使用 Python/PIL 检测 HSV 颜色空间(来自 RGB)中的阈值 [英] Detecting thresholds in HSV color space (from RGB) using Python / PIL

问题描述

我想将一个 RGB 图像转换为黑白 RGB 图像，如果像素的 HSV 值在一定范围内则为黑色，否则为白色.

I want to take an RGB image and convert it to a black and white RGB image, where a pixel is black if its HSV value is between a certain range and white otherwise.

目前我创建了一个新图像，然后通过遍历其数据创建一个新像素值列表，然后 .putdata() 该列表形成新图像.

Currently I create a new image, then create a list of new pixel values by iterating through its data, then .putdata() that list to form the new image.

感觉应该有一种更快的方法来做到这一点，例如使用 .point()，但似乎 .point() 没有得到给定的像素，而是从 0 到 255 的值.是否有 .point() 变换但在像素上?

It feels like there should be a much faster way of doing this, e.g. with .point(), but it seems .point() doesn't get given pixels but values from 0 to 255 instead. Is there a .point() transform but on pixels?

推荐答案

好的，这确实有效(修复了一些溢出错误):

Ok, this does work (fixed some overflow errors):

import numpy, Image
i = Image.open(fp).convert('RGB')
a = numpy.asarray(i, int)

R, G, B = a.T

m = numpy.min(a,2).T
M = numpy.max(a,2).T

C = M-m #chroma
Cmsk = C!=0

# Hue
H = numpy.zeros(R.shape, int)
mask = (M==R)&Cmsk
H[mask] = numpy.mod(60*(G-B)/C, 360)[mask]
mask = (M==G)&Cmsk
H[mask] = (60*(B-R)/C + 120)[mask]
mask = (M==B)&Cmsk
H[mask] = (60*(R-G)/C + 240)[mask]
H *= 255
H /= 360 # if you prefer, leave as 0-360, but don't convert to uint8

# Value
V = M

# Saturation
S = numpy.zeros(R.shape, int)
S[Cmsk] = ((255*C)/V)[Cmsk]

# H, S, and V are now defined as integers 0-255

它基于维基百科对HSV的定义.当我有更多时间时，我会查看它.肯定有加速，也许还有错误.如果你找到了，请告诉我.干杯.

It is based on the Wikipedia's definition of HSV. I'll look it over as I get more time. There are definitely speedups and maybe bugs. Please let me know if you find any. cheers.

结果:

从这个色轮开始:

我得到了这些结果:

色调:

值:

饱和度:

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