VisualMatch

Efficient image search using high-dimensional techniques for accurate similarity retrieval.

Project for DB2 course that allows you to perform image search based on similarity using the K-Nearest Neighbors (KNN) algorithm. It provides a user-friendly interface where you can upload an image and retrieve the most similar images from a pre-populated image database.

Features

• Upload an image to search for similar images using a 128 dimension vector per image.
• Specify the value of K to control the number of similar images to retrieve.
• Display the best match image along with other relevant search results.
• Show the names and distances of the search results.
• FAISS implementation
• R-Tree implementation
• Sequential implementation
• Comparison between search algorithms.

Installation

1. Clone the repository:

git clone https://github.com/your-username/image-search-app.git
# then open the folder

Recomended: Use conda to manage the environment and easily install all libraries

Updates an environment called my_env with the data from the .yaml

$ conda env update -n my_env --file environment.yaml

Create a new environment

$ conda env create -f environment.yml

Activate the environment

$ conda activate bdd3

If you want to use pip instead, use:

pip install -r requirements.txt

Libraries:

• Python 3.10 (Python 3.11 won't work)
• Flask
• NumPy
• Face Recognition
• Faiss (for high-dimensional search)
• Sequential and Rtree (for alternative KNN search methods)

Usage

[Optional] Run the startup.py file, it will create the encodings (encodings.pickle) and the r-tree. It will take a few minutes if done with the HOG algorithm and a couple hours if done with CNN.

python startup.py

Then just run the app

python app.py

Access the application by visiting http://localhost:5000 or http://127.0.0.1:5000 in your web browser.

Upload an image using the provided interface.

Enter the value of K (number of similar images to retrieve) if not, it's 5 by default.

Additional Search Methods

To implement alternative KNN search methods (such as sequential search or Rtree), follow these steps:

The Flask route

@app.route('/upload', methods=['GET', 'POST'])
def upload_pic(): 

calls the search method function

results, names, similarity_scores, best_result = highd_knn(img, numeric_value)

The search method function

# loads the "Database"
r_data = pickle.loads(open("encodings.pickle", "rb").read())

# turns the given image into a cv2 image
img_array = np.frombuffer(img, np.uint8)
image = cv2.imdecode(img_array, cv2.IMREAD_COLOR)

# compares and generates results
# finally returns

# images[1:] -> resultados (without the best result) <class 'list' <class 'str'> >
# f_names[1:] -> names (without the [0] which is the best result) <class 'list' <class 'str'> >
# D[0][1:] -> all distances (generated by FAISS algorithm) <class 'numpy.ndarray' <class 'numpy.float32'> >
# images[0] -> best result image path <class 'str'>

return images[1:], f_names[1:], D[0], images[0]

With that in consideration, the frontend should work with the new search algorithm !

INFORME:

Para el informe, ingrese a la carpeta others