https://github.com/erenisci/computer-vision

Comprehensive computer vision portfolio featuring face detection, object tracking, deep learning models, YOLO, GANs, and advanced image processing techniques.
https://github.com/erenisci/computer-vision

autoencoders classification cnn computer-vision deep-dream deep-learning face-detection face-recognition gans haar-cascade image-segmentation object-detection object-tracking opencv python style-transfer yolo

Last synced: 3 months ago
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Comprehensive computer vision portfolio featuring face detection, object tracking, deep learning models, YOLO, GANs, and advanced image processing techniques.

• Host: GitHub
• URL: https://github.com/erenisci/computer-vision
• Owner: erenisci
• Created: 2025-06-04T09:38:49.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2025-07-21T09:16:52.000Z (12 months ago)
• Last Synced: 2025-07-21T11:22:27.000Z (12 months ago)
• Topics: autoencoders, classification, cnn, computer-vision, deep-dream, deep-learning, face-detection, face-recognition, gans, haar-cascade, image-segmentation, object-detection, object-tracking, opencv, python, style-transfer, yolo
• Language: Jupyter Notebook
• Homepage:
• Size: 65.3 MB
• Stars: 0
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Computer Vision

This repository includes a structured collection of **computer vision projects and exercises**, covering a full spectrum of concepts and implementations, such as:

- **Face Detection and Recognition**

  - Understanding the intuition behind **Cascade** and **HOG (Histogram of Oriented Gradients)** classifiers.

  - Implementing face detection using **OpenCV** and **Dlib**.

  - Detecting additional objects like **cars, clocks, eyes, and full human bodies**.

  - Comparing the performance of **Haarcascade, HOG, and CNN-based detectors**.

  - Detecting and recognizing faces using **images and live webcam feeds**.

  - Exploring the **LBPH (Local Binary Patterns Histograms)** algorithm for face recognition.

- **Object Tracking**

  - Learning the intuition behind **KCF (Kernelized Correlation Filters)** and **CSRT (Discriminative Correlation Filter with Channel and Spatial Reliability)** trackers.

  - Tracking objects in videos using the **OpenCV tracking API**.

- **Neural Networks and Image Classification**

  - Gaining a solid understanding of neural network theory, including **perceptrons, activation functions, weight updates, backpropagation, and gradient descent**.

  - Implementing **dense (fully connected) neural networks** for image classification.

  - Extracting pixels and engineered features from images to build effective models.

- **Convolutional Neural Networks (CNNs)**

  - Learning the theory and practical implementation of CNNs using **Python and TensorFlow**.

  - Applying **transfer learning and fine-tuning** for high-accuracy classification.

  - Using CNNs to classify **human emotions** (happy, anger, disgust, fear, surprise, neutral) in images and videos.

- **Advanced Deep Learning Techniques**

  - Compressing images using **linear and convolutional autoencoders**.

  - Detecting objects in images and videos using **YOLO (You Only Look Once)**.

  - Recognizing **gestures and actions** in video sequences.

  - Generating **hallucinogenic images** with **Deep Dream**.

  - Applying **style transfer** to blend the style of famous artworks into photos.

  - Creating entirely new, synthetic images using **Generative Adversarial Networks (GANs)**.

  - Performing **image segmentation** to extract meaningful structures and regions from images and videos.

This repository serves as a **comprehensive, hands-on portfolio** for mastering fundamental and advanced computer vision concepts.

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### Repository Structure

#### 00 - Face Detection

- `face_detection.camera.py` – Real-time face detection using a webcam.

- `face_detection.ipynb` – Face detection on static images.

#### 01 - Face Recognition

- `face_recognition.camera.py` – Real-time face recognition.

- `face_recognition.ipynb` – Face recognition on images.

- `preprocess_for_camera.ipynb` – Preprocessing for building a face recognition dataset.

#### 02 - Object Tracking

- `csrt_object_tracking.py` – Object tracking using CSRT tracker.

- `kcf_object_tracking.py` – Object tracking using KCF tracker.

#### 03 - Neural Network Image Classification

- `neural_network_for_image_classification.ipynb` – Basic neural network for image classification.

- `homework.ipynb` – Related assignment.

#### 04 - Convolutional Neural Network (CNN) Classification

- `convolutional_neural_network_for_image_classification.ipynb` – CNN for image classification.

- `homework.ipynb` – Related assignment.

#### 05 - Transfer Learning and Fine-Tuning

- `transfer_learning.ipynb` – Transfer learning using pre-trained models.

- `homework.ipynb` – Related assignment.

#### 06 - Classification of Emotions

- `classification_of_emotions.ipynb` – Classifying emotions from facial expressions.

- `homework.ipynb` – Related assignment.

#### 07 - Autoencoders for Image Compression

- `autoencoders_for_image_compression.ipynb` – Image compression with autoencoders.

- `homework.ipynb` – Related assignment.

#### 08 - Object Detection with YOLO

- `object_detection_with_yolo.ipynb` – Object detection using YOLO.

#### 09 - Recognition of Gestures and Actions

- `recognition_of_gestures_and_actions.ipynb` – Gesture and action recognition.

#### 10 - Deep Dream

- `deep_dream.ipynb` – Visual transformations using Google’s DeepDream.

#### 11 - Style Transfer

- `style_transfer.ipynb` – Applying artistic style transfer to images.

#### 12 - Generative Adversarial Networks (GANs)

- `gans.ipynb` – Generating synthetic images with GANs.

- `homework.ipynb` – Related assignment.

#### 13 - Image Segmentation

- `image_segmentation.ipynb` – Performing image segmentation tasks.

---

## Notes

- Each directory in this repository is **self-contained** and can be run independently.

- Camera-based scripts (`*_camera.py`) are designed for **real-time execution**.

- All examples are provided for **educational purposes** to practice computer vision techniques.

---

## Course Source

These projects were developed as part of the  

**[Udemy - Computer Vision Masterclass](https://www.udemy.com/course/computer-vision-masterclass/)**,  

which covers both foundational and advanced topics in computer vision.

---

## License

This repository is intended **solely for educational use**.  

All scripts and notebooks are based on course exercises and are meant to help students explore  

image processing, machine learning, and computer vision concepts.