podgorskiy

#include <glm/matrix.hpp>

class Frustum
{
public:
	Frustum() {}

	// m = ProjectionMatrix * ViewMatrix 
	Frustum(glm::mat4 m);

podgorskiy

#pragma once
#include <Eigen/Dense>
#include <glm/matrix.hpp>

template<typename T, int m, int n>
inline glm::mat<m, n, float, glm::precision::highp> E2GLM(const Eigen::Matrix<T, m, n>& em)
{
	glm::mat<m, n, float, glm::precision::highp> mat;
	for (int i = 0; i < m; ++i)
	{

podgorskiy

#pragma once
#include <glm/glm.hpp>
#include <limits>

namespace glm
{
	template <int Dim, typename Type>
	class aabb
	{
	public:

podgorskiy

# Copyright 2018 Stanislav Pidhorskyi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#  http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

podgorskiy

import matplotlib.pyplot as plt
import numpy as np
import math


def find_maximum(f, min_x, max_x, epsilon=1e-3):
    def binary_search(l, r, fl, fr, epsilon):
        mid = l + (r - l) / 2
        fm = f(mid)
        binary_search.eval_count += 1

podgorskiy

import torch
from torch import nn
import numpy as np


class RotationMatrix(torch.nn.Module):
    def __init__(self):
        super(RotationMatrix, self).__init__()
        self.betta = nn.Parameter(torch.tensor(np.random.randn(3), dtype=torch.float32), requires_grad=True)

podgorskiy

import numpy as np
from random import random


def optimize(f, t_scale, steps=10000, tolerance=1e-6, step_decay_exp=0.01):
    def random_rotation(size):
        shape = [size, size]
        a = np.random.normal(-1.0, 1.0, shape)
        u, s, v = np.linalg.svd(a, full_matrices=False)
        return u

podgorskiy

# Copyright 2020 Stanislav Pidhorskyi
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#  http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

podgorskiy

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import contextlib
import datetime
import io
import json
import logging
import numpy as np
import os
import pycocotools.mask as mask_util
from fvcore.common.file_io import PathManager, file_lock

podgorskiy

% Encoding: UTF-8

@article{alina2018openimages,
  author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Tom Duerig and Vittorio Ferrari},
  title = {The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale},
  year = {2018},
  journal = {arXiv:1811.00982}
}
@inproceedings{rodrigo2019iteractive,
  author = {Rodrigo Benenson and Stefan Popov and Vittorio Ferrari},