instantMesh/src/data/objaverse.py

import os, sys
import math
import json
import importlib
from pathlib import Path

import cv2
import random
import numpy as np
from PIL import Image
import webdataset as wds
import pytorch_lightning as pl

import torch
import torch.nn.functional as F
from torch.utils.data import Dataset
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from torchvision import transforms

from src.utils.train_util import instantiate_from_config
from src.utils.camera_util import (
    FOV_to_intrinsics, 
    center_looking_at_camera_pose, 
    get_surrounding_views,
)


class DataModuleFromConfig(pl.LightningDataModule):
    def __init__(
        self, 
        batch_size=8, 
        num_workers=4, 
        train=None, 
        validation=None, 
        test=None, 
        **kwargs,
    ):
        super().__init__()

        self.batch_size = batch_size
        self.num_workers = num_workers

        self.dataset_configs = dict()
        if train is not None:
            self.dataset_configs['train'] = train
        if validation is not None:
            self.dataset_configs['validation'] = validation
        if test is not None:
            self.dataset_configs['test'] = test
    
    def setup(self, stage):

        if stage in ['fit']:
            self.datasets = dict((k, instantiate_from_config(self.dataset_configs[k])) for k in self.dataset_configs)
        else:
            raise NotImplementedError

    def train_dataloader(self):

        sampler = DistributedSampler(self.datasets['train'])
        return wds.WebLoader(self.datasets['train'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, sampler=sampler)

    def val_dataloader(self):

        sampler = DistributedSampler(self.datasets['validation'])
        return wds.WebLoader(self.datasets['validation'], batch_size=1, num_workers=self.num_workers, shuffle=False, sampler=sampler)

    def test_dataloader(self):

        return wds.WebLoader(self.datasets['test'], batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False)


class ObjaverseData(Dataset):
    def __init__(self,
        root_dir='objaverse/',
        meta_fname='valid_paths.json',
        input_image_dir='rendering_random_32views',
        target_image_dir='rendering_random_32views',
        input_view_num=6,
        target_view_num=2,
        total_view_n=32,
        fov=50,
        camera_rotation=True,
        validation=False,
    ):
        self.root_dir = Path(root_dir)
        self.input_image_dir = input_image_dir
        self.target_image_dir = target_image_dir

        self.input_view_num = input_view_num
        self.target_view_num = target_view_num
        self.total_view_n = total_view_n
        self.fov = fov
        self.camera_rotation = camera_rotation

        with open(os.path.join(root_dir, meta_fname)) as f:
            filtered_dict = json.load(f)
        paths = filtered_dict['good_objs']
        self.paths = paths
        
        self.depth_scale = 4.0
            
        total_objects = len(self.paths)
        print('============= length of dataset %d =============' % len(self.paths))

    def __len__(self):
        return len(self.paths)

    def load_im(self, path, color):
        '''
        replace background pixel with random color in rendering
        '''
        pil_img = Image.open(path)

        image = np.asarray(pil_img, dtype=np.float32) / 255.
        alpha = image[:, :, 3:]
        image = image[:, :, :3] * alpha + color * (1 - alpha)

        image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float()
        alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float()
        return image, alpha
    
    def __getitem__(self, index):
        # load data
        while True:
            input_image_path = os.path.join(self.root_dir, self.input_image_dir, self.paths[index])
            target_image_path = os.path.join(self.root_dir, self.target_image_dir, self.paths[index])

            indices = np.random.choice(range(self.total_view_n), self.input_view_num + self.target_view_num, replace=False)
            input_indices = indices[:self.input_view_num]
            target_indices = indices[self.input_view_num:]

            '''background color, default: white'''
            bg_white = [1., 1., 1.]
            bg_black = [0., 0., 0.]

            image_list = []
            alpha_list = []
            depth_list = []
            normal_list = []
            pose_list = []

            try:
                input_cameras = np.load(os.path.join(input_image_path, 'cameras.npz'))['cam_poses']
                for idx in input_indices:
                    image, alpha = self.load_im(os.path.join(input_image_path, '%03d.png' % idx), bg_white)
                    normal, _ = self.load_im(os.path.join(input_image_path, '%03d_normal.png' % idx), bg_black)
                    depth = cv2.imread(os.path.join(input_image_path, '%03d_depth.png' % idx), cv2.IMREAD_UNCHANGED) / 255.0 * self.depth_scale
                    depth = torch.from_numpy(depth).unsqueeze(0)
                    pose = input_cameras[idx]
                    pose = np.concatenate([pose, np.array([[0, 0, 0, 1]])], axis=0)

                    image_list.append(image)
                    alpha_list.append(alpha)
                    depth_list.append(depth)
                    normal_list.append(normal)
                    pose_list.append(pose)

                target_cameras = np.load(os.path.join(target_image_path, 'cameras.npz'))['cam_poses']
                for idx in target_indices:
                    image, alpha = self.load_im(os.path.join(target_image_path, '%03d.png' % idx), bg_white)
                    normal, _ = self.load_im(os.path.join(target_image_path, '%03d_normal.png' % idx), bg_black)
                    depth = cv2.imread(os.path.join(target_image_path, '%03d_depth.png' % idx), cv2.IMREAD_UNCHANGED) / 255.0 * self.depth_scale
                    depth = torch.from_numpy(depth).unsqueeze(0)
                    pose = target_cameras[idx]
                    pose = np.concatenate([pose, np.array([[0, 0, 0, 1]])], axis=0)

                    image_list.append(image)
                    alpha_list.append(alpha)
                    depth_list.append(depth)
                    normal_list.append(normal)
                    pose_list.append(pose)

            except Exception as e:
                print(e)
                index = np.random.randint(0, len(self.paths))
                continue

            break
        
        images = torch.stack(image_list, dim=0).float()                 # (6+V, 3, H, W)
        alphas = torch.stack(alpha_list, dim=0).float()                 # (6+V, 1, H, W)
        depths = torch.stack(depth_list, dim=0).float()                 # (6+V, 1, H, W)
        normals = torch.stack(normal_list, dim=0).float()               # (6+V, 3, H, W)
        w2cs = torch.from_numpy(np.stack(pose_list, axis=0)).float()    # (6+V, 4, 4)
        c2ws = torch.linalg.inv(w2cs).float()

        normals = normals * 2.0 - 1.0
        normals = F.normalize(normals, dim=1)
        normals = (normals + 1.0) / 2.0
        normals = torch.lerp(torch.zeros_like(normals), normals, alphas)

        # random rotation along z axis
        if self.camera_rotation:
            degree = np.random.uniform(0, math.pi * 2)
            rot = torch.tensor([
                [np.cos(degree), -np.sin(degree), 0, 0],
                [np.sin(degree), np.cos(degree), 0, 0],
                [0, 0, 1, 0],
                [0, 0, 0, 1],
            ]).unsqueeze(0).float()
            c2ws = torch.matmul(rot, c2ws)

            # rotate normals
            N, _, H, W = normals.shape
            normals = normals * 2.0 - 1.0
            normals = torch.matmul(rot[:, :3, :3], normals.view(N, 3, -1)).view(N, 3, H, W)
            normals = F.normalize(normals, dim=1)
            normals = (normals + 1.0) / 2.0
            normals = torch.lerp(torch.zeros_like(normals), normals, alphas)

        # random scaling
        if np.random.rand() < 0.5:
            scale = np.random.uniform(0.8, 1.0)
            c2ws[:, :3, 3] *= scale
            depths *= scale

        # instrinsics of perspective cameras
        K = FOV_to_intrinsics(self.fov)
        Ks = K.unsqueeze(0).repeat(self.input_view_num + self.target_view_num, 1, 1).float()

        data = {
            'input_images': images[:self.input_view_num],     # (6, 3, H, W)
            'input_alphas': alphas[:self.input_view_num],           # (6, 1, H, W) 
            'input_depths': depths[:self.input_view_num],           # (6, 1, H, W)
            'input_normals': normals[:self.input_view_num],         # (6, 3, H, W)
            'input_c2ws': c2ws_input[:self.input_view_num],         # (6, 4, 4)
            'input_Ks': Ks[:self.input_view_num],                   # (6, 3, 3)

            # lrm generator input and supervision
            'target_images': images[self.input_view_num:],          # (V, 3, H, W)
            'target_alphas': alphas[self.input_view_num:],          # (V, 1, H, W)
            'target_depths': depths[self.input_view_num:],          # (V, 1, H, W)
            'target_normals': normals[self.input_view_num:],        # (V, 3, H, W)
            'target_c2ws': c2ws[self.input_view_num:],              # (V, 4, 4)
            'target_Ks': Ks[self.input_view_num:],                  # (V, 3, 3)

            'depth_available': 1,
        }
        return data


class ValidationData(Dataset):
    def __init__(self,
        root_dir='objaverse/',
        input_view_num=6,
        input_image_size=256,
        fov=50,
    ):
        self.root_dir = Path(root_dir)
        self.input_view_num = input_view_num
        self.input_image_size = input_image_size
        self.fov = fov

        self.paths = sorted(os.listdir(self.root_dir))
        print('============= length of dataset %d =============' % len(self.paths))

        cam_distance = 2.5
        azimuths = np.array([30, 90, 150, 210, 270, 330])
        elevations = np.array([30, -20, 30, -20, 30, -20])
        azimuths = np.deg2rad(azimuths)
        elevations = np.deg2rad(elevations)

        x = cam_distance * np.cos(elevations) * np.cos(azimuths)
        y = cam_distance * np.cos(elevations) * np.sin(azimuths)
        z = cam_distance * np.sin(elevations)

        cam_locations = np.stack([x, y, z], axis=-1)
        cam_locations = torch.from_numpy(cam_locations).float()
        c2ws = center_looking_at_camera_pose(cam_locations)
        self.c2ws = c2ws.float()
        self.Ks = FOV_to_intrinsics(self.fov).unsqueeze(0).repeat(6, 1, 1).float()

        render_c2ws = get_surrounding_views(M=8, radius=cam_distance)
        render_Ks = FOV_to_intrinsics(self.fov).unsqueeze(0).repeat(render_c2ws.shape[0], 1, 1)
        self.render_c2ws = render_c2ws.float()
        self.render_Ks = render_Ks.float()

    def __len__(self):
        return len(self.paths)

    def load_im(self, path, color):
        '''
        replace background pixel with random color in rendering
        '''
        pil_img = Image.open(path)
        pil_img = pil_img.resize((self.input_image_size, self.input_image_size), resample=Image.BICUBIC)

        image = np.asarray(pil_img, dtype=np.float32) / 255.
        if image.shape[-1] == 4:
            alpha = image[:, :, 3:]
            image = image[:, :, :3] * alpha + color * (1 - alpha)
        else:
            alpha = np.ones_like(image[:, :, :1])

        image = torch.from_numpy(image).permute(2, 0, 1).contiguous().float()
        alpha = torch.from_numpy(alpha).permute(2, 0, 1).contiguous().float()
        return image, alpha
    
    def __getitem__(self, index):
        # load data
        input_image_path = os.path.join(self.root_dir, self.paths[index])

        '''background color, default: white'''
        # color = np.random.uniform(0.48, 0.52)
        bkg_color = [1.0, 1.0, 1.0]

        image_list = []
        alpha_list = []

        for idx in range(self.input_view_num):
            image, alpha = self.load_im(os.path.join(input_image_path, f'{idx:03d}.png'), bkg_color)
            image_list.append(image)
            alpha_list.append(alpha)
        
        images = torch.stack(image_list, dim=0).float()                     # (6+V, 3, H, W)
        alphas = torch.stack(alpha_list, dim=0).float()                 # (6+V, 1, H, W)

        data = {
            'input_images': images,                 # (6, 3, H, W)
            'input_alphas': alphas,             # (6, 1, H, W)
            'input_c2ws': self.c2ws,            # (6, 4, 4)
            'input_Ks': self.Ks,                # (6, 3, 3)

            'render_c2ws': self.render_c2ws,
            'render_Ks': self.render_Ks,
        }
        return data