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load_EV copy.py
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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_EV
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 load_EV_data(basedir, half_res=False, testskip=1):
splits = ['train', 'val', 'test']
metas = {}
with open(os.path.join(basedir, 'transforms.json'), 'r') as fp:
metas['train'] = json.load(fp)
all_imgs = []
all_poses = []
all_intrinsics = []
counts = [0]
for s in splits:
if s == 'train':
meta = metas[s]
imgs = []
poses = []
intrinsics = []
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'][2:])
imgs.append(imageio.imread(fname))
poses.append(np.array(frame['transform_matrix']))
focal = frame['fl_x']
cx = frame['cx']
cy = frame['cy']
K = np.array([[focal, 0, cx], [0, focal, cy], [0, 0, 1] ])
intrinsics.append(K)
for i in range(len(imgs)): imgs[i] = (imgs[i] / 255.).astype(np.float32)
#imgs = (np.array(imgs) / 255.).astype(np.float32) # keep all 4 channels (RGBA)
poses = np.array(poses).astype(np.float32)
intrinsics = np.array(intrinsics).astype(np.float32)
counts.append(counts[-1] + len(imgs))
# counts.append(counts[-1] + imgs.shape[0])
all_imgs.append(imgs)
all_poses.append(poses)
all_intrinsics.append(intrinsics)
else:
imgs = []
poses = []
intrinsics = []
imgs.append(imageio.imread(fname))
poses.append(np.array(frame['transform_matrix']))
intrinsics.append(K)
counts.append(counts[-1] + len(imgs))
all_imgs.append(imgs)
all_poses.append(poses)
all_intrinsics.append(intrinsics)
i_split = [np.arange(counts[i], counts[i+1]) for i in range(3)]
imgs = [item for sublist in all_imgs for item in sublist]
#imgs = np.concatenate(all_imgs, 0)
poses = np.concatenate(all_poses, 0)
intrinsics = np.concatenate(all_intrinsics, 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(-180,180,10+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_EV(metas["train"], near=2.0, far=6.0)
'''
zz = render_poses.cpu().numpy()
out = {'frames': []}
for i in range(zz.shape[0]):
frame = {}
frame['transform_matrix'] = zz[i, ...].tolist()
out['frames'].append(frame)
with open("/home/ubuntu/HashNeRF-pytorch/logs/cam.json", "w") as outfile: json.dump(out, outfile, indent=2)
'''
return imgs, poses, render_poses, intrinsics, i_split, bounding_box