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renderer.py
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renderer.py
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import os
os.environ['PYOPENGL_PLATFORM'] = 'osmesa'
import torch
from torchvision.utils import make_grid
import numpy as np
import trimesh
try:
import pyrender
except:
pass
try:
from opendr.renderer import ColoredRenderer
from opendr.lighting import LambertianPointLight, SphericalHarmonics
from opendr.camera import ProjectPoints
except:
pass
import neural_renderer as nr
from utils.densepose_methods import DensePoseMethods
from skimage.transform import resize
class Renderer:
"""
Renderer used for visualizing the SMPL model
Code adapted from https://github.com/vchoutas/smplify-x
"""
def __init__(self, focal_length=5000, img_res=224, faces=None):
self.renderer = pyrender.OffscreenRenderer(viewport_width=img_res,
viewport_height=img_res,
point_size=1.0)
self.focal_length = focal_length
self.camera_center = [img_res // 2, img_res // 2]
self.faces = faces
def visualize_tb(self, vertices, camera_translation, images):
vertices = vertices.cpu().numpy()
camera_translation = camera_translation.cpu().numpy().copy()
images = images.cpu()
images_np = np.transpose(images.numpy(), (0,2,3,1))
rend_imgs = []
for i in range(vertices.shape[0]):
rend_img = torch.from_numpy(np.transpose(self.__call__(vertices[i], camera_translation[i], images_np[i]), (2,0,1))).float()
rend_imgs.append(images[i])
rend_imgs.append(rend_img)
rend_imgs = make_grid(rend_imgs, nrow=2)
return rend_imgs
def __call__(self, vertices, camera_translation, image):
material = pyrender.MetallicRoughnessMaterial(
metallicFactor=0.2,
alphaMode='OPAQUE',
baseColorFactor=(0.8, 0.3, 0.3, 1.0))
camera_translation[0] *= -1.
mesh = trimesh.Trimesh(vertices, self.faces)
rot = trimesh.transformations.rotation_matrix(
np.radians(180), [1, 0, 0])
mesh.apply_transform(rot)
mesh = pyrender.Mesh.from_trimesh(mesh, material=material)
scene = pyrender.Scene(ambient_light=(0.5, 0.5, 0.5))
scene.add(mesh, 'mesh')
camera_pose = np.eye(4)
camera_pose[:3, 3] = camera_translation
camera = pyrender.IntrinsicsCamera(fx=self.focal_length, fy=self.focal_length,
cx=self.camera_center[0], cy=self.camera_center[1])
scene.add(camera, pose=camera_pose)
light = pyrender.DirectionalLight(color=[1.0, 1.0, 1.0], intensity=1)
light_pose = np.eye(4)
light_pose[:3, 3] = np.array([0, -1, 1])
scene.add(light, pose=light_pose)
light_pose[:3, 3] = np.array([0, 1, 1])
scene.add(light, pose=light_pose)
light_pose[:3, 3] = np.array([1, 1, 2])
scene.add(light, pose=light_pose)
color, rend_depth = self.renderer.render(scene, flags=pyrender.RenderFlags.RGBA)
color = color.astype(np.float32) / 255.0
valid_mask = (rend_depth > 0)[:,:,None]
output_img = (color[:, :, :3] * valid_mask +
(1 - valid_mask) * image)
return output_img
# https://github.com/classner/up/blob/master/up_tools/camera.py
def rotateY(points, angle):
"""Rotate all points in a 2D array around the y axis."""
ry = np.array([
[np.cos(angle), 0., np.sin(angle)],
[0., 1., 0. ],
[-np.sin(angle), 0., np.cos(angle)]
])
return np.dot(points, ry)
def rotateX( points, angle ):
"""Rotate all points in a 2D array around the x axis."""
rx = np.array([
[1., 0., 0. ],
[0., np.cos(angle), -np.sin(angle)],
[0., np.sin(angle), np.cos(angle) ]
])
return np.dot(points, rx)
def rotateZ( points, angle ):
"""Rotate all points in a 2D array around the z axis."""
rz = np.array([
[np.cos(angle), -np.sin(angle), 0. ],
[np.sin(angle), np.cos(angle), 0. ],
[0., 0., 1. ]
])
return np.dot(points, rz)
class opendr_render(object):
def __init__(self, ratio=1, color=None):
self.ratio = ratio
self.color = color
def render(self, image, cam, K, verts, face):
## Create OpenDR renderer
rn = ColoredRenderer()
## Assign attributes to renderer
w, h = (224 * self.ratio, 224 * self.ratio)
f = np.array([K[0, 0], K[1, 1]]) * float(self.ratio)
c = np.array([K[0, 2], K[1, 2]]) * float(self.ratio)
t = np.array([cam[1], cam[2], 2 * K[0, 0] / (224. * cam[0] + 1e-9)])
rn.camera = ProjectPoints(v=verts, rt=np.zeros(3), t=t, f=f, c=c, k=np.zeros(5))
rn.frustum = {'near': 1., 'far': 100., 'width': w, 'height': h}
albedo = np.ones_like(verts)*.9
if self.color is not None:
color0 = self.color
color1 = self.color
color2 = self.color
else:
# white
color0 = np.array([1, 1, 1])
color1 = np.array([1, 1, 1])
color2 = np.array([0.7, 0.7, 0.7])
rn.set(v=verts, f=face, bgcolor=np.zeros(3))
yrot = np.radians(120)
rn.vc = LambertianPointLight(
f=rn.f,
v=rn.v,
num_verts=len(rn.v),
light_pos=rotateY(np.array([-200, -100, -100]), yrot),
vc=albedo,
light_color=color0)
# Construct Left Light
rn.vc += LambertianPointLight(
f=rn.f,
v=rn.v,
num_verts=len(rn.v),
light_pos=rotateY(np.array([800, 10, 300]), yrot),
vc=albedo,
light_color=color1)
# Construct Right Light
rn.vc += LambertianPointLight(
f=rn.f,
v=rn.v,
num_verts=len(rn.v),
light_pos=rotateY(np.array([-500, 500, 1000]), yrot),
vc=albedo,
light_color=color2)
img_orig = np.transpose(image, (1, 2, 0))
img_resized = resize(img_orig, (img_orig.shape[0] * self.ratio, img_orig.shape[1] * self.ratio), anti_aliasing=True)
img_smpl = img_resized.copy()
img_smpl[rn.visibility_image != 4294967295] = rn.r[rn.visibility_image != 4294967295]
rn.set(v=rotateY(verts, np.radians(90)), f=face, bgcolor=np.zeros(3))
render_smpl = rn.r
render_smpl_rgba = np.zeros((render_smpl.shape[0], render_smpl.shape[1], 4))
render_smpl_rgba[:, :, :3] = render_smpl
render_smpl_rgba[:, :, 3][rn.visibility_image != 4294967295] = 255
return img_orig, img_resized, img_smpl, render_smpl_rgba
class IUV_Renderer(object):
'''
Renderer for generating IUV maps
Ref: H. Zhang et al. Learning 3D Human Shape and Pose from Dense Body Parts
'''
def __init__(self, orig_size=224, out_size=56, focal_length=5000.):
self.orig_size = orig_size
self.out_size = out_size
self.focal_length = focal_length
K = np.array([[self.focal_length, 0., self.orig_size / 2.],
[0., self.focal_length, self.orig_size / 2.],
[0., 0., 1.]])
R = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]])
t = np.array([0, 0, 5])
if self.orig_size != 224:
rander_scale = self.orig_size / float(224)
K[0, 0] *= rander_scale
K[1, 1] *= rander_scale
K[0, 2] *= rander_scale
K[1, 2] *= rander_scale
self.K = torch.FloatTensor(K[None, :, :])
self.R = torch.FloatTensor(R[None, :, :])
self.t = torch.FloatTensor(t[None, None, :])
self.coco_plus2coco = [14, 15, 16, 17, 18, 9, 8, 10, 7, 11, 6, 3, 2, 4, 1, 5, 0]
DP = DensePoseMethods()
vert_mapping = DP.All_vertices.astype('int64') - 1
self.vert_mapping = torch.from_numpy(vert_mapping)
faces = DP.FacesDensePose
faces = faces[None, :, :]
self.faces = torch.from_numpy(faces.astype(np.int32))
num_part = float(np.max(DP.FaceIndices))
textures = np.array(
[(DP.FaceIndices[i] / num_part, np.mean(DP.U_norm[v]), np.mean(DP.V_norm[v])) for i, v in
enumerate(DP.FacesDensePose)])
textures = textures[None, :, None, None, None, :]
self.textures = torch.from_numpy(textures.astype(np.float32))
self.renderer = nr.Renderer(camera_mode='projection', image_size=self.out_size, fill_back=False, anti_aliasing=False,
dist_coeffs=torch.FloatTensor([[0.] * 5]), orig_size=self.orig_size)
self.renderer.light_intensity_directional = 0.0
self.renderer.light_intensity_ambient = 1.0
def verts2uvimg(self, verts, cam):
''' render IUV images of given SMPL vertices and camera.
Args:
verts (tensor): [B, 6890, 3] SMPL vertices
cam (tensor): [B, 3] camera (s, x, y)
Return:
iuv_image (tensor): [B, 3, self.out_size, self.out_size] IUV images
'''
batch_size = verts.size(0)
K, R, t = self.camera_matrix(cam)
# map verts to its DensePose version
vertices = verts[:, self.vert_mapping, :]
iuv_image = self.renderer(vertices, self.faces.to(verts.device).expand(batch_size, -1, -1),
self.textures.to(verts.device).expand(batch_size, -1, -1, -1, -1, -1).clone(),
K=K, R=R, t=t,
mode='rgb',
dist_coeffs=torch.FloatTensor([[0.] * 5]).to(verts.device))
return iuv_image
def camera_matrix(self, cam):
'''
Args:
cam (tensor): [B, 3] camera (s, x, y)
'''
batch_size = cam.size(0)
K = self.K.repeat(batch_size, 1, 1)
R = self.R.repeat(batch_size, 1, 1)
t = torch.stack([cam[:, 1], cam[:, 2], 2 * self.focal_length/(self.orig_size * cam[:, 0] + 1e-9)], dim=-1)
t = t.unsqueeze(1)
if cam.is_cuda:
device_id = cam.get_device()
K = K.cuda(device_id)
R = R.cuda(device_id)
t = t.cuda(device_id)
return K, R, t