https://github.com/harshjuly12/hand-gesture-recognition-using-convolutional-neural-networks-cnn
Hand Gesture Recognition Using CNN employs Convolutional Neural Networks to classify hand gestures from near-infrared images. The project involves dataset preprocessing, CNN model training, and evaluation for accurate gesture recognition.
https://github.com/harshjuly12/hand-gesture-recognition-using-convolutional-neural-networks-cnn
cnn cnn-classification cnn-keras computer-vision
Last synced: 18 days ago
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Hand Gesture Recognition Using CNN employs Convolutional Neural Networks to classify hand gestures from near-infrared images. The project involves dataset preprocessing, CNN model training, and evaluation for accurate gesture recognition.
- Host: GitHub
- URL: https://github.com/harshjuly12/hand-gesture-recognition-using-convolutional-neural-networks-cnn
- Owner: harshjuly12
- License: mit
- Created: 2023-11-28T18:14:25.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-07-07T15:43:38.000Z (about 1 year ago)
- Last Synced: 2024-11-12T21:22:41.731Z (11 months ago)
- Topics: cnn, cnn-classification, cnn-keras, computer-vision
- Language: Jupyter Notebook
- Homepage:
- Size: 93.8 KB
- Stars: 6
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
Hand Gesture Recognition Using Convolutional Neural Networks (CNN)
## Table of Contents
1. [Project Overview](#project-overview)
2. [Dataset](#dataset)
3. [Project Structure](#project-structure)
4. [Requirements](#requirements)
5. [Installation](#installation)
6. [Usage](#usage)
7. [Example](#example)
8. [Analysis and Results](#analysis-and-results)
9. [Contributing](#contributing)
10. [License](#license)
11. [Author](#author)
## Project Overview
This project implements a hand gesture recognition system using Convolutional Neural Networks (CNN). The dataset comprises near-infrared images acquired by the Leap Motion sensor, featuring 10 different hand gestures performed by 10 subjects (5 men and 5 women).
## Dataset
The dataset includes 10 categories of hand gestures:
- 01_palm
- 02_l
- 03_fist
- 04_fist_moved
- 05_thumb
- 06_index
- 07_ok
- 08_palm_moved
- 09_c
- 10_down
Images are resized to 50x50 pixels and converted to grayscale for processing.
## Project Structure
The project is structured as follows:
- **Data Loading and Preprocessing**: Images are loaded from the dataset, resized, and converted to grayscale.
- **Model Building**: A Convolutional Neural Network (CNN) architecture is defined using Keras.
- **Model Training**: The CNN model is trained on the processed dataset.
- **Evaluation**: Model performance is evaluated using accuracy and other relevant metrics.
## Requirements
To run the project, you need the following libraries:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- Plotly
- Scikit-learn
## Installation
1. Clone the repository:
```sh
git clone
cd HandGestureRecognitionUsingCNN
```
2. Install the required Python libraries:
```sh
pip install -r requirements.txt
```
## Usage
1. Navigate to the project directory:
```sh
cd HandGestureRecognitionUsingCNN
```
2. Run the Jupyter notebook for detailed steps and execution:
```sh
jupyter notebook HandGestureRecognitionUsingCNN.ipynb
```
## Example
# Import necessary libraries
```sh
import warnings
warnings.filterwarnings('ignore')
import keras
import matplotlib.pyplot as plt
import os
import cv2
import numpy as np
from keras.layers import Conv2D, Activation, MaxPool2D, Dense, Flatten, Dropout
# Define categories and image size
CATEGORIES = ["01_palm", "02_l", "03_fist", "04_fist_moved", "05_thumb", "06_index", "07_ok", "08_palm_moved", "09_c", "10_down"]
IMG_SIZE = 50
# Path to the dataset
data_path = "../input/leapgestrecog/leapGestRecog"
# Load and preprocess images
image_data = []
for dr in os.listdir(data_path):
for category in CATEGORIES:
class_index = CATEGORIES.index(category)
path = os.path.join(data_path, dr, category)
for img in os.listdir(path):
try:
img_arr = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
image_data.append([cv2.resize(img_arr, (IMG_SIZE, IMG_SIZE)), class_index])
except Exception as e:
pass
```
## Analysis and Results
The notebook contains the following steps:
1. Importing Libraries: Importing necessary libraries for analysis and visualization.
2. Data Exploration: Exploring the dataset to understand the distribution and relationships between different variables.
3. Data Preprocessing: Preparing the data for clustering by scaling the features.
4. K-means Clustering: Implementing K-means clustering to group customers into segments.
5. Visualization: Visualizing the clusters to interpret the results.
## Contributing
Contributions are welcome! Please open an issue or submit a pull request for any improvements or bug fixes.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## Author
For any questions or suggestions, please contact:
- Harsh Singh: [harshjuly12@gmail.com](harshjuly12@gmail.com)
- GitHub: [harshjuly12](https://github.com/harshjuly12)