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.

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

To install the necessary libraries, use the following command:

pip install -r requirements.txt

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

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.

The image_similarity.ipynb notebook contains the following sections:

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.

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.

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.

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.

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.

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.

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.

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.

Description: This cell runs the main function.

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

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

This project is licensed under the MIT License.