Merge remote-tracking branch 'upstream/3.4' into merge-3.4

samples/dnn/fast_neural_style.py

@@ -14,7 +14,7 @@ parser.add_argument('--median_filter', default=0, type=int, help='Kernel size of
 args = parser.parse_args()
 
 net = cv.dnn.readNetFromTorch(cv.samples.findFile(args.model))
-net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV);
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
 
 if args.input:
     cap = cv.VideoCapture(args.input)

samples/dnn/mobilenet_ssd_accuracy.py

@@ -27,7 +27,7 @@ args = parser.parse_args()
 
 ### Get OpenCV predictions #####################################################
 net = cv.dnn.readNetFromTensorflow(cv.samples.findFile(args.weights), cv.samples.findFile(args.prototxt))
-net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV);
+net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
 
 detections = []
 for imgName in os.listdir(args.images):

samples/dnn/text_detection.py

@@ -134,7 +134,7 @@ def main():
             for j in range(4):
                 p1 = (vertices[j][0], vertices[j][1])
                 p2 = (vertices[(j + 1) % 4][0], vertices[(j + 1) % 4][1])
-                cv.line(frame, p1, p2, (0, 255, 0), 1);
+                cv.line(frame, p1, p2, (0, 255, 0), 1)
 
         # Put efficiency information
         cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))

samples/dnn/tf_text_graph_common.py

@@ -21,7 +21,7 @@ def tokenize(s):
                 elif token:
                     tokens.append(token)
                     token = ""
-            isString = (symbol == '\"' or symbol == '\'') ^ isString;
+            isString = (symbol == '\"' or symbol == '\'') ^ isString
 
         elif symbol == '{' or symbol == '}' or symbol == '[' or symbol == ']':
             if token:

samples/dnn/tf_text_graph_ssd.py

@@ -122,7 +122,7 @@ def createSSDGraph(modelPath, configPath, outputPath):
     print('Input image size: %dx%d' % (image_width, image_height))
 
     # Read the graph.
-    inpNames = ['image_tensor']
+    _inpNames = ['image_tensor']
     outNames = ['num_detections', 'detection_scores', 'detection_boxes', 'detection_classes']
 
     writeTextGraph(modelPath, outputPath, outNames)