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.
Related
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.
I am interested in porting some of my old fractal imaging programs over from Borland C to python. In Borland C, the putpixel command would place a specified color pixel within a rasterized graphical field. Is there a simple way to do this in matplotlib?
So the answer depends on what you're trying to do here. matplotlib has a lot of utilities for working with representing image data, this might give a good starting point for getting familiar with matplotlib's workflow. You can directly edit the values of the numpy array that you're using matplotlib to visualize, but matplotlib doesn't give you access to the data that you're rendering.
I imagine that you already have written some colormap and other rendering tools tools, but to get an idea of what matplotlib might have built in, I recommend looking at this example. It's a simple Mandelbrot, escape time, but it makes use of nonlinear colormapping and shading.
In my experience, I've normally computed the fractal as a 2D numpy array, and then allowed matplotlib to handle the coloring, and scaling of the final output image. Matplotlib doesn't work like the canvas experience it sounds like you're used to using. I'd recommend filling a numpy array with the desired pixel values as you've computed them, and then sending that array off to matplotlib to be rendered.
After posting this I discovered that there is a putpixel command in PIL (Python Imaging Library), which has tools for dealing with pixel oriented graphics. Matplotlib can also do the job as suggested by the answer above.
I had a problem where I need to search for a pattern (present as a numpy ndarray) within another image (also present as a numpy ndarray) and compute a template match (minimum difference position in the image). My question is... is there any in-built image that I can possibly use in the Python Imaging Library or Numpy or anything possible that can do this without me manually writing a function to do so???
Thank you....
This is likely best done as an inverse convolution or correlation. Numpy/scipy has code to do both.
edit: including a little example.
Go here for the ipython notebook file: http://nbviewer.ipython.org/4020770/
I made a little gaussian and then use scipy.signal.correlate2d with the original image and a small subset.
you can see that the highest values of the correlation are centered around where the subset of the image was taken. note that for large kernels or images, this code can take a while (because correlation is expensive)
I would like to generate 2D images of 3D books with custom covers on demand.
Ideally, I'd like to import a 3D model of a book (created by an artist), change the cover texture to the custom one, and export a bitmap image (jpeg, png, etc...). I'm fairly ignorant about 3D graphics, so I'm not sure if that's possible or feasible, but it describes what I want to do. Another method would be fine if it accomplishes something similar. Like maybe I could start with a rendered 2D image and distort the custom cover somehow then put it in the right place over the original image?
It would be best if I could do this using Python, but if that's not possible, I'm open to other solutions.
Any suggestions on how to accomplish this?
Sure it's possible.
Blender would probably be overkill, but you can script blender with python, so that's one solution.
The latter solution is (I'm pretty sure) what most of those e-book cover generators do, which is why they always look a little off.
The PIL is an excellent tool for manipulating images and pixel data, so if you wanted to distort your own, that would be a great tool to look at, and if it goes too slow it's trivial to convert the image to a numpy array so you can get some speedup.
i need to segment an image into regions .i'm using pil.i found no module to segment image in pil. I need this segmented regions as a list or dictionary.
Actually i'm trying to compare the images for similarity in content aware fashion.for that i need to segment the image. i tried segwin tool but it is drawing another image(which is not required and also time consuming)
thans in advance
The easiest way to segment an image into regions is creating an other image called labelmap. The "region 1" is represented by all the 1 valued pixels within the labelmap, and so on. If you need the pixels of the "region 3" you just binarize the labelmap with a thershold equal to 3 and multiply the result with the original image.
Like Oliver I advise WrapItk.
For this task i prefer numpy and scipy. In terms of image processing these two have all you need. For array math i recommend numexpr. Take a look at http://docs.scipy.org/doc/scipy/reference/ndimage.html
Take a look at the PIL Handbook, you can use the "crop" function to get a subregion of the image.
You might want to try the python bindings for ITK, a segmentation tool in C++.