[1,0,0,0],

[0,1,0,0],

[0,0,1,t],

[1,0,0,0],

[0,np.cos(phi),-np.sin(phi),0],

[0,np.sin(phi), np.cos(phi),0],

[np.cos(th),0,-np.sin(th),0],

[0,1,0,0],

[np.sin(th),0, np.cos(th),0],

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

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 idx_test, frame in enumerate(meta['frames'][::skip]):

fname = frame['file_path']

if s == 'test':

print(f"{idx_test}th test frame: {fname}")

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]

focal = float(meta['frames'][0]['intrinsic_matrix'][0][0])

K = meta['frames'][0]['intrinsic_matrix']

print(f"Focal: {focal}")

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, 3))

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()

near = np.floor(min(metas['train']['near'], metas['test']['near']))

far = np.ceil(max(metas['train']['far'], metas['test']['far']))