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renderer.py
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renderer.py
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import json
import os
# os.environ['PYOPENGL_PLATFORM'] = 'osmesa'
import torch
import trimesh
import numpy as np
from pare.utils.vis_utils import get_smpl_faces
from skimage.transform import resize
from torchvision.utils import make_grid
import torch.nn.functional as F
from core import path_config, constants
from utils.densepose_methods import DensePoseMethods
from .geometry import convert_to_full_img_cam
from utils.imutils import crop
# try:
# import math
# import pyrender
# from pyrender.constants import RenderFlags
# except ModuleNotFoundError:
# print('Failed to import *pyrender*. Please ignore the warning if opendr is appointed to render results.')
from pytorch3d.structures.meshes import Meshes
# from pytorch3d.renderer.mesh.renderer import MeshRendererWithFragments
from pytorch3d.renderer import (
look_at_view_transform,
FoVPerspectiveCameras,
PerspectiveCameras,
AmbientLights,
PointLights,
RasterizationSettings,
BlendParams,
MeshRenderer,
MeshRasterizer,
SoftPhongShader,
SoftSilhouetteShader,
HardPhongShader,
HardGouraudShader,
HardFlatShader,
TexturesVertex
)
import logging
logger = logging.getLogger(__name__)
# class WeakPerspectiveCamera(pyrender.Camera):
# def __init__(self,
# scale,
# translation,
# znear=pyrender.camera.DEFAULT_Z_NEAR,
# zfar=None,
# name=None):
# super(WeakPerspectiveCamera, self).__init__(
# znear=znear,
# zfar=zfar,
# name=name,
# )
# self.scale = scale
# self.translation = translation
#
# def get_projection_matrix(self, width=None, height=None):
# P = np.eye(4)
# P[0, 0] = self.scale[0]
# P[1, 1] = self.scale[1]
# P[0, 3] = self.translation[0] * self.scale[0]
# P[1, 3] = -self.translation[1] * self.scale[1]
# P[2, 2] = -1
# return P
# class PyRenderer:
# def __init__(self, resolution=(224, 224), orig_img=False, wireframe=False, scale_ratio=1., vis_ratio=1.):
# self.resolution = (resolution[0] * scale_ratio, resolution[1] * scale_ratio)
# # self.scale_ratio = scale_ratio
#
# self.faces = {'smplx': get_model_faces('smplx'),
# 'smpl': get_model_faces('smpl'),
# 'mano': get_model_faces('mano'),
# 'flame': get_model_faces('flame'),
# }
# self.orig_img = orig_img
# self.wireframe = wireframe
# self.renderer = pyrender.OffscreenRenderer(
# viewport_width=self.resolution[0],
# viewport_height=self.resolution[1],
# point_size=1.0
# )
#
# self.vis_ratio = vis_ratio
#
# # set the scene
# self.scene = pyrender.Scene(bg_color=[0.0, 0.0, 0.0, 0.0], ambient_light=(0.3, 0.3, 0.3))
#
# light = pyrender.PointLight(color=np.array([1.0, 1.0, 1.0]) * 0.2, intensity=1)
#
# yrot = np.radians(120) # angle of lights
#
# light_pose = np.eye(4)
# light_pose[:3, 3] = [0, -1, 1]
# self.scene.add(light, pose=light_pose)
#
# light_pose[:3, 3] = [0, 1, 1]
# self.scene.add(light, pose=light_pose)
#
# light_pose[:3, 3] = [1, 1, 2]
# self.scene.add(light, pose=light_pose)
#
# spot_l = pyrender.SpotLight(color=np.ones(3), intensity=15.0,
# innerConeAngle=np.pi / 3, outerConeAngle=np.pi / 2)
#
# light_pose[:3, 3] = [1, 2, 2]
# self.scene.add(spot_l, pose=light_pose)
#
# light_pose[:3, 3] = [-1, 2, 2]
# self.scene.add(spot_l, pose=light_pose)
#
# # light_pose[:3, 3] = [-2, 2, 0]
# # self.scene.add(spot_l, pose=light_pose)
#
# # light_pose[:3, 3] = [-2, 2, 0]
# # self.scene.add(spot_l, pose=light_pose)
#
# self.colors_dict = {
# 'red': np.array([0.5, 0.2, 0.2]),
# 'pink': np.array([0.7, 0.5, 0.5]),
# 'neutral': np.array([0.7, 0.7, 0.6]),
# # 'purple': np.array([0.5, 0.5, 0.7]),
# 'purple': np.array([0.55, 0.4, 0.9]),
# 'green': np.array([0.5, 0.55, 0.3]),
# 'sky': np.array([0.3, 0.5, 0.55]),
# 'white': np.array([1.0, 0.98, 0.94]),
# }
#
# def __call__(self, verts, faces=None, img=np.zeros((224, 224, 3)), cam=np.array([1, 0, 0]),
# focal_length=[5000, 5000], camera_rotation=np.eye(3), crop_info=None,
# angle=None, axis=None, mesh_filename=None, color_type=None, color=[1.0, 1.0, 0.9], iwp_mode=True,
# crop_img=True, mesh_type='smpl', scale_ratio=1., rgba_mode=False):
#
# if faces is None:
# faces = self.faces[mesh_type]
# mesh = trimesh.Trimesh(vertices=verts, faces=faces, process=False)
#
# Rx = trimesh.transformations.rotation_matrix(math.radians(180), [1, 0, 0])
# mesh.apply_transform(Rx)
#
# if mesh_filename is not None:
# mesh.export(mesh_filename)
#
# if angle and axis:
# R = trimesh.transformations.rotation_matrix(math.radians(angle), axis)
# mesh.apply_transform(R)
#
# cam = cam.copy()
# if iwp_mode:
# resolution = np.array(img.shape[:2]) * scale_ratio
# if len(cam) == 4:
# sx, sy, tx, ty = cam
# # sy = sx
# camera_translation = np.array([tx, ty, 2 * focal_length[0] / (resolution[0] * sy + 1e-9)])
# elif len(cam) == 3:
# sx, tx, ty = cam
# sy = sx
# camera_translation = np.array([- tx, ty, 2 * focal_length[0] / (resolution[0] * sy + 1e-9)])
# render_res = resolution
# self.renderer.viewport_width = render_res[1]
# self.renderer.viewport_height = render_res[0]
# else:
# if crop_info['opt_cam_t'] is None:
# camera_translation = convert_to_full_img_cam(
# pare_cam=cam[None],
# bbox_height=crop_info['bbox_scale'] * 200.,
# bbox_center=crop_info['bbox_center'],
# img_w=crop_info['img_w'],
# img_h=crop_info['img_h'],
# focal_length=focal_length[0],
# )
# else:
# camera_translation = crop_info['opt_cam_t']
# if torch.is_tensor(camera_translation):
# camera_translation = camera_translation[0].cpu().numpy()
# camera_translation = camera_translation.copy()
# camera_translation[0] *= -1
# if 'img_h' in crop_info and 'img_w' in crop_info:
# render_res = (int(crop_info['img_h'][0]), int(crop_info['img_w'][0]))
# else:
# render_res = img.shape[:2] if type(img) is not list else img[0].shape[:2]
# self.renderer.viewport_width = render_res[1]
# self.renderer.viewport_height = render_res[0]
# camera_rotation = camera_rotation.T
# camera = pyrender.IntrinsicsCamera(fx=focal_length[0], fy=focal_length[1],
# cx=render_res[1] / 2., cy=render_res[0] / 2.)
#
# if color_type != None:
# color = self.colors_dict[color_type]
#
# material = pyrender.MetallicRoughnessMaterial(
# metallicFactor=0.2,
# roughnessFactor=0.6,
# alphaMode='OPAQUE',
# baseColorFactor=(color[0], color[1], color[2], 1.0)
# )
#
# mesh = pyrender.Mesh.from_trimesh(mesh, material=material)
#
# mesh_node = self.scene.add(mesh, 'mesh')
#
# camera_pose = np.eye(4)
# camera_pose[:3, :3] = camera_rotation
# camera_pose[:3, 3] = camera_rotation @ camera_translation
# cam_node = self.scene.add(camera, pose=camera_pose)
#
# if self.wireframe:
# render_flags = RenderFlags.RGBA | RenderFlags.ALL_WIREFRAME | RenderFlags.SHADOWS_SPOT
# else:
# render_flags = RenderFlags.RGBA | RenderFlags.SHADOWS_SPOT
#
# rgb, _ = self.renderer.render(self.scene, flags=render_flags)
# if crop_info is not None and crop_img:
# crop_res = img.shape[:2]
# rgb, _, _ = crop(rgb, crop_info['bbox_center'][0], crop_info['bbox_scale'][0], crop_res)
#
# valid_mask = (rgb[:, :, -1] > 0)[:, :, np.newaxis]
#
# image_list = [img] if type(img) is not list else img
#
# return_img = []
# for item in image_list:
# if scale_ratio != 1:
# orig_size = item.shape[:2]
# item = resize(item, (orig_size[0] * scale_ratio, orig_size[1] * scale_ratio), anti_aliasing=True)
# item = (item * 255).astype(np.uint8)
# output_img = rgb[:, :, :-1] * valid_mask * self.vis_ratio + (1 - valid_mask * self.vis_ratio) * item
# # output_img[valid_mask < 0.5] = item[valid_mask < 0.5]
# # if scale_ratio != 1:
# # output_img = resize(output_img, (orig_size[0], orig_size[1]), anti_aliasing=True)
# if rgba_mode:
# output_img_rgba = np.zeros((output_img.shape[0], output_img.shape[1], 4))
# output_img_rgba[:, :, :3] = output_img
# output_img_rgba[:, :, 3][valid_mask[:, :, 0]] = 255
# output_img = output_img_rgba.astype(np.uint8)
# image = output_img.astype(np.uint8)
# return_img.append(image)
# return_img.append(item)
#
# if type(img) is not list:
# # if scale_ratio == 1:
# return_img = return_img[0]
#
# self.scene.remove_node(mesh_node)
# self.scene.remove_node(cam_node)
#
# return return_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 IUV_Renderer(object):
# img_size=[height,width]
def __init__(self, focal_length=constants.FOCAL_LENGTH, orig_size=(224, 224), output_size=(56, 56), mode='iuv',
device=torch.device('cuda'), mesh_type='smpl'):
self.focal_length = focal_length
self.orig_size = orig_size
self.output_size = output_size
if mode in ['iuv']:
if mesh_type == 'smpl':
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)) # [1, 13774, 3], torch.int32
num_part = float(np.max(DP.FaceIndices))
self.num_part = num_part
dp_vert_pid_fname = 'data/dp_vert_pid.npy'
if os.path.exists(dp_vert_pid_fname):
dp_vert_pid = list(np.load(dp_vert_pid_fname))
else:
print('creating data/dp_vert_pid.npy')
dp_vert_pid = []
for v in range(len(vert_mapping)):
for i, f in enumerate(DP.FacesDensePose):
if v in f:
dp_vert_pid.append(DP.FaceIndices[i])
break
np.save(dp_vert_pid_fname, np.array(dp_vert_pid))
textures_vts = np.array(
[(dp_vert_pid[i] / num_part, DP.U_norm[i], DP.V_norm[i]) for i in
range(len(vert_mapping))])
self.textures_vts = torch.from_numpy(textures_vts[None].astype(np.float32)) # (1, 7829, 3)
# elif mode == 'pncc':
# self.vert_mapping = None
# self.faces = torch.from_numpy(get_model_faces(mesh_type)[None].astype(np.int32)) # mano: torch.Size([1, 1538, 3])
# textures_vts = get_model_tpose(mesh_type).unsqueeze(0) # mano: torch.Size([1, 778, 3])
#
# texture_min = torch.min(textures_vts) - 0.001
# texture_range = torch.max(textures_vts) - texture_min + 0.001
# self.textures_vts = (textures_vts - texture_min) / texture_range
# elif mode in ['seg']:
# self.vert_mapping = None
# body_model = 'smpl'
#
# self.faces = torch.from_numpy(get_smpl_faces().astype(np.int32)[None])
#
# with open(os.path.join(path_config.SMPL_MODEL_DIR, '{}_vert_segmentation.json'.format(body_model)), 'rb') as json_file:
# smpl_part_id = json.load(json_file)
#
# v_id = []
# for k in smpl_part_id.keys():
# v_id.extend(smpl_part_id[k])
#
# v_id = torch.tensor(v_id)
# n_verts = len(torch.unique(v_id))
# num_part = len(constants.SMPL_PART_ID.keys())
# self.num_part = num_part
#
# seg_vert_pid = np.zeros(n_verts)
# for k in smpl_part_id.keys():
# seg_vert_pid[smpl_part_id[k]] = constants.SMPL_PART_ID[k]
#
# print('seg_vert_pid', seg_vert_pid.shape)
# textures_vts = seg_vert_pid[:, None].repeat(3, axis=1) / num_part
# print('textures_vts', textures_vts.shape)
# # textures_vts = np.array(
# # [(seg_vert_pid[i] / num_part,) * 3 for i in
# # range(n_verts)])
# self.textures_vts = torch.from_numpy(textures_vts[None].astype(np.float32))
K = np.array([[self.focal_length, 0., self.orig_size[1] / 2.],
[0., self.focal_length, self.orig_size[0] / 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[0] != 224:
rander_scale_w = self.orig_size[1] / float(224)
rander_scale_h = self.orig_size[0] / float(224)
K[0, 0] *= rander_scale_w
K[1, 1] *= rander_scale_h
K[0, 2] *= rander_scale_w
K[1, 2] *= rander_scale_h
self.K = torch.FloatTensor(K[None, :, :])
self.R = torch.FloatTensor(R[None, :, :])
self.t = torch.FloatTensor(t[None, None, :])
camK = F.pad(self.K, (0, 1, 0, 1), "constant", 0)
camK[:, 2, 2] = 0
camK[:, 3, 2] = 1
camK[:, 2, 3] = 1
self.K = camK
self.device = device
lights = AmbientLights(device=self.device)
raster_settings = RasterizationSettings(
image_size=output_size,
blur_radius=0,
faces_per_pixel=1,
)
self.renderer = MeshRenderer(
rasterizer=MeshRasterizer(
raster_settings=raster_settings
),
shader=HardFlatShader(
device=self.device,
lights=lights,
blend_params=BlendParams(background_color=[0, 0, 0], sigma=0.0, gamma=0.0)
)
)
def camera_matrix(self, cam):
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[0] * cam[:, 0] + 1e-9)], dim=-1)
if cam.is_cuda:
# device_id = cam.get_device()
K = K.to(cam.device)
R = R.to(cam.device)
t = t.to(cam.device)
return K, R, t
def verts2iuvimg(self, verts, cam, iwp_mode=True):
batch_size = verts.size(0)
K, R, t = self.camera_matrix(cam)
if self.vert_mapping is None:
vertices = verts
else:
vertices = verts[:, self.vert_mapping, :]
mesh = Meshes(vertices, self.faces.to(verts.device).expand(batch_size, -1, -1))
mesh.textures = TexturesVertex(verts_features=self.textures_vts.to(verts.device).expand(batch_size, -1, -1))
cameras = PerspectiveCameras(device=verts.device, R=R, T=t, K=K, in_ndc=False,
image_size=[self.orig_size])
iuv_image = self.renderer(mesh, cameras=cameras)
iuv_image = iuv_image[..., :3].permute(0, 3, 1, 2)
return iuv_image
class Depth_Renderer(object):
def __init__(self, focal_length=constants.FOCAL_LENGTH, orig_size=(224, 224), output_size=(56, 56), mode='depth', device=torch.device('cuda'),
mesh_type='smpl'):
self.focal_length = focal_length
self.orig_size = orig_size
self.output_size = output_size
if mode in ['depth']:
if mesh_type == 'smpl':
faces = get_smpl_faces()
faces = faces[None, :, :]
self.faces = torch.from_numpy(faces.astype(np.int32))
K = np.array([[self.focal_length, 0., self.orig_size[1] / 2.],
[0., self.focal_length, self.orig_size[0] / 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[0] != 224:
rander_scale_w = self.orig_size[1] / float(224)
rander_scale_h = self.orig_size[0] / float(224)
K[0, 0] *= rander_scale_w
K[1, 1] *= rander_scale_h
K[0, 2] *= rander_scale_w
K[1, 2] *= rander_scale_h
self.K = torch.FloatTensor(K[None, :, :])
self.R = torch.FloatTensor(R[None, :, :])
self.t = torch.FloatTensor(t[None, None, :])
camK = F.pad(self.K, (0, 1, 0, 1), "constant", 0)
camK[:, 2, 2] = 0
camK[:, 3, 2] = 1
camK[:, 2, 3] = 1
self.K = camK
self.device = device
self.raster_settings_mesh = RasterizationSettings(
image_size=output_size,
blur_radius=np.log(1.0 / 1e-4) * 1e-7,
bin_size=-1,
faces_per_pixel=30,
)
self.meshRas = MeshRasterizer(raster_settings=self.raster_settings_mesh)
def camera_matrix(self, cam):
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[0] * cam[:, 0] + 1e-9)], dim=-1)
if cam.is_cuda:
# device_id = cam.get_device()
K = K.to(cam.device)
R = R.to(cam.device)
t = t.to(cam.device)
return K, R, t
def verts2depthimg(self, verts, cam, iwp_mode=True):
batch_size = verts.size(0)
K, R, t = self.camera_matrix(cam)
vertices = verts
mesh = Meshes(vertices, self.faces.to(verts.device).expand(batch_size, -1, -1))
mesh.textures = TexturesVertex(verts_features=(mesh.verts_normals_padded() + 1.0) * 0.5)
cameras = PerspectiveCameras(device=verts.device, R=R, T=t, K=K, in_ndc=False,
image_size=[self.orig_size])
fragments = self.meshRas(mesh, cameras=cameras)
depth_image = fragments.zbuf[..., 0]
depth_image = 1. / depth_image
depth_image=torch.where(depth_image==-1.,torch.tensor(0.).to(depth_image.device),depth_image)
depth_image=depth_image.unsqueeze(1)
return depth_image