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import os
# if 'PYOPENGL_PLATFORM' not in os.environ:
# os.environ['PYOPENGL_PLATFORM'] = 'egl'
import torch
from torchvision.utils import make_grid
import numpy as np
import pyrender
import trimesh
import cv2
import torch.nn.functional as F
from .render_openpose import render_openpose
def create_raymond_lights():
import pyrender
thetas = np.pi * np.array([1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0])
phis = np.pi * np.array([0.0, 2.0 / 3.0, 4.0 / 3.0])
nodes = []
for phi, theta in zip(phis, thetas):
xp = np.sin(theta) * np.cos(phi)
yp = np.sin(theta) * np.sin(phi)
zp = np.cos(theta)
z = np.array([xp, yp, zp])
z = z / np.linalg.norm(z)
x = np.array([-z[1], z[0], 0.0])
if np.linalg.norm(x) == 0:
x = np.array([1.0, 0.0, 0.0])
x = x / np.linalg.norm(x)
y = np.cross(z, x)
matrix = np.eye(4)
matrix[:3,:3] = np.c_[x,y,z]
nodes.append(pyrender.Node(
light=pyrender.DirectionalLight(color=np.ones(3), intensity=1.0),
matrix=matrix
))
return nodes
class MeshRenderer:
def __init__(self, cfg, faces=None):
self.cfg = cfg
self.focal_length = cfg.EXTRA.FOCAL_LENGTH
self.img_res = cfg.MODEL.IMAGE_SIZE
self.renderer = pyrender.OffscreenRenderer(viewport_width=self.img_res,
viewport_height=self.img_res,
point_size=1.0)
self.camera_center = [self.img_res // 2, self.img_res // 2]
self.faces = faces
def visualize(self, vertices, camera_translation, images, focal_length=None, nrow=3, padding=2):
images_np = np.transpose(images, (0,2,3,1))
rend_imgs = []
for i in range(vertices.shape[0]):
fl = self.focal_length
rend_img = torch.from_numpy(np.transpose(self.__call__(vertices[i], camera_translation[i], images_np[i], focal_length=fl, side_view=False), (2,0,1))).float()
rend_img_side = torch.from_numpy(np.transpose(self.__call__(vertices[i], camera_translation[i], images_np[i], focal_length=fl, side_view=True), (2,0,1))).float()
rend_imgs.append(torch.from_numpy(images[i]))
rend_imgs.append(rend_img)
rend_imgs.append(rend_img_side)
rend_imgs = make_grid(rend_imgs, nrow=nrow, padding=padding)
return rend_imgs
def visualize_tensorboard(self, vertices, camera_translation, images, pred_keypoints, gt_keypoints, focal_length=None, nrow=5, padding=2):
images_np = np.transpose(images, (0,2,3,1))
rend_imgs = []
pred_keypoints = np.concatenate((pred_keypoints, np.ones_like(pred_keypoints)[:, :, [0]]), axis=-1)
pred_keypoints = self.img_res * (pred_keypoints + 0.5)
gt_keypoints[:, :, :-1] = self.img_res * (gt_keypoints[:, :, :-1] + 0.5)
keypoint_matches = [(1, 12), (2, 8), (3, 7), (4, 6), (5, 9), (6, 10), (7, 11), (8, 14), (9, 2), (10, 1), (11, 0), (12, 3), (13, 4), (14, 5)]
for i in range(vertices.shape[0]):
fl = self.focal_length
rend_img = torch.from_numpy(np.transpose(self.__call__(vertices[i], camera_translation[i], images_np[i], focal_length=fl, side_view=False), (2,0,1))).float()
rend_img_side = torch.from_numpy(np.transpose(self.__call__(vertices[i], camera_translation[i], images_np[i], focal_length=fl, side_view=True), (2,0,1))).float()
body_keypoints = pred_keypoints[i, :25]
extra_keypoints = pred_keypoints[i, -19:]
for pair in keypoint_matches:
body_keypoints[pair[0], :] = extra_keypoints[pair[1], :]
pred_keypoints_img = render_openpose(255 * images_np[i].copy(), body_keypoints) / 255
body_keypoints = gt_keypoints[i, :25]
extra_keypoints = gt_keypoints[i, -19:]
for pair in keypoint_matches:
if extra_keypoints[pair[1], -1] > 0 and body_keypoints[pair[0], -1] == 0:
body_keypoints[pair[0], :] = extra_keypoints[pair[1], :]
gt_keypoints_img = render_openpose(255*images_np[i].copy(), body_keypoints) / 255
rend_imgs.append(torch.from_numpy(images[i]))
rend_imgs.append(rend_img)
rend_imgs.append(rend_img_side)
rend_imgs.append(torch.from_numpy(pred_keypoints_img).permute(2,0,1))
rend_imgs.append(torch.from_numpy(gt_keypoints_img).permute(2,0,1))
rend_imgs = make_grid(rend_imgs, nrow=nrow, padding=padding)
return rend_imgs
def __call__(self, vertices, camera_translation, image, focal_length=5000, text=None, resize=None, side_view=False, baseColorFactor=(1.0, 1.0, 0.9, 1.0), rot_angle=90):
renderer = pyrender.OffscreenRenderer(viewport_width=image.shape[1],
viewport_height=image.shape[0],
point_size=1.0)
material = pyrender.MetallicRoughnessMaterial(
metallicFactor=0.0,
alphaMode='OPAQUE',
baseColorFactor=baseColorFactor)
camera_translation[0] *= -1.
mesh = trimesh.Trimesh(vertices.copy(), self.faces.copy())
if side_view:
rot = trimesh.transformations.rotation_matrix(
np.radians(rot_angle), [0, 1, 0])
mesh.apply_transform(rot)
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(bg_color=[0.0, 0.0, 0.0, 0.0],
ambient_light=(0.3, 0.3, 0.3))
scene.add(mesh, 'mesh')
camera_pose = np.eye(4)
camera_pose[:3, 3] = camera_translation
camera_center = [image.shape[1] / 2., image.shape[0] / 2.]
camera = pyrender.IntrinsicsCamera(fx=focal_length, fy=focal_length,
cx=camera_center[0], cy=camera_center[1])
scene.add(camera, pose=camera_pose)
light_nodes = create_raymond_lights()
for node in light_nodes:
scene.add_node(node)
color, rend_depth = renderer.render(scene, flags=pyrender.RenderFlags.RGBA)
color = color.astype(np.float32) / 255.0
valid_mask = (color[:, :, -1] > 0)[:, :, np.newaxis]
if not side_view:
output_img = (color[:, :, :3] * valid_mask +
(1 - valid_mask) * image)
else:
output_img = color[:, :, :3]
if resize is not None:
output_img = cv2.resize(output_img, resize)
output_img = output_img.astype(np.float32)
renderer.delete()
return output_img
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