Image Similarity Comparison using VGG16

This project compares the similarity between images using features extracted from the VGG16 pre-trained deep learning model. It uses cosine similarity to compute the similarity scores between feature vectors.

Project Structure

📦image_similarity
 ┣ 📂data
 ┃ ┗ 📂images
 ┃ ┃ ┣ 📜cat1.jpg
 ┃ ┃ ┣ 📜cat2.jpg
 ┃ ┃ ┣ 📜dog1.jpg
 ┃ ┃ ┗ 📜dog2.jpg
 ┣ 📜LICENSE.txt
 ┣ 📜README.md
 ┣ 📜image_similarity.ipynb
 ┗ 📜requirements.txt

Requirements

To install the necessary libraries, use the following command:

pip install -r requirements.txt

requirements.txt

opencv-python
numpy
matplotlib
scikit-image
scikit-learn
keras
tensorflow

How to Run the Project

1. Clone the repository to your local machine.
2. Ensure you have Python installed.
3. Install the required libraries using the requirements.txt file:

   pip install -r requirements.txt

4. Place your images in the data/images/ directory.
5. Open and run the image_similarity.ipynb notebook to compare the images.

image_similarity.ipynb

The image_similarity.ipynb notebook contains the following sections:

Section 1: Import Libraries

Description: This cell imports all the necessary libraries for image processing, feature extraction, and similarity calculation. These include:

• opencv-python for image processing.
• numpy for numerical operations.
• matplotlib for plotting.
• scikit-image for additional image processing functions.
• scikit-learn for similarity calculations.
• keras and tensorflow for using the pre-trained VGG16 model and deep learning operations.

Purpose: To ensure that all required libraries are imported and available for use in subsequent cells.

Section 2: Load and Preprocess Image Function

Description: This cell defines a function load_and_preprocess_image that:

• Loads an image from the specified path.
• Resizes the image to a target size (224x224) required for VGG16.
• Applies necessary preprocessing steps like scaling pixel values using keras's preprocess_input.

Purpose: To handle the loading and preprocessing of images, preparing them for input into the VGG16 model.

Section 3: Extract Features Using VGG16

Description: This cell defines a function extract_vgg16_features that:

• Loads the pre-trained VGG16 model with weights trained on ImageNet.
• Creates a new model that outputs features from the 'fc1' layer of VGG16.
• Extracts and flattens the features from the preprocessed image.

Purpose: To use the pre-trained VGG16 model to extract deep features from the preprocessed image, which are used for comparing the images.

Section 4: Calculate Similarity Function

Description: This cell defines a function calculate_similarity that:

• Computes the cosine similarity between two given feature vectors using scikit-learn's cosine_similarity function.

Purpose: To calculate the cosine similarity between two feature vectors, providing a measure of similarity that ranges between -1 and 1.

Section 5: Display Images Function

Description: This cell defines a function display_images that:

• Displays a list of images along with their titles in a single figure using matplotlib.

Purpose: To visually display the images along with their titles, helping to verify the images being compared and understand the context of the similarity scores.

Section 6: Plot Similarities Function

Description: This cell defines a function plot_similarities that:

• Creates a bar plot to visualize the pairwise similarity scores between the images using matplotlib.

Purpose: To provide an intuitive visual representation of the similarity scores, showing how similar each pair of images is based on the extracted features.

Section 7: Compare Images Function

Description: This cell defines a function compare_images that:

• Loads and preprocesses each image.
• Extracts features from each image using the VGG16 model.
• Calculates pairwise similarities between the images.
• Displays the images.
• Plots the similarity scores.
• Prints the similarity results.

Purpose: To orchestrate the complete image comparison process by integrating all the previously defined functions, from loading and preprocessing images to displaying results.

Section 8: Main Function

Description: This cell defines the main function that:

• Specifies the list of image paths to be compared.
• Calls the compare_images function to execute the comparison.

Purpose: To act as the entry point for the script, specifying the images to compare and initiating the comparison process.

Section 9: Run the Main Function

Description: This cell runs the main function.

Purpose: To start the image comparison process when the notebook is executed.

Contributing

If you would like to contribute to this project, please fork the repository and submit a pull request with your improvements.

License

This project is licensed under the MIT License.