如何使用opencv从皮肤图像中去除头发? [英] How to remove hair from skin images using opencv?

问题描述

我正在识别皮肤斑点.为此，我处理了许多具有不同噪点的图像.这些噪音之一是头发，因为我在污渍(ROI)区域上有头发图像.如何减少或消除这些类型的图像噪点?

I am working with recognition of skin spots. For this, I work with a number of images with different noises. One of these noises are the hairs, because I have images with hairs over the area of ​​the stain (ROI). How to decrease or remove these types of image noise?

下面的代码会减少头发所在的区域，但不会删除感兴趣区域(ROI)上方的头发.

The code below decreases the area where hairs are, but does not remove hairs that are above the area of ​​interest (ROI).

import numpy as np
import cv2

IMD = 'IMD436'
# Read the image and perfrom an OTSU threshold
img = cv2.imread(IMD+'.bmp')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh =     cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

# Remove hair with opening
kernel = np.ones((2,2),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)

# Combine surrounding noise with ROI
kernel = np.ones((6,6),np.uint8)
dilate = cv2.dilate(opening,kernel,iterations=3)

# Blur the image for smoother ROI
blur = cv2.blur(dilate,(15,15))

# Perform another OTSU threshold and search for biggest contour
ret, thresh =     cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
contours, hierarchy =     cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)

# Create a new mask for the result image
h, w = img.shape[:2]
mask = np.zeros((h, w), np.uint8)

# Draw the contour on the new mask and perform the bitwise operation
cv2.drawContours(mask, [cnt],-1, 255, -1)
res = cv2.bitwise_and(img, img, mask=mask)

# Display the result
cv2.imwrite(IMD+'.png', res)
cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

退出:

如何从感兴趣区域的顶部去除头发?

How can I remove hair from the top of my region of interest?

使用的图像:

推荐答案

我正在回复您在相关帖子上的标签.据我了解，您和另一所大学正在共同开展一个项目，以在皮肤上定位痣.因为我想我已经在一个类似的问题上为你们中的一个或两个人提供了帮助，并且已经提到了去除头发是非常棘手和困难的任务.如果您去除图像上的头发，则会丢失信息，并且无法替换图像的那部分(程序或算法无法猜测出头发下的内容，但可以进行估算).正如我在其他文章中提到的那样，您可以做些什么，我认为最好的方法是学习深度神经网络，并为脱毛做自己的事.您可以在Google水印去除深度神经网络"中进行搜索，然后了解我的意思.话虽如此，您的代码似乎并未提取示例图像中给出的所有ROI(摩尔).我已经举了另一个例子，说明如何更好地提取痣.基本上，应该在转换为二进制文件之前执行关闭操作，这样会得到更好的结果.

I am responding to your tag on a related post. As I understand you and another colege are working together on a project to locate the moles on the skin? Because I think I have already gave help to one or maybe both of you on similar questions and already mentioned that the removal of the hair is very tricky and difficult task. If you remove the hair on the image you lose information and you can't replace that part of the image (no program or alghorithm can guess what is under the hair - but it can make an estimation). What you could do as I mentioned in other posts and I think that it would be the best approach is to learn about deep neural networks and make your own for the hair removal. You can google "watermark removal deep neural network" and see what I mean. That being said, your code does not seem to extract all ROIs (the moles) you have given in the example image. I have made another example on how you can better extract the moles. Basically you should perform closing before transforming to binary and you will get better results.

对于第二部分-脱毛，如果您不想建立神经网络，我认为可以选择另一种解决方案，即计算包含痣的区域的平均像素强度.然后遍历每个像素，并就像素与均值的差异做出某种判断.头发似乎呈现出比痣区域暗的像素.因此，当您找到像素时，请用不属于此标准的近邻像素替换它.在示例中，我提出了一个简单的逻辑，该逻辑不适用于所有图像，但可以作为示例.为了提供一个完全可操作的解决方案，您应该制定一个更好，更复杂的算法，我想这将花费相当长的时间.希望能有所帮助！干杯！

For the second part - hair removal, if you do not wish to make a neural network, I think that alternative solution could be, that you calculate the mean pixel intesity of the region that contains the mole. Then iterate throug every pixel and make some sort of criteria on how much can the pixel differ from the mean. Hair seem to be presented with pixels that are darker than the mole area. So when you find the pixel, replace it with the neigbour pixel that does not fall in this criteria. In the example I have made a simple logic which will not work with every image but it can serve as an example. To make a fully operational solution you should make a better, more complex alghorithm which I guess will take quite some time. Hope it helps a bit! Cheers!

import numpy as np
import cv2
from PIL import Image

# Read the image and perfrom an OTSU threshold
img = cv2.imread('skin2.png')
kernel = np.ones((15,15),np.uint8)

# Perform closing to remove hair and blur the image
closing = cv2.morphologyEx(img,cv2.MORPH_CLOSE,kernel, iterations = 2)
blur = cv2.blur(closing,(15,15))

# Binarize the image
gray = cv2.cvtColor(blur,cv2.COLOR_BGR2GRAY)
_, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)


# Search for contours and select the biggest one
_, contours, hierarchy =     cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
cnt = max(contours, key=cv2.contourArea)

# Create a new mask for the result image
h, w = img.shape[:2]
mask = np.zeros((h, w), np.uint8)

# Draw the contour on the new mask and perform the bitwise operation
cv2.drawContours(mask, [cnt],-1, 255, -1)
res = cv2.bitwise_and(img, img, mask=mask)

# Calculate the mean color of the contour
mean = cv2.mean(res, mask = mask)
print(mean)

# Make some sort of criterion as the ratio hair vs. skin color varies
# thus makes it hard to unify the threshold.
# NOTE that this is only for example and it will not work with all images!!!

if mean[2] >182:
    bp = mean[0]/100*35
    gp = mean[1]/100*35
    rp = mean[2]/100*35   

elif 182 > mean[2] >160:
    bp = mean[0]/100*30
    gp = mean[1]/100*30
    rp = mean[2]/100*30

elif 160>mean[2]>150:
    bp = mean[0]/100*50
    gp = mean[1]/100*50
    rp = mean[2]/100*50

elif 150>mean[2]>120:
    bp = mean[0]/100*60
    gp = mean[1]/100*60
    rp = mean[2]/100*60

else:
    bp = mean[0]/100*53
    gp = mean[1]/100*53
    rp = mean[2]/100*53

# Write temporary image
cv2.imwrite('temp.png', res)

# Open the image with PIL and load it to RGB pixelpoints
mask2 = Image.open('temp.png')
pix = mask2.load()
x,y = mask2.size

# Itearate through the image and make some sort of logic to replace the pixels that
# differs from the mean of the image
# NOTE that this alghorithm is for example and it will not work with other images

for i in range(0,x):
    for j in range(0,y):
        if -1<pix[i,j][0]<bp or -1<pix[i,j][1]<gp or -1<pix[i,j][2]<rp:
            try:
                pix[i,j] = b,g,r
            except:
                pix[i,j] = (int(mean[0]),int(mean[1]),int(mean[2]))
        else:
            b,g,r = pix[i,j]

# Transform the image back to cv2 format and mask the result         
res = np.array(mask2)
res = res[:,:,::-1].copy()
final = cv2.bitwise_and(res, res, mask=mask)

# Display the result
cv2.imshow('img', final)
cv2.waitKey(0)
cv2.destroyAllWindows()

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