In order to utilize the app, the latest version of OpenCV Manager (also found on Google Play) must be downloaded and installed correctly. This app has been successfully tested on Android 2.3.4 and Android 4.3.
At the moment this can considered an initial tech demo, please feel free to play around with it. The source code for this implementation can also be found in github link below!
OpenCV Manager needed . If not installed, the application will ask download.
It takes at least two faces saved so you can begin to recognize
Training Mode: Write the name of the person, focus and when it begins to appear a box locating a face press "Rec". Press Rec repeatedly to store different gestures
Find mode. Focus on one face and if recognized, its name appears. An icon will appear green, yellow or red depending on the degree of confidence in recognizing
Button "View All": See the faces stored.
+ Version 2.2: Storage moved to internal storage.
Source code: https://github.com/ayuso2013/face-recognition
This app compares the reference image to the images in the added list. It uses ORB feature detector with BRIEF descriptor extractors. It uses BRUTEFORCE_HAMMING method to match the descriptors. Also, based on preference, app can also check the homography of the matched keypoints with LMED (least of median squares) and remove the outliners to give a better result.
How to use:
1 - Move the vision wherever you want, take pics along the way by using ADD button.
2 - You can also take a reference picture, that you want to be recognized. When you click it, it will be shown on the right of the camera view.
3 - When you are done, and make sure you have added images and have a reference image, click the Find Match button. It will calculate the keypoints of each image and the reference image and apply the matching algorithm.
4 - After matching and comparing, it will show the best match among all matches.
5 - You can also use Homography Off/On switch to provide more accurate results.
6 - Also, you can use Images only/with matches switch to show which keypoints are matched. REMEMBER, you have to click find match again to see the effect of Homography and Matches switch.
This app is still in development, actually being used by another project but I put it on for cool demonstration!
Source code available: https://github.com/mustafaakin/image-matcher
We parallelized the algorithm using the Vector Fabrics Pareon tool. The results are shown for single, two and four application threads.
This demo should be run on dual or quad core machines.
This demo is only interesting to programmers who need to write parallelized C or C++ code.
Modes currently supported:
-HDR Capture & Tonemapping
-Neon Gradient Edges
ViewerCV is simply an *open source* demo app for fun...
!! There is no point to it !!
[ SEO: real-time, viewer, camera, live, computer vision, opencv ]
HowTo (Button info):
-'Menu' changes effect
-'Mode' cycles options for that effect
-'Menu>Settings' adjust resolution
Load scaled pictures
Color plane extraction - RGB, HSV, HSL, XYZ, YCrCb
Binary convolution functions like Dilate, Erode, etc
Save post-processed pictures
Camera Classifier é um aplicativo que permite realizar a classificação de imagens utilizando o descritor BIC, com classificador 3NN com a biblioteca OpenCV. Seu código fonte é aberto e disponível em https://github.com/nihey/CameraClassifier
Simple edge detection should be fluent for everybody but face detection need more processing power.
Use Neon for colorspace transformation
Face detection optimization is done thanks to Bill Mc Cord
for more information please visit: http://www.lfe.mw.tum.de/en/mdt
The Mobile Detection Task (MDT) is a derivative of the Detection Response Tasks
(see e.g. http://www.lfe.mw.tum.de/en/arduino-drt) allowing mobile, wireless experimentation through the use of a nomadic device (Android smart phone). Since the MDT is not within the DRT ISO Standard (currently under development), this task is referred to a MDT rather than a DRT, despite similar task qualities.
The basic idea behind the MDT is to implement the DRT protocol on a smart phone and to use the hardware of the device (touch screen, display, vibration motor, WLAN, etc.) to asses the attentive effects of cognitive workload. The display (visual) and vibration motor (tactile) are used to present participants with signals, and the touch screen or an external button can be used to communicate participant responses. Data can be logged locally on the system or smart phone and can even be wirelessly transmitted (depending on the phones capabilities). These connections can also be used to control and monitor an experiment.
All these capabilities are possible with the use of a single device you already likely possess!). The source is intentionally open source (GPL) so that you can adjust it to your project needs.
Important note: The absolute reaction time values are not as accurate as you may expect. Nevertheless, sometimes useful to compare relatively experimental conditions or different systems under test.
If you connect wires and equipment to a subject, please do so with caution and take precautionary measures (e.g. beware of electrical safety, etc.).
If experimental setups include a driving task, special care must be taken. e.g. no cables or device parts should interfere with being able to safely perform the driving task. As a general piece of advice, please think about what can fail while preparing the experiment (something becomes loose or gets stuck to other parts,...) and take every measure possible to avoid such occurrences.
* Phone's positioning sensors for location feedback (GPS, Mag, Accel)
* Realtime video streaming phone-to-tablet (MJPEG)
* Motor control on Arduino (PID with encoder feedback)
With this APK you can see the functionallity of the Synchronized Scrolling Library I created. If you are interested, you can find the source code for the library and the samples at GitHub here: https://github.com/xrigau/Synchronized-Scrolling/tree/master/XaviRigau-SynchronizedScrolling
I'll be writing some posts on how to use it in my blog. If you want to check it it's: http://blog.xavirigau.com
Thanks for watching!
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OpenCV library is used by other applications for image enhancement, panorama stitching, object detection and recognition, etc. OpenCV Manager provides the best version of the OpenCV for your hardware. It also receives the latest stability and performance updates for the library.