podgorskiy

#pragma once
#define _USE_MATH_DEFINES
#include <math.h>
#include <vector>
#include <functional>

namespace misc
{
	std::vector<float> GaussKernel(float sigma, int kernelSize, int sampleCount = 1000);

podgorskiy

import numpy as np
from scipy.optimize import curve_fit
from matplotlib import pyplot as plt


t = np.arange(380.0, 781.0, 5.0)
s = np.asarray([
    [0.0014, 0.0000, 0.0065], [0.0022, 0.0001, 0.0105], [0.0042, 0.0001, 0.0201],
    [0.0076, 0.0002, 0.0362], [0.0143, 0.0004, 0.0679], [0.0232, 0.0006, 0.1102],
    [0.0435, 0.0012, 0.2074], [0.0776, 0.0022, 0.3713], [0.1344, 0.0040, 0.6456],

podgorskiy

TEX = max_print_line=10000 pdflatex -shell-escape -interaction=nonstopmode -file-line-error -halt-on-error
BIB = bibtex
VIEWER = evince

.PHONY: all view

view : rebuild
	$(VIEWER) main.pdf &

rebuild: clean all

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},

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

# 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

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

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 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

# 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,