vpeopleonatank

Installing i3-gaps

Dependencies

i3-gaps has some packages that are required for it to work so install these things:

sudo apt install libxcb1-dev libxcb-keysyms1-dev libpango1.0-dev libxcb-util0-dev libxcb-icccm4-dev libyajl-dev libstartup-notification0-dev libxcb-randr0-dev libev-dev libxcb-cursor-dev libxcb-xinerama0-dev libxcb-xkb-dev libxkbcommon-dev libxkbcommon-x11-dev autoconf xutils-dev libtool

You also need to install libxcb-xrm-dev, but I got Unable to locate package libxcb-xrm-dev when trying to install from the apt repositories on Ubuntu 16.04. If this happens to you, just install it from source using these commands:

mkdir tmp

	from tensorflow import keras
	import tensorflow as tf
	from tensorflow import keras
	from keras.preprocessing.text import Tokenizer
	from keras.preprocessing.sequence import pad_sequences
	from pydantic import BaseModel, Field
	import re

	def tokenize(x):
	x_tk = Tokenizer()

	import cv2
	import numpy as np
	import math
	import skimage.io
	import xml.etree.ElementTree as ET
	import glob
	import os
	import shutil

	def read_label(xml_file: str):

	#!/bin/bash

	# This installs alacritty terminal on ubuntu (https://github.com/jwilm/alacritty)
	# You have to have rust/cargo installed for this to work

	# Install required tools
	sudo apt-get install -y cmake libfreetype6-dev libfontconfig1-dev xclip

	# Download, compile and install Alacritty
	git clone https://github.com/jwilm/alacritty

	from __future__ import print_function
	import requests
	import json
	import cv2

	addr = 'http://localhost:5000'
	test_url = addr + '/api/test'

	# prepare headers for http request
	content_type = 'image/jpeg'

	# Modified from labelme2coco repository
	# Usage: python convert_matterport_json_to_coco.py path_to_json.json

	import argparse
	import glob
	import json
	import os

	import numpy as np
	import PIL.Image

	# Place file config in a folder in {MMDETECTION_ROOT}/configs
	_base_ = [
	'../_base_/models/mask_rcnn_r50_fpn.py',
	'../_base_/datasets/coco_instance.py',
	'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
	]
	# Use r101 model
	model = dict(
	pretrained='torchvision://resnet101',
	backbone=dict(depth=101),

	dataset_type = 'CocoDataset'
	Classes = ('Hand')
	data_root = 'data/handdata/'
	img_norm_cfg = dict(
	mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
	train_pipeline = [
	dict(type='LoadImageFromFile'),
	dict(
	type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=False),
	dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),