如何改善边缘检测并从图像中去除背景？

Question

我正在使用下面的代码删除图像的背景并仅突出显示我的感兴趣区域 (ROI)，但是，该算法在某些图像中的行为方式错误，丢弃了污点 (ROI) 并删除了背景。

import numpy as np
import cv2

#Read the image and perform threshold
img = cv2.imread('photo.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('photo.png', res)
#cv2.imshow('img', res)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

我不知道我是否理解正确，因为当我运行您的代码时，我没有得到您发布的输出（退出）。如果您只想获得痣，则无法通过简单的阈值处理来完成，因为痣太靠近边界，而且如果您仔细查看图像，您会发现它有某种框架。然而，有一种简单的方法可以对这个图像执行此操作，但在其他情况下它可能不起作用。您可以在图像上绘制假边框并将 ROI 与其他噪声区域分开。然后为您希望显示的轮廓设置一个阈值。干杯！

例子：

#Import all necessery libraries
import numpy as np
import cv2

#Read the image and perform threshold and get its height and weight
img = cv2.imread('moles.png')
h, w = img.shape[:2]

# Transform to gray colorspace and blur the image.
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(5,5),0)

# Make a fake rectangle arround the image that will seperate the main contour.
cv2.rectangle(blur, (0,0), (w,h), (255,255,255), 10)

# Perform Otsu threshold.
_,thresh = cv2.threshold(blur,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

# Create a mask for bitwise operation
mask = np.zeros((h, w), np.uint8)

# Search for contours and iterate over contours. Make threshold for size to
# eliminate others.
_, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)

for i in contours:
    cnt = cv2.contourArea(i)
    if 1000000 >cnt > 100000:
        cv2.drawContours(mask, [i],-1, 255, -1)


# Perform the bitwise operation.
res = cv2.bitwise_and(img, img, mask=mask)

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

结果：