| from keras.models import load_model | |
| import numpy as np | |
| labels = ['Chalky Soil', 'Mary Soil', 'Sand', 'Slit SOil', 'Alluvial Soil', 'Black Soil', 'Clay Soil', 'Red Soil'] | |
| MODEL_PATH = r"D:\Official\Reseach Projects\Official Project\soil-type-classification\models\DenseNet121v2_95.h5" | |
| def load_CNN_model(): | |
| model = load_model(MODEL_PATH) | |
| print("model loaded") | |
| return model | |
| def classify_image(img): | |
| model = load_CNN_model() | |
| prediction = model.predict(img) | |
| predicted_class = np.argmax(prediction) | |
| predicted_label = labels[predicted_class] | |
| accuracy = prediction[0][predicted_class] | |
| return predicted_label, accuracy | |