Update app.py

app.py

@@ -36,58 +36,6 @@ IR_Model_tst.load_state_dict(torch.load(file_savingfolder+'IR_Model'+ext+'.pth',
 #IR_Model_tst.to(device)
 IR_Model_tst.eval()
 
-
-
-def standarize_point(d, dim=128, flip = False):
-    if flip:
-        d = -d
-    return d/dim - 0.5
-
-def destandarize_point(d, dim=128, flip = False):
-    if flip:
-        d = -d
-    return dim*(d + 0.5)
-
-def generate_standard_elips(N_samples = 100, a= 1,b = 1):
-    radius = 0.25
-    center = 0
-    N_samples1 = int(N_samples/2 - 1)
-    N_samples2 = N_samples - N_samples1
-    x1 = torch.distributions.uniform.Uniform(center-radius,center + radius).sample([N_samples1])
-    x1_ordered = torch.sort(x1).values
-    y1 = center + b*torch.sqrt(radius**2 - ((x1_ordered-center)/a)**2)
-    x2 = torch.distributions.uniform.Uniform(center-radius,center + radius).sample([N_samples2])
-    x2_ordered = torch.sort(x2, descending=True).values
-    y2 = center - b*torch.sqrt(radius**2 - ((x2_ordered-center)/a)**2)
-    x = torch.cat([x1_ordered, x2_ordered])
-    y = torch.cat([y1, y2])
-    return x, y
-
-def transform_standard_points(Affine_mat, x,y):
-    XY = torch.ones([3,x.shape[0]])
-    XY[0,:]= x
-    XY[1,:]= y
-    XYt = torch.matmul(Affine_mat.to('cpu').detach(),XY)
-    xt0 = XYt[0]
-    yt0 = XYt[1]
-    return xt0, yt0
-
-def wrap_points(img, x_source, y_source, l=1):
- for i in range(len(y_source)):
-   x0 = x_source[i].int()
-   y0 = y_source[i].int()
-   img[:,:,x0-l:x0+l,y0-l:y0+l] = 0
- return img
-
-
-def wrap_imge_cropped(Affine_mtrx, source_img, dim1=224, dim2=128):
-  source_img224 = torch.nn.ZeroPad2d(int((dim1-dim2)/2))(source_img)
-  grd = torch.nn.functional.affine_grid(Affine_mtrx, size=source_img224.shape,align_corners=False)
-  wrapped_img = torch.nn.functional.grid_sample(source_img224, grid=grd,
-                                                mode='bilinear', padding_mode='zeros', align_corners=False)
-  wrapped_img = torchvision.transforms.CenterCrop((dim2, dim2))(wrapped_img)
-  return wrapped_img
-
 def imgnt_reg(img1,img2):#, model_selected):
     #fixed_images = np.empty((1, 128, 128, 3))
     #moving_images = np.empty((1, 128, 128, 3))