cv2.INTERSECT_NONE

cv2.INTERSECT_NONE是OpenCV库中的一个常量，用于表示两个矩形或轮廓之间的相交关系。具体来说，它表示两个矩形或轮廓没有相交部分。

在OpenCV中，可以使用cv2.intersectConvexConvex()函数来计算两个凸多边形之间的相交关系。该函数返回一个整数值，表示相交的类型。其中，cv2.INTERSECT_NONE就是其中之一，表示两个凸多边形没有相交部分。

相关问题

cv2.rotatedrectangleintersecti

on function is used to calculate the intersection area between two rotated rectangles. The function is called `cv2.rotatedRectangleIntersection(rect1, rect2)`. It takes two parameters, `rect1` and `rect2`, which represent the rotated rectangles.

Here is an example usage of the function:

import cv2
import numpy as np

# Define the rotated rectangles
rect1 = ((100, 100), (200, 100), 30)
rect2 = ((150, 150), (100, 200), 45)

# Calculate the intersection area
intersection = cv2.rotatedRectangleIntersection(rect1, rect2)

# Check if there is an intersection
if intersection[0] == cv2.INTERSECT_NONE:
    print("No intersection between the rectangles.")
elif intersection[0] == cv2.INTERSECT_PARTIAL:
    print("Partial intersection between the rectangles.")
elif intersection[0] == cv2.INTERSECT_FULL:
    print("Full intersection between the rectangles.")

# If there is an intersection, retrieve the intersection rectangle
if intersection[0] != cv2.INTERSECT_NONE:
    intersection_rect = cv2.boxPoints(intersection[1])
    intersection_rect = np.int0(intersection_rect)

In this example, `rect1` and `rect2` are defined as tuples representing the center coordinates, size (width and height), and rotation angle of each rectangle. The function returns a tuple with two elements. The first element represents the type of intersection (`INTERSECT_NONE`, `INTERSECT_PARTIAL`, or `INTERSECT_FULL`), and the second element is the intersection rectangle if there is an intersection.

Note that this function requires OpenCV library to be installed on your machine.

python opencv 图像对比_Python Opencv中用compareHist函数进行直方图比较对比图片

要使用compareHist函数进行图像直方图比较，需要先将要比较的两幅图像的直方图计算出来。具体步骤如下：

1. 导入必要的库：

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

2. 加载要比较的两幅图像：

img1 = cv2.imread('image1.png')
img2 = cv2.imread('image2.png')

3. 将图像转为HSV空间，并计算直方图：

hsv1 = cv2.cvtColor(img1, cv2.COLOR_BGR2HSV)
hsv2 = cv2.cvtColor(img2, cv2.COLOR_BGR2HSV)

hist1 = cv2.calcHist([hsv1], [0, 1], None, [180, 256], [0, 180, 0, 256])
hist2 = cv2.calcHist([hsv2], [0, 1], None, [180, 256], [0, 180, 0, 256])

其中，第一个参数是要计算直方图的图像，第二个参数是通道，[0, 1] 表示计算H和S通道的直方图，第三个参数是掩膜，这里不使用，第四个参数是直方图的大小，第五个参数是每个通道的取值范围。

4. 使用compareHist函数进行直方图比较：

# 使用相关性比较方法
corr = cv2.compareHist(hist1, hist2, cv2.HISTCMP_CORREL)
print('Correlation:', corr)

# 使用卡方比较方法
chi = cv2.compareHist(hist1, hist2, cv2.HISTCMP_CHISQR)
print('Chi-Square:', chi)

# 使用交叉熵比较方法
cross = cv2.compareHist(hist1, hist2, cv2.HISTCMP_INTERSECT)
print('Intersection:', cross)

# 使用巴氏距离比较方法
bhat = cv2.compareHist(hist1, hist2, cv2.HISTCMP_BHATTACHARYYA)
print('Bhattacharyya:', bhat)

其中，第一个参数是要比较的直方图1，第二个参数是要比较的直方图2，第三个参数是比较方法，可以选择相关性、卡方、交叉熵或巴氏距离。

5. 可以将比较结果可视化：

plt.subplot(121), plt.imshow(cv2.cvtColor(img1, cv2.COLOR_BGR2RGB))
plt.title('Image 1'), plt.xticks([]), plt.yticks([])
plt.subplot(122), plt.imshow(cv2.cvtColor(img2, cv2.COLOR_BGR2RGB))
plt.title('Image 2'), plt.xticks([]), plt.yticks([])
plt.show()

这样就可以使用Python Opencv中的compareHist函数进行图像直方图比较了。