| import tensorflow as tf
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| import numpy as np
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| from PIL import Image
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| import os
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| from tensorflow.keras.layers import Layer
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|
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| class CustomScaleLayer(Layer):
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| def __init__(self, scale=1.0, **kwargs):
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| super(CustomScaleLayer, self).__init__(**kwargs)
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| self.scale = scale
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| def call(self, inputs):
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| if isinstance(inputs, (list, tuple)):
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| x = tf.add_n(inputs)
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| else:
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| x = inputs
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| return x * self.scale
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|
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| def get_config(self):
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| config = super().get_config()
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| config.update({"scale": self.scale})
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| return config
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| def load_model(model_path):
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| try:
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| model = tf.keras.models.load_model(model_path, custom_objects={'CustomScaleLayer': CustomScaleLayer})
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| print("Model loaded successfully!")
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| return model
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| except Exception as e:
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| print(f"Error loading model: {e}")
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| return None
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| def preprocess_image(image_path, target_size=(299, 299), normalize=True):
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| try:
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| img = Image.open(image_path)
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| if img.mode != 'RGB':
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| img = img.convert('RGB')
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| img = img.resize(target_size)
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| img_array = np.array(img)
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| if normalize:
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| img_array = img_array / 255.0
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| img_array = np.expand_dims(img_array, axis=0)
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| return img_array
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| except Exception as e:
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| print(f"Error preprocessing image: {e}")
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| return None
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| def predict_disease(model, image_array):
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| try:
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| prediction = model.predict(image_array)
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| predicted_class = np.argmax(prediction[0])
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| confidence = prediction[0][predicted_class]
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| return predicted_class, confidence, prediction[0]
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| except Exception as e:
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| print(f"Error making prediction: {e}")
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| return None, None, None
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| def get_class_name(class_index):
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| classes = [
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| "Apple___Apple_scab", "Apple___Black_rot", "Apple___Cedar_apple_rust", "Apple___healthy",
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| "Blueberry___healthy", "Cherry_(including_sour)___Powdery_mildew", "Cherry_(including_sour)___healthy",
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| "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot", "Corn_(maize)___Common_rust_",
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| "Corn_(maize)___Northern_Leaf_Blight", "Corn_(maize)___healthy", "Grape___Black_rot",
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| "Grape___Esca_(Black_Measles)", "Grape___Leaf_blight_(Isariopsis_Leaf_Spot)", "Grape___healthy",
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| "Orange___Haunglongbing_(Citrus_greening)", "Peach___Bacterial_spot", "Peach___healthy",
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| "Pepper,_bell___Bacterial_spot", "Pepper,_bell___healthy", "Potato___Early_blight",
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| "Potato___Late_blight", "Potato___healthy", "Raspberry___healthy", "Soybean___healthy",
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| "Squash___Powdery_mildew", "Strawberry___Leaf_scorch", "Strawberry___healthy",
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| "Tomato___Bacterial_spot", "Tomato___Early_blight", "Tomato___Late_blight", "Tomato___Leaf_Mold",
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| "Tomato___Septoria_leaf_spot", "Tomato___Spider_mites Two-spotted_spider_mite",
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| "Tomato___Target_Spot", "Tomato___Tomato_Yellow_Leaf_Curl_Virus", "Tomato___Tomato_mosaic_virus",
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| "Tomato___healthy"
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| ]
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| if 0 <= class_index < len(classes):
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| return classes[class_index]
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| else:
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| return "Unknown"
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| def main():
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| model_path = "Pretrained_model.h5"
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| sample_image_path = "sample_image.jpg"
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| if not os.path.exists(model_path):
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| print(f"Model file not found at: {model_path}")
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| return
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|
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| if not os.path.exists(sample_image_path):
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| print(f"Image file not found at: {sample_image_path}")
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| return
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|
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| model = load_model(model_path)
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| if model is None:
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| return
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| has_rescaling = any(isinstance(layer, tf.keras.layers.Rescaling) for layer in model.layers)
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| image_array = preprocess_image(sample_image_path, target_size=(299, 299), normalize=not has_rescaling)
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| if image_array is None:
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| return
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|
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| predicted_class, confidence, all_predictions = predict_disease(model, image_array)
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| if predicted_class is not None:
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| class_name = get_class_name(predicted_class)
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| print(f"\nPrediction Results:")
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| print(f"Predicted Class: {class_name}")
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| print(f"Confidence: {confidence:.2%}")
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| print(f"Class Index: {predicted_class}")
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| top_3_indices = np.argsort(all_predictions)[-3:][::-1]
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| print(f"\nTop 3 Predictions:")
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| for i, idx in enumerate(top_3_indices):
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| class_name = get_class_name(idx)
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| confidence = all_predictions[idx]
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| print(f"{i+1}. {class_name}: {confidence:.2%}")
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|
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| if __name__ == "__main__":
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| main()
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|
