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renderer.py
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renderer.py
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import torch,os,imageio,sys
import numpy as np
from tqdm.auto import tqdm
import matplotlib.pyplot as plt
from dataLoader.ray_utils import get_rays
from models.tensoRF import TensorVM, TensorCP, TensorVMSplit
from utils import visualize_depth_numpy
from loss import rgb_ssim, rgb_lpips
from dataLoader.ray_utils import ndc_rays_blender
def OctreeRender_trilinear_fast(rays, tensorf, mask=None, chunk=4096, N_samples=-1, ndc_ray=False, white_bg=True, is_train=False, device='cuda'):
rgbs, alphas, depth_maps, weights, uncertainties, num_samples = [], [], [], [], [], []
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, num_valid_samples = tensorf(rays_chunk, mask, 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)
num_samples.append(float(num_valid_samples))
return torch.cat(rgbs), None, torch.cat(depth_maps), None, None, sum(num_samples)
def create_gif(path_to_dir, name_gif):
if os.path.exists(path_to_dir):
filenames = os.listdir(path_to_dir)
filenames = sorted(filenames, key=lambda x: int(x.split('.')[0]))
images = []
for filename in filenames:
images.append(imageio.imread(f'{path_to_dir}/{filename}'))
kargs = {"duration": 5.0}
imageio.mimsave(name_gif, images, "GIF", **kargs)
else:
return
@torch.no_grad()
def save_rendered_image_per_train(train_dataset, test_dataset, tensorf, renderer, step, logs, savePath=None, chunk=4096, 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+"/rgb", exist_ok=True)
os.makedirs(savePath+"/rgbd", exist_ok=True)
os.makedirs(savePath+f"/plot/vis_every", exist_ok=True)
try:
tqdm._instances.clear()
except Exception:
pass
near_far = train_dataset.near_far
idxs = list(range(0, train_dataset.all_rays.shape[0], 1))
train_rgb_map = None
train_depth_map = None
img_eval_interval = 1
for idx, samples in enumerate(train_dataset.all_rays[0::img_eval_interval]):
W, H = train_dataset.img_wh
rays = samples.view(-1,samples.shape[-1])
rgb_map, _, depth_map, _, _, _ = renderer(
rays,
tensorf,
chunk=chunk,
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()
train_depth_map, _ = visualize_depth_numpy(depth_map.numpy(),near_far)
train_rgb_map = (rgb_map.numpy() * 255).astype('uint8')
near_far = test_dataset.near_far
img_eval_interval = 1
idxs = list(range(0, test_dataset.all_rays.shape[0], img_eval_interval))
test_rgb_map = None
test_depth_map = None
for idx, samples in enumerate(test_dataset.all_rays[0::img_eval_interval]):
W, H = test_dataset.img_wh
rays = samples.view(-1,samples.shape[-1])
rgb_map, _, depth_map, _, _, _ = renderer(
rays,
tensorf,
chunk=chunk,
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()
test_depth_map, _ = visualize_depth_numpy(depth_map.numpy(),near_far)
test_rgb_map = (rgb_map.numpy() * 255).astype('uint8')
# rgb_map = np.concatenate((rgb_map, depth_map), axis=1)
if savePath is not None:
loss = logs["mse"]
train_psnr = logs["train_psnr"]
test_psnr = logs["test_psnr"]
# Plot the rgb, depth and the loss plot.
fig, ax = plt.subplots(nrows=3, ncols=2, figsize=(15, 20))
ax[0][0].imshow(train_rgb_map)
ax[0][0].set_title(f"Predicted train Image: {step:03d}")
ax[0][1].imshow(train_depth_map)
ax[0][1].set_title(f"Train Image with Depth Map: {step:03d}")
ax[1][0].imshow(test_rgb_map)
ax[1][0].set_title(f"Predicted test Image: {step:03d}")
ax[1][1].imshow(test_depth_map)
ax[1][1].set_title(f"Test Image with Depth Map: {step:03d}")
W, H = train_dataset.img_wh
ax[2][0].plot(loss)
ax[2][0].set_title(f"Loss Plot: {step:03d}")
ax[2][0].set_box_aspect(H/W)
ax[2][1].plot(train_psnr, label='Train')
ax[2][1].plot(test_psnr, label='Test')
ax[2][1].set_title(f"Train test psnr Plot: {step:03d}")
ax[2][1].set_box_aspect(H/W)
savefig = fig.savefig(f"{savePath}/plot/vis_every/{step:03d}.png")
plt.close()
@torch.no_grad()
def evaluation(test_dataset, tensorf, renderer, savePath=None, N_vis=5, prtx='', chunk=4096, N_samples=-1,
white_bg=False, ndc_ray=False, compute_extra_metrics=True, device='cuda'):
PSNRs, gt_rgb_maps, 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+"/prediction", exist_ok=True)
os.makedirs(savePath+"/ground_truth", exist_ok=True)
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, _, _, _ = renderer(
rays,
tensorf,
chunk=chunk,
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)
if len(test_dataset.all_rgbs):
gt_rgb = test_dataset.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')
gt_rgb_map = (gt_rgb.numpy() * 255).astype('uint8')
rgb_maps.append(rgb_map)
gt_rgb_maps.append(gt_rgb_map)
depth_maps.append(depth_map)
if savePath is not None:
imageio.imwrite(f'{savePath}/prediction/{prtx}{idx:03d}.png', rgb_map)
imageio.imwrite(f'{savePath}/ground_truth/{prtx}{idx:03d}.png', gt_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=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)
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