HyzeAI/HyzeACR-Polaris-Dataset

HyzeACR (Astronomical Character Recognition)

An Image Classification model that detects galaxies, moons, planets, and nebulaes using TensorFlow and Keras

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The Live Demo

Go to https://hyzeacr.netlify.app

HyzeACR Polaris

HyzeACR Polaris is an AI-powered astronomy image classification model designed to identify and categorize space objects from images with high accuracy.

Built as part of the Hyze ecosystem, Polaris focuses on real-world astronomical data and supports a wide range of celestial object classes.

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Features

• Classifies multiple types of space objects
• Deep learning–based image recognition
• Designed for astronomy research and exploration
• Fast and efficient inference
• Built for integration with Hyze AI systems

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Supported Classes

HyzeACR Polaris can detect and classify the following categories:

• Moon
• Nebula
• Black Hole / Quasar
• Lensed Quasar
• Earth
• Exoplanet
• Solar System Planets
• Unknown
• Star Clusters
• Galaxy
• Galaxy Cluster

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Use Cases

• Astronomy research and education
• Space image labeling and organization
• AI-powered space exploration tools
• Integration into astronomy apps and platforms
• Dataset preprocessing and classification

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Usage

Python (Keras)

from keras.models import load_model
from PIL import Image, ImageOps
import numpy as np

np.set_printoptions(suppress=True)

model = load_model("keras_Model.h5", compile=False)
class_names = open("labels.txt", "r").readlines()

data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

image = Image.open("<IMAGE_PATH>").convert("RGB")
size = (224, 224)
image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)

image_array = np.asarray(image)
normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1

data[0] = normalized_image_array

prediction = model.predict(data)
index = np.argmax(prediction)
class_name = class_names[index]
confidence_score = prediction[0][index]

print("Class:", class_name[2:], end="")
print("Confidence Score:", confidence_score)

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Python (OpenCV + Keras)

from keras.models import load_model
import cv2
import numpy as np

np.set_printoptions(suppress=True)

model = load_model("keras_Model.h5", compile=False)
class_names = open("labels.txt", "r").readlines()

camera = cv2.VideoCapture(0)

while True:
    ret, image = camera.read()
    image = cv2.resize(image, (224, 224), interpolation=cv2.INTER_AREA)

    cv2.imshow("Webcam Image", image)

    image = np.asarray(image, dtype=np.float32).reshape(1, 224, 224, 3)
    image = (image / 127.5) - 1

    prediction = model.predict(image)
    index = np.argmax(prediction)
    class_name = class_names[index]
    confidence_score = prediction[0][index]

    print("Class:", class_name[2:], end="")
    print("Confidence Score:", str(np.round(confidence_score * 100))[:-2], "%")

    if cv2.waitKey(1) == 27:
        break

camera.release()
cv2.destroyAllWindows()

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Google Coral (Edge TPU)

Install dependencies:

python3 -m pip install --extra-index-url https://google-coral.github.io/py-repo/ pycoral~=2.0 Pillow opencv-python opencv-contrib-python

Run inference:

import cv2
from pycoral.utils.dataset import read_label_file
from pycoral.utils.edgetpu import make_interpreter
from pycoral.adapters import common, classify

modelPath = '<PATH_TO_MODEL>'
labelPath = '<PATH_TO_LABELS>'

def classifyImage(interpreter, image):
    size = common.input_size(interpreter)
    common.set_input(interpreter, cv2.resize(image, size, interpolation=cv2.INTER_CUBIC))
    interpreter.invoke()
    return classify.get_classes(interpreter)

def main():
    interpreter = make_interpreter(modelPath)
    interpreter.allocate_tensors()
    labels = read_label_file(labelPath)

    cap = cv2.VideoCapture(0)
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        frame = cv2.flip(frame, 1)
        results = classifyImage(interpreter, frame)

        cv2.imshow('frame', frame)
        print(f'Label: {labels[results[0].id]}, Score: {results[0].score}')

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    cap.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

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Contributing

Contributions are welcome! Feel free to open issues or submit pull requests to improve the model, dataset, or performance.

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License

This project is licensed under the MIT License.

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Part of Hyze

HyzeACR Polaris is part of the Hyze AI ecosystem, focused on building powerful, accessible AI tools across multiple domains.

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Support

If you like this project, consider starring the repo and sharing it!

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