FiDA-3D-Trellis/trellis/datasets/structured_latent2render.py

import os
from PIL import Image
import json
import numpy as np
import torch
import utils3d.torch
from ..modules.sparse.basic import SparseTensor
from .components import StandardDatasetBase


class SLat2Render(StandardDatasetBase):
    """
    Dataset for Structured Latent and rendered images.
    
    Args:
        roots (str): paths to the dataset
        image_size (int): size of the image
        latent_model (str): latent model name
        min_aesthetic_score (float): minimum aesthetic score
        max_num_voxels (int): maximum number of voxels
    """
    def __init__(
        self,
        roots: str,
        image_size: int,
        latent_model: str,
        min_aesthetic_score: float = 5.0,
        max_num_voxels: int = 32768,
    ):
        self.image_size = image_size
        self.latent_model = latent_model
        self.min_aesthetic_score = min_aesthetic_score
        self.max_num_voxels = max_num_voxels
        self.value_range = (0, 1)
        
        super().__init__(roots)
        
    def filter_metadata(self, metadata):
        stats = {}
        metadata = metadata[metadata[f'latent_{self.latent_model}']]
        stats['With latent'] = len(metadata)
        metadata = metadata[metadata['aesthetic_score'] >= self.min_aesthetic_score]
        stats[f'Aesthetic score >= {self.min_aesthetic_score}'] = len(metadata)
        metadata = metadata[metadata['num_voxels'] <= self.max_num_voxels]
        stats[f'Num voxels <= {self.max_num_voxels}'] = len(metadata)
        return metadata, stats

    def _get_image(self, root, instance):
        with open(os.path.join(root, 'renders', instance, 'transforms.json')) as f:
            metadata = json.load(f)
        n_views = len(metadata['frames'])
        view = np.random.randint(n_views)
        metadata = metadata['frames'][view]
        fov = metadata['camera_angle_x']
        intrinsics = utils3d.torch.intrinsics_from_fov_xy(torch.tensor(fov), torch.tensor(fov))
        c2w = torch.tensor(metadata['transform_matrix'])
        c2w[:3, 1:3] *= -1
        extrinsics = torch.inverse(c2w)

        image_path = os.path.join(root, 'renders', instance, metadata['file_path'])
        image = Image.open(image_path)
        alpha = image.getchannel(3)
        image = image.convert('RGB')
        image = image.resize((self.image_size, self.image_size), Image.Resampling.LANCZOS)
        alpha = alpha.resize((self.image_size, self.image_size), Image.Resampling.LANCZOS)
        image = torch.tensor(np.array(image)).permute(2, 0, 1).float() / 255.0
        alpha = torch.tensor(np.array(alpha)).float() / 255.0
        
        return {
            'image': image,
            'alpha': alpha,
            'extrinsics': extrinsics,
            'intrinsics': intrinsics,
        }
    
    def _get_latent(self, root, instance):
        data = np.load(os.path.join(root, 'latents', self.latent_model, f'{instance}.npz'))
        coords = torch.tensor(data['coords']).int()
        feats = torch.tensor(data['feats']).float()
        return {
            'coords': coords,
            'feats': feats,
        }

    @torch.no_grad()
    def visualize_sample(self, sample: dict):
        return sample['image']

    @staticmethod
    def collate_fn(batch):
        pack = {}
        coords = []
        for i, b in enumerate(batch):
            coords.append(torch.cat([torch.full((b['coords'].shape[0], 1), i, dtype=torch.int32), b['coords']], dim=-1))
        coords = torch.cat(coords)
        feats = torch.cat([b['feats'] for b in batch])
        pack['latents'] = SparseTensor(
            coords=coords,
            feats=feats,
        )
        
        # collate other data
        keys = [k for k in batch[0].keys() if k not in ['coords', 'feats']]
        for k in keys:
            if isinstance(batch[0][k], torch.Tensor):
                pack[k] = torch.stack([b[k] for b in batch])
            elif isinstance(batch[0][k], list):
                pack[k] = sum([b[k] for b in batch], [])
            else:
                pack[k] = [b[k] for b in batch]

        return pack

    def get_instance(self, root, instance):
        image = self._get_image(root, instance)
        latent = self._get_latent(root, instance)
        return {
            **image,
            **latent,
        }
        

class Slat2RenderGeo(SLat2Render):
    def __init__(
        self,
        roots: str,
        image_size: int,
        latent_model: str,
        min_aesthetic_score: float = 5.0,
        max_num_voxels: int = 32768,
    ):
        super().__init__(
            roots,
            image_size,
            latent_model,
            min_aesthetic_score,
            max_num_voxels,
        )
        
    def _get_geo(self, root, instance):
        verts, face = utils3d.io.read_ply(os.path.join(root, 'renders', instance, 'mesh.ply'))
        mesh = {
            "vertices" : torch.from_numpy(verts),
            "faces" : torch.from_numpy(face),
        }
        return  {
            "mesh" : mesh,
        }
        
    def get_instance(self, root, instance):
        image = self._get_image(root, instance)
        latent = self._get_latent(root, instance)
        geo = self._get_geo(root, instance)
        return {
            **image,
            **latent,
            **geo,
        }