Develop Reference

Eliminate the background (the common points) of 3 images - OpenCV

Forgive me but I'm new in OpenCV.
I would like to delete the common background in 3 images, where there is a landscape and a man.
I tried some subtraction codes but I can't solve the problem.
I would like output each image only with the man and without landscape
Are there in OpenCV Algorithms what do this do? (then without any manual operation so no markers or other)
I tried this python code CV - Extract differences between two images
but not works because in my case i don't have an image with only background (without man).
I thinks that good solution should to Compare all the images and save those "points" that are the same at least in an image.
In this way I can extrapolate a background (which we call "Result.jpg") and finally analyze each image and cut those portions that are also present in "Result.jpg".
You say it's a good idea? Do you have other simplest ideas?

Without semantic segmentation, you can't do that.
Because all you can compute is where two images differ, and this does not give you the silhouette of the person, but an overlapping of two silhouettes. You'll never know the exact outline.

How can I compute orthographical projection image from perspective projection image?

My goal is to transform an image captured by a camera and transform that image to orthographical image without effects of perspective.
I have a few objects of known size on a surface. I have a camera, placed above and directed to those objects, as exemplified in the scene. The camera is capturing images as in image captured by the camera. I want to get an orthographical image of the environment as in orthographical image I want to get.
I have read few posts, but did not really understand their relevance to my problem, as I am not expert on these transforms. The answer from this question made me think it is possible, although I did not get how.
I would appreciate a clear explanation or pointing a clear tutorial, using Python or Lua if possible.
Any help is appreciated.

This was not possible without distorting the image. A straightforward explanation is that the perspective causes some parts of the image to be not visible, for example the white line in the marked area is not visible, and there could be something small that we are not able to observe. For those parts, the algorithm is supposed to produce some kind of prediction based on heuristics.

Interpolating blacked out ROI in an image with python

I have been browsing the internet and stack overflow in order to find a solution to my problem, but to no avail.
So here is my problem:
Problem
I have a series of images with specific ROIs, where I detect a signal change. In order to extract the signal I need subtract the background of the image from the actual signal. Unfortunately I can't just subtract the images, as this doesn't delete the background noise sufficiently.
Solution Idea
What I want to do is to cut out (black out) my ROIs and then do an interpolation across the entire "reduced" image. Then I want to fill in the blacked out ROIs again via interpolation. This way I can get an idea of what the background below my signal is actually doing. I have been playing around with griddata, RectBivariateSpline, but I haven't found a way that works.
So far I have been doing this in MATLAB with the function scatteredInterpolant, but I would like to do it in python.
Below an image series, that describes the concept. One can see the third image being slightly blurry in the before blacked out ROIs.
Imageprocessing concept
So, does python provide a solution or way, which is similar to MATLABs scatteredInterpolant or how could I best tackle this problem?
Thank you.

Object Detection using opencv python

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.

CAPTCHAs Image Manipulation using Pillow

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.