB)" : 78
"YOLOv8l (100MB)" : 85
📈 Benchmarks are continuously updated as new optimizations and models are implemented
Contributions are welcome! Please read our Contributing Guidelines for details on how to submit pull requests, report issues, and contribute to the project.
This project is licensed under the terms of the MIT License.
Thanks to these wonderful people:
Special thanks to all contributors and the open-source community for making this project possible!