计算机视觉相关代码片段（Python）

本文记载了计算机视觉相关的代码片段，是由Python实现的。

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1直方图均衡化

Python计算机视觉：基本操作与直方图
图解直方图均衡化及其Python实现

# -*- coding: cp936 -*-from PIL import Imagefrom numpy import *import matplotlib.pyplot as pltdef histeq(im, nbr_bins=256):    imhist,bins = histogram(im.flatten(), nbr_bins, normed=True)    cdf = imhist.cumsum()    cdf = 255*cdf/cdf[-1]    im2 = interp(im.flatten(),bins[:-1], cdf)    return im2.reshape(im.shape), cdfim = array(Image.open('dog.jpg').convert('L')) #图像与源码在同一个文件夹下plt.figure('hist', figsize=(8,8))plt.subplot(221) plt.imshow(im,plt.cm.gray) #原始图像plt.subplot(222)plt.hist(im.flatten(), bins=256, normed=1, edgecolor='None', facecolor='red') #原始图像直方图im2,cdf = histeq(im)plt.subplot(223) plt.imshow(im2,plt.cm.gray) #均衡化图像plt.subplot(224)plt.hist(im2.flatten(), bins=256, normed=1, edgecolor='None', facecolor='red') #均衡化直方图plt.show()

运行结果

2 主成分分析法（PCA）

机器学习中的数学(4)-线性判别分析(LDA)，主成分分析(PCA)
机器学习中的数学(5)-强大的矩阵奇异值分解(SVD)及其应用
待补充

3 Harris角点检测器

3.1 单张图片检测

Harris角点
Harris角点检测

from PIL import Imagefrom pylab import *from numpy import *from scipy.ndimage import filtersdef compute_harris_response(im,sigma=3):    """在一幅灰度图像中，对每个像素计算Harris角点检测器响应函数"""    #计算导数    imx = zeros(im.shape)    filters.gaussian_filter(im, (sigma, sigma), (0,1), imx)    imy = zeros(im.shape)    filters.gaussian_filter(im, (sigma, sigma), (1,0), imy)    #计算Harris矩阵的分量    Wxx = filters.gaussian_filter(imx*imx, sigma)    Wxy = filters.gaussian_filter(imx*imy, sigma)    Wyy = filters.gaussian_filter(imy*imy, sigma)    #计算特征值和迹    Wdet = Wxx*Wyy - Wxy**2    Wtr = Wxx + Wyy    return Wdet / Wtrdef get_harris_points(harrisim, min_dist=10, threshold=0.1):    """从一幅Harris响应图像中返回角点。min_dist为分割点和图像边界的最小像素数目"""    #寻找高于阈值的候选角点    corner_threshold = harrisim.max()*threshold    harrisim_t = (harrisim>corner_threshold)*1    #得到候选点的坐标    coords = array(harrisim_t.nonzero()).T    #以及它们的Harris响应值    candidate_values = [harrisim[c[0],c[1]] for c in coords]    #对候选点按照Harris响应值进行排序    index = argsort(candidate_values)    #将可行点的位置保存到数组中    allowed_locations = zeros(harrisim.shape)    allowed_locations[min_dist:-min_dist, min_dist:-min_dist] = 1    #按照min_distancce原则，选择最佳Harris点    filtered_coords = []    for i in index:        if allowed_locations[coords[i,0],coords[i,1]] == 1:            filtered_coords.append(coords[i])            allowed_locations[(coords[i,0]-min_dist):(coords[i,0]+min_dist),(coords[i,1]-min_dist):(coords[i,1]+min_dist)]=0    return filtered_coordsdef plot_harris_points(image,filterd_coords):    """绘制图像中检测到的角点"""    figure()    gray()    imshow(image)    plot([p[1] for p in filtered_coords], [p[0] for p in filtered_coords], "*")    axis('off')    show()#读入图像im = array(Image.open('./data/empire.jpg').convert('L'))#检测harris角点harrisim = compute_harris_response(im)#harris响应函数harrisim1 = 255-harrisimfigure()gray()#画出Harris响应图subplot(141)imshow(harrisim1)print harrisim1.shapeaxis('off')axis('equal')threshold = [0.01, 0.05, 0.1]for i,thres in enumerate(threshold):    filtered_coords = get_harris_points(harrisim, 6, thres)    subplot(1,4,i+2)    imshow(im)    print im.shape    plot([p[1] for p in filtered_coords], [p[0] for p in filtered_coords], "*")    axis('off')show()

运行结果

3.2 两张图片匹配

from PIL import Imagefrom pylab import *from numpy import *from scipy.ndimage import filtersdef imresize(im, sz):    pil_im = Image.fromarray(uint8(im))    return array(pil_im.resize(sz))def compute_harris_response(im,sigma=3):    """在一幅灰度图像中，对每个像素计算Harris角点检测器响应函数"""    #计算导数    imx = zeros(im.shape)    filters.gaussian_filter(im, (sigma, sigma), (0,1), imx)    imy = zeros(im.shape)    filters.gaussian_filter(im, (sigma, sigma), (1,0), imy)    #计算Harris矩阵的分量    Wxx = filters.gaussian_filter(imx*imx, sigma)    Wxy = filters.gaussian_filter(imx*imy, sigma)    Wyy = filters.gaussian_filter(imy*imy, sigma)    #计算特征值和迹    Wdet = Wxx*Wyy - Wxy**2    Wtr = Wxx + Wyy    return Wdet / Wtrdef get_harris_points(harrisim, min_dist=10, threshold=0.1):    """从一幅Harris响应图像中返回角点。min_dist为分割点和图像边界的最小像素数目"""    #寻找高于阈值的候选角点    corner_threshold = harrisim.max()*threshold    harrisim_t = (harrisim>corner_threshold)*1    #得到候选点的坐标    coords = array(harrisim_t.nonzero()).T    #以及它们的Harris响应值    candidate_values = [harrisim[c[0],c[1]] for c in coords]    #对候选点按照Harris响应值进行排序    index = argsort(candidate_values)    #将可行点的位置保存到数组中    allowed_locations = zeros(harrisim.shape)    allowed_locations[min_dist:-min_dist, min_dist:-min_dist] = 1    #按照min_distancce原则，选择最佳Harris点    filtered_coords = []    for i in index:        if allowed_locations[coords[i,0],coords[i,1]] == 1:            filtered_coords.append(coords[i])            allowed_locations[(coords[i,0]-min_dist):(coords[i,0]+min_dist),(coords[i,1]-min_dist):(coords[i,1]+min_dist)]=0    return filtered_coordsdef get_descriptors(image, filtered_coords, wid=5):    """对于每个返回的点，返回点周围2*wid+1个像素的值（假设选取的点min_distance>wid）"""    desc = []    for coords in filtered_coords:        patch = image[coords[0]-wid:coords[0]+wid+1,                      coords[1]-wid:coords[1]+wid+1].flatten()        desc.append(patch)    return descdef match(desc1, desc2, threshold=0.5):    """对于第一幅图像中的每个角点描述子，使用归一化互相关，选取它在第二幅图像中的匹配角点"""    n = len(desc1[0])    #点对的距离    d = -ones((len(desc1),len(desc2)))    for i in range(len(desc1)):        for j in range(len(desc2)):            d1 = (desc1[i]-mean(desc1[i])) / std(desc1[i])            d2 = (desc2[j]-mean(desc2[j])) / std(desc2[j])            ncc_value = sum(d1*d2)/(n-1)            if ncc_value > threshold:                d[i,j] = ncc_value    ndx = argsort(-d)    matchscores = ndx[:,0]    return matchscoresdef match_twosided(desc1, desc2, threshold=0.5):    """两边对称版本的match()"""    matches_12 = match(desc1, desc2, threshold)    matches_21 = match(desc2, desc1, threshold)    ndx_12 = where(matches_12>=0)[0]    #去除非对称的匹配    for n in ndx_12:        if matches_21[matches_12[n]] != n:            matches_12[n] = -1    return matches_12def appendimages(im1, im2):    """返回将两幅图像并排拼接成的一幅新图像"""    #选取具有最少行数的图像，然后填充足够的空行    rows1 = im1.shape[0]    rows2 = im2.shape[0]    if rows1 < rows2:        im1 = concatenate((im1, zeros((rows2-rows1,im1.shape[1]))),axis=0)    elif rows1 >rows2:        im2 = concatenate((im2, zeros((rows1-rows2,im2.shape[1]))),axis=0)    return concatenate((im1,im2), axis=1)def plot_matches(im1,im2,locs1,locs2,matchscores,show_below=True):    """ 显示一幅带有连接匹配之间连线的图片        输入：im1, im2(数组图像), locs1,locs2（特征位置）,matchscores(match()的输出)，        show_below（如果图像应该显示在匹配的下方）    """    im3=appendimages(im1,im2)    if show_below:        im3=vstack((im3,im3))    imshow(im3)    cols1 = im1.shape[1]    for i,m in enumerate(matchscores):        if m>0:            plot([locs1[i][1],locs2[m][1]+cols1],[locs1[i][0],locs2[m][0]],'c')    axis('off')im1 = array(Image.open('./data/sf_view1.jpg').convert("L"))im2 = array(Image.open('./data/sf_view2.jpg').convert("L"))im1 = imresize(im1, (im1.shape[1]/2, im1.shape[0]/2))im2 = imresize(im2, (im2.shape[1]/2, im2.shape[0]/2))wid=5harrisim = compute_harris_response(im1,5)filtered_coords1 = get_harris_points(harrisim, wid+1)d1 = get_descriptors(im1, filtered_coords1, wid)harrisim = compute_harris_response(im2,5)filtered_coords2 = get_harris_points(harrisim, wid+1)d2 = get_descriptors(im2, filtered_coords2, wid)print 'start matching'matches = match_twosided(d1,d2)#print matchesfigure()gray()plot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)show()

运行结果