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working depth render

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luni
2022-12-28 16:10:54 +00:00
parent 156d7c0245
commit 0652130e65
2 changed files with 63 additions and 11 deletions
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data/dataset.py
+33 -2
View File
@@ -38,7 +38,7 @@ class Dataset(Dataset):
return len(self.frames)
# TODO: make it work kwith two part outfits
# TODO: make it work with two part outfits
def __getitem__(self, idx):
frame : Frame = self.frames[idx]
path = os.path.join(self.folder, frame.id)
@@ -46,6 +46,7 @@ class Dataset(Dataset):
mesh_posed = self.reader.read_garment_vertices(frame.id, frame.garment, frame.frame_nr)
mask = np.load(os.path.join(path, frame.garment + '_mask.npy'))
# TODO: what's the difference between masking and reading .obj file
faces = mask_faces(self.body_faces, mask)
# faces = quads2tris(self.reader.read_garment_topology(frame.id, frame.garment))
# body = readPC2Frame(os.path.join(path, 'Body_posed.pc16'), frame.frame_nr, True)
@@ -55,14 +56,44 @@ class Dataset(Dataset):
info = self.reader.read_info(frame.id)
combined = torch.Tensor(np.concatenate((body, mesh_posed[mask, :]))).to('cuda')
combined_faces = torch.Tensor(np.concatenate((body_faces, faces + body.shape[0]))).to('cuda')
combined_faces_int = torch.Tensor(np.concatenate((body_faces, faces + body.shape[0])).astype(np.int32)).to('cuda')
idx = 36000
x = combined[idx, :].cpu().detach().numpy()
y = np.array(info['camLoc'], dtype=np.float32)
dist = np.sqrt(np.sum((x - y) ** 2))
start = time.perf_counter()
# depth_image = self.render.render(mesh_posed[mask, :], faces, info['camLoc'][0])[:,:,0]
# depth_image = self.render.render(combined, combined_faces, info['camLoc'][0])[:,:,0]
deodr_depth = self.deodr_render.render(cam=info['camLoc'].astype(np.float32), mesh=combined.cpu().numpy(), faces=combined_faces.cpu().numpy().astype(np.int32))
depth_image = self.render(combined, combined_faces, info['camLoc'])
# deodr_depth = self.deodr_render.render(cam=info['camLoc'].astype(np.float32), mesh=combined, faces=combined_faces.cpu().numpy().astype(np.int32))
t = time.perf_counter() - start
print(t)
start = time.perf_counter()
depth_image, v2i = self.render(combined, combined_faces, info['camLoc'])
t = time.perf_counter() - start
print(t)
depth_image = depth_image.cpu().numpy()
dist_img = depth_image[v2i[idx, 1], v2i[idx, 0]]
depth_image[v2i[idx, 1], v2i[idx, 0]] = 100
combined = combined.cpu().numpy()
diff = np.abs(dist - dist_img)
import plotly.express as ex
import plotly.graph_objects as go
ex.imshow(deodr_depth[:,:,0]).show()
ex.imshow(depth_image).show()
go.Figure(data = [go.Scatter3d(x=combined[:, 0], y=combined[:, 1], z=combined[:, 2], marker=dict(size=3, line=dict(width=0)), mode='markers'),
go.Scatter3d(x=[combined[idx, 0]], y=[combined[idx, 1]], z=[combined[idx, 2]], marker=dict(size=10, line=dict(width=0)), mode='markers'),
]).show()
# v2i_coverage = np.zeros((480, 640), dtype=np.float32)
# v2i_coverage[v2i[:,0], v2i[:, 1]] = 1
# ex.imshow(v2i_coverage).show()
# image = self.transforms(image)
return { 'mesh_posed': mesh_posed, 'depth_image': depth_image, 'mask': mask }
data/render.py
+30 -9
View File
@@ -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