如何在 OpenCV Python 中匹配图像形状?

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我们使用 cv2.matchShapes() 函数来匹配两个图像形状。此函数返回一个度量，显示图像形状之间的相似性。此函数使用 Hu-Moments 来计算度量值。度量值越低，图像形状之间的相似度越高。

在下面的例子中，我们将匹配来自不同图像的形状以及来自单个图像的形状。

语法

我们使用以下语法来匹配两个图像形状 -

ret = cv2.matchShapes(cnt1,cnt1,1,0.0)

其中，

cnt1 - 第一个图像形状的轮廓点。
cnt2 - 第二个图像形状的轮廓点

步骤

您可以使用以下步骤匹配两个图像形状 -

导入所需的库。在以下所有 Python 示例中，所需的 Python 库是 OpenCV。确保您已经安装了它。

import cv2

使用 cv2.imread() 将输入图像读取为灰度图像。

img1 = cv2.imread('star.png',0)
img2 = cv2.imread('star1.png',0)

对灰度图像应用阈值以创建二值图像。

ret,thresh1 = cv2.threshold(img1,150,255,0)
ret,thresh2 = cv2.threshold(img1,150,255,0)

使用查找二值图像中形状的轮廓cv2.findContours() 函数。

contours1, _ = cv2.findContours(thresh1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours2, _ = cv2.findContours(thresh2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

从每幅图像中选择特定轮廓，并应用形状匹配函数 cv2.matchShapes() 传递所选轮廓。

cnt1=contours1[0]
cnt2=contours2[0]
ret12 = cv2.matchShapes(cnt1, cnt2, 1, 0.0)

打印结果值，即图像形状匹配度量。值越低，匹配越好。

print("匹配图像 1 与图像 2:", ret12)

让我们看一些例子以便更好地理解。

示例 1

在此程序中，我们匹配两个图像形状。每个图像包含一个形状。我们还将每个图像中的形状与其自身进行匹配。

# import required libraries
import cv2

# Read two images as grayscale images
img1 = cv2.imread('star.png',0)
img2 = cv2.imread('star1.png',0)

# Apply thresholding on the images to convert to binary images
ret, thresh1 = cv2.threshold(img1, 127, 255,0)
ret, thresh2 = cv2.threshold(img2, 127, 255,0)

# find the contours in the binary image
contours1,hierarchy = cv2.findContours(thresh1,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
print("Number of Shapes detected in Image 1:",len(contours))
cnt1 = contours1[0]

contours2,hierarchy = cv2.findContours(thresh2,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
print("Number of Shapes detected in Image 2:",len(contours))
cnt2 = contours2[0]

# Compute the match scores
ret11 = cv2.matchShapes(cnt1,cnt1,1,0.0)
ret22 = cv2.matchShapes(cnt2,cnt2,1,0.0)
ret12 = cv2.matchShapes(cnt1,cnt2,1,0.0)

# print the matching scores
print("Matching Image 1 with itself:", ret11)
print("Matching Image 2 with itself:", ret22)
print("Matching Image 1 with Image 2:", ret12)

将下面的图像视为上述程序中提到的"star.png"和"pentagon.png"的输入图像。

输出

执行时，上述代码将产生以下输出 −

Number of Shapes detected in Image 1: 1 
Number of Shapes detected in Image 2: 1 
Matching Image 1 with itself: 0.0 
Matching Image 2 with itself: 0.0 
Matching Image 1 with Image 2: 0.6015851094057714

示例 2

在此程序中，我们匹配图像中的形状。我们在图像中检测到三种形状。

import cv2
import numpy as np
img = cv2.imread('convexhull.png')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

ret,thresh = cv2.threshold(gray,100,255,0)
contours,hierarchy = cv2.findContours(thresh, cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
print("Number of Shapes detected:",len(contours))

# draw contour and shape number
for i, cnt in enumerate(contours):
   M = cv2.moments(cnt)
   x1, y1 = cnt[0,0]
   img1 = cv2.drawContours(img, [cnt], -1, (0,255,255), 3)
   cv2.putText(img1, f'Shape:{i+1}', (x1, y1), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)

cnt1 = contours[0]
cnt2 = contours[1]
cnt3= contours[2]

ret11 = cv2.matchShapes(cnt1,cnt1,1,0.0)
ret12 = cv2.matchShapes(cnt1,cnt2,1,0.0)
ret23 = cv2.matchShapes(cnt2,cnt3,1,0.0)
ret31 = cv2.matchShapes(cnt3,cnt1,1,0.0)

print("Matching Shape 1 with itself:", ret11)
print("Matching Shape 1 with Shape 2:", ret12)
print("Matching Shape 2 with Shape 3:", ret23)
print("Matching Shape 3 with Shape 1:", ret31)

cv2.imshow("Shapes", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

我们将在此程序中使用以下图像和输入文件 -

输出

执行时，上述代码将产生以下输出 -

Number of Shapes detected: 3 
Matching Shape 1 with itself: 0.0 
Matching Shape 1 with Shape 2: 0.15261042892128207 
Matching Shape 2 with Shape 3: 0.9192709496955178 
Matching Shape 3 with Shape 1: 0.7521097407160106

我们得到以下窗口，显示输出 -