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from torch.utils.data import Dataset
import os
import json
import numpy as np
import torch
import imageio
import math
import cv2
from torchvision import transforms
def cartesian_to_spherical(xyz):
ptsnew = np.hstack((xyz, np.zeros(xyz.shape)))
xy = xyz[:,0]**2 + xyz[:,1]**2
z = np.sqrt(xy + xyz[:,2]**2)
theta = np.arctan2(np.sqrt(xy), xyz[:,2]) # for elevation angle defined from Z-axis down
#ptsnew[:,4] = np.arctan2(xyz[:,2], np.sqrt(xy)) # for elevation angle defined from XY-plane up
azimuth = np.arctan2(xyz[:,1], xyz[:,0])
return np.array([theta, azimuth, z])
def get_T(T_target, T_cond):
theta_cond, azimuth_cond, z_cond = cartesian_to_spherical(T_cond[None, :])
theta_target, azimuth_target, z_target = cartesian_to_spherical(T_target[None, :])
d_theta = theta_target - theta_cond
d_azimuth = (azimuth_target - azimuth_cond) % (2 * math.pi)
d_z = z_target - z_cond
d_T = torch.tensor([d_theta.item(), math.sin(d_azimuth.item()), math.cos(d_azimuth.item()), d_z.item()])
return d_T
def get_spherical(T_target, T_cond):
theta_cond, azimuth_cond, z_cond = cartesian_to_spherical(T_cond[None, :])
theta_target, azimuth_target, z_target = cartesian_to_spherical(T_target[None, :])
d_theta = theta_target - theta_cond
d_azimuth = (azimuth_target - azimuth_cond) % (2 * math.pi)
d_z = z_target - z_cond
d_T = torch.tensor([math.degrees(d_theta.item()), math.degrees(d_azimuth.item()), d_z.item()])
return d_T
class RTMV(Dataset):
def __init__(self, root_dir='datasets/RTMV/google_scanned',\
first_K=64, resolution=256, load_target=False):
self.root_dir = root_dir
self.scene_list = sorted(next(os.walk(root_dir))[1])
self.resolution = resolution
self.first_K = first_K
self.load_target = load_target
def __len__(self):
return len(self.scene_list)
def __getitem__(self, idx):
scene_dir = os.path.join(self.root_dir, self.scene_list[idx])
with open(os.path.join(scene_dir, 'transforms.json'), "r") as f:
meta = json.load(f)
imgs = []
poses = []
for i_img in range(self.first_K):
meta_img = meta['frames'][i_img]
if i_img == 0 or self.load_target:
img_path = os.path.join(scene_dir, meta_img['file_path'])
img = imageio.imread(img_path)
img = cv2.resize(img, (self.resolution, self.resolution), interpolation = cv2.INTER_LINEAR)
imgs.append(img)
c2w = meta_img['transform_matrix']
poses.append(c2w)
imgs = (np.array(imgs) / 255.).astype(np.float32) # (RGBA) imgs
imgs = torch.tensor(self.blend_rgba(imgs)).permute(0, 3, 1, 2)
imgs = imgs * 2 - 1. # convert to stable diffusion range
poses = torch.tensor(np.array(poses).astype(np.float32))
return imgs, poses
def blend_rgba(self, img):
img = img[..., :3] * img[..., -1:] + (1. - img[..., -1:]) # blend A to RGB
return img
class GSO(Dataset):
def __init__(self, root_dir='datasets/GoogleScannedObjects',\
split='val', first_K=5, resolution=256, load_target=False, name='render_mvs'):
self.root_dir = root_dir
with open(os.path.join(root_dir, '%s.json' % split), "r") as f:
self.scene_list = json.load(f)
self.resolution = resolution
self.first_K = first_K
self.load_target = load_target
self.name = name
def __len__(self):
return len(self.scene_list)
def __getitem__(self, idx):
scene_dir = os.path.join(self.root_dir, self.scene_list[idx])
with open(os.path.join(scene_dir, 'transforms_%s.json' % self.name), "r") as f:
meta = json.load(f)
imgs = []
poses = []
for i_img in range(self.first_K):
meta_img = meta['frames'][i_img]
if i_img == 0 or self.load_target:
img_path = os.path.join(scene_dir, meta_img['file_path'])
img = imageio.imread(img_path)
img = cv2.resize(img, (self.resolution, self.resolution), interpolation = cv2.INTER_LINEAR)
imgs.append(img)
c2w = meta_img['transform_matrix']
poses.append(c2w)
imgs = (np.array(imgs) / 255.).astype(np.float32) # (RGBA) imgs
mask = imgs[:, :, :, -1]
imgs = torch.tensor(self.blend_rgba(imgs)).permute(0, 3, 1, 2)
imgs = imgs * 2 - 1. # convert to stable diffusion range
poses = torch.tensor(np.array(poses).astype(np.float32))
return imgs, poses
def blend_rgba(self, img):
img = img[..., :3] * img[..., -1:] + (1. - img[..., -1:]) # blend A to RGB
return img
class WILD(Dataset):
def __init__(self, root_dir='data/nerf_wild',\
first_K=33, resolution=256, load_target=False):
self.root_dir = root_dir
self.scene_list = sorted(next(os.walk(root_dir))[1])
self.resolution = resolution
self.first_K = first_K
self.load_target = load_target
def __len__(self):
return len(self.scene_list)
def __getitem__(self, idx):
scene_dir = os.path.join(self.root_dir, self.scene_list[idx])
with open(os.path.join(scene_dir, 'transforms_train.json'), "r") as f:
meta = json.load(f)
imgs = []
poses = []
for i_img in range(self.first_K):
meta_img = meta['frames'][i_img]
if i_img == 0 or self.load_target:
img_path = os.path.join(scene_dir, meta_img['file_path'])
img = imageio.imread(img_path + '.png')
img = cv2.resize(img, (self.resolution, self.resolution), interpolation = cv2.INTER_LINEAR)
imgs.append(img)
c2w = meta_img['transform_matrix']
poses.append(c2w)
imgs = (np.array(imgs) / 255.).astype(np.float32) # (RGBA) imgs
imgs = torch.tensor(self.blend_rgba(imgs)).permute(0, 3, 1, 2)
imgs = imgs * 2 - 1. # convert to stable diffusion range
poses = torch.tensor(np.array(poses).astype(np.float32))
return imgs, poses
def blend_rgba(self, img):
img = img[..., :3] * img[..., -1:] + (1. - img[..., -1:]) # blend A to RGB
return img |