问题描述
我有 2 个从 cv2.findContours()
接收到的轮廓(cont1
和 cont2
).我怎么知道它们是否相交?我不需要坐标,我只需要一个布尔值 True
或 False
.
我尝试了不同的方法,并且已经尝试过使用
if ((cont1 & cont2).area() > 0):
...但得到数组没有方法Area()"的错误
...cont1array = cv2.findContours(binary1, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]cont2array = cv2.findContours(binary2, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]...对于 cont1array 中的 cont1:对于 cont2array 中的 cont2:打印(cont1")打印(续 1)打印(类型(cont1))打印(cont2")打印(续 2)打印(类型(cont2))>如果 cont1 和 cont2 相交:#我不知道如何检查相交打印(是的,它们相交")别的:打印(不,他们不相交")# 续 1# [[172 302]# [261 301]# [262 390]# [173 391]]# <class 'numpy.ndarray'># cont2# [[ 0 0]# [0699]# [499 699]# [499 0]]# <class 'numpy.ndarray'>一旦你有了 cv2.findContours()
的两个轮廓,你就可以使用按位 AND
检测交叉点的操作.具体来说,我们可以使用
检测到的轮廓
我们现在将检测到的两个轮廓传递给函数并获得此交集数组:
[[假假假...假假假][假假假...假假假][假假假...假假假]...[假假假...假假假][假假假...假假假][假假假...假假假]]
我们检查 intersection
数组以查看 True
是否存在.我们将在轮廓相交的地方获得 True
或 1
,在轮廓相交的地方获得 False
或 0
.>
返回intersection.any()
这样我们就得到了
错误
完整代码
导入 cv2将 numpy 导入为 np定义轮廓相交(原始图像,轮廓1,轮廓2):# 两个单独的轮廓试图检查交点轮廓 = [轮廓 1,轮廓 2]# 创建与原始图像大小相同的填充零的图像空白 = np.zeros(original_image.shape[0:2])# 将每个轮廓复制到自己的图像中并用1"填充image1 = cv2.drawContours(blank.copy(), 轮廓, 0, 1)image2 = cv2.drawContours(blank.copy(), 轮廓, 1, 1)# 对两幅图像使用逻辑与运算# 由于这两个图像已按位 AND 应用于它,# 应该有一个'1'或'True'的地方有交集# 和一个'0'或'False',它没有相交交集 = np.logical_and(image1, image2)# 检查交集数组中是否有'1'返回intersection.any()original_image = cv2.imread("base.png")图像 = original_image.copy()cv2.imshow(原始",图像)灰色 = cv2.cvtColor(图像,cv2.COLOR_BGR2GRAY)cv2.imshow(灰色",灰色)模糊 = cv2.GaussianBlur(gray, (5,5), 0)cv2.imshow(模糊",模糊)阈值 = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]cv2.imshow(阈值",阈值)轮廓 = cv2.findContours(threshold.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)# 根据 OpenCV 版本,cv.findContours 返回的参数数量# 是 2 或 3轮廓 = 轮廓 [1] 如果 len(轮廓) == 3 否则轮廓 [0]轮廓列表 = []对于轮廓中的 c:contour_list.append(c)cv2.drawContours(image, [c], 0, (0,255,0), 2)打印(轮廓相交(原始图像,轮廓列表[0],轮廓列表[1]))cv2.imshow(轮廓",图像)cv2.waitKey(0)
I have 2 contours (cont1
and cont2
) received from cv2.findContours()
. How do I know if they intersect or not? I don't need coordinates, I only need a boolean True
or False
.
I have attempted different ways and already tried to do a check with
if ((cont1 & cont2).area() > 0):
...but got the error that the array has no method "Area()"
...
cont1array = cv2.findContours(binary1, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]
cont2array = cv2.findContours(binary2, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]
...
for cont1 in cont1array:
for cont2 in cont2array:
print("cont1")
print(cont1)
print(type(cont1))
print("cont2")
print(cont2)
print(type(cont2))
> if cont1 and cont2 intersect: #i dont know how check intersect
print("yes they intersect")
else:
print("no they do not intersect")
# cont1
# [[172 302]
# [261 301]
# [262 390]
# [173 391]]
# <class 'numpy.ndarray'>
# cont2
# [[ 0 0]
# [ 0 699]
# [499 699]
# [499 0]]
# <class 'numpy.ndarray'>
Once you have the two contours from cv2.findContours()
, you can use a bitwise AND
operation to detect intersection. Specifically, we can use np.logical_and()
. The idea is to create two separate images for each contour and then use the logical AND
operation on them. Any points that have a positive value (1
or True
) will be points of intersection. So since you're only looking to obtain a boolean value of whether there is intersection, we can check the intersected image to see if there is a single positive value. Essentially, if the entire array is False
then there was no intersection between the contours. But if there is a single True
, then the contours touched and thus intersect.
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()
Example
Original Image
Detected Contour
We now pass the two detected contours to the function and obtain this intersection array:
[[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]]
We check the intersection
array to see if True
exists. We will obtain a True
or 1
where the contours intersect and False
or 0
where they do not.
return intersection.any()
Thus we obtain
Full code
import cv2
import numpy as np
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 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 3
contours = 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)
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