I am trying to detect a barcode with pyzbar.
If get the following image up on my computer screen and scan it with my mobile phone camera then it is instantly able to provide me with the qr code information.
However, if I use Pyzbar to analyse the raw image file then I am not able to get any results.
How can I improve the accuracy of pyzbar?
Related
I'm using the OpenCV framework in iOS Xcode objc, is there a way that I could process the image feed from the video camera and allow the user to touch an object on the screen then we use some functionality in OpenCV to highlight it.
Here is graphically what I mean. The first image shows an example of what the user might see in the video feed:
Then when they tap on the screen on the ipad i want to use OpenCV feature/object detecting to process the area they've clicked to highlight the area. Would look something like this if they clicked the ipad:
Any ideas on how this would be achievable in objc OpenCV?
I can see quite easily how we could achieve this using trained templates of the iPad to match it using OpenCV algorithms but I want to try and get it dynamic so users can just touch anything in the screen and we'll take it from there?
Explanation: why should we use the segmentation approach
According to my understanding, the task which you are trying to solve is segmentation of objects, regardless to their identity.
The object recognition approach is one way to do it. But it has two major downsides:
It requires you to train an object classifier, and to collect a dataset which contains a respectable amount of examples of objects which you would like to recognize. If you choose to take a classifier which is already trained - it won'y necessarily work on any type of object which you would like to detect.
Most of the object recognition solutions find a bounding box around the recognized object, but they don't perform a complete segmentation of it. The segmentation part requires extra effort.
Therefore, I believe that the best way for your case is to use an image segmentation algorithms. More precisly, we'll be using the GrabCut segmentation algorithm.
The GrabCut algorithm
This is an iterative algorithm with two stages:
initial stage - the user specify a bounding box around the object.
given this bounding box the algorithm estimates the color distribution of foreground (the object) and the background by using GMM, followed by a graph cut optimization for finding the optimal boundaries between the foreground and the background.
In the next stage, the user may correct the segmentation if needed, by supplying scribbles of the foreground and the background. The algorithm fixes the model accordingly and perform a new segmentation based on the updated information.
Using this approach has pros and cons.
The pros:
The segmentation algorithm is easy to implement with openCV.
It enables the user to fix segmentation errors if needed.
It doesn't relies on a collecting a dataset and training a classifier.
The main con is that you will need an extra source of information from the user beside of a single tap on the screen. This information will be a bounding box around the object, and in some cases - additional scribbles will be required to correct the segmentation.
Code
Luckily, there is an implementation of this algorithm in OpenCV. The user Itseez create a simple and easy to use sample for using OpenCV's GrabCut algorithm, which can be found here: https://github.com/Itseez/opencv/blob/master/samples/cpp/grabcut.cpp
Application usage:
The application receives a path to an image file as an command line argument input. It renders the image onto the screen and the user is required to supply an initial bounding rect.
The user can press 'n' in order to perform the segmentation for the current iteration or press 'r' to revert his operation.
After choosing a rect, the segmentation is calculated. If the user wants to correct it, he may choose to add foreground or background scribbles by pressing shift+left and Ctrl+left accordingly.
Examples
Segmenting the iPod:
Segmenting the pen:
You Can do it by Training a Classifier of Ipad images using opencv Haar Classifiers and then detecting Ipad images in a given frame.
Now based on coordinates of the touch check if that area overlapped with detected Ipad image area. If it does Drawbounding box on the detected Object.Means from there on you can proceed towards processing your detected ipad image.
Repeat the above procedure for Number of objects that you want to detect.
The task which you are trying to solve is "Object proposal". It doesn't work very accurate and this results are very new.
This two articles give you a good overview of methods for this:
https://pdollar.wordpress.com/2013/12/10/a-seismic-shift-in-object-detection/
https://pdollar.wordpress.com/2013/12/22/generating-object-proposals/
To have state-of-the-art results, look for latest CVPR papers on Object proposals. Quite often they have code available to test.
I am trying to organize my trading card collection digitally and am working on building a scanner using ocr to detect the names of my collection.
I need to use a webcam to snap a single image of each card in question. Snapping the image doesn't seem to be to difficult, but I need help determining how to get OpenCV to capture only a specific part of that image for OCR to work with. I'm trying to capture just the text portion of the image so that the artwork on the cards doesn't interfere with the OCR.
If my card will be placed in the same physical location each time, is there a way to get OpenCV to take an image and focus on just the area of the image that I'm interested in.
Thank You
Sour Jack
I am not sure I understand the problem. Do you want to use your OCR algorithm always on the same portion of the snapshot? If so, you can try something like:
roi = img[y:y+height, x:x+width]
There is more information here: http://answers.opencv.org/question/29260/how-to-save-a-rectangular-roi/
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A video can be edit by deleting some frames from it.that can be done by deleting consecutive frames or random frames in that video.
we need to detect the tampered videos, we hope to do this by detecting frames which has been altered , simply we need to have an algorithm for way to identify deleted frames.Because we are creating a tool which could use for tamper detection as a video surveillance tool.Which could use in law enforcements and courts
Is there any method to identify that frames of a video has been deleted by malicious attacks, there are already available methods such as watermarking, digital signature, but we need an algorithm to detect that frames by using proper algorithm.
In general, whatever image sequence I am handed, some or all of it could have come from a real camera, from Photoshop or from a 3D renderer, and there is no general purpose image processing technique that will be able to tell the difference based on analysis of the image content alone, either well enough to stand up in court, or, indeed, at all.
You'll need to embed some easy to recognise yet difficult to fake, relocate or tamper with signal into the video at recording time. You've tagged the question "opencv", but the fields you want to investigate are cryptography and watermarking, not computer vision.
Have the video surveillance equipment use public key crypto to visibly watermark each frame with an identifier unique to the piece of equipment, a timestamp, a frame sequence number and a hash or other suitable function of the frame image, using a scheme robust to compression.
Tampering with the video sequence will then require either knowledge of the device's private key, or removal of the watermark. This isn't great (keeping the private key secret will be a logistical headache) but is probably the best solution you can get.
this can't be done in general. However some approaches may be possible.
the used video format may support frame wise meta data that stores the index or time index and that is not touched during editing
the image sensor itself may be configured to write some meta data to some specific region of the image
you may have some external reference that was imaged by the camera and may help identify missing frames
precise clock
fast blinking indicator
some uniform motion
I wish to display an image through my projector via MATlab. The projected image should be full sized without any figure handle bars (menu bar, the grey stuff which encompasses a figure etc).
Similar to a normal presentation when the projector projects the complete slide or image, I want to do the same using MATlab as my platform. Any thoughts or idea? Can we access the projector using MATlab? My first thoughts were to send data to the corresponding printer IP but that doesn't seem to work :/
If you know the relevant C++ command or method to do this, please suggest a link or a library, so that I may try and import it on my MATlab platform.
Reason for doing this: Projector-Camera calibration for photo-metric correction of my projector display output.
Assuming your projector is set as a second display, you can do something very simple. Get the monitor position information and set the figure frame to be the monitor size
// plot figure however you want
monitorFrames = get(0,'MonitorPositions');
secondMonitor = monitorFrames(2,:);
secondMonitor(3) = secondMonitor(3)-monitorFrames(1,3);
set(gcf,'Position',secondMonitor);
This will put the figure window onto the second monitor and have it take up the whole screen.
You can then use this to do whatever calibration you need, and shift this window around as necessary.
NOTE:
In no way am I saying this is the ideal solution. It is quick and dirty, and will not use any outside libraries.
UPDATE
If the above solution does not suit your specific needs, what you could always do is save the plot as an image, then have your MATLAB script, call a c++ script that opens the image and makes it full screen.
This is non-trivial. For Windows you can use the WindowAPI submission to the MATLAB File Exchange. With the WindowAPI function installed you can do
WindowAPI(FigH, 'Position', 'full');
For Mac and Linux you can use wrappers around OpenGL to do low level plotting, but you cannot use standard MATLAB figure windows. One nice implementation is PsychToolbox.
I am developing a sample camera and I am able to control image sensor directly. The sensors gives out Bayer image and I need to do show images as live view.
I looked at debayering codes and also white balancing. Is there any library in C/C++ that can help me in this process?
Since I need to have live view, I need to do these things very fast and hence I need algorithms that are very fast.
For example, I can change the RGB gains on sensor and hence I need an algorithm that act at that level, instead of acting on generated image.
Is there any library that help to save images in raw format?
simplecv has a function for white balance control:
simplecv project web site