Sourcery Starbot ⭐ refactored lamhoangtung/LineHTR

src/DataLoader.py

@@ -51,15 +51,17 @@ def __init__(self, filePath, batchSize, imgSize, maxTextLen):
 
         # Read json lables file
         # Dataset folder should contain a labels.json file inside, with key is the file name of images and value is the label
-        with open(filePath + 'labels.json') as json_data:
+        with open(f'{filePath}labels.json') as json_data:
             label_file = json.load(json_data)
 
         # Log
         print("Loaded", len(label_file), "images")
 
         # Put sample into list
-        for fileName, gtText in label_file.items():
-            self.samples.append(Sample(gtText, filePath + fileName))
+        self.samples.extend(
+            Sample(gtText, filePath + fileName)
+            for fileName, gtText in label_file.items()
+        )
 
         self.charList = list(open(FilePaths.fnCharList).read())
 

src/Model.py

@@ -199,19 +199,19 @@ def setupCTC(self, ctcIn3d):
 
     def setupTF(self):
         """ Initialize TensorFlow """
-        print('Python: ' + sys.version)
-        print('Tensorflow: ' + tf.__version__)
+        print(f'Python: {sys.version}')
+        print(f'Tensorflow: {tf.__version__}')
         sess = tf.Session()  # Tensorflow session
         saver = tf.train.Saver(max_to_keep=5)  # Saver saves model to file
         modelDir = '../model/'
         latestSnapshot = tf.train.latest_checkpoint(
             modelDir)  # Is there a saved model?
         # If model must be restored (for inference), there must be a snapshot
         if self.mustRestore and not latestSnapshot:
-            raise Exception('No saved model found in: ' + modelDir)
+            raise Exception(f'No saved model found in: {modelDir}')
         # Load saved model if available
         if latestSnapshot:
-            print('Init with stored values from ' + latestSnapshot)
+            print(f'Init with stored values from {latestSnapshot}')
             saver.restore(sess, latestSnapshot)
         else:
             print('Init with new values')
@@ -247,7 +247,7 @@ def toSpare(self, texts):
     def decoderOutputToText(self, ctcOutput):
         """ Extract texts from output of CTC decoder """
         # Contains string of labels for each batch element
-        encodedLabelStrs = [[] for i in range(Model.batchSize)]
+        encodedLabelStrs = [[] for _ in range(Model.batchSize)]
         # Word beam search: label strings terminated by blank
         if self.decoderType == DecoderType.WordBeamSearch:
             blank = len(self.charList)