algorithm - 如何在存在重叠和噪声的情况下从图像中分割主要形状？

Question

我们如何根据引用图像将左图转换为右图？

Answer 1

这是一个想法:

加载图像，转换为灰度，以及二值图像的 Otsu 阈值

找到轮廓并用白色填充它

现在我们有一个二值图像，我们可以执行形态学操作。根据您尝试提取的对象，我们可以创建不同的结构内核。对于矩形，我们可以使用 cv2.MORPH_RECT ，对于椭圆，我们可以删除具有更大内核大小的水平部分并使用 cv2.MORPH_ELLIPSE .

然后我们过滤剩余的轮廓并找到矩形和椭圆的旋转边界框

这是过程的可视化




对于椭圆




由于您没有指定语言，这里是 Python 的实现

import cv2
import numpy as np

# Load image, convert to grayscale, Otsu's threshold for binary image
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Find contours and fill in contour with white
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(thresh, [c], 0, 255, -1)

# Rectangle ----------------------------------------
# Morph open to separate rectangular contour
rectangular_kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (20, 20))
rect = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, rectangular_kernel, iterations=4)

# Find contours and draw rotated rectangle onto image
cnts = cv2.findContours(rect, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
isolated_rect = cv2.minAreaRect(cnts[0])
box = np.int0(cv2.boxPoints(isolated_rect))
cv2.drawContours(image, [box], 0, (36,255,12), 3)
# Rectangle ----------------------------------------

# Ellipse ----------------------------------------
# Morph open to separate elliptical contour
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (50,10))
ellipse = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)

# Find contours and filter for ellipse
cnts = cv2.findContours(ellipse, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
# Filter using contour area, could also use Aspect Ratio or contour approximation
for c in cnts:
    area = cv2.contourArea(c)
    if area > 20000:
        cv2.drawContours(image, [c], 0, (136,15,212), 3)
# Ellipse ----------------------------------------


# Display
cv2.imshow('image', image)
cv2.imshow('thresh', thresh)
cv2.imshow('rect', rect)
cv2.imshow('ellipse', ellipse)
cv2.waitKey()