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# import decord
# decord.bridge.set_bridge('torch')
from torch.utils.data import Dataset
from einops import rearrange
from typing import Literal, Tuple, Optional, Any
import glob
import os
import json
import random
import cv2
import math
import numpy as np
import torch
from PIL import Image
class MVDiffusionDatasetV1(Dataset):
def __init__(
self,
root_dir: str,
num_views: int,
bg_color: Any,
img_wh: Tuple[int, int],
validation: bool = False,
num_validation_samples: int = 64,
num_samples: Optional[int] = None,
caption_path: Optional[str] = None,
elevation_range_deg: Tuple[float,float] = (-90, 90),
azimuth_range_deg: Tuple[float, float] = (0, 360),
):
self.all_obj_paths = sorted(glob.glob(os.path.join(root_dir, "*/*")))
if not validation:
self.all_obj_paths = self.all_obj_paths[:-num_validation_samples]
else:
self.all_obj_paths = self.all_obj_paths[-num_validation_samples:]
if num_samples is not None:
self.all_obj_paths = self.all_obj_paths[:num_samples]
self.all_obj_ids = [os.path.basename(path) for path in self.all_obj_paths]
self.num_views = num_views
self.bg_color = bg_color
self.img_wh = img_wh
def get_bg_color(self):
if self.bg_color == 'white':
bg_color = np.array([1., 1., 1.], dtype=np.float32)
elif self.bg_color == 'black':
bg_color = np.array([0., 0., 0.], dtype=np.float32)
elif self.bg_color == 'gray':
bg_color = np.array([0.5, 0.5, 0.5], dtype=np.float32)
elif self.bg_color == 'random':
bg_color = np.random.rand(3)
elif isinstance(self.bg_color, float):
bg_color = np.array([self.bg_color] * 3, dtype=np.float32)
else:
raise NotImplementedError
return bg_color
def load_image(self, img_path, bg_color, return_type='np'):
# not using cv2 as may load in uint16 format
# img = cv2.imread(img_path, cv2.IMREAD_UNCHANGED) # [0, 255]
# img = cv2.resize(img, self.img_wh, interpolation=cv2.INTER_CUBIC)
# pil always returns uint8
img = np.array(Image.open(img_path).resize(self.img_wh))
img = img.astype(np.float32) / 255. # [0, 1]
assert img.shape[-1] == 4 # RGBA
alpha = img[...,3:4]
img = img[...,:3] * alpha + bg_color * (1 - alpha)
if return_type == "np":
pass
elif return_type == "pt":
img = torch.from_numpy(img)
else:
raise NotImplementedError
return img
def __len__(self):
return len(self.all_obj_ids)
def __getitem__(self, index):
obj_path = self.all_obj_paths[index]
obj_id = self.all_obj_ids[index]
with open(os.path.join(obj_path, 'meta.json')) as f:
meta = json.loads(f.read())
num_views_all = len(meta['locations'])
num_groups = num_views_all // self.num_views
# random a set of 4 views
# the data is arranged in ascending order of the azimuth angle
group_ids = random.sample(range(num_groups), k=2)
cond_group_id, tgt_group_id = group_ids
cond_location = meta['locations'][cond_group_id * self.num_views + random.randint(0, self.num_views - 1)]
tgt_locations = meta['locations'][tgt_group_id * self.num_views : tgt_group_id * self.num_views + self.num_views]
# random an order
start_id = random.randint(0, self.num_views - 1)
tgt_locations = tgt_locations[start_id:] + tgt_locations[:start_id]
cond_elevation = cond_location['elevation']
cond_azimuth = cond_location['azimuth']
tgt_elevations = [loc['elevation'] for loc in tgt_locations]
tgt_azimuths = [loc['azimuth'] for loc in tgt_locations]
elevations = [ele - cond_elevation for ele in tgt_elevations]
azimuths = [(azi - cond_azimuth) % (math.pi * 2) for azi in tgt_azimuths]
elevations = torch.as_tensor(elevations).float()
azimuths = torch.as_tensor(azimuths).float()
elevations_cond = torch.as_tensor([cond_elevation] * self.num_views).float()
bg_color = self.get_bg_color()
img_tensors_in = [
self.load_image(os.path.join(obj_path, cond_location['frames'][0]['name']), bg_color, return_type='pt').permute(2, 0, 1)
] * self.num_views
img_tensors_out = []
for loc in tgt_locations:
img_path = os.path.join(obj_path, loc['frames'][0]['name'])
img_tensor = self.load_image(img_path, bg_color, return_type="pt").permute(2, 0, 1)
img_tensors_out.append(img_tensor)
img_tensors_in = torch.stack(img_tensors_in, dim=0).float() # (Nv, 3, H, W)
img_tensors_out = torch.stack(img_tensors_out, dim=0).float() # (Nv, 3, H, W)
camera_embeddings = torch.stack([elevations_cond, elevations, azimuths], dim=-1) # (Nv, 3)
return {
'elevations_cond': elevations_cond,
'elevations_cond_deg': torch.rad2deg(elevations_cond),
'elevations': elevations,
'azimuths': azimuths,
'elevations_deg': torch.rad2deg(elevations),
'azimuths_deg': torch.rad2deg(azimuths),
'imgs_in': img_tensors_in,
'imgs_out': img_tensors_out,
'camera_embeddings': camera_embeddings
}
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