如何改善边缘检测并从图像中去除背景？

Question

我正在使用以下代码删除图像的背景并仅突出显示我感兴趣的区域（ROI），但是，该算法在某些图像中的行为方式不正确，会丢弃污点（ROI）并与背景。

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，因为在运行您的代码时，我没有得到您发布（退出）的输出。如果您只想获取痣，则不能仅仅通过阈值来完成，因为痣太靠近边界，而且如果您查看图像closley，您会发现它具有某种框架。但是，有一种简单的方法可以对此图像执行此操作，但在其他情况下可能无法使用。您可以在图像上绘制假边框，并将ROI与其他噪点区分开。然后为要显示的轮廓设定一个阈值。干杯！

示例：

#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：