basic | Gradle搭建的 SpringBoot Vue 前后端分离（含移动端）且合并打包的基本框架 | Command Line Interface library

This can easily be done using list comprehension

I had used document.getElementById('1').contentWindow.jQuery('#left').jstree(true); to get instance from iframe with id='1'. In order to listen to right iframe(with id='2') if any menu has been clicked, I used document.getElementById('2').contentWindow.jQuery('#right').on("changed.jstree",function(e,data){}). I get the instance of left iframe within this function. By using this instance, I has deselect previous selection, select current selection, and open children of selected menu.

index-12.html

The problem is that your CSS selectors include parentheses () and dollar signs $. These symbols already have a special meaning. See:

• () - Are parentheses allowed in CSS selectors?
• $ - [attribute$=value] Selector

You can escape these characters using a backslash \.

try require('dotenv').config()

My research of the major syntax and functionality differences from http://www.greenend.org.uk/rjk/tech/regexp.html:

• . in GNU grep does not match null bytes and newlines (but does match newlines when used with --null-data), while Perl, everything except \n is matched.
• [...] in GNU grep defines POSIX bracket expressions, while Perl uses "character" classes. I'm not sure on the details. See http://www.greenend.org.uk/rjk/tech/regexp.html#bracketexpression
• "In basic regular expressions the meta-characters ?, +, {, |, (, and ) lose their special meaning; instead use the backslashed versions \?, \+, \{, \|, \(, and \)." From https://www.gnu.org/software/grep/manual/html_node/Basic-vs-Extended.html. ERE matches PCRE syntax.
• GNU grep \w and \W are the same as [[:alnum:]] and [^[:alnum]], while Perl uses alphanumeric and underscore.
• GNU grep has \< and \> for start and end of word.

Perl supports much more additional functionality:

• "nongreedy {}" with syntax re{...}?
• additional anchors and character types \A, \C, \d, \D, \G, \p, \P, \s, \S, \X. \Z, \z.
• (?#comment)
• shy grouping (?:re), shy grouping + modifiers (?modifiers:re)
• lookahead and negative lookahead (?=re) and (?!re), lookbehind and negative lookbehind (?<=p) and (?
• Atomic groups (?>re)
• Conditional expression (?(cond)re)
• ... and more, see man pcresyntax

Tesseract takes a lang variable that you can expand to include different languages if they're installed. I've used the UB Mannheim (https://github.com/UB-Mannheim/tesseract/wiki) installation which includes a ton of languages supported.

To get better and more accurate results, the best thing to do is to process the image before handing it to Tesseract. Set a white/black threshold so that you have black text on white background with no shading. I'm not sure how to do this in Node, but I've done it with Python's OpenCV library.

If that font doesn't get you decent results with the out of the box, then you'll want to train your own, yes. This blog post walks through the process in great detail: https://towardsdatascience.com/simple-ocr-with-tesseract-a4341e4564b6. It revolves around using the jTessBoxEditor to hand-label the objects detected in the images you're using.

Edit: In brief, the process to train your own:

1. Install jTessBoxEditor (https://sourceforge.net/projects/vietocr/files/jTessBoxEditor/). Requires Java Runtime installed as well.
2. Collect your training images. They want to be .tiffs. I found I got fairly accurate results with not a whole lot of images that had a good sample of all the characters I wanted to detect. Maybe 30/40 images. It's tedious, so you don't want to do TOO many, but need enough in order to get a good sampling.
3. Use jTessBoxEditor to merge all the images into a single .tiff
4. Create a training label file (.box)j. This is done with Tesseract itself. tesseract your_language.font.exp0.tif your_language.font.exp0 makebox
5. Now you can open the box file in jTessBoxEditor and you'll see how/where it detected the characters. Bounding boxes and what character it saw. The tedious part: Hand fix all the bounding boxes and characters to accurately represent what is in the images. Not joking, it's tedious. Slap some tv episodes up and just churn through it.
6. Train the tesseract model itself

• save a file: font_properties who's content is font 0 0 0 0 0
• run the following commands:

tesseract num.font.exp0.tif font_name.font.exp0 nobatch box.train

unicharset_extractor font_name.font.exp0.box

shapeclustering -F font_properties -U unicharset -O font_name.unicharset font_name.font.exp0.tr

mftraining -F font_properties -U unicharset -O font_name.unicharset font_name.font.exp0.tr

cntraining font_name.font.exp0.tr

You should, in there close to the end see some output that looks like this:

Master shape_table:Number of shapes = 10 max unichars = 1 number with multiple unichars = 0

That number of shapes should roughly be the number of characters present in all the image files you've provided.

If it went well, you should have 4 files created: inttemp normproto pffmtable shapetable. Rename them all with the prefix of your_language from before. So e.g. your_language.inttemp etc.

Then run:

combine_tessdata your_language

The file: your_language.traineddata is the model. Copy that into your Tesseract's data folder. On Windows, it'll be like: C:\Program Files x86\tesseract\4.0\tessdata and on Linux it's probably something like /usr/shared/tesseract/4.0/tessdata.

Then when you run Tesseract, you'll pass the lang=your_language. I found best results when I still passed an existing language as well, so like for my stuff it was still English I was grabbing, just funny fonts. So I still wanted the English as well, so I'd pass: lang=your_language+eng.

The simplest solution is to stick with a one-to-many relationship. Use one table and store one comment per row, with references to the post and the comment author, and a timestamp so you can sort the comments chronologically.

You seem uncertain about whether you need a "threaded comment" hierarchy. This is more complex, so if you don't need it, don't bother.

If you do need to show comment threads, then you should learn about running recursive queries in MySQL 8.0: https://dev.mysql.com/doc/refman/8.0/en/with.html#common-table-expressions-recursive

You still only need one table. Don't create a second table for sub-comments. Just store comments like in your one-to-many example, but each comment may link to its "parent" comment when it is a reply.

Another solution that many sites use is to skip implementing their own comment system, and just embed a comment service like Disqus. That's likely to be much more reliable and safe than yours. But if you're doing this as a learning exercise, that's worthwhile too.

I would suggest recreating the dictionary using a dictionary comprehension along with enumerate.

First note that a token must be obtained from the server ! A token is required to make some API calls due to security concerns. There are usually at least two types of tokens:

• Access token: You use it to make API calls (as in the Authorization header above). But this token usually expires after a short period of time.
• Refresh token: Use this token to refresh the access token after it has expired.

You should use requests-oauthlib in addition with requests.
https://pypi.org/project/requests-oauthlib/
But first, read the available token acquisition workflows:
https://requests-oauthlib.readthedocs.io/en/latest/oauth2_workflow.html#available-workflows
and choose the right workflow that suits your purposes. (The most frequently used is Web App workflow)
Then, implement the workflow in your code to obtain the token. Once a valid token is obtained you can use it to make various API calls.

As a side note: be sure to refresh token if required.