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# Render utils for PyTorch3D
# Adapted and improved from: https://github.com/ThunderVVV/HaWoR/blob/main/lib/vis/renderer.py
import torch
import numpy as np
from typing import List, Tuple, Union
from pytorch3d.renderer import (
PerspectiveCameras,
MeshRenderer,
MeshRasterizer,
SoftPhongShader,
RasterizationSettings,
PointLights,
TexturesVertex
)
from pytorch3d.structures import Meshes
from pytorch3d.renderer.camera_conversions import _cameras_from_opencv_projection
def update_intrinsics_from_bbox(
K_org: torch.Tensor, bbox: torch.Tensor
) -> Tuple[torch.Tensor, List[Tuple[int, int]]]:
"""
Update intrinsic matrix K according to the given bounding box.
Args:
K_org (torch.Tensor): Original intrinsic matrix of shape (B, 3, 3).
bbox (torch.Tensor): Bounding boxes of shape (B, 4) in (left, top, right, bottom) format.
Returns:
K_new (torch.Tensor): Updated intrinsics with shape (B, 4, 4).
image_sizes (List[Tuple[int, int]]): List of image sizes (height, width) for each bbox.
"""
device, dtype = K_org.device, K_org.dtype
# Initialize 4x4 intrinsic matrix
K_new = torch.zeros((K_org.shape[0], 4, 4), device=device, dtype=dtype)
K_new[:, :3, :3] = K_org.clone()
K_new[:, 2, 2] = 0
K_new[:, 2, -1] = 1
K_new[:, -1, 2] = 1
image_sizes = []
for idx, box in enumerate(bbox):
left, top, right, bottom = box
cx, cy = K_new[idx, 0, 2], K_new[idx, 1, 2]
# Adjust principal point according to bbox
new_cx = cx - left
new_cy = cy - top
# Compute new width and height
new_height = max(bottom - top, 1)
new_width = max(right - left, 1)
# Flip principal point coordinates if needed
new_cx = new_width - new_cx
new_cy = new_height - new_cy
K_new[idx, 0, 2] = new_cx
K_new[idx, 1, 2] = new_cy
image_sizes.append((int(new_height), int(new_width)))
return K_new, image_sizes
class Renderer():
"""
Renderer class using PyTorch3D for mesh rendering with Phong shading.
Attributes:
width (int): Target image width.
height (int): Target image height.
focal_length (Union[float, Tuple[float, float]]): Camera focal length(s).
device (torch.device): Device to run rendering on.
renderer (MeshRenderer): PyTorch3D mesh renderer.
cameras (PerspectiveCameras): Camera object.
lights (PointLights): Lighting setup for rendering.
"""
def __init__(
self,
width: int,
height: int,
focal_length: Union[float, Tuple[float, float]],
device: torch.device,
bin_size: int = 512,
max_faces_per_bin: int = 200000,
):
self.width = width
self.height = height
self.focal_length = focal_length
self.device = device
# Initialize camera parameters
self._initialize_camera_params()
# Set up lighting
self.lights = PointLights(
device=device,
location = ((0.0, -1.5, -1.5),),
ambient_color=((0.75, 0.75, 0.75),),
diffuse_color=((0.25, 0.25, 0.25),),
specular_color=((0.02, 0.02, 0.02),)
)
# Initialize renderer
self._create_renderer(bin_size, max_faces_per_bin)
def _create_renderer(self, bin_size: int, max_faces_per_bin: int):
"""
Create the PyTorch3D MeshRenderer with rasterizer and shader.
"""
self.renderer = MeshRenderer(
rasterizer=MeshRasterizer(
raster_settings=RasterizationSettings(
image_size=self.image_sizes[0],
blur_radius=1e-5,
bin_size=bin_size,
max_faces_per_bin=max_faces_per_bin,
)
),
shader=SoftPhongShader(
device=self.device,
lights=self.lights,
),
)
def _initialize_camera_params(self):
"""
Initialize camera intrinsics and extrinsics.
"""
# Extrinsics (identity rotation and zero translation)
self.R = torch.eye(3, device=self.device).unsqueeze(0)
self.T = torch.zeros(1, 3, device=self.device)
# Intrinsics
if isinstance(self.focal_length, (list, tuple)):
fx, fy = self.focal_length
else:
fx = fy = self.focal_length
self.K = torch.tensor(
[[fx, 0, self.width / 2],
[0, fy, self.height / 2],
[0, 0, 1]],
device=self.device,
dtype=torch.float32,
).unsqueeze(0)
self.bboxes = torch.tensor([[0, 0, self.width, self.height]], dtype=torch.float32)
self.K_full, self.image_sizes = update_intrinsics_from_bbox(self.K, self.bboxes)
# Create PyTorch3D cameras
self.cameras = self._create_camera_from_cv()
def _create_camera_from_cv(
self,
R: torch.Tensor = None,
T: torch.Tensor = None,
K: torch.Tensor = None,
image_size: torch.Tensor = None,
) -> PerspectiveCameras:
"""
Create a PyTorch3D camera from OpenCV-style intrinsics and extrinsics.
"""
if R is None:
R = self.R
if T is None:
T = self.T
if K is None:
K = self.K
if image_size is None:
image_size = torch.tensor(self.image_sizes, device=self.device)
cameras = _cameras_from_opencv_projection(R, T, K, image_size)
return cameras
def render(
self,
verts_list: List[torch.Tensor],
faces_list: List[torch.Tensor],
colors_list: List[torch.Tensor],
) -> Tuple[np.ndarray, np.ndarray]:
"""
Render a batch of meshes into an RGB image and mask.
Args:
verts_list (List[torch.Tensor]): List of vertex tensors.
faces_list (List[torch.Tensor]): List of face tensors.
colors_list (List[torch.Tensor]): List of per-vertex color tensors.
Returns:
rend (np.ndarray): Rendered RGB image as uint8 array.
mask (np.ndarray): Boolean mask of rendered pixels.
"""
all_verts = []
all_faces = []
all_colors = []
vertex_offset = 0
for verts, faces, colors in zip(verts_list, faces_list, colors_list):
all_verts.append(verts)
all_colors.append(colors)
all_faces.append(faces + vertex_offset) # Offset face indices
vertex_offset += verts.shape[0]
# Combine all meshes into a single mesh for rendering
all_verts = torch.cat(all_verts, dim=0)
all_faces = torch.cat(all_faces, dim=0)
all_colors = torch.cat(all_colors, dim=0)
mesh = Meshes(
verts=[all_verts], # batch_size=1
faces=[all_faces],
textures=TexturesVertex(all_colors.unsqueeze(0)),
)
# Render the image
images = self.renderer(mesh, cameras=self.cameras, lights=self.lights)
rend = np.clip(images[0, ..., :3].cpu().numpy() * 255, 0, 255).astype(np.uint8)
mask = images[0, ..., -1].cpu().numpy() > 0
return rend, mask |