Oleksandr Melnyk FreeFly19

	import numpy as np
	import cv2
	from scipy.spatial.transform import Rotation as R

	if __name__ == '__main__':
	# Generating a random point cloud within specified spatial limits
	num_points = 100 # Reduced number of points for clarity
	points_3d = np.random.rand(num_points, 3) # Random points in 3D
	# Scale and shift points to fit within the specified range
	points_3d[:, 0] = points_3d[:, 0] * 1000 - 500 # X: -500 to 500 mm

	import argparse
	import dataclasses
	import json
	import math
	import threading
	import time
	from typing import List

	import matplotlib
	import matplotlib.animation as animation

	import json
	from argparse import ArgumentParser


	def convert(input_file, output_file):
	with open(input_file, 'r') as fin:
	data2 = json.load(fin)

	features = data2['features']
	with open(output_file, 'w') as fout:

	import numpy as np
	import torch
	import torchvision
	from torch.utils.data import DataLoader
	from torchvision.transforms import ToTensor

	train_dataset = torchvision.datasets.MNIST('data', train=True, transform=ToTensor(), download=True)
	test_dataset = torchvision.datasets.MNIST('data', train=False, transform=ToTensor(), download=True)

	batch_size = 16

	import torch

	w = torch.tensor([5.], requires_grad=True)
	lr = 0.6

	w_mom_grad = 0
	momentum_coef = 0.9
	weight_decay = 0.001

	for i in range(10):

	import torch

	x = torch.tensor([1.5,2.8,15.,19.5])
	y = torch.tensor([0.,0.,1.,1.])

	w = torch.tensor([-.3], requires_grad=True)
	b = torch.tensor([0.1232154], requires_grad=True)


	def model(x):

	import time

	import av
	import tqdm
	from av.video import VideoStream


	desired_fps = 2

	timestamp = int(time.time())

	client
	# put the line below to disable tunneling outcoming trafic
	route-nopull
	....