Quickly remove the text without having to create a custom prompt.
Edit the file: C:\Program Files\Git\etc\profile.d\git-prompt.sh
else
TITLEPREFIX=$MSYSTEM
fi| #!/bin/sh | |
| # Converts a mysqldump file into a Sqlite 3 compatible file. It also extracts the MySQL `KEY xxxxx` from the | |
| # CREATE block and create them in separate commands _after_ all the INSERTs. | |
| # Awk is choosen because it's fast and portable. You can use gawk, original awk or even the lightning fast mawk. | |
| # The mysqldump file is traversed only once. | |
| # Usage: $ ./mysql2sqlite mysqldump-opts db-name | sqlite3 database.sqlite | |
| # Example: $ ./mysql2sqlite --no-data -u root -pMySecretPassWord myDbase | sqlite3 database.sqlite |
| #!/bin/bash | |
| cd /tmp || exit | |
| read -r -p "[s]table or [b]eta? [sS/bB] " choice | |
| choice=${choice,,} # tolower | |
| if [[ ! "$choice" =~ ^(s|b)$ ]]; then | |
| exit | |
| fi | |
| if [[ "$choice" = "s" ]]; then | |
| url=https://dl.pstmn.io/download/latest/linux64 | |
| name=Postman |
People
:bowtie: |
π :smile: |
π :laughing: |
|---|---|---|
π :blush: |
π :smiley: |
:relaxed: |
π :smirk: |
π :heart_eyes: |
π :kissing_heart: |
π :kissing_closed_eyes: |
π³ :flushed: |
π :relieved: |
π :satisfied: |
π :grin: |
π :wink: |
π :stuck_out_tongue_winking_eye: |
π :stuck_out_tongue_closed_eyes: |
π :grinning: |
π :kissing: |
π :kissing_smiling_eyes: |
π :stuck_out_tongue: |
| #!/usr/bin/env node | |
| var options = require('minimist')(process.argv.slice(2), { default: { | |
| cport: 8888 | |
| }}); | |
| var disable = options.disable || options.d; | |
| var enable = options.enable || options.e; | |
| var status = options.s || options.status; | |
| var child = require('child_process'); |
| #!/bin/bash | |
| MYSQL_USER="root" | |
| MYSQL_PASS="1234" | |
| MAIL="voce@servidor.com.br" | |
| REMOTE_REPOSITORY=https://bitbucket.org/username/backup-banco | |
| DIRECTORY="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && pwd )" | |
| cd "$DIRECTORY" | |
| COMMIT_COUNT=`git rev-list HEAD --count` |
Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.
The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.
On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:
####### 1. A low-resolution photo of road signs
| #/usr/bin/env bash | |
| # MIT Β© Sindre Sorhus - sindresorhus.com | |
| # git hook to run a command after `git pull` or `git merge` if a specified file was changed | |
| # Run `chmod +x post-merge` to make it executable then put it into `.git/hooks/`. | |
| changed_files="$(git diff-tree -r --name-only --no-commit-id ORIG_HEAD HEAD)" | |
| check_run() { | |
| echo "$changed_files" | grep --quiet "$1" && eval "$2" |