Histogram of grayscale values in numpy image
I have loaded an image into a numpy array and want to plot its color values in a histogram.
import numpy as np from skimage import io from skimage import color img = io.imread('img.jpg') img = color.rgb2gray(img) unq = np.unique(img) unq = np.sort(unq)
When we check the value unq
, we will see something like
array([ 5.65490196e-04, 8.33333333e-04, 1.13098039e-03, ...,
7.07550980e-01, 7.09225490e-01, 7.10073725e-01])
which still has too many values for matplotlib
, so my idea was to iterate over unq
and remove every value that only deviates x
from its predecessor.
dels = []
for i in range(1, len(unq)):
if abs(unq[i]-unq[i-1]) < 0.0003:
dels.append(i)
unq = np.delete(unq, dels)
While this method works, it is very inefficient as it does not use optimized versions of numpy.
Is there a numpy function for me?
Just noticed that my algorithm is losing information on how often the color occurs. Let me try to fix this.
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If you just want to compute the histogram, you can use np.histogram
:
bin_counts, bin_edges = np.histogram(img, bins, ...)
This bins
can be either the number of bins or a vector defining the top and bottom edges of the edges.
If you want to plot a histogram, the simplest way would be to use plt.hist
:
bin_counts, bin_edges, patches = plt.hist(img.ravel(), bins, ...)
Note that I used img.ravel()
to flatten the image array before calculating the histogram. If you pass a 2D array to plt.hist()
, it will treat each row as a separate data series, which is not needed here.
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