Neural-Network/PreProcessing.py at master · Ivanodib/Neural-Network · GitHub

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# -*- coding: utf-8 -*-
"""
Created on Sun Sep 27 09:32:14 2020

@author: Ivano Dibenedetto mat. 654678
"""

import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from tqdm import tqdm
import random
import pickle


DATADIR = "Covid19_dataset_global"

CATEGORIES = ["Covid", "Polmonite"]
IMG_SIZE = 250
training_data = []


def create_training_data():

    for category in CATEGORIES:
        path = os.path.join(DATADIR, category)
        class_num = CATEGORIES.index(category)

        for img in tqdm(os.listdir(path)):

            img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
            new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
            training_data.append([new_array, class_num])


create_training_data()

print(len(training_data))
random.shuffle(training_data)

X = []
y = []


for features, label in training_data:
    X.append(features)
    y.append(label)

X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)

pickle_out = open("X2.pickle", "wb")
pickle.dump(X, pickle_out)
pickle_out.close()

pickle_out = open("y2.pickle", "wb")
pickle.dump(y, pickle_out)
pickle_out.close()