luni/reconstruction
1
0
Fork 0
Code Issues 6 Pull Requests Packages Projects 1 Releases Wiki Activity

got root joint location

Browse Source
This commit is contained in:
luni
2022-12-30 12:57:08 +00:00
parent e5780de5e5
commit 51b6219021
3 changed files with 192 additions and 181 deletions
Download Patch File Download Diff File Expand all files Collapse all files
data/dataset.py
+36 -25
View File
@@ -46,21 +46,20 @@ class Dataset(Dataset):
# mesh_posed = readPC2Frame(os.path.join(path, frame.garment + '_registered.pc16'), frame.frame_nr, True)
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'))
mesh_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 = mask_faces(self.body_faces, mesh_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)
body, body_faces = self.reader.read_human(frame.id, frame.frame_nr)
body, body_faces, root_joint = self.reader.read_human(frame.id, frame.frame_nr)
body_faces = quads2tris(body_faces)
info = self.reader.read_info(frame.id)
combined = torch.Tensor(np.concatenate((body, mesh_posed[mask, :]))).to('cuda')
combined = torch.Tensor(np.concatenate((body, mesh_posed[mesh_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()
idx_frame = 36000
x = combined[idx_frame, :].cpu().detach().numpy()
y = np.array(info['camLoc'], dtype=np.float32)
dist = np.sqrt(np.sum((x - y) ** 2))
@@ -69,35 +68,42 @@ class Dataset(Dataset):
# 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))
# 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)
print(f'deodr time: {time.perf_counter() - start}')
start = time.perf_counter()
depth_image, depth_mask, v2i, K = self.render(combined, combined_faces, info['camLoc'])
t = time.perf_counter() - start
print(t)
pc = kornia.geometry.depth.depth_to_3d(depth_image[None,None,:,:], K, normalize_points=True)
pc = pc[0,:,depth_mask].cpu().numpy()
depth_image, depth_mask, v2i, K, camera = self.render(combined, combined_faces, info['camLoc'])
print(f'pytorch3d time: {time.perf_counter() - start}')
start = time.perf_counter()
# TODO: is it sure I need this K, and not some transformed one?
normal_image = get_normals_from_depth(depth_image[None,:,:], depth_mask[None,:,:], K)
print(f'normals time: {time.perf_counter() - start}')
root_joint_pixel = camera.transform_points_screen(torch.tensor(root_joint.astype(np.float32), device=combined.get_device()))[0,:2]
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
# Testing
# pc = kornia.geometry.depth.depth_to_3d(depth_image[None,None,:,:], K, normalize_points=True)
# pc = pc[0,:,depth_mask].cpu().numpy()
# 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)
# diff = np.abs(dist - dist_img)
import plotly.express as ex
# import plotly.express as ex
import plotly.graph_objects as go
# ex.imshow(deodr_depth[:,:,0]).show()
# ex.imshow(depth_image).show()
ex.imshow(normal_image[0].cpu().numpy()).show()
# ex.imshow(normal_image[0].cpu().numpy()).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()
# trans = np.array(info['trans'], dtype=np.float32)[:, self.frames[idx].frame_nr]
trans = root_joint[0,:]
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'),
go.Scatter3d(x=[trans[0]], y=[trans[1]], z=[trans[2]], marker=dict(size=10, line=dict(width=0)), mode='markers'),
]).show()
# go.Figure(data = [go.Scatter3d(x=pc[0,:], y=pc[1,:], z=pc[2,:], marker=dict(size=3, line=dict(width=0)), mode='markers'),
# ]).show()
@@ -107,7 +113,12 @@ class Dataset(Dataset):
# ex.imshow(v2i_coverage).show()
# image = self.transforms(image)
return { 'mesh_posed': mesh_posed, 'depth_image': depth_image, 'mask': mask }
return { 'mesh_posed': mesh_posed,
'depth_image': depth_image,
'mesh_mask': mesh_mask,
'depth_mask': depth_mask,
'normal_image': normal_image
}
if __name__ == '__main__':
data/render.py
+1 -1
View File
@@ -198,4 +198,4 @@ class DepthRenderer(nn.Module):
# import plotly.express as ex
# ex.imshow(depth.cpu().numpy()).show()
return depth, mask, v2i, K
return depth, mask, v2i, K, cameras