better preprocessor for training data

src/preprocessor.py

@@ -98,6 +98,7 @@ class Preprocessor:
         # data augmentation
         img = img.astype(np.float)
         if self.data_augmentation:
+
             # photometric data augmentation
             if random.random() < 0.25:
                 def rand_odd():
@@ -128,13 +129,34 @@ class Preprocessor:
             target = np.ones(self.img_size[::-1]) * 255
             img = cv2.warpAffine(img, M, dsize=self.img_size, dst=target, borderMode=cv2.BORDER_TRANSPARENT)
 
-            # photometric data augmentation
-            if random.random() < 0.5:
-                img = img * (0.25 + random.random() * 0.75)
-            if random.random() < 0.25:
-                img = np.clip(img + (np.random.random(img.shape) - 0.5) * random.randint(1, 25), 0, 255)
-            if random.random() < 0.1:
-                img = 255 - img
+            # # photometric data augmentation
+            # if random.random() < 0.5:
+            #     img = img * (0.25 + random.random() * 0.75)
+            # if random.random() < 0.25:
+            #     img = np.clip(img + (np.random.random(img.shape) - 0.5) * random.randint(1, 25), 0, 255)
+            # if random.random() < 0.1:
+            #     img = 255 - img
+
+            #random line on picture
+
+
+            img=img.astype(np.uint8)
+            img = cv2.blur(img,(3,3))
+            img =  cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
+                    cv2.THRESH_BINARY,11,2)
+            
+            start_hight =32- random.randint(0, int(32/4))
+            end_hight =32- random.randint(0, int(32/4))
+            thickness = random.randint(1, 3)
+
+            #draw line on image, starting at 0,start_hight, ending at w, end_hight
+            cv2.line(img, (0, start_hight), (127, end_hight), (0), thickness)
+
+            # show_img = img.astype(np.uint8)
+            # cv2.imshow('img', show_img)
+            # cv2.waitKey(1000)
+            img = img.astype(np.float)
+            
 
         # no data augmentation
         else:

src/webserver.py

@@ -18,7 +18,7 @@ csv_path = '../tns.csv'
 
 char_list = htr.char_list_from_file()
 chars = ''.join(char_list)
-word_chars = open('../model/wordCharList.txt').read().splitlines()[0]
+#word_chars = open('../model/wordCharList.txt').read().splitlines()[0]
 matrikel_numbers = []
 
 @app.route('/getNames', methods=['GET'])
@@ -43,10 +43,7 @@ def predictNach():
     model_name.corpus = open('../data/Nachname.txt').read().split()[:200]
 
     #check time of infer_batch
-    start = time.time()
     recognized, probability = htr_model.Model.infer_batch(model_name, batch)
-    end = time.time()
-    print("infer_batch time: ", end-start)
 
 
     processed_image = processed_image + 0.5