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import os, sys
import math
import spaces
import json
import importlib
import time
from .data.online_render_dataloader import load_obj
import glm
from pathlib import Path
import cv2
import torchvision
import random
from tqdm import tqdm
import numpy as np
from PIL import Image
import sys
# from .src.utils.mesh import Mesh
import nvdiffrast.torch as dr
from .src.utils import obj, mesh, render_utils, render
import torch
import torch.nn.functional as F
import random
from kiui.cam import orbit_camera
import itertools
# from .src.utils.material import Material
# from .utils.camera_util import (
# FOV_to_intrinsics,
# center_looking_at_camera_pose,
# get_circular_camera_poses,
# )
os.environ["OPENCV_IO_ENABLE_OPENEXR"]="1"
import re
def sample_spherical(phi, theta, cam_radius):
theta = np.deg2rad(theta)
phi = np.deg2rad(phi)
z = cam_radius * np.cos(phi) * np.sin(theta)
x = cam_radius * np.sin(phi) * np.sin(theta)
y = cam_radius * np.cos(theta)
return x, y, z
def load_mipmap(env_path):
diffuse_path = os.path.join(env_path, "diffuse.pth")
diffuse = torch.load(diffuse_path, map_location=torch.device('cpu'))
specular = []
for i in range(6):
specular_path = os.path.join(env_path, f"specular_{i}.pth")
specular_tensor = torch.load(specular_path, map_location=torch.device('cpu'))
specular.append(specular_tensor)
return [specular, diffuse]
ENV = load_mipmap("models/lrm/env_mipmap/6")
materials = (0.0,0.9)
def random_scene():
train_res = [512, 512]
cam_near_far = [0.1, 1000.0]
fovy = np.deg2rad(50)
spp = 1
cam_radius = 3.5
layers = 1
iter_res = 512
proj_mtx = render_utils.perspective(fovy, train_res[1] / train_res[0], cam_near_far[0], cam_near_far[1])
all_azimuths = np.array([0, 90, 180, 270])
all_elevations = np.array([60, 90, 90, 120])
# all_azimuths = np.array([0])
# all_elevations = np.array([60])
all_mv = []
all_campos = []
all_mvp = []
for index, (azimuths, elevations) in enumerate(zip(all_azimuths, all_elevations)):
x, y, z = sample_spherical(azimuths, elevations, cam_radius)
eye = glm.vec3(x, y, z)
at = glm.vec3(0.0, 0.0, 0.0)
up = glm.vec3(0.0, 1.0, 0.0)
view_matrix = glm.lookAt(eye, at, up)
mv = torch.from_numpy(np.array(view_matrix))
mvp = proj_mtx @ (mv) #w2c
campos = torch.linalg.inv(mv)[:3, 3]
all_mv.append(mv[None, ...].cuda())
all_campos.append(campos[None, ...].cuda())
all_mvp.append(mvp[None, ...].cuda())
return all_mv, all_mvp, all_campos
@spaces.GPU
def rendering(ref_mesh):
GLCTX = dr.RasterizeCudaContext()
all_mv, all_mvp, all_campos = random_scene()
iter_res = [512, 512]
iter_spp = 1
layers = 1
all_albedo = []
all_alpha = []
all_image = []
all_ccm = []
all_depth = []
all_normal = []
for i in range(len(all_mv)):
mvp = all_mvp[i]
campos = all_campos[i]
with torch.no_grad():
buffer_dict = render.render_mesh(GLCTX, ref_mesh, mvp, campos, [ENV], None, None,
materials, iter_res, spp=iter_spp, num_layers=layers, msaa=True,
background=None, gt_render=True)
image = buffer_dict['shaded'][0]
albedo = (buffer_dict['albedo'][0]).clamp(0., 1.)
alpha = buffer_dict['mask'][0][:, :, 3:]
ccm = buffer_dict['ccm'][0][...,:3]
alpha = buffer_dict['mask'][0][...,:3]
albedo = buffer_dict['albedo'][0].clamp(0., 1.)
# breakpoint()
ccm = ccm * alpha
depth = buffer_dict['depth'][0]
normal = buffer_dict['gb_normal'][0]
all_image.append(image)
all_albedo.append(albedo)
all_alpha.append(alpha)
all_ccm.append(ccm)
all_depth.append(depth)
all_normal.append(normal)
all_albedo = torch.stack(all_albedo)
all_alpha = torch.stack(all_alpha)
all_ccm = torch.stack(all_ccm)
all_normal = torch.stack(all_normal)
all_image = torch.stack(all_image)
all_depth = torch.stack(all_depth)
# breakpoint()
return all_image.detach(), all_albedo.detach(), all_alpha.detach(), all_ccm.detach(), all_depth.detach(), all_normal.detach()
def render_mesh(mesh_path):
ref_mesh = load_obj(mesh_path, return_attributes=False)
ref_mesh = mesh.auto_normals(ref_mesh)
ref_mesh = mesh.compute_tangents(ref_mesh)
ref_mesh.rotate_x_90()
# print(f"start ==> {mesh_path}")
rgb, albedo, alpha, ccm, depth, normal = rendering(ref_mesh)
depth = depth[...,:3] * alpha
# breakpoint()
torchvision.utils.save_image(rgb.permute(0, 3, 1, 2), f"debug_image/{mesh_path.split('/')[-1].split('.')[0]}_rgb.png")
torchvision.utils.save_image(albedo.permute(0, 3, 1, 2), f"debug_image/{mesh_path.split('/')[-1].split('.')[0]}_albedo.png")
torchvision.utils.save_image(alpha.permute(0, 3, 1, 2), f"debug_image/{mesh_path.split('/')[-1].split('.')[0]}_alpha.png")
torchvision.utils.save_image(ccm.permute(0, 3, 1, 2), f"debug_image/{mesh_path.split('/')[-1].split('.')[0]}_ccm.png")
torchvision.utils.save_image(depth.permute(0, 3, 1, 2), f"debug_image/{mesh_path.split('/')[-1].split('.')[0]}_depth.png", normalize=True)
torchvision.utils.save_image(normal.permute(0, 3, 1, 2), f"debug_image/{mesh_path.split('/')[-1].split('.')[0]}_normal.png")
print(f"end ==> {mesh_path}")
if __name__ == '__main__':
render_mesh("./meshes_online/bubble_mart_blue/bubble_mart_blue.obj")
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