optimize code

sessions/session-4/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)
@@ -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
\ No newline at end of file
+        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         
\ No newline at end of file

sessions/session-4/window.py

@@ -5,14 +5,9 @@ from PyQt5.QtWidgets import (QAction, QApplication, QWidget,QGridLayout)
 #from trainWidget import TrainWidget
 #from testWidget import TestWidget
 from ubuwidgets import (Button, ComboWidget, Board)
-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 import svm, metrics
 from sklearn.metrics import accuracy_score
 from sklearn.externals import joblib
 
@@ -24,6 +19,7 @@ class MainWindow(QWidget):
         self.trainButton = Button(self, "Train", self.on_train)
         self.saveCombo = ComboWidget(self, "Label: ", "Save", self.on_save)
         self.testCombo = ComboWidget(self, "Result: ", "Test", self.on_test, True)
+
         grid = QGridLayout()
         grid.addWidget(self.scribbleArea,1,0)
         grid.addWidget(self.clearButton,2,0)
@@ -37,54 +33,10 @@ class MainWindow(QWidget):
     def on_clear(self):
         self.scribbleArea.clearImage()
         
-    def data_prep_train(self):
-        print('preporcessing..')
-        list_of_label = os.listdir('Image/')
-        digit_data = list()
-        digit_label = list()
-        for label in list_of_label:
-            label_path = 'Image/'+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)
-                #list_im = str(arr_im.tolist())
-                
-                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   
 
-    def data_prep_test(self):   
-        im = Image.open('Test/temp.png')
-        iar = np.array(im)
-        #list_im = str(arr_im.tolist())
-        
-        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 on_train(self):
         
-        digit_data, digit_label = self.data_prep_train()
+        digit_data, digit_label = self.scribbleArea.data_prep_train()
         
         print('training..')  
         # Create a classifier: a support vector classifier
@@ -94,22 +46,23 @@ class MainWindow(QWidget):
         classifier.fit(digit_data, digit_label)
         #save model
         joblib.dump(classifier, 'svmModel.pkl')
-        
-        expected = digit_label
+
         predicted = classifier.predict(digit_data)
         print("Classification report for classifier %s:\n%s\n"
-              % (classifier, metrics.classification_report(expected, predicted)))
-        print("Confusion matrix:\n%s" % metrics.confusion_matrix(expected, predicted))
+              % (classifier, metrics.classification_report(digit_label, predicted)))
+        print("Confusion matrix:\n%s" % metrics.confusion_matrix(digit_label, predicted))
         print("\naccuracy = ",accuracy_score(digit_label, predicted))   
         
     def on_test(self):
         print("testing...")
         clf = joblib.load('svmModel.pkl')
         self.scribbleArea.saveImageTest()
-        test_data = self.data_prep_test()
+        test_data = self.scribbleArea.data_prep_test()
         predicted = clf.predict([test_data])
-        print("ผลการทำนาย คือ", predicted)
-        
+        result = "ผลการทำนาย คือ "+predicted[0]
+        self.testCombo.set_text(result)
+        print("ผลการทำนาย คือ", predicted)         
+               
     def on_save(self):
         print('on_save')
         self.scribbleArea.saveImage(self.saveCombo.text())