Update app.py

app.py

@@ -7,11 +7,11 @@ import torch
 import kornia as K
 from kornia.core import Tensor
 
-def edge_detection(file, detector): 
+def edge_detection(filepath, detector): 
 
-    img_bgr: np.ndarray = cv2.imread(file, cv2.IMREAD_COLOR)
+    img_bgr: np.ndarray = cv2.imread(filepath, cv2.IMREAD_COLOR)
 
-    x_bgr: torch.Tensor = K.utils.image_to_tensor(img_bgr)  # CxHxWx
+    x_bgr: torch.Tensor = K.utils.image_to_tensor(img_bgr)
     x_bgr = x_bgr[None,...].float() / 255.
 
     x_rgb: torch.Tensor = K.color.bgr_to_rgb(x_bgr)
@@ -19,28 +19,28 @@ def edge_detection(file, detector):
     
 
     if detector == '1st order derivates in x':
-        grads: torch.Tensor = K.filters.spatial_gradient(x_gray, order=1)  # BxCx2xHxW
+        grads: torch.Tensor = K.filters.spatial_gradient(x_gray, order=1) 
         grads_x = grads[:, :, 0]
         grads_y = grads[:, :, 1]
         
         output = K.utils.tensor_to_image(1. - grads_x.clamp(0., 1.))
     
     elif detector == '1st order derivates in y':
-        grads: torch.Tensor = K.filters.spatial_gradient(x_gray, order=1)  # BxCx2xHxW
+        grads: torch.Tensor = K.filters.spatial_gradient(x_gray, order=1)
         grads_x = grads[:, :, 0]
         grads_y = grads[:, :, 1]
         
         output = K.utils.tensor_to_image(1. - grads_y.clamp(0., 1.))
         
     elif detector == '2nd order derivatives in x':
-        grads: torch.Tensor = K.filters.spatial_gradient(x_gray, order=2)  # BxCx2xHxW
+        grads: torch.Tensor = K.filters.spatial_gradient(x_gray, order=2) 
         grads_x = grads[:, :, 0]
         grads_y = grads[:, :, 1]
         
         output = K.utils.tensor_to_image(1. - grads_x.clamp(0., 1.))
         
     elif detector == '2nd order derivatives in y':
-        grads: torch.Tensor = K.filters.spatial_gradient(x_gray, order=2)  # BxCx2xHxW
+        grads: torch.Tensor = K.filters.spatial_gradient(x_gray, order=2)
         grads_x = grads[:, :, 0]
         grads_y = grads[:, :, 1]