text: import os
  import torch
  import numpy as np
  import imageio 
  import json
  import torch.nn.functional as F
  import cv2
  
  from utils import get_bbox3d_for_blenderobj
  
  trans_t = lambda t : torch.Tensor([
      [1,0,0,0],
      [0,1,0,0],
      [0,0,1,t],
      [0,0,0,1]]).float()
  
  rot_phi = lambda phi : torch.Tensor([
      [1,0,0,0],
      [0,np.cos(phi),-np.sin(phi),0],
      [0,np.sin(phi), np.cos(phi),0],
      [0,0,0,1]]).float()
  
  rot_theta = lambda th : torch.Tensor([
      [np.cos(th),0,-np.sin(th),0],
      [0,1,0,0],
      [np.sin(th),0, np.cos(th),0],
      [0,0,0,1]]).float()
  
  
  def pose_spherical(theta, phi, radius):
      c2w = trans_t(radius)
      c2w = rot_phi(phi/180.*np.pi) @ c2w
      c2w = rot_theta(theta/180.*np.pi) @ c2w
      c2w = torch.Tensor(np.array([[-1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]])) @ c2w
      return c2w
  
  def pose_spherical_edited(theta, phi, radius):
      c2w = trans_t(-radius)
      c2w = rot_phi(phi/180.*np.pi) @ c2w
      c2w = rot_theta(theta/180.*np.pi) @ c2w
      c2w = trans_t(+radius) @ c2w
      c2w = torch.Tensor(np.array([[-1,0,0,0],[0,0,1,0],[0,1,0,0],[0,0,0,1]])) @ c2w
      return c2w
  
  def load_blender_data(basedir, half_res=False, testskip=1):
      splits = ['train', 'val', 'test']
      metas = {}
      for s in splits:
          with open(os.path.join(basedir, 'transforms_{}.json'.format(s)), 'r') as fp:
              metas[s] = json.load(fp)
  
      all_imgs = []
      all_poses = []
      counts = [0]
      for s in splits:
          meta = metas[s]
          imgs = []
          poses = []
          if s=='train' or testskip==0:
              skip = 1
          else:
              skip = testskip
              
          for frame in meta['frames'][::skip]:
              fname = os.path.join(basedir, frame['file_path'] + '.png')
              imgs.append(imageio.imread(fname))
              poses.append(np.array(frame['transform_matrix']))
          imgs = (np.array(imgs) / 255.).astype(np.float32) # keep all 4 channels (RGBA)
          poses = np.array(poses).astype(np.float32)
          counts.append(counts[-1] + imgs.shape[0])
          all_imgs.append(imgs)
          all_poses.append(poses)
      
      i_split = [np.arange(counts[i], counts[i+1]) for i in range(3)]
      
      imgs = np.concatenate(all_imgs, 0)
      poses = np.concatenate(all_poses, 0)
      
      H, W = imgs[0].shape[:2]
      camera_angle_x = float(meta['camera_angle_x'])
      focal = .5 * W / np.tan(.5 * camera_angle_x)
      
      render_poses = torch.stack([pose_spherical(angle, -30.0, 4.0) for angle in np.linspace(-180,180,40+1)[:-1]], 0)
      
      if half_res:
          H = H//2
          W = W//2
          focal = focal/2.
  
          imgs_half_res = np.zeros((imgs.shape[0], H, W, 4))
          for i, img in enumerate(imgs):
              imgs_half_res[i] = cv2.resize(img, (W, H), interpolation=cv2.INTER_AREA)
          imgs = imgs_half_res
          # imgs = tf.image.resize_area(imgs, [400, 400]).numpy()
  
      bounding_box = get_bbox3d_for_blenderobj(metas["train"], H, W, near=2.0, far=6.0)
          
      return imgs, poses, render_poses, [H, W, focal], i_split, bounding_box
  
  
  