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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()

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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):

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#!/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

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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'

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# 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

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ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQDUMi6jd45sZ1CuUyFr/DyoM0zmvvwnNWSa1P78jLLVyyNrbBwrL3MSb2TYPal1hFW0vmxS9F+HJAbeuMJ8kp5fxjq+wipf+OzDpIFvYdSkOGQb2N51MsglT6qDw+vgfFJ4JfVcso+9CgCntgn5HJdOXbd8reHyzASV0LQ9gmDBtz8A0SnrDkh3znYLcuERtuf6vRhdKHljHu5Mny6CjG4+e6+ZOM7QxxBYzEtmNk96CT4cVkKP9G+xXN5gXMZfEEHCXiimJdkhwBc7gPzfDZc67y/4QB69G+EOgOX6Hbnvp4Kx4mFxaCMo2VU391m3JE5or04xbdUtpuq8BG2G6NrB root@f24bae967576

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# 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),

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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),

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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

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{"lastUpload":"2018-11-24T13:33:06.172Z","extensionVersion":"v3.2.0"}