viewcrafter.py

import sys
sys.path.append('./extern/dust3r')
from dust3r.inference import inference, load_model
from dust3r.utils.image import load_images
from dust3r.image_pairs import make_pairs
from dust3r.cloud_opt import global_aligner, GlobalAlignerMode
from dust3r.utils.device import to_numpy
import trimesh
import torch
import numpy as np
import torchvision
import os
import copy
import cv2  
from PIL import Image
import pytorch3d
print(pytorch3d.__version__)
from pytorch3d.structures import Pointclouds
from torchvision.utils import save_image
import torch.nn.functional as F
import torchvision.transforms as transforms
from PIL import Image
from utils.pvd_utils import *
from omegaconf import OmegaConf
from pytorch_lightning import seed_everything
from utils.diffusion_utils import instantiate_from_config,load_model_checkpoint,image_guided_synthesis
from pathlib import Path
from torchvision.utils import save_image

class ViewCrafter:
    def __init__(self, opts, gradio = False):
        self.opts = opts
        self.device = opts.device
        self.setup_dust3r()
        self.setup_diffusion() #fixme
        # initialize ref images, pcd
        if not gradio:
            self.images, self.img_ori = self.load_initial_images(image_dir=self.opts.image_dir)
            self.run_dust3r(input_images=self.images)
        
    def run_dust3r(self, input_images,clean_pc = False):
        pairs = make_pairs(input_images, scene_graph='complete', prefilter=None, symmetrize=True)
        output = inference(pairs, self.dust3r, self.device, batch_size=self.opts.batch_size)

        mode = GlobalAlignerMode.PointCloudOptimizer #if len(self.images) > 2 else GlobalAlignerMode.PairViewer
        scene = global_aligner(output, device=self.device, mode=mode)
        if mode == GlobalAlignerMode.PointCloudOptimizer:
            loss = scene.compute_global_alignment(init='mst', niter=self.opts.niter, schedule=self.opts.schedule, lr=self.opts.lr)

        if clean_pc:
            self.scene = scene.clean_pointcloud()
        else:
            self.scene = scene

    def render_pcd(self,pts3d,imgs,masks,views,renderer,device):
        
        imgs = to_numpy(imgs)
        pts3d = to_numpy(pts3d)

        if masks == None:
            pts = torch.from_numpy(np.concatenate([p for p in pts3d])).view(-1, 3).to(device)
            col = torch.from_numpy(np.concatenate([p for p in imgs])).view(-1, 3).to(device)
        else:
            # masks = to_numpy(masks)
            pts = torch.from_numpy(np.concatenate([p[m] for p, m in zip(pts3d, masks)])).to(device)
            col = torch.from_numpy(np.concatenate([p[m] for p, m in zip(imgs, masks)])).to(device)
        
        color_mask = torch.ones(col.shape).to(device)

        # point_cloud_mask = Pointclouds(points=[pts],features=[color_mask]).extend(views)
        point_cloud = Pointclouds(points=[pts], features=[col]).extend(views)
        images = renderer(point_cloud)
        # view_masks = renderer(point_cloud_mask)
        return images, None
    
    def run_render(self, pcd, imgs,masks, H, W, camera_traj,num_views,use_cpu=False):
        if use_cpu:
            device = torch.device("cpu")
        else:
            device = self.device
        render_setup = setup_renderer(camera_traj, image_size=(H,W))
        renderer = render_setup['renderer']
        render_results, viewmask = self.render_pcd(pcd, imgs, masks, num_views,renderer,device)
        return render_results, viewmask

    
    def run_diffusion(self, renderings):

        prompts = [self.opts.prompt]
        videos = (renderings * 2. - 1.).permute(3,0,1,2).unsqueeze(0).to(self.device)
        condition_index = [0]
        with torch.no_grad(), torch.cuda.amp.autocast():
            # [1,1,c,t,h,w]
            batch_samples = image_guided_synthesis(self.diffusion, prompts, videos, self.noise_shape, self.opts.n_samples, self.opts.ddim_steps, self.opts.ddim_eta, \
                               self.opts.unconditional_guidance_scale, self.opts.cfg_img, self.opts.frame_stride, self.opts.text_input, self.opts.multiple_cond_cfg, self.opts.timestep_spacing, self.opts.guidance_rescale, condition_index)

            # save_results_seperate(batch_samples[0], self.opts.save_dir, fps=8)
            # torch.Size([1, 3, 25, 576, 1024]) [-1,1]

        return torch.clamp(batch_samples[0][0].permute(1,2,3,0), -1., 1.) 

    def nvs_single_view(self, gradio=False):
        # 最后一个view为 0 pose
        c2ws = self.scene.get_im_poses().detach()[1:] 
        principal_points = self.scene.get_principal_points().detach()[1:] #cx cy
        focals = self.scene.get_focals().detach()[1:] 
        shape = self.images[0]['true_shape']
        H, W = int(shape[0][0]), int(shape[0][1])
        pcd = [i.detach() for i in self.scene.get_pts3d(clip_thred=self.opts.dpt_trd)] # a list of points of size whc
        depth = [i.detach() for i in self.scene.get_depthmaps()]
        depth_avg = depth[-1][H//2,W//2] #以图像中心处的depth(z)为球心旋转
        radius = depth_avg*self.opts.center_scale #缩放调整

        ## change coordinate
        c2ws,pcd =  world_point_to_obj(poses=c2ws, points=torch.stack(pcd), k=-1, r=radius, elevation=self.opts.elevation, device=self.device)

        imgs = np.array(self.scene.imgs)
        
        masks = None

        if self.opts.mode == 'single_view_nbv':
            ## 输入candidate->渲染mask->最大mask对应的pose作为nbv
            ## nbv模式下self.opts.d_theta[0], self.opts.d_phi[0]代表search space中的网格theta, phi之间的间距; self.opts.d_phi[0]的符号代表方向,分为左右两个方向
            ## FIXME hard coded candidate view数量, 以left为例,第一次迭代从[左,左上]中选取, 从第二次开始可以从[左,左上,左下]中选取
            num_candidates = 2
            candidate_poses,thetas,phis = generate_candidate_poses(c2ws, H, W, focals, principal_points, self.opts.d_theta[0], self.opts.d_phi[0],num_candidates, self.device)
            _, viewmask = self.run_render([pcd[-1]], [imgs[-1]],masks, H, W, candidate_poses,num_candidates,use_cpu=False)
            nbv_id = torch.argmin(viewmask.sum(dim=[1,2,3])).item()
            save_image( viewmask.permute(0,3,1,2), os.path.join(self.opts.save_dir,f"candidate_mask0_nbv{nbv_id}.png"), normalize=True, value_range=(0, 1))
            theta_nbv = thetas[nbv_id]
            phi_nbv = phis[nbv_id]
            # generate camera trajectory from T_curr to T_nbv
            camera_traj,num_views = generate_traj_specified(c2ws, H, W, focals, principal_points, theta_nbv, phi_nbv, self.opts.d_r[0],self.opts.video_length, self.device)
            # 重置elevation
            self.opts.elevation -= theta_nbv
        elif self.opts.mode == 'single_view_target':
            camera_traj,num_views = generate_traj_specified(c2ws, H, W, focals, principal_points, self.opts.d_theta[0], self.opts.d_phi[0], self.opts.d_r[0],self.opts.video_length, self.device)
        elif self.opts.mode == 'single_view_txt':
            if not gradio:
                with open(self.opts.traj_txt, 'r') as file:
                    lines = file.readlines()
                    phi = [float(i) for i in lines[0].split()]
                    theta = [float(i) for i in lines[1].split()]
                    r = [float(i) for i in lines[2].split()]
            else: 
                phi, theta, r = self.gradio_traj
            # device = torch.device("cpu")
            device = self.device
            camera_traj,num_views = generate_traj_txt(c2ws, H, W, focals, principal_points, phi, theta, r,self.opts.video_length, device,viz_traj=True, save_dir = self.opts.save_dir)
            # camera_traj,num_views = generate_traj_txt(c2ws, H, W, focals, principal_points, phi, theta, r,self.opts.video_length, self.device,viz_traj=True, save_dir = self.opts.save_dir)
        else:
            raise KeyError(f"Invalid Mode: {self.opts.mode}")

        render_results, viewmask = self.run_render([pcd[-1]], [imgs[-1]],masks, H, W, camera_traj,num_views,use_cpu=False)
        render_results = render_results.to(self.device)
        render_results = F.interpolate(render_results.permute(0,3,1,2), size=(576, 1024), mode='bilinear', align_corners=False).permute(0,2,3,1)
        render_results[0] = self.img_ori
        if self.opts.mode == 'single_view_txt':
            if phi[-1]==0. and theta[-1]==0. and r[-1]==0.:
                render_results[-1] = self.img_ori
        # torch.Size([25, 576, 1024, 3]), [0,1]
        # save_pointcloud_with_normals([imgs[-1]], [pcd[-1]], msk=None, save_path=os.path.join(self.opts.save_dir,'pcd0.ply') , mask_pc=False, reduce_pc=False)
        diffusion_results = self.run_diffusion(render_results)
        save_video((diffusion_results + 1.0) / 2.0, os.path.join(self.opts.save_dir, 'diffusion0.mp4'))
        return render_results

    def nvs_sparse_view(self,iter):

        c2ws = self.scene.get_im_poses().detach()
        principal_points = self.scene.get_principal_points().detach()
        focals = self.scene.get_focals().detach()
        shape = self.images[0]['true_shape']
        H, W = int(shape[0][0]), int(shape[0][1])
        pcd = [i.detach() for i in self.scene.get_pts3d(clip_thred=self.opts.dpt_trd)] # a list of points of size whc
        depth = [i.detach() for i in self.scene.get_depthmaps()]
        depth_avg = depth[0][H//2,W//2] #以ref图像中心处的depth(z)为球心旋转
        radius = depth_avg*self.opts.center_scale #缩放调整

        ## masks for cleaner point cloud
        self.scene.min_conf_thr = float(self.scene.conf_trf(torch.tensor(self.opts.min_conf_thr)))
        masks = self.scene.get_masks()
        depth = self.scene.get_depthmaps()
        bgs_mask = [dpt > self.opts.bg_trd*(torch.max(dpt[40:-40,:])+torch.min(dpt[40:-40,:])) for dpt in depth]
        masks_new = [m+mb for m, mb in zip(masks,bgs_mask)] 
        masks = to_numpy(masks_new)

        ## render, 从c2ws[0]即ref image对应的相机开始
        imgs = np.array(self.scene.imgs)

        if self.opts.mode == 'single_view_ref_iterative':
            c2ws,pcd =  world_point_to_obj(poses=c2ws, points=torch.stack(pcd), k=0, r=radius, elevation=self.opts.elevation, device=self.device)
            camera_traj,num_views = generate_traj_specified(c2ws[0:1], H, W, focals[0:1], principal_points[0:1], self.opts.d_theta[iter], self.opts.d_phi[iter], self.opts.d_r[iter],self.opts.video_length, self.device)
            render_results, viewmask = self.run_render(pcd, imgs,masks, H, W, camera_traj,num_views)
            render_results = F.interpolate(render_results.permute(0,3,1,2), size=(576, 1024), mode='bilinear', align_corners=False).permute(0,2,3,1)
            render_results[0] = self.img_ori
        elif self.opts.mode == 'single_view_1drc_iterative':
            self.opts.elevation -= self.opts.d_theta[iter-1]
            c2ws,pcd =  world_point_to_obj(poses=c2ws, points=torch.stack(pcd), k=-1, r=radius, elevation=self.opts.elevation, device=self.device)
            camera_traj,num_views = generate_traj_specified(c2ws[-1:], H, W, focals[-1:], principal_points[-1:], self.opts.d_theta[iter], self.opts.d_phi[iter], self.opts.d_r[iter],self.opts.video_length, self.device)
            render_results, viewmask = self.run_render(pcd, imgs,masks, H, W, camera_traj,num_views)
            render_results = F.interpolate(render_results.permute(0,3,1,2), size=(576, 1024), mode='bilinear', align_corners=False).permute(0,2,3,1)
            render_results[0] = (self.images[-1]['img_ori'].squeeze(0).permute(1,2,0)+1.)/2.
        elif self.opts.mode == 'single_view_nbv':
            c2ws,pcd =  world_point_to_obj(poses=c2ws, points=torch.stack(pcd), k=-1, r=radius, elevation=self.opts.elevation, device=self.device)
            ## 输入candidate->渲染mask->最大mask对应的pose作为nbv
            ## nbv模式下self.opts.d_theta[0], self.opts.d_phi[0]代表search space中的网格theta, phi之间的间距; self.opts.d_phi[0]的符号代表方向,分为左右两个方向
            ## FIXME hard coded candidate view数量, 以left为例,第一次迭代从[左,左上]中选取, 从第二次开始可以从[左,左上,左下]中选取
            num_candidates = 3
            candidate_poses,thetas,phis = generate_candidate_poses(c2ws[-1:], H, W, focals[-1:], principal_points[-1:], self.opts.d_theta[0], self.opts.d_phi[0], num_candidates, self.device)
            _, viewmask = self.run_render(pcd, imgs,masks, H, W, candidate_poses,num_candidates)
            nbv_id = torch.argmin(viewmask.sum(dim=[1,2,3])).item()
            save_image(viewmask.permute(0,3,1,2), os.path.join(self.opts.save_dir,f"candidate_mask{iter}_nbv{nbv_id}.png"), normalize=True, value_range=(0, 1))
            theta_nbv = thetas[nbv_id]
            phi_nbv = phis[nbv_id]   
            # generate camera trajectory from T_curr to T_nbv
            camera_traj,num_views = generate_traj_specified(c2ws[-1:], H, W, focals[-1:], principal_points[-1:], theta_nbv, phi_nbv, self.opts.d_r[0],self.opts.video_length, self.device)
            # 重置elevation
            self.opts.elevation -= theta_nbv    
            render_results, viewmask = self.run_render(pcd, imgs,masks, H, W, camera_traj,num_views)
            render_results = F.interpolate(render_results.permute(0,3,1,2), size=(576, 1024), mode='bilinear', align_corners=False).permute(0,2,3,1)
            render_results[0] = (self.images[-1]['img_ori'].squeeze(0).permute(1,2,0)+1.)/2. 
        else:
            raise KeyError(f"Invalid Mode: {self.opts.mode}")

        save_video(render_results, os.path.join(self.opts.save_dir, f'render{iter}.mp4'))
        save_pointcloud_with_normals(imgs, pcd, msk=masks, save_path=os.path.join(self.opts.save_dir, f'pcd{iter}.ply') , mask_pc=True, reduce_pc=False)
        diffusion_results = self.run_diffusion(render_results)
        save_video((diffusion_results + 1.0) / 2.0, os.path.join(self.opts.save_dir, f'diffusion{iter}.mp4'))
        # torch.Size([25, 576, 1024, 3])
        return diffusion_results
    
    def nvs_single_view_ref_iterative(self):

        all_results = []
        sample_rate = 6
        idx = 1 #初始包含1张ref image
        for itr in range(0, len(self.opts.d_phi)):
            if itr == 0:
                self.images = [self.images[0]] #去掉后一份copy
                diffusion_results_itr = self.nvs_single_view()
                # diffusion_results_itr = torch.randn([25, 576, 1024, 3]).to(self.device)
                diffusion_results_itr = diffusion_results_itr.permute(0,3,1,2)
                all_results.append(diffusion_results_itr)
            else:
                for i in range(0+sample_rate, diffusion_results_itr.shape[0], sample_rate):
                    self.images.append(get_input_dict(diffusion_results_itr[i:i+1,...],idx,dtype = torch.float32))
                    idx += 1
                self.run_dust3r(input_images=self.images, clean_pc=True)
                diffusion_results_itr = self.nvs_sparse_view(itr)
                # diffusion_results_itr = torch.randn([25, 576, 1024, 3]).to(self.device)
                diffusion_results_itr = diffusion_results_itr.permute(0,3,1,2)
                all_results.append(diffusion_results_itr)
        return all_results

    def nvs_single_view_1drc_iterative(self):

        all_results = []
        sample_rate = 6
        idx = 1 #初始包含1张ref image
        for itr in range(0, len(self.opts.d_phi)):
            if itr == 0:
                self.images = [self.images[0]] #去掉后一份copy
                diffusion_results_itr = self.nvs_single_view()
                # diffusion_results_itr = torch.randn([25, 576, 1024, 3]).to(self.device)
                diffusion_results_itr = diffusion_results_itr.permute(0,3,1,2)
                all_results.append(diffusion_results_itr)
            else:
                for i in range(0+sample_rate, diffusion_results_itr.shape[0], sample_rate):
                    self.images.append(get_input_dict(diffusion_results_itr[i:i+1,...],idx,dtype = torch.float32))
                    idx += 1
                self.run_dust3r(input_images=self.images, clean_pc=True)
                diffusion_results_itr = self.nvs_sparse_view(itr)
                # diffusion_results_itr = torch.randn([25, 576, 1024, 3]).to(self.device)
                diffusion_results_itr = diffusion_results_itr.permute(0,3,1,2)
                all_results.append(diffusion_results_itr)
        return all_results

    def nvs_single_view_nbv(self):
        # lef and right
        # d_theta and a_phi 是搜索空间的顶点间隔
        all_results = []
        ## FIXME: hard coded
        sample_rate = 6
        max_itr = 3

        idx = 1 #初始包含1张ref image
        for itr in range(0, max_itr):
            if itr == 0:
                self.images = [self.images[0]] #去掉后一份copy
                diffusion_results_itr = self.nvs_single_view()
                # diffusion_results_itr = torch.randn([25, 576, 1024, 3]).to(self.device)
                diffusion_results_itr = diffusion_results_itr.permute(0,3,1,2)
                all_results.append(diffusion_results_itr)
            else:
                for i in range(0+sample_rate, diffusion_results_itr.shape[0], sample_rate):
                    self.images.append(get_input_dict(diffusion_results_itr[i:i+1,...],idx,dtype = torch.float32))
                    idx += 1
                self.run_dust3r(input_images=self.images, clean_pc=True)
                diffusion_results_itr = self.nvs_sparse_view(itr)
                # diffusion_results_itr = torch.randn([25, 576, 1024, 3]).to(self.device)
                diffusion_results_itr = diffusion_results_itr.permute(0,3,1,2)
                all_results.append(diffusion_results_itr)
        return all_results

    def setup_diffusion(self):
        seed_everything(self.opts.seed)

        config = OmegaConf.load(self.opts.config)
        model_config = config.pop("model", OmegaConf.create())

        ## set use_checkpoint as False as when using deepspeed, it encounters an error "deepspeed backend not set"
        model_config['params']['unet_config']['params']['use_checkpoint'] = False
        model = instantiate_from_config(model_config)
        model = model.to(self.device)
        model.cond_stage_model.device = self.device
        model.perframe_ae = self.opts.perframe_ae
        assert os.path.exists(self.opts.ckpt_path), "Error: checkpoint Not Found!"
        model = load_model_checkpoint(model, self.opts.ckpt_path)
        model.eval()
        self.diffusion = model

        h, w = self.opts.height // 8, self.opts.width // 8
        channels = model.model.diffusion_model.out_channels
        n_frames = self.opts.video_length
        self.noise_shape = [self.opts.bs, channels, n_frames, h, w]

    def setup_dust3r(self):
        self.dust3r = load_model(self.opts.model_path, self.device)
    
    def load_initial_images(self, image_dir):
        ## load images
        ## dict_keys(['img', 'true_shape', 'idx', 'instance', 'img_ori']),张量形式
        images = load_images([image_dir], size=512,force_1024 = True)
        img_ori = (images[0]['img_ori'].squeeze(0).permute(1,2,0)+1.)/2. # [576,1024,3] [0,1]

        # img_ori = Image.open(image_dir).convert('RGB')

        # transform = transforms.Compose([
        #     transforms.Resize((576, 1024)),  
        #     transforms.ToTensor(), 
        #     transforms.Normalize((0., 0., 0.), (1., 1., 1.))  # 归一化到[-1,1]，如果要归一化到[0,1]，请使用transforms.Normalize((0., 0., 0.), (1., 1., 1.))
        # ])

        # img_ori = transform(img_ori).permute(1,2,0).to(self.device)
        if len(images) == 1:
            images = [images[0], copy.deepcopy(images[0])]
            images[1]['idx'] = 1

        return images, img_ori
    
    def run_traj(self,i2v_input_image, i2v_elevation, i2v_center_scale, i2v_pose):
        self.opts.elevation = float(i2v_elevation)
        self.opts.center_scale = float(i2v_center_scale)
        i2v_d_phi,i2v_d_theta,i2v_d_r = [i for i in i2v_pose.split(';')]
        self.gradio_traj = [float(i) for i in i2v_d_phi.split()],[float(i) for i in i2v_d_theta.split()],[float(i) for i in i2v_d_r.split()]
        transform = transforms.Compose([
            transforms.Resize((576,1024)),
            # transforms.CenterCrop((576,1024)),
            ])
        torch.cuda.empty_cache()
        img_tensor = torch.from_numpy(i2v_input_image).permute(2, 0, 1).unsqueeze(0).float().to(self.device)
        img_tensor = (img_tensor / 255. - 0.5) * 2
        image_tensor_resized = transform(img_tensor) #1,3,h,w
        images = get_input_dict(image_tensor_resized,idx = 0,dtype = torch.float32)
        images = [images, copy.deepcopy(images)]
        images[1]['idx'] = 1
        self.images = images
        self.img_ori = (image_tensor_resized.squeeze(0).permute(1,2,0) + 1.)/2.

        # self.images: torch.Size([1, 3, 288, 512]), [-1,1]
        # self.img_ori:  torch.Size([576, 1024, 3]), [0,1]
        # self.images, self.img_ori = self.load_initial_images(image_dir=i2v_input_image)
        self.run_dust3r(input_images=self.images)
        render_results = self.nvs_single_view(gradio=True)
        save_video(render_results, os.path.join(self.opts.save_dir, 'render0.mp4'))
        traj_dir = os.path.join(self.opts.save_dir, "viz_traj.mp4")
        return traj_dir
    
    def run_gen(self,i2v_steps, i2v_seed):
        self.opts.ddim_steps = i2v_steps
        seed_everything(i2v_seed)
        render_dir = os.path.join(self.opts.save_dir, 'render0.mp4')
        video = imageio.get_reader(render_dir, 'ffmpeg')
        frames = []
        for frame in video:
            frame = frame / 255.0
            frames.append(frame)
        frames = np.array(frames)
        ##torch.Size([25, 576, 1024, 3])
        render_results = torch.from_numpy(frames).to(self.device).half()

        gen_dir = os.path.join(self.opts.save_dir, "diffusion0.mp4")
        diffusion_results = self.run_diffusion(render_results)
        save_video((diffusion_results + 1.0) / 2.0, os.path.join(self.opts.save_dir, 'diffusion0.mp4'))
        return gen_dir

    def run_both(self,i2v_input_image, i2v_elevation, i2v_center_scale, i2v_pose,i2v_steps, i2v_seed):
        self.opts.ddim_steps = i2v_steps
        seed_everything(i2v_seed)
        self.opts.elevation = float(i2v_elevation)
        self.opts.center_scale = float(i2v_center_scale)
        i2v_d_phi,i2v_d_theta,i2v_d_r = [i for i in i2v_pose.split(';')]
        self.gradio_traj = [float(i) for i in i2v_d_phi.split()],[float(i) for i in i2v_d_theta.split()],[float(i) for i in i2v_d_r.split()]
        torch.cuda.empty_cache()
        img_tensor = torch.from_numpy(i2v_input_image).permute(2, 0, 1).unsqueeze(0).float().to(self.device)
        img_tensor = (img_tensor / 255. - 0.5) * 2
        image_tensor_resized = center_crop_image(img_tensor) #1,3,h,w
        images = get_input_dict(image_tensor_resized,idx = 0,dtype = torch.float32)
        images = [images, copy.deepcopy(images)]
        images[1]['idx'] = 1
        self.images = images
        self.img_ori = (image_tensor_resized.squeeze(0).permute(1,2,0) + 1.)/2.

        # self.images: torch.Size([1, 3, 288, 512]), [-1,1]
        # self.img_ori:  torch.Size([576, 1024, 3]), [0,1]
        # self.images, self.img_ori = self.load_initial_images(image_dir=i2v_input_image)
        self.run_dust3r(input_images=self.images)
        self.nvs_single_view(gradio=True)
        traj_dir = os.path.join(self.opts.save_dir, "viz_traj.mp4")
        gen_dir = os.path.join(self.opts.save_dir, "diffusion0.mp4")
        return gen_dir,traj_dir,