I want to develop app which will recognize object(like monument or something) present in front of camera using OpenCv and then it will show information about it.
So the question is how to recognize object(like monument or something) shape or compare to images with OpenCV?
And what is the best method for doing this?
It would be good if there was some kind of samples or tutorials for object detection and comparison.
Thank you.
The best method for what you ask is using local features detectors like OpenCV's SIFT, SURF and ORB, for example.
You need at least one picture from the object you want to detect. Afterwards, those algorithms can compare that image with other images to see if they are similar enough.
Here is the Documentation for the algorithms.
ORB and others:
http://docs.opencv.org/modules/features2d/doc/feature_detection_and_description.html
SURF and SIFT ('nonfree'):
http://docs.opencv.org/modules/nonfree/doc/feature_detection.html
The way these algorithms work for that task is by selecting interesting points for each image, and compare them to see if they match. If several matches are found, it is most likely the images have the same object.
Tutorials (from Feature Detection and below):
http://docs.opencv.org/doc/tutorials/features2d/table_of_content_features2d/table_of_content_features2d.html
You can also find C++ samples related to this topic here (samples are also within OpenCV download package):
eg. "matching_to_many_images.cpp"
"video_homography.cpp"
http://code.opencv.org/projects/opencv/repository/revisions/master/show/samples/cpp
And Android Java samples here (unrelated but also helpful):
http://code.opencv.org/projects/opencv/repository/revisions/master/show/samples/android
Or Python samples which are actually the more updated ones for this topic (at the time this post was written):
http://code.opencv.org/projects/opencv/repository/revisions/master/show/samples/python2
As a final note, like #BDFun said in the comment, this is not trivial to do.
More - if you want an overview of OpenCV Feature detection and description, check this post.
Related
I am building an android app,
And I want to add the ability that when I take a picture of a dog, I will search in my DB that consist picture of dogs, I want to find the pictures that is the same dog or at least quite similar to him.
Is there any library or github that can be used to compare pictures of dogs?
I'm pretty new at this.
thanks!
You need to use some image processing framework.., Like opencv i have used it for detecting the difference in two images and showing rectangle over the difference area. You can also go for google tensorflow.
Well, if you want to detect images containing dogs, you need a harcascade and you have to train it with positive and negative samples, so that it would learn how to find dogs within images. And answer is not so short it is a machine learning library. If you want to detect dogs are present in given image or not , with c++ and opencv, i can help by providing sample basic programs too
Personally i have used opencv it is great fun in learning and there is also very active forum like stackoverflow for them :
This is the link of opencv forum
Hope it helps :)
I'm working on android application and it should allow users to take photos using camera and the application reads the text and numbers in the photo.
I don't know where to start from android studio. is there any good suggestion on github that could help me???
thanks in advance.
As others said OpenCV or OCR is the way to go.
Google maintains one OCR library called as "Tesseract" (reminds me of Avengers :P).
To make the job little easier there is a fork of Tesseract called as Tess-Two
It combines some other useful tools like Leptonica (image processing library). Build instructions are given on the Readme file.
To get started you can check out very easy to use OCR library
Easy OCR Library Android which uses Tess-Two under the hood.
Again usage instructions are in the Readme file. It is already built so you don't need to build Tess-Two.
You can try to use OpenCV library. Its abbreviature is Open Computer Vision Library. It has a reputation similar to OpenGL. There must be articles about yor issue in which library is ised. It can be linked static or dynamic using runtime application called OpenCV Manager (available in Google Play). You can use it both in Java and C++ code. Hope, it helps
PS i have an own example of it use.
https://github.com/androidovshchik/ProhibitingSignDetector
i could give suggest about how i would do that if i needed to .
first of all you need to photo the picture only black and white .
then cheack the min black pixels in row that will define a letter .(you dont want any shadow to recognize as potential letter.
try and learn progress (any camera have diffrent resulution so it need to be some % of the picture row pixels.)
after that evrey letter have diffrent shape so you need to do for loop 5 times in diffrent angel until you get to the third gap of black rows.
after that some huge switch and if to get to the right letter need to do big research about the gap inside the letter proportion.
to have a little dataBase could help if you wanted to get more then one font .
again i not sure its the right way but that what i would do.
have fun :)
You may try to find some Optical Character Recognition (OCR) library for Java
Check Java OCR, tess-two, Aprise. And explore stackoverflow searching other OCR solutions.
Implementing your own OCR lib may be very difficult so think is it really necessary for your task.
I would like to make an Android Application that captures an image and searches it for coins and paper notes and then determines the value of the money in the image.
Additionally, the output of the system will be such that it can be understood by a blind person.
What functions and techniques in openCV would suit these tasks?
What limitations and development hurdles can I expect?
Assuming you already know how to program android apps,you need to do the following:-
Download the OpenCV SDK and set it up with the IDE.
Recognising shapes will be a huge part of your project, see the contour detection example that is present with the SDK examples. Your primary goal will be detecting a circle. Later you need to adapt your algorithm according to the currency. This will be of particular interest to you.
Learn the different image processing techniques like thresholding for better accurate results. Understanding what a Mat object is and how it can be manipulated is important.
Finally improve the accuracy of your algorithm, as sometimes lighting conditions make the difference between a good review and a dissatisfied user.
I must create an Android app that recognizes some objects from the camera (car steering wheel, car wheel). I tried with Haar classifier but without success and I'm running out of time (it's a school project). So I decided to look for another way. I found some other methods for my goal - ORB. I found what should I do in this answer. My problem is that things are messed up in my head. Can you give me a step-by-step answer of what to do to implement the answer from the question in the link I gave:
From extracting the feature points to training the KD tree and using it for every frame from the camera.
Bonus questions:
Can you give a definition of feature point? It's something I couldn't exactly understand.
Will be the detecting slow using ORB? I know OpenCV can be used in native android, wouldn't that make the things faster?
I need to create this app as soon as possible. Please help!
I am currently developing a similar application. I would recommend getting something working with a single reference image first for a couple of reasons:
It's easier to do and understand if you're just starting out, and you can change it later.
For android applications you have limited processing capabilities so more images = lower fps.
You should have a look at the OpenCV tutorials which are quite helpful. Once you go through the “OpenCV for Android SDK” section and understand the three tutorials you can pretty easily add in functionality that will allow you to analyse the video feed.
The basic logic path I'd recommend following when making the app is:
Read in the reference image.
Create and use your FeatureDetector, DescriptorExtractor and DescriptorMatcher.
Use the above to detect keypoints and then descrive keypoints (the first two, don't forget to convert it to a mat and then to greyscale).
Every time you get a frame from your camera repeat step 3. on it and then compare the keypoints in the images (with the third part of 2.).
Use the result to determine if there is a match (if there is then draw a box around it or something).
Get a new frame.
Try making it to work for a single object and then add in others later. Another thing you could add is a screen at the start to allow users to pick what they want to search for.
Also ORB is reasonably fast, especially compared to SIFT and SURF. I get about 3fps on a HTC One with a single reference image.
I have successfully integrated tesseract into my android app and it reads whatever the image that I capture but with very less accuracy. But most of the time I do not get the correct text after capturing because some text around the region of interest is also getting captured.
All I want to read is all text from a rectangular area, accurately, without capturing the edges of the rectangle. I have done some research and posted on stackoverflow about this two times, but still did not get a happy result!
Following are the 2 posts that I made:
https://stackoverflow.com/questions/16663504/extract-text-from-a-captured-image?noredirect=1#comment23973954_16663504
Extracting information from captured image in android
I am not sure whether to go ahead with tesseract or use openCV
Including the many links and answers from others, I think it's good to take a step back and note that there are actually two fundamental steps to optical character recognition (OCR):
Text Detection: This is the title and focus of your question, and it is concerned with localizing regions in an image that contain text.
Text Recognition: This is where the actual recognition happens, where the localized image regions from detection get segmented character-by-character and classified. This is also where tools like Tesseract come into play.
Now, there are also two general settings in which OCR is applied:
Controlled: These are images taken from a scanner or similar in-nature where the target is a document and things like perspective, scale, font, orientation, background consistency, etc are pretty docile.
Uncontrolled/Scene: These are the more natural and in-the-wild photos, e.g. those taken from a camera, where you are trying to recognize a street sign, shop name, etc.
Tesseract as-is is most applicable to the "controlled" setting. And in general, but for scene OCR especially, "re-training" Tesseract will not directly improve detection, but may improve recognition.
If you are looking to improve scene text detection, see this work; and if you are looking at improving scene text recognition, see this work. Since you asked about detection, the detection reference uses maximally stable extremal regions (MSER), which has a plethora of implementation resources, e.g. see here.
There's also a text detection project here specifically for Android too:
https://github.com/dreamdragon/text-detection
As many have noted, keep in mind that recognition is still an open research challenge.
The solution to improving the OCR output is to
either use more training data to train it better
filter it's input using some Linear Filter (grayscaling, high-contrasting, blurring)
In the chat we posted a number of links describing filtering techniques used in OCRing, but sample code wasn't posted.
Some of the links posted were
Improving input for OCR
How to train Tesseract
Text enhancement using asymmetric filters <-- this paper is easily found on google, and should be read fully as it quite clearly illustrates and demonstrates necessary steps before OCR-processing the image.
OCR Classification