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https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision
This is my undergraduate project at Ferdowsi University of Mashhad, focusing comprehensively on computer vision.
https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision
cnn computer-vision deep-learning image-classification image-generation image-manipulation image-segmentation keras object-detection python pytorch-lightning
Last synced: 9 days ago
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This is my undergraduate project at Ferdowsi University of Mashhad, focusing comprehensively on computer vision.
- Host: GitHub
- URL: https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision
- Owner: alirezasaharkhiz9
- License: mit
- Created: 2024-09-20T10:54:05.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-17T16:31:04.000Z (13 days ago)
- Last Synced: 2025-01-17T17:34:37.922Z (13 days ago)
- Topics: cnn, computer-vision, deep-learning, image-classification, image-generation, image-manipulation, image-segmentation, keras, object-detection, python, pytorch-lightning
- Language: Jupyter Notebook
- Homepage:
- Size: 69.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Undergraduate Project - Computer Vision
### Overview
This undergraduate project focuses on exploring and developing advanced techniques in computer vision, structured into two main sections: Classical Computer Vision and Modern Deep Learning-based Computer Vision. The project begins by covering the fundamentals of image processing, video analysis, and classical object detection algorithms. In the second part, using cutting-edge tools like PyTorch, convolutional neural networks (CNN) will be implemented. The project also delves into modern techniques such as transfer learning, pre-trained models, and the emerging Vision Transformers. Furthermore, efficient object detection with YOLO and facial recognition using deep learning will be thoroughly explored.
### Table of Contents
1. [Project Structure](#project-structure)
2. [Modern CV vs Classical CV](#modern-cv-vs-classical-cv)
3. [Roadmap](#roadmap)
4. [Requirements](#requirements)
5. [References](#references)
### Project Structure
The project will be organized into the following sections:
#### 1. [Classical Computer Vision](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/tree/main/Classical%20Computer%20Vision)
- Image Processing
- [Image processing](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Classical%20Computer%20Vision/ImageProcessing.ipynb)
- Video Processing
- [Working with Video](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Classical%20Computer%20Vision/WorkingWithVideo.ipynb)
- Object Detection
- [Object Detection](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Classical%20Computer%20Vision/ObjectDetection.ipynb)
- Face Detection
- [Face Detection](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Classical%20Computer%20Vision/FaceDetection.ipynb)
- OCR
- [Text Detection](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Classical%20Computer%20Vision/TextDetection.ipynb)
#### 2. [Modern Computer Vision - Deep Learning](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/tree/main/Modern%20Computer%20Vision)
- Image Classification
- [Cnn Model theory](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/CnnModelTheory.ipynb)
- [Cnn With Keras (Normal, Pneumonia and Tuberculosis Lungs)](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/CnnWithKeras.ipynb)
- [CNN With Pytorch Lightning (Fundus Glaucoma Detection)](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/CnnWithPytorchLightning.ipynb)
- [Residual Network With Pytorch Lightning (Normal, Pneumonia and Tuberculosis Lungs)](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/ResidualNetworkWithPytorchLightning.ipynb)
- Image Manipulation
- [AutoEncoder With Keras (The main objective of creating this dataset is to create autoencoder network that can colorized grayscale landscape images)](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/AutoEncoderWithKeras.ipynb)
- Image Generation
- [DCGAN With Keras (GAN using the MNIST dataset)](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/DCGANWithKeras.ipynb)
- Image Segmentation
- [Lungs Segmentation Using U-Net architecture](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/LungsSegmentationUsingU_Net.ipynb)
- [Tumor Segmentation Using Yolo v11](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/TumorSegmentationUsingYolo.ipynb)
- Object Detection
- [Object Detection Using YOLO v5 (road-detection)](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/ObjectDetectionUsingYOLOv5.ipynb)
- [Object Detection Using YOLO v8 (Bone Fracture Detection)](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/ObjectDetectionUsingYOLOv8.ipynb)
- [Object Detection Using YOLO v11 (Tumor Detection)](https://github.com/alirezasaharkhiz9/undergraduate-project-computer-vision/blob/main/Modern%20Computer%20Vision/TumorDetectionUsingYolov11.ipynb)
- Object tracking
- ?
- Face Recognition
- ?
### Modern CV vs Classical CV
| **Topic** | **Classical Computer Vision** | **Modern Computer Vision (Using Deep Learning)** |
|-----------------|-----------------------|---------------------------------|
| **Definition and Core Principles** | Based on hand-crafted algorithms and engineered features. | Based on deep neural networks and learning from large datasets. |
| **Feature Extraction Method** | Features are extracted manually using algorithms like SIFT, SURF, and HOG. | Features are automatically learned by convolutional neural networks (CNNs). |
| **Accuracy** | Accuracy is usually limited and optimization for complex problems is harder. | Generally achieves much higher accuracy, especially in complex tasks like image recognition and classification. |
| **Amount of Data Required** | Requires less data, but models are usually less optimized. | Requires large amounts of data for effective learning. |
| **Computational Complexity** | Generally lighter and simpler in computation. | Heavier in computation and requires powerful hardware (GPUs). |
### Roadmap
The project will progress through the following phases:
1. **Phase 1**: Implementing Classical Computer Vision Techniques
2. **Phase 2**: Building and Training Deep Learning Models with PyTorch and tensorflow, keras
### Requirements
To run the project, you'll need:
- Python 3.x
- tools: Jupyter Lab, Colab, lightning studio
- Required libraries listed in requirements.txt.
You can install the dependencies using:
``` bash
pip install -r requirements.txt
```
### References:
- Ayyadevara, V. K., & Reddy, Y. (2024). Modern Computer Vision with PyTorch - Second Edition: A practical roadmap from deep learning fundamentals to advanced applications and Generative AI (2nd ed.). Packt Publishing.
- Elgendy, M. (2020). Deep Learning for Vision Systems (1st ed.). Manning.
- Ratan, R. D. (2024). Modern Computer Vision GPT, PyTorch, Keras, OpenCV4 in 2024! Next-Gen Computer Vision: YOLOv8, DINO-GPT4V, OpenCV4, Face Recognition, GenerativeAI, Diffusion Models & Transformers [Online course]. Udemy.