| import keras.backend as K | |
| from keras.engine import InputSpec | |
| from keras.layers import Conv2D | |
| from keras.utils import conv_utils | |
| class Deconv2D(Conv2D): | |
| """Deconvolution layer. |
| from keras.engine.topology import Layer | |
| import numpy as np | |
| import tensorflow as tf | |
| class MaxPooling2D(Layer): | |
| def __init__(self, pool_size = 2, stride = None, padding = 'VALID', **kwargs): | |
| self.pool_size = pool_size | |
| assert(isinstance(self.pool_size, int)) |
| import numpy as np | |
| def mutual_nn_matcher(descriptors1, descriptors2): | |
| descriptors1 = descriptors1 / np.linalg.norm(descriptors1, axis=1)[:, np.newaxis] | |
| descriptors2 = descriptors2 / np.linalg.norm(descriptors2, axis=1)[:, np.newaxis] | |
| similarity = descriptors1 @ descriptors2.transpose() | |
| nn12 = np.argmax(similarity, axis=1) | |
| nn21 = np.argmax(similarity, axis=0) | |
| ids1 = np.arange(similarity.shape[0]) | |
| valid_matches = (ids1 == nn21[nn12]) |
This is a short tutorial for compiling COLMAP on Leonhard with GCC 6.3.0. Qt installation is quite large so COLMAP cannot be compiled in the home directories.
Create a file called colmap_startup.sh in your home directory containing the following:
export DATA=/cluster/project/infk/cvg/$USER
| def compute_reconstruction_statistics(reference_model_path): | |
| # Images w. intrinsics and extrinsics. | |
| with open(os.path.join(reference_model_path, 'cameras.txt'), 'r') as f: | |
| raw_cameras = f.readlines()[3 :] | |
| cameras = {} | |
| for raw_line in raw_cameras: | |
| split_line = raw_line.strip('\n').split(' ') | |
| cameras[int(split_line[0])] = split_line[1 :] |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class GeM(nn.Module): | |
| def __init__(self, p=3, eps=1e-6): | |
| # Based on https://arxiv.org/abs/1711.02512. | |
| super().__init__() | |
| self.p = torch.nn.parameter.Parameter(torch.ones(1) * p) |
| \RequirePackage[% | |
| style = alphabetic, | |
| autocite = inline, | |
| backend = biber, | |
| doi = false, | |
| url = true, | |
| giveninits = true, | |
| hyperref = true, | |
| sortcites = true, | |
| minalphanames = 1, |
| from pathlib import Path | |
| from scantools.capture import Capture | |
| path = Path('/media/SSD2/ms_eth_dataset/datasets/release_public/v2.0') | |
| for location in ['LIN', 'CAB', 'HGE']: | |
| print(location) | |
| capture = Capture.load(path / location) | |
| for session_id in ['map', 'query_hololens', 'query_phone', 'query_val_hololens', 'query_val_phone']: |