将RGB图像转换为黑白PIL手部识别

Question

我试图用 python 编写与此处所写相同的内容，但我的代码没有产生好的结果。我的目标是获取 RGB 图像，调整大小并转换为 YCbCr，然后将背景像素值设置为 0，将手部像素值设置为 1。有人可以帮我使用 PIL 在 python 中编写此代码吗？

（我试图复制的代码，我在步骤 3-6 时遇到了一些问题）

function image_out = processSkinImage(filename)

    Step 1...

    % Read the image

    original = imread(filename);

    ...

    Step 2...

    % Resize the image to 50x50

    image_resized = imresize(original, scale);

    [M N Z] = size(image_resized);

    % Initialize the output image

    image_out = zeros(height,width);

    image_out = zeros(M,N);

    ...

    Step 3...

    % Convert the image from RGB to YCbCr

    img_ycbcr = rgb2ycbcr(image_resized);

    Cb = img_ycbcr(:,:,2);

    Cr = img_ycbcr(:,:,3);

    ...

    Step 4...

    % Get the central color of the image

    % Expected the hand to be in the central of the image

    central_color = img_ycbcr(int32(M/2),int32(N/2),:);

    Cb_Color = central_color(:,:,2);

    Cr_Color = central_color(:,:,3);

    % Set the range

    Cb_Difference = 15;

    Cr_Difference = 10;

    ...

    Step 5...

    % Detect skin pixels

    [r,c,v] = find(Cb>=Cb_Color-Cr_Difference & Cb<=Cb_Color+Cb_Difference & Cr>=Cr_Color-Cr_Difference & Cr<=Cr_Color+Cr_Difference);

    ...

    Step 6...

    % Mark detected pixels

    for i=1:match_count

        image_out(r(i),c(i)) = 1;

    end

end

这就是我写的代码：

from PIL import Image as im

image = im.open('/Users/eitan/Desktop/eell.jpg')

image = image.resize((50,50), im.NEAREST)

grayScale = image.convert(mode='L')

width, height = grayScale.size

mid_pixel=grayScale.getpixel((width/2,height/2))

print (mid_pixel)

pixels = grayScale.load()

for i in range(grayScale.size[0]):    # for every col:

    for j in range(grayScale.size[1]):    # For every row

        if grayScale.getpixel((i,j)) < mid_pixel+40 and grayScale.getpixel((i,j)) > mid_pixel-15:

            pixels[i,j] = 255

        else:

            pixels[i, j] = 0

grayScale.show()

如果有人可以帮助我用python编写这段代码，那就太好了！

Answer 1

你可以像这样处理它，我使用的是 HSV 颜色空间而不是 YCbCr 颜色空间：#!/usr/bin/env python3import numpy as npfrom PIL import Image# Open image and convert to HSV colourspaceim = Image.open('hand.png').convert('HSV')# Convert to Numpy arrayni = np.array(im)# Get H, S and V of central pixel - consider taking a median of a larger area hereh,s,v = ni[int(ni.shape[0]/2), int(ni.shape[1]/2)]# Separate each channel to own arrayH = ni[:,:,0]S = ni[:,:,1]V = ni[:,:,2]# Permissible +/- tolerances on each channeldeltah = 20deltas = 80deltav = 50# Make masks of pixels with acceptable H, S and Vhmask = np.where((H > h-deltah) & (H < h+deltah), 255, 0).astype(np.uint8)smask = np.where((S > s-deltas) & (S < s+deltas), 255, 0).astype(np.uint8)vmask = np.where((V > v-deltav) & (V < v+deltav), 255, 0).astype(np.uint8)# Save as images for inspectionImage.fromarray(hmask).save('hmask.png')Image.fromarray(smask).save('smask.png')Image.fromarray(vmask).save('vmask.png')生成的色调蒙版：产生的饱和度遮罩：结果值掩码：然后，您可以将掩码与或或组合在一起以获得更复杂的掩码组合。