Comments (2)
I tried to just run a forward pass like this to render a simple square. But I am getting segmentation fault. I am trying with 2d images hence I used z coordinate as 0 in vertices.
"""
Example 2. Optimizing vertices.
"""
from __future__ import division
import os
import argparse
import glob
import torch
import torch.nn as nn
import numpy as np
from skimage.io import imread, imsave
import tqdm
import imageio
import neural_renderer as nr
current_dir = os.path.dirname(os.path.realpath(__file__))
data_dir = os.path.join(current_dir, 'data')
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
# load .obj
#vertices, faces = nr.load_obj(filename_obj)
vertices = torch.from_numpy(np.array([[0, 0, 0], [0, 1, 0], [1, 0, 0], [1, 1, 0]]).reshape(4, 3).astype(np.float32)).float()
faces = torch.from_numpy(np.array([[0, 1, 2], [0, 2, 3]]).reshape(2, 3).astype(np.int64)).int()
self.vertices = nn.Parameter(vertices[None, :, :])
self.register_buffer('faces', faces[None, :, :])
texture_size = 2
textures = torch.ones(1, self.faces.shape[1], texture_size, texture_size, texture_size, 3, dtype=torch.float32)
self.register_buffer('textures', textures)
print(self.vertices.shape)
print(self.faces.shape)
# create textures
#texture_size = 2
#textures = torch.ones(1, self.faces.shape[1], texture_size, texture_size, texture_size, 3, dtype=torch.float32)
#self.register_buffer('textures', textures)
# load reference image
#image_ref = torch.from_numpy(imread(filename_ref).astype(np.float32).mean(-1) / 255.)[None, ::]
#self.register_buffer('image_ref', image_ref)
# setup renderer
renderer = nr.Renderer(camera_mode='look_at')
self.renderer = renderer
def forward(self):
self.renderer.eye = nr.get_points_from_angles(0, 0, 0)
image = self.renderer(self.vertices, self.faces, textures=self.textures, mode=None)
#loss = torch.sum((image - self.image_ref[None, :, :])**2)
return image
model = Model().cuda()
img = model()
print(img.shape)
from neural_renderer.
@shivamsaboo17 were you trying to render a UV pass like this? Did you figure it out?
from neural_renderer.
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from neural_renderer.