I would like to detect labels with barcodes. These labels all look very similar but each label has a different barcode/wording on it.
I have tried template matching but to no avail.
One constraint that I am faced with is that there are other barcodes in the image, but I only want labels with this format of barcodes.
Could anyone hint me some other algorithms.
Thank you!
I don't have much experience with barcodes, but you might want to have a look at Canny Edge detector. It's quite simple to understand and should help you in your task.
If I had to do anything like this, I would first try to rotate the image until the lines are vertical, use canny and then extract the 'on/off' pattern of each set of bars.
This PyImageSearch page is a good starting point.
To reject codes of other formats, you can use a bar code reader api, or come up with some rules regarding the format.
Related
Sorry if this is a recurring question, but I can't find the right keywords for this search.
I need to develop a system for visual recognition of labels attached to products from a warehouse. I'm using a fixed focus camera, so the idea is to use a label with some code with 6 alphanumeric digits printed in a large font. Then this system would be responsible for performing ROI extraction and applying OCR to recognize the objects in the scene.
My main problem is the ROI extraction part. I tried to use template matching, but due to the difficulties with scale and rotation, it doesn't seem to be the right technique for the application. I also tried to use feature matching, but the results are still insufficient.
My question is how could I develop the label to facilitate ROI extraction? Could I use something like apriltags to simplify the homography?
Thanks in advance!
I was asked to recognize logo in an image using opencv. The lecturer told me that I don't have to do logo detection but logo recognition only. I am using opencv in c++. Can I know the easiest way to do it??
Ps: newbie in computer vision.
It largely depends on your kind of images.
If your logo occupies say 90% of the image, you don't need detection, since you are probably good with color histograms.
If the logo is small compared to the image, you should "find" the logo, in order to focus your comparison on that and not on the background clutter.
There could be multiple logos on the same image?
The logo is always fully visible?
The logo is rigid? Or could be deformed? (think for example of a logo on a shirt or a small bottle)
Assuming that you have a single complete rigid logo to find, the simplest thing to try is template matching.
A more accurate approach is to match descriptors.
You can also see a related topic on SO here
Other more robust approaches would require to build constellations of keypoints on your reference logo, and match those constellations on the target image.
Last, but not least, have fun on Google!
I agree with #Miki , you need to do template matching, my recomendation to you is to use sum of square differences and only use a rigid transformation, you can find a lot of information here. The last is one of the best books that I've red is simple to understand and it have the major part of the equations step by step.
I am trying to use the OpenCVs Background Subtractor class MOG2 to seperate a person moving infront of a camera. I got everything set up and working nicely. But the resulting mask I am getting looks something like this:
(default settings)
Now what I would like to get is something like this:
(bad gimp skills :D)
I have already tryed to mess around with the parameter described in the docu, but all I managed to accomplish was something the looked like a motion blur effect...
So I was hopeing somebody with a better understanding of the algorithm or somebody who has already done something similar might be able to help me!
Thanks in advance, Foaly
I am working also with that and what I've seen is that this algorithm needs a good calibration to accomplish that goal, because you should be aware that this algorithm try to put in the background some pixels that don't show changes, e.g. in your skin major part of the pixels have the same color maybe this is the reason. I recommend to you that use other kind of methods (using zncc) if you want to use an application like the one that is showed in your question.
So I guess this were our image processing skills come into play. The first thing I would do is make the lines on the image thicker and join it it. We can use the following:
1) I would want the lines thicker. Use Morphological operators tutorial on Morphological operators with Otsu's method. This paper worked for me when I did my ear biometrics http://www4.comp.polyu.edu.hk/~csajaykr/myhome/papers/PR2011.pdf
2) Fill in connected components using opencv and clean the image
3) Segment human profile
Currently i am working on opencv. I have an image with a text. And i want to find out the style(Bold, Italic) of the text. How can i achieve this? Thanks
What you can do is (assuming a letter by letter approach)
Using segmentation techniques you first segment out the letters
Using the segmented letters,compare against your owns data set of pre-segmented/pre-filtered letters to find the font style.
Comparison can be done using various features, SIFT,SURF,BRISK,Harris corners, template matching, or your come up with something of your own. My best guess would be to go with HAAR-features and training.
Once you get a set of features for a letter, matching for closest candidate against your pre-filtered dataset can be achieved using different techniques such as KNN, euclidean distance, etc If you use HAAR features, OpenCV can help alot in retrieval.
Eventually you might ending doing some OCR which includes font style.
OpenCV has a set of built in feature descriptors which you can read here
Good Luck!
This might help you, I know it's not exact. But it will suffice for my similar project.
"Typefont is an experimental library that detects the font of a text in a image."
https://github.com/Vasile-Peste/Typefont
I'm developing application that uses DirectShow combined with C++.
Its main goal is to capture users' faces.
I have reached the phase when I capture a image from my webcam.
The problem is I need an intelligent render. In fact, I need that render to be able to detect a face inside a rectangle.
I'm wondring if there is a filter that I can use for this purpose,
or if I need to create my own custmized filter.
If so enlighten my mind.
It would look like this:
I need to understand how I can draw a recangle in my render in the first place. Because otherwise, even if I know the algorithm, I will not be able to apply it. This is my main goal now.
I have some idea but I don't know if they are correct. I think I need to grab each frame separately and apply some modification in some pixels, like what's drawn in the live render.
Have a look at OpenCV
Quick look inside and I found this.
Making your own "filter" that works well is no easy job.
Are you talking about automatic detection of where there is something like a human face in the shot you have taken with the webcam? In this case object detection algorithms like Viola-Jones might be interesting for you.
If a commercial package is an option, you can use the Montivision Filter SDK which includes filters that should do the job out of the box. They offer a free eval which is perfect for experimentation.