working depth render

data/render.py

@@ -1,15 +1,22 @@
+import os
+import sys
+import torch
 import numpy as np
 
-
 from deodr import Camera, Scene3D
 from deodr.triangulated_mesh import TriMesh
+from deodr.pytorch.triangulated_mesh_pytorch import ColoredTriMeshPytorch
+from deodr.pytorch.differentiable_renderer_pytorch import Scene3DPytorch, CameraPytorch
+
+sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
+from cloth3d.util import proj
 
 
 class RenderCore:
     def __init__(self, max_depth=0, depth_scale=1):
         self.mesh = None
         self.depthScale = depth_scale
-        self.scene = Scene3D(0)
+        self.scene = Scene3D()
         self.set_max_depth(max_depth)
 		
     def set_mesh(self, vertices, faces):
@@ -141,6 +148,8 @@ from pytorch3d.renderer import (
     TexturesVertex
 )
 from pytorch3d.structures import Meshes
+from pytorch3d.utils import cameras_from_opencv_projection
+
 
 class DepthRenderer(nn.Module):
     def __init__(self) -> None:
@@ -149,13 +158,21 @@ class DepthRenderer(nn.Module):
 
 
     def forward(self, verts, faces, cam):
-        R, T = look_at_view_transform(2.7, 0, 0) 
+        # R, T = look_at_view_transform(2.7, 0, 0) 
         # cameras = FoVPerspectiveCameras(device='cuda', R=R, T=T)
-        ext = extrinsic(cam)
-        cameras = PerspectiveCameras(device='cuda', R=ext[None, :, :3], T=ext[None, :, 3])
+
+        ext = torch.tensor(extrinsic(cam).astype(np.float32), device='cuda')
+        # ext = proj(cam)
+        fp = np.array([240 * (480 / 36), 320 * (640 / 36)], dtype=np.float32)
+        img_size = np.array([480, 640], dtype=np.float32)
+        # K = np.eye(4, dtype=np.float32)
+        # K[:3,:3] = intrinsic()
+        # cameras = PerspectiveCameras(device='cuda', R=ext[None, :, :3], T=ext[None, :, 3], focal_length=1,)# principal_point=fp[None, :], image_size=img_size[None, :])
+
+        cameras = cameras_from_opencv_projection(R=ext[None, :, :3], tvec=ext[None, :, 3], camera_matrix=torch.tensor(intrinsic()[None, :, :], device='cuda'), image_size=torch.tensor(img_size[None, :], device='cuda'))
 
         raster_settings = RasterizationSettings(
-                            image_size=512, 
+                            image_size=(480, 640), 
                             blur_radius=0.0, 
                             faces_per_pixel=1, 
                             bin_size=0
@@ -166,14 +183,18 @@ class DepthRenderer(nn.Module):
                     raster_settings=raster_settings
                 )
         output = rasterizer(Meshes(verts[None,:,:], faces[None,:,:]))
+        v2i = cameras.transform_points_screen(verts).cpu().numpy().astype(np.int32)[:,:2]
         depth = output.zbuf[0,:,:,0]
 
         mask = depth != -1
         # dmin = depth.min()
         dmean = depth[mask].mean()
         # dmax = depth[mask].max()
-        depth[mask] = depth[mask] - dmean
-        depth[~mask] = 0.0
+        # depth[mask] = depth[mask] - dmean
+        # depth[~mask] = 0.0
         # mask = mask.astype(np.float32)
 
-        return depth
+        # import plotly.express as ex
+        # ex.imshow(depth.cpu().numpy()).show()
+
+        return depth, v2i