fix to graphics.softras/transform.Projection (#127)

Signed-off-by: Michele Sanna <sanna@arrival.com>

examples/renderers/NMR/neural_renderer/renderer.py

@@ -68,7 +68,7 @@ def __init__(self, image_size=256, anti_aliasing=True, background_color=[0,0,0],
             self.perspective = perspective
             self.viewing_angle = viewing_angle
             self.eye = [0, 0, -(1. / math.tan(math.radians(self.viewing_angle)) + 1)]
-            self.camera_direction = [0, 0, 1]
+            self.camera_direction = camera_direction
         else:
             raise ValueError('Camera mode has to be one of projection, look or look_at')
 

examples/renderers/SoftRas/soft_renderer/transform.py

@@ -37,10 +37,10 @@ def __init__(self, P, dist_coeffs=None, orig_size=512):
 
         if isinstance(self.P, np.ndarray):
             self.P = torch.from_numpy(self.P).cuda()
-        if self.P is None or P.ndimension() != 3 or self.P.shape[1] != 3 or self.P.shape[2] != 4:
+        if self.P is None or self.P.ndimension() != 3 or self.P.shape[1] != 3 or self.P.shape[2] != 4:
             raise ValueError('You need to provide a valid (batch_size)x3x4 projection matrix')
         if dist_coeffs is None:
-            self.dist_coeffs = torch.cuda.FloatTensor([[0., 0., 0., 0., 0.]]).repeat(P.shape[0], 1)
+            self.dist_coeffs = torch.cuda.FloatTensor([[0., 0., 0., 0., 0.]]).repeat(self.P.shape[0], 1)
 
     def forward(self, vertices):
         vertices = srf.projection(vertices, self.P, self.dist_coeffs, self.orig_size)
@@ -77,7 +77,7 @@ def __init__(self, camera_direction=[0,0,1], perspective=True, viewing_angle=30,
         self.viewing_angle = viewing_angle
         self.viewing_scale = viewing_scale
         self._eye = eye
-        self.camera_direction = [0, 0, 1]
+        self.camera_direction = camera_direction
 
         if self._eye is None:
             self._eye = [0, 0, -(1. / math.tan(math.radians(self.viewing_angle)) + 1)]

kaolin/graphics/softras/transform.py

@@ -39,10 +39,10 @@ def __init__(self, P, dist_coeffs=None, orig_size=512):
 
         if isinstance(self.P, np.ndarray):
             self.P = torch.from_numpy(self.P).cuda()
-        if self.P is None or P.ndimension() != 3 or self.P.shape[1] != 3 or self.P.shape[2] != 4:
+        if self.P is None or self.P.ndimension() != 3 or self.P.shape[1] != 3 or self.P.shape[2] != 4:
             raise ValueError('You need to provide a valid (batch_size)x3x4 projection matrix')
         if dist_coeffs is None:
-            self.dist_coeffs = torch.cuda.FloatTensor([[0., 0., 0., 0., 0.]]).repeat(P.shape[0], 1)
+            self.dist_coeffs = torch.cuda.FloatTensor([[0., 0., 0., 0., 0.]]).repeat(self.P.shape[0], 1)
 
     def forward(self, vertices):
         vertices = srf.projection(vertices, self.P, self.dist_coeffs, self.orig_size)
@@ -79,7 +79,7 @@ def __init__(self, camera_direction=[0,0,1], perspective=True, viewing_angle=30,
         self.viewing_angle = viewing_angle
         self.viewing_scale = viewing_scale
         self._eye = eye
-        self.camera_direction = [0, 0, 1]
+        self.camera_direction = camera_direction
 
         if self._eye is None:
             self._eye = [0, 0, -(1. / math.tan(math.radians(self.viewing_angle)) + 1)]