cataract.py

import tensorflow as tf
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 generated by teachablemachine
model = tf.saved_model.load("./converted_savedmodel/model.savedmodel")

# Load the labels
class_names = open("./converted_savedmodel/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
def getImg(x):
    image = Image.open(x).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(data)
    index = np.argmax(prediction)
    class_name = class_names[index]
    confidence_score = prediction[0][index]

    # Print prediction and confidence score
    confidence_score = float(confidence_score)*100
    return class_name[2:],confidence_score