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import os, torch, cv2, re
import numpy as np
from PIL import Image
import torch.nn.functional as F
import torchvision.transforms as T
# Misc
img2mse = lambda x, y: torch.mean((x - y) ** 2)
mse2psnr = lambda x: -10. * torch.log(x) / torch.log(torch.Tensor([10.]))
to8b = lambda x: (255 * np.clip(x, 0, 1)).astype(np.uint8)
mse2psnr2 = lambda x: -10. * np.log(x) / np.log(10.)
def get_psnr(imgs_pred, imgs_gt):
psnrs = []
for (img, tar) in zip(imgs_pred, imgs_gt):
psnrs.append(mse2psnr2(np.mean((img - tar.cpu().numpy()) ** 2)))
return np.array(psnrs)
def init_log(log, keys):
for key in keys:
log[key] = torch.tensor([0.0], dtype=float)
return log
def visualize_depth_numpy(depth, minmax=None, cmap=cv2.COLORMAP_JET):
"""
depth: (H, W)
"""
x = np.nan_to_num(depth) # change nan to 0
if minmax is None:
mi = np.min(x[x > 0]) # get minimum positive depth (ignore background)
ma = np.max(x)
else:
mi, ma = minmax
x = (x - mi) / (ma - mi + 1e-8) # normalize to 0~1
x = (255 * x).astype(np.uint8)
x_ = cv2.applyColorMap(x, cmap)
return x_, [mi, ma]
def visualize_depth(depth, minmax=None, cmap=cv2.COLORMAP_JET):
"""
depth: (H, W)
"""
if type(depth) is not np.ndarray:
depth = depth.cpu().numpy()
x = np.nan_to_num(depth) # change nan to 0
if minmax is None:
mi = np.min(x[x > 0]) # get minimum positive depth (ignore background)
ma = np.max(x)
else:
mi, ma = minmax
x = (x - mi) / (ma - mi + 1e-8) # normalize to 0~1
x = (255 * x).astype(np.uint8)
x_ = Image.fromarray(cv2.applyColorMap(x, cmap))
x_ = T.ToTensor()(x_) # (3, H, W)
return x_, [mi, ma]
def abs_error_numpy(depth_pred, depth_gt, mask):
depth_pred, depth_gt = depth_pred[mask], depth_gt[mask]
return np.abs(depth_pred - depth_gt)
def abs_error(depth_pred, depth_gt, mask):
depth_pred, depth_gt = depth_pred[mask], depth_gt[mask]
err = depth_pred - depth_gt
return np.abs(err) if type(depth_pred) is np.ndarray else err.abs()
def acc_threshold(depth_pred, depth_gt, mask, threshold):
"""
computes the percentage of pixels whose depth error is less than @threshold
"""
errors = abs_error(depth_pred, depth_gt, mask)
acc_mask = errors < threshold
return acc_mask.astype('float') if type(depth_pred) is np.ndarray else acc_mask.float()
def to_tensor_cuda(data, device, filter):
for item in data.keys():
if item in filter:
continue
if type(data[item]) is np.ndarray:
data[item] = torch.tensor(data[item], dtype=torch.float32, device=device)
else:
data[item] = data[item].float().to(device)
return data
def to_cuda(data, device, filter):
for item in data.keys():
if item in filter:
continue
data[item] = data[item].float().to(device)
return data
def tensor_unsqueeze(data, filter):
for item in data.keys():
if item in filter:
continue
data[item] = data[item][None]
return data
def filter_keys(dict):
dict.pop('N_samples')
if 'ndc' in dict.keys():
dict.pop('ndc')
if 'lindisp' in dict.keys():
dict.pop('lindisp')
return dict
def sub_selete_data(data_batch, device, idx, filtKey=[],
filtIndex=['view_ids_all', 'c2ws_all', 'scan', 'bbox', 'w2ref', 'ref2w', 'light_id', 'ckpt',
'idx']):
data_sub_selete = {}
for item in data_batch.keys():
data_sub_selete[item] = data_batch[item][:, idx].float() if (
item not in filtIndex and torch.is_tensor(item) and item.dim() > 2) else data_batch[item].float()
if not data_sub_selete[item].is_cuda:
data_sub_selete[item] = data_sub_selete[item].to(device)
return data_sub_selete
def detach_data(dictionary):
dictionary_new = {}
for key in dictionary.keys():
dictionary_new[key] = dictionary[key].detach().clone()
return dictionary_new
def read_pfm(filename):
file = open(filename, 'rb')
color = None
width = None
height = None
scale = None
endian = None
header = file.readline().decode('utf-8').rstrip()
if header == 'PF':
color = True
elif header == 'Pf':
color = False
else:
raise Exception('Not a PFM file.')
dim_match = re.match(r'^(\d+)\s(\d+)\s$', file.readline().decode('utf-8'))
if dim_match:
width, height = map(int, dim_match.groups())
else:
raise Exception('Malformed PFM header.')
scale = float(file.readline().rstrip())
if scale < 0: # little-endian
endian = '<'
scale = -scale
else:
endian = '>' # big-endian
data = np.fromfile(file, endian + 'f')
shape = (height, width, 3) if color else (height, width)
data = np.reshape(data, shape)
data = np.flipud(data)
file.close()
return data, scale
from torch.optim.lr_scheduler import CosineAnnealingLR, MultiStepLR
# from warmup_scheduler import GradualWarmupScheduler
def get_scheduler(hparams, optimizer):
eps = 1e-8
if hparams.lr_scheduler == 'steplr':
scheduler = MultiStepLR(optimizer, milestones=hparams.decay_step,
gamma=hparams.decay_gamma)
elif hparams.lr_scheduler == 'cosine':
scheduler = CosineAnnealingLR(optimizer, T_max=hparams.num_epochs, eta_min=eps)
else:
raise ValueError('scheduler not recognized!')
# if hparams.warmup_epochs > 0 and hparams.optimizer not in ['radam', 'ranger']:
# scheduler = GradualWarmupScheduler(optimizer, multiplier=hparams.warmup_multiplier,
# total_epoch=hparams.warmup_epochs, after_scheduler=scheduler)
return scheduler
#### pairing ####
def get_nearest_pose_ids(tar_pose, ref_poses, num_select):
'''
Args:
tar_pose: target pose [N, 4, 4]
ref_poses: reference poses [M, 4, 4]
num_select: the number of nearest views to select
Returns: the selected indices
'''
dists = np.linalg.norm(tar_pose[:, None, :3, 3] - ref_poses[None, :, :3, 3], axis=-1)
sorted_ids = np.argsort(dists, axis=-1)
selected_ids = sorted_ids[:, :num_select]
return selected_ids
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