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。
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 Image
from numpy import *
import matplotlib.pyplot as plt
def 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), cdf
im = 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
from PIL import Image
from pylab import *
from numpy import *
from scipy.ndimage import filters
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 / Wtr
def 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
#得到候选点的座标
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_coords
def 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-harrisim
figure()
gray()
#画出Harris响应图
subplot(141)
imshow(harrisim1)
print harrisim1.shape
axis('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 Image
from pylab import *
from numpy import *
from scipy.ndimage import filters
def 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 / Wtr
def 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
#得到候选点的座标
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_coords
def 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 desc
def 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 matchscores
def 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_12
def 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=5
harrisim = 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 matches
figure()
gray()
plot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)
show()运行结果
