We have some legacy code written in C that uses GraphicMagik to manipulate an image i.e Rotate the Image
We would like to write some tests in Python to test this code, I can use OpenCV for resizing, but how do I determine if a rotation of the Image against the Original has suceeded?
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With a program, I am producing an SVG image with dimensions of 400px x 400px. However, I would like to crop the bottom of this SVG image off, based off of a variable that dictates how much of the bottom of the image should be cropped in pixels.
This SVG image is being generated with pyCairo with surface = cairo.SVGSurface("output.svg", WIDTH, HEIGHT) and ctx = cairo.Context(surface).
Although the HEIGHT variable is a constant and isn't changed, after I perform some operations on the surface object, I would like to be able to resize it once more. I can use the Pillow Image object to crop PNGs, but it does not support SVGs.
I have also tried to open the svg file with open("output.svg"). However, if I try to read it, I am unable to and it shows up as blank, thus making it unmodifiable.
Is there any way in Python to either crop an SVG image or modify its size after it has been modified with pycairo?
The answer above is incomplete and at least for me doesn't solve the problem.
A SVG can simply be cropped (trimmed, clipped, cut) using vpype with the crop or trim and translate commands.
import vpype_cli as vp
#vp.excute("read test.svg translate 300 400 trim 30 20 write output.svg")
vpype_cli.execute("read test.svg crop 0cm 0cm 10cm 20cm write output.svg")
Playing around with the parameters should lead to the desired crop.
Took some time to find this, as most answers say it cant be done, which is ridiculous.
You cannot crop SVG like you crop PNG because in the latter you can just drop pixels, while for the former you have defined paths that can't be easily recomputed.
If you're sure there's nothing in the part you are about to "crop", you can use set_context_size to make the svg context/canvas smaller while preserving ratio and size inside.
I am trying to apply fisheye effect on a normal image using opencv-fisheye module.
For converting a fisheye image to normal image, 'cv2.fisheye.undistortimage' api can be used. I am not sure how to get the fisheye image with the help of 'cv2.fisheye.distortPoints' function. Any working sample code/algorithm is much appreciated.
I have a database of original images and for each original images there are various cropped versions.
This is an example of how the image look like:
Original
Horizontal Crop
Square Crop
This is a very simple example, but most images are like this, some might taken a smaller section of the original image than others.
I was looking at OpenCV in python but I'm very new to this kind of image processing.
The idea is to be able to save the cropping information separate from the image to save space and then generate all the cropping and different aspect ratio on the fly with a cache system instead.
The method you are looking for is called "template matching". You find examples here
https://docs.opencv.org/trunk/d4/dc6/tutorial_py_template_matching.html
For your problem, given the large images, it might be a good idea to constrain the search space by resizing both images by the same factor. So that searching a position that isn't as precise, but allows then to constrain the actual full pixel sized search to a smaller region around that point.
I have an image that represents the elevation of some area. But the drone that made it didn't necessarily go in a straight line(although image is always rectangular). I also have gps coordinates generated every 20cm of the way.
How can I "bend" this rectangular image (curve/mosaic) so that it represents the curved path that the drone actually went through? (in python)
I haven't managed to write any code as I have no idea what is the name of this "warping" of the image. Please find the attached image as a wanted end state, and normal horizontal letters as a start state.
There might be a better answer, but I guess you could use the remapping functions of openCV for that.
The process would look like that :
From your data, get your warping function. This will be a function that maps (x,y) pixel values from your input image I to (x,y) pixel values from your output image O
Compute the size needed in the output image to host your whole warped image, and create it
Create two maps, mapx and mapy, which will tell the pixel coordinates in I for every pixel in 0 (that's, in a sense, the inverse of your warping function)
Apply OpenCV remap function (which is better than simply applying your maps because it interpolates if the output image is larger than the input)
Depending on your warping function, it might be very simple, or close to impossible to apply this technique.
You can find an example with a super simple warping function here : https://docs.opencv.org/2.4/doc/tutorials/imgproc/imgtrans/remap/remap.html
More complex examples can be looked at in OpenCV doc and code when looking at distortion and rectification of camera images.
I have a working program in C++ that generates data for a Mandelbrot Set. I am able to get the color map image of the Mandelbrot set using gnuplot. In order to save the image, I just take a screenshot which doesn't give a very accurate image.
how I can use a Python script in order to produce and save the image.
The Python Imaging Library is the standard means to produce 2D images and image files in Python.
Source code Draw a Mandelbrot Set (Python)
PIL and NumPy ofcourse ;)