如何使用opencv从皮肤图像中删除头发？

Question

我正在努力识别皮肤斑点.为此,我使用了许多具有不同噪声的图像.这些噪音中的一个是毛发,因为我在污渍区域(ROI)上有毛发的图像.如何减少或消除这些类型的图像噪音？

下面的代码减少了毛发的区域,但不会去除感兴趣区域(ROI)上方的毛发.

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()

出口:

如何从我感兴趣的区域顶部去除头发？

使用的图像:

Answer 1

我正在相关帖子上回复您的标签。据我了解，您和另一所大学正在合作开展一个项目来定位皮肤上的痣？因为我想我已经在类似的问题上为你们一个或两个人提供了帮助，并且已经提到脱毛是一项非常棘手和困难的任务。如果删除图像上的头发，您就会丢失信息，并且无法替换图像的该部分（没有程序或算法可以猜测头发下面的内容 - 但它可以做出估计）。正如我在其他帖子中提到的，你可以做的，我认为最好的方法是了解深度神经网络并制作自己的脱毛网络。你可以谷歌“水印去除深度神经网络”并明白我的意思。话虽这么说，您的代码似乎并未提取您在示例图像中给出的所有 ROI（痣）。我还举了另一个例子来说明如何更好地去除痣。基本上你应该在转换为二进制之前执行关闭，你会得到更好的结果。

对于第二部分 - 脱毛，如果您不想制作神经网络，我认为替代解决方案可能是计算包含痣的区域的平均像素强度。然后迭代每个像素并制定某种标准来确定像素与平均值的差异有多大。头发似乎呈现出比痣区域更暗的像素。因此，当您找到该像素时，请将其替换为不符合此条件的相邻像素。在这个例子中，我做了一个简单的逻辑，它并不适用于每个图像，但它可以作为一个例子。为了制定一个完全可操作的解决方案，你应该制定一个更好、更复杂的算法，我想这将需要相当长的时间。希望它能有点帮助！干杯!

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()