OpenCV计算机视觉实战 - 图像特征(Harris+Sift)检测和全景图像拼接

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最新推荐文章于 2025-03-19 21:21:20 发布

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文章标签： opencv

原文链接：https://blog.youkuaiyun.com/sinat_29950703/article/details/108038818

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一、Harris 角点检测

角点检测算法基本思想：使用一个固定窗口在图像上进行任意方向上的滑动，比较滑动前与滑动后两种情况，窗口中的像素灰度变化程度，如果存在任意方向上的滑动，都有着较大灰度变化，那么我们可以认为该窗口中存在角点。

Harris 算法的原理与公式解析

在一副图像中可能存在以下三类点：平面点，边界点和角点
在这里插入图片描述
用下面这张图两条线做个夹角，表示三种点的位置。当它们移动的时候，灰度值会发生变化
平面点：x 和 y 方向变化不大
边界点：x 或 y 方向变化大
角点：x 和 y 方向的变化都大

做减法：是为了获得平移前后的差异
平方：是为了获得这样一个变化的趋势，负数就无意义了，因为-5 比 2的变化幅度大；而且对这趋势做了个增强
在这里插入图片描述

度量角点响应

在这里插入图片描述
总结一下Harris算法流程

获取点数据后,计算Ix 和 Iy
整合矩阵,求特征值
比较特征值大小
非极大值抑制NMS,过滤掉不是那么角的

详细解析 https://blog.youkuaiyun.com/linqianbi/article/details/78930239

OpenCV中的Harris操作

cv2.cornerHarris(img, blockSize, k)
img：数据类型为 ﬂoat32 的入图像
blockSize：角点检测中指定区域的大小，前面提到的w(u,v),可能是个高斯函数
ksize：就像Sobel求导中使用的窗口大小
k：取值参数为 [0,04,0.06]. 默认0.04
返回dst：输出图像

import cv2 
import numpy as np

img = cv2.imread(‘test_1.jpg’)
print (‘img.shape:’,img.shape)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# gray = np.float32(gray)
dst = cv2.cornerHarris(gray, 2, 3, 0.04)
print (‘dst.shape:’,dst.shape)

dst.max是最大值,一定是角点
只要dst中满足0.01倍的最大值，就判定为角点，要求较高的也可以调大点

# 设置为其他倍数
img[dst>0.01*dst.max()]=[0,0,255]
cv2.imshow('dst',img) 
cv2.waitKey(0) 
cv2.destroyAllWindows()

 
 
 
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二、Sift 角点检测

1.图像尺度空间

在一定的范围内，无论物体是大还是小，人眼都可以分辨出来，然而计算机要有相同的能力却很难，所以要让机器能够对物体在不同尺度下有一个统一的认知，就需要考虑图像在不同的尺度下都存在的特点，对图像进行不同尺度的改变。

尺度空间的获取通常使用高斯模糊来实现
在这里插入图片描述

高斯函数对图像做滤波操作，得到高斯模糊后的结果

不同σ的高斯函数决定了对图像的平滑程度，越大的σ值对应的图像越模糊。

2.多分辨率金字塔

金字塔有多个层级
同一层级中，有不同的高斯滤波

3.高斯差分金字塔（DOG）

在这里插入图片描述

两个高斯模糊后的特征相减，得到DOG
然后怎么找极值点？每个点都要跟周围的点比较。继续往下看

4.DoG空间极值检测

为了寻找尺度空间的极值点，每个像素点要和其图像域（同一尺度空间-8个点）和尺度域（相邻的尺度空间-上下各9个共18个）的所有相邻点（共26）进行比较，当其大于（或者小于）所有相邻点时，该点就是极值点。如下图所示，中间的检测点要和其所在图像的3×3邻域8个像素点，以及其相邻的上下两层的3×3领域18个像素点，共26个像素点进行比较。
在这里插入图片描述
最上/下层无法比较，故4层特征图中只有2层才能检测极值点。
但这步检测的离散极值点不一定是最终的极值点，故需要精确定位

5.关键点的精确定位

这些候选关键点是DOG空间的局部极值点，而且这些极值点均为离散的点，精确定位极值点的一种方法是，对尺度空间DoG函数进行曲线拟合，计算其极值点，从而实现关键点的精确定位。
在这里插入图片描述

6.消除边界响应

在这里插入图片描述
类似于Harris角点中的角点相应R值，Tr(H)² / Det(H)，大于10的是边界，要进行过滤操作

7.特征点的主方向

在这里插入图片描述
每个特征点可以得到三个信息(x,y,σ,θ)，即位置、尺度（m(x,y)）和方向。具有多个方向的关键点可以被复制成多份，然后将方向值分别赋给复制后的特征点，一个特征点就产生了多个坐标、尺度相等，但是方向不同的特征点。

然后咋用这些点？看下面

8.生成特征描述

在完成关键点的梯度计算后，使用直方图统计邻域内像素的梯度和方向。
为了简单起见，只统计8个方向（0-45, 45-90, 90-135,135-180, 180-235, 235-270, 270-315, 315-360）
在这里插入图片描述
为了保证特征矢量的旋转不变性，要以特征点为中心，在附近邻域内将坐标轴旋转θ角度，即将坐标轴旋转为特征点的主方向。

旋转之后的主方向为中心取8x8的窗口，求每个像素的梯度幅值和方向，箭头方向代表梯度方向，长度代表梯度幅值，然后利用高斯窗口对其进行加权运算，最后在每个4x4的小块上绘制8个方向的梯度直方图，计算每个梯度方向的累加值，即可形成一个种子点，即每个特征的由4个种子点组成，每个种子点有8个方向的向量信息。
在这里插入图片描述
论文中建议对每个关键点使用4x4共16个种子点来描述，这样一个关键点就会产生128维的SIFT特征向量。

OpenCV中的Sift操作

OpenCV大于3.4版本就对sift进行专利保护了，所以想免费使用的，看看sift效果的，降低一下版本吧

卸载 pip uninstall opencv-python 
安装 pip install opencv-contrib-python==3.4.1.15 
里面有很多包 pip install opencv-contrib-python==3.4.1.15

 
 
 
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import cv2
import numpy as np

img = cv2.imread(‘test_1.jpg’)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
print(cv2.version) # 3.4.1.15

得到特征点 kp, 直接用drawKeypoints绘制关键点

sift = cv2.xfeatures2d.SIFT_create()
kp = sift.detect(gray, None)

img = cv2.drawKeypoints(gray, kp, img)

cv2.imshow(‘drawKeypoints’, img)
cv2.waitKey(0)
cv2.destroyAllWindows()

计算特征 sift.compute
还是关键点kp 和关键点相应的特征des （每个关键点都转化为128维向量）

kp, des = sift.compute(gray, kp)
print (np.array(kp).shape)	# (6827,) 原来是list, 用np转化一下, 就可以看shape了
# des.shape					# (6827, 128)
# des[0]
'''
array([  0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,  21.,   8.,   0.,
         0.,   0.,   0.,   0.,   0., 157.,  31.,   3.,   1.,   0.,   0.,
         2.,  63.,  75.,   7.,  20.,  35.,  31.,  74.,  23.,  66.,   0.,
         0.,   1.,   3.,   4.,   1.,   0.,   0.,  76.,  15.,  13.,  27.,
         8.,   1.,   0.,   2., 157., 112.,  50.,  31.,   2.,   0.,   0.,
         9.,  49.,  42., 157., 157.,  12.,   4.,   1.,   5.,   1.,  13.,
         7.,  12.,  41.,   5.,   0.,   0., 104.,   8.,   5.,  19.,  53.,
         5.,   1.,  21., 157.,  55.,  35.,  90.,  22.,   0.,   0.,  18.,
         3.,   6.,  68., 157.,  52.,   0.,   0.,   0.,   7.,  34.,  10.,
        10.,  11.,   0.,   2.,   6.,  44.,   9.,   4.,   7.,  19.,   5.,
        14.,  26.,  37.,  28.,  32.,  92.,  16.,   2.,   3.,   4.,   0.,
         0.,   6.,  92.,  23.,   0.,   0.,   0.], dtype=float32)
'''

 
 
 
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三、特征匹配

cv2.drawMatches(imageA, kpsA, imageB, kpsB, matches[:10], None, flags=2) # 对两个图像关键点进行连线操作
参数说明：imageA和imageB表示图片，kpsA和kpsB表示关键点， matches表示进过cv2.BFMatcher获得的匹配的索引值，也有距离， flags表示有几个图像

书籍的SIFT特征点连接：
第一步:使用sift.detectAndComputer找出关键点和sift特征向量
第二步：构建BFMatcher()蛮力匹配器，bf.match匹配sift特征向量，使用的是欧式距离
第三步：根据匹配结果matches.distance对matches按照距离进行排序
第四步：进行画图操作，使用cv2.drawMatches进行画图操作

bf.match(des1, des2) 1 对 1 匹配
bf.knnMatch(des1, des2, k=2) k对最佳匹配，此处是1个点最多可以跟2个点匹配

import cv2 
import numpy as np

img1 = cv2.imread(‘box.png’, 0)
img2 = cv2.imread(‘box_in_scene.png’, 0)
def cv_show(name,img):
cv2.imshow(name, img)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv_show(‘img1’,img1)
cv_show(‘img2’,img2)

# 第一步：构造sift，求解出特征点和sift特征向量
sift = cv2.xfeatures2d.SIFT_create()
kp1, des1 = sift.detectAndCompute(img1, None)
kp2, des2 = sift.detectAndCompute(img2, None)

# 第二步：构造BFMatcher()蛮力匹配，匹配sift特征向量距离最近对应组分
# crossCheck 表示两个特征点要互相匹，
# 例如A中的第i个特征点与B中的第j个特征点最近的,并且B中的第j个特征点到A中的第i个特征点也是
# NORM_L2: 归一化数组的(欧几里德距离)，如果其他特征计算方法需要考虑不同的匹配计算方式
bf = cv2.BFMatcher(crossCheck=True)

# 获得匹配的结果【1 对 1 匹配】
matches = bf.match(des1, des2)

#第三步：对匹配的结果按照距离进行排序操作
matches = sorted(matches, key=lambda x: x.distance)

# 第四步：使用cv2.drawMacthes进行画图操作
# 只画前10个点
img3 = cv2.drawMatches(img1, kp1, img2, kp2, matches[:10], None,flags=2)
cv_show(‘img3’,img3)

# 【k对最佳匹配】
bf = cv2.BFMatcher()
matches = bf.knnMatch(des1, des2, k=2)
good = []
for m, n in matches:
if m.distance < 0.75 * n.distance:
good.append([m])

# good所有点都显示,就会有很多连线了
img4 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,good,None,flags=2)
cv_show(‘img4’,img4)

在这里插入图片描述

四、图像拼接

from Stitcher import Stitcher
import cv2

# 读取拼接图片
imageA = cv2.imread(“left_01.png”)
imageB = cv2.imread(“right_01.png”)

# 把图片拼接成全景图
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)

# 显示所有图片
cv2.imshow(“Image A”, imageA)
cv2.imshow(“Image B”, imageB)
cv2.imshow(“Keypoint Matches”, vis)
cv2.imshow(“Result”, result)
cv2.waitKey(0)
cv2.destroyAllWindows()

在这里插入图片描述

Stitcher的 stitch函数检测流程 简述：

img A 和 img B做sift检测
匹配特征点（具体看matchKeypoints流程）
提取匹配结果，其中H是单应性矩阵
对A进行H的视角变换
将图片B传入result图片最左端
检测是否需要显示图片匹配

matchKeypoints流程：

建立暴力匹配器
k对匹配
当筛选后的匹配对大于4时，计算视角变换矩阵
其中findHomography：计算多个二维点对之间的最优单映射变换矩阵 H(3行x3列)，使用最小均方误差或者RANSAC（原理，匹配效果）方法

Stitcher.py 完整代码如下：

import numpy as np
import cv2

class Stitcher:

<span class="token comment"># 拼接函数</span>
<span class="token keyword">def</span> <span class="token function">stitch</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> images<span class="token punctuation">,</span> ratio<span class="token operator">=</span><span class="token number">0.75</span><span class="token punctuation">,</span> reprojThresh<span class="token operator">=</span><span class="token number">4.0</span><span class="token punctuation">,</span>showMatches<span class="token operator">=</span><span class="token boolean">False</span><span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token comment">#获取输入图片</span>
    <span class="token punctuation">(</span>imageB<span class="token punctuation">,</span> imageA<span class="token punctuation">)</span> <span class="token operator">=</span> images
    <span class="token comment"># 1.检测A、B图片的SIFT关键特征点，并计算特征描述子</span>
    <span class="token punctuation">(</span>kpsA<span class="token punctuation">,</span> featuresA<span class="token punctuation">)</span> <span class="token operator">=</span> self<span class="token punctuation">.</span>detectAndDescribe<span class="token punctuation">(</span>imageA<span class="token punctuation">)</span>
    <span class="token punctuation">(</span>kpsB<span class="token punctuation">,</span> featuresB<span class="token punctuation">)</span> <span class="token operator">=</span> self<span class="token punctuation">.</span>detectAndDescribe<span class="token punctuation">(</span>imageB<span class="token punctuation">)</span>

    <span class="token comment"># 2.匹配两张图片的所有特征点，返回匹配结果</span>
    M <span class="token operator">=</span> self<span class="token punctuation">.</span>matchKeypoints<span class="token punctuation">(</span>kpsA<span class="token punctuation">,</span> kpsB<span class="token punctuation">,</span> featuresA<span class="token punctuation">,</span> featuresB<span class="token punctuation">,</span> ratio<span class="token punctuation">,</span> reprojThresh<span class="token punctuation">)</span>

    <span class="token comment"># 如果返回结果为空，没有匹配成功的特征点，退出算法</span>
    <span class="token keyword">if</span> M <span class="token keyword">is</span> <span class="token boolean">None</span><span class="token punctuation">:</span>
        <span class="token keyword">return</span> <span class="token boolean">None</span>

    <span class="token comment"># 3. 否则，提取匹配结果</span>
    <span class="token comment"># H是3x3视角变换矩阵      </span>
    <span class="token punctuation">(</span>matches<span class="token punctuation">,</span> H<span class="token punctuation">,</span> status<span class="token punctuation">)</span> <span class="token operator">=</span> M
    <span class="token comment"># 4.将图片A进行视角变换，result是变换后图片</span>
    result <span class="token operator">=</span> cv2<span class="token punctuation">.</span>warpPerspective<span class="token punctuation">(</span>imageA<span class="token punctuation">,</span> H<span class="token punctuation">,</span> <span class="token punctuation">(</span>imageA<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span> <span class="token operator">+</span> imageB<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">,</span> imageA<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
    self<span class="token punctuation">.</span>cv_show<span class="token punctuation">(</span><span class="token string">'result'</span><span class="token punctuation">,</span> result<span class="token punctuation">)</span>
    <span class="token comment"># 5.将图片B传入result图片最左端</span>
    result<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">:</span>imageB<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">:</span>imageB<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">]</span> <span class="token operator">=</span> imageB
    self<span class="token punctuation">.</span>cv_show<span class="token punctuation">(</span><span class="token string">'result'</span><span class="token punctuation">,</span> result<span class="token punctuation">)</span>
    <span class="token comment"># 6.检测是否需要显示图片匹配</span>
    <span class="token keyword">if</span> showMatches<span class="token punctuation">:</span>
        <span class="token comment"># 生成匹配图片</span>
        vis <span class="token operator">=</span> self<span class="token punctuation">.</span>drawMatches<span class="token punctuation">(</span>imageA<span class="token punctuation">,</span> imageB<span class="token punctuation">,</span> kpsA<span class="token punctuation">,</span> kpsB<span class="token punctuation">,</span> matches<span class="token punctuation">,</span> status<span class="token punctuation">)</span>
        <span class="token comment"># 返回结果</span>
        <span class="token keyword">return</span> <span class="token punctuation">(</span>result<span class="token punctuation">,</span> vis<span class="token punctuation">)</span>

    <span class="token comment"># 返回匹配结果</span>
    <span class="token keyword">return</span> result
<span class="token keyword">def</span> <span class="token function">cv_show</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span>name<span class="token punctuation">,</span>img<span class="token punctuation">)</span><span class="token punctuation">:</span>
    cv2<span class="token punctuation">.</span>imshow<span class="token punctuation">(</span>name<span class="token punctuation">,</span> img<span class="token punctuation">)</span>
    cv2<span class="token punctuation">.</span>waitKey<span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">)</span>
    cv2<span class="token punctuation">.</span>destroyAllWindows<span class="token punctuation">(</span><span class="token punctuation">)</span>

<span class="token keyword">def</span> <span class="token function">detectAndDescribe</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> image<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token comment"># 将彩色图片转换成灰度图</span>
    gray <span class="token operator">=</span> cv2<span class="token punctuation">.</span>cvtColor<span class="token punctuation">(</span>image<span class="token punctuation">,</span> cv2<span class="token punctuation">.</span>COLOR_BGR2GRAY<span class="token punctuation">)</span>

    <span class="token comment"># 建立SIFT生成器</span>
    descriptor <span class="token operator">=</span> cv2<span class="token punctuation">.</span>xfeatures2d<span class="token punctuation">.</span>SIFT_create<span class="token punctuation">(</span><span class="token punctuation">)</span>
    <span class="token comment"># 检测SIFT特征点，并计算描述子</span>
    <span class="token punctuation">(</span>kps<span class="token punctuation">,</span> features<span class="token punctuation">)</span> <span class="token operator">=</span> descriptor<span class="token punctuation">.</span>detectAndCompute<span class="token punctuation">(</span>image<span class="token punctuation">,</span> <span class="token boolean">None</span><span class="token punctuation">)</span>

    <span class="token comment"># 将结果转换成NumPy数组</span>
    kps <span class="token operator">=</span> np<span class="token punctuation">.</span>float32<span class="token punctuation">(</span><span class="token punctuation">[</span>kp<span class="token punctuation">.</span>pt <span class="token keyword">for</span> kp <span class="token keyword">in</span> kps<span class="token punctuation">]</span><span class="token punctuation">)</span>

    <span class="token comment"># 返回特征点集，及对应的描述特征</span>
    <span class="token keyword">return</span> <span class="token punctuation">(</span>kps<span class="token punctuation">,</span> features<span class="token punctuation">)</span>

<span class="token keyword">def</span> <span class="token function">matchKeypoints</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> kpsA<span class="token punctuation">,</span> kpsB<span class="token punctuation">,</span> featuresA<span class="token punctuation">,</span> featuresB<span class="token punctuation">,</span> ratio<span class="token punctuation">,</span> reprojThresh<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token comment"># 1.建立暴力匹配器</span>
    matcher <span class="token operator">=</span> cv2<span class="token punctuation">.</span>BFMatcher<span class="token punctuation">(</span><span class="token punctuation">)</span>

    <span class="token comment"># 2.使用KNN检测来自A、B图的SIFT特征匹配对，K=2</span>
    rawMatches <span class="token operator">=</span> matcher<span class="token punctuation">.</span>knnMatch<span class="token punctuation">(</span>featuresA<span class="token punctuation">,</span> featuresB<span class="token punctuation">,</span> <span class="token number">2</span><span class="token punctuation">)</span>

    matches <span class="token operator">=</span> <span class="token punctuation">[</span><span class="token punctuation">]</span>
    <span class="token keyword">for</span> m <span class="token keyword">in</span> rawMatches<span class="token punctuation">:</span>
        <span class="token comment"># 当最近距离跟次近距离的比值小于ratio值时，保留此匹配对</span>
        <span class="token keyword">if</span> <span class="token builtin">len</span><span class="token punctuation">(</span>m<span class="token punctuation">)</span> <span class="token operator">==</span> <span class="token number">2</span> <span class="token operator">and</span> m<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">.</span>distance <span class="token operator">&lt;</span> m<span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">.</span>distance <span class="token operator">*</span> ratio<span class="token punctuation">:</span>
        <span class="token comment"># 存储两个点在featuresA, featuresB中的索引值</span>
            matches<span class="token punctuation">.</span>append<span class="token punctuation">(</span><span class="token punctuation">(</span>m<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">.</span>trainIdx<span class="token punctuation">,</span> m<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">.</span>queryIdx<span class="token punctuation">)</span><span class="token punctuation">)</span>

    <span class="token comment"># 3.当筛选后的匹配对大于4时，计算视角变换矩阵</span>
    <span class="token keyword">if</span> <span class="token builtin">len</span><span class="token punctuation">(</span>matches<span class="token punctuation">)</span> <span class="token operator">&gt;</span> <span class="token number">4</span><span class="token punctuation">:</span>    <span class="token comment"># 至少4对,8个方程的单应性矩阵</span>
        <span class="token comment"># 获取匹配对的点坐标</span>
        ptsA <span class="token operator">=</span> np<span class="token punctuation">.</span>float32<span class="token punctuation">(</span><span class="token punctuation">[</span>kpsA<span class="token punctuation">[</span>i<span class="token punctuation">]</span> <span class="token keyword">for</span> <span class="token punctuation">(</span>_<span class="token punctuation">,</span> i<span class="token punctuation">)</span> <span class="token keyword">in</span> matches<span class="token punctuation">]</span><span class="token punctuation">)</span>
        ptsB <span class="token operator">=</span> np<span class="token punctuation">.</span>float32<span class="token punctuation">(</span><span class="token punctuation">[</span>kpsB<span class="token punctuation">[</span>i<span class="token punctuation">]</span> <span class="token keyword">for</span> <span class="token punctuation">(</span>i<span class="token punctuation">,</span> _<span class="token punctuation">)</span> <span class="token keyword">in</span> matches<span class="token punctuation">]</span><span class="token punctuation">)</span>

        <span class="token comment"># 计算视角变换矩阵</span>
        <span class="token punctuation">(</span>H<span class="token punctuation">,</span> status<span class="token punctuation">)</span> <span class="token operator">=</span> cv2<span class="token punctuation">.</span>findHomography<span class="token punctuation">(</span>ptsA<span class="token punctuation">,</span> ptsB<span class="token punctuation">,</span> cv2<span class="token punctuation">.</span>RANSAC<span class="token punctuation">,</span> reprojThresh<span class="token punctuation">)</span>

        <span class="token comment"># 返回结果</span>
        <span class="token keyword">return</span> <span class="token punctuation">(</span>matches<span class="token punctuation">,</span> H<span class="token punctuation">,</span> status<span class="token punctuation">)</span>

    <span class="token comment"># 4.如果匹配对小于4时，返回None</span>
    <span class="token keyword">return</span> <span class="token boolean">None</span>

<span class="token keyword">def</span> <span class="token function">drawMatches</span><span class="token punctuation">(</span>self<span class="token punctuation">,</span> imageA<span class="token punctuation">,</span> imageB<span class="token punctuation">,</span> kpsA<span class="token punctuation">,</span> kpsB<span class="token punctuation">,</span> matches<span class="token punctuation">,</span> status<span class="token punctuation">)</span><span class="token punctuation">:</span>
    <span class="token comment"># 初始化可视化图片，将A、B图左右连接到一起</span>
    <span class="token punctuation">(</span>hA<span class="token punctuation">,</span> wA<span class="token punctuation">)</span> <span class="token operator">=</span> imageA<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token number">2</span><span class="token punctuation">]</span>
    <span class="token punctuation">(</span>hB<span class="token punctuation">,</span> wB<span class="token punctuation">)</span> <span class="token operator">=</span> imageB<span class="token punctuation">.</span>shape<span class="token punctuation">[</span><span class="token punctuation">:</span><span class="token number">2</span><span class="token punctuation">]</span>
    vis <span class="token operator">=</span> np<span class="token punctuation">.</span>zeros<span class="token punctuation">(</span><span class="token punctuation">(</span><span class="token builtin">max</span><span class="token punctuation">(</span>hA<span class="token punctuation">,</span> hB<span class="token punctuation">)</span><span class="token punctuation">,</span> wA <span class="token operator">+</span> wB<span class="token punctuation">,</span> <span class="token number">3</span><span class="token punctuation">)</span><span class="token punctuation">,</span> dtype<span class="token operator">=</span><span class="token string">"uint8"</span><span class="token punctuation">)</span>
    vis<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">:</span>hA<span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">:</span>wA<span class="token punctuation">]</span> <span class="token operator">=</span> imageA
    vis<span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">:</span>hB<span class="token punctuation">,</span> wA<span class="token punctuation">:</span><span class="token punctuation">]</span> <span class="token operator">=</span> imageB

    <span class="token comment"># 联合遍历，画出匹配对</span>
    <span class="token keyword">for</span> <span class="token punctuation">(</span><span class="token punctuation">(</span>trainIdx<span class="token punctuation">,</span> queryIdx<span class="token punctuation">)</span><span class="token punctuation">,</span> s<span class="token punctuation">)</span> <span class="token keyword">in</span> <span class="token builtin">zip</span><span class="token punctuation">(</span>matches<span class="token punctuation">,</span> status<span class="token punctuation">)</span><span class="token punctuation">:</span>
        <span class="token comment"># 当点对匹配成功时，画到可视化图上</span>
        <span class="token keyword">if</span> s <span class="token operator">==</span> <span class="token number">1</span><span class="token punctuation">:</span>
            <span class="token comment"># 画出匹配对</span>
            ptA <span class="token operator">=</span> <span class="token punctuation">(</span><span class="token builtin">int</span><span class="token punctuation">(</span>kpsA<span class="token punctuation">[</span>queryIdx<span class="token punctuation">]</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token builtin">int</span><span class="token punctuation">(</span>kpsA<span class="token punctuation">[</span>queryIdx<span class="token punctuation">]</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
            ptB <span class="token operator">=</span> <span class="token punctuation">(</span><span class="token builtin">int</span><span class="token punctuation">(</span>kpsB<span class="token punctuation">[</span>trainIdx<span class="token punctuation">]</span><span class="token punctuation">[</span><span class="token number">0</span><span class="token punctuation">]</span><span class="token punctuation">)</span> <span class="token operator">+</span> wA<span class="token punctuation">,</span> <span class="token builtin">int</span><span class="token punctuation">(</span>kpsB<span class="token punctuation">[</span>trainIdx<span class="token punctuation">]</span><span class="token punctuation">[</span><span class="token number">1</span><span class="token punctuation">]</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
            cv2<span class="token punctuation">.</span>line<span class="token punctuation">(</span>vis<span class="token punctuation">,</span> ptA<span class="token punctuation">,</span> ptB<span class="token punctuation">,</span> <span class="token punctuation">(</span><span class="token number">0</span><span class="token punctuation">,</span> <span class="token number">255</span><span class="token punctuation">,</span> <span class="token number">0</span><span class="token punctuation">)</span><span class="token punctuation">,</span> <span class="token number">1</span><span class="token punctuation">)</span>

    <span class="token comment"># 返回可视化结果</span>
    <span class="token keyword">return</span> vis