adpa.yaml

import cv2
import numpy as np
from sklearn.ensemble import RandomForestClassifier

class SatelliteImageProcessor:
    def __init__(self):
        self.model = RandomForestClassifier()

    def train_model(self, images, labels):
        # Flatten images and train model
        flattened_images = [img.flatten() for img in images]
        self.model.fit(flattened_images, labels)

    def classify_image(self, image):
        flattened_image = image.flatten().reshape(1, -1)
        return self.model.predict(flattened_image)

# Example usage
processor = SatelliteImageProcessor()
# Assuming images and labels are predefined lists of images and their corresponding labels
processor.train_model(images, labels)
test_image = cv2.imread('test_image.jpg', cv2.IMREAD_GRAYSCALE)
classification = processor.classify_image(test_image)
print("Classified as:", classification)