这是一个简单的脚本，它可以提取二值图像中最大的白色斑点。因为图像中最大的白色斑点是前景（也是你要找的形状），所以这应该会给予你预期的结果。
它基本上得到了所有的外部轮廓，并保留了面积最大的轮廓。然后在一张新的图像上绘制它。
这是源代码，我使用this图像，因为你没有提供原始.

# Imports:
import cv2
import numpy as np

# image path
path = "D://opencvImages//"

# Reading an image in default mode:
inputImage = cv2.imread(path + "testBlob.png")

# Grayscale conversion:
grayscaleImage = cv2.cvtColor(inputImage, cv2.COLOR_BGR2GRAY)

# Threshold via Otsu:
# Note the image inversion:
_, binaryImage = cv2.threshold(grayscaleImage, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

# Store a copy of the input image:
biggestBlob = binaryImage.copy()
# Set initial values for the
# largest contour:
largestArea = 0
largestContourIndex = 0

# Find the contours on the binary image:
contours, hierarchy = cv2.findContours(binaryImage, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)

# Get the largest contour in the contours list:
for i, cc in enumerate(contours):
    # Find the area of the contour:
    area = cv2.contourArea(cc)
    # Store the index of the largest contour:
    if area > largestArea:
        largestArea = area
        largestContourIndex = i

# Once we get the biggest blob, paint it black:
tempMat = binaryImage.copy()
cv2.drawContours(tempMat, contours, largestContourIndex, (0, 0, 0), -1, 8, hierarchy)

# Erase smaller blobs:
biggestBlob = biggestBlob - tempMat

# Show the result:
cv2.imshow("biggestBlob", biggestBlob)
cv2.waitKey(0)

这就是结果：