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import tensorflow as tf

import matplotlib.pyplot as plt

from PIL import Image

mnist = tf.keras.datasets.mnist

import cv2

mnist = tf.keras.datasets.mnist

(x_train, y_train),(x_test, y_test) = mnist.load_data()

x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([

  tf.keras.layers.Flatten(),

  tf.keras.layers.Dense(512, activation=tf.nn.relu),

  tf.keras.layers.Dropout(0.2),

  tf.keras.layers.Dense(10, activation=tf.nn.softmax)

])

model.compile(optimizer='adam',

              loss='sparse_categorical_crossentropy',

              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)

score = model.evaluate(x_test, y_test)

############ 추가 소스 ##################

img = cv2.imread("F:/tensordata/number/tensorflow_model03.png", cv2.IMREAD_GRAYSCALE)

plt.imshow(img)

plt.show()

img = cv2.resize(255-img, (28, 28))

test_num = img.flatten() / 255.0

test_num = test_num.reshape((-1, 28, 28, 1))

print('The Answer is ', model.predict_classes(test_num))

plt.show() 

 #-*-coding:utf-8-*-

import cv2

import numpy as np

from matplotlib import pyplot as plt

imgFile1 = 'F:/Python/images/heart01.png'

imgFile2 = 'F:/Python/images/heart02.png'

# 이미지 읽기

img1 = cv2.imread(imgFile1, 1);

img2 = cv2.imread(imgFile2, 1);

# 사이즈 조절

img1 = cv2.resize(img1,(230,260))

img2 = cv2.resize(img2,(230,260))

# 이미지 붙이기

addv = cv2.vconcat([img1, img2])

addh = cv2.hconcat([img1, img2])

cv2.imshow('imgv',addv)

cv2.imshow('imgh',addh)

cv2.waitKey(0)

#-*-coding:utf-8-*-

import cv2

import numpy as np

from matplotlib import pyplot as plt

imgFile1 = 'F:/Python/images/heart01.png'

imgFile2 = 'F:/Python/images/heart02.png'

# 이미지 읽기

img1 = cv2.imread(imgFile1, 1);

img2 = cv2.imread(imgFile2, 1);

# 이미지 사이즈 조절

img1 = cv2.resize(img1,(230,260))

img2 = cv2.resize(img2,(230,260))

# 이미지 붙이기

addv = np.vstack((img1, img2))

addh = np.hstack((img1, img2))

cv2.imshow('imgv',addv)

cv2.imshow('imgh',addh)

cv2.waitKey(0)

import numpy as np

import cv2

imgFile1 = 'C:/opencv/threshold01.png'

img1 = cv2.imread(imgFile1, cv2.IMREAD_GRAYSCALE)

ret1, addWeight1 = cv2.threshold(img1, 127, 255, cv2.THRESH_BINARY)

ret2, addWeight2 = cv2.threshold(img1, 127, 255, cv2.THRESH_BINARY_INV)

ret3, addWeight3 = cv2.threshold(img1, 127, 255, cv2.THRESH_TRUNC)

ret4, addWeight4 = cv2.threshold(img1, 127, 255, cv2.THRESH_TOZERO)

ret5, addWeight5 = cv2.threshold(img1, 127, 255, cv2.THRESH_TOZERO_INV)

cv2.imshow('THRESH_BINARY', addWeight1)

cv2.imshow('THRESH_BINARY_INV', addWeight2)

cv2.imshow('THRESH_TRUNC', addWeight3)

cv2.imshow('THRESH_TOZERO', addWeight4)

cv2.imshow('THRESH_TOZERO_INV', addWeight5)

cv2.waitKey(0)

import numpy as np

import cv2

imgFile1 = 'F:/Python/images/image_calcu001.png'

imgFile2 = 'F:/Python/images/image_calcu002.png'

# image read

img1 = cv2.imread(imgFile1)

img2 = cv2.imread(imgFile2)

bit_and = cv2.bitwise_and(img1, img2)

bit_or = cv2.bitwise_or(img2, img1)

bit_not = cv2.bitwise_not(img2)

bit_xor = cv2.bitwise_xor(img2, img1)

cv2.imshow("bit_and", bit_and)

cv2.imshow("bit_or", bit_or)

cv2.imshow("bit_not", bit_not)

cv2.imshow("bit_xor", bit_xor)

cv2.waitKey(0)