def convert_disparity_to_saveable(disparity, normalize=True):
# takes in a single disparity map of shape height x width.
# can be saved via: imageio.imwrite(filename, convert_disparity_to_saveable(disparity))
converted_disparity = (disparity / np.max(disparity)
if normalize else disparity.copy())
converted_disparity = to8b(
converted_disparity) # height x width. int values in [0,255].
return converted_disparity
def save_depthmap(filename, depth, H, W):
os.makedirs(os.path.dirname(filename), exist_ok=True)
disp_map_label = 1 - 1. / torch.max(
torch.ones_like(depth).cuda() * 1e-10, depth)
disp_map_label = torch.reshape(disp_map_label, (H, W))
rgb8_label = to8b(disp_map_label.cpu().detach().numpy())
imageio.imwrite(filename, rgb8_label)
def update_canvas(self, images, disps=None):
for i, image in enumerate(images):
resized_rgb = F.interpolate(image.unsqueeze(dim=0),
size=(self.size,
self.size)).squeeze(dim=0)
self.real_images_array[i].src = renormalize.as_url(resized_rgb)
self.real_canvas_array[i].image = renormalize.as_url(image)
self.real_canvas_array[i].resized_image = renormalize.as_url(
resized_rgb)
if disps is not None:
disp_img = torch.from_numpy(to8b(to_disp_img(
disps[i]))).unsqueeze(dim=0) / 255.
resized_disp = F.interpolate(disp_img.unsqueeze(dim=0),
size=(self.size,
self.size)).squeeze(dim=0)
self.real_canvas_array[i].resized_disp = renormalize.as_url(
resized_disp)
self.real_canvas_array[i].disp = renormalize.as_url(disp_img)
if self.editing_canvas.index >= 0:
self.editing_canvas.image = self.real_canvas_array[
self.editing_canvas.index].image
def convert_disparity_to_phong(disparity, normalize=True):
converted_disparity = (disparity / np.max(disparity)
if normalize else disparity.copy())
converted_disparity = to8b(
visualize_disparity_with_blinn_phong(converted_disparity))
return converted_disparity # height x width x 3. int values in [0,255].
def convert_disparity_to_jet(disparity, normalize=True):
converted_disparity = (disparity / np.max(disparity)
if normalize else disparity.copy())
converted_disparity = to8b(
visualize_disparity_with_jet_color_scheme(converted_disparity))
return converted_disparity # height x width x 3. int values in [0,255].
def convert_rgb_to_saveable(rgb):
# input: float values in [0,1]
# output: int values in [0,255]
return to8b(rgb)
def free_viewpoint_rendering(args):
# memory vs. speed and quality
frames_at_a_time = 10 # set to 1 to reduce memory requirements
only_rgb = False # set to True to reduce memory requirements. Needs to be False for some scene editing to work.
# determine output name
if args.camera_path == "spiral":
output_name = args.deformations + "_" + args.camera_path
elif args.camera_path == "fixed":
output_name = (args.deformations + "_" + args.camera_path + "_" +
str(args.fixed_view))
elif args.camera_path == "input_reconstruction":
output_name = args.deformations + "_" + args.camera_path
else:
raise RuntimeError("invalid --camera_path argument")
if args.forced_background_stabilization is not None:
output_name += "_fbs_" + str(args.forced_background_stabilization)
if args.motion_factor is not None:
output_name += "_exaggeration_" + str(args.motion_factor)
if args.foreground_removal is not None:
output_name += "_removal_" + str(args.foreground_removal)
if args.render_canonical:
output_name += "_canonical"
output_folder = os.path.join(args.input, "output", output_name)
create_folder(output_folder)
# load Nerf network
(
render_kwargs_train,
render_kwargs_test,
start,
grad_vars,
load_weights_into_network,
checkpoint_dict,
get_training_ray_bending_latents,
load_llff_dataset,
raw_render_path,
render_convenient,
convert_rgb_to_saveable,
convert_disparity_to_saveable,
convert_disparity_to_jet,
convert_disparity_to_phong,
store_ray_bending_mesh_visualization,
to8b,
) = _setup_nonrigid_nerf_network(args.input)
print("sucessfully loaded nerf network", flush=True)
# load dataset
ray_bending_latents = (get_training_ray_bending_latents()
) # shape: frames x latent_size
(
images,
poses,
all_rotations,
all_translations,
bds,
render_poses,
render_rotations,
render_translations,
i_train,
i_val,
i_test,
near,
far,
dataset_extras,
) = load_llff_dataset(render_kwargs_train_=render_kwargs_train,
render_kwargs_test_=render_kwargs_test
) # load dataset that this nerf was trained on
print("sucessfully loaded dataset", flush=True)
# determine subset
if args.deformations == "train":
indices = i_train
poses = poses[i_train]
ray_bending_latents = ray_bending_latents[i_train]
images = images[i_train]
print("rendering training set")
elif args.deformations == "test":
indices = i_test
poses = poses[i_test]
ray_bending_latents = ray_bending_latents[i_test]
images = images[i_test]
print("rendering test set")
elif args.deformations == "all":
print("rendering training and test set")
else:
raise RuntimeError("invalid --deformations argument")
copy_over_groundtruth_images = False
if copy_over_groundtruth_images:
groundtruth_images_folder = os.path.join(output_folder, "groundtruth")
create_folder(groundtruth_images_folder)
for i, rgb in enumerate(images):
rgb = convert_rgb_to_saveable(rgb)
file_prefix = os.path.join(groundtruth_images_folder,
str(i).zfill(6))
imageio.imwrite(file_prefix + ".png", rgb)
# determine camera poses and latent codes
num_poses = poses.shape[0]
intrinsics = dataset_extras["intrinsics"]
if args.camera_path == "input_reconstruction":
poses = poses
intrinsics = [
intrinsics[dataset_extras["imageid_to_viewid"][i]]
for i in range(num_poses)
]
elif args.camera_path == "fixed":
poses = torch.stack(
[torch.Tensor(poses[args.fixed_view]) for _ in range(num_poses)],
0) # N x 3 x 4
intrinsics = [
intrinsics[dataset_extras["imageid_to_viewid"][args.fixed_view]]
for _ in range(num_poses)
]
elif args.camera_path == "spiral":
# poses = np.stack(_spiral_poses(poses, bds, num_poses), axis=0)
poses = []
while len(poses) < num_poses:
poses += [render_pose for render_pose in render_poses]
poses = np.stack(poses, axis=0)[:num_poses]
intrinsics = [
intrinsics[dataset_extras["imageid_to_viewid"][0]]
for _ in range(num_poses)
]
else:
# poses has shape N x ... and ray_bending_latents has shape N x ...
# Can design custom camera paths here.
# poses is indexed with imageid
# ray_bending_latents is indexed with timestepid
# intrinsics is indexed with viewid
# images is indexed with imageid
raise RuntimeError
# example with time interpolation from a fixed camera view
num_target_frames = 500
latent_indices = np.linspace(0,
ray_bending_latents.shape[0] - 1,
num=num_target_frames)
start_indices = np.floor(latent_indices).astype(np.int)
end_indices = np.ceil(latent_indices).astype(np.int)
start_latents = ray_bending_latents[
start_indices] # num_target_frames x latent_size
end_latents = ray_bending_latents[
end_indices] # num_target_frames x latent_size
interpolation_factors = latent_indices - start_indices # shape: num_target_frames. should be in [0,1]
interpolation_factors = torch.Tensor(interpolation_factors).reshape(
-1, 1) # num_target_frames x 1
ray_bending_latents = end_latents * interpolation_factors + start_latents * (
1. - interpolation_factors)
fixed_camera = 0
poses = torch.stack([
torch.Tensor(poses[fixed_camera]) for _ in range(num_target_frames)
], 0) # N x 3 x 4
intrinsics = [
intrinsics[dataset_extras["imageid_to_viewid"][fixed_camera]]
for _ in range(num_target_frames)
]
latents = ray_bending_latents
latents = latents.detach().cuda()
# rendering
correspondence_rgbs = []
rigidities = []
rgbs = []
disps = []
num_output_frames = poses.shape[0]
for start_index in range(0, num_output_frames, frames_at_a_time):
end_index = np.min([start_index + frames_at_a_time, num_output_frames])
print(
"rendering " + str(start_index) + " to " + str(end_index) +
" out of " + str(num_output_frames),
flush=True,
)
subposes = poses[start_index:end_index]
sublatents = [latents[i] for i in range(start_index, end_index)]
# render
returned = render_convenient(
poses=subposes,
ray_bending_latents=sublatents,
intrinsics=intrinsics,
with_ray_bending=not args.render_canonical,
detailed_output=not only_rgb,
rigidity_test_time_cutoff=args.forced_background_stabilization,
motion_factor=args.motion_factor,
foreground_removal=args.foreground_removal)
if only_rgb:
subrgbs, subdisps = returned
else:
subrgbs, subdisps, details_and_rest = returned
print("finished rendering", flush=True)
rgbs += [image for image in subrgbs]
disps += [image for image in subdisps]
if only_rgb:
correspondence_rgbs += [None for _ in subrgbs]
rigidities += [None for _ in subrgbs]
continue
# determine correspondences
# details_and_rest: list, one entry per image. each image has first two dimensions height x width.
min_point = np.array(
checkpoint_dict["scripts_dict"]["min_nerf_volume_point"]).reshape(
1, 1, 3)
max_point = np.array(
checkpoint_dict["scripts_dict"]["max_nerf_volume_point"]).reshape(
1, 1, 3)
for i, image_details in enumerate(details_and_rest):
# visibility_weight is the weight of the influence that each sample has on the final rgb value. so they sum to at most 1.
accumulated_visibility = torch.cumsum(
torch.Tensor(image_details["fine_visibility_weights"]).cuda(),
dim=-1) # height x width x point samples
median_indices = torch.min(
torch.abs(accumulated_visibility - 0.5), dim=-1
)[1] # height x width. visibility goes from 0 to 1. 0.5 is the median, so treat it as "most likely to be on the actually visible surface"
# visualize canonical correspondences as RGB
height, width = median_indices.shape
surface_pixels = (
image_details["fine_input_pts"].reshape(
height * width, -1,
3)[np.arange(height * width),
median_indices.cpu().reshape(-1), :].reshape(
height, width, 3)
) # height x width x 3. median_indices contains the index of one ray sample for each pixel. this ray sample is selected in this line of code.
correspondence_rgb = (surface_pixels - min_point) / (max_point -
min_point)
number_of_small_rgb_voxels = 100 # break the canonical space into smaller voxels. each voxel covers the entire RGB space [0,1]^3. makes it easier to visualize small changes. leads to a 3D checkerboard pattern
if number_of_small_rgb_voxels > 1:
correspondence_rgb *= number_of_small_rgb_voxels
correspondence_rgb = correspondence_rgb - correspondence_rgb.astype(
int)
correspondence_rgbs.append(correspondence_rgb)
# visualize rigidity
if "fine_rigidity_mask" in image_details:
rigidity = (image_details["fine_rigidity_mask"].reshape(
height * width,
-1)[np.arange(height * width),
median_indices.cpu().reshape(-1)].reshape(
height, width)) # height x width. values in [0,1]
rigidities.append(rigidity)
else:
rigidities.append(None)
rgbs = np.stack(rgbs, axis=0)
disps = np.stack(disps, axis=0)
correspondence_rgbs = np.stack(correspondence_rgbs, axis=0)
use_rigidity = rigidities[0] is not None
# store results
# for i, (rgb, disp, correspondence_rgb, rigidity) in zip(indices, (zip(rgbs, disps, correspondence_rgbs, rigidities))):
for i, (rgb, disp, correspondence_rgb, rigidity) in enumerate(
zip(rgbs, disps, correspondence_rgbs, rigidities)):
print("storing image " + str(i) + " / " + str(rgbs.shape[0]),
flush=True)
rgb = convert_rgb_to_saveable(rgb)
disp_saveable = convert_disparity_to_saveable(disp)
disp_jet = convert_disparity_to_jet(disp)
disp_phong = convert_disparity_to_phong(disp)
if not only_rgb:
correspondence_rgb = convert_rgb_to_saveable(correspondence_rgb)
if use_rigidity:
rigidity_saveable = convert_disparity_to_saveable(rigidity,
normalize=False)
rigidity_jet = convert_disparity_to_jet(rigidity, normalize=False)
file_postfix = "_" + str(i).zfill(6) + ".png"
imageio.imwrite(os.path.join(output_folder, "rgb" + file_postfix), rgb)
if not only_rgb:
imageio.imwrite(
os.path.join(output_folder, "correspondences" + file_postfix),
correspondence_rgb,
)
if use_rigidity:
imageio.imwrite(
os.path.join(output_folder, "rigidity" + file_postfix),
rigidity_saveable,
)
imageio.imwrite(
os.path.join(output_folder, "rigidity_jet" + file_postfix),
rigidity_jet)
imageio.imwrite(os.path.join(output_folder, "disp" + file_postfix),
disp_saveable)
imageio.imwrite(os.path.join(output_folder, "disp_jet" + file_postfix),
disp_jet)
imageio.imwrite(
os.path.join(output_folder, "disp_phong" + file_postfix),
disp_phong)
# movies
file_prefix = os.path.join(output_folder, "video_")
try:
print("storing RGB video...", flush=True)
imageio.mimwrite(
file_prefix + "rgb.mp4",
convert_rgb_to_saveable(rgbs),
fps=args.output_video_fps,
quality=9,
)
if not only_rgb:
print("storing correspondence RGB video...", flush=True)
imageio.mimwrite(
file_prefix + "correspondences.mp4",
convert_rgb_to_saveable(correspondence_rgbs),
fps=args.output_video_fps,
quality=9,
)
print("storing disparity video...", flush=True)
imageio.mimwrite(
file_prefix + "disp.mp4",
convert_disparity_to_saveable(disps),
fps=args.output_video_fps,
quality=9,
)
print("storing disparity jet video...", flush=True)
imageio.mimwrite(
file_prefix + "disp_jet.mp4",
np.stack([convert_disparity_to_jet(disp) for disp in disps],
axis=0),
fps=args.output_video_fps,
quality=9,
)
print("storing disparity phong video...", flush=True)
imageio.mimwrite(
file_prefix + "disp_phong.mp4",
np.stack([convert_disparity_to_phong(disp) for disp in disps],
axis=0),
fps=args.output_video_fps,
quality=9,
)
if use_rigidity:
rigidities = np.stack(rigidities, axis=0)
print("storing rigidity video...", flush=True)
imageio.mimwrite(
file_prefix + "rigidity.mp4",
convert_disparity_to_saveable(rigidities, normalize=False),
fps=args.output_video_fps,
quality=9,
)
print("storing rigidity jet video...", flush=True)
imageio.mimwrite(
file_prefix + "rigidity_jet.mp4",
np.stack(
[
convert_disparity_to_jet(rigidity, normalize=False)
for rigidity in rigidities
],
axis=0,
),
fps=args.output_video_fps,
quality=9,
)
except:
print(
"imageio.mimwrite() failed. maybe ffmpeg is not installed properly?"
)
# evaluation of background stability
if args.camera_path == "fixed":
standard_deviations = np.std(rgbs, axis=0)
averaged_standard_devations = 10 * np.mean(standard_deviations,
axis=-1)
from matplotlib import cm
color_mapping = np.array([cm.jet(i)[:3] for i in range(256)])
max_value = 1
min_value = 0
averaged_standard_devations = np.clip(
averaged_standard_devations, a_max=max_value, a_min=min_value
) / max_value # cut off above max_value. result is normalized to [0,1]
averaged_standard_devations = (
255. * averaged_standard_devations).astype(
'uint8') # now contains int in [0,255]
original_shape = averaged_standard_devations.shape
averaged_standard_devations = color_mapping[
averaged_standard_devations.flatten()]
averaged_standard_devations = averaged_standard_devations.reshape(
original_shape + (3, ))
imageio.imwrite(os.path.join(output_folder, "standard_deviations.png"),
averaged_standard_devations)
# quantitative evaluation
if args.camera_path == "input_reconstruction":
try:
from PerceptualSimilarity import lpips
perceptual_metric = lpips.LPIPS(net='alex')
except:
print(
"Perceptual LPIPS metric not found. Please see the README for installation instructions"
)
perceptual_metric = None
create_error_maps = True # whether to write out error images instead of just computing scores
naive_error_folder = os.path.join(output_folder, "naive_errors/")
create_folder(naive_error_folder)
ssim_error_folder = os.path.join(output_folder, "ssim_errors/")
create_folder(ssim_error_folder)
to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
def visualize_with_jet_color_scheme(image):
from matplotlib import cm
color_mapping = np.array([cm.jet(i)[:3] for i in range(256)])
max_value = 1.0
min_value = 0.0
intermediate = np.clip(
image, a_max=max_value, a_min=min_value
) / max_value # cut off above max_value. result is normalized to [0,1]
intermediate = (255. * intermediate).astype(
'uint8') # now contains int in [0,255]
original_shape = intermediate.shape
intermediate = color_mapping[intermediate.flatten()]
intermediate = intermediate.reshape(original_shape + (3, ))
return intermediate
mask = None
scores = {}
from skimage.metrics import structural_similarity as ssim
for i, (groundtruth, generated) in enumerate(zip(images, rgbs)):
if mask is None: # undistortion leads to masked-out black pixels in groundtruth
mask = (np.sum(groundtruth, axis=-1) == 0.)
groundtruth[mask] = 0.
generated[mask] = 0.
# PSNR
mse = np.mean((groundtruth - generated)**2)
psnr = -10. * np.log10(mse)
# SSIM
# https://scikit-image.org/docs/dev/api/skimage.metrics.html#skimage.metrics.structural_similarity
returned = ssim(groundtruth,
generated,
data_range=1.0,
multichannel=True,
gaussian_weights=True,
sigma=1.5,
use_sample_covariance=False,
full=create_error_maps)
if create_error_maps:
ssim_error, ssim_error_image = returned
else:
ssim_error = returned
# perceptual metric
if perceptual_metric is None:
lpips = 1.
else:
def numpy_to_pytorch(np_image):
torch_image = 2 * torch.from_numpy(
np_image) - 1 # height x width x 3. must be in [-1,+1]
torch_image = torch_image.permute(2, 0,
1) # 3 x height x width
return torch_image.unsqueeze(0) # 1 x 3 x height x width
lpips = perceptual_metric.forward(
numpy_to_pytorch(groundtruth), numpy_to_pytorch(generated))
lpips = float(lpips.detach().reshape(1).numpy()[0])
scores[i] = {"psnr": psnr, "ssim": ssim_error, "lpips": lpips}
if create_error_maps:
# MSE-style
error = np.linalg.norm(groundtruth - generated,
axis=-1) / np.sqrt(
1 + 1 + 1) # height x width
error *= 10. # exaggarate error
error = np.clip(error, 0.0, 1.0)
error = to8b(visualize_with_jet_color_scheme(
error)) # height x width x 3. int values in [0,255]
filename = os.path.join(naive_error_folder,
'error_{:03d}.png'.format(i))
imageio.imwrite(filename, error)
# SSIM
filename = os.path.join(ssim_error_folder,
'error_{:03d}.png'.format(i))
ssim_error_image = to8b(
visualize_with_jet_color_scheme(
1. - np.mean(ssim_error_image, axis=-1)))
imageio.imwrite(filename, ssim_error_image)
averaged_scores = {}
averaged_scores["average_psnr"] = np.mean(
[score["psnr"] for score in scores.values()])
averaged_scores["average_ssim"] = np.mean(
[score["ssim"] for score in scores.values()])
averaged_scores["average_lpips"] = np.mean(
[score["lpips"] for score in scores.values()])
print(averaged_scores, flush=True)
scores.update(averaged_scores)
import json
with open(os.path.join(output_folder, "scores.json"),
"w") as json_file:
json.dump(scores, json_file, indent=4)
def render_path(render_poses,
styles,
hwfs,
chunk,
render_kwargs,
nfs=None,
gt_imgs=None,
alpha_cache=None,
feature_cache=None,
weights_cache=None,
savedir=None,
maximum=1000,
get_cached=None,
get_rgbs=False,
verbose=True,
cb=None,
update_cb=None):
render_kwargs['network_fine'].get_cached = get_cached
rgbs = []
disps = []
alphas = []
features = []
weights = []
total_psnr = 0.
total_psnr0 = 0
N = len(render_poses)
s = N // maximum if len(render_poses) > maximum else 1
if gt_imgs is not None:
gt_imgs = gt_imgs[::s].cuda()
render_poses = render_poses[::s].cuda()
styles = styles[::s].cuda()
hwfs = hwfs[::s].cuda()
iterator = zip(render_poses, styles)
if verbose:
iterator = tqdm(iterator, total=len(styles))
for i, (c2w, style) in enumerate(iterator):
if cb is not None:
cb(i)
H, W, focal = hwfs[i]
H, W = int(H), int(W)
if nfs is not None:
near, far = nfs[i]
render_kwargs.update({'near': near, 'far': far})
alpha = alpha_cache[i] if alpha_cache is not None else None
feature = feature_cache[i] if feature_cache is not None else None
weight = weights_cache[i] if weights_cache is not None else None
rgb, disp, acc, additional = render(H,
W,
focal,
style=style,
chunk=chunk,
weights=weight,
c2w=c2w[:3, :4],
alpha=alpha,
feature=feature,
**render_kwargs)
if 'rgb0' in additional:
rgb0 = additional['rgb0']
if gt_imgs is not None:
gt_img = gt_imgs[i]
mse_loss = img2mse(rgb, gt_img)
psnr = mse2psnr(mse_loss)
total_psnr += psnr.item()
if 'rgb0' in additional:
mse_loss0 = img2mse(rgb0, gt_img)
psnr0 = mse2psnr(mse_loss0)
total_psnr0 += psnr0.item()
rgbs.append(rgb.cpu().numpy())
disps.append(disp.cpu().numpy())
if update_cb:
update_cb(i, rgbs[-1])
if get_cached:
alphas.append(additional['raw_alpha'])
features.append(additional['features'])
weights.append(additional['weights0'])
if savedir is not None:
rgb8 = to8b(rgbs[-1])
disp8 = to8b(to_disp_img(disps[-1]))
imageio.imwrite(os.path.join(savedir, '{:04d}_rgb.png'.format(i)),
rgb8)
imageio.imwrite(os.path.join(savedir, '{:04d}_disp.png'.format(i)),
disp8)
if gt_imgs is not None:
gt_img = to8b((gt_imgs[i]).cpu().numpy())
imageio.imwrite(
os.path.join(savedir, '{:04d}_gt.png'.format(i)), gt_img)
if gt_imgs is not None:
with open(os.path.join(savedir, 'log.txt'), 'a+') as f:
torchvision.utils.save_image(
torch.tensor(rgbs).cpu().permute(0, 3, 1, 2), 'rgbs.png')
torchvision.utils.save_image(
torch.tensor(gt_imgs).cpu().permute(0, 3, 1, 2), 'gt.png')
msg = f'psnr0, psnr1, {total_psnr0/len(render_poses)}, {total_psnr/len(render_poses)}'
f.write(msg + '\n')
print(msg)
total_psnr = total_psnr / len(rgbs)
rgbs = np.stack(rgbs, 0)
disps = np.stack(disps, 0)
if get_cached:
render_kwargs['network_fine'].get_cached = None
return rgbs, disps, total_psnr, alphas, features, weights
else:
return rgbs, disps, total_psnr
def log_qualitative_results(writer,
metalearning_iter,
scene_id,
save_dir,
render_poses,
poses,
i_split,
hwf,
chunk,
render_kwargs_test,
images,
N_importance,
timesteps,
use_viewdirs=True,
render_test_set=False):
H, W, focal = hwf
testsavedir = os.path.join(
save_dir, 'testset_iter/{:06d}/scene{}'.format(metalearning_iter,
scene_id))
os.makedirs(testsavedir, exist_ok=True)
split_timesteps = []
for i in i_split:
split_timesteps.extend([timesteps[i]] * H * W)
split_timesteps = np.asarray(split_timesteps)
render_path(poses[i_split],
hwf,
split_timesteps,
chunk,
render_kwargs_test,
gt_imgs=images[i_split],
savedir=testsavedir)
# Log a rendered validation view to Tensorboard
img_i = np.random.choice(i_split)
target = images[img_i]
pose = poses[img_i, :3, :4]
split_timestep = [timesteps[img_i]] * (H * W)
rgb, disp, acc, extras = render(H,
W,
focal,
split_timestep,
chunk=chunk,
c2w=pose,
**render_kwargs_test)
psnr = mse2psnr(img2mse(rgb, target))
# Save out the validation image for Tensorboard-free monitoring
testimgdir = os.path.join(save_dir, 'tboard_val_imgs')
if not os.path.exists(testimgdir):
os.makedirs(testimgdir, exist_ok=True)
imageio.imwrite(
os.path.join(testimgdir,
'{:06d}_{}.png'.format(metalearning_iter, scene_id)),
to8b(rgb))
writer.add_image(f'rgb/{scene_id}',
np.squeeze(to8b(rgb)[tf.newaxis], axis=0),
metalearning_iter,
dataformats='HWC')
writer.add_image(f'disp/{scene_id}',
np.squeeze(disp[tf.newaxis, ..., tf.newaxis], axis=0),
metalearning_iter,
dataformats='HWC')
writer.add_image(f'acc/{scene_id}',
np.squeeze(acc[tf.newaxis, ..., tf.newaxis], axis=0),
metalearning_iter,
dataformats='HWC')
writer.add_image(f'rgb_holdout/{scene_id}',
np.squeeze(target[tf.newaxis], axis=0),
metalearning_iter,
dataformats='HWC')
if N_importance > 0:
writer.add_image(f'rgb0/{scene_id}',
np.squeeze(to8b(extras['rgb0'])[tf.newaxis], axis=0),
metalearning_iter,
dataformats='HWC')
writer.add_image(f'disp0/{scene_id}',
np.squeeze(extras['disp0'][tf.newaxis, ...,
tf.newaxis],
axis=0),
metalearning_iter,
dataformats='HWC')
writer.add_image(f'z_std/{scene_id}',
np.squeeze(extras['z_std'][tf.newaxis, ...,
tf.newaxis],
axis=0),
metalearning_iter,
dataformats='HWC')
# Save videos
if render_test_set:
sorted_timesteps = sorted(list(set(timesteps)))
rgbs, disps = render_timesteps(poses[i_split[0]],
hwf,
sorted_timesteps,
chunk,
render_kwargs_test,
savedir=testsavedir)
moviebase = os.path.join(
save_dir, '{}_temporal_{:06d}_'.format(scene_id,
metalearning_iter))
imageio.mimwrite(moviebase + 'rgb.mp4', to8b(rgbs), fps=30, quality=8)
imageio.mimwrite(moviebase + 'disp.mp4',
to8b(disps / np.max(disps)),
fps=30,
quality=8)
if use_viewdirs:
render_kwargs_test['c2w_staticcam'] = render_poses[0][:3, :4]
rgbs_still, _ = render_path(render_poses, hwf, sorted_timesteps,
chunk, render_kwargs_test)
render_kwargs_test['c2w_staticcam'] = None
imageio.mimwrite(moviebase + 'rgb_still.mp4',
to8b(rgbs_still),
fps=30,
quality=8)
def __init__(self,
instance,
config,
use_cached=True,
expname=None,
edit_type=None,
num_canvases=9,
shape_params='fusion_shape_branch',
color_params='color_branch',
randneg=8192,
device='cuda:0'):
super().__init__(style=dict(
border="3px solid gray", padding="8px", display="inline-block"))
torch.set_default_tensor_type('torch.cuda.FloatTensor' if device ==
'cuda:0' else 'cpu')
self.edit_type = edit_type
self.instance = instance
self.num_canvases = num_canvases
self.shape_params = shape_params
self.color_params = color_params
self.size = IMG_SIZE
self.randneg = randneg
self.device = device
self.msg_out = labwidget.Div()
self.editing_canvas = paintwidget.PaintWidget(image='',
width=self.size * 3,
height=self.size * 3).on(
'mask',
self.change_mask)
self.editing_canvas.index = -1
self.copy_canvas = paintwidget.PaintWidget(image='',
width=self.size * 2,
height=self.size * 2).on(
'mask', self.copy)
self.copy_mask = None
inline = dict(display='inline', border="2px solid gray")
self.toggle_rgbs_disps_btn = labwidget.Button(
'show depth', style=inline).on('click', self.toggle_rgb_disps)
self.positive_mask_btn = labwidget.Button(self.pad('edit color'),
style=inline).on(
'click',
self.positive_mask)
self.addition_mask_btn = labwidget.Button(self.pad('add shape'),
style=inline).on(
'click', self.add)
self.sigma_mask_btn = labwidget.Button(self.pad('remove shape'),
style=inline).on(
'click', self.sigma_mask)
self.color_from_btn = labwidget.Button(self.pad('transfer color'),
style=inline).on(
'click', self.color_from)
self.shape_from_btn = labwidget.Button(self.pad('transfer shape'),
style=inline).on(
'click', self.shape_from)
self.execute_btn = labwidget.Button(self.pad('execute'),
style=inline).on(
'click', self.execute_edit)
self.brushsize_textbox = labwidget.Textbox(5,
desc='brushsize: ',
size=3).on(
'value',
self.change_brushsize)
self.target = None
self.use_color_cache = True
self.color_style = dict(display='inline', border="2px solid white")
trn = transforms.Compose(
[transforms.Resize(32),
transforms.ToTensor()])
bg_img = trn(Image.open('bg.png').convert('RGB'))
bg_img = renormalize.as_url(bg_img * 2 - 1)
self.color_pallete = [
labwidget.Image(src=bg_img,
style=self.color_style).on('click', self.set_color)
]
self.color_pallete[-1].index = 0
self.color_pallete[-1].color_type = 'bg'
for color in mean_colors.colors.values():
image = torch.zeros(3, 32, 32)
image[0, :, :] = color[0]
image[1, :, :] = color[1]
image[2, :, :] = color[2]
image = image / 255. * 2 - 1
self.color_pallete.append(
labwidget.Image(src=renormalize.as_url(image),
style=self.color_style).on(
'click', self.set_color))
self.color_pallete[-1].index = len(self.color_pallete) - 1
self.color_pallete[-1].color_type = 'color'
# TODO: Highlight the white box with black for clarity
self.color = None
self.mask_type = None
self.real_canvas_array = []
self.real_images_array = []
self.positive_masks = []
train, test, optimizer, styles = load_model(instance,
config,
expname=expname)
poses, hwfs, cache, args = load_dataset(instance,
config,
num_canvases=num_canvases,
N_instances=styles.shape[0],
expname=expname,
use_cached=use_cached)
self.parentdir = load_config(config).expname
self.expname = expname if expname else self.parentdir
self.savedir = os.path.join(self.expname, str(instance))
os.makedirs(self.savedir, exist_ok=True)
self.poses = poses.to(device)
self.cache = cache
self.chunk = args.chunk
self.near = args.blender_near
self.far = args.blender_far
self.nfs = [[self.near, self.far]] * self.poses.shape[0]
self.hwfs = hwfs.to(device)
self.old_fine_network = dict(
copy.deepcopy(test['network_fine']).named_parameters())
self.train_kwargs = train
self.test_kwargs = test
self.optimizer = None
self.all_instance_styles = styles
self.instance_style = styles[instance].unsqueeze(dim=0).to(device)
if cache is not None:
self.weights = cache['weights']
self.alphas = cache['alphas']
self.features = cache['features']
else:
self.weights = None
self.alphas = None
self.features = None
self.trn = transforms.Compose(
[transforms.Resize(128),
transforms.ToTensor()])
self.transfer_instances_array = [
labwidget.Image(src='').on('click', self.change_target)
for _ in range(12)
]
self.addition_instances_array = [
labwidget.Image(src='').on('click', self.change_target)
for _ in range(12)
]
images, disps = self.render(self.poses,
self.instance_style,
verbose=False,
get_disps=True)
for i, image in enumerate(images):
resized = F.interpolate(image.unsqueeze(dim=0),
size=(self.size, self.size)).squeeze(dim=0)
disp_img = torch.from_numpy(to8b(to_disp_img(
disps[i]))).unsqueeze(dim=0) / 255.
resized_disp = F.interpolate(disp_img.unsqueeze(dim=0),
size=(self.size,
self.size)).squeeze(dim=0)
self.real_images_array.append(
labwidget.Image(src=renormalize.as_url(resized)).on(
'click', self.set_editing_canvas))
self.real_images_array[-1].index = i
self.real_canvas_array.append(
paintwidget.PaintWidget(image=renormalize.as_url(image),
width=self.size * 3,
height=self.size * 3).on(
'mask', self.change_mask))
self.real_canvas_array[-1].index = i
self.real_canvas_array[-1].negative_mask = ''
self.real_canvas_array[-1].resized_image = renormalize.as_url(
resized)
self.real_canvas_array[-1].resized_disp = renormalize.as_url(
resized_disp)
self.real_canvas_array[-1].disp = renormalize.as_url(disp_img)
self.real_canvas_array[-1].orig = renormalize.as_url(image)
self.positive_masks.append(torch.zeros(image.shape).cpu())
self.show_rgbs = True
self.change_brushsize()
self.editname_textbox = labwidget.Datalist(choices=self.saved_names(),
style=inline)
self.save_btn = labwidget.Button('save',
style=inline).on('click', self.save)
self.load_btn = labwidget.Button('load',
style=inline).on('click', self.load)
1) * N_per_instance
all_poses = all_poses[ps:pe]
if args.video:
all_poses, all_hwfs = generate_flythrough(all_poses[0].cpu(),
all_hwfs[0],
num_poses=100)
nfs = [[writer.near, writer.far]] * all_poses.shape[0]
styles = writer.instance_style.repeat((all_poses.shape[0], 1))
with torch.no_grad():
print(f'Saving samples in {savedir}')
rgbs, disps, psnr = render_path(all_poses,
styles,
all_hwfs,
writer.chunk,
writer.test_kwargs,
nfs=nfs,
savedir=savedir,
verbose=True)
if args.video:
imageio.mimwrite(os.path.join(savedir, 'video.mp4'),
to8b(rgbs),
fps=30,
quality=8)
imageio.mimwrite(os.path.join(savedir, 'disps.mp4'),
to8b(disps / np.max(disps)),
fps=30,
quality=8)