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("<IMAGE_PATH>").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)
