opencv_python FLANN近邻匹配笔记

Dear 丶 2023-05-29 06:49 17阅读 0赞

# opencv\_python FLANN近邻匹配笔记 #

## FLANN邻近搜索 ##

FLANN库全称是Fast Library for Approximate Nearest Neighbors，它是目前最完整的（近似）最近邻开源库。不但实现了一系列查找算法，还包含了一种自动选取最快算法的机制。  
使用FLANN的搜索，整体来说分为两步，一是建立索引，二是搜索。

代码实现：

import cv2
    import numpy as np
    from matplotlib import pyplot as plt
    
    def FLANN():
        queryImage = cv2.imread('img/left_01.png', 0)
        trainingImage = cv2.imread('img/right_01.png', 0)
    
        # 创建SIFT检测和计算
        sift = cv2.xfeatures2d.SIFT_create()
        kp1, des1 = sift.detectAndCompute(queryImage, None)
        kp2, des2 = sift.detectAndCompute(trainingImage, None)
    
        # FLANN匹配参数,定义FLANN匹配器，使用KNN算法实现匹配
        # 这里使用FLANN_INDEX_KDTREE，5kd-trees和50 checks迭代
        FLANN_INDEX_KDTREE = 1
        indexParams = dict(algorithm=1, trees=5)
        searchParams = dict(check=100)
    
        flann = cv2.FlannBasedMatcher(indexParams, searchParams)
        matches = flann.knnMatch(des1, des2, k=2)
    
        # 根据matches生成相同长度的matchesMask列表，列表元素为[0, 0]
        matchesMask = [[0, 0] for i in range(len(matches))]
    
        # 去除错误匹配
        # 比值检测认为第一个匹配和第二个匹配的比值小于一个给定的值（一般是0.5）
        for i, (m, n) in enumerate(matches):
            if m.distance < 0.5*n.distance:
                matchesMask[i] = [1, 0]
        # 将图像显示
        # mathColor是两图的匹配连接线，连接线与matchesMask相关
        # singlePointColor是勾画关键点
        drawParams = dict(matchColor=(0, 255, 0),
                          singlePointColor=(255, 0, 0),
                          matchesMask=matchesMask,
                          flags=0)
        resultImage = cv2.drawMatchesKnn(queryImage, kp1, trainingImage, kp2, matches,
                                         None, **drawParams)
        return resultImage
    
    if __name__ == '__main__':
        resultImage = FLANN()
        plt.imshow(resultImage)
        plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTYyOTgy_size_16_color_FFFFFF_t_70]

## 单应性变换(Homography) ##

单应性是一个条件，该条件表明当两幅图像中的一副出现投影畸变时，它们还能彼此匹配。  
一个平面到另一个平面的映射关系。  
两张图分别有四个相对位置相同的点，Homography就是一个变换（3\*3矩阵），将一张图中的点映射到另一张图中对应的点。  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTYyOTgy_size_16_color_FFFFFF_t_70 1]  
![在这里插入图片描述][20191105115753842.png]  
RANSAC  
随机一致性采样RANSAC是一种鲁棒的模型拟合算法，能够从有外点的数据中拟合准确的模型。

代码实现：

import cv2
    import numpy as np
    from matplotlib import pyplot as plt
    
    MIN_MATCH_COUNT = 10
    
    queryImage = cv2.imread('img/box.png', 0)
    trainingImage = cv2.imread('img/box_in_scene.png', 0)
    
    sift = cv2.xfeatures2d.SIFT_create()
    kp1, des1 = sift.detectAndCompute(queryImage, None)
    kp2, des2 = sift.detectAndCompute(trainingImage, None)
    
    FLANN_INDEX_KDTREE = 1
    indexParams = dict(algorithm=1, trees=5)
    searchParams = dict(check=100)
    
    flann = cv2.FlannBasedMatcher(indexParams, searchParams)
    matches = flann.knnMatch(des1, des2, k=2)
    
    good = []
    for m, n in matches:
        if m.distance < 0.5*n.distance:
            good.append(m)
    
    if len(good) > MIN_MATCH_COUNT:
        src_pts = np.float32([kp1[m.queryIdx].pt for m in good]).reshape(-1, 1, 2)
        dst_pts = np.float32([kp2[m.trainIdx].pt for m in good]).reshape(-1, 1, 2)
    
        M, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)
        matchesMask = mask.ravel().tolist()
    
        h, w = queryImage.shape
        pts = np.float32([[0, 0], [0, h-1], [w-1, h-1], [w-1, 0]]).reshape(-1, 1, 2)
        dst = cv2.perspectiveTransform(pts, M)
    
        trainingImage = cv2.polylines(trainingImage, [np.int32(dst)], True, 255, 3, cv2.LINE_AA)
    
    else:
        print("Not enough matches are found %d/%d" % (len(good), MIN_MATCH_COUNT))
        matchesMask = None
    
    draw_params = dict(matchColor=(0, 255, 0), singlePointColor=None, matchesMask=matchesMask, flags=2)
    
    img3 = cv2.drawMatches(queryImage, kp1, trainingImage, kp2, good, None, **draw_params)
    
    plt.imshow(img3, 'gray'), plt.show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTYyOTgy_size_16_color_FFFFFF_t_70 2]

### 参考文章： ###

https://www.jianshu.com/p/57fc7068cfee

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[20191105115753842.png]: https://img-blog.csdnimg.cn/20191105115753842.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTYyOTgy_size_16_color_FFFFFF_t_70 2]: https://img-blog.csdnimg.cn/20191105115838621.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzMzNTYyOTgy,size_16,color_FFFFFF,t_70