Computer-Vision

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Computer-Vision

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# 📸 Computer Vision with OpenCV & AI Roadmap

> A complete all-in-one guide to Computer Vision covering OpenCV, Image Processing, and Machine Learning.

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## 📖 Introduction

Computer Vision (CV) enables machines to understand images and videos.

This repository includes:
- OpenCV basics (hands-on)
- Image processing techniques
- Full CV pipeline
- Feature extraction
- Machine learning models
- Real-world projects

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## 🧠 OpenCV Basics (CampusX Series)

### 📌 Topics Covered

1. Image Basics  
2. Reading an Image  
3. RGB → Grayscale Conversion  
4. RGB Color Channels  
5. Image Resizing  
6. Flipping Images  
7. Cropping Images  
8. Saving Images  
9. Drawing Shapes & Text  
10. OpenCV Events  
11. Advanced Events  
12. Cropping Tool Project  
13. Working with Videos

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## 🔄 Image Processing

### 📐 Geometric Transformations
- Translation  
- Scaling  
- Rotation  
- Affine Transformation  
- Perspective (Homography) Transformation

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### 🌫️ Filtering & Smoothing
- Gaussian Blur  
- Median Blur  
- Bilateral Filter

---

### 🧱 Morphological Operations
- Erosion  
- Dilation  
- Opening  
- Closing

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### 🎚️ Intensity Transformations
- Histogram Equalization

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## ⚙️ Complete Computer Vision Pipeline

### 1️⃣ Image Acquisition & Input
- Image loading  
- Image resizing  
- Color space conversion

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### 2️⃣ Preprocessing
- Noise reduction  
- Normalization  
- Thresholding  
- Contrast enhancement

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### 3️⃣ Feature Extraction

#### 🔹 Edge Features
- Sobel  
- Canny

#### 🔹 Corner Features
- Harris  
- FAST

#### 🔹 Local Descriptors
- SIFT  
- SURF  
- ORB

#### 🔹 Shape Descriptors
- HOG (Histogram of Oriented Gradients)

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### 4️⃣ Feature Representation
- Feature vector construction  
- Bag of Visual Words (BoVW)  
- Feature normalization  
- PCA (optional)

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### 5️⃣ Machine Learning Models

#### 📈 Linear Models
- Logistic Regression  
- Linear SVM

#### 📊 Margin-Based Models
- Kernel SVM

#### 📏 Distance-Based Models
- k-Nearest Neighbors (kNN)

#### 🌳 Tree-Based Models
- Decision Tree  
- Random Forest

#### 🎲 Probabilistic Models
- Naive Bayes  
- Gaussian Mixture Models (GMM)

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### 6️⃣ Prediction & Inference
- Image classification  
- Image matching  
- Image recognition  
- Similarity-based retrieval

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### 7️⃣ Evaluation Metrics
- Accuracy  
- Precision / Recall  
- Confusion matrix  
- Cross-validation

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## 💡 Project Ideas

### 🟢 Beginner
- Image Resizer Tool  
- Grayscale Converter  
- Drawing App using OpenCV

### 🟡 Intermediate
- Face Detection System  
- Document Scanner (Perspective Transform)  
- Image Filters App

### 🔴 Advanced
- Image Classification System  
- Object Detection System  
- Image Search Engine  
- Real-time Video Processing App

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## 🛠️ Tech Stack

- Python 🐍  
- OpenCV  
- NumPy  
- Scikit-learn  
- Matplotlib

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## 📌 Goals

- Learn OpenCV from scratch  
- Understand full CV pipeline  
- Apply ML on image data  
- Build real-world projects

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## 🤝 Contributing

Feel free to:
- Add projects  
- Improve code  
- Share notes

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## ⭐ Support

If this helps you:
- Star ⭐ the repo  
- Share with others

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## 📢 Credits

- CampusX OpenCV Series  
- roadmap.sh community roadmap  
- OpenCV documentation

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## 🚀 Final Note

> "Learn Computer Vision by building — not just watching."

Stay consistent and keep building!

Computer-Vision

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