predict.py

import tensorflow as tf
from tensorflow import keras
import cv2
import matplotlib.pyplot as plt
# import  numpy as np
# SIZE = 120
# model = keras.models.load_model('model.h5')
# categories = ["NonDemented", "MildDemented", "ModerateDemented", "VeryMildDemented"]
#
# nimage = cv2.imread("mild.jpg", cv2.IMREAD_GRAYSCALE)
# image = cv2.resize(nimage,(SIZE,SIZE))
# image = image/255.0
# prediction = model.predict(np.array(image).reshape(-1,SIZE,SIZE,1))
# pclass = np.argmax(prediction)
# plt.imshow(image,cmap="gray")
# pValue = "Prediction: {0}".format(categories[int(pclass)])
# plt.title(pValue)
# realvalue = "Real Value 1"
# plt.figtext(0,0,realvalue)
# plt.show()
import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras
import tensorflow as tf
from model import createModel


def predict(SIZE):
    categories = ["NonDemented", "MildDemented", "ModerateDemented", "VeryMildDemented"]

    path = "./test/"
    images = []
    for img in os.listdir(path):
        data = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
        new_data = cv2.resize(data, (SIZE, SIZE))
        new_data = new_data / 255.0
        images.append(new_data)
    model = createModel()
    title = os.listdir('./test')
    x = 0
    for img ,indx,value in images,enumerate(title):
        image = np.array(img).reshape(-1, SIZE, SIZE, 1)
        prediction = model.predict(image)
        plt.imshow(img, cmap="gray")
        ptitle = "Prediction: {0}".format(categories[np.argmax(prediction)])

        plt.figtext(0, 0, title[indx])
        plt.title(ptitle)
        plt.show()
        print(prediction)


    print(len(images), len(title))


predict(120)