Graphic map color images using matplotlib

Question

I am trying to create a color histogram of an image. I use PIL to read image files and try to build them through matplotlib.

im = Image.open(sys.argv[1]) w, h = im.size colors = im.getcolors(w*h) #Returns a list [(pixel_count, (R, G, B))] 

Update . After some trial and error, this code displays a histogram, but not colors! (Takes a lot of time, consuming tons of downloads even for 320x480 JPEG)

 for idx, c in enumerate(colors): plt.bar(idx, c[0], color=hexencode(c[1])) plt.show() 

Where

 def hexencode(rgb): return '#%02x%02x%02x' % rgb 

When executed, the program starts consuming infinite memory, and the display does not appear. OS memory usage has gone from <380 MB to> 2.5 GB in a few minutes; after which I stopped execution. How can i solve the problem?

Here is an example of a color histogram of an image with dominant red hues:

Answer 1

I am tired of your update code and it worked fine. Here is what I am trying to do:

 import PIL from PIL import Image from matplotlib import pyplot as plt im = Image.open('./color_gradient.png') w, h = im.size colors = im.getcolors(w*h) def hexencode(rgb): r=rgb[0] g=rgb[1] b=rgb[2] return '#%02x%02x%02x' % (r,g,b) for idx, c in enumerate(colors): plt.bar(idx, c[0], color=hexencode(c[1])) plt.show() 

Update:

I think matplotlib is trying to put a black border around each bar? If there are many bars, should the bar be thin in order to have color? If you have a toolbar, you can zoom in on the chart and see that the bars really have color. Thus, if you specify the color of the edges:

 for idx, c in enumerate(colors): plt.bar(idx, c[0], color=hexencode(c[1]),edgecolor=hexencode(c[1])) 

It works!

Image for processing:

Result:

Profiling
Sort by tottime:

  ncalls tottime percall cumtime percall filename:lineno(function) 1 23.424 23.424 24.672 24.672 {built-in method mainloop} 460645 8.626 0.000 8.626 0.000 {numpy.core.multiarray.array} 22941 7.909 0.000 18.447 0.001 C:\Python27\lib\site-packages\matplotlib\artist.py:805(get_aliases) 6814123 3.900 0.000 3.900 0.000 {method 'startswith' of 'str' objects} 22941 2.244 0.000 2.244 0.000 {dir} 276714 2.140 0.000 2.140 0.000 C:\Python27\lib\weakref.py:243(__init__) 4336835 2.029 0.000 2.029 0.000 {getattr} 1927044 1.962 0.000 3.027 0.000 C:\Python27\lib\site-packages\matplotlib\artist.py:886(is_alias) 114811 1.852 0.000 3.883 0.000 C:\Python27\lib\site-packages\matplotlib\colors.py:317(to_rgba) 69559 1.653 0.000 2.841 0.000 C:\Python27\lib\site-packages\matplotlib\path.py:86(__init__) 68869 1.425 0.000 11.700 0.000 C:\Python27\lib\site-packages\matplotlib\patches.py:533(_update_patch_transform) 161205 1.316 0.000 1.618 0.000 C:\Python27\lib\site-packages\matplotlib\cbook.py:381(is_string_like) 1 1.232 1.232 1.232 1.232 {gc.collect} 344698 1.116 0.000 1.513 0.000 C:\Python27\lib\site-packages\matplotlib\cbook.py:372(iterable) 22947 1.111 0.000 3.768 0.000 {built-in method draw_path} 276692 1.024 0.000 3.164 0.000 C:\Python27\lib\site-packages\matplotlib\transforms.py:80(__init__) 2 1.021 0.510 1.801 0.900 C:\Python27\lib\site-packages\matplotlib\colors.py:355(to_rgba_array) 22947 0.818 0.000 14.677 0.001 C:\Python27\lib\site-packages\matplotlib\patches.py:371(draw) 183546/183539 0.793 0.000 2.030 0.000 C:\Python27\lib\site-packages\matplotlib\units.py:117(get_converter) 138006 0.756 0.000 1.267 0.000 C:\Python27\lib\site-packages\matplotlib\transforms.py:126(set_children) 

Sorted by cumulative time

 ncalls tottime percall cumtime percall filename:lineno(function) 1 0.001 0.001 84.923 84.923 C:\Python27\test.py:23(imageProcess) 1 0.013 0.013 44.079 44.079 C:\Python27\lib\site-packages\matplotlib\pyplot.py:2080(bar) 1 0.286 0.286 43.825 43.825 C:\Python27\lib\site-packages\matplotlib\axes.py:4556(bar) 1 0.000 0.000 40.533 40.533 C:\Python27\lib\site-packages\matplotlib\pyplot.py:123(show) 1 0.000 0.000 40.533 40.533 C:\Python27\lib\site-packages\matplotlib\backend_bases.py:69(__call__) 22943 0.171 0.000 24.964 0.001 C:\Python27\lib\site-packages\matplotlib\patches.py:508(__init__) 1 0.000 0.000 24.672 24.672 C:\Python27\lib\site-packages\matplotlib\backends\backend_tkagg.py:68(mainloop) 1 0.000 0.000 24.672 24.672 C:\Python27\lib\lib-tk\Tkinter.py:323(mainloop) 1 23.424 23.424 24.672 24.672 {built-in method mainloop} 22947 0.499 0.000 24.654 0.001 C:\Python27\lib\site-packages\matplotlib\patches.py:55(__init__) 22941 0.492 0.000 20.180 0.001 C:\Python27\lib\site-packages\matplotlib\artist.py:1136(setp) 22941 0.135 0.000 18.730 0.001 C:\Python27\lib\site-packages\matplotlib\artist.py:788(__init__) 22941 7.909 0.000 18.447 0.001 C:\Python27\lib\site-packages\matplotlib\artist.py:805(get_aliases) 72/65 0.071 0.001 17.118 0.263 {built-in method call} 24/12 0.000 0.000 17.095 1.425 C:\Python27\lib\lib-tk\Tkinter.py:1405(__call__) 22941 0.188 0.000 16.647 0.001 C:\Python27\lib\site-packages\matplotlib\axes.py:1476(add_patch) 1 0.000 0.000 15.861 15.861 C:\Python27\lib\site-packages\matplotlib\backends\backend_tkagg.py:429(show) 1 0.000 0.000 15.861 15.861 C:\Python27\lib\lib-tk\Tkinter.py:909(update) 1 0.000 0.000 15.846 15.846 C:\Python27\lib\site-packages\matplotlib\backends\backend_tkagg.py:219(resize) 1 0.000 0.000 15.503 15.503 C:\Python27\lib\site-packages\matplotlib\backends\backend_tkagg.py:238(draw) 

It seems that all the time is spent on matplotlib. If you want to speed it up, can you find another charting tool, reduce the number of bars, try to do it yourself with a rectangle on the canvas?

Dates:

• Sent above code: 75s
• Drawing a line for each of them, for example plt.plot ([n, n], [0, count], etc.): 95s

Answer 2

I am working on a similar problem and stumbled upon this topic, being 5 years late. For larger images, the accepted answer was too long to simply “generate a color histogram of the image”.

I used openCV instead of PIL to generate histograms, and it is much faster.

 import cv2 import numpy as np from matplotlib import pyplot as plt file0 = 'image.jpg' img = cv2.imread(file0) color = ('b','g','r') plt.figure() for i,col in enumerate(color): histr = cv2.calcHist([img],[i],None,[256],[0,256]) plt.plot(histr,color = col) plt.xlim([0,256]) plt.show()