Computer Vision Projects

This repository encompasses a collection of seven distinct computer vision projects, each designed to address specific challenges and applications within the field. The projects demonstrate various techniques and methodologies in computer vision, utilizing a range of technologies and frameworks.

Tech Stack

The projects in this repository leverage the following technologies and libraries:

• Programming Languages: Python
• Libraries and Frameworks:
  • OpenCV
  • TensorFlow
  • Keras
  • NumPy
  • Matplotlib

Installation Instructions

To set up the repository locally:

1. Clone the Repository:

   git clone https://github.com/fahad-git/computer-vision-projects.git

2. Navigate to the Repository Directory:

   cd computer-vision-projects

3. Create a Virtual Environment:

   python -m venv venv

4. Activate the Virtual Environment:
   • On Windows:

     venv\Scripts\activate

   • On Unix or MacOS:

     source venv/bin/activate

5. Install Dependencies:

   pip install -r requirements.txt

Note: Each project may have additional dependencies or setup instructions detailed in their respective sections below.

Contributing Guidelines

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Commit your changes (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.

Please ensure your code adheres to the project's coding standards and includes appropriate tests.

License

This repository is licensed under the MIT License. See the LICENSE file for more details.

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Below are the individual projects contained in this repository:

1. Autoencoder

Purpose: Develop an autoencoder model for unsupervised feature learning and dimensionality reduction.

Tech Stack:

• Python
• TensorFlow
• Keras

Installation Instructions: Navigate to the Autoencoder directory and ensure the required libraries are installed as per the main installation instructions.

Usage Guide: Run the autoencoder.py script to train the model on the dataset.

Features:

• Encoder and decoder architecture
• Reconstruction of input data

Dependencies:

• TensorFlow
• Keras

2. Autoencoder Fashion MNIST Dataset Model

Purpose: Implement an autoencoder specifically trained on the Fashion MNIST dataset for feature extraction.

Tech Stack:

• Python
• TensorFlow
• Keras

Installation Instructions: Navigate to the Autoencoder_fashion_mnist_dataset_model directory.

Usage Guide: Execute the fashion_mnist_autoencoder.py script to train and evaluate the model.

Features:

• Training on Fashion MNIST dataset
• Visualization of reconstructed images

Dependencies:

• TensorFlow
• Keras

3. Coil 20 Unprocessed

Purpose: Explore object recognition using the unprocessed COIL-20 dataset.

Tech Stack:

• Python
• OpenCV
• NumPy

Installation Instructions: Navigate to the Coil_20_Unproc directory.

Usage Guide: Use the coil20_processing.py script to preprocess and analyze the dataset.

Features:

• Data preprocessing
• Feature extraction

Dependencies:

• OpenCV
• NumPy

4. Dog Cat Classification

Purpose: Classify images of dogs and cats using a convolutional neural network.

Tech Stack:

• Python
• TensorFlow
• Keras

Installation Instructions: Navigate to the Dog_Cat_Classification directory.

Usage Guide: Run the dog_cat_classifier.py script to train and test the model.

Features:

• Image classification
• Data augmentation

Dependencies:

• TensorFlow

5. Caltech 101 Dataset Model

Purpose:
Create and evaluate a model trained on the Caltech 101 dataset to classify objects into various categories.

Tech Stack:

• Python
• TensorFlow
• Keras

Installation Instructions:

1. Navigate to the caltech_101_dataset_Model directory.
2. Ensure the Caltech 101 dataset is available or downloaded.
3. Follow the repository's general installation instructions for dependencies.

Usage Guide:

• Run the caltech_101_model.py script to train and evaluate the model.
• Ensure the dataset is correctly structured before execution.

Features:

• Multi-class classification on Caltech 101 dataset.
• Support for training visualization using Matplotlib.

Dependencies:

• TensorFlow
• Keras

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6. Flower Recognition

Purpose:
Recognize and classify different types of flowers using image classification techniques.

Tech Stack:

• Python
• TensorFlow
• Keras

Installation Instructions:

1. Navigate to the flower_recognition directory.
2. Install the required dependencies listed in the requirements.txt file.

Usage Guide:

• Execute the flower_recognition.py script to train the model on the flower dataset.
• Follow any dataset preprocessing steps mentioned in the script.

Features:

• Classification of flower species.
• Visualization of training and testing accuracy.

Dependencies:

• TensorFlow
• Keras

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7. Weather Image Dataset

Purpose:
Classify weather conditions (e.g., sunny, cloudy, rainy) based on image data.

Tech Stack:

• Python
• TensorFlow
• Keras

Installation Instructions:

1. Navigate to the weather_image_dataset directory.
2. Ensure the weather dataset is available locally or configured for download.
3. Follow the general installation steps to set up the environment.

Usage Guide:

• Run the weather_classification.py script to train and test the model.
• Use the trained model to predict weather conditions on new images.

Features:

• Classification of images into weather categories.
• Pretrained model support for transfer learning (if applicable).

Dependencies:

• TensorFlow
• Keras