python - 如何使用opencv从皮肤图像中去除头发？

Question

我正在研究皮肤斑点的识别。为此，我处理了许多具有不同噪声的图像。这些噪音之一是毛发，因为我的图像在污渍（ROI）区域上有毛发。如何减少或消除这些类型的图像噪声？

下面的代码会减少毛发所在的区域，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：

标签： pythonimageopencvimage-processing

Answer 1

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中的一个或两个提供过帮助，并且已经提到去除头发是非常棘手和困难的任务。如果您删除图像上的头发，您将丢失信息并且您无法替换图像的那部分（没有程序或算法可以猜测头发下的内容 - 但它可以做出估计）。正如我在其他帖子中提到的那样，您可以做什么，我认为最好的方法是学习深度神经网络并自己制作脱毛。你可以谷歌“去水印深度神经网络”，看看我的意思。话虽如此，您的代码似乎没有提取您在示例图像中给出的所有 ROI（痣）。我举了另一个例子，说明如何更好地提取痣。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您计算包含痣的区域的平均像素强度。然后遍历每个像素，并就像素与平均值的差异制定某种标准。头发似乎呈现出比痣区域更暗的像素。因此，当您找到像素时，将其替换为不属于此标准的相邻像素。在这个例子中，我做了一个简单的逻辑，它不适用于每个图像，但它可以作为一个例子。要制定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，我想这需要相当长的时间。希望它有点帮助！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()