optimize code2

sessions/session-3/ubuwidgets.py

@@ -2,6 +2,17 @@ from PyQt5.QtCore import Qt, QDir, QFile, QPoint, QRect, QSize
 from PyQt5.QtWidgets import QWidget, QPushButton, QLineEdit, QLabel, QGridLayout
 from PyQt5.QtGui import QImage, QImageWriter, QPainter, QPen, qRgb, QPixmap
 
+import os
+import matplotlib.pyplot as plt
+
+# Import datasets, classifiers and performance metrics
+from sklearn import datasets, svm, metrics
+from PIL import Image
+import numpy as np
+import math
+from sklearn.metrics import accuracy_score
+from sklearn.externals import joblib
+
 class Button(QWidget):
     def __init__(self,parent=None, label="Button", on_click=None):
         super(Button,self).__init__(parent)
@@ -67,7 +78,7 @@ class Board(QWidget):
                 tmpN = int(file[:-4:])
                 maxf = max(maxf,tmpN)
         visibleImage.save('tmp.png')
-        pixmap = QPixmap('tmp.png').scaled(16,16)
+        pixmap = QPixmap('tmp.png').scaled(64,64)
         pixmap.save(label+'/'+str(max(cnt,maxf)+1)+'.png')
         QFile.remove('tmp.png')
         
@@ -79,7 +90,7 @@ class Board(QWidget):
             QDir().mkdir('Test')
 
         visibleImage.save('tmp.png')
-        pixmap = QPixmap('tmp.png').scaled(16,16)
+        pixmap = QPixmap('tmp.png').scaled(64,64)
         pixmap.save('Test/temp.png')
         QFile.remove('tmp.png')
 
@@ -128,4 +139,47 @@ class Board(QWidget):
         newImage.fill(qRgb(255,255,255))
         painter = QPainter(newImage)
         painter.drawImage(QPoint(0,0), image)
-        self.image = newImage
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+        self.image = newImage
+
+    def data_prep_test(self):   
+        im = Image.open('Test/temp.png')
+        iar = np.array(im)
+        
+        width, height = im.size
+        test_data = np.zeros(width*height)
+        
+        test_data = np.zeros(64*64)
+        count = 0
+        for i in range(len(iar)):
+            for j in range(len(iar[i])):
+                test_data[count] = math.floor(((np.sum(iar[i][j][:3]/765))))
+                #print(test_data[count])
+                count+=1
+        return test_data  
+
+    def data_prep_train(self):
+        print('preporcessing..')
+        folder_name = 'Image/'
+        list_of_label = os.listdir(folder_name)
+        digit_data = list()
+        digit_label = list()
+        for label in list_of_label:
+            label_path = folder_name+str(label)+'/'
+            list_of_data = os.listdir(label_path)
+           #print(list_of_data)
+            for data in list_of_data:
+                im = Image.open(label_path+'/'+data)
+                arr_im = np.array(im)
+                width, height = im.size
+                gen_data = np.zeros(width*height)
+                count = 0
+                for i in range(len(arr_im)):
+                    for j in range(len(arr_im[i])):
+                        #ทำ Normalize ด้วยการ (R+G+B) หาร 765 มาจาก (255+255+255)
+                        gen_data[count] = math.floor(((np.sum(arr_im[i][j][:3]/765))))
+                        count+=1
+                digit_data.append( gen_data )
+                digit_label.append( label )     
+        digit_data = np.array(digit_data)  #ข้อมูล
+        digit_label = np.array(digit_label) # label ของข้อมูล  
+        return digit_data,digit_label         
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