https://github.com/ritwika-das-gupta/sports_celebrity_image_classification
In this Python code snippet, we will walk through the process of building a celebrity face recognition system using OpenCV (Open Source Computer Vision Library) and machine learning. The goal of this project is to detect and recognize faces of celebrities in images.
https://github.com/ritwika-das-gupta/sports_celebrity_image_classification
feature-engineering gridsearchcv numpy opencv pandas python svm-classifier tkinter wavelet-transform
Last synced: 3 months ago
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In this Python code snippet, we will walk through the process of building a celebrity face recognition system using OpenCV (Open Source Computer Vision Library) and machine learning. The goal of this project is to detect and recognize faces of celebrities in images.
- Host: GitHub
- URL: https://github.com/ritwika-das-gupta/sports_celebrity_image_classification
- Owner: Ritwika-Das-Gupta
- Created: 2023-09-13T05:40:10.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-09-13T10:36:15.000Z (almost 2 years ago)
- Last Synced: 2025-01-24T16:16:06.012Z (5 months ago)
- Topics: feature-engineering, gridsearchcv, numpy, opencv, pandas, python, svm-classifier, tkinter, wavelet-transform
- Language: Jupyter Notebook
- Homepage:
- Size: 9.13 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sports Person Image Classification
GitHub repository for a machine learning-based sports personality image classification system. This project covers data collection, preprocessing, model training, and performance evaluation. Open-source under the MIT License.
LINK TO DOWNLOAD THE IMAGES DATASET: https://drive.google.com/drive/folders/1vavIn1flpJv3LWHO6noYuUwuD2ETkoPU?usp=drive_link
## Introduction
Welcome to the Sports Person Image Classification project repository! This project focuses on utilizing machine learning techniques to recognize and classify images of sports personalities. Whether you're a sports enthusiast, developer, or researcher, this repository offers tools and resources for accurate image recognition in the world of sports.
## Table of Contents
- [Introduction](#introduction)
- [Key Features](#key-features)
- [Getting Started](#getting-started)
- [Usage](#usage)
- [Dataset](#dataset)
- [Model Training](#model-training)
- [Evaluation](#evaluation)
- [Testing with GUI](#testing-with-gui)
## Introduction
Recognizing sports icons in images is a valuable task in today's digital world. This project provides a solution for automated image classification of sports personalities, making it useful for sports apps, fan websites, and sports analytics tools.
## Key Features
- **Machine Learning Models**: Utilize state-of-the-art image classification techniques.
- **Data Collection**: Scripts for gathering sports personality images.
- **Data Preprocessing**: Tools to clean and preprocess the dataset.
- **Model Training**: Train models to recognize athletes and sports icons.
- **Evaluation Metrics**: Assess model accuracy and performance.
## Getting Started
### Overview
The code aims to detect and recognize faces of celebrities in images. It follows these key steps:
#### Importing Libraries and Loading an Image
#### Face Detection using Haar Cascade
#### Creating a Cropped Image
#### Feature Engineering with Wavelet Transform
#### Model Training (Support Vector Machine, SVM)
#### Model Selection using GridSearchCV
#### Saving the Trained Model and Class Dictionary
#### Creating a GUI for Image Upload and Testing
#### Face Detection using Haar Cascade
The code uses Haar Cascade classifiers to detect faces and eyes in the image. It makes use of OpenCV for this purpose.
#### Feature Engineering with Wavelet Transform
A wavelet transform is applied to the cropped image to enhance its features, making it easier to distinguish facial features like eyes, nose, and lips.
#### Model Training
The dataset is prepared for training the machine learning model. Both raw and wavelet-transformed images are used as features. The chosen model is a Support Vector Machine (SVM).
#### Model Selection using GridSearchCV
Different machine learning models and hyperparameters are explored using GridSearchCV to find the best-performing model.
#### Saving the Model
The trained model and a class dictionary that maps class labels to celebrity names are saved for later use.
#### Creating a GUI for Image Upload and Testing
Tkinter is used to create a graphical user interface (GUI) that allows users to upload an image for celebrity recognition.
#### Image Preprocessing and Prediction
When a user uploads an image, it undergoes preprocessing, face detection, feature engineering, and is then used for prediction. The recognized celebrity's name is displayed as the output.
This project is open-source and available under the MIT License.