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 ;)
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I am expanding my limited Python knowledge by converting some MATLAB image analysis code to Python. I am following Image manipulation and processing using Numpy and Scipy. The code in Section 2.6.1 saves an image using both imageio.imsave and face.tofile, where type(face)=<class 'imageio.core.util.Array>'.
I am trying to understand why there are two ways to export an image. I tried web-searching tofile, but got numpy.ndarray.tofile. It's very sparse, and doesn't seem to be specific to images. I also looked for imageio.core.util.Array.tofile, but wasn't able to find anything.
Why are there two ways to export files? And why does imageio.core.util.Array.tofile seem to be un-findable online?
The difference is in what the two functions write in the file.
imageio.imsave() saves a conventional image, like a picture or photo, in JPEG/PNG format that can be viewed with an image viewer like GIMP, feh, eog, Photoshop or MSxPaint.
tofile() saves in a Numpy-compatible format that only Numpy (and a small number of other Python tools) use.
I'm looking for OpenCV or other Python function that displays a NumPy array as an image like this:
Referenced from this tutorial.
What function creates this kind of grey-scale image with pixel values display?
Is there a color image equivalent?
MATLAB has a function called showPixelValues().
The best way to do this is to search "heat map" or "confusion matrix" rather than image, then there are two good options:
Using matplotlib only, with imshow() and text() as building blocks the solution is actually not that hard, and here are some examples.
Using seaborn which is a data visualisation package and the solution is essentially a one-liner using seaborn.heatmap() as shown in these examples.
My problem was really tunnel vision, coming at it from an image processing mindset, and not thinking about what other communities have a need to display the same thing even if they call it by a different name.
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.
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?
I am working on a GTK program that manipulates an image based on user input. I am using pypng to read the image and am now looking for a way to create a Gtk.Image from a pixel list without creating an intermediate image file.
Not entirely easy to do in Python directly. You could do this, using GtkImage, which can load images from a GdkPixbuf.Pixbuf using a method called from_pixdata().
A higher level of doing this is using (for example) the PIL (in python2) or Pillow (python3). There, you can construct images using the Image module and the PIL.Image.frombytes(), PIL.Image.fromstring(), and other methods.
There are also functions in matplotlib which convert arrays immediately to images.