How to remove image noises using opencv - python?

Question

I am working with skin images, in the recognition of skin spots, and due to the presence of noises, mainly by the presence of hair, it is more complicated this work.

I have an example image in which I work on trying to highlight only the skin stain, but due to the large amount of hair, the algorithm is not effective. With this, I would like you to help me develop an algorithm for removal or decrease the amount of hair and so be able to highlight only my area of interest (ROI), which are the spots.

Algorithm used to highlight skin spots:

import numpy as np
import cv2

#Read the image and perform threshold
img = cv2.imread('IMD006.bmp')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.medianBlur(gray,5)
_,thresh = cv2.threshold(blur,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)

#Display the result
cv2.imwrite('IMD006.png', res)
#cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

Example of image used:

Exit:

The algorithm behaves as expected for hairless images, as shown below:

How to treat these noises to the point of improving my region of interest?

Answer 1

Solution

• Creation of the mask with:
  • morphologyEx with cv2.MORPH_CLOSE
  • cv2.blur to blur the image
  • Binarization with the flag cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU
  • Floodfill to eliminate the corners of the mask
• Cut out the original image or found by the mask, with logic AND
• Canny Edges to find the hair strands and then dilation
• Remove from the cropped image the one found by Canny Edges, using OR logic
• Use Alexandru Telea’s Inpaint interpolation to fill the empty spaces, predicting what should be in that empty place.

Sample Code

import numpy as np
import cv2
import urllib.request


# Abrir a imagem do link ou img = cv2.imread(diretorio_da_imagem//nome_do_arquivo)
resp = urllib.request.urlopen("https://i.stack.imgur.com/pgW91.png")
img = np.asarray(bytearray(resp.read()), dtype="uint8")
img = cv2.imdecode(img, cv2.IMREAD_COLOR)

kernel = np.ones((3,3),np.uint8)

# Utilização do morphologyEx e blur
closing = cv2.morphologyEx(img,cv2.MORPH_CLOSE,kernel, iterations = 2)
blur = cv2.blur(closing,(15,15))

# Binarização
gray = cv2.cvtColor(blur,cv2.COLOR_BGR2GRAY)
_, mask = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
cv2.imshow('Binarização',mask)
cv2.waitKey(0)

#Preenche os quatro cantos da imagem binária
w, h = mask.shape[::-1]
cv2.floodFill(mask, None, (0, 0), 0)
cv2.floodFill(mask, None, (w-1, 0), 0)
cv2.floodFill(mask, None, (0, h-1), 0)
cv2.floodFill(mask, None, (w-1, h-1), 0)
cv2.imshow('mask',mask)
cv2.waitKey(0)

#Lógica AND para obter da imagem original a encontrada pela criação do mask
img = cv2.bitwise_and(img, img, mask=mask )
cv2.imshow('AND',img)
cv2.waitKey(0)

#Canny Edges
edges = cv2.Canny(img, 100,200)
dilate = cv2.dilate(edges,kernel,iterations=1)
dilate = cv2.bitwise_not(dilate)
cv2.imshow('Canny',dilate)
cv2.waitKey(0)

#Lógica OR para retirar da imagem original os pêlos encontrados
img = cv2.bitwise_or(img, img, mask=dilate )
cv2.imshow('Edges',img)
cv2.waitKey(0)

#Interpolação da imagem para preencher os vazios
dilate = cv2.bitwise_not(dilate)
inpaint = cv2.inpaint(img, dilate, 3,cv2.INPAINT_TELEA)
cv2.imshow('InPaint', inpaint)
cv2.waitKey(0)

cv2.destroyAllWindows()

Results

Binarization

Floodfill

Logic AND

Canny Edges

Logic OR

Interpolation Inpaint