重叠轮廓

Question

我正在尝试在图像上绘制多个轮廓，到目前为止，我已经设法通过应用不同的阈值来绘制轮廓。唯一的问题是，大多数轮廓区域是重叠的，因此我在这里讨论如何处理它。我理想地希望的是，每当有重叠时，都应将轮廓分成各个区域。例如，与Conceptual image中一样，有4个区域（轮廓）为橙色，绿色，蓝色和黑色。每当有重叠时，都应分为紫色区域。似乎很棘手，我什至不确定是否可行。如果没有，我希望所有重叠部分合并。谁能帮助解决这个问题？ Sample image

import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
im = cv.imread('images/sample.jpg')
imgray = cv.cvtColor(im, cv.COLOR_BGR2GRAY)
ret1, thresh1 = cv.threshold(imgray, 30, 80, 0)
ret2, thresh2 = cv.threshold(imgray, 80, 110, 0)
ret3, thresh3 = cv.threshold(imgray, 110, 150, 0)
ret4, thresh4 = cv.threshold(imgray, 150, 200, 0)
ret5, thresh5 = cv.threshold(imgray, 200, 255, 0)
_,contours1, hierarchy1 = cv.findContours(thresh1, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
_,contours2, hierarchy2 = cv2.findContours(thresh2,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
_,contours3, hierarchy3 = cv2.findContours(thresh3,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
_,contours4, hierarchy4 = cv2.findContours(thresh4,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
_,contours5, hierarchy5 = cv2.findContours(thresh5,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)



cv2.drawContours(im, contours1, -1, (0, 0, 255), 1)
cv2.drawContours(im, contours2, -1, (0, 255, 0), 1)
cv2.drawContours(im, contours3, -1, (0, 0, 255), 1)
cv2.drawContours(im, contours4, -1, (10, 200, 200), 1)
cv2.drawContours(im, contours5, -1, (255, 255, 0), 1)

cv2.imshow("im",im)
cv2.waitKey(0)

Answer 1

如前所述，从轮廓生成遮罩，然后从原始遮罩计算成对的相交以及“排他”部分，肯定会为您提供所需的区域，但是这种方法往往会很昂贵也一样从您的示例图像和代码中，我无法弄清您实际上想要做什么，因此我坚持使用一些非常基本的示例来说明这种方法。

import cv2
import numpy as np
from matplotlib import pyplot as plt

# Generate some dummy images, whose (main) contours overlap
img1 = cv2.circle(np.zeros((400, 400, 3), np.uint8), (150, 150), 100, (0, 255, 0), cv2.FILLED)
img2 = cv2.rectangle(np.zeros((400, 400, 3), np.uint8), (175, 175), (325, 325), (0, 0, 255), cv2.FILLED)

# Find contours (OpenCV 4.x)
contours1, _ = cv2.findContours(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
contours2, _ = cv2.findContours(cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

# Find contours (OpenCV 3.x)
#_, contours1, _ = cv2.findContours(cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
#_, contours2, _ = cv2.findContours(cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

# Generate masks of (main) contours; Attention: Hard-coded selection of first contour here!
mask1 = cv2.drawContours(np.zeros((400, 400), np.uint8), [contours1[0]], -1, 255, cv2.FILLED)
mask2 = cv2.drawContours(np.zeros((400, 400), np.uint8), [contours2[0]], -1, 255, cv2.FILLED)

# Find intersection of both masks
mask_combined = cv2.bitwise_and(mask1, mask2)

# Generate "exclusive" masks, i.e. masks without the intersection parts
mask1_excl = cv2.bitwise_xor(mask1, mask_combined)
mask2_excl = cv2.bitwise_xor(mask2, mask_combined)

# Visualization
plt.figure()
plt.subplot(3, 3, 1), plt.imshow(img1), plt.ylabel('img1')
plt.subplot(3, 3, 2), plt.imshow(img2), plt.ylabel('img2')
plt.subplot(3, 3, 3), plt.imshow(img1 + img2), plt.ylabel('img1 + img2')
plt.subplot(3, 3, 4), plt.imshow(mask1, cmap='gray'), plt.ylabel('mask1')
plt.subplot(3, 3, 5), plt.imshow(mask2, cmap='gray'), plt.ylabel('mask2')
plt.subplot(3, 3, 6), plt.imshow(mask_combined, cmap='gray'), plt.ylabel('mask_combined')
plt.subplot(3, 3, 7), plt.imshow(mask1_excl, cmap='gray'), plt.ylabel('mask1_excl')
plt.subplot(3, 3, 8), plt.imshow(mask2_excl, cmap='gray'), plt.ylabel('mask2_excl')
plt.subplot(3, 3, 9), plt.imshow(mask_combined, cmap='gray'), plt.ylabel('mask_combined')
plt.show()

可视化：

现在，必须对每个轮廓元组都进行此操作-不仅要成对，因为可以有三个或更多轮廓的交点。跟踪所有这些生成的掩码等将很可能占用大量内存，但计算量并不大。最后，所有方法都需要以某种方式将结果区域存储为某种掩码。

希望有帮助！