thirdparty/TRELLIS/trellis/representations/mesh/flexicubes/examples/render.py

# Copyright (c) 2023, NVIDIA CORPORATION & AFFILIATES.  All rights reserved.
#
# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
import numpy as np
import copy
import math
from ipywidgets import interactive, HBox, VBox, FloatLogSlider, IntSlider

import torch
import nvdiffrast.torch as dr
import kaolin as kal
import util

###############################################################################
# Functions adapted from https://github.com/NVlabs/nvdiffrec
###############################################################################

def get_random_camera_batch(batch_size, fovy = np.deg2rad(45), iter_res=[512,512], cam_near_far=[0.1, 1000.0], cam_radius=3.0, device="cuda", use_kaolin=True):
    if use_kaolin:
        camera_pos = torch.stack(kal.ops.coords.spherical2cartesian(
            *kal.ops.random.sample_spherical_coords((batch_size,), azimuth_low=0., azimuth_high=math.pi * 2,
                                                    elevation_low=-math.pi / 2., elevation_high=math.pi / 2., device='cuda'),
            cam_radius
        ), dim=-1)
        return kal.render.camera.Camera.from_args(
            eye=camera_pos + torch.rand((batch_size, 1), device='cuda') * 0.5 - 0.25,
            at=torch.zeros(batch_size, 3),
            up=torch.tensor([[0., 1., 0.]]),
            fov=fovy,
            near=cam_near_far[0], far=cam_near_far[1],
            height=iter_res[0], width=iter_res[1],
            device='cuda'
        )
    else:
        def get_random_camera():
            proj_mtx = util.perspective(fovy, iter_res[1] / iter_res[0], cam_near_far[0], cam_near_far[1])
            mv     = util.translate(0, 0, -cam_radius) @ util.random_rotation_translation(0.25)
            mvp    = proj_mtx @ mv
            return mv, mvp
        mv_batch = []
        mvp_batch = []
        for i in range(batch_size):
            mv, mvp = get_random_camera()
            mv_batch.append(mv)
            mvp_batch.append(mvp)
        return torch.stack(mv_batch).to(device), torch.stack(mvp_batch).to(device)

def get_rotate_camera(itr, fovy = np.deg2rad(45), iter_res=[512,512], cam_near_far=[0.1, 1000.0], cam_radius=3.0, device="cuda", use_kaolin=True):
    if use_kaolin:
        ang = (itr / 10) * np.pi * 2
        camera_pos = torch.stack(kal.ops.coords.spherical2cartesian(torch.tensor(ang), torch.tensor(0.4), -torch.tensor(cam_radius)))
        return kal.render.camera.Camera.from_args(
            eye=camera_pos,
            at=torch.zeros(3),
            up=torch.tensor([0., 1., 0.]),
            fov=fovy,
            near=cam_near_far[0], far=cam_near_far[1],
            height=iter_res[0], width=iter_res[1],
            device='cuda'
        )
    else:
        proj_mtx = util.perspective(fovy, iter_res[1] / iter_res[0], cam_near_far[0], cam_near_far[1])

        # Smooth rotation for display.
        ang    = (itr / 10) * np.pi * 2
        mv     = util.translate(0, 0, -cam_radius) @ (util.rotate_x(-0.4) @ util.rotate_y(ang))
        mvp    = proj_mtx @ mv
        return mv.to(device), mvp.to(device)

glctx = dr.RasterizeGLContext()
def render_mesh(mesh, camera, iter_res, return_types = ["mask", "depth"], white_bg=False, wireframe_thickness=0.4):
    vertices_camera = camera.extrinsics.transform(mesh.vertices)
    face_vertices_camera = kal.ops.mesh.index_vertices_by_faces(
        vertices_camera, mesh.faces
    )

    # Projection: nvdiffrast take clip coordinates as input to apply barycentric perspective correction.
    # Using `camera.intrinsics.transform(vertices_camera) would return the normalized device coordinates.
    proj = camera.projection_matrix().unsqueeze(1)
    proj[:, :, 1, 1] = -proj[:, :, 1, 1]
    homogeneous_vecs = kal.render.camera.up_to_homogeneous(
        vertices_camera
    )
    vertices_clip = (proj @ homogeneous_vecs.unsqueeze(-1)).squeeze(-1)
    faces_int = mesh.faces.int()

    rast, _ = dr.rasterize(
        glctx, vertices_clip, faces_int, iter_res)

    out_dict = {}
    for type in return_types:
        if type == "mask" :
            img = dr.antialias((rast[..., -1:] > 0).float(), rast, vertices_clip, faces_int)
        elif type == "depth":
            img = dr.interpolate(homogeneous_vecs, rast, faces_int)[0]
        elif type == "wireframe":
            img = torch.logical_or(
                torch.logical_or(rast[..., 0] < wireframe_thickness, rast[..., 1] < wireframe_thickness),
                (rast[..., 0] + rast[..., 1]) > (1. - wireframe_thickness)
            ).unsqueeze(-1)
        elif type == "normals" :
            img = dr.interpolate(
                mesh.face_normals.reshape(len(mesh), -1, 3), rast,
                torch.arange(mesh.faces.shape[0] * 3, device='cuda', dtype=torch.int).reshape(-1, 3)
            )[0]
        if white_bg:
            bg = torch.ones_like(img)
            alpha = (rast[..., -1:] > 0).float() 
            img = torch.lerp(bg, img, alpha)
        out_dict[type] = img

        
    return out_dict

def render_mesh_paper(mesh, mv, mvp, iter_res, return_types = ["mask", "depth"], white_bg=False):
    '''
    The rendering function used to produce the results in the paper.
    '''
    v_pos_clip = util.xfm_points(mesh.vertices.unsqueeze(0), mvp)  # Rotate it to camera coordinates
    rast, db = dr.rasterize(
        dr.RasterizeGLContext(), v_pos_clip, mesh.faces.int(), iter_res)

    out_dict = {}
    for type in return_types:
        if type == "mask" :
            img = dr.antialias((rast[..., -1:] > 0).float(), rast, v_pos_clip, mesh.faces.int()) 
        elif type == "depth":
            v_pos_cam = util.xfm_points(mesh.vertices.unsqueeze(0), mv)
            img, _ = util.interpolate(v_pos_cam, rast, mesh.faces.int())
        elif type == "normal" :
            normal_indices = (torch.arange(0, mesh.nrm.shape[0], dtype=torch.int64, device='cuda')[:, None]).repeat(1, 3)
            img, _ = util.interpolate(mesh.nrm.unsqueeze(0).contiguous(), rast, normal_indices.int())
        elif type == "vertex_normal":
            img, _ = util.interpolate(mesh.v_nrm.unsqueeze(0).contiguous(), rast, mesh.faces.int())
            img = dr.antialias((img + 1) * 0.5, rast, v_pos_clip, mesh.faces.int()) 
        if white_bg:
            bg = torch.ones_like(img)
            alpha = (rast[..., -1:] > 0).float() 
            img = torch.lerp(bg, img, alpha)
        out_dict[type] = img
    return out_dict

class SplitVisualizer():
    def __init__(self, lh_mesh, rh_mesh, height, width):
        self.lh_mesh = lh_mesh
        self.rh_mesh = rh_mesh
        self.height = height
        self.width = width
        self.wireframe_thickness = 0.4
        

    def render(self, camera):
        lh_outputs = render_mesh(
            self.lh_mesh, camera, (self.height, self.width),
            return_types=["normals", "wireframe"], wireframe_thickness=self.wireframe_thickness
        )
        rh_outputs = render_mesh(
            self.rh_mesh, camera, (self.height, self.width),
            return_types=["normals", "wireframe"], wireframe_thickness=self.wireframe_thickness
        )
        outputs = {
            k: torch.cat(
                [lh_outputs[k][0].permute(1, 0, 2), rh_outputs[k][0].permute(1, 0, 2)],
                dim=0
            ).permute(1, 0, 2) for k in ["normals", "wireframe"]
        }
        return {
            'img': (outputs['wireframe'] * ((outputs['normals'] + 1.) / 2.) * 255).to(torch.uint8),
            'normals': outputs['normals']
        }

    def show(self, init_camera):
        visualizer = kal.visualize.IpyTurntableVisualizer(
            self.height, self.width * 2, copy.deepcopy(init_camera), self.render,
            max_fps=24, world_up_axis=1)

        def slider_callback(new_wireframe_thickness):
            """ipywidgets sliders callback"""
            with visualizer.out: # This is in case of bug
                self.wireframe_thickness = new_wireframe_thickness
                # this is how we request a new update
                visualizer.render_update()
                
        wireframe_thickness_slider = FloatLogSlider(
            value=self.wireframe_thickness,
            base=10,
            min=-3,
            max=-0.4,
            step=0.1,
            description='wireframe_thickness',
            continuous_update=True,
            readout=True,
            readout_format='.3f',
        )
        
        interactive_slider = interactive(
            slider_callback,
            new_wireframe_thickness=wireframe_thickness_slider,
        )
        
        full_output = VBox([visualizer.canvas, interactive_slider])
        display(full_output, visualizer.out)

class TimelineVisualizer():
    def __init__(self, meshes, height, width):
        self.meshes = meshes
        self.height = height
        self.width = width
        self.wireframe_thickness = 0.4
        self.idx = len(meshes) - 1

    def render(self, camera):
        outputs = render_mesh(
            self.meshes[self.idx], camera, (self.height, self.width),
            return_types=["normals", "wireframe"], wireframe_thickness=self.wireframe_thickness
        )

        return {
            'img': (outputs['wireframe'] * ((outputs['normals'] + 1.) / 2.) * 255).to(torch.uint8)[0],
            'normals': outputs['normals'][0]
        }

    def show(self, init_camera):
        visualizer = kal.visualize.IpyTurntableVisualizer(
            self.height, self.width, copy.deepcopy(init_camera), self.render,
            max_fps=24, world_up_axis=1)

        def slider_callback(new_wireframe_thickness, new_idx):
            """ipywidgets sliders callback"""
            with visualizer.out: # This is in case of bug
                self.wireframe_thickness = new_wireframe_thickness
                self.idx = new_idx
                # this is how we request a new update
                visualizer.render_update()

        wireframe_thickness_slider = FloatLogSlider(
            value=self.wireframe_thickness,
            base=10,
            min=-3,
            max=-0.4,
            step=0.1,
            description='wireframe_thickness',
            continuous_update=True,
            readout=True,
            readout_format='.3f',
        )

        idx_slider = IntSlider(
            value=self.idx,
            min=0,
            max=len(self.meshes) - 1,
            description='idx',
            continuous_update=True,
            readout=True
        )

        interactive_slider = interactive(
            slider_callback,
            new_wireframe_thickness=wireframe_thickness_slider,
            new_idx=idx_slider
        )
        full_output = HBox([visualizer.canvas, interactive_slider])
        display(full_output, visualizer.out)