Update codes.py

codes.py

@@ -88,6 +88,65 @@ def load_image_pil_accelerated(image_path, dim=128):
     return tensor
 
 
+def workaround_matrix(Affine_mtrx0, acc = 2):
+    # To find the equivalent torch-compatible matrix from a correct matrix set acc=2 #This will be needed for transforming an image
+    # To find the correct Affine matrix from Torch compatible matrix set acc=0.5
+    Affine_mtrx_adj = inv_AM(Affine_mtrx0)
+    Affine_mtrx_adj[:,:,2]*=acc
+    return Affine_mtrx_adj
+
+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 preprocess_image(image_path, dim = 128):
     img = torch.zeros([1,3,dim,dim])
     img[0] = load_image_pil_accelerated(image_path, dim)