pose encoder model

utils/data_utils.py

@@ -113,10 +113,10 @@ def proj(camLoc):
 	return intrinsic().dot(extrinsic(camLoc))
 
 
-def get_camera(cam, img_size, device):
+def get_camera(cam, img_size, device, res_x=640, res_y=480):
     size = np.array(img_size, dtype=np.float32)
     ext = torch.tensor(extrinsic(cam).astype(np.float32), device=device)
-    K = torch.tensor(intrinsic()[None, :, :], device=device)
+    K = torch.tensor(intrinsic(res_x, res_y)[None, :, :], device=device)
     cameras = cameras_from_opencv_projection(R=ext[None, :, :3], tvec=ext[None, :, 3], camera_matrix=K, image_size=torch.tensor(size[None, :], device=device))
     return cameras, K
 

utils/pose_coloring.py

@@ -35,14 +35,14 @@ class PoseRenderer(Module):
         super().__init__()
         colors = np.load('custom_data/pose_colors.npy')
         self.textures = TexturesVertex(verts_features=torch.from_numpy(colors).to(device)[None, :, :])
-        self.cam = torch.tensor([5.0, 0.0, 0.0], device=device).cpu().numpy()
+        self.cam = torch.tensor([3.0, 0.0, 0.0], device=device).cpu().numpy()
 
 
     def forward(self, verts: torch.Tensor, faces: torch.Tensor):
-        cameras, K = get_camera(self.cam, [480, 640], verts.get_device())
+        cameras, K = get_camera(self.cam, [512, 512], verts.get_device(), res_x=512, res_y=512)
 
         raster_settings = RasterizationSettings(
-                            image_size=(480, 640), 
+                            image_size=(512, 512), 
                             blur_radius=0.0, 
                             faces_per_pixel=1, 
                             bin_size=0
@@ -63,7 +63,7 @@ if __name__ == '__main__':
     smpl = SMPL(path='superSMPL', device='cuda')
     pose = torch.zeros((1,72), device='cuda')
     # generate random pose
-    pose[:,3:] = torch.randn((1,69), device='cuda') * 0.1
+    # pose[:,3:] = torch.randn((1,69), device='cuda') * 0.1
     body, _ = smpl(pose, torch.zeros((1,11), device='cuda'), remove=False)
     colors = np.zeros((body.shape[1], 3))