tree-classification

import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import numpy as np import cv2 import os

Define model architecture

def create_model(): model = keras.Sequential([ layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)), layers.MaxPooling2D((2, 2)), layers.Conv2D(64, (3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Conv2D(128, (3, 3), activation='relu'), layers.MaxPooling2D((2, 2)), layers.Flatten(), layers.Dense(128, activation='relu'), layers.Dense(5, activation='softmax') # Assuming 5 types of trees ])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

return model

Load and preprocess image

def preprocess_image(image_path): img = cv2.imread(image_path) img = cv2.resize(img, (128, 128)) img = img / 255.0 # Normalize return np.expand_dims(img, axis=0)

Load trained model or create a new one

model = create_model()

Example prediction

def predict_tree(image_path): classes = ['Fir', 'Spruce', 'Pine', 'Cedar', 'Christmas Tree'] # Example classes img = preprocess_image(image_path) prediction = model.predict(img) predicted_class = classes[np.argmax(prediction)]

print(f"Predicted Tree Type: {predicted_class}")
return predicted_class

Example usage

predict_tree("example_tree.jpg")

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