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import argparse
import BboxTools as bbt
import gradio as gr
import numpy as np
from PIL import Image
from pytorch3d.renderer import RasterizationSettings, PerspectiveCameras, MeshRasterizer, MeshRenderer, HardPhongShader, BlendParams, camera_position_from_spherical_angles, look_at_rotation, PointLights
from pytorch3d.renderer import TexturesVertex as Textures
from pytorch3d.structures import Meshes
import torch
mesh_paths = {
"Aeroplane": "CAD_selected/aeroplane.off",
"Bicycle": "CAD_selected/bicycle.off",
"Boat": "CAD_selected/boat.off",
"Bottle": "CAD_selected/bottle.off",
"Bus": "CAD_selected/bus.off",
"Car": "CAD_selected/car.off",
"Chair": "CAD_selected/chair.off",
"Diningtable": "CAD_selected/diningtable.off",
"Motorbike": "CAD_selected/motorbike.off",
"Sofa": "CAD_selected/sofa.off",
"Train": "CAD_selected/train.off",
"Tvmonitor": "CAD_selected/tvmonitor.off",
}
def parse_args():
parser = argparse.ArgumentParser(description='Render off')
parser.add_argument('--azimuth', type=float)
parser.add_argument('--elevation', type=float)
parser.add_argument('--theta', type=float)
parser.add_argument('--dist', type=float)
parser.add_argument('--category', type=str)
parser.add_argument('--unit', type=str)
parser.add_argument('--img_id', type=int)
return parser.parse_args()
def rotation_theta(theta, device_=None):
# cos -sin 0
# sin cos 0
# 0 0 1
if type(theta) == float:
if device_ is None:
device_ = 'cpu'
theta = torch.ones((1, 1, 1)).to(device_) * theta
else:
if device_ is None:
device_ = theta.device
theta = theta.view(-1, 1, 1)
mul_ = torch.Tensor([[1, 0, 0, 0, 1, 0, 0, 0, 0], [0, -1, 0, 1, 0, 0, 0, 0, 0]]).view(1, 2, 9).to(device_)
bia_ = torch.Tensor([0] * 8 + [1]).view(1, 1, 9).to(device_)
# [n, 1, 2]
cos_sin = torch.cat((torch.cos(theta), torch.sin(theta)), dim=2).to(device_)
# [n, 1, 2] @ [1, 2, 9] + [1, 1, 9] => [n, 1, 9] => [n, 3, 3]
trans = torch.matmul(cos_sin, mul_) + bia_
trans = trans.view(-1, 3, 3)
return trans
def campos_to_R_T(campos, theta, device='cpu', at=((0, 0, 0),), up=((0, 1, 0), )):
R = look_at_rotation(campos, at=at, device=device, up=up) # (n, 3, 3)
R = torch.bmm(R, rotation_theta(theta, device_=device))
T = -torch.bmm(R.transpose(1, 2), campos.unsqueeze(2))[:, :, 0] # (1, 3)
return R, T
def load_off(off_file_name, to_torch=False):
file_handle = open(off_file_name)
file_list = file_handle.readlines()
n_points = int(file_list[1].split(' ')[0])
all_strings = ''.join(file_list[2:2 + n_points])
array_ = np.fromstring(all_strings, dtype=np.float32, sep='\n')
all_strings = ''.join(file_list[2 + n_points:])
array_int = np.fromstring(all_strings, dtype=np.int32, sep='\n')
array_ = array_.reshape((-1, 3))
if not to_torch:
return array_, array_int.reshape((-1, 4))[:, 1::]
else:
return torch.from_numpy(array_), torch.from_numpy(array_int.reshape((-1, 4))[:, 1::])
def pre_process_mesh_pascal(verts):
verts = torch.cat((verts[:, 0:1], verts[:, 2:3], -verts[:, 1:2]), dim=1)
return verts
def render(azimuth, elevation, theta, dist, category, unit, img_id):
azimuth = float(azimuth)
elevation = float(elevation)
theta = float(theta)
dist = float(dist)
h, w = 256, 256
render_image_size = max(h, w)
crop_size = (256, 256)
device = 'cpu'
cameras = PerspectiveCameras(focal_length=12.0, device=device)
raster_settings = RasterizationSettings(
image_size=render_image_size,
blur_radius=0.0,
faces_per_pixel=1,
bin_size=0
)
raster_settings1 = RasterizationSettings(
image_size=render_image_size // 8,
blur_radius=0.0,
faces_per_pixel=1,
bin_size=0
)
rasterizer = MeshRasterizer(
cameras=cameras,
raster_settings=raster_settings1
)
lights = PointLights(device=device, location=((2.0, 2.0, -2.0),))
phong_renderer = MeshRenderer(
rasterizer=MeshRasterizer(
cameras=cameras,
raster_settings=raster_settings
),
shader=HardPhongShader(device=device, lights=lights, cameras=cameras)
)
x3d, xface = load_off(mesh_paths[category])
x3d = x3d * 1.0
verts = torch.from_numpy(x3d).to(device)
verts = pre_process_mesh_pascal(verts)
faces = torch.from_numpy(xface).to(device)
verts_rgb = torch.ones_like(verts)[None]
# verts_rgb = torch.ones_like(verts)[None] * torch.Tensor(color).view(1, 1, 3).to(verts.device)
textures = Textures(verts_rgb.to(device))
meshes = Meshes(verts=[verts], faces=[faces], textures=textures)
# meshes = Meshes(verts=[verts], faces=[faces])
C = camera_position_from_spherical_angles(dist, elevation, azimuth, degrees=(unit=='Degree'), device=device)
R, T = campos_to_R_T(C, theta, device=device)
image = phong_renderer(meshes_world=meshes.clone(), R=R, T=T)
image = image[:, ..., :3]
box_ = bbt.box_by_shape(crop_size, (render_image_size // 2,) * 2)
bbox = box_.bbox
image = image[:, bbox[0][0]:bbox[0][1], bbox[1][0]:bbox[1][1], :]
image = torch.squeeze(image).detach().cpu().numpy()
image = np.array((image / image.max()) * 255).astype(np.uint8)
cx, cy = (128, 128)
dx = int(-cx + w/2)
dy = int(-cy + h/2)
image_pad = np.pad(image, ((abs(dy), abs(dy)), (abs(dx), abs(dx)), (0, 0)), mode='edge')
image = image_pad[dy+abs(dy):dy+abs(dy)+image.shape[0], dx+abs(dx):dx+abs(dx)+image.shape[1]]
Image.fromarray(image).save(f'{img_id:05d}.png')
if __name__ == '__main__':
args = parse_args()
render(args.azimuth, args.elevation, args.theta, args.dist, args.category, args.unit, args.img_id)
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