| from keras.models import load_model | |
| from PIL import Image, ImageOps | |
| import numpy as np | |
| # Load the model | |
| model = load_model('keras_model.h5') | |
| # 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('<IMAGE_PATH>') | |
| #resize the image to a 224x224 with the same strategy as in TM2: | |
| #resizing the image to be at least 224x224 and then cropping from the center | |
| size = (224, 224) | |
| image = ImageOps.fit(image, size, Image.ANTIALIAS) | |
| #turn the image into a numpy array | |
| image_array = np.asarray(image) | |
| # Normalize the image | |
| normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1 | |
| # Load the image into the array | |
| data[0] = normalized_image_array | |
| # run the inference | |
| prediction = model.predict(data) | |
| print(prediction) | |