Annotations | Showing use of Java6 annotation processors | Build Tool library
kandi X-RAY | Annotations Summary
kandi X-RAY | Annotations Summary
This is a sample project demonstrating usefulness of compile time annotations on creating URLStreamHandlerFactory ready for modular usage. Declaratively registered, scalable, and simple to write. Read more at
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Top functions reviewed by kandi - BETA
- Returns an URL stream handler
- Look for a URL stream handler
- Read the content of a URL line from the given URL
- Find a URL stream handler by protocol
- Process the method signature
- Generate a wrapper around a method
- Implements the getComplements method
- Open a URLConnection
- Registers the global proxy factory
Annotations Key Features
Annotations Examples and Code Snippets
Lines of Code : 26
License : No License
Copy@RuntimePermissions
class MainActivity : AppCompatActivity(), View.OnClickListener {
@NeedsPermission(Manifest.permission.CAMERA)
fun showCamera() {
supportFragmentManager.beginTransaction()
.replace(R.id.sample_conte @Hint("hint1")
@Hint("hint2")
class Person {}
Hint hint = Person.class.getAnnotation(Hint.class);
System.out.println(hint); // null
Hints hints1 = Person.class.getAnnotation(Hints.class);
System.out.println(hints1.value().length) @interface Hints {
Hint[] value();
}
@Repeatable(Hints.class)
@interface Hint {
String value();
}
def _annotate_ndarray_lines(
array_lines, tensor, np_printoptions=None, offset=0):
"""Generate annotations for line-by-line begin indices of tensor text.
Parse the numpy-generated text representation of a numpy ndarray to
determine the ind def update_image_and_anno(
img_list: list, anno_list: list, flip_type: int = 1
) -> tuple[list, list, list]:
"""
- img_list : list of all images
- anno_list : list of all annotations of specific image
- flip_type : 0 is vertica @Override
public boolean process(Set annotations, RoundEnvironment roundEnv) {
for (TypeElement annotation : annotations) {
Set annotatedElements = roundEnv.getElementsAnnotatedWith(annotation);
Map> annotated Community Discussions
Trending Discussions on Annotations
QUESTION
I would like to extract the definitions from the book The Navajo Language: A Grammar and Colloquial Dictionary by Young and Morgan. They look like this (very blurry):
I tried running it through the Google Cloud Vision API, and got decent results, but it doesn't know what to do with these "special" letters with accent marks on them, or the curls and lines on/through them. And because of the blurryness (there are no alternative sources of the PDF), it gets a lot of them wrong. So I'm thinking of doing it from scratch in Tesseract. Note the term is bold and the definition is not bold.
How can I use Node.js and Tesseract to get basically an array of JSON objects sort of like this:
...ANSWER
Answered 2021-Jun-15 at 20:17Tesseract 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:
- Install jTessBoxEditor (https://sourceforge.net/projects/vietocr/files/jTessBoxEditor/). Requires Java Runtime installed as well.
- 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.
- Use jTessBoxEditor to merge all the images into a single .tiff
- Create a training label file (.box)j. This is done with Tesseract itself.
tesseract your_language.font.exp0.tif your_language.font.exp0 makebox - 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.
- Train the tesseract model itself
- save a file:
font_propertieswho's content isfont 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.
QUESTION
i am working on a map app with some overlays (annotations, circles, polygons). And i also have UISwitches to appear/disappear them. For the annotation is easy: .add / .remove, it works.
...ANSWER
Answered 2021-Jun-15 at 19:49You can use MKMapView.removeOverlays call to do this.
QUESTION
This is the model I have so far:
...ANSWER
Answered 2021-Jun-15 at 01:10In Kotlin you have to instantiate properties with backing field in the construction (getting them from constructor, assigning them some value, or fill them in init blocks). And the only exception is lateinit var. In the first code, you're getting them in constructor. But in second one, they're introduced without being initialize so compiler asks you to either fill them, or convert them to non-backing field by providing getter and setters.
But if you want to make the first code Serializable you have to simply make that implement Serializable like this:
QUESTION
The Question
How do I best execute memory-intensive pipelines in Apache Beam?
Background
I've written a pipeline that takes the Naemura Bird dataset and converts the images and annotations to TF Records with TF Examples of the required format for the TF object detection API.
I tested the pipeline using DirectRunner with a small subset of images (4 or 5) and it worked fine.
The Problem
When running the pipeline with a bigger data set (day 1 of 3, ~21GB) it crashes after a while with a non-descriptive SIGKILL.
I do see a memory peak before the crash and assume that the process is killed because of a too high memory load.
I ran the pipeline through strace. These are the last lines in the trace:
ANSWER
Answered 2021-Jun-15 at 13:51Multiple things could cause this behaviour, because the pipeline runs fine with less Data, analysing what has changed could lead us to a resolution.
Option 1 : clean your input dataThe third line of the logs you provide might indicate that you're processing unclean data in your bigger pipeline mmap(NULL, could mean that | "Get Content" >> beam.Map(lambda x: x.read_utf8()) is trying to read a null value.
Is there an empty file somewhere ? Are your files utf8 encoded ?
Option 2 : use smaller files as inputI'm guessing using the fileio.ReadMatches() will try to load into memory the whole file, if your file is bigger than your memory, this could lead to errors. Can you split your data into smaller files ?
If files are too big for your current machine with a DirectRunner you could try to use an on-demand infrastructure using another runner on the Cloud such as DataflowRunner
QUESTION
i am trying to put 2 vertical lines on a chart.JS chart using the annotations plugin. i am using the following versions: chart.js = 2.8.0 annotations plugin = 0.5.7
here's the JSFiddle
please see my code below:
...ANSWER
Answered 2021-Jun-15 at 12:30You have to provide both annotations as object in 1 array, not an array containing objects containing arrays, see example:
QUESTION
I am trying to install jenkins on my kubernetes cluster under jenkins namespace. When I deploy my pv and pvc, the pv remains available and does not bind to my pvc.
Here is my yamls:
...ANSWER
Answered 2021-Jun-15 at 09:52Based on the storage class spec, I think the problem is the volumeBindingMode being set as WaitForFirstConsumer which means the PV will remain unbound until there is a Pod to consume it.
You can change it Immediate to allow the PV to be bound immediately without requiring to create a Pod.
You can read about the different volume binding modes in detail in the docs.
QUESTION
TL;DR: Interested in knowing if it's possible to use Abstract Base Classes as a mixin in the way I'd like to, or if my approach is fundamentally misguided.
I have a Flask project I've been working on. As part of my project, I've implemented a "RememberingDict" class. It's a simple subclass of dict, with a handful of extra features tacked on: it remembers its creation time, it knows how to pickle/save itself to a disk, and it knows how to open/unpickle itself from a disk:
...ANSWER
Answered 2021-Jun-15 at 03:43You can get around the problems of subclassing dict by subclassing collections.UserDict instead. As the docs say:
Class that simulates a dictionary. The instance’s contents are kept in a regular dictionary, which is accessible via the data attribute of UserDict instances. If initialdata is provided, data is initialized with its contents; note that a reference to initialdata will not be kept, allowing it be used for other purposes.
Essentially, it's a thin regular-class wrapper around a dict. You should be able to use it with multiple inheritance as an abstract base class, as you do with AbstractRememberingDict.
QUESTION
I don't understand how to apply hashicorp vault to inject secrets in my app.
The following link shows a couple of examples https://www.vaultproject.io/docs/platform/k8s/injector/examples
I used the environment variables example from the same post. But it seems not all the env variables are injected into the app. For instance, ENVs in one of my layouts don't seem to get applied meta property="og:title" content="#{ENV['NAME']}" - shows no value. But the app is running, /vault/secrets/... has files with contents.
Here's a part of the Deployment config of my app.
When there're multiple secrets/templates, the Deployment is going to look ugly.
There's absolutely no description for configmap example but this is probably what I should be using instead of env.
...ANSWER
Answered 2021-Apr-18 at 18:36If you want to inject the vault secret into the deployment pod what you can do
There is one great project on Github Vault-CRD in java: https://github.com/DaspawnW/vault-crd
Vault CRD for sharing Vault Secrets with Kubernetes. It injects & sync values from Vault to Kubernetes secret. You can use these secrets as environment variables inside pod.
the flow goes something like : vault to Kubernetes secret > and that secrets get injected into deployment using YAML same as configmap
apart from this there is also another nice method of sidecar pattern.
for that, there is a very nice tutorial: https://github.com/hashicorp/hands-on-with-vault-on-kubernetes
another one : https://www.hashicorp.com/blog/injecting-vault-secrets-into-kubernetes-pods-via-a-sidecar
QUESTION
I am having a problem using Kubernetes Ingress with a ASP.NET core web API.
Lets say I have a web API with three controllers (simplified code to demonstrate three routes /, /ep1, /ep2):
...ANSWER
Answered 2021-Jun-14 at 18:57Routing within the app should be handled by the app. So, there should be no need to define dynamic paths. Try this.
QUESTION
I'd like to test that all Hibernate association annotations (@ManyToOne, @OneToMany, @OneToOne, @ManyToMany) are using fetch = FetchType.LAZY. This is what works:
ANSWER
Answered 2021-Jun-14 at 20:00What you want can be built with Java stream operations:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install Annotations
You can use Annotations like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the Annotations component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .
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