Is there any tool or methods to do automation testing for games developed in cocos2d-x?
FYI: Appium and other tools try to find Android and IOS elements, I don't use them in the games.
I highly recommend you to use Image Recognition for Mobile App Game Testing, Appium too supports Image recognition.
You can use applitools for doing automatic image comparison with Appium. In fact Applitools automates not only the capturing of the screenshot, but also it's validation, and has sophisticated algorithms to avoid false failures of the image comparison. Applitools' Selenium SDK works out of the box with Appium as well as with any other WebDriver implementation, and is cross-platform and cross-device. Applitools have a free registration plan, and the SDKs are open source and available on GitHub.
For More Information Use below links
Image Recognition Part 1
and Image Recognition Part 2 also take a look at Sikuli
Related
I'm looking to capture an image from my usb webcam in LabVIEW 2018. I've looked at older posts (the one from Lava working with the 'free' portions of V&M Toolkit, another webcam test that hangs my computer when trying to run and a few others). What is the best way to do this in the newer LabVIEWs? All the examples I've seen (none of which run correctly or well) are all from 2011-ish timeframe.
It depends on the task (like, for what you are going to use camera), but you could use NI Vision Acquisition Software - which provides set of functions to access the camera, acquire images and videos and process them (basically, IMAQ drivers is what you need). Or, if you are going to use your camera for some kind of test application (vision inspection) - then you'd better check Vision Builder for Automated Inspection.
Those are the easiest (but not the cheapest) ways to acquire images from the various cameras using LabVIEW.
UPDATE:
License scheme for the software could be found here - Licensing National Instruments Vision Software. Description of each software component is also here - Does My Camera Use NI-IMAQ, NI-IMAQdx or NI-IMAQ I/O?. So in order to use 3rd party USB camera, one need to have NI-IMAQdx, which requires license.
I'm trying to build an app to make image recognition using the phone camera... I saw a lot of videos where using the camara the app identify where is the person or which feelings they have or things like that in real time.
I need to do a built an app like this, I know it's not an easy task, but I need to know which technologies can be use in order to achieve this in a mobile app?
Is it tensor flow?
Are there some libraries that helps to achieve this?
Or do I need to build a full Machine Learning with IA app?
Sorry to make such a general question but I need some insights.
Redgards
If you are trying to do this for the iOS platform, you could use a starter kit here: https://developer.ibm.com/patterns/build-an-ios-game-powered-by-core-ml-and-watson-visual-recognition/ for step-by-step instructions.
https://github.com/IBM/rainbow is a repo which it references.
You train your vision model on the IBM Cloud using Watson Visual Recognition, which just needs example images to learn from. Then you download the model into your iOS app and deploy with XCode. It will "scan" the live camera feed for the classes defined in your model.
I see you tagged TF (which is not part of this starter kit) but if you're open to other technologies, I think it would be very helpful.
I am trying to do some work using Kinect and the Kinect SDK.
I was wondering whether it is possible to detect facial expressions (e.g. wink, smile etc) using the Kinect SDK Or, getting raw data that can help in recognizing these.
Can anyone kindly suggest any links for this ? Thanks.
I am also working on this and i considered 2 options:
Face.com API:
there is a C# client library and there are a lot of examples in their documentation
EmguCV
This guy Talks about the basic face detection using EmguCV and Kinect SDK and you can use this to recognize faces
Presently i stopped developing this but if you complete this please post a link to your code.
This is currently not featured within the Kinect for Windows SDK due to the limitations of Kinect in producing high-resolution images. That being said, libraries such as OpenCV and AForge.NET have been sucessfuly used to detected finger and facial recognition from both the raw images that are returned from Kinect, and also RGB video streams from web cams. I would use this computer vision libraries are a starting point.
Just a note, MS is releasing the "Kinect for PC" along with a new SDK version in february. This has a new "Near Mode" which will offer better resolution for close-up images. Face and finger recognition might be possible with this. You can read a MS press release here, for example:
T3.com
The new Kinect SDK1.5 is released and contains the facial detection and recognition
you can download the latest SDK here
and check this website for more details about kinect face tracking
Is there any code out there, that I can use in Cocoa, to recognize text from photos? Let's say I snap a photo with my iPhone of a page of a book. I'd like to capture the text in it.
There is the Tesseract OCR toolkit that is an open source OCR engine, currently maintained by Google. "Olipion" created a cross compilation tutorial to get in on the iPhone. I would say that this is a good place to start.
However, there are reasons why you might not want to to OCR on the Phone even if you could. Some of these include:
Even the new iPhone 4's processor is not that fast and since you app can't really run in the background doing the processing, the user experience might not be optimal.
Running OCR on a mobile device would probably be a killer for battery life.
Every time you would want to update the OCR engine everybody who installed your app would have to upgrade.
For an always connected mobile device running the OCR on a server somewhere would be probably better. You could upgrade your OCR software easily, you could run much more powerful algorithms then a mobile device could handle and so on.
I am not so sure that you would be able to get good results from photos taken using a mobile camera -- accuracy of OCR systems goes way down with the kind of poorly lit, noisy, distorted images likely to be captured using a phone camera.
As far as commercial products out there, there is Evernote that gives you a OCR capability if you buy their premium service.
As an alternative to machine OCR, there is always Mechanical Turk, where you could pay people small amount to do the OCR for you. Would probably do better at transcription given the image source.
I am developing a website exclusively for mobile browsers.
What guidelines should I follow to optimize the site for mobile development?
My main concerns:
Most mobile devices have propriety browsers. How can the app be tested on those different browsers (testing on an actual device is not possible due to security restrictions)?
How to optimize the site for different screen sizes?
How to make the app touch friendly?
How to detect orientation of devices (in devices that come with an accelerometer)?
How to check that the device is not a desktop/laptop?
Things that I have used when designing mobile websites.
Find out the range of devices that you are planning to support. Some questions that you can ask are
Are u going to support only smartphones
What platforms are u planning to support ( iPhone, Android, Symbian ? )
A lot of you questions can be answered by the kind of Analytics that you are able to gather. If you have very less statistics then you can follow this strategy to start with.
Separate out the target range of devices into
simple ( basic phones with minimum browsing capabilities. ) - Design a very simple plain vanilla site for them.
medium ( older generation smartphones with browsers with poor javascript support ) - Design a site that has slightly better features.
Highend smartphones ( iPhone, Android, WebOS ) - Provide jazzy features that these phones support.
Use a device detection library like WURFL / .Mobi for device detection and WALL for dynamic rendering of content.
You can use .Mobi to detect an HTML5 compliant mobile browser. That way, you can take advantage of HTML5 capabilities in the devices that support it.
For testing you can follow this approach
test on browsers - Firefox / Safari / Opera have plugins to alter USER_AGENT and can simulate mobile testing.
Test on simulators - All the device platforms provide free to download emulators
If needed try device emulation products like device anywhere / perfecto.
I hope I was able to clarify atleast some of you questions. :)
The definitive guide has to be the W3C Mobile Web Best Practices: http://www.w3.org/TR/mobile-bp/ Don't let the length of it put you off - I find it much easier to read than other W3C specs. The key section is the Best Practice Statements, divided into bite-size chunks, often with an example. There's also a recent and extensive mobile web optimization guide here: http://dev.opera.com/articles/view/the-mobile-web-optimization-guide/ (disclaimer: I work for Opera)
Q1 Most mobile devices have propriety browsers. How can the app be tested on those different browsers (testing on an actual device is not possible due to security restrictions)?
The answer depends on how many devices you want to test and support.
iPhone: device and simulator are available.
Android: devices and emulator are available
Other mobile phones?
check http://www.deviceanywhere.com
Of course, you need to pay service fee. But i think its reasonable.
Q2 How to optimize the site for different screen sizes?
iphone4
WVGA854
WVGA800
VGA
HVGA
QVGA
QCIF+
Making contents for all different size is difficult. So have to make a choice about screen size and supported models.
Q3 How to make the app touch friendly?
It is your design issue.
Q4 How to detect orientation of devices (in devices that come with an accelerometer)?
Android and iOS has special message about such event. You have to follow such message.
Of course, you need both landscape and portlait layout.
Q5 How to check that the device is not a desktop/laptop?
You can use User-Agent header or IP address. But IP address is not good method.