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renderer.py
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import os
import pdb
import sys
import imageio
import torch
from tqdm.auto import tqdm
from dataLoader.ray_utils import get_rays, ndc_rays_blender
from models.tensoRF import (AlphaGridMask, TensorCP, TensorVM, TensorVMSplit,
raw2alpha)
from utils import *
class SimpleSampler:
def __init__(self, total, batch):
self.total = total
self.batch = batch
self.curr = total
self.ids = None
def nextids(self):
self.curr+=self.batch
if self.curr + self.batch > self.total:
self.ids = torch.LongTensor(np.random.permutation(self.total))
self.curr = 0
return self.ids[self.curr:self.curr+self.batch]
def OctreeRender_trilinear_fast(rays, tensorf, chunk=4096, N_samples=-1, ndc_ray=False, white_bg=True, is_train=False, device='cuda'):
rgbs, alphas, depth_maps, weights, uncertainties, z_val = [], [], [], [], [], []
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)
rgb_map, depth_map, z_vals, weight = tensorf(rays_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)
weights.append(weight)
z_val.append(z_vals)
return torch.cat(rgbs), None, torch.cat(depth_maps), torch.cat(weights), torch.cat(z_val)
@torch.no_grad()
def evaluation(test_dataset, tensorf, args, renderer, savePath=None, N_vis=5,
prtx='', N_samples=-1, white_bg=True, ndc_ray=False, compute_extra_metrics=True,
device='cuda', video_gen=False, N_iter=-1, preview=False, stitching=True):
"""
preview: if True, rendering in support views; if False, rendering in training views (inpainting views)
"""
PSNRs, rgb_maps, depth_maps = [], [], []
ssims,l_alex,l_vgg=[],[],[]
stitching=False
if video_gen:
stitching=False
if savePath is not None:
os.makedirs(savePath, exist_ok=True)
if not stitching:
os.makedirs(os.path.join(savePath, 'rgbs'), exist_ok=True)
os.makedirs(os.path.join(savePath, 'depths'), exist_ok=True)
if test_dataset.split == 'train':
if preview:
all_rays = test_dataset.all_rays_sprt_split
all_rgbs = None # test_dataset.all_rgbs_sprt_split
else:
all_rays = test_dataset.all_rays_gen_split[:N_iter+1]
all_rgbs = test_dataset.all_rgbs_gen_split[:N_iter+1]
elif test_dataset.split == 'test' and N_iter>=0:
all_rays = test_dataset.all_rays_split[:N_iter+1]
all_rgbs = None
else:
all_rays = test_dataset.all_rays_split
all_rgbs = None
try:
tqdm._instances.clear()
except Exception:
pass
near_far = test_dataset.near_far
img_eval_interval = 1 if N_vis < 0 else max(all_rays.shape[0] // N_vis,1)
idxs = list(range(0, all_rays.shape[0], img_eval_interval))
for idx, samples in tqdm(enumerate(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, _, _ = renderer(rays, tensorf, chunk=args.batch_size, N_samples=N_samples,
ndc_ray=ndc_ray, white_bg = white_bg, device=device)
rgb_map = rgb_map.clamp(0.0, 1.0)
rgb_map, depth_map = rgb_map.reshape(H, W, 3).cpu(), depth_map.reshape(H, W).cpu()
depth_map = depth_map - args.push_depth + 0.8
depth_map = np.maximum(depth_map.numpy(), 0)
depth_map, _ = visualize_depth_numpy(depth_map, near_far, colorize=True)
if all_rgbs is not None and compute_extra_metrics:
gt_rgb = all_rgbs[idxs[idx]].view(H, W, 3)
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_maps.append(rgb_map)
depth_maps.append(depth_map)
if savePath is not None:
if stitching and all_rgbs is not None:
gt_rgb = (gt_rgb.numpy() * 255).astype('uint8')
rgb_map = np.concatenate((rgb_map, depth_map, gt_rgb), axis=1)
imageio.imwrite(f'{savePath}/{prtx}{idx:03d}.png', rgb_map)
elif stitching and all_rgbs is None:
rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
imageio.imwrite(f'{savePath}/{prtx}{idx:03d}.png', rgb_map)
else:
imageio.imwrite(f'{savePath}/rgbs/{prtx}{idx:03d}_rgb.png', rgb_map)
imageio.imwrite(f'{savePath}/depths/{prtx}{idx:03d}_depth.png', depth_map)
if video_gen:
imageio.mimwrite(f'{savePath}/{prtx}video.mp4', np.stack(rgb_maps), fps=30, quality=9)
imageio.mimwrite(f'{savePath}/{prtx}depthvideo.mp4', np.stack(depth_maps), fps=30, quality=9)
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)
try:
tqdm._instances.clear()
except Exception:
pass
near_far = test_dataset.near_far
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, _, _ = renderer(rays, tensorf, chunk=8192, N_samples=N_samples,
ndc_ray=ndc_ray, white_bg = white_bg, device=device)
rgb_map = rgb_map.clamp(0.0, 1.0)
rgb_map, depth_map = rgb_map.reshape(H, W, 3).cpu(), depth_map.reshape(H, W).cpu()
depth_map, _ = visualize_depth_numpy(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)
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