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| from keras.models import load_model # TensorFlow is required for Keras to work | |
| from PIL import Image, ImageOps # Install pillow instead of PIL | |
| import numpy as np | |
| # Disable scientific notation for clarity | |
| np.set_printoptions(suppress=True) | |
| # Load the model | |
| model = load_model("keras_Model.h5", compile=False) | |
| # Load the labels | |
| class_names = open("labels.txt", "r").readlines() | |
| # Create the array of the right shape to feed into the keras model | |
| # The 'length' or number of images you can put into the array is | |
| # determined by the first position in the shape tuple, in this case 1 | |
| data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32) | |
| # Replace this with the path to your image | |
| image = Image.open("./Data/spiral/testing/healthy/V04HE01.png").convert("RGB") | |
| # resizing the image to be at least 224x224 and then cropping from the center | |
| size = (224, 224) | |
| image = ImageOps.fit(image, size, Image.Resampling.LANCZOS) | |
| # turn the image into a numpy array | |
| image_array = np.asarray(image) | |
| # Normalize the image | |
| normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1 | |
| # Load the image into the array | |
| data[0] = normalized_image_array | |
| # Predicts the model | |
| prediction = model.predict(data) | |
| index = np.argmax(prediction) | |
| class_name = class_names[index] | |
| confidence_score = prediction[0][index] | |
| # Print prediction and confidence score | |
| print("Class:", class_name[2:], end="") | |
| print("Confidence Score:", confidence_score) | |