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Zero
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import os
import cv2
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import matplotlib as mpl
import os
import sys
os.environ["PYOPENGL_PLATFORM"] = "egl"
from pytorch3d.structures import Meshes, Pointclouds
from pytorch3d.renderer import (
PointLights,
DirectionalLights,
PerspectiveCameras,
Materials,
SoftPhongShader,
RasterizationSettings,
MeshRenderer,
MeshRendererWithFragments,
MeshRasterizer,
TexturesVertex,
PointsRasterizationSettings,
PointsRenderer,
PointsRasterizer,
AlphaCompositor
)
import torch
import torch.nn as nn
def vis_keypoints_with_skeleton(img, kps, kps_lines, kp_thresh=0.4, alpha=1):
# Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv.
cmap = plt.get_cmap('rainbow')
colors = [cmap(i) for i in np.linspace(0, 1, len(kps_lines) + 2)]
colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors]
# Perform the drawing on a copy of the image, to allow for blending.
kp_mask = np.copy(img)
# Draw the keypoints.
for l in range(len(kps_lines)):
i1 = kps_lines[l][0]
i2 = kps_lines[l][1]
p1 = kps[0, i1].astype(np.int32), kps[1, i1].astype(np.int32)
p2 = kps[0, i2].astype(np.int32), kps[1, i2].astype(np.int32)
if kps[2, i1] > kp_thresh and kps[2, i2] > kp_thresh:
cv2.line(
kp_mask, p1, p2,
color=colors[l], thickness=2, lineType=cv2.LINE_AA)
if kps[2, i1] > kp_thresh:
cv2.circle(
kp_mask, p1,
radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA)
if kps[2, i2] > kp_thresh:
cv2.circle(
kp_mask, p2,
radius=3, color=colors[l], thickness=-1, lineType=cv2.LINE_AA)
# Blend the keypoints.
return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0)
def vis_keypoints(img, kps, alpha=1):
# Convert from plt 0-1 RGBA colors to 0-255 BGR colors for opencv.
cmap = plt.get_cmap('rainbow')
colors = [cmap(i) for i in np.linspace(0, 1, len(kps) + 2)]
colors = [(c[2] * 255, c[1] * 255, c[0] * 255) for c in colors]
# Perform the drawing on a copy of the image, to allow for blending.
kp_mask = np.copy(img)
# Draw the keypoints.
for i in range(len(kps)):
p = kps[i][0].astype(np.int32), kps[i][1].astype(np.int32)
cv2.circle(kp_mask, p, radius=3, color=colors[i], thickness=-1, lineType=cv2.LINE_AA)
# Blend the keypoints.
return cv2.addWeighted(img, 1.0 - alpha, kp_mask, alpha, 0)
def render_mesh(mesh, face, cam_param, bkg, blend_ratio=1.0, return_bg_mask=False, R=None, T=None, return_fragments=False):
mesh = mesh.cuda()[None,:,:]
face = torch.LongTensor(face.astype(np.int64)).cuda()[None,:,:]
cam_param = {k: v.cuda()[None,:] for k,v in cam_param.items()}
render_shape = (bkg.shape[0], bkg.shape[1]) # height, width
batch_size, vertex_num = mesh.shape[:2]
textures = TexturesVertex(verts_features=torch.ones((batch_size,vertex_num,3)).float().cuda())
mesh = torch.stack((-mesh[:,:,0], -mesh[:,:,1], mesh[:,:,2]),2) # reverse x- and y-axis following PyTorch3D axis direction
mesh = Meshes(mesh, face, textures)
if R is None:
cameras = PerspectiveCameras(focal_length=cam_param['focal'],
principal_point=cam_param['princpt'],
device='cuda',
in_ndc=False,
image_size=torch.LongTensor(render_shape).cuda().view(1,2))
else:
cameras = PerspectiveCameras(focal_length=cam_param['focal'],
principal_point=cam_param['princpt'],
device='cuda',
in_ndc=False,
image_size=torch.LongTensor(render_shape).cuda().view(1,2),
R=R,
T=T)
raster_settings = RasterizationSettings(image_size=render_shape, blur_radius=0.0, faces_per_pixel=1, bin_size=0)
rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings).cuda()
lights = PointLights(device='cuda')
shader = SoftPhongShader(device='cuda', cameras=cameras, lights=lights)
materials = Materials(
device='cuda',
specular_color=[[0.0, 0.0, 0.0]],
shininess=0.0
)
# render
with torch.no_grad():
renderer = MeshRendererWithFragments(rasterizer=rasterizer, shader=shader)
images, fragments = renderer(mesh, materials=materials)
# background masking
is_bkg = (fragments.zbuf <= 0).float().cpu().numpy()[0]
render = images[0,:,:,:3].cpu().numpy()
fg = render * blend_ratio + bkg/255 * (1 - blend_ratio)
render = fg * (1 - is_bkg) * 255 + bkg * is_bkg
ret = [render]
if return_bg_mask:
ret.append(is_bkg)
if return_fragments:
ret.append(fragments)
return tuple(ret)
def rasterize_mesh(mesh, face, cam_param, height, width, return_bg_mask=False, R=None, T=None):
mesh = mesh.cuda()[None,:,:]
face = face.long().cuda()[None,:,:]
cam_param = {k: v.cuda()[None,:] for k,v in cam_param.items()}
render_shape = (height, width)
batch_size, vertex_num = mesh.shape[:2]
textures = TexturesVertex(verts_features=torch.ones((batch_size,vertex_num,3)).float().cuda())
mesh = torch.stack((-mesh[:,:,0], -mesh[:,:,1], mesh[:,:,2]),2) # reverse x- and y-axis following PyTorch3D axis direction
mesh = Meshes(mesh, face, textures)
if R is None:
cameras = PerspectiveCameras(focal_length=cam_param['focal'],
principal_point=cam_param['princpt'],
device='cuda',
in_ndc=False,
image_size=torch.LongTensor(render_shape).cuda().view(1,2))
else:
cameras = PerspectiveCameras(focal_length=cam_param['focal'],
principal_point=cam_param['princpt'],
device='cuda',
in_ndc=False,
image_size=torch.LongTensor(render_shape).cuda().view(1,2),
R=R,
T=T)
raster_settings = RasterizationSettings(image_size=render_shape, blur_radius=0.0, faces_per_pixel=1, bin_size=0)
rasterizer = MeshRasterizer(cameras=cameras, raster_settings=raster_settings).cuda()
# render
fragments = rasterizer(mesh)
ret = [fragments]
if return_bg_mask:
# background masking
is_bkg = (fragments.zbuf <= 0).float().cpu().numpy()[0]
ret.append(is_bkg)
return tuple(ret)
def rasterize_points(points, cam_param, height, width, return_bg_mask=False, R=None, T=None, to_cpu=False, points_per_pixel=5, radius=0.01):
points = torch.stack((-points[:, 0], -points[:, 1], points[:, 2]), 1) # reverse x- and y-axis following PyTorch3D axis direction
device = points.device
if len(points.shape) == 2:
points = [points]
pointclouds = Pointclouds(points=points)
cam_param = {k: v.to(device)[None,:] for k,v in cam_param.items()}
render_shape = (height, width) # height, width
if R is None:
cameras = PerspectiveCameras(focal_length=cam_param['focal'],
principal_point=cam_param['princpt'],
device=device,
in_ndc=False,
image_size=torch.LongTensor(render_shape).to(device).view(1,2))
else:
cameras = PerspectiveCameras(focal_length=cam_param['focal'],
principal_point=cam_param['princpt'],
device=device,
in_ndc=False,
image_size=torch.LongTensor(render_shape).to(device).view(1,2),
R=R,
T=T)
raster_settings = PointsRasterizationSettings(image_size=render_shape, radius=radius, points_per_pixel=points_per_pixel, max_points_per_bin=82000)
rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings).to(device)
# render
fragments = rasterizer(pointclouds)
# background masking
ret = [fragments]
if return_bg_mask:
if to_cpu:
is_bkg = (fragments.zbuf <= 0).all(dim=-1, keepdim=True).float().cpu().numpy()[0]
else:
is_bkg = (fragments.zbuf <= 0).all(dim=-1, keepdim=True).float()[0]
ret.append(is_bkg)
return tuple(ret)
def render_points(points, cam_param, bkg, blend_ratio=1.0, return_bg_mask=False, R=None, T=None, return_fragments=False, rgbs=None):
points = torch.stack((-points[:, 0], -points[:, 1], points[:, 2]), 1) # reverse x- and y-axis following PyTorch3D axis direction
if rgbs is None:
rgbs = torch.ones_like(points)
if len(points.shape) == 2:
points = [points]
rgbs = [rgbs]
pointclouds = Pointclouds(points=points, features=rgbs).cuda()
cam_param = {k: v.cuda()[None,:] for k,v in cam_param.items()}
render_shape = (bkg.shape[0], bkg.shape[1]) # height, width
if R is None:
cameras = PerspectiveCameras(focal_length=cam_param['focal'],
principal_point=cam_param['princpt'],
device='cuda',
in_ndc=False,
image_size=torch.LongTensor(render_shape).cuda().view(1,2))
else:
cameras = PerspectiveCameras(focal_length=cam_param['focal'],
principal_point=cam_param['princpt'],
device='cuda',
in_ndc=False,
image_size=torch.LongTensor(render_shape).cuda().view(1,2),
R=R,
T=T)
raster_settings = PointsRasterizationSettings(image_size=render_shape, radius=0.01, points_per_pixel=5)
rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings).cuda()
# render
with torch.no_grad():
fragments = rasterizer(pointclouds)
renderer = PointsRenderer(rasterizer=rasterizer, compositor=AlphaCompositor(background_color=(0, 0, 0)))
images = renderer(pointclouds)
# background masking
is_bkg = (fragments.zbuf <= 0).all(dim=-1, keepdim=True).float().cpu().numpy()[0]
render = images[0,:,:,:3].cpu().numpy()
fg = render * blend_ratio + bkg/255 * (1 - blend_ratio)
render = fg * (1 - is_bkg) * 255 + bkg * is_bkg
ret = [render]
if return_bg_mask:
ret.append(is_bkg)
if return_fragments:
ret.append(fragments)
return tuple(ret)
class RenderMesh(nn.Module):
def __init__(self, image_size, obj_filename=None, faces=None, device='cpu'):
super(RenderMesh, self).__init__()
self.device = device
self.image_size = image_size
if obj_filename is not None:
verts, faces, aux = load_obj(obj_filename, load_textures=False)
self.faces = faces.verts_idx
elif faces is not None:
import numpy as np
self.faces = torch.tensor(faces.astype(np.int32))
else:
raise NotImplementedError('Must have faces.')
self.raster_settings = RasterizationSettings(image_size=image_size, blur_radius=0.0, faces_per_pixel=1)
self.lights = PointLights(device=device, location=[[0.0, 0.0, 3.0]])
def _build_cameras(self, transform_matrix, focal_length, principal_point=None, intr=None):
batch_size = transform_matrix.shape[0]
screen_size = torch.tensor(
[self.image_size, self.image_size], device=self.device
).float()[None].repeat(batch_size, 1)
if principal_point is None:
principal_point = torch.zeros(batch_size, 2, device=self.device).float()
# print("==="*16, "principle_points:", principal_point)
# print("==="*16, "focal_length:", focal_length)
if intr is None:
cameras_kwargs = {
'principal_point': principal_point, 'focal_length': focal_length,
'image_size': screen_size, 'device': self.device,
}
else:
cameras_kwargs = {
'principal_point': principal_point, 'focal_length': torch.tensor([intr[0, 0], intr[1, 1]]).unsqueeze(0),
'image_size': screen_size, 'device': self.device,
}
cameras = PerspectiveCameras(**cameras_kwargs, R=transform_matrix[:, :3, :3], T=transform_matrix[:, :3, 3])
return cameras
def forward(
self, vertices, cameras=None, transform_matrix=None, focal_length=None, principal_point=None, only_rasterize=False, intr=None,
):
if cameras is None:
cameras = self._build_cameras(transform_matrix, focal_length, principal_point=principal_point, intr=intr)
faces = self.faces[None].repeat(vertices.shape[0], 1, 1)
# Initialize each vertex to be white in color.
verts_rgb = torch.ones_like(vertices) # (1, V, 3)
textures = TexturesVertex(verts_features=verts_rgb.to(self.device))
mesh = Meshes(
verts=vertices.to(self.device),
faces=faces.to(self.device),
textures=textures
)
renderer = MeshRendererWithFragments(
rasterizer=MeshRasterizer(cameras=cameras, raster_settings=self.raster_settings),
shader=SoftPhongShader(cameras=cameras, lights=self.lights, device=self.device)
)
render_results, fragments = renderer(mesh)
render_results = render_results.permute(0, 3, 1, 2)
if only_rasterize:
return fragments
images = render_results[:, :3]
alpha_images = render_results[:, 3:]
images[alpha_images.expand(-1, 3, -1, -1)<0.5] = 0.0
return images*255, alpha_images
class RenderPoints(nn.Module):
def __init__(self, image_size, obj_filename=None, device='cpu'):
super(RenderPoints, self).__init__()
self.device = device
self.image_size = image_size
if obj_filename is not None:
verts = load_obj(obj_filename, load_textures=False)
self.raster_settings = PointsRasterizationSettings(image_size=image_size, radius=0.01, points_per_pixel=1)
self.lights = PointLights(device=device, location=[[0.0, 0.0, 3.0]])
def _build_cameras(self, transform_matrix, focal_length, principal_point=None):
batch_size = transform_matrix.shape[0]
screen_size = torch.tensor(
[self.image_size, self.image_size], device=self.device
).float()[None].repeat(batch_size, 1)
if principal_point is None:
principal_point = torch.zeros(batch_size, 2, device=self.device).float()
# print("==="*16, "principle_points:", principal_point)
# print("==="*16, "focal_length:", focal_length)
cameras_kwargs = {
'principal_point': principal_point, 'focal_length': focal_length,
'image_size': screen_size, 'device': self.device,
}
cameras = PerspectiveCameras(**cameras_kwargs, R=transform_matrix[:, :3, :3], T=transform_matrix[:, :3, 3])
return cameras
def forward(
self, vertices, cameras=None, transform_matrix=None, focal_length=None, principal_point=None, only_rasterize=False
):
if cameras is None:
cameras = self._build_cameras(transform_matrix, focal_length, principal_point=principal_point)
# Initialize each vertex to be white in color.
verts_rgb = torch.ones_like(vertices) # (1, V, 3)
pointclouds = Pointclouds(points=vertices, features=verts_rgb).cuda()
# render
rasterizer = PointsRasterizer(cameras=cameras, raster_settings=self.raster_settings).cuda()
if only_rasterize:
fragments = rasterizer(pointclouds)
return fragments
renderer = PointsRenderer(rasterizer=rasterizer, compositor=AlphaCompositor(background_color=(0, 0, 0)))
render_results = renderer(pointclouds).permute(0, 3, 1, 2)
images = render_results[:, :3]
alpha_images = render_results[:, 3:]
return images*255, alpha_images |