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import io | ||
import os | ||
import torch | ||
from skimage.io import imread | ||
import numpy as np | ||
import cv2 | ||
from tqdm import tqdm_notebook as tqdm | ||
import base64 | ||
from IPython.display import HTML | ||
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# Util function for loading meshes | ||
from pytorch3d.io import load_objs_as_meshes | ||
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from IPython.display import HTML | ||
from base64 import b64encode | ||
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# Data structures and functions for rendering | ||
from pytorch3d.structures import Meshes, Textures | ||
from pytorch3d.renderer import ( | ||
look_at_view_transform, | ||
OpenGLOrthographicCameras, | ||
PointLights, | ||
DirectionalLights, | ||
Materials, | ||
RasterizationSettings, | ||
MeshRenderer, | ||
MeshRasterizer, | ||
TexturedSoftPhongShader, | ||
HardPhongShader | ||
) | ||
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def set_renderer(): | ||
# Setup | ||
device = torch.device("cuda:0") | ||
torch.cuda.set_device(device) | ||
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# Initialize an OpenGL perspective camera. | ||
R, T = look_at_view_transform(2.0, 0, 180) | ||
cameras = OpenGLOrthographicCameras(device=device, R=R, T=T) | ||
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raster_settings = RasterizationSettings( | ||
image_size=512, | ||
blur_radius=0.0, | ||
faces_per_pixel=1, | ||
bin_size = None, | ||
max_faces_per_bin = None | ||
) | ||
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lights = PointLights(device=device, location=((2.0, 2.0, 2.0),)) | ||
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renderer = MeshRenderer( | ||
rasterizer=MeshRasterizer( | ||
cameras=cameras, | ||
raster_settings=raster_settings | ||
), | ||
shader=HardPhongShader( | ||
device=device, | ||
cameras=cameras, | ||
lights=lights | ||
) | ||
) | ||
return renderer | ||
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def get_verts_rgb_colors(obj_path): | ||
rgb_colors = [] | ||
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f = open(obj_path) | ||
lines = f.readlines() | ||
for line in lines: | ||
ls = line.split(' ') | ||
if len(ls) == 7: | ||
rgb_colors.append(ls[-3:]) | ||
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return np.array(rgb_colors, dtype='float32')[None, :, :] | ||
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def generate_video_from_obj(obj_path, video_path, renderer): | ||
# Setup | ||
device = torch.device("cuda:0") | ||
torch.cuda.set_device(device) | ||
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# Load obj file | ||
verts_rgb_colors = get_verts_rgb_colors(obj_path) | ||
verts_rgb_colors = torch.from_numpy(verts_rgb_colors).to(device) | ||
textures = Textures(verts_rgb=verts_rgb_colors) | ||
wo_textures = Textures(verts_rgb=torch.ones_like(verts_rgb_colors)*0.75) | ||
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# Load obj | ||
mesh = load_objs_as_meshes([obj_path], device=device) | ||
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# Set mesh | ||
vers = mesh._verts_list | ||
faces = mesh._faces_list | ||
mesh_w_tex = Meshes(vers, faces, textures) | ||
mesh_wo_tex = Meshes(vers, faces, wo_textures) | ||
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# create VideoWriter | ||
fourcc = cv2. VideoWriter_fourcc(*'MP4V') | ||
out = cv2.VideoWriter(video_path, fourcc, 20.0, (1024,512)) | ||
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for i in tqdm(range(90)): | ||
R, T = look_at_view_transform(1.8, 0, i*4, device=device) | ||
images_w_tex = renderer(mesh_w_tex, R=R, T=T) | ||
images_w_tex = np.clip(images_w_tex[0, ..., :3].cpu().numpy(), 0.0, 1.0)[:, :, ::-1] * 255 | ||
images_wo_tex = renderer(mesh_wo_tex, R=R, T=T) | ||
images_wo_tex = np.clip(images_wo_tex[0, ..., :3].cpu().numpy(), 0.0, 1.0)[:, :, ::-1] * 255 | ||
image = np.concatenate([images_w_tex, images_wo_tex], axis=1) | ||
out.write(image.astype('uint8')) | ||
out.release() | ||
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def video(path): | ||
mp4 = open(path,'rb').read() | ||
data_url = "data:video/mp4;base64," + b64encode(mp4).decode() | ||
return HTML('<video width=500 controls loop> <source src="%s" type="video/mp4"></video>' % data_url) |