Python计算机视觉编程(二)---SIFT、Harris特征

小咪咪 2022-03-07 17:50 435阅读 0赞

## 图像局部描述符 ##

##### 本文工作 #####

**Harris**

1.  角点检测
2.  在图像间寻找对应点

**SIFT**

1.  检测关键点
2.  描述子匹配
3.  地理标记图像匹配

**工具包的安装**

1.  vlfeat
2.  Graphviz
3.  pydot

##### 检测感兴趣点 #####

对比Harris和SIFT，将Harris角点检测的显示在了图像的最后，这两种算法选择了不同的坐标。

# -*- coding: utf-8 -*-
    from PIL import Image
    from pylab import *
    from PCV.localdescriptors import sift
    from PCV.localdescriptors import harris
    
    # 添加中文字体支持
    from matplotlib.font_manager import FontProperties
    font = FontProperties(fname=r"c:\windows\fonts\SimSun.ttc", size=14)
    
    imname = 'E:\\study_work\\python\\images\\school4.jpg'
    im = array(Image.open(imname).convert('L'))
    sift.process_image(imname, 'school.sift')
    l1, d1 = sift.read_features_from_file('school.sift')
    
    figure()
    gray()
    subplot(131)
    sift.plot_features(im, l1, circle=False)
    title(u'SIFT特征',fontproperties=font)
    subplot(132)
    sift.plot_features(im, l1, circle=True)
    title(u'用圆圈表示SIFT特征尺度',fontproperties=font)
    
    # 检测harris角点
    harrisim = harris.compute_harris_response(im)
    
    subplot(133)
    filtered_coords = harris.get_harris_points(harrisim, 6, 0.1)
    imshow(im)
    plot([p[1] for p in filtered_coords], [p[0] for p in filtered_coords], '*')
    axis('off')
    title(u'Harris角点',fontproperties=font)
    
    show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70]

##### 描述子匹配 #####

**Harris**  
Harris角点检测器可以给出图像中检测到兴趣点，但它并没有提供在图像间对兴趣点进行比较的方法，我们需要在每个角点添加描述子，以及对这些描述子进行比较。

# -*- coding: utf-8 -*-
    from pylab import *
    from PIL import Image
    
    from PCV.localdescriptors import harris
    from PCV.tools.imtools import imresize
    
    """
    This is the Harris point matching example in Figure 2-2.
    """
    
    # Figure 2-2下面的图
    im1 = array(Image.open("E:\\study_work\\python\\images\\school11.jpg").convert("L"))
    im2 = array(Image.open("E:\\study_work\\python\\images\\school12.jpg").convert("L"))
    
    # resize加快匹配速度
    im1 = imresize(im1, (im1.shape[1]/2, im1.shape[0]/2))
    im2 = imresize(im2, (im2.shape[1]/2, im2.shape[0]/2))
    
    wid = 5
    harrisim = harris.compute_harris_response(im1, 5)
    filtered_coords1 = harris.get_harris_points(harrisim, wid+1)
    d1 = harris.get_descriptors(im1, filtered_coords1, wid)
    
    harrisim = harris.compute_harris_response(im2, 5)
    filtered_coords2 = harris.get_harris_points(harrisim, wid+1)
    d2 = harris.get_descriptors(im2, filtered_coords2, wid)
    
    print 'starting matching'
    matches = harris.match_twosided(d1, d2)
    figure()
    gray()
    harris.plot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)
    show()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 1]  
如图，可以看到结果中有许多错误的匹配，而SIFT能够较好地提高特征描述点检测与描述。

**SIFT**

import cv2
    import numpy as np
    
    def drawMatchesKnn_cv2(img1_gray, kp1, img2_gray, kp2, goodMatch):
        h1, w1 = img1_gray.shape[:2]
        h2, w2 = img2_gray.shape[:2]
    
        vis = np.zeros((max(h1, h2), w1 + w2, 3), np.uint8)
        vis[:h1, :w1] = img1_gray
        vis[:h2, w1:w1 + w2] = img2_gray
    
        p1 = [kpp.queryIdx for kpp in goodMatch]
        p2 = [kpp.trainIdx for kpp in goodMatch]
    
        post1 = np.int32([kp1[pp].pt for pp in p1])
        post2 = np.int32([kp2[pp].pt for pp in p2]) + (w1, 0)
    
        for (x1, y1), (x2, y2) in zip(post1, post2):
            cv2.line(vis, (x1, y1), (x2, y2), (0, 0, 255))
    
        cv2.namedWindow("match", cv2.WINDOW_NORMAL)
        cv2.imshow("match", vis)
    
    img1_gray = cv2.imread("E:\\study_work\\python\\images\\school11.jpg")
    img2_gray = cv2.imread("E:\\study_work\\python\\images\\school12.jpg")
    
    sift = cv2.SIFT()
    # sift = cv2.SURF()
    
    kp1, des1 = sift.detectAndCompute(img1_gray, None)
    kp2, des2 = sift.detectAndCompute(img2_gray, None)
    
    # BFmatcher with default parms
    bf = cv2.BFMatcher(cv2.NORM_L2)
    matches = bf.knnMatch(des1, des2, k=2)
    
    goodMatch = []
    for m, n in matches:
        if m.distance < 0.50 * n.distance:
            goodMatch.append(m)
    
    drawMatchesKnn_cv2(img1_gray, kp1, img2_gray, kp2, goodMatch[:20])
    
    cv2.waitKey(0)
    cv2.destroyAllWindows()

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 2]  
SIFT是采用opencv-python进行实践的，由结果可以看出同前面的harris对比，SIFT检测出的匹配较为精准

##### 地理标记图像匹配 #####

自己准备一个图像集，博主的图像集存放在E:/study\_work/python/images中  
此算法是用于对图像的地理位置进行匹配并描绘出关系图（注：图像一定要resize以下，不然太大，对电脑性能有影响）

# -*- coding: utf-8 -*-
    from pylab import *
    from PIL import Image
    from PCV.localdescriptors import sift
    from PCV.tools import imtools
    import pydot
    
    """ This is the example graph illustration of matching images from Figure 2-10.
    To download the images, see ch2_download_panoramio.py."""
    
    #download_path = "panoimages"  # set this to the path where you downloaded the panoramio images
    #path = "/FULLPATH/panoimages/"  # path to save thumbnails (pydot needs the full system path)
    
    download_path = "E:/study_work/python/images"  # set this to the path where you downloaded the panoramio images
    path = "E:/study_work/python/images/"  # path to save thumbnails (pydot needs the full system path)
    
    # list of downloaded filenames
    imlist = imtools.get_imlist(download_path)
    nbr_images = len(imlist)
    
    # extract features
    featlist = [imname[:-3] + 'sift' for imname in imlist]
    for i, imname in enumerate(imlist):
        sift.process_image(imname, featlist[i])
    
    matchscores = zeros((nbr_images, nbr_images))
    
    for i in range(nbr_images):
        for j in range(i, nbr_images):  # only compute upper triangle
            print 'comparing ', imlist[i], imlist[j]
            l1, d1 = sift.read_features_from_file(featlist[i])
            l2, d2 = sift.read_features_from_file(featlist[j])
            matches = sift.match_twosided(d1, d2)
            nbr_matches = sum(matches > 0)
            print 'number of matches = ', nbr_matches
            matchscores[i, j] = nbr_matches
    print "The match scores is: \n", matchscores
    
    # copy values
    for i in range(nbr_images):
        for j in range(i + 1, nbr_images):  # no need to copy diagonal
            matchscores[j, i] = matchscores[i, j]
    
    #可视化
    
    threshold = 2  # min number of matches needed to create link
    
    g = pydot.Dot(graph_type='graph')  # don't want the default directed graph
    
    for i in range(nbr_images):
        for j in range(i + 1, nbr_images):
            if matchscores[i, j] > threshold:
                # first image in pair
                im = Image.open(imlist[i])
                im.thumbnail((100, 100))
                filename = path + str(i) + '.png'
                im.save(filename)  # need temporary files of the right size
                g.add_node(pydot.Node(str(i), fontcolor='transparent', shape='rectangle', image=filename))
    
                # second image in pair
                im = Image.open(imlist[j])
                im.thumbnail((100, 100))
                filename = path + str(j) + '.png'
                im.save(filename)  # need temporary files of the right size
                g.add_node(pydot.Node(str(j), fontcolor='transparent', shape='rectangle', image=filename))
    
                g.add_edge(pydot.Edge(str(i), str(j)))
    g.write_png('jimei.png')

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 3]  
关键点匹配是采用Kd树的数据结构来完成搜索，具体原理见[SIFT算法原理][SIFT]，如上图所示，此算法得到两组图像，由图像内容可看出匹配地理位置得出的结果还是较为准确的。

安装工具包  
1、安装VLFeat  
进行上述事项前，需要先安装开源工具包VLFeat,可以在[www.vlfeat.org][]上下载，如下图所示（ps:要下载**0.9.20**版本，不能下载0.9.21版本的，0.9.21版本不稳定，博主已经掉过坑）  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 4]![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 5]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 6]  
下载完之后解压（博主电脑是windows64位的，所以选择win64），复制此文件夹并将win64放进PCV所在目录  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 7]  
放到此目录下（这是博主的PCV所在地，你们根据自己的做相应的改变）![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 8]  
打开PCV中的localdescriptors文件夹，找到sift.py并打开  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 9]  
在sift.py中找到如下代码，并将cmmd中的路径改为sift.exe的所在路径（就是刚刚存放在同PCV目录的win64文件夹的里头），这里要特别注意以下，sift.exe路径后一定要加个空格！这可是个大坑

def process_image(imagename,resultname,params="--edge-thresh 10 --peak-thresh 5"):
        """ process an image and save the results in a file"""
        # path = os.path.abspath(os.path.join(os.path.dirname("__file__"),os.path.pardir))
    	# path = path+"\\utils\\win32vlfeat\\sift.exe "
        if imagename[-3:] != 'pgm':
    	    #create a pgm file
    		 im = Image.open(imagename).convert('L')
    		 im.save('tmp.pgm')
    		 imagename = 'tmp.pgm'
        cmmd = str("D:\\Pycharm\\test\\win64_vlfeat\\sift.exe "+imagename+" --output="+resultname+
    				" "+params)
        os.system(cmmd)
        print 'processed', imagename, 'to', resultname

好了，现在vlfeat工具包安装完成  
2、安装Graphviz和pydot  
**注意**：一定要先安装Graphviz!注意顺序  
**安装Graphviz**  
在[官网][Link 1]上下载graphviz-2.38.msi  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 10]  
下载好之后双击graphviz-2.38.msi，选择安装路径（这个路径要记住，后面要配置环境变量）  
安装完成之后配置环境变量  
右键我的电脑->属性->高级系统设置->环境变量->系统变量->Path，在Path里添加Graphviz-2.38文件夹下的bin  
![在这里插入图片描述][20190317133517238.png]  
在命令行界面中输入dot -version检测是否安装成功，出现如下界面则说明安装成功  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 11]  
**安装pydot**  
在命令行界面中输入`pip install pydot==1.1.0`  
在python工具下运行

import pydot
    
    g = pydot.Dot(graph_type='graph')
    g.add_node(pydot.Node(str(0), fontcolor='transparent'))
    for i in range(5):
      g.add_node(pydot.Node(str(i + 1)))
      g.add_edge(pydot.Edge(str(0), str(i + 1)))
      for j in range(5):
        g.add_node(pydot.Node(str(j + 1) + '0' + str(i + 1)))
        g.add_edge(pydot.Edge(str(j + 1) + '0' + str(i + 1), str(j + 1)))
    g.write_png(E:/study_work/python/images/test.png', prog='neato')

出现下图则说明安装成功  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 12]

*参考*  
[Pytthon计算机视觉编程第二章][Pytthon]

[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70]: /images/20220307/683a9d9d4a4c4a47bb1202dd7f1577f8.png
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[SIFT]: https://blog.csdn.net/qq_37374643/article/details/88606351
[www.vlfeat.org]: http://www.vlfeat.org/
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[Link 1]: https://graphviz.gitlab.io/_pages/Download/Download_windows.html
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM3Mzc0NjQz_size_16_color_FFFFFF_t_70 10]: /images/20220307/77ee5413dbbb4238a65cd158228ffd34.png
[20190317133517238.png]: /images/20220307/d024b6fee5304389a678d2bb6ad2bbb7.png
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[Pytthon]: http://yongyuan.name/pcvwithpython/chapter2.html#sec-2-2-3