Accuracy Up++

sessions/session-4/ubuwidgets.py

@@ -71,6 +71,17 @@ class Board(QWidget):
         pixmap.save(label+'/'+str(max(cnt,maxf)+1)+'.png')
         QFile.remove('tmp.png')
         
+    def saveImageTest(self):
+        import os
+        visibleImage = self.image
+        self.resizeImage(visibleImage,self.size())
+        if QDir('Test').exists() == False:
+            QDir().mkdir('Test')
+
+        visibleImage.save('tmp.png')
+        pixmap = QPixmap('tmp.png').scaled(64,64)
+        pixmap.save('Test/temp.png')
+        QFile.remove('tmp.png')
 
     def paintEvent(self, event):
         painter = QPainter(self)
@@ -117,4 +128,4 @@ class Board(QWidget):
         newImage.fill(qRgb(255,255,255))
         painter = QPainter(newImage)
         painter.drawImage(QPoint(0,0), image)
-        self.image = newImage
+        self.image = newImage
\ No newline at end of file

sessions/session-4/window.py

@@ -37,13 +37,8 @@ class MainWindow(QWidget):
     def on_clear(self):
         self.scribbleArea.clearImage()
         
-    def data_prep(self):
+    def data_prep_train(self):
         print('preporcessing..')
-        
-    def on_train(self):
-        self.data_prep()
-        
-        print('training..')
         list_of_label = os.listdir('Image/')
         digit_data = list()
         digit_label = list()
@@ -54,7 +49,7 @@ class MainWindow(QWidget):
             for data in list_of_data:
                 im = Image.open(label_path+'/'+data)
                 arr_im = np.array(im)
-                list_im = str(arr_im.tolist())
+                #list_im = str(arr_im.tolist())
                 
                 width, height = im.size
                 gen_data = np.zeros(width*height)
@@ -62,16 +57,38 @@ class MainWindow(QWidget):
                 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)))
+                        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()
         
+        print('training..')  
         # Create a classifier: a support vector classifier
-        classifier = svm.SVC(gamma=0.001, C=.1)
+        classifier = svm.SVC(gamma=0.001)
         
         # We learn the digits on the first half of the digits
         classifier.fit(digit_data, digit_label)
@@ -83,32 +100,20 @@ class MainWindow(QWidget):
         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))
-        print("\naccuracy = ",accuracy_score(digit_label, predicted))        
-        
-        
-    def on_save(self):
-        print('on_save')
-        self.scribbleArea.saveImage(self.saveCombo.text())
-        self.scribbleArea.clearImage()
+        print("\naccuracy = ",accuracy_score(digit_label, predicted))   
         
     def on_test(self):
         print("testing...")
         clf = joblib.load('svmModel.pkl')
-        
-        print('preporcessing..')
-        i = Image.open('test/t.png')
-        iar = np.array(i)
-        test_data = np.zeros(64*64)
-        len(iar[0])
-        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
+        self.scribbleArea.saveImageTest()
+        test_data = self.data_prep_test()
         predicted = clf.predict([test_data])
-        print(predicted)
+        print("ผลการทำนาย คือ", predicted)
         
+    def on_save(self):
+        print('on_save')
+        self.scribbleArea.saveImage(self.saveCombo.text())
+        self.scribbleArea.clearImage()
         
 if __name__ == '__main__':
     import sys