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https://github.com/savinrazvan/image_similarity
Compare image similarity using features extracted from the pre-trained VGG16 model. This project leverages cosine similarity for accurate visual similarity assessment, making it ideal for image retrieval and duplicate detection.
https://github.com/savinrazvan/image_similarity
computer-vision convolutional-neural-networks cosine-similarity deep-learning feature-extraction image-comparison image-processing image-similarity keras pre-trained-models python vgg16
Last synced: 2 months ago
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Compare image similarity using features extracted from the pre-trained VGG16 model. This project leverages cosine similarity for accurate visual similarity assessment, making it ideal for image retrieval and duplicate detection.
- Host: GitHub
- URL: https://github.com/savinrazvan/image_similarity
- Owner: SavinRazvan
- License: mit
- Created: 2024-08-01T13:28:06.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-08-01T13:37:55.000Z (5 months ago)
- Last Synced: 2024-10-10T08:23:03.977Z (2 months ago)
- Topics: computer-vision, convolutional-neural-networks, cosine-similarity, deep-learning, feature-extraction, image-comparison, image-processing, image-similarity, keras, pre-trained-models, python, vgg16
- Language: Jupyter Notebook
- Homepage:
- Size: 1.5 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# 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
```bash
📦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:
```bash
pip install -r requirements.txt
```
### requirements.txt
```plaintext
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:
```bash
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.