Merge branch 4.x

modules/dnn/misc/objc/gen_dict.json

@@ -8,7 +8,9 @@
             "(Net*)readNetFromONNX:(NSString*)onnxFile" : { "readNetFromONNX" : {"name" : "readNetFromONNXFile"} },
             "(Net*)readNetFromONNX:(ByteVector*)buffer" : { "readNetFromONNX" : {"name" : "readNetFromONNXBuffer"} },
             "(Net*)readNetFromTensorflow:(NSString*)model config:(NSString*)config" : { "readNetFromTensorflow" : {"name" : "readNetFromTensorflowFile"} },
-            "(Net*)readNetFromTensorflow:(ByteVector*)bufferModel bufferConfig:(ByteVector*)bufferConfig" : { "readNetFromTensorflow" : {"name" : "readNetFromTensorflowBuffer"} }
+            "(Net*)readNetFromTensorflow:(ByteVector*)bufferModel bufferConfig:(ByteVector*)bufferConfig" : { "readNetFromTensorflow" : {"name" : "readNetFromTensorflowBuffer"} },
+            "(Net*)readNetFromTFLite:(NSString*)model" : { "readNetFromTFLite" : {"name" : "readNetFromTFLiteFile"} },
+            "(Net*)readNetFromTFLite:(ByteVector*)buffer" : { "readNetFromTFLite" : {"name" : "readNetFromTFLiteBuffer"} }
         },
         "Net": {
             "(void)forward:(NSMutableArray<Mat*>*)outputBlobs outputName:(NSString*)outputName" : { "forward" : {"name" : "forwardOutputBlobs"} },

modules/dnn/misc/python/test/test_dnn.py

@@ -119,7 +119,7 @@ class dnn_test(NewOpenCVTests):
             inp = np.random.standard_normal([1, 2, 10, 11]).astype(np.float32)
             net.setInput(inp)
             net.forward()
-        except BaseException as e:
+        except BaseException:
             return False
         return True
 
@@ -153,6 +153,41 @@ class dnn_test(NewOpenCVTests):
         target = target.transpose(2, 0, 1).reshape(1, 3, height, width)  # to NCHW
         normAssert(self, blob, target)
 
+    def test_blobFromImageWithParams(self):
+        np.random.seed(324)
+
+        width = 6
+        height = 7
+        stddev = np.array([0.2, 0.3, 0.4])
+        scalefactor = 1.0/127.5 * stddev
+        mean = (10, 20, 30)
+
+        # Test arguments names.
+        img = np.random.randint(0, 255, [4, 5, 3]).astype(np.uint8)
+
+        param = cv.dnn.Image2BlobParams()
+        param.scalefactor = scalefactor
+        param.size = (6, 7)
+        param.mean = mean
+        param.swapRB=True
+        param.datalayout = cv.dnn.DNN_LAYOUT_NHWC
+
+        blob = cv.dnn.blobFromImageWithParams(img, param)
+        blob_args = cv.dnn.blobFromImageWithParams(img, cv.dnn.Image2BlobParams(scalefactor=scalefactor, size=(6, 7), mean=mean,
+                                                                      swapRB=True, datalayout=cv.dnn.DNN_LAYOUT_NHWC))
+        normAssert(self, blob, blob_args)
+
+        target2 = cv.resize(img, (width, height), interpolation=cv.INTER_LINEAR).astype(np.float32)
+        target2 = target2[:,:,[2, 1, 0]]  # BGR2RGB
+        target2[:,:,0] -= mean[0]
+        target2[:,:,1] -= mean[1]
+        target2[:,:,2] -= mean[2]
+
+        target2[:,:,0] *= scalefactor[0]
+        target2[:,:,1] *= scalefactor[1]
+        target2[:,:,2] *= scalefactor[2]
+        target2 = target2.reshape(1, height, width, 3)  # to NHWC
+        normAssert(self, blob, target2)
 
     def test_model(self):
         img_path = self.find_dnn_file("dnn/street.png")