@@ -264,7 +264,7 @@ def msrf(input_size=(256,256,3),input_size_2=(256,256,1)):
264264 ss = Conv2D (1 ,kernel_size = (1 ,1 ),padding = 'same' )(ss )
265265 edge_out = Activation ('sigmoid' ,name = 'edge_out' )(ss )
266266 #######canny edge
267- # canny = cv2.Canny(np.asarray(inputs),10,100)
267+ canny = cv2 .Canny (np .asarray (inputs ),10 ,100 )
268268 cat = Concatenate ()([edge_out ,canny ])
269269 cw = Conv2D (1 ,kernel_size = (1 ,1 ),padding = 'same' )(cat )
270270 acts = Activation ('sigmoid' )(cw )
@@ -314,8 +314,10 @@ def msrf(input_size=(256,256,3),input_size_2=(256,256,1)):
314314 n14 = Conv2D (32 , 3 , activation = 'relu' , padding = 'same' , kernel_initializer = 'he_normal' )(n14_input )
315315 n14 = BatchNormalization ()(n14 )
316316 n14 = Add ()([n14 ,n14_input ])
317+ n14 = Concatenate ()([n14 ,edge ])
317318 n14 = Conv2D (32 , 3 , activation = 'relu' , padding = 'same' , kernel_initializer = 'he_normal' )(n14 )
318319 x = Conv2D (1 ,(1 ,1 ), strides = (1 ,1 ), padding = "same" ,activation = 'sigmoid' ,name = 'x' )(n14 )
320+
319321 model = Model (inputs = [inputs_img ,canny ],outputs = [x ,edge_out ,pred2 ,pred4 ])
320322 return model
321323
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