一旦有了的两个轮廓
cv2.findContours()，就可以使用按位
AND运算来检测相交。具体来说，我们可以使用
np.logical_and()。想法是为每个轮廓创建两个单独的图像，然后
AND对它们使用逻辑运算。具有正值（
1或
True）的任何点都将是交点。因此，由于您只想获取是否存在相交的布尔值，因此我们可以检查相交的图像以查看是否存在单个正值。本质上，如果整个数组都
False存在，则轮廓之间就没有交集。但是，如果只有一个
True，则轮廓会接触并因此相交。
def contourIntersect(original_image, contour1, contour2): # Two separate contours trying to check intersection on contours = [contour1, contour2] # Create image filled with zeros the same size of original image blank = np.zeros(original_image.shape[0:2]) # Copy each contour into its own image and fill it with '1' image1 = cv2.drawContours(blank.copy(), contours, 0, 1) image2 = cv2.drawContours(blank.copy(), contours, 1, 1) # Use the logical AND operation on the two images # Since the two images had bitwise and applied to it, # there should be a '1' or 'True' where there was intersection # and a '0' or 'False' where it didnt intersect intersection = np.logical_and(image1, image2) # Check if there was a '1' in the intersection return intersection.any()
例
原始图片
检测到轮廓
现在，我们将两个检测到的轮廓传递给函数，并获得此交集数组：
[[False False False ... False False False] [False False False ... False False False] [False False False ... False False False] ... [False False False ... False False False] [False False False ... False False False] [False False False ... False False False]]
我们检查
intersection数组以查看是否
True存在。我们将获得一个
True或
1轮廓相交的地方，
False或
0它们不相交的地方。
return intersection.any()
这样我们得到
假
完整代码
import cv2import numpy as npdef contourIntersect(original_image, contour1, contour2): # Two separate contours trying to check intersection on contours = [contour1, contour2] # Create image filled with zeros the same size of original image blank = np.zeros(original_image.shape[0:2]) # Copy each contour into its own image and fill it with '1' image1 = cv2.drawContours(blank.copy(), contours, 0, 1) image2 = cv2.drawContours(blank.copy(), contours, 1, 1) # Use the logical AND operation on the two images # Since the two images had bitwise AND applied to it, # there should be a '1' or 'True' where there was intersection # and a '0' or 'False' where it didnt intersect intersection = np.logical_and(image1, image2) # Check if there was a '1' in the intersection array return intersection.any()original_image = cv2.imread("base.png")image = original_image.copy()cv2.imshow("original", image)gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)cv2.imshow("gray", gray)blurred = cv2.GaussianBlur(gray, (5,5), 0)cv2.imshow("blur", blurred)threshold = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]cv2.imshow("thresh", threshold)contours = cv2.findContours(threshold.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# Depending on OpenCV version, number of arguments return by cv.findContours # is either 2 or 3contours = contours[1] if len(contours) == 3 else contours[0]contour_list = []for c in contours: contour_list.append(c) cv2.drawContours(image, [c], 0, (0,255,0), 2)print(contourIntersect(original_image, contour_list[0], contour_list[1]))cv2.imshow("contour", image)cv2.waitKey(0)
