211123

#이미지 컬러있는 버전으로 나오는 거
'''
import cv2
import numpy as np
img = cv2.imread('./images/meat.jpeg', cv2.IMREAD_GRAYSCALE)

#hist,bins = np.histogram(img.ravel(),256,[0,256])

cv2.imshow('color', img)

cv2.waitKey()
cv2.destroyAllwindows()
'''

#히스토그램
from typing import List, Any

import numpy

'''import cv2
from matplotlib import pyplot as plt
img = cv2.imread('./images/meat.jpeg')
color = ('b','g','r')
for i,col in enumerate(color):
    histr = cv2.calcHist([img],[i],None,[256],[0,256])
    plt.plot(histr,color = col)
    plt.xlim([0,256])
plt.show()
'''
'''
import cv2
import numpy as np

img = cv2.imread('./images/meat.jpeg')

x=320; y=150; w=50; h=50        # roi 좌표
roi = img[y:y+h, x:x+w]         # roi 지정        ---①

print(roi.shape)                # roi shape, (50,50,3)
cv2.rectangle(roi, (0,0), (h-10, w+100), (0,0,255)) # roi 전체에 사각형 그리기 ---②
cv2.imshow("img", img)

key = cv2.waitKey(0)
print(key)
cv2.destroyAllWindows()
'''
'''
import cv2
import numpy as np
import matplotlib.pylab as plt
# haarcascade 불러오기
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')


# 이미지 불러오기
img = cv2.imread('./images2/face2.jpg')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img2 = cv2.imread('./images2/face2.jpg')


# 얼굴 찾기
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
k = 0
loc = []
for (x, y, w, h) in faces:
    #face = cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
    loc.append([x, y])

    #print(loc)
    # 눈 찾기
    roi_color = img[y:y + h, x:x + w]   # jooseok
    roi_gray = gray[y:y + h, x:x + w]

    if k==0:
        roi_first = gray[y:y + h, x:x + w]

    #print(roi_gray)
    cv2.imwrite('./images/result' + str(k) + '.jpg', roi_color)
    k += 1
    for i in range(len(roi_first)):
        for j in range(len(roi_first[i])):
            img[i+y][j+x] = roi_first[i][j]            # 얼굴부분을 한 사람 얼굴로 전부 바꾸기

    eyes = eye_cascade.detectMultiScale(gray)
    for (ex, ey, ew, eh) in eyes:
        roi_color1 = img2[ey:ey+eh, ex:ex+ew]
        for l in range(len(roi_color1)):
            for t in range(len(roi_color1[l])):
                img[l+ey][t+ex] = roi_color1[l][t]    #눈부분만 컬러로 (각얼굴에 맞게)

        #cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (0, 255, 0), 2)



# 영상 출력
cv2.imshow('image', img)
from matplotlib import pyplot as plt

img = cv2.imread('./images2/face2.jpg',0)
'''
'''
color = ('b','g','r')
for i,col in enumerate(color):
    histr = cv2.calcHist([img],[i],None,[256],[0,256])
    plt.plot(histr,color = col)
    plt.xlim([0,256])
plt.show()
'''
'''
img2 = cv2.equalizeHist(img)
img = cv2.resize(img,(400,400))
img2 = cv2.resize(img2,(400,400))

dst = np.hstack((img, img2))
cv2.imshow('img',dst)
cv2.waitKey()
cv2.destroyAllWindows()
'''
'''

clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
img2 = clahe.apply(img)

img = cv2.resize(img,(400,400))
img2 = cv2.resize(img2,(400,400))

dst = np.hstack((img, img2))
cv2.imshow('img',dst)
#print(loc)

key = cv2.waitKey(0)
cv2.destroyAllWindows()


import glob
target_dir = './images/'
files = glob.glob(target_dir+'*.*')
#print(files)

from PIL import Image

def imgresize(files,x_size,y_size):
    file_list = []
    for file in files:
        image = Image.open(file)
        resized_file = image.resize((x_size,y_size))
        file_list.append(resized_file)

thumb_sizeX = 100
thumb_sizeY = 100
file_list = imgresize(files, thumb_sizeX,thumb_sizeY)

def imgMerge(file_list,sizeX,sizeY,numX):
    new_image = Image.new("RGB",(sizeX*numX,sizeY*(len(file_list)//numX+1)),(256,256,256))
    count_X = 0
    count_Y = 0
    for index in range(len(file_list)):
        area = ((count_X*sizeX),count_Y*sizeY,(count_X+1)*sizeX,(count_Y+1)*sizeY)
        new_image.paste(file_list[index],area)

        if(index+1)%numX ==0:
            count_Y+=1
            count_X =0
        else:
            count_X +=1
    new_image.show()
    new_image.save('./images2/face2','jpg')

numX = 3
imgMerge(file_list,thumb_sizeX, thumb_sizeY, numX)
'''
import tensorflow as tf
'''


# 1. MNIST 데이터셋 임포트
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 2. 데이터 전처리
x_train, x_test = x_train/255.0, x_test/255.0

# 3. 모델 구성
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(512, activation=tf.nn.relu),
    tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])

# 4. 모델 컴파일
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# 5. 모델 훈련
model.fit(x_train, y_train, epochs=20)

# 6. 정확도 평가
test_loss, test_acc = model.evaluate(x_test, y_test)
print('테스트 정확도:', test_acc)
'''

'''
import sys
import tensorflow as tf
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from PyQt5.QtCore import *
import numpy as np
class MyApp(QMainWindow):

    def __init__(self):
        super().__init__()
        self.image = QImage(QSize(400, 400), QImage.Format_RGB32)
        self.image.fill(Qt.white)
        self.drawing = False
        self.brush_size = 30
        self.brush_color = Qt.black
        self.last_point = QPoint()
        self.loaded_model = None
        self.initUI()

    def initUI(self):
        menubar = self.menuBar()
        menubar.setNativeMenuBar(False)
        filemenu = menubar.addMenu('File')

        load_model_action = QAction('Load model', self)
        load_model_action.setShortcut('Ctrl+L')
        load_model_action.triggered.connect(self.load_model)

        save_action = QAction('Save', self)
        save_action.setShortcut('Ctrl+S')
        save_action.triggered.connect(self.save)

        clear_action = QAction('Clear', self)
        clear_action.setShortcut('Ctrl+C')
        clear_action.triggered.connect(self.clear)

        filemenu.addAction(load_model_action)
        filemenu.addAction(save_action)
        filemenu.addAction(clear_action)

        self.statusbar = self.statusBar()

        self.setWindowTitle('MNIST Classifier')
        self.setGeometry(300, 300, 400, 400)
        self.show()

    def paintEvent(self, e):
        canvas = QPainter(self)
        canvas.drawImage(self.rect(), self.image, self.image.rect())

    def mousePressEvent(self, e):
        if e.button() == Qt.LeftButton:
            self.drawing = True
            self.last_point = e.pos()

    def mouseMoveEvent(self, e):
        if (e.buttons() & Qt.LeftButton) & self.drawing:
            painter = QPainter(self.image)
            painter.setPen(QPen(self.brush_color, self.brush_size, Qt.SolidLine, Qt.RoundCap))
            painter.drawLine(self.last_point, e.pos())
            self.last_point = e.pos()
            self.update()

    def mouseReleaseEvent(self, e):
        if e.button() == Qt.LeftButton:
            self.drawing = False

            arr = np.zeros((28, 28))
            for i in range(28):
                for j in range(28):
                    arr[j, i] = 1 - self.image.scaled(28, 28).pixelColor(i, j).getRgb()[0] / 255.0
            arr = arr.reshape(-1, 28, 28)

            if self.loaded_model:
                pred = self.loaded_model.predict(arr)[0]
                pred_num = str(np.argmax(pred))
                self.statusbar.showMessage('숫자 ' + pred_num + '입니다.')

    def load_model(self):
        fname, _ = QFileDialog.getOpenFileName(self, 'Load Model', '')

        if fname:
            self.loaded_model = tf.keras.models.load_model(fname)
            self.statusbar.showMessage('Model loaded.')

    def save(self):
        fpath, _ = QFileDialog.getSaveFileName(self, 'Save Image', '', "PNG(*.png);;JPEG(*.jpg *.jpeg);;All Files(*.*) ")

        if fpath:
            self.image.scaled(28, 28).save(fpath)

    def clear(self):
        self.image.fill(Qt.white)
        self.update()
        self.statusbar.clearMessage()


if __name__ == '__main__':
    app = QApplication(sys.argv)
    ex = MyApp()
    sys.exit(app.exec_())
'''

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt

#from google.colab import auth
#from google.colab import drive

import cv2
import numpy as np

#drive.mount('/content/gdrive/') # google drive address

#import glob
import os
scr = os.listdir('./long numbers/')

'''
plt.title('The number I write')
plt.imshow(myNum, cmap='gray')
print(myNum.shape)
'''


# data input
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
print(x_train.shape, y_train.shape, x_test.shape, y_test.shape)

# view Images
'''
plt.figure(figsize=(10, 10))
c = 0

for x in range(2):
  for y in range(5):
    plt.subplot(2, 5, c+1)
    plt.imshow(x_train[c], cmap='gray')
    c+=1
plt.show()

print(y_train[:10])
'''

# make a model
model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])

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

model.summary()

model.fit(x_train, y_train, epochs=1)
model.evaluate(x_test, y_test)

# test my Image data

myNum = np.array(myNum)

#v = model.predict(np.reshape(myNum, (1,28,28)))
#print(v)
#print(max(v[0]))
#이미지 파일 하나 찝어와서 안의 숫자 인식, 출력
sum = 0
dataVV = []
s = []
#for i in range(3):
for i in range(2):
    #path = os.path.join('./number/'+str(scr[i]))
    src = cv2.imread('./long numbers/result'+str(i)+'.png', cv2.IMREAD_GRAYSCALE)
    dataVV.append(src)
    s.append()

dst = []
for i in range(0,140,28):
    dst.append(dataVV[0:28, 0:28].copy())
    

5 < dataVV[0]

for i in range(0,len(dataVV)):
    ret, binary = cv2.threhold(dataVV[i])

for i in range(0,len(dataVV)):
    ret, binary = cv2.threshold(dataVV[i], 170, 255, cv2.THRESH_BINARY_INV)
    scr = np.asarray(cv2.resize(binary,dsize=(28, 28), interpolation=cv2.INTER_AREA))/255
    myNum = np.array(scr)
    v = model.predict(np.reshape(myNum, (1, 28, 28)))
    s.append(list(v[i]).index(max(v[i])))
    sum += int(s.pop())*(10*(i+1))

print(sum)

'''
import random
from PIL import Image

width1 = 28*5
height1 = 28
path1 = './numbers/0.png'
path2 = './numbers/1.png'
path3 = './numbers/2.png'

list_input = []
for i in range(20):
    #v = cv2.imread('./numbers/'+str(i)+'.png', cv2.IMREAD_GRAYSCALE)
    #v = numpy.array(v)
    v = Image.open('./numbers/'+str(i)+'.png')
    list_input.append(v)
    #list_input[i] = cv2.imread('./numbers/'+str(i)+'.png', cv2.IMREAD_GRAYSCALE)

idx =0

for i in range(3):
    idx +=1

for j in range(20):
    result0 = Image.new("L",(width1,height1))
    for i in range(5):
        v = random.randrange(0,19)
        result0.paste(im = list_input[v],box = (i*28, 0))

    result0.save('./numbers/result'+str(j)+'.png'.format(idx))

print(sum)

'''


r = [];
ret , binary = cv2.threshold(src,170,255,cv2.THRESH_BINARY_INV)
if (type(scr) == type(None)):
    pass
else:
    scr = np.asarray(cv2.resize(binary,dsize=(28, 28), interpolation=cv2.INTER_AREA))/255
    #vv = np.asarray(cv2.resize(binary, dsize=(28, 28), interpolation=cv2.INTER_AREA))/255
    #dataVV.append()
    myNum = np.array(binary)
    v = model.predict(np.reshape(myNum, (1, 28, 28)))
    s = list(v[0]).index(max(v[0]))
    r.append(s);
    for i in range(len(r),0):
        a = s.pop()
        sum += a*(10**len(r))


print('>',sum)