偏振图像-如何找到每个RGB值的平均值 [英] Polarizing an image--how to find the average of each RGB value

问题描述

要进行图像处理练习，我必须编写一个对图像应用各种效果的程序.效果之一是灰度，可以找到RGB值的平均值((红色+绿色+蓝色)/3).但是，要使图像偏振，我需要首先找到每个单独分量的平均值(即所有红色值/红色像素数).然后遍历行和列(使用像素，红色值，绿色值和蓝色值的计数器)作为查找平均值的方式是否合适?有没有更有效的方法?

For an exercise in image processing, I have to write a program that applies various effects to images. One of the effects was grayscale, which was done find the average of RGB values ((red+green+blue)/3). To polarize an image, however, I need to first find the averages of each individual component (i.e. all red values/number of red pixels). Would it be appropriate to then loop through the rows and columns (with a counter for pixels, red values, green values, and blue values) as a way to find the average? Is there a more efficient way?

此外，像素基于平均像素值极化.如果平均R为100，平均G为200，平均B为300，并且像素的R为150，G 150，而B为100，则极化像素将为255、0和0."我不明白这种关系吗?是不是如果当前值小于平均值，则极化为0/大于平均值，极化为255?

Also, pixels are polarized based on the average pixel value. "If average R is 100, average G is 200, and average B is 300 and a pixel has R 150, G 150, and B, 100, the polarized pixel would be 255, 0, and 0." I don't understand the relationship? Is it if the current value is less than the average, then it would be polarized to 0/more than the average, polarized to 255?

推荐答案

计算图像的平均RGB值:

Calculate average RGB value of an image:

    import numpy as np

    # load your image in a variable named `im`, then calculate average RGB value
    np.array(im).mean(axis=(0,1))

    # It gives a tuple like (71.710743801652896, 103.11570247933884, 64.165289256198349)

您不需要进行迭代，numpy可以满足所有需求:)

You don't need to iterate, numpy has it all :)

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