sergeyprokudin

import numpy as np

def load_colmap_sparse_points(points3d_path):
  '''Load COLMAP points3D.txt as numpy array
  '''
  with open(points3d_path, 'r') as f:
    points_txt = f.readlines()[3:]

  n_points = len(points_txt)
  points_np = np.zeros([n_points, 6])

sergeyprokudin

def get_img_uv_coords(img):
  """
  Get u,v coords for image pixels
  """
  
  img_height, img_width = img.shape[0], img.shape[1]
  
  uv_map = np.zeros([img_height, img_width, 2])
  u = np.arange(0, img_height)
  v = np.arange(0, img_width)

sergeyprokudin

import numpy as np
def get_rotation_mat(theta=np.pi, axis=0):
  # https://en.wikipedia.org/wiki/Rotation_matrix

  sin = np.sin(theta)
  cos = np.cos(theta)

  if axis==0:
    R = np.asarray([[1,   0,    0],
                    [0, cos, -sin],

sergeyprokudin

import numpy as np
import matplotlib.pyplot as plt

def show_img(img, figsize=(10, 10)):
    '''
    Show image using matplotlib
    
    Parameters
    ----------
    img : array of shape [img_width, img_height, 3]

sergeyprokudin

import numpy as np

def pdist(X1, X2):
    """Computes the matrix of pairwise distances between X1 and X2.

    The implementation is based on the following observation for matrices:
    (X1-X2)^2 = X1^2 - 2*X1*X2 + X2^2
        
    Parameters
    ----------

sergeyprokudin

import numpy as np

from keras.layers import Input, Dense
from keras.models import Model, Sequential


def dnn(n_inputs, n_outputs, n_hidden_layers=3, hlayer_size=128, probabilistic=True):
    """Defines simple DNN model
    """
    x_input = Input(shape=[n_inputs])

sergeyprokudin

import numpy as np
from sklearn.neighbors import NearestNeighbors


def chamfer_distance(x, y, metric='l2', direction='bi'):
    """Chamfer distance between two point clouds

    Parameters
    ----------
    x: numpy array [n_points_x, n_dims]

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# details:
# https://stackoverflow.com/questions/11367736/matplotlib-consistent-font-using-latex/27697390
from matplotlib import rc
import matplotlib
matplotlib.rcParams['mathtext.fontset'] = 'stix'
matplotlib.rcParams['font.family'] = 'STIXGeneral'

sergeyprokudin

"""
Simple point cloud visualization with pyntcloud library

"""
import pyntcloud as pynt
import pandas as pd
import numpy as np


def show_cloud(xyz, rgb=None, initial_point_size=0.01):

sergeyprokudin

import open3d as o3d
import trimesh
import numpy as np


def ball_pivoting_reconstruction(xyz, radii=None):
    """Given a 3D point cloud, get unstructured mesh using ball pivoting algorithm
    
    Based on this stack overflow code snippet:
    https://stackoverflow.com/questions/56965268/how-do-i-convert-a-3d-point-cloud-ply-into-a-mesh-with-faces-and-vertices