DataSetup1.py

import pickle
import random
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from tqdm import tqdm
DATADIR = "C:/Kaggle/PetImages"
IMG_SIZE = 50

CATEGORIES = ["Dog", "Cat"]

# for category in CATEGORIES:  # do dogs and cats
#     path = os.path.join(DATADIR, category)  # create path to dogs and cats
#     for img in os.listdir(path):  # iterate over each image per dogs and cats
#         img_array = cv2.imread(os.path.join(path, img),
#                                cv2.IMREAD_GRAYSCALE)  # convert to array
#         IMG_SIZE = 100
#         new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))

#         plt.imshow(new_array, cmap='gray')  # graph it
#         plt.show()  # display!

#         break  # we just want one for now so break
#     break  # ...and one more!

training_data = []


def create_training_data():
    for category in CATEGORIES:  # do dogs and cats

        path = os.path.join(DATADIR, category)  # create path to dogs and cats
        # get the classification  (0 or a 1). 0=dog 1=cat
        class_num = CATEGORIES.index(category)

        for img in tqdm(os.listdir(path)):  # iterate over each image per dogs and cats
            try:
                img_array = cv2.imread(os.path.join(
                    path, img), cv2.IMREAD_GRAYSCALE)  # convert to array
                # resize to normalize data size
                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
                # add this to our training_data
                training_data.append([new_array, class_num])
            except Exception as e:  # in the interest in keeping the output clean...
                pass
            # except OSError as e:
            #    print("OSErrroBad img most likely", e, os.path.join(path,img))
            # except Exception as e:
            #    print("general exception", e, os.path.join(path,img))


create_training_data()

# random.shuffle(training_data)
print(len(training_data))
# for sample in training_data[:10]:
#     print(sample[1])


X = []
y = []

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



# print(X[0].reshape(-1, IMG_SIZE, IMG_SIZE, 1))

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


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

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