单眼三维成像是依据多带带监控摄像头健身运动仿真模拟双目视觉获得物件和空间里的三维视觉信息内容,下面本文关键为大家介绍了对于如何依据python完成单眼三维成像的资料,原文中依据案例编码推荐的十分详尽,必须的小伙伴可以借鉴一下
一、单眼三维成像简述
客观现实的物件是三维立体的,而我用监控摄像头获得的图象是二维动画的,但我们可以依据二维图像认知总体目标三维信息内容。三维重建技术要以相对应的形式解决图象从而获得电子计算机可以识别三维立体信息内容,从而对于目标展开分析。而单眼三维成像乃是依据多带带监控摄像头健身运动来仿真模拟双目视觉,从而得到物件和空间里的三维视觉信息内容,在其中,单眼是指多带带监控摄像头。
二、完成全过程
对其物件开展单眼三维成像的过程当中,有关软件环境如下所示:
matplotlib3.3.4
numpy1.19.5
opencv-contrib-python3.4.2.16
opencv-python3.4.2.16
pillow8.2.0
python3.6.2
其复建主要包括下列流程:
(1)镜头的校准
def camera_calibration(ImagePath): #循环中断 criteria=(cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER,30,0.001) #棋盘格尺寸(棋盘格的交叉点的个数) row=11 column=8 objpoint=np.zeros((row*column,3),np.float32) objpoint[:,:2]=np.mgrid[0:row,0:column].T.reshape(-1,2) objpoints=[]#3d point in real world space imgpoints=[]#2d points in image plane. batch_images=glob.glob(ImagePath+'/*.jpg') for i,fname in enumerate(batch_images): img=cv2.imread(batch_images<i>) imgGray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #find chess board corners ret,corners=cv2.findChessboardCorners(imgGray,(row,column),None) #if found,add object points,image points(after refining them) if ret: objpoints.append(objpoint) corners2=cv2.cornerSubPix(imgGray,corners,(11,11),(-1,-1),criteria) imgpoints.append(corners2) #Draw and display the corners img=cv2.drawChessboardCorners(img,(row,column),corners2,ret) cv2.imwrite('Checkerboard_Image/Temp_JPG/Temp_'+str(i)+'.jpg',img) print("成功提取:",len(batch_images),"张图片角点!") ret,mtx,dist,rvecs,tvecs=cv2.calibrateCamera(objpoints,imgpoints,imgGray.shape[::-1],None,None)
(2)图象图像匹配及配对
def epipolar_geometric(Images_Path,K): IMG=glob.glob(Images_Path) img1,img2=cv2.imread(IMG[0]),cv2.imread(IMG[1]) img1_gray=cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY) img2_gray=cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY) #Initiate SURF detector SURF=cv2.xfeatures2d_SURF.create() #compute keypoint&descriptions keypoint1,descriptor1=SURF.detectAndCompute(img1_gray,None) keypoint2,descriptor2=SURF.detectAndCompute(img2_gray,None) print("角点数量:",len(keypoint1),len(keypoint2)) #Find point matches bf=cv2.BFMatcher(cv2.NORM_L2,crossCheck=True) matches=bf.match(descriptor1,descriptor2) print("匹配点数量:",len(matches)) src_pts=np.asarray([keypoint1[m.queryIdx].pt for m in matches]) dst_pts=np.asarray([keypoint2[m.trainIdx].pt for m in matches]) #plot knn_image=cv2.drawMatches(img1_gray,keypoint1,img2_gray,keypoint2,matches[:-1],None,flags=2) image_=Image.fromarray(np.uint8(knn_image)) image_.save("MatchesImage.jpg") #Constrain matches to fit homography retval,mask=cv2.findHomography(src_pts,dst_pts,cv2.RANSAC,100.0) #We select only inlier points points1=src_pts[mask.ravel()==1] points2=dst_pts[mask.ravel()==1]
(3)三维成像
points1=cart2hom(points1.T) points2=cart2hom(points2.T) #plot fig,ax=plt.subplots(1,2) ax[0].autoscale_view('tight') ax[0].imshow(cv2.cvtColor(img1,cv2.COLOR_BGR2RGB)) ax[0].plot(points1[0],points1[1],'r.') ax[1].autoscale_view('tight') ax[1].imshow(cv2.cvtColor(img2,cv2.COLOR_BGR2RGB)) ax[1].plot(points2[0],points2[1],'r.') plt.savefig('MatchesPoints.jpg') fig.show() # points1n=np.dot(np.linalg.inv(K),points1) points2n=np.dot(np.linalg.inv(K),points2) E=compute_essential_normalized(points1n,points2n) print('Computed essential matrix:',(-E/E[0][1])) P1=np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0]]) P2s=compute_P_from_essential(E) ind=-1 for i,P2 in enumerate(P2s): #Find the correct camera parameters d1=reconstruct_one_point(points1n[:,0],points2n[:,0],P1,P2) #Convert P2 from camera view to world view P2_homogenous=np.linalg.inv(np.vstack([P2,[0,0,0,1]])) d2=np.dot(P2_homogenous[:3,:4],d1) if d1[2]>0 and d2[2]>0: ind=i P2=np.linalg.inv(np.vstack([P2s[ind],[0,0,0,1]]))[:3,:4] Points3D=linear_triangulation(points1n,points2n,P1,P2) fig=plt.figure() fig.suptitle('3D reconstructed',fontsize=16) ax=fig.gca(projection='3d') ax.plot(Points3D[0],Points3D[1],Points3D[2],'b.') ax.set_xlabel('x axis') ax.set_ylabel('y axis') ax.set_zlabel('z axis') ax.view_init(elev=135,azim=90) plt.savefig('Reconstruction.jpg') plt.show()
下面,我们一起来详尽看看每个流程的实际完成:
(1)镜头的校准
在咱们在日常生活中有许多照相机,如手机上面的照相机、数码照相机及程序模块型照相机这些,每个镜头的主要参数都是不一样的,即照相机拍出来的手机照片屏幕分辨率、方式等。假定大家在开展物件三维成像时,事前并不了解大家镜头的引流矩阵主要参数,那样,大家就应该算出镜头的引流矩阵主要参数,这个流程就叫镜头的校准。相机标定的有关基本原理我不讲了,在网上许多人讲的挺详尽的。其校准的实际完成如下所示:
在其中,cv2.calibrateCamera函数公式求出来的mtx引流矩阵即是K引流矩阵。
当改动好相对应主要参数并进行校准后,我们能导出棋盘格的角点照片来看看是不是已正式获取棋盘格的角点,导出角点图如下所示:
图1:棋盘格角点获取
(2)图象图像匹配及配对
在所有三维成像的过程当中,这步是相当重要的,也是比较繁杂的一歩,照片图像匹配的好与坏决定你最后复建实际效果。
在照片特征点获取优化算法中,主要有三种优化算法比较常见,分别是:SIFT优化算法、SURF优化算法及其ORB优化算法。依据全面分析比照,大家在这里一歩中选用SURF优化算法的方式对图形的特征点开展获取。3种算法的特征点获取实际效果比照如果你们有兴趣能去网上搜索来看一下,在这个也不逐个考察了。实际完成如下所示:
图2:特征点获取
(3)三维成像
大家寻找图形的特征点并彼此配对后,则可开始启动三维成像了,实际完成如下所示:
图3:三维成像
三、结果
从复建得到的结果来说,单眼三维成像实际效果通常,我觉得可能和这几个方面有一定关系:
(1)图片拍摄方式。假如是开展单眼三维成像每日任务,在拍摄图片的时候最好维持平行移动照相机,且最好是正面拍摄,即不必横着拍或特殊视角照相;
(2)拍摄的时候周围环境影响。选择拍的地址最好是维持单一化,降低不相干一个物体影响;
(3)照相灯源难题。选择的照相场所要确保适宜的色度(详细情况要试才发现你的灯源是不是合格),另外就是挪动照相机的时候一定要确保上一时时刻刻和此阶段的灯源统一性。
实际上,单眼三维成像效果的确通常,即使将各个方面情形都打满,很有可能所得到的复建效果也是并不是很好。或是大家可以选择选用双眼三维成像,双眼三维成像实际效果肯定要比单目地比较好的,在推进是可能就不便一(亿)一点,嘿嘿。其实并没有多不少实际操作,主要是整2个相机拍摄和校准2个照相机不便点,其它的都不重要了。
四、编码
此次试验的所有编码如下所示:
GitHub:https://github.com/DeepVegChicken/Learning-3DReconstruction
import cv2 import json import numpy as np import glob from PIL import Image import matplotlib.pyplot as plt plt.rcParams['font.sans-serif']=['SimHei'] plt.rcParams['axes.unicode_minus']=False def cart2hom(arr): """Convert catesian to homogenous points by appending a row of 1s :param arr:array of shape(num_dimension x num_points) :returns:array of shape((num_dimension+1)x num_points) """ if arr.ndim==1: return np.hstack([arr,1]) return np.asarray(np.vstack([arr,np.ones(arr.shape[1])])) def compute_P_from_essential(E): """Compute the second camera matrix(assuming P1=[I 0]) from an essential matrix.E=[t]R :returns:list of 4 possible camera matrices. """ U,S,V=np.linalg.svd(E) #Ensure rotation matrix are right-handed with positive determinant if np.linalg.det(np.dot(U,V))<0: V=-V #create 4 possible camera matrices(Hartley p 258) W=np.array([[0,-1,0],[1,0,0],[0,0,1]]) P2s=[np.vstack((np.dot(U,np.dot(W,V)).T,U[:,2])).T, np.vstack((np.dot(U,np.dot(W,V)).T,-U[:,2])).T, np.vstack((np.dot(U,np.dot(W.T,V)).T,U[:,2])).T, np.vstack((np.dot(U,np.dot(W.T,V)).T,-U[:,2])).T] return P2s def correspondence_matrix(p1,p2): p1x,p1y=p1[:2] p2x,p2y=p2[:2] return np.array([ p1x*p2x,p1x*p2y,p1x, p1y*p2x,p1y*p2y,p1y, p2x,p2y,np.ones(len(p1x)) ]).T return np.array([ p2x*p1x,p2x*p1y,p2x, p2y*p1x,p2y*p1y,p2y, p1x,p1y,np.ones(len(p1x)) ]).T def scale_and_translate_points(points): """Scale and translate image points so that centroid of the points are at the origin and avg distance to the origin is equal to sqrt(2). :param points:array of homogenous point(3 x n) :returns:array of same input shape and its normalization matrix """ x=points[0] y=points[1] center=points.mean(axis=1)#mean of each row cx=x-center[0]#center the points cy=y-center[1] dist=np.sqrt(np.power(cx,2)+np.power(cy,2)) scale=np.sqrt(2)/dist.mean() norm3d=np.array([ [scale,0,-scale*center[0]], [0,scale,-scale*center[1]], [0,0,1] ]) return np.dot(norm3d,points),norm3d def compute_image_to_image_matrix(x1,x2,compute_essential=False): """Compute the fundamental or essential matrix from corresponding points (x1,x2 3*n arrays)using the 8 point algorithm. Each row in the A matrix below is constructed as [x'*x,x'*y,x',y'*x,y'*y,y',x,y,1] """ A=correspondence_matrix(x1,x2) #compute linear least square solution U,S,V=np.linalg.svd(A) F=V[-1].reshape(3,3) #constrain F.Make rank 2 by zeroing out last singular value U,S,V=np.linalg.svd(F) S[-1]=0 if compute_essential: S=[1,1,0]#Force rank 2 and equal eigenvalues F=np.dot(U,np.dot(np.diag(S),V)) return F def compute_normalized_image_to_image_matrix(p1,p2,compute_essential=False): """Computes the fundamental or essential matrix from corresponding points using the normalized 8 point algorithm. :input p1,p2:corresponding points with shape 3 x n :returns:fundamental or essential matrix with shape 3 x 3 """ n=p1.shape[1] if p2.shape[1]!=n: raise ValueError('Number of points do not match.') #preprocess image coordinates p1n,T1=scale_and_translate_points(p1) p2n,T2=scale_and_translate_points(p2) #compute F or E with the coordinates F=compute_image_to_image_matrix(p1n,p2n,compute_essential) #reverse preprocessing of coordinates #We know that P1'E P2=0 F=np.dot(T1.T,np.dot(F,T2)) return F/F[2,2] def compute_fundamental_normalized(p1,p2): return compute_normalized_image_to_image_matrix(p1,p2) def compute_essential_normalized(p1,p2): return compute_normalized_image_to_image_matrix(p1,p2,compute_essential=True) def skew(x): """Create a skew symmetric matrix*A*from a 3d vector*x*. Property:np.cross(A,v)==np.dot(x,v) :param x:3d vector :returns:3 x 3 skew symmetric matrix from*x* """ return np.array([ [0,-x[2],x[1]], [x[2],0,-x[0]], [-x[1],x[0],0] ]) def reconstruct_one_point(pt1,pt2,m1,m2): """ pt1 and m1*X are parallel and cross product=0 pt1 x m1*X=pt2 x m2*X=0 """ A=np.vstack([ np.dot(skew(pt1),m1), np.dot(skew(pt2),m2) ]) U,S,V=np.linalg.svd(A) P=np.ravel(V[-1,:4]) return P/P[3] def linear_triangulation(p1,p2,m1,m2): """ Linear triangulation(Hartley ch 12.2 pg 312)to find the 3D point X where p1=m1*X and p2=m2*X.Solve AX=0. :param p1,p2:2D points in homo.or catesian coordinates.Shape(3 x n) :param m1,m2:Camera matrices associated with p1 and p2.Shape(3 x 4) :returns:4 x n homogenous 3d triangulated points """ num_points=p1.shape[1] res=np.ones((4,num_points)) for i in range(num_points): A=np.asarray([ (p1[0,i]*m1[2,:]-m1[0,:]), (p1[1,i]*m1[2,:]-m1[1,:]), (p2[0,i]*m2[2,:]-m2[0,:]), (p2[1,i]*m2[2,:]-m2[1,:]) ]) _,_,V=np.linalg.svd(A) X=V[-1,:4] res[:,i]=X/X[3] return res def writetofile(dict,path): for index,item in enumerate(dict): dict[item]=np.array(dict[item]) dict[item]=dict[item].tolist() js=json.dumps(dict) with open(path,'w')as f: f.write(js) print("参数已成功保存到文件") def readfromfile(path): with open(path,'r')as f: js=f.read() mydict=json.loads(js) print("参数读取成功") return mydict def camera_calibration(SaveParamPath,ImagePath): #循环中断 criteria=(cv2.TERM_CRITERIA_EPS+cv2.TERM_CRITERIA_MAX_ITER,30,0.001) #棋盘格尺寸 row=11 column=8 objpoint=np.zeros((row*column,3),np.float32) objpoint[:,:2]=np.mgrid[0:row,0:column].T.reshape(-1,2) objpoints=[]#3d point in real world space imgpoints=[]#2d points in image plane. batch_images=glob.glob(ImagePath+'/*.jpg') for i,fname in enumerate(batch_images): img=cv2.imread(batch_images<i>) imgGray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) #find chess board corners ret,corners=cv2.findChessboardCorners(imgGray,(row,column),None) #if found,add object points,image points(after refining them) if ret: objpoints.append(objpoint) corners2=cv2.cornerSubPix(imgGray,corners,(11,11),(-1,-1),criteria) imgpoints.append(corners2) #Draw and display the corners img=cv2.drawChessboardCorners(img,(row,column),corners2,ret) cv2.imwrite('Checkerboard_Image/Temp_JPG/Temp_'+str(i)+'.jpg',img) print("成功提取:",len(batch_images),"张图片角点!") ret,mtx,dist,rvecs,tvecs=cv2.calibrateCamera(objpoints,imgpoints,imgGray.shape[::-1],None,None) dict={'ret':ret,'mtx':mtx,'dist':dist,'rvecs':rvecs,'tvecs':tvecs} writetofile(dict,SaveParamPath) meanError=0 for i in range(len(objpoints)): imgpoints2,_=cv2.projectPoints(objpoints<i>,rvecs<i>,tvecs<i>,mtx,dist) error=cv2.norm(imgpoints<i>,imgpoints2,cv2.NORM_L2)/len(imgpoints2) meanError+=error print("total error:",meanError/len(objpoints)) def epipolar_geometric(Images_Path,K): IMG=glob.glob(Images_Path) img1,img2=cv2.imread(IMG[0]),cv2.imread(IMG[1]) img1_gray=cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY) img2_gray=cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY) #Initiate SURF detector SURF=cv2.xfeatures2d_SURF.create() #compute keypoint&descriptions keypoint1,descriptor1=SURF.detectAndCompute(img1_gray,None) keypoint2,descriptor2=SURF.detectAndCompute(img2_gray,None) print("角点数量:",len(keypoint1),len(keypoint2)) #Find point matches bf=cv2.BFMatcher(cv2.NORM_L2,crossCheck=True) matches=bf.match(descriptor1,descriptor2) print("匹配点数量:",len(matches)) src_pts=np.asarray([keypoint1[m.queryIdx].pt for m in matches]) dst_pts=np.asarray([keypoint2[m.trainIdx].pt for m in matches]) #plot knn_image=cv2.drawMatches(img1_gray,keypoint1,img2_gray,keypoint2,matches[:-1],None,flags=2) image_=Image.fromarray(np.uint8(knn_image)) image_.save("MatchesImage.jpg") #Constrain matches to fit homography retval,mask=cv2.findHomography(src_pts,dst_pts,cv2.RANSAC,100.0) #We select only inlier points points1=src_pts[mask.ravel()==1] points2=dst_pts[mask.ravel()==1] points1=cart2hom(points1.T) points2=cart2hom(points2.T) #plot fig,ax=plt.subplots(1,2) ax[0].autoscale_view('tight') ax[0].imshow(cv2.cvtColor(img1,cv2.COLOR_BGR2RGB)) ax[0].plot(points1[0],points1[1],'r.') ax[1].autoscale_view('tight') ax[1].imshow(cv2.cvtColor(img2,cv2.COLOR_BGR2RGB)) ax[1].plot(points2[0],points2[1],'r.') plt.savefig('MatchesPoints.jpg') fig.show() # points1n=np.dot(np.linalg.inv(K),points1) points2n=np.dot(np.linalg.inv(K),points2) E=compute_essential_normalized(points1n,points2n) print('Computed essential matrix:',(-E/E[0][1])) P1=np.array([[1,0,0,0],[0,1,0,0],[0,0,1,0]]) P2s=compute_P_from_essential(E) ind=-1 for i,P2 in enumerate(P2s): #Find the correct camera parameters d1=reconstruct_one_point(points1n[:,0],points2n[:,0],P1,P2) #Convert P2 from camera view to world view P2_homogenous=np.linalg.inv(np.vstack([P2,[0,0,0,1]])) d2=np.dot(P2_homogenous[:3,:4],d1) if d1[2]>0 and d2[2]>0: ind=i P2=np.linalg.inv(np.vstack([P2s[ind],[0,0,0,1]]))[:3,:4] Points3D=linear_triangulation(points1n,points2n,P1,P2) return Points3D def main(): CameraParam_Path='CameraParam.txt' CheckerboardImage_Path='Checkerboard_Image' Images_Path='SubstitutionCalibration_Image/*.jpg' #计算相机参数 camera_calibration(CameraParam_Path,CheckerboardImage_Path) #读取相机参数 config=readfromfile(CameraParam_Path) K=np.array(config['mtx']) #计算3D点 Points3D=epipolar_geometric(Images_Path,K) #重建3D点 fig=plt.figure() fig.suptitle('3D reconstructed',fontsize=16) ax=fig.gca(projection='3d') ax.plot(Points3D[0],Points3D[1],Points3D[2],'b.') ax.set_xlabel('x axis') ax.set_ylabel('y axis') ax.set_zlabel('z axis') ax.view_init(elev=135,azim=90) plt.savefig('Reconstruction.jpg') plt.show() if __name__=='__main__': main()
综上所述,这篇文章就给大家介绍到这里了,希望可以给大家带来帮助。
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