import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from tqdm import tqdm
import random
DATADIR = "Products”
CATEGORIES = ["colgate"]
training data = []
IMG_SIZE = 50
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)):
      try:
        img_array = cv2.imread(os.path.join(path, img), cv2. IMREAD_GRAYSCALE)
        new_array = cv2.resize (img_array, (IMG_SIZE, IMG_SIZE))
        traning_data. append([new_array, class_num]) 
      except Exception as  e:
          pass
create_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)

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