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])

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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
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 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],

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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 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

5.251361131668090820e-01 9.620525240898132324e-01 7.196764349937438965e-01
5.226933956146240234e-01 9.544755220413208008e-01 7.539969086647033691e-01
5.285560488700866699e-01 9.512906074523925781e-01 7.259945869445800781e-01
5.315514206886291504e-01 9.564292430877685547e-01 6.891824007034301758e-01
5.269100069999694824e-01 9.467492103576660156e-01 7.527468204498291016e-01
5.214509367942810059e-01 9.482995271682739258e-01 7.740847468376159668e-01
5.324009060859680176e-01 9.465931057929992676e-01 6.968711614608764648e-01
5.352808237075805664e-01 9.493461251258850098e-01 6.598548889160156250e-01
5.359319448471069336e-01 9.241502285003662109e-01 6.846292018890380859e-01
5.381639003753662109e-01 9.202622175216674805e-01 6.565521359443664551e-01

sergeyprokudin

newmtl material_1
map_Kd rp_mei_posed_001_dif.jpg

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sergeyprokudin

import open3d as o3d
import numpy as np

def poisson_open3d(ply_path, depth=8):
  
  pcd = o3d.io.read_point_cloud(ply_path)
  mesh, densities = o3d.geometry.TriangleMesh.create_from_point_cloud_poisson(pcd, depth=8)
  vertices_to_remove = densities < np.quantile(densities, 0.02)
  mesh.remove_vertices_by_mask(vertices_to_remove)