《Python计算机视觉编程》一书中关于增强现实茶壶显示的程序

大家好，我是第一次写CSDN博客，也是刚开始学习用Python进行计算机视觉编程，有很多不懂和不足的地方，希望大家多包涵。以下纯粹是我个人的一些实际操作经历。
在《Python计算机视觉编程》一书中，有关于增强现实AR的一部分编程程序。尤其是一段在相片上显示一个虚拟茶壶的程序，在网上（包括CSDN）有很多程序。这里我个人觉得这2篇文章算不错的，给出链接。第一个是https://blog.csdn.net/Dujing2019/article/details/91691202，第二个是https://blog.csdn.net/titansm/article/details/89057184。但直接使用这些程序，不一定能得到它们说的同样的效果和结果（至少我不是），所以我贴出自己的代码，以供大家参考。

1、一些基本的我就不说了，例如软件和包的安装等，大家参考以上两个链接和其他文章，这里我说点和其它文章不一样的地方。对了，我的环境是Win7 64位，装了anaconda3和python3.6。
2、首先是以上两个链接中都会提到的sift.process_image等等函数，你会发现文章中根本没细说这写函数的内容是什么。其实这些函数在《Python计算机视觉编程》一书中有详细提及，只是以上链接文章没提罢了，这里我把所有相关的程序待会在下面都列出来。
3、sift.process_image函数是将.bmp文件转变成.sift文件。那么如何转变呢？这里你需要下载一个vlfeat-0.9.20-bin.tar.gz压缩包（注意必须是0.9.20版本，0.9.21版本没用），解压缩后将其中/bin/win32中的sift.exe、vl.dll、vl.lib拷贝到和你这个AR python程序同一个文件夹下。很奇怪我是64位版本为什么用win32中的文件吧？其实我也奇怪，但我试过了，用win64根本不行，只有win32可以，我也不知道为什么。
4、另外，你在安装完PyOpenGL后，需要将OpenGL库文件（由于我安装了anaconda3，因此我的OpenGL库文件在Continuum\anaconda3\Lib\site-packages\OpenGL）下的DLLS文件夹中的部分东西删除，只保留gle_AUTHORS、gle_COPYING、gle_COPYING.src、gle64.vc14.dll、glut64.vc14.dll这六个文件即可。否则在编译函数draw_teapot时会跳出一个莫名其妙的错误。
5、这一点非常关键，我找遍了好多文章，最后终于在第二个链接中找到了，也就是width和height的值。一定不能用大多数文章里使用的1000和747，要用你自己那个.bmp的像素。比如我的.bmp文件的像素是553和369（可以点击右键–>属性–>详细信息看到该文件的像素）。
另外关于这一点，我做了若干次尝试：当width,height = 553,369时，效果OK；当width,height = 553,300时，效果也OK；当width不是553时，哪怕是554或552，出来的背景效果都是极度扭曲的。
还有，width和height的值不能太大，否则编译时就会报错，什么内存读取溢出或错误OSError: exception: access violation reading 0x0000000008FC7000。网上很多人说这是64位版本问题，算是一个bug，具体我也搞不清楚。所以你在生成.bmp图片时，就要记住图片像素值不能太大。
好，接下来我就贴代码了。

首先生成一个空文件sift.py，将它和其它AR python程序放在同一个文件夹。sift.py里的程序是：

from PIL import Image
from numpy import *
from pylab import *
import osdef process_image(imagename, resultname, params='--edge-thresh 10 --peak-thresh 5'):if imagename[-3:] != 'pgm':im = Image.open(imagename).convert('L')im.save('tmp.pgm')imagename = 'tmp.pgm'cmmd = str("sift " + imagename + " --output=" + resultname + " " + params)os.system(cmmd)print(str("processed " + imagename + " to " + resultname))def read_features_from_file(filename):f = loadtxt(filename)return (f[:, :4], f[:, 4:])def match(desc1,desc2):desc1 = array([d/linalg.norm(d) for d in desc1])desc2 = array([d/linalg.norm(d) for d in desc2])dist_ratio = 0.6desc1_size = desc1.shapematchscores = zeros((desc1_size[0],1),'int')desc2t = desc2.Tfor i in range(desc1_size[0]):dotprods = dot(desc1[i,:],desc2t)dotprods = 0.9999*dotprodsindx = argsort(arccos(dotprods))if arccos(dotprods)[indx[0]] < dist_ratio*arccos(dotprods)[indx[1]]:matchscores[i] = int(indx[0])return matchscoresdef match_twosided(desc1,desc2):matches_12 = match(desc1, desc2)matches_21 = match(desc2, desc1)ndx_12 = matches_12.nonzero()[0]for n in ndx_12:if matches_21[matches_12[n]] != n:matches_12[n] = 0return matches_12

然后生成一个空文件homography.py，将它和其它AR python程序放在同一个文件夹。homography.py里的程序是：

import os
from PIL import Image
from numpy import *
from pylab import *
from scipy import ndimage
import ransacdef make_homog(points):return vstack((points,ones((1,points.shape[1]))))class RansacModel(object):def __init__(self,debug=False):self.debug = debugdef fit(self,data):data = data.Tfp = data[:3,:4]tp = data[3:,:4]return H_from_points(fp,tp)def get_error(self,data,H):data = data.Tfp = data[:3]tp = data[3:]fp_transformed = dot(H,fp)for i in range(3):fp_transformed[i] /= fp_transformed[2]return sqrt(sum((tp-fp_transformed)**2,axis=0))def H_from_ransac(fp,tp,model,maxiter=1000,match_threshold=10):data = vstack((fp,tp))H,ransac_data = ransac.ransac(data.T,model,4,maxiter,match_threshold,10,return_all=True)return H,ransac_data['inliers']

再生成一个空文件ransac.py，将它和其它AR python程序放在同一个文件夹。ransac.py里的程序是（ransac.py其实网上能下载，这里我把代码都贴出来吧）：

import numpy
import scipy # use numpy if scipy unavailable
import scipy.linalg # use numpy if scipy unavailable## Copyright (c) 2004-2007, Andrew D. Straw. All rights reserved.## Redistribution and use in source and binary forms, with or without
## modification, are permitted provided that the following conditions are
## met:##     * Redistributions of source code must retain the above copyright
##       notice, this list of conditions and the following disclaimer.##     * Redistributions in binary form must reproduce the above
##       copyright notice, this list of conditions and the following
##       disclaimer in the documentation and/or other materials provided
##       with the distribution.##     * Neither the name of the Andrew D. Straw nor the names of its
##       contributors may be used to endorse or promote products derived
##       from this software without specific prior written permission.## THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
## "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
## LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
## A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
## OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
## SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
## LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
## DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
## THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
## (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
## OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.def ransac(data,model,n,k,t,d,debug=False,return_all=False):"""fit model parameters to data using the RANSAC algorithmThis implementation written from pseudocode found at
http://en.wikipedia.org/w/index.php?title=RANSAC&oldid=116358182{{{
Given:data - a set of observed data pointsmodel - a model that can be fitted to data pointsn - the minimum number of data values required to fit the modelk - the maximum number of iterations allowed in the algorithmt - a threshold value for determining when a data point fits a modeld - the number of close data values required to assert that a model fits well to data
Return:bestfit - model parameters which best fit the data (or nil if no good model is found)
iterations = 0
bestfit = nil
besterr = something really large
while iterations < k {maybeinliers = n randomly selected values from datamaybemodel = model parameters fitted to maybeinliersalsoinliers = empty setfor every point in data not in maybeinliers {if point fits maybemodel with an error smaller than tadd point to alsoinliers}if the number of elements in alsoinliers is > d {% this implies that we may have found a good model% now test how good it isbettermodel = model parameters fitted to all points in maybeinliers and alsoinliersthiserr = a measure of how well model fits these pointsif thiserr < besterr {bestfit = bettermodelbesterr = thiserr}}increment iterations
}
return bestfit
}}}
"""iterations = 0bestfit = Nonebesterr = numpy.infbest_inlier_idxs = Nonewhile iterations < k:maybe_idxs, test_idxs = random_partition(n,data.shape[0])maybeinliers = data[maybe_idxs,:]test_points = data[test_idxs]maybemodel = model.fit(maybeinliers)test_err = model.get_error( test_points, maybemodel)also_idxs = test_idxs[test_err < t] # select indices of rows with accepted pointsalsoinliers = data[also_idxs,:]if debug:'''print 'test_err.min()',test_err.min()print 'test_err.max()',test_err.max()print 'numpy.mean(test_err)',numpy.mean(test_err)print 'iteration %d:len(alsoinliers) = %d'%(iterations,len(alsoinliers))'''if len(alsoinliers) > d:betterdata = numpy.concatenate( (maybeinliers, alsoinliers) )bettermodel = model.fit(betterdata)better_errs = model.get_error( betterdata, bettermodel)thiserr = numpy.mean( better_errs )if thiserr < besterr:bestfit = bettermodelbesterr = thiserrbest_inlier_idxs = numpy.concatenate( (maybe_idxs, also_idxs) )iterations+=1if bestfit is None:raise ValueError("did not meet fit acceptance criteria")if return_all:return bestfit, {'inliers':best_inlier_idxs}else:return bestfitdef random_partition(n,n_data):"""return n random rows of data (and also the other len(data)-n rows)"""all_idxs = numpy.arange( n_data )numpy.random.shuffle(all_idxs)idxs1 = all_idxs[:n]idxs2 = all_idxs[n:]return idxs1, idxs2class LinearLeastSquaresModel:"""linear system solved using linear least squaresThis class serves as an example that fulfills the model interfaceneeded by the ransac() function."""def __init__(self,input_columns,output_columns,debug=False):self.input_columns = input_columnsself.output_columns = output_columnsself.debug = debugdef fit(self, data):A = numpy.vstack([data[:,i] for i in self.input_columns]).TB = numpy.vstack([data[:,i] for i in self.output_columns]).Tx,resids,rank,s = scipy.linalg.lstsq(A,B)return xdef get_error( self, data, model):A = numpy.vstack([data[:,i] for i in self.input_columns]).TB = numpy.vstack([data[:,i] for i in self.output_columns]).TB_fit = scipy.dot(A,model)err_per_point = numpy.sum((B-B_fit)**2,axis=1) # sum squared error per rowreturn err_per_pointdef test():# generate perfect input datan_samples = 500n_inputs = 1n_outputs = 1A_exact = 20*numpy.random.random((n_samples,n_inputs) )perfect_fit = 60*numpy.random.normal(size=(n_inputs,n_outputs) ) # the modelB_exact = scipy.dot(A_exact,perfect_fit)assert B_exact.shape == (n_samples,n_outputs)# add a little gaussian noise (linear least squares alone should handle this well)A_noisy = A_exact + numpy.random.normal(size=A_exact.shape )B_noisy = B_exact + numpy.random.normal(size=B_exact.shape )if 1:# add some outliersn_outliers = 100all_idxs = numpy.arange( A_noisy.shape[0] )numpy.random.shuffle(all_idxs)outlier_idxs = all_idxs[:n_outliers]non_outlier_idxs = all_idxs[n_outliers:]A_noisy[outlier_idxs] =  20*numpy.random.random((n_outliers,n_inputs) )B_noisy[outlier_idxs] = 50*numpy.random.normal(size=(n_outliers,n_outputs) )# setup modelall_data = numpy.hstack( (A_noisy,B_noisy) )input_columns = range(n_inputs) # the first columns of the arrayoutput_columns = [n_inputs+i for i in range(n_outputs)] # the last columns of the arraydebug = Falsemodel = LinearLeastSquaresModel(input_columns,output_columns,debug=debug)linear_fit,resids,rank,s = scipy.linalg.lstsq(all_data[:,input_columns],all_data[:,output_columns])# run RANSAC algorithmransac_fit, ransac_data = ransac(all_data,model,50, 1000, 7e3, 300, # misc. parametersdebug=debug,return_all=True)if 1:import pylabsort_idxs = numpy.argsort(A_exact[:,0])A_col0_sorted = A_exact[sort_idxs] # maintain as rank-2 arrayif 1:pylab.plot( A_noisy[:,0], B_noisy[:,0], 'k.', label='data' )pylab.plot( A_noisy[ransac_data['inliers'],0], B_noisy[ransac_data['inliers'],0], 'bx', label='RANSAC data' )else:pylab.plot( A_noisy[non_outlier_idxs,0], B_noisy[non_outlier_idxs,0], 'k.', label='noisy data' )pylab.plot( A_noisy[outlier_idxs,0], B_noisy[outlier_idxs,0], 'r.', label='outlier data' )pylab.plot( A_col0_sorted[:,0],numpy.dot(A_col0_sorted,ransac_fit)[:,0],label='RANSAC fit' )pylab.plot( A_col0_sorted[:,0],numpy.dot(A_col0_sorted,perfect_fit)[:,0],label='exact system' )pylab.plot( A_col0_sorted[:,0],numpy.dot(A_col0_sorted,linear_fit)[:,0],label='linear fit' )pylab.legend()pylab.show()if __name__=='__main__':test()

接下来是主程序了。生成一个空文件AR_Test.py，将它和其它AR python程序放在同一个文件夹。AR_Test.py里的程序是（这里基本就是直接复制黏贴链接一和链接二了）：

from pylab import *
from OpenGL.GL import *
from OpenGL.GLU import *
from OpenGL.GLUT import *
import pygame, pygame.image
from pygame.locals import *
from numpy import *
import homography,camera,sift
import math
from PIL import Imagedef cube_points(c, wid):p = []# bottomp.append([c[0]-wid, c[1]-wid, c[2]-wid])p.append([c[0]-wid, c[1]+wid, c[2]-wid])p.append([c[0]+wid, c[1]+wid, c[2]-wid])p.append([c[0]+wid, c[1]-wid, c[2]-wid])p.append([c[0]-wid, c[1]-wid, c[2]-wid]) #same as first to close plot# topp.append([c[0]-wid, c[1]-wid, c[2]+wid])p.append([c[0]-wid, c[1]+wid, c[2]+wid])p.append([c[0]+wid, c[1]+wid, c[2]+wid])p.append([c[0]+wid, c[1]-wid, c[2]+wid])p.append([c[0]-wid, c[1]-wid, c[2]+wid]) #same as first to close plot# vertical sidesp.append([c[0]-wid, c[1]-wid, c[2]+wid])p.append([c[0]-wid, c[1]+wid, c[2]+wid])p.append([c[0]-wid, c[1]+wid, c[2]-wid])p.append([c[0]+wid, c[1]+wid, c[2]-wid])p.append([c[0]+wid, c[1]+wid, c[2]+wid])p.append([c[0]+wid, c[1]-wid, c[2]+wid])p.append([c[0]+wid, c[1]-wid, c[2]-wid])return array(p).Tdef my_calibration(sz):row, col = szfx = 2555*col/2592fy = 2586*row/1936K = diag([fx, fy, 1])K[0, 2] = 0.5*colK[1, 2] = 0.5*rowreturn Kdef set_projection_from_camera(K):  # 获取视图glMatrixMode(GL_PROJECTION)glLoadIdentity()fx = K[0, 0]fy = K[1, 1]fovy = 2 * math.atan(0.5 * height / fy) * 180 / math.piaspect = (width * fy) / (height * fx)# 定义近和远的剪裁平面near = 0.1far = 100.0# 设定透视gluPerspective(fovy, aspect, near, far)glViewport(0, 0, width, height)def set_modelview_from_camera(Rt):  # 获取矩阵glMatrixMode(GL_MODELVIEW)glLoadIdentity()# 围绕x轴将茶壶旋转90度，使z轴向上Rx = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]])# 获得旋转的最佳逼近R = Rt[:, :3]U, S, V = np.linalg.svd(R)R = np.dot(U, V)R[0, :] = -R[0, :]  # 改变x轴的符号# 获得平移量t = Rt[:, 3]# 获得4*4的的模拟视图矩阵M = np.eye(4)M[:3, :3] = np.dot(R, Rx)M[:3, 3] = t# 转置并压平以获取列序数值M = M.Tm = M.flatten()# 将模拟视图矩阵替换成新的矩阵glLoadMatrixf(m)def draw_background(imname):# 载入背景图像bg_image = pygame.image.load(imname).convert()bg_data = pygame.image.tostring(bg_image, 'RGBX', 1)  # 将图像转为字符串描述glMatrixMode(GL_MODELVIEW)  # 将当前矩阵指定为投影矩阵glLoadIdentity()  # 把矩阵设为单位矩阵#glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)  # 清楚颜色、深度缓冲glEnable(GL_TEXTURE_2D)  # 纹理映射glBindTexture(GL_TEXTURE_2D, glGenTextures(1))glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, width, height, 0, GL_RGBA, GL_UNSIGNED_BYTE, bg_data)#glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_NEAREST)glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_NEAREST)# 绑定纹理glBegin(GL_QUADS)glTexCoord2f(0.0, 0.0);glVertex3f(-1.0, -1.0, -1.0)glTexCoord2f(1.0, 0.0);glVertex3f(1.0, -1.0, -1.0)glTexCoord2f(1.0, 1.0);glVertex3f(1.0, 1.0, -1.0)glTexCoord2f(0.0, 1.0);glVertex3f(-1.0, 1.0, -1.0)glEnd()glDeleteTextures(1)  # 清除纹理def draw_teapot(size):  # 红色茶壶glEnable(GL_LIGHTING)glEnable(GL_LIGHT0)glEnable(GL_DEPTH_TEST)glClear(GL_DEPTH_BUFFER_BIT)# 绘制红色茶壶glMaterialfv(GL_FRONT, GL_AMBIENT, [0, 0, 0, 0])glMaterialfv(GL_FRONT, GL_DIFFUSE, [0.5, 0.0, 0.0, 0.0])glMaterialfv(GL_FRONT, GL_SPECULAR, [0.7, 0.6, 0.6, 0.0])glMaterialf(GL_FRONT, GL_SHININESS, 0.25 * 128.0)glutSolidTeapot(size)def drawFunc(size):  # 白色茶壶glRotatef(0.5, 5, 5, 0)  # (角度,x,y,z)glutWireTeapot(size)# 刷新显示glFlush()def load_and_draw_model(filename):glEnable(GL_LIGHTING)glEnable(GL_LIGHT0)glEnable(GL_DEPTH_TEST)glClear(GL_DEPTH_BUFFER_BIT)glMaterialfv(GL_FRONT,GL_AMBIENT,[0,0,0,0])glMaterialfv(GL_FRONT, GL_DIFFUSE, [0.5, 0.75, 1.0, 0.0])glMaterialf(GL_FRONT, GL_SHININESS, 0.25*128.0)import objloaderobj = objloader.OBJ(filename,swapyz=True)glCallList(obj.gl_list)#这里一定要是.bmp图片的分辨率，不要迷信网上的1000,747
width,height = 553,369
def setup():pygame.init()pygame.display.set_mode((width,height),OPENGL | DOUBLEBUF)pygame.display.set_caption("OpenGL AR demo")# 计算特征，先对着一本书竖着排一张book0.jpg，再对着这本书横着拍一张book1.jpg
sift.process_image('book0.jpg', 'im0.sift')
l0, d0 = sift.read_features_from_file('im0.sift')
sift.process_image('book1.jpg', 'im1.sift')
l1, d1 = sift.read_features_from_file('im1.sift')# 匹配特征，计算单应性矩阵
matches = sift.match_twosided(d0, d1)
ndx = matches.nonzero()[0]
fp = homography.make_homog(l0[ndx, :2].T)
ndx2 = [int(matches[i]) for i in ndx]
tp = homography.make_homog(l1[ndx2, :2].T)model = homography.RansacModel()
H, inliers = homography.H_from_ransac(fp, tp, model)# 计算照相机标定矩阵
K = my_calibration((height, width))
# 位于边长为0.2，z=0平面上的三维点
box = cube_points([0, 0, 0.1], 0.1)# 投影第一幅图下个上底部的正方形
cam1 = camera.Camera(hstack((K, dot(K, array([[0], [0], [-1]])))))
# 底部正方形上的点
box_cam1 = cam1.project(homography.make_homog(box[:, :5]))# 使用H将点变换到第二幅图像中
box_trans = homography.normalize(dot(H, box_cam1))# 从cam1和H中计算第二个照相机矩阵
cam2 = camera.Camera(dot(H, cam1.P))
A = dot(linalg.inv(K), cam2.P[:, :3])
A = array([A[:, 0], A[:, 1], cross(A[:, 0], A[:, 1])]).T
cam2.P[:, :3] = dot(K, A)
# 使用第二个照相机矩阵投影
box_cam2 = cam2.project(homography.make_homog(box))#这一段程序是在book图上画一个AR立方体
point = array([1,1,0,1]).T
print(homography.normalize(dot(dot(H,cam1.P),point)))
print(cam2.project(point))
im0 = array(Image.open('book0.jpg'))
im1 = array(Image.open('book1.jpg'))
figure()
imshow(im0)
plot(box_cam1[0,:],box_cam1[1,:],linewidth=3)
figure()
imshow(im1)
plot(box_trans[0,:],box_trans[1,:],linewidth=3)
figure()
imshow(im1)
plot(box_cam2[0,:],box_cam2[1,:],linewidth=3)
show()Rt = dot(linalg.inv(K),cam2.P)
setup()
draw_background("book1.bmp")
set_projection_from_camera(K)
set_modelview_from_camera(Rt)
draw_teapot(0.1)
#drawFunc(0.1)pygame.display.flip()
while True:for event in pygame.event.get():if event.type==pygame.QUIT:sys.exit()

好，到这里基本结束了。第一次写CSDN博客，不足之处还望大家海涵。希望对你有帮助！

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