I am having to solve the problem where I have to detect the tire dot code in a given tire image and recognize the characters in the tire.
Sample :-
https://www.tirerack.com/images/tires/tiretech/determining_age/Post_2000_Full_Dot.jpg
I am using OPENCV to perform this task and referred the following link for reference https://github.com/schollz/python-ocr.
I have also used a Canny Edge and SWT detection to perform the task, but it has also not helped. I used the libpillowfight library for the same.
This approach is able to contrast the image but not able to extract the characters efficiently.
Can anyone suggest a better way to achieve this. Help appreciated.
Related
I want to detect excel logo in this kind of image :
!Edited Image where I want to detect excel logo.
So I extracted each logo and used them as "training" set.
I tried by using Brute-Force Matching with ORB Descriptors and Brute-Force Matching with SIFT Descriptors and Ratio Test as you can find here : https://docs.opencv.org/4.x/dc/dc3/tutorial_py_matcher.html.
I never had any matches. I thought I was doing it wrong so I use the exact same code but on the same kind of images you can see in the example and it works correctly.
My two hypothesis are : Excel logo is too simple so not easy to recognize. The logo in the image are too small. Maybe I'm wrong and someone here could help me.
Maybe I'm not using the best method or the best parameters but I don't have any idea to improve my researches.
I am trying to detect the pdf417 barcode (2D barcode) from an image using python.
I will be receiving images of IDs where there is a barcode in them but it might not always be straight. So I am looking for an effective way to DETECT the pdf417 barcode using Python.
I tried all of the available methods that I could find (that uses python)
e.g.,
pdf417decoder: requires the image to be cut exactly around the barcode just like in the image below:
pyzbar: only detects 1D barcodes
python-zxing and zxing: didn't detect any of the pdf417 barcodes that I tried (around 10 different IDs - different country)
Barcode-detection: this is a DL approach that uses YOLO-V3 to detect barcodes, but again (after trying it), it only detects 1D barcodes...
Is there a method that I missed?
Am I using a wrong approach towards this problem?
Possible solution that I am thinking of: using computer vision (some filters and transformations) to detect a box that has black and white dots... Something similar to this.
Thanks!
After various trials, I ended up using an approach of template matching by OpenCV.
You need to precisely choose your template image that will be the search reference of your algorithm. You need to feed it some grayscaled images.
Then, you need to choose the boxes that have a result higher than a certain threshold (for me 0.55). Then apply NMS (non max suppression) to filter out the noisy boxes.
But keep in mind that there are many edge cases to encounter. If someone is interested to see the complete solution, please let me know.
So I have been asked to motion deblur a frame captured from a video, I am kind of new to this deblur filters so need help. The video does not contain any noise, just a vertical motion blur. I am not allowed to use skimage, or any other library except cv2. It would be a great help even if what technique or function I have to use comes to know. Thanks!
You can use the Motion Deblur Filter of opencv, if you specifically want to use opencv.
Following is the link to its documentation, which is fairly easy to understand:
http://amroamroamro.github.io/mexopencv/opencv/weiner_deconvolution_demo_gui.html
You can go for skimage as well. It has many function like deconvolution which can help in deblurring images.
I think that for this kind of problem you have to use the recent deep learning techniques. They outperform the classical approaches. I recommend to look on github for a repository that would already provide a trained network that can deblur the same kind of blur that you have.
I never tried it, but this could be a nice candidate.
fig:Shoe in the red circle is to be detected
I am trying to create a python script using cv2 that can recognize the shoe of the baller and determine whether the shoe is beyond, on or before the white line(refer to the image).
I have no idea about any kind of approach to use, what kind of algorithms might be helpful. Need some guideline, please help!
(Image is attached)
I realize this would work better as a comment because it isn't a full answer, but I don't have enough rep yet to leave comments, haha.
You may be interested in OpenCV's Canny Edge detection algorithm:
http://docs.opencv.org/trunk/da/d22/tutorial_py_canny.html
This will allow you to find shapes within your image.
Also, you can find similarly colored blobs using SimpleBlobDetector:
https://www.learnopencv.com/blob-detection-using-opencv-python-c/
This should make it fairly easy to detect the white line.
In order to detect a more complex object like the shoe, you'll probably have to make something like a object detection cascade file and use a CascadeClassifier to find it:
http://docs.opencv.org/2.4/doc/tutorials/objdetect/cascade_classifier/cascade_classifier.html#cascade-classifier
http://johnallen.github.io/opencv-object-detection-tutorial/
Basically, you take a bunch of pictures to "teach" what the object looks like, and output that info to a file that a CascadeClassifier can use to detect objects in input images. It may be hard to distinguish between different brands of shoe though, if you need it to be that specific. Also, you may need to adjust the input images (saturation, brightness, etc) before trying to detect objects in order to get good results.
As an exercise, I'm attempting to break the following CAPTCHA:
It doesn't seem like it would be too difficult to break as the edges seems to fairly solid and noise should be relatively easy to remove. Problem is, I have very little experience with image manipulation. Currently I'm using Python with the Pillow library to manipulate the CAPTCHA image, after which it will be passed into Tesseract for OCR.
In the following code I attempt to bring out the edges by sharpening the image and the convert the image to black and white
from PIL import Image, ImageFilter
try:
img = Image.open("Captcha.jpg")
except:
print("Can't load captcha.")
exit()
# Bring out the edges by sharpening.
out = img.filter(ImageFilter.SHARPEN)
out = out.convert("L")
out = out.point(lambda x: 0 if x<136 else 255, "1")
width, height = out.size
out = out.resize((width*5, height*5), Image.NEAREST)
out.save("captcha_modified.png")
At this point I see the following:
However, Tesseract is still unable to read the characters. As an experiment, I used good ol' mspaint to manually modify the image to a point to where it could be read by Tesseract:
So if can get the image to that point, I think Tesseract will do a fairly good job at detecting characters. So my current thoughts are that I need to enhance the edges and reduce the noise the image. Also, I imagine it would be easier for Tesseract to detect the letters if the letters will filled in rather than outlined, but I have not idea how I'd do this.
Any suggestions on how to go about this? Is there a better way to process the images?
I am short on time so this answer may not be incredibly useful but goes over my own 2 algorithms exactly. There isn't much code but a few method reccomendations. It is a good idea to use code rather than MS Paint.With code its actually really easy to break a captcha and achieve above 50% success rate. Behavioral recognition may be a better security mechanism or may be an additional one.
A. Edge Detection Method you use:
Edge detection really isn't necessary. In this case, just use the getpixel((x,y)) function and fill in the area between the bounding lines, recognizing to fill at lines 1,3,5;etc. and turn off the fill after intersection 2,4,6;etc. Luckilly, you chose an easy Captcha so edge detection is a decent solution without decluttering,rotating, and re-alignment.
B. Manipulation Method:
Another method I use utilizes OpenCV and pillow as well. I am really busy but am posting a blog article on this later at druid5.wordpress.com/ which will contain code examples of this method. Since it isn't illegal to get through them, at least I am told, I use the method I will post to collect data all the time. Mostly, contrast and detail from pillow, some basic clutter removal with stats, re-alignment with a basic dfs, and rotation (performable with opencv or easily with a kernal). Tesseract is a good choice for open source but it isn't too hard to create an OCR with opencv either.
This exercies is a decent introduction to OpenCV, PIL (pillow), image manipulation with math, and some other things that help with everything from robotics to AI.
Using flow control to find the failed conditions and try different routes may be necessary but the aim should always be a generic solution.