- import tensorflow.keras
- from PIL import Image, ImageOps
- import numpy as np
- # Disable scientific notation for clarity
- np.set_printoptions(suppress=True)
- # Load the model
- model = tensorflow.keras.models.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('test_photo.jpg')
- #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)
- # display the resized image
- image.show()
- # 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)
[text] aaaaaa
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