chore: remove b&w filters post-processing step

filters.py

@@ -87,6 +87,7 @@ class TorchConv(nn.Module):
         # Ensure the kernel has a proper shape
         # If the kernel has a 1D shape, a (1, 1) kernel is used for each in_channels
         if len(kernel_shape) == 1:
+            self.kernel = self.kernel.repeat(self.n_out_channels)
             kernel = self.kernel.reshape(
                 self.n_out_channels,
                 self.n_in_channels // self.groups,
@@ -151,7 +152,6 @@ class Filter:
         self.onnx_model = None
         self.fhe_circuit = None
         self.divide = None
-        self.repeat_out_channels = False
 
         # Instantiate the torch module associated to the given filter name 
         if filter_name == "identity":
@@ -174,19 +174,16 @@ class Filter:
             # post-processing in order to retrieve the correct result
             kernel = [299, 587, 114]
 
-            self.torch_model = TorchConv(kernel, n_out_channels=1, groups=1)
+            self.torch_model = TorchConv(kernel)
 
             # Define the value used when for dividing the output values in post-processing
             self.divide = 1000
 
-            # Indicate that the out_channels will need to be repeated, as Gradio requires all 
-            # images to have a RGB format, even for grayscaled ones
-            self.repeat_out_channels = True
 
         elif filter_name == "blur":
             kernel = np.ones((3, 3))
 
-            self.torch_model = TorchConv(kernel, n_out_channels=3, groups=3)
+            self.torch_model = TorchConv(kernel, groups=3)
 
             # Define the value used when for dividing the output values in post-processing
             self.divide = 9
@@ -198,7 +195,7 @@ class Filter:
                 [0, -1, 0],
             ]
 
-            self.torch_model = TorchConv(kernel, n_out_channels=3, groups=3)
+            self.torch_model = TorchConv(kernel, groups=3)
 
         elif filter_name == "ridge detection":
             kernel = [
@@ -209,12 +206,8 @@ class Filter:
 
             # Additionally to the convolution operator, the filter will subtract a given threshold
             # value to the result in order to better display the ridges
-            self.torch_model = TorchConv(kernel, n_out_channels=1, groups=1, threshold=900)
+            self.torch_model = TorchConv(kernel, threshold=900)
 
-            # Indicate that the out_channels will need to be repeated, as Gradio requires all 
-            # images to have a RGB format, even for grayscaled ones. Ridge detection images are 
-            # ususally displayed as such
-            self.repeat_out_channels = True
 
     def compile(self):
         """Compile the filter on a representative inputset."""
@@ -266,8 +259,4 @@ class Filter:
         # Clip the image's values to proper RGB standards as filters don't handle such constraints
         output_image = output_image.clip(0, 255)
 
-        # Gradio requires all images to follow a RGB format
-        if self.repeat_out_channels:
-            output_image = output_image.repeat(3, axis=2)
-
         return output_image

filters/black and white/deployment/client.zip

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filters/black and white/deployment/server.zip

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filters/blur/deployment/client.zip

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filters/blur/deployment/server.zip

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filters/identity/deployment/client.zip

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filters/inverted/deployment/client.zip

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filters/inverted/deployment/server.zip

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filters/ridge detection/deployment/client.zip

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filters/ridge detection/deployment/server.zip

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filters/rotate/deployment/client.zip

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filters/rotate/deployment/server.zip

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filters/sharpen/deployment/client.zip

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