import numpy as np
import matplotlib.pyplot as plt
import os
import cv2

DATADIR = "I:/Datasets/CatsDogs"
CATEGORIES = ["Dog", "Cat"]
for category in CATEGORIES:
    path = os.path.join(DATADIR, category) #path to cats or digs dir
    for img in os.listdir(path):
        img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
        plt.imshow(img_array, cmap="gray")
        plt.show()
        break
    break

---

IMG_SIZE = 60

new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
plt.imshow(new_array, cmap = 'gray')
plt.show()

---

training_data = []

def create_training_data():
    for category in CATEGORIES:
        path = os.path.join(DATADIR, category) #path to cats or digs dir
        class_num = CATEGORIES.index(category)
        for img in 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))
                training_data.append([new_array, class_num])
            except Exception as e:
                pass
            
create_training_data()

---

print(len(training_data))

---

import random

random.shuffle(training_data)

---

for sample in training_data:
    print(sample[1])

---

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_out.close()

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

---

pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)

---

X[1]

---

NEW DOCUMENT THINGY

---
import tensorflow as tf
import numpy as np
import cv2
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
import pickle

X = pickle.load(open("X.pickle", "rb"))
y = pickle.load(open("y.pickle", "rb"))

X = X/255.0

model = Sequential()

model.add(Conv2D(64, (3,3), input_shape = X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(64))

model.add(Dense(1))
model.add(Activation('sigmoid'))

model.compile(loss="binary_crossentropy",
             optimizer="adam",
             metrics=['accuracy'])

model.fit(X, y, batch_size=32, epochs=1, validation_split=0.1)

---

IMG_SIZE = 60

userpic = cv2.imread("C:/Users/_reg/Pictures/My Photo's/148611_1703005778829_3639558_n.jpg")

cv2.resize(userpic, (IMG_SIZE, IMG_SIZE))

print(userpic.shape)

np.array(userpic).reshape(1, IMG_SIZE, IMG_SIZE, 1)

model.predict(userpic)