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renderer.py
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renderer.py
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import math
import torch,os,imageio,sys
from tqdm.auto import tqdm
from dataLoader.ray_utils import get_rays
from models.tensoRF import TensorVM, TensorCP, raw2alpha, TensorVMSplit, AlphaGridMask
from utils import *
from dataLoader.ray_utils import ndc_rays_blender
from dataLoader.llff import LLFFDataset
from opt import args
import scipy.io as sio
def OctreeRender_trilinear_fast(rays, tensorf, chunk=args.chunk_size, N_samples=-1, ndc_ray=False, white_bg=True, \
is_train=False, device='cuda', **kargs):
poseids, filterids = kargs['poseids'], kargs['filterids']
filters = LLFFDataset.filters_back
rgbs, alphas, depth_maps, weights, uncertainties, dist_losses, spec_maps = [], [], [], [], [], [], []
N_rays_all = rays.shape[0]
for chunk_idx in range(N_rays_all // chunk + int(N_rays_all % chunk > 0)):
rays_chunk = rays[chunk_idx * chunk:(chunk_idx + 1) * chunk].to(device)
poseids_chunk = poseids[chunk_idx * chunk:(chunk_idx + 1) * chunk].to(device)
filters_chunk = filters[filterids[chunk_idx * chunk:(chunk_idx + 1) * chunk].reshape(-1)].to(device)
rgb_map, depth_map, dist_loss, spec_map, phi = tensorf(rays_chunk, poseids_chunk, filters_chunk, is_train=is_train, white_bg=white_bg, \
ndc_ray=ndc_ray, N_samples=N_samples)
rgbs.append(rgb_map)
depth_maps.append(depth_map)
dist_losses.append(dist_loss)
spec_maps.append(spec_map)
return None if args.render_test_exhibition else torch.cat(rgbs), None, torch.cat(depth_maps), None, None, \
torch.cat(dist_losses).mean(), torch.cat(spec_maps), phi
@torch.no_grad()
def evaluation(test_dataset:LLFFDataset,tensorf, args, renderer, savePath=None, N_vis=5, prtx='', N_samples=-1,
white_bg=False, ndc_ray=False, compute_extra_metrics=True, device='cuda'):
PSNRs, rgb_maps, depth_maps = [], [], []
ssims,l_alex,l_vgg=[],[],[]
os.makedirs(savePath, exist_ok=True)
os.makedirs(savePath+"/rgbd", exist_ok=True)
os.makedirs(savePath+"/spec", exist_ok=True)
# delete old spec.mat
del_list = os.listdir(f'{savePath}/spec')
for f in del_list:
file_path = os.path.join(f'{savePath}/spec', f)
if os.path.isfile(file_path):
os.remove(file_path)
try:
tqdm._instances.clear()
except Exception:
pass
near_far = test_dataset.near_far
img_eval_interval = 1 if N_vis < 0 else max(test_dataset.all_rays.shape[0] // N_vis,1)
idxs = list(range(0, test_dataset.all_rays.shape[0], img_eval_interval))
for idx, samples in tqdm(enumerate(test_dataset.all_rays[0::img_eval_interval]), file=sys.stdout):
W, H = test_dataset.img_wh
rays = samples.view(-1,samples.shape[-1])
rgb_map, _, depth_map, _, _, _, spec_map, _ = \
renderer(rays, tensorf, N_samples=N_samples, ndc_ray=ndc_ray, white_bg = white_bg, device=device, \
poseids=test_dataset.all_poses[idxs[idx]], filterids=test_dataset.all_filtersIdx[idxs[idx]])
rgb_map = rgb_map.clamp(0.0, 1.0)
rgb_map, depth_map, spec_map = rgb_map.reshape(H, W, args.observation_channel).cpu(), depth_map.reshape(H, W).cpu(), spec_map.reshape(H, W, args.spec_channel).cpu().numpy()
depth_map_raw = depth_map.numpy().copy()
if rgb_map.shape[-1] != 1:
depth_map, _ = visualize_depth_numpy(depth_map.numpy(),near_far)
else:
depth_map = visualize_depth_numpy_mono(depth_map.numpy(),near_far)
if len(test_dataset.all_rgbs):
gt_rgb = test_dataset.all_rgbs[idxs[idx]].view(H, W, args.observation_channel)
loss = torch.mean((rgb_map - gt_rgb) ** 2)
PSNRs.append(-10.0 * np.log(loss.item()) / np.log(10.0))
if compute_extra_metrics:
ssim = rgb_ssim(rgb_map, gt_rgb, 1)
l_a = rgb_lpips(gt_rgb.numpy(), rgb_map.numpy(), 'alex', tensorf.device)
l_v = rgb_lpips(gt_rgb.numpy(), rgb_map.numpy(), 'vgg', tensorf.device)
ssims.append(ssim)
l_alex.append(l_a)
l_vgg.append(l_v)
rgb_map = (rgb_map.numpy() * 255).astype('uint8')
# rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
rgb_maps.append(rgb_map)
depth_maps.append(depth_map)
if savePath is not None:
# imageio.imwrite(f'{savePath}/{prtx}{idx:03d}.png', rgb_map)
rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
imageio.imwrite(f'{savePath}/rgbd/{prtx}{idx:03d}.png', rgb_map)
# save spec.mat
sio.savemat(f'{savePath}/spec/{prtx}{idx:03d}.mat', {'spec': spec_map})
sio.savemat(f'{savePath}/rgbd/{prtx}{idx:03d}_depth.mat', {'depth': depth_map_raw})
# imageio.mimwrite(f'{savePath}/{prtx}video.mp4', np.stack(rgb_maps), fps=30, quality=10)
# imageio.mimwrite(f'{savePath}/{prtx}depthvideo.mp4', np.stack(depth_maps), fps=30, quality=10)
if PSNRs:
psnr = np.mean(np.asarray(PSNRs))
if compute_extra_metrics:
ssim = np.mean(np.asarray(ssims))
l_a = np.mean(np.asarray(l_alex))
l_v = np.mean(np.asarray(l_vgg))
np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr, ssim, l_a, l_v]))
else:
np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr]))
return PSNRs
@torch.no_grad()
def evaluation_path(test_dataset,tensorf, c2ws, renderer, savePath=None, N_vis=5, prtx='', N_samples=-1,
white_bg=False, ndc_ray=False, compute_extra_metrics=True, device='cuda'):
PSNRs, rgb_maps, depth_maps = [], [], []
ssims,l_alex,l_vgg=[],[],[]
os.makedirs(savePath, exist_ok=True)
os.makedirs(savePath+"/rgbd", exist_ok=True)
os.makedirs(savePath+"/spec", exist_ok=True)
try:
tqdm._instances.clear()
except Exception:
pass
near_far = test_dataset.near_far
ones_filtersIdx = torch.LongTensor([[0]])
for idx, c2w in tqdm(enumerate(c2ws)):
W, H = test_dataset.img_wh
c2w = torch.FloatTensor(c2w)
rays_o, rays_d = get_rays(test_dataset.directions, c2w) # both (h*w, 3)
if ndc_ray:
rays_o, rays_d = ndc_rays_blender(H, W, test_dataset.focal[0], 1.0, rays_o, rays_d)
rays = torch.cat([rays_o, rays_d], 1) # (h*w, 6)
rgb_map, _, depth_map, _, _, _, spec_map, _ = \
renderer(rays, tensorf, N_samples=N_samples, ndc_ray=ndc_ray, white_bg = white_bg, device=device, \
poseids=ones_filtersIdx.expand((rays.shape[0], -1)), filterids=ones_filtersIdx.expand((rays.shape[0], -1)))
rgb_map = rgb_map.clamp(0.0, 1.0)
rgb_map, depth_map, spec_map = rgb_map.reshape(H, W, args.observation_channel).cpu(), depth_map.reshape(H, W).cpu(), spec_map.reshape(H, W, args.spec_channel).cpu().numpy()
if rgb_map.shape[-1] != 1:
depth_map, _ = visualize_depth_numpy(depth_map.numpy(),near_far)
else:
depth_map = visualize_depth_numpy_mono(depth_map.numpy(),near_far)
rgb_map = (rgb_map.numpy() * 255).astype('uint8')
# rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
rgb_maps.append(rgb_map)
depth_maps.append(depth_map)
if savePath is not None:
imageio.imwrite(f'{savePath}/{prtx}{idx:03d}.png', rgb_map)
rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
imageio.imwrite(f'{savePath}/rgbd/{prtx}{idx:03d}.png', rgb_map)
sio.savemat(f'{savePath}/spec/{prtx}{idx:03d}.mat', {'spec': spec_map})
imageio.mimwrite(f'{savePath}/{prtx}video.mp4', np.stack(rgb_maps), fps=30, quality=8)
imageio.mimwrite(f'{savePath}/{prtx}depthvideo.mp4', np.stack(depth_maps), fps=30, quality=8)
if PSNRs:
psnr = np.mean(np.asarray(PSNRs))
if compute_extra_metrics:
ssim = np.mean(np.asarray(ssims))
l_a = np.mean(np.asarray(l_alex))
l_v = np.mean(np.asarray(l_vgg))
np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr, ssim, l_a, l_v]))
else:
np.savetxt(f'{savePath}/{prtx}mean.txt', np.asarray([psnr]))
return PSNRs
@torch.no_grad()
def exhibition(test_dataset,tensorf, c2ws, renderer, savePath=None, N_vis=5, prtx='', N_samples=-1,
white_bg=False, ndc_ray=False, device='cuda', scale=False, **kargs):
rgb_maps, depth_maps = [], []
os.makedirs(savePath, exist_ok=True)
os.makedirs(savePath+"/rgbd", exist_ok=True)
os.makedirs(savePath+"/spec", exist_ok=True)
try:
tqdm._instances.clear()
except Exception:
pass
near_far = test_dataset.near_far
ones_filtersIdx = torch.LongTensor([[0]])
filtersets = kargs['filtersets']
ssfs = kargs['ssfs']
lights = kargs['lights']
try:
lightOrigin = torch.from_numpy(sio.loadmat(args.exhibition_lightorigin_path)['light_spec'][:, args.band_start_idx:]).cuda()
lightOrigin = (1 / lightOrigin.mean()) * lightOrigin # normalize to one mean
except Exception as e:
print('Just warning you, seems you did not provide lightorgin file.')
for idx, c2w in tqdm(enumerate(c2ws)):
times1 = math.ceil(len(c2ws) / len(filtersets))
if idx % times1 == 0:
filter = torch.from_numpy(filtersets[idx // times1]).cuda()
times2 = math.ceil(len(c2ws) / len(ssfs))
if idx % times2 == 0:
ssf = torch.from_numpy(ssfs[idx // times2]).cuda()
if len(lights) != 0:
if idx % math.ceil(len(c2ws) / len(lights)) == 0:
light = torch.from_numpy(lights[idx // math.ceil(len(c2ws) / len(lights))]).cuda()
else:
light = None
W, H = test_dataset.img_wh
c2w = torch.FloatTensor(c2w)
rays_o, rays_d = get_rays(test_dataset.directions, c2w) # both (h*w, 3)
if ndc_ray:
rays_o, rays_d = ndc_rays_blender(H, W, test_dataset.focal[0], 1.0, rays_o, rays_d)
rays = torch.cat([rays_o, rays_d], 1) # (h*w, 6)
_, _, depth_map, _, _, _, spec_map, _ = \
renderer(rays, tensorf, N_samples=N_samples, ndc_ray=ndc_ray, white_bg = white_bg, device=device, \
poseids=ones_filtersIdx.expand((rays.shape[0], -1)), filterids=ones_filtersIdx.expand((rays.shape[0], -1)))
if light is not None:
spec_map = spec_map * light / lightOrigin
rgb_map = ((spec_map * filter) @ ssf)
if scale:
rgb_map = (0.6 / torch.quantile(rgb_map.reshape(-1), 0.95)) * rgb_map
rgb_map = rgb_map.clamp(0,1)
rgb_map, depth_map, spec_map = rgb_map.reshape(H, W, 3).cpu(), depth_map.reshape(H, W).cpu(), spec_map.reshape(H, W, args.spec_channel).cpu().numpy()
if rgb_map.shape[-1] != 1:
depth_map, _ = visualize_depth_numpy(depth_map.numpy(),near_far)
else:
depth_map = visualize_depth_numpy_mono(depth_map.numpy(),near_far)
rgb_map = (rgb_map.numpy() * 255).astype('uint8')
# rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
rgb_maps.append(rgb_map)
depth_maps.append(depth_map)
if savePath is not None:
imageio.imwrite(f'{savePath}/{prtx}{idx:03d}.png', rgb_map)
imageio.mimwrite(f'{savePath}/{prtx}video.mp4', np.stack(rgb_maps), fps=30, quality=8)
imageio.mimwrite(f'{savePath}/{prtx}depthvideo.mp4', np.stack(depth_maps), fps=30, quality=8)