python查看图片颜色统计_查找图像的RGB像素颜色计数的最快方法

I have a use case where i have to find the consecutive rgb pixel color count of each frame of live video after searching i found a piece of code which does the same thing but performance wise it take around ~ 3 sec to give me output but in my case i have to do this calculation as fast as possible may be 25 frames in 1 seconds. Can someone help me to figure out how to do this by refactoring the below code

from PIL import Image

import timeit

starttime = timeit.default_timer()

with Image.open("netflix.png") as image:

color_count = {}

width, height = image.size

print(width,height)

rgb_image = image.convert('RGB')

for x in range(width):

for y in range(height):

rgb = rgb_image.getpixel((x, y))

if rgb in color_count:

color_count[rgb] += 1

else:

color_count[rgb] = 1

print('Pixel Count per Unique Color:')

print('-' * 30)

print(len(color_count.items()))

print("The time difference is :", timeit.default_timer() - starttime)

output:

Pixel Count per Unique Color:

130869

The time difference is : 3.9660612

解决方案

You need to use Numpy, or OpenCV, for fast image processing in Python. I made a 9-colour version of Paddington:

from PIL import Image

import numpy as np

# Open Paddington and make sure he is RGB - not palette

im = Image.open('paddington.png').convert('RGB')

# Make into Numpy array

na = np.array(im)

# Arrange all pixels into a tall column of 3 RGB values and find unique rows (colours)

colours, counts = np.unique(na.reshape(-1,3), axis=0, return_counts=1)

print(colours)

print(counts)

Results

[[ 14 48 84]

[ 19 21 30]

[ 33 108 163]

[ 33 152 190]

[ 72 58 58]

[ 96 154 210]

[180 89 64]

[205 210 200]

[208 151 99]]

[20389 40269 12820 1488 17185 25371 17050 16396 9032]

That means there are 20,389 pixels of RGB(14,48,84), and so on.

That takes 125ms on my Mac for a 400x400 image, which will give you 8 fps, so you better have at least 4 CPU cores and use all of them to get 25+ fps.

Update

I think you can actually go significantly faster than this. If you take the dot-product of each of the pixels with [1,256,65536], you will get a single 24-bit number for each pixel, rather than 3 8-bit numbers. It is then a lot faster to find the unique values. That looks like this:

# Open Paddington and make sure he is RGB - not palette

im = Image.open('paddington.png').convert('RGB')

# Make into Numpy array

na = np.array(im)

# Make a single 24-bit number for each pixel

f = np.dot(na.astype(np.uint32),[1,256,65536])

nColours = len(np.unique(f)) # prints 9

That takes 4ms rather than 125ms on my Mac :-)

Keywords: Python, Numpy, PIL/Pillow, image processing, count unique colours, count colors.