Vector Forge Logo

Python >= 3.10

🐍 Vector Forge is a Python package designed for easy transformation of various data types into feature vectors.

Python PyTorch Keras

💡Core ideas

🌄 For image embeddings, Vector Forge uses pre-trained networks, which means the models have already learned features from a large set, for example - ImageNet. When we use these models in Vector Forge, we remove the last classification layer and return embeddings. This way, we get a bunch of numbers (a vector) representing the image, which can be used in many different tasks like finding similar images, semantic search, clustering, classification and many more.

📄 Text embeddings are a way to convert words or sentences into numbers, making it possible for computers to understand and process them. In Vector Forge, the CLIP ViT-B/32 model is utilized to generate one of those embeddings. When you provide any text, be it a single word or a sentence, to CLIP, it transforms this text into a fixed-size vector. Each vector has a consistent length, no matter how long or short the original text is. This consistency in size is valuable, especially when comparing different pieces of text or measuring how similar a piece of text is to an image.

🧩 Features

⚙️ Requirements

📦 Supported models

Model Name Implementation Parameter Value Supports Image Supports Text Embedding Size
CLIP ViT-B/32 PyTorch Models.CLIP_B_P32 ✅ ✅ (512,)
CLIP ViT-L/14 PyTorch Models.CLIP_L_P14 ✅ ✅ (768,)
CLIP ViT-B/32 OpenVino™ OpenVino™ Models.CLIP_B_P32_OV ✅ ✅ (512,)
CLIP ViT-L/14 OpenVino™ OpenVino™ Models.CLIP_L_P14_OV ✅ ✅ (768,)
VGG16 Keras Models.VGG16 ✅ ❌ (512,)
VGG19 Keras Models.VGG19 ✅ ❌ (512,)
Xception Keras Models.Xception ✅ ❌ (2048,)

🎛️ Usage

You can work on many images at once or just one piece of text with simple commands, making it a breeze to get your data ready for further use or analysis.

🔧 Installation

pip install vector_forge

🔌 Create a vectorizer

Import the necessary classes or functions

from vector_forge import Vectorizer

Default vectorizer

By default, the vectorizer is CLIP ViT-B/32, as it works for text and images.

vectorizer = Vectorizer()

Text to Vector

Example how to convert a text prompt to a vector.

text_embedding = vectorizer.text_to_vector("Nice text!")

Image to Vector

Example how to convert to convert image from path to vector.

image_embedding = vectorizer.image_to_vector("/path/to/image.jpg")

Change the vectorizer to use a different model

Example how to change the vectorizer model, in this example to Xception.
Keep in mind, that not all models work for for text prompts. If you want to compare image and texts, I recommend using CLIP ViT-B/32.

from vector_forge import Vectorizer, Models

vectorizer = Vectorizer(model=Models.Xception)

Return types

In Vector Forge, you have the flexibility to choose the format in which the vectors are returned. This is controlled by the return_type parameter available in the image_to_vector and text_to_vector methods of the Vectorizer class. Here are the available return types along with examples:

a) return_type=”numpy”

This is the default return type. Vectors are returned as NumPy arrays.

image_embedding = vectorizer.image_to_vector("/path/to/image.jpg", return_type="numpy")
# Output: array([0.0234, 0.0345, ..., 0.0456])
# Shape: (2048,) for Xception, (512,) for VGG16, VGG19 and CLIP ViT-B/32, (768, ) for CLIP ViT-L/14

b) return_type=”str”

Vectors are returned as a string representation of the NumPy array.

image_embedding = vectorizer.image_to_vector("/path/to/image.jpg", return_type="str")
# Output: "[0.0234, 0.0345, ..., 0.0456]"

c) return_type=”list”

Vectors are returned as a list of values.

image_embedding = vectorizer.image_to_vector("/path/to/image.jpg", return_type="list")
# Output: [0.0234, 0.0345, ..., 0.0456]

d) return_type=”2darray”

Vectors are returned as a 2-dimensional NumPy array, where each vector is a row in the array. This format is especially useful when you want to compute similarities or perform other vectorized operations.

image_embedding = vectorizer.image_to_vector("/path/to/image.jpg", return_type="2darray")
# Output: array([[0.0234, 0.0345, ..., 0.0456]])
# Shape: (1, 2048)  # for Xception, (1, 512) for VGG16, VGG19 and CLIP ViT-B/32, (1, 768) for CLIP ViT-L/14

Batch Processing for images

Vector Forge can process multiple images in a folder in one go. Just provide the folder path, and the load_from_folder method will handle the rest.

# Convert all valid images in a folder to vectors
for vector in vectorizer.load_from_folder("/path/to/folder"):
    print(vector.shape)

You can specify the return_type, save_to_index, and file_info_extractor parameters to control the output format, to save the file paths of processed images to an index file, and to execute a custom function on each file for additional information extraction, respectively.

# Example with return_type and save_to_index
for vector in vectorizer.load_from_folder("/path/to/folder", return_type="2darray", save_to_index="paths.txt"):
    print(vector.shape)

from vector_forge.info_extractors import get_file_info

# Example with additional information to each file
for vector, dimension in vectorizer.load_from_folder("/path/to/folder", file_info_extractor=get_file_info):
    print(vector.shape)

Image preprocessing

Vector Forge provides a collection of image preprocessing functions to help prepare images for vectorization. These functions can be found in the image_preprocessors. You can also specify your own custom image preprocessing function.

from vector_forge.image_preprocessors import resize_image

# Create a Vectorizer instance with the resize_image function as the image preprocessor
resize_fn = lambda img: resize_image(img, width=600)
vectorizer = Vectorizer(image_preprocessor=resize_fn)
vector = vectorizer.image_to_vector(input_image='/path/to/image.jpg')

from vector_forge.image_preprocessors import convert_to_grayscale

# Create a Vectorizer instance with the convert_to_grayscale function as the image preprocessor
vectorizer = Vectorizer(image_preprocessor=convert_to_grayscale)
vector = vectorizer.image_to_vector(input_image='/path/to/image.jpg')

🧪 A complete example

from vector_forge import Vectorizer
from vector_forge.image_preprocessors import resize_image
from sklearn.metrics.pairwise import cosine_similarity


def compute_similarity(vectorizer, text, image_path):
    # Convert text and image to vectors with return type '2darray'
    text_embedding = vectorizer.text_to_vector(text, return_type="2darray")
    image_embedding = vectorizer.image_to_vector(image_path, return_type="2darray")

    # Compute cosine similarity
    similarity = cosine_similarity(text_embedding, image_embedding)[0][0]
    return similarity


# Create a vectorizer with the default CLIP ViT-B/32 model and a custom image preprocessor
resize_fn = lambda img: resize_image(img, width=600)
vectorizer = Vectorizer(image_preprocessor=resize_fn)

# Define text and image paths
text = "A couple of birds"
image_path_1 = "vector_forge/test_data/birds.jpg"  # adapt paths accordingly
image_path_2 = "vector_forge/test_data/sample.jpg"  # adapt paths accordingly

# Compute and print similarity scores
similarity_1 = compute_similarity(vectorizer, text, image_path_1)
similarity_2 = compute_similarity(vectorizer, text, image_path_2)

print(f"Similarity between text and first image: {similarity_1}")
print(f"Similarity between text and second image: {similarity_2}")

Complete example how to use file_info_extractor, which can extract some valuable information from files.

from vector_forge import Vectorizer, Models
from vector_forge.info_extractors import get_colors

# Create a Vectorizer instance
vectorizer = Vectorizer(model=Models.Xception)

# Define the path to your folder containing images
folder_path = '/path/to/images'

# Process all images in the specified folder
for vector, colors in vectorizer.load_from_folder(folder_path, file_info_extractor=get_colors):
    # Print the vector shape and image dimensions
    print(f'Vector shape: {vector.shape}')
    print(f'Image colors: {colors}')

▶️ Tutorials

⚠️ Disclaimer

Vector Forge is provided as-is, without warranty of any kind. Users should employ the library at their own risk. It’s important to test and validate the library’s results in your specific context to ensure it meets your needs. Performance and accuracy can vary based on data and use cases. We encourage all users to thoroughly verify the library’s outputs and consider them as one of many tools in their toolkit.

🔮 Future features

Images

Texts