context | Context - Using old smartphones as stationary IoT devices

There are two approaches that are possible. The first is to use a suborchestrator for each job so that each suborchestrator handles just a specific job. Here is the docs for this approach https://docs.microsoft.com/en-us/azure/azure-functions/durable/durable-functions-sub-orchestrations?tabs=csharp Example from docs seem to be alike to yours.

The other is to use ContinueWith so that each job has its own "chain"

The issue you are having has nothing to do with templates. You cannot invoke std::async() on a member function without binding it to an instance. Wrapping the call in a lambda does the trick.

Here's an example:

I finally found the solution to the problem:

There is no public interface in the Magnification API that allows clients to apply filtering (other than color transforms). This used to be possible, but the MagSetImageScalingCallback API was deprecated in Windows 7:

This function works only when Desktop Window Manager (DWM) is off.

Even if it is still available, it will no longer work as designed. From Desktop Window Manager is always on:

In Windows 8, Desktop Window Manager (DWM) is always ON and cannot be disabled by end users and apps.

With that, you're pretty much out of luck trying to replicate the results of the Magnifier application's upscaler using the Magnification API.

The Magnifier application appears to be using undocumented API calls to accomplish the upscaling effects. Running dumpbin /IMPORTS magnify.exe | findstr "Mag" lists some of the public APIs, as well as the following:

• MagSetLensUseBitmapSmoothing
• MagSetFullscreenUseBitmapSmoothing

Unless you are willing to reverse-engineer those API calls, you're going to have to spend your time on another project, or look into a different solution.

A note on the upscaling algorithm: If you look closely you'll notice that the upscaled image doesn't exhibit any of the artifacts associated with smoothing algorithms.

The image isn't blurred in any way. Instead, it shows sharp edges everywhere. I don't know what upscaling algorithm is at work here. Wikipedia's entry on Pixel-art scaling algorithms lists some that have very similar properties. It might well be one of those, or a modified version thereof.

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.

I'm thinking the suspect is an unsuccessful migration. Let's undo it and try again

You can use simply intent.putExtra instead of worrying about which variant like put_____Extra to use.

When extracting the value, you can use intent.extras to get the Bundle and then you can use get() on the Bundle and cast to the appropriate type. This is easier than trying to figure out which intent.get____Extra function to use to extract it, since you will have to cast it anyway.

The below code works whether your data class is Serializeable or Parcelable. You don't need to use arrays, because ArrayLists themselves are Serializeable, but you do need to convert from MutableList to ArrayList.

There are several points to correct in your code:

• You need to return a Promise when all the asynchronous job is completed. See this doc for more details.
• If you use the await keyword, you need to declare the function async, see here.
• A QuerySnapshot has a forEach() method
• You can get the DocumentReference of a doc from the QuerySnapshot just by using the ref property.

The following should therefore do the trick:

You need to set input image for the filter and take care of the interoperately between Image and UImage