└── README.md


/README.md:
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 1 | # tree-classification
 2 | import tensorflow as tf
 3 | from tensorflow import keras
 4 | from tensorflow.keras import layers
 5 | import numpy as np
 6 | import cv2
 7 | import os
 8 | 
 9 | # Define model architecture
10 | def create_model():
11 |     model = keras.Sequential([
12 |         layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)),
13 |         layers.MaxPooling2D((2, 2)),
14 |         layers.Conv2D(64, (3, 3), activation='relu'),
15 |         layers.MaxPooling2D((2, 2)),
16 |         layers.Conv2D(128, (3, 3), activation='relu'),
17 |         layers.MaxPooling2D((2, 2)),
18 |         layers.Flatten(),
19 |         layers.Dense(128, activation='relu'),
20 |         layers.Dense(5, activation='softmax')  # Assuming 5 types of trees
21 |     ])
22 |     
23 |     model.compile(optimizer='adam',
24 |                   loss='sparse_categorical_crossentropy',
25 |                   metrics=['accuracy'])
26 |     
27 |     return model
28 | 
29 | # Load and preprocess image
30 | def preprocess_image(image_path):
31 |     img = cv2.imread(image_path)
32 |     img = cv2.resize(img, (128, 128))
33 |     img = img / 255.0  # Normalize
34 |     return np.expand_dims(img, axis=0)
35 | 
36 | # Load trained model or create a new one
37 | model = create_model()
38 | 
39 | # Example prediction
40 | def predict_tree(image_path):
41 |     classes = ['Fir', 'Spruce', 'Pine', 'Cedar', 'Christmas Tree']  # Example classes
42 |     img = preprocess_image(image_path)
43 |     prediction = model.predict(img)
44 |     predicted_class = classes[np.argmax(prediction)]
45 |     
46 |     print(f"Predicted Tree Type: {predicted_class}")
47 |     return predicted_class
48 | 
49 | # Example usage
50 | # predict_tree("example_tree.jpg")
51 | ear
52 | 


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