This repository contains resources and examples for learning computer vision using Python and OpenCV.
- Udemy Course: Computer Vision Masterclass
- OpenCV Documentation
- Dlib Documentation
- Google Colab
- Jupyter Notebooks
- Python: The primary programming language used for computer vision tasks in this repository.
- OpenCV: An open-source computer vision library that provides a wide range of tools for image processing and computer vision tasks.
- Dlib: A modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real-world problems, including face detection and recognition.
- Google Colab: A free cloud service that allows you to run Jupyter notebooks in the cloud, making it easier to share and collaborate on code.
- Jupyter Notebooks: An interactive computing environment that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.
- GitHub: A platform for version control and collaboration, allowing you to host and review code, manage projects, and build software alongside millions of other developers.
- Haar Cascades: A machine learning-based approach for object detection that uses a cascade of classifiers to detect objects in images.
- HOG (Histogram of Oriented Gradients): A feature descriptor used in computer vision and image processing for object detection.
- CNN (Convolutional Neural Networks): A class of deep neural networks commonly used for analyzing visual imagery, particularly effective for tasks like image classification and object detection.
- Face Detection: A computer vision technique that identifies and locates human faces in digital images or videos.
- Face Recognition: A technology capable of identifying or verifying a person from a digital image or a video frame by comparing and analyzing patterns based on the person's facial features.
- Object Detection: A computer vision technique that identifies and locates objects within an image or video, often using bounding boxes to indicate the position of detected objects.
- Image Processing: The manipulation and analysis of digital images using algorithms to enhance, transform, or extract information from the images.
- TensorFlow: An open-source machine learning framework developed by Google, widely used for building and training deep learning models, including CNNs for computer vision tasks.
- scikit-learn: A popular machine learning library in Python that provides simple and efficient tools for data mining and data analysis, including metrics for evaluating model performance.
- Matplotlib: A plotting library for Python that provides a wide range of tools for creating static, animated, and interactive visualizations, often used for displaying images and results in computer vision projects.
- NumPy: A fundamental package for scientific computing in Python, providing support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays, commonly used in computer vision for image manipulation and processing.
- Pandas: A powerful data manipulation and analysis library for Python, often used for handling and analyzing structured data in computer vision projects, such as datasets of images and their corresponding labels.
- Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow, used for building and training deep learning models, including CNNs for image classification and object detection tasks in computer vision.
- Basic understanding of Python programming.
- Familiarity with machine learning concepts is helpful but not required.
- A computer with Python installed and access to the internet for downloading necessary libraries and resources.
- An IDE or code editor (e.g., Jupyter Notebook, VS Code) for writing and running Python code.
- Basic knowledge of computer vision concepts is beneficial for understanding the course material.
- Optional: A webcam or a collection of images for testing face detection and recognition techniques.
- Optional: A GPU for faster processing when working with CNNs, although it is not required for learning and experimenting with the techniques covered in the course.
- Optional: Familiarity with Git and GitHub for version control and collaboration, especially if you plan to contribute to the repository or share your work.
- Optional: Basic understanding of deep learning frameworks (e.g., TensorFlow, PyTorch) can be helpful when working with CNNs, but it is not necessary for the face detection techniques covered in the course.
- Optional: A willingness to explore and experiment with different computer vision techniques beyond the course material, as this field is rapidly evolving and there are many exciting developments to explore.
Computer vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world. It involves the development of algorithms and techniques that allow machines to process and analyze images and videos, enabling them to perform tasks such as object detection, face recognition, image classification, and more.
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01.FaceDetection.ipynb: This notebook demonstrates how to perform face detection using Haar cascades, HOG, and CNN methods using OpenCV and Dlib libraries. It includes examples of how to load images, detect faces, and visualize the results.
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02.FaceDetection.Camera.py: This script captures video from a webcam and performs real-time face detection using Haar cascades. It displays the video feed with detected faces highlighted by rectangles.
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01.FaceRecognition.ipynb: This notebook demonstrates how to perform face recognition using OpenCV and Dlib libraries. It includes examples of how to load images, extract facial features, and compare faces for recognition using different methods such as LBPH (Local Binary Patterns Histograms), face detectors.
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02.CaptureFace.Pycharm.py: This script captures faces from a webcam feed and saves them as images. It uses OpenCV to access the webcam, detect faces, and save the detected faces to disk. The script allows you to specify the number of samples to capture and includes functionality to stop the capture process by pressing a key.
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03.FaceRecognition.ipynb: This notebook demonstrates how to perform face recognition using a CNN (Convolutional Neural Network) approach. It includes examples of how to load images, preprocess them, and train a CNN model for face recognition using lbph (Local Binary Patterns Histograms) and face detectors.
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04.FaceRecognition.Camera.py: This script captures video from a webcam and performs real-time face recognition using a trained CNN model. It detects faces in the video feed, extracts facial features, and compares them to the trained model to recognize individuals in real-time.
- 01.Object.Tracking.py: This script demonstrates how to perform object tracking using OpenCV's built-in tracking algorithms. It captures video from a specified source (e.g., a video file or webcam), allows the user to select a region of interest (ROI) for tracking, and then tracks the selected object across frames in real-time. The script uses the CSRT (Channel and Spatial Reliability Tracking) algorithm for tracking, which is known for its accuracy and robustness in various tracking scenarios.
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01.Image-Classification.All.Pixels.ipynb: This notebook demonstrates how to perform image classification using all pixels as features. It includes examples of how to load images, preprocess them, and train a simple neural network for image classification.
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02.Image-Classification.FeatureExtraction.OpenCv.ipynb: This notebook demonstrates how to perform image classification using feature extraction techniques with OpenCV. It includes examples of how to extract features from images using methods such as SIFT (Scale-Invariant Feature Transform) and SURF (Speeded-Up Robust Features), and then use these features for image classification.
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03.Image-Classification.Cats&Dogs.ipynb: This notebook demonstrates how to perform image classification on a dataset of cats and dogs. It includes examples of how to load images, preprocess them, and train a neural network for binary classification.
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01.ImageClassification.CNN.ipynb: This notebook demonstrates how to perform image classification using a Convolutional Neural Network (CNN). It includes examples of how to load images, preprocess them, build and train a CNN model, and evaluate its performance.
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02.Image-Classification.Cats&Dogs.CNN.ipynb: This notebook demonstrates how to perform image classification on a dataset of cats and dogs using a Convolutional Neural Network (CNN). It includes examples of how to load images, preprocess them, build and train a CNN model, and evaluate its performance.
- 01.Transfer-Learning-&-Fine-Tunning.ipynb : This notebook demonstrates how to perform transfer learning and fine-tuning using pre-trained models for image classification tasks. It includes examples of how to load a pre-trained model, modify its architecture for a specific task, and fine-tune the model on a new dataset.
- 02.Transfer-Learning-&-Fine-Tunning-Cats&Dogs.ipynb : This notebook demonstrates how to perform transfer learning and fine-tuning on a dataset of cats and dogs using a pre-trained model. It includes examples of how to load a pre-trained model, modify its architecture for binary classification, and fine-tune the model on the cats and dogs dataset.
- 01.Emotion-Classification.ipynb : This notebook demonstrates how to perform emotion classification using a Convolutional Neural Network (CNN). It includes examples of how to load images, preprocess them, build and train a CNN model for emotion classification, and evaluate its performance.
- 02.Emotion-Classification-Hw.ipynb : This notebook provides a hands-on exercise for emotion classification using a Convolutional Neural Network (CNN). It includes instructions for downloading a dataset, preprocessing the images, building and training a CNN model for emotion classification, and evaluating its performance.
This repository provides a comprehensive introduction to computer vision using Python and OpenCV, covering essential concepts and techniques for face detection and recognition. By exploring the provided resources and examples, you can gain a solid foundation in computer vision and start building your own applications in this exciting field.