├── .gitattributes ├── requirements.txt ├── Data ├── 001.JPG ├── 002.JPG ├── 003.JPG ├── 004.JPG ├── 005.JPG ├── 006.JPG ├── 003.txt ├── 001.txt ├── 002.txt ├── 004.txt ├── 006.txt ├── 005.txt └── 物点坐标.TXT ├── Result ├── 002_result.JPG ├── 004_result.JPG ├── 光束法平差实际精度.csv ├── 光束法平差理论精度.csv └── 光束法平差结果.csv ├── BeamAdjustment.exe ├── README.md └── BeamAdjustment.py /.gitattributes: -------------------------------------------------------------------------------- 1 | BeamAdjustment.exe filter=lfs diff=lfs merge=lfs -text 2 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.17.1 2 | pandas==0.23.4 3 | opencv-python==4.1.1 4 | -------------------------------------------------------------------------------- /Data/001.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Stone-sy/BeamAdjustment/HEAD/Data/001.JPG -------------------------------------------------------------------------------- /Data/002.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Stone-sy/BeamAdjustment/HEAD/Data/002.JPG -------------------------------------------------------------------------------- /Data/003.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Stone-sy/BeamAdjustment/HEAD/Data/003.JPG -------------------------------------------------------------------------------- /Data/004.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Stone-sy/BeamAdjustment/HEAD/Data/004.JPG -------------------------------------------------------------------------------- /Data/005.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Stone-sy/BeamAdjustment/HEAD/Data/005.JPG -------------------------------------------------------------------------------- /Data/006.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Stone-sy/BeamAdjustment/HEAD/Data/006.JPG -------------------------------------------------------------------------------- /Result/002_result.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Stone-sy/BeamAdjustment/HEAD/Result/002_result.JPG -------------------------------------------------------------------------------- /Result/004_result.JPG: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Stone-sy/BeamAdjustment/HEAD/Result/004_result.JPG -------------------------------------------------------------------------------- /Result/光束法平差实际精度.csv: -------------------------------------------------------------------------------- 1 | ux(mm),uy(mm),uz(mm) 2 | 0.06191848953287761,0.06767077491694709,0.23857583785694797 3 | -------------------------------------------------------------------------------- /BeamAdjustment.exe: -------------------------------------------------------------------------------- 1 | version https://git-lfs.github.com/spec/v1 2 | oid sha256:da3380552956aecea3d5ab05a65cadc55751cf5ec1107a3cd46f34fa9c1a22d1 3 | size 374419057 4 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | BeamAdjustment 2 | ================= 3 | 2020年数字摄影测量实习--光束法平差 4 | 5 | **** 6 | ## 目录 7 | * 使用方法 8 | * [直接运行exe文件](#直接运行exe文件) 9 | * [运行源代码](#运行源代码) 10 | * 光束法平差内容 11 | * 文件结构 12 | 13 | 使用方法 14 | ------ 15 | 16 | #### 直接运行exe文件 17 | 点击BeamAdjustment.exe，稍微等1分钟左右，弹框会显示程序运行进度。程序结果输出到Result文件夹中。 18 | #### 运行源代码 19 | 1. 安装环境依赖 20 | ```shell 21 | pip install -r requirements.txt 22 | ``` 23 | 24 | 2. 运行代码 25 | ```shell 26 | python BeamAdjustment.py 27 | ``` 28 | 29 | 光束法平差内容 30 | ------ 31 | #### 后方交会 32 | 已知像点坐标和控制点坐标，利用像点坐标纠正、后方交会迭代，获得相片的外方位元素。 33 | #### 前方交会 34 | 已知相片的外方位元素和像点坐标，通过前方交会，获得加密点坐标。 35 | #### 光束法平差 36 | 已知相片的外方位元素、像点坐标、加密点坐标，以此为初值进行光束法平差的迭代。之所以进行后方交会和前方交会，就是为了获得更好的初值，不然迭代不收敛。光束法平差的结果是更加准确的相片的外方位元素和加密点坐标。 37 | #### 精度评定 38 | 将光束法平差的结果、理论精度、实际精度输出到Result文件夹中对应csv里。 39 | #### 核线 40 | 在相片002.jpg和相片004.jpg上绘制通过控制点4507点的同名核线。 41 | 42 | 文件结构 43 | ------ 44 | #### BeamAdjustment 45 | > Data ：存放已知的像点坐标、控制点坐标和相片
46 | > Result ：存放光束法平差输出结果、精度评定和核线绘制结果
47 | > BeamAdjustment.py ：源代码
48 | > BeamAdjustment.exe ：可执行程序
49 | > requirements.txt ：所需python包及版本
50 | > Readme.md ：使用须知
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3407,2662.387861582865,-130.3195434781257,-5415.435524821507,0.07099937932217981,0.06812147168735659,0.2494620955282304,0.268165590399518 94 | 3408,2659.5729564083867,-430.39478244465596,-5415.957754726815,0.0711714818516929,0.06953246852391846,0.24893608663185401,0.2680845374794628 95 | 3409,2660.3551942677877,-730.9472762298382,-5414.346036514143,0.07188085471223847,0.07344078192243443,0.2485749086897634,0.2689793504224157 96 | 2401,2658.872694098411,1785.8928008134246,-5907.146843399251,0.08120061886425835,0.12410669602978634,0.30165071741642696,0.33613861399906647 97 | 2402,2661.3318726984585,1365.6697384863958,-5909.99777915265,0.07898964154682668,0.10711096751575111,0.2993133242433727,0.32756768598791125 98 | 2403,2662.734111247288,1040.7911052101213,-5910.533326610328,0.07729489662428884,0.09526870330622862,0.29729895470426226,0.3216166900862838 99 | 2404,2663.1852992927393,624.7760119729677,-5912.254405848718,0.07672100652292076,0.08333893763838929,0.29556392716189617,0.31652729172687155 100 | 2405,2663.006732732241,285.39202722226025,-5914.332437582009,0.0767781539954339,0.07730781602431323,0.2946950614152759,0.3141919199660438 101 | 2406,2662.095859011626,22.802678538236112,-5914.922917032616,0.07737747608278539,0.0751278434408548,0.294300162361545,0.3134390725979251 102 | 2407,2662.4411747433023,-275.8984921790423,-5914.616023452251,0.0779973081877968,0.07482763013861603,0.2937463026860647,0.31300102980411554 103 | 2408,2659.5710663876953,-576.5421234142576,-5913.503930137984,0.07781455269119124,0.07741559612611794,0.29270423683172586,0.3126084602075501 104 | 4501,3684.1920325769734,1894.1552794002612,-4900.496518588496,0.07298822126932016,0.11191795057897157,0.22191725583088276,0.2590370177047401 105 | 4502,3685.36798056129,1678.791632415697,-4897.193794637091,0.07173676726739142,0.10307719464880634,0.22027362287551316,0.253557766138897 106 | 4503,3686.044836711808,1375.5671380969336,-4901.135203193287,0.07160985542701888,0.09275995467741709,0.21949546198155037,0.24881848487708716 107 | 4504,3686.8821625494847,1039.9332884425346,-4901.023300922032,0.07212939409975795,0.08300209833886531,0.21883659503608197,0.244911112750808 108 | 4505,3688.106224759176,671.46479557169,-4900.794714895975,0.07263826063192061,0.07399340555220739,0.21817287661829157,0.24155898878895923 109 | 4506,3687.74219848955,378.1929524233354,-4900.214548988074,0.07229573746613242,0.06857249314186924,0.217169757684177,0.23893841073395422 110 | 4507,3691.3401346776145,100.70685792527138,-4900.347313744168,0.07119036309968874,0.06497355550164491,0.2156622224787046,0.2362198656278619 111 | 4508,3690.1918126312007,-276.46806470297935,-4899.068042063992,0.07029698105342017,0.06326248257298296,0.21384693414846112,0.23382540172363256 112 | 4509,3686.574,-637.607,-4899.495,0.0,0.0,0.0,0.0 113 | 2501,3687.408828520514,1861.689744429033,-5893.718024277442,0.08455281328227006,0.1275509668295507,0.3059059741639162,0.3420480849270671 114 | 2502,3690.0344991155557,1588.1035455976667,-5897.61665830653,0.08519055859855917,0.1169182240188689,0.3054785828321716,0.33800069075552625 115 | 2503,3693.6971547242088,1257.031988737365,-5897.89691368442,0.08592487025626779,0.10474662388564923,0.30464471640693674,0.3334116701296777 116 | 2504,3693.664332462778,868.232996075526,-5898.724890692485,0.08560526551050666,0.09196532618369783,0.3031561255792038,0.328160812985634 117 | 2505,3693.440610373947,509.4078217279088,-5897.3729696010505,0.0841963407891984,0.0824583343208365,0.3009358837411025,0.32318850045181996 118 | 2506,3692.6080923751465,270.84009450832286,-5897.7082355786715,0.08295238933137054,0.0772774095421741,0.2992566562537193,0.32001162984099324 119 | 2507,3691.699365882393,-114.57422997682154,-5898.480427288475,0.08243341184652955,0.07341488809609774,0.2976714643714253,0.3174796275099409 120 | 2508,3686.9141109120783,-524.4470147944642,-5899.243491503492,0.08244459634860589,0.07578648773988986,0.2967116318859215,0.31714112897484553 121 | 2509,3687.9575902438855,-840.9099516903187,-5899.159483810468,0.08328159609910021,0.08165836734513941,0.2966534102500406,0.31875877873286296 122 | 2510,3686.20508648524,-1050.9374372665268,-5899.2428645081645,0.08429134627740692,0.08715154418227807,0.29690234014325323,0.3207045716748568 123 | 4601,4711.826,1827.08,-4889.11,0.0,0.0,0.0,0.0 124 | 4602,4711.141377641673,1662.6055853456166,-4885.624009419327,0.09812670341218875,0.1078371229472798,0.22967820517794288,0.2720473726060438 125 | 4603,4711.89893480846,1391.689941411771,-4888.490863261294,0.09620973943920265,0.09655843457249705,0.22756250653106952,0.2652631516602966 126 | 4604,4712.5542812153135,1071.2821117509466,-4888.223116188794,0.09487315671211308,0.08491672743503452,0.22569139653611053,0.25913003093662645 127 | 2601,4717.313,1864.731,-5892.505,0.0,0.0,0.0,0.0 128 | 2602,4719.103621209777,1551.85997803859,-5896.6571373218085,0.11309209616052106,0.11739314145965578,0.3134203905313003,0.3532751237725918 129 | 2603,4718.007595732464,1157.1328700682107,-5896.706111922863,0.11336279271158559,0.10287011725533159,0.31252359922356415,0.34800055153281945 130 | 2604,4718.310828793979,839.1248662213287,-5897.331938893943,0.11384605626703562,0.0930228182745185,0.3121469805655084,0.34503609481249203 131 | 2605,4717.190775376625,514.0043358193356,-5895.7601752443625,0.11322804800920078,0.08465145521179297,0.3105273695298808,0.34119452948808426 132 | 2606,4717.77521465486,167.45926448584328,-5896.7617431950475,0.11185513305822202,0.07814471543866597,0.3082647171098348,0.33711319042314125 133 | 2607,4718.745487215765,-137.59105017710826,-5896.682176445714,0.1107139557590322,0.07491278135556102,0.3063007143876378,0.33419998869631384 134 | 2608,4717.297075664844,-515.2921262443601,-5896.39563549368,0.11017246363952088,0.07648678009738016,0.30487991265866626,0.3330765083532949 135 | 2609,4718.157,-836.973,-5892.781,0.0,0.0,0.0,0.0 136 | ,dXs(mm),dYs(mm),dZs(mm),dPhi(rad),dOmega(rad),dKappa(rad) 137 | 0,0.03786054578639186,0.026132027293544587,0.06661825021517732,4.4325677607155e-06,4.279948910382423e-06,1.2701726420453941e-05 138 | 1,0.03508893646085696,0.026476725273102037,0.0650971129694318,4.458605088442256e-06,4.318076261394224e-06,1.2481759443134742e-05 139 | 2,0.02885505051256973,0.028185356688201206,0.06823027213982721,4.321160685485421e-06,4.206871158279976e-06,1.2628496798364786e-05 140 | 3,0.028638450752968638,0.0280390648214192,0.06843278541807138,4.333157141511511e-06,4.205637050988738e-06,1.2664786452126002e-05 141 | 4,0.04253225023606012,0.0265543960557857,0.07483200144179342,4.461560470891703e-06,4.276861752197547e-06,1.362612761588583e-05 142 | 5,0.04330422203716715,0.028070312593578842,0.0716722469795795,4.574475266228231e-06,4.360309847876432e-06,1.3250749093940557e-05 143 | -------------------------------------------------------------------------------- /BeamAdjustment.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Mon Jun 29 16:38:31 2020 4 | 5 | 数字摄影测量实习 6 | 光束法平差 7 | 1 后方交会 8 | 2 前方交会 9 | 3 光束法平差 10 | 4 精度评定 11 | 5 核线 12 | 13 | @author: shi'ying 14 | """ 15 | import numpy as np 16 | import math as m 17 | import pandas as pd 18 | import cv2 19 | 20 | path='./Data' 21 | result_path='./Result' 22 | 23 | #读取像点坐标 24 | def read_xy(file): 25 | file=path+'/'+file 26 | point=[[] for i in range(3)] 27 | with open(file,'r')as f: 28 | next(f) 29 | lines=f.readlines() 30 | for line in lines: 31 | a,b,c=[i for i in line.split()] 32 | point[0].append(a) #点号 33 | point[1].append(b) #x坐标 34 | point[2].append(c) #y坐标 35 | f.close() 36 | return point 37 | 38 | #读取物方点坐标 39 | def read_XY(file): 40 | file=path+'/'+file 41 | point=[[] for i in range(4)] 42 | with open(file,'r')as f: 43 | next(f) 44 | lines=f.readlines() 45 | for line in lines: 46 | a,b,c,d=[i for i in line.split()] 47 | point[0].append(a) #点号 48 | point[1].append(b) #x坐标 49 | point[2].append(c) #y坐标 50 | point[3].append(d) #z坐标 51 | f.close() 52 | return point 53 | 54 | #将像素坐标转换为像平面坐标，单位从像素变成mm 55 | def xyplane(p): 56 | x=p[1] 57 | y=p[2] 58 | #平移到像主点为原点的坐标系（此时还是像素坐标） 59 | x=[float(i)-5344.0/2 for i in x] 60 | y=[4008.0/2-float(i) for i in y] 61 | K1 = -5.00793e-009 62 | K2 = 1.83462e-016 63 | P1 = -2.24419e-008 64 | P2 = 1.76820e-008 65 | x0=47.48571 66 | y0=12.02756 67 | #畸变纠正(像素坐标) 68 | for i in range(0,len(x)): 69 | r2=(x[i]-x0)**2+(y[i]-y0)**2 70 | delx=(x[i]-x0)*(K1*r2+K2*r2**2)+P1*(r2+2*(x[i]-x0)**2)+2*P2*(x[i]-x0)*(y[i]-y0) 71 | dely=(y[i]-y0)*(K1*r2+K2*r2**2)+P2*(r2+2*(y[i]-y0)**2)+2*P1*(x[i]-x0)*(y[i]-y0) 72 | x[i]=x[i]-delx 73 | y[i]=y[i]-dely 74 | #转成mm单位 75 | x[i]=x[i]*25.4/300 76 | y[i]=y[i]*25.4/300 77 | p[1]=x 78 | p[2]=y 79 | return p 80 | 81 | #计算旋转矩阵R 82 | def calR(angle): #angle=[fi,w,k] 外方位元素 83 | fi=angle[0] 84 | w=angle[1] 85 | k=angle[2] 86 | a1=m.cos(fi)*m.cos(k)-m.sin(fi)*m.sin(w)*m.sin(k) 87 | a2=-m.cos(fi)*m.sin(k)-m.sin(fi)*m.sin(w)*m.cos(k) 88 | a3=-m.sin(fi)*m.cos(w) 89 | b1=m.cos(w)*m.sin(k) 90 | b2=m.cos(w)*m.cos(k) 91 | b3=-m.sin(w) 92 | c1=m.sin(fi)*m.cos(k)+m.cos(fi)*m.sin(w)*m.sin(k) 93 | c2=-m.sin(fi)*m.sin(k)+m.cos(fi)*m.sin(w)*m.cos(k) 94 | c3=m.cos(fi)*m.cos(w) 95 | #R=[[a1,a2,a3],[b1,b2,b3],[c1,c2,c3]] 96 | return a1,a2,a3,b1,b2,b3,c1,c2,c3 97 | 98 | #后方交会 99 | def BundleAdjustment(point1,pointXY): 100 | point1=xyplane(point1) #像素坐标转像平面坐标 101 | kzd=[2201,2209,4201,4211,4601,4509,2601,2609] 102 | kzdnum=[] 103 | 104 | for i in kzd: 105 | for k in range(0,len(point1[0])): 106 | if int(point1[0][k])==i: 107 | kzdnum.append(k) 108 | X=[float(pointXY[1][i]) for i in kzdnum] 109 | Y=[float(pointXY[2][i]) for i in kzdnum] 110 | Z=[float(pointXY[3][i]) for i in kzdnum]#提取出对应的物方点坐标 111 | x=[float(point1[1][i]) for i in kzdnum] 112 | y=[float(point1[2][i]) for i in kzdnum]#提取对应的像点坐标 113 | 114 | f=4547.93519*25.4/300 115 | x0=47.48571*25.4/300 116 | y0=12.02756*25.4/300 117 | 118 | #初值 119 | Xs=np.mean(np.array(X)) 120 | Ys=np.mean(np.array(Y)) 121 | Zs=-f 122 | fi=0 123 | w=0 124 | k=0 125 | 126 | angle=[fi,w,k] 127 | a1,a2,a3,b1,b2,b3,c1,c2,c3=calR(angle) 128 | count=0 129 | while True: 130 | angle=[fi,w,k] 131 | a1,a2,a3,b1,b2,b3,c1,c2,c3=calR(angle) 132 | A=[] 133 | #B=[] 134 | L=[] 135 | 136 | count+=1 137 | 138 | for i in range(0,len(kzdnum)): 139 | X0=a1*(X[i]-Xs)+b1*(Y[i]-Ys)+c1*(Z[i]-Zs) 140 | Y0=a2*(X[i]-Xs)+b2*(Y[i]-Ys)+c2*(Z[i]-Zs) 141 | Z0=a3*(X[i]-Xs)+b3*(Y[i]-Ys)+c3*(Z[i]-Zs) 142 | 143 | #计算像点坐标近似值 144 | x1=x0-f*X0/Z0 145 | y1=y0-f*Y0/Z0 146 | 147 | #计算误差矩阵 148 | a11=(a1*f+a3*(x[i]-x0))/Z0 149 | a12=(b1*f+b3*(x[i]-x0))/Z0 150 | a13=(c1*f+c3*(x[i]-x0))/Z0 151 | a14=(y[i]-y0)*m.sin(w)-((x[i]-x0)/f*((x[i]-x0)*m.cos(k)-(y[i]-y0)*m.sin(k))+f*m.cos(k))*m.cos(w) 152 | a15=-f*m.sin(k)-(x[i]-x0)/f*((x[i]-x0)*m.sin(k)+(y[i]-y0)*m.cos(k)) 153 | a16=y[i]-y0 154 | a21=(a2*f+a3*(y[i]-y0))/Z0 155 | a22=(b2*f+b3*(y[i]-y0))/Z0 156 | a23=(c2*f+c3*(y[i]-y0))/Z0 157 | a24=-(x[i]-x0)*m.sin(w)-((y[i]-y0)/f*((x[i]-x0)*m.cos(k)-(y[i]-y0)*m.sin(k))-f*m.sin(k))*m.cos(w) 158 | a25=-f*m.cos(k)-(y[i]-y0)/f*((x[i]-x0)*m.sin(k)+(y[i]-y0)*m.cos(k)) 159 | a26=-(x[i]-x0) 160 | 161 | A.append([a11,a12,a13,a14,a15,a16]) 162 | #B.append([-a11,-a12,-a13]) 163 | A.append([a21,a22,a23,a24,a25,a26]) 164 | #B.append([-a21,-a22,-a23]) 165 | 166 | l1=x[i]-x1 167 | l2=y[i]-y1 168 | L.append([l1]) 169 | L.append([l2]) 170 | 171 | #将A\B矩阵和L矩阵转换成numpy数组进行运算 172 | A=np.array(A) 173 | #B=np.array(B) 174 | L=np.array(L) 175 | 176 | #解法方程 177 | #t=(A.T@A-A.T@B@np.linalg.inv(B.T@B)@(B.T@A))@(A.T@L-A.T@B@np.linalg.inv(B.T@B)@(B.T@L)) 178 | t=np.matmul(np.matmul(np.linalg.inv(A.T@A),A.T),L) 179 | Xs+=float(t[0]) 180 | Ys+=float(t[1]) 181 | Zs+=float(t[2]) 182 | fi+=float(t[3]) 183 | w+=float(t[4]) 184 | k+=float(t[5]) 185 | if abs(t[3])<0.00001 and abs(t[4])<0.00001 and abs(t[5])<0.00001 or count>20: 186 | break 187 | 188 | return [Xs,Ys,Zs,fi,w,k],count 189 | 190 | #前方交会 191 | def FrontAdjustment(xy,allXs): 192 | f=4547.93519*25.4/300 193 | x0=47.48571*25.4/300 194 | y0=12.02756*25.4/300 195 | 196 | wf=[] 197 | for i in range(0,len(xy[0][0])): 198 | B=[] 199 | L=[] 200 | for n in range(0,len(allXs)): 201 | a1,a2,a3,b1,b2,b3,c1,c2,c3=calR(allXs[n][3:]) 202 | x1=xy[n][0][i]-x0 203 | y1=xy[n][1][i]-y0 204 | l1=f*a1+x1*a3 205 | l2=f*b1+x1*b3 206 | l3=f*c1+x1*c3 207 | lx=l1*allXs[n][0]+l2*allXs[n][1]+l3*allXs[n][2] 208 | l4=f*a2+y1*a3 209 | l5=f*b2+y1*b3 210 | l6=f*c2+y1*c3 211 | ly=l4*allXs[n][0]+l5*allXs[n][1]+l6*allXs[n][2] 212 | B.append([l1,l2,l3]) 213 | B.append([l4,l5,l6]) 214 | L.append([lx]) 215 | L.append([ly]) 216 | B=np.array(B) 217 | L=np.array(L) 218 | wf.append([i[0] for i in (np.linalg.inv(B.T@B)@B.T@L).tolist()]) 219 | WuFang=[] 220 | kzd=[2201,2209,4201,4211,4601,4509,2601,2609] 221 | for i in range(0,len(point1[0])): 222 | if (int(point1[0][i]) in kzd): 223 | WuFang.append([pointXY[0][i],float(pointXY[1][i]),float(pointXY[2][i]),float(pointXY[3][i])]) 224 | else: 225 | WuFang.append([point1[0][i],wf[i][0],wf[i][1],wf[i][2]]) 226 | return WuFang 227 | 228 | #光束法平差 229 | def BeamAdjustment(WuFang, nxy, allXs): 230 | count=0 231 | while True: 232 | A=np.zeros((2*6*132,6*6)) 233 | B=np.zeros((2*6*132,124*3)) 234 | L=np.zeros((2*6*132,1)) 235 | for n in range(0,len(allXs)): 236 | Xs,Ys,Zs=allXs[n][0:3] 237 | fi,w,k=allXs[n][3:] 238 | a1,a2,a3,b1,b2,b3,c1,c2,c3=calR([fi,w,k]) 239 | A1=[] 240 | B2=np.zeros((2*132,124*3)) 241 | L1=[] 242 | k=0 243 | 244 | for i in range(0,len(xy[0][0])): 245 | B1=[] 246 | #提取第一张相片的第一个点 247 | x=nxy[n][1][i] 248 | y=nxy[n][2][i] 249 | X=WuFang[i][1] 250 | Y=WuFang[i][2] 251 | Z=WuFang[i][3] 252 | X0=a1*(X-Xs)+b1*(Y-Ys)+c1*(Z-Zs) 253 | Y0=a2*(X-Xs)+b2*(Y-Ys)+c2*(Z-Zs) 254 | Z0=a3*(X-Xs)+b3*(Y-Ys)+c3*(Z-Zs) 255 | 256 | #计算像点坐标近似值 257 | x1=x0-f*X0/Z0 258 | y1=y0-f*Y0/Z0 259 | 260 | #计算误差矩阵 261 | a11=(a1*f+a3*(x-x0))/Z0 262 | a12=(b1*f+b3*(x-x0))/Z0 263 | a13=(c1*f+c3*(x-x0))/Z0 264 | a14=(y-y0)*m.sin(w)-((x-x0)/f*((x-x0)*m.cos(k)-(y-y0)*m.sin(k))+f*m.cos(k))*m.cos(w) 265 | a15=-f*m.sin(k)-(x-x0)/f*((x-x0)*m.sin(k)+(y-y0)*m.cos(k)) 266 | a16=y-y0 267 | a21=(a2*f+a3*(y-y0))/Z0 268 | a22=(b2*f+b3*(y-y0))/Z0 269 | a23=(c2*f+c3*(y-y0))/Z0 270 | a24=-(x-x0)*m.sin(w)-((y-y0)/f*((x-x0)*m.cos(k)-(y-y0)*m.sin(k))-f*m.sin(k))*m.cos(w) 271 | a25=-f*m.cos(k)-(y-y0)/f*((x-x0)*m.sin(k)+(y-y0)*m.cos(k)) 272 | a26=-(x-x0) 273 | 274 | A1.append([a11,a12,a13,a14,a15,a16]) 275 | A1.append([a21,a22,a23,a24,a25,a26]) 276 | B1.append([-a11,-a12,-a13]) 277 | B1.append([-a21,-a22,-a23]) 278 | 279 | l1=x-x1 280 | l2=y-y1 281 | L1.append([l1]) 282 | L1.append([l2]) 283 | #循环一张相片后，得到一张相片132个点的264个方程的系数，都是 284 | if (int(nxy[0][0][i]) not in kzd): 285 | B2[2*i:2*i+2,3*k:3*k+3]=np.array(B1).reshape(2,3) 286 | k+=1 287 | A[2*132*n:2*132*(n+1),6*n:6*(n+1)]=A1 288 | B[2*132*n:2*132*(n+1),0:124*3]=B2 289 | L[2*132*n:2*132*(n+1),0:1]=L1 290 | 291 | N11=A.T@A 292 | N12=A.T@B 293 | N22=B.T@B 294 | u1=A.T@L 295 | u2=B.T@L 296 | a=N11-N12@np.linalg.inv(N22)@N12.T 297 | b=u1-N12@np.linalg.inv(N22)@u2 298 | t_=np.linalg.inv(a)@b 299 | c=N22-N12.T@np.linalg.inv(N11)@N12 300 | d=u2-N12.T@np.linalg.inv(N11)@u1 301 | X_=np.linalg.inv(c)@d 302 | 303 | #处理解 304 | for i in range(0,6): 305 | allXs[i][0]+=t_[6*i] 306 | allXs[i][1]+=t_[6*i+1] 307 | allXs[i][2]+=t_[6*i+2] 308 | allXs[i][3]+=t_[6*i+3] 309 | allXs[i][4]+=t_[6*i+4] 310 | allXs[i][5]+=t_[6*i+5] 311 | 312 | j=0 313 | for i in range(0,len(xy[0][0])): 314 | if (int(nxy[0][0][i]) not in kzd): 315 | WuFang[i][1]+=float(X_[j*3]) 316 | WuFang[i][2]+=float(X_[j*3+1]) 317 | WuFang[i][3]+=float(X_[j*3+2]) 318 | j+=1 319 | 320 | sum1=0.0 321 | for i in range(0,int(len(t_)/6)): 322 | sum1+=sum(t_[6*i+3:6*i+6]) #线元素之和 323 | #print('第{}次迭代'.format(count)) 324 | count+=1 325 | 326 | if sum1<0.00001: 327 | break 328 | 329 | print("光束法平差 迭代了 {}次，其结果已写入Result文件夹 ：".format(count)) 330 | 331 | #坐标差 332 | ZuoBiaoCha(WuFang, pointXY,allXs) 333 | 334 | #理论精度 335 | accuracy_eval(np.hstack((A,B)),np.vstack((t_,X_)),L) 336 | 337 | #实际精度 338 | autual_eval(WuFang,pointXY) 339 | 340 | return WuFang,allXs 341 | 342 | #坐标差 343 | def ZuoBiaoCha(WuFang,pointXY,allXs): 344 | p=np.array(pointXY,dtype=np.float).T 345 | WuFang=np.array(WuFang,dtype=np.float) 346 | temp=np.zeros((WuFang.shape[0],WuFang.shape[1]+4),dtype=np.float) 347 | temp[:,4:7]=WuFang[:,1:]-p[:,1:] 348 | temp[:,0:4]=WuFang[:,:] 349 | temp[:,7]=np.sqrt(temp[:,4]**2+temp[:,5]**2+temp[:,6]**2) 350 | data=pd.DataFrame(temp) 351 | data.to_csv(result_path+'/光束法平差结果.csv',header=['Point','X(mm)','Y(mm)','Z(mm)','dX(mm)','dY(mm)','dZ(mm)','dP(mm)'],index=False) 352 | data4=pd.DataFrame(np.array(np.array(allXs).reshape(6,6),dtype=np.float)) 353 | data4.to_csv(result_path+'/光束法平差结果.csv',mode='a',header=['Xs(mm)','Ys(mm)','Zs(mm)','Phi(rad)','Omega(rad)','Kappa(rad)']) 354 | print("【光束法平差结果】：加密点坐标（含计算坐标与已知坐标的差）和外方位元素") 355 | 356 | #理论精度 357 | def accuracy_eval(B,X,L): 358 | V=B@X-L 359 | Qii=np.linalg.inv(B.T@B) 360 | #中误差 361 | m0=m.sqrt(V.T@V/(np.shape(B)[0]-np.shape(X)[0])) 362 | theory_m=m0*np.array([m.sqrt(i) for i in Qii.diagonal()]) 363 | temp1=[] 364 | for i in range(0,6): 365 | temp1.append(theory_m[6*i:6*i+6]) 366 | temp2=[] 367 | for i in range(0,124): 368 | temp2.append(theory_m[36+3*i:36+3*i+3]) 369 | for i in range(0,len(point1[0])): 370 | if(int(point1[0][i]) in kzd): 371 | temp2=np.insert(temp2,i,np.zeros((1,3)),axis=0) 372 | delta=np.zeros((len(WuFang),4)) 373 | delta[:,0:3]=np.array(temp2) 374 | delta[:,3]=[m.sqrt(i[0]**2+i[1]**2+i[2]**2) for i in delta[:,0:3]] 375 | m0=pd.DataFrame(np.array([m0])) 376 | m0.to_csv(result_path+'/光束法平差理论精度.csv',header=['Mean square error'],index=False) 377 | data1=np.hstack((WuFang,delta)) 378 | data1=pd.DataFrame(data1) 379 | data1.to_csv(result_path+'/光束法平差理论精度.csv',mode='a',header=['Point','X(mm)','Y(mm)','Z(mm)','dX(mm)','dY(mm)','dZ(mm)','dP(mm)'],index=False) 380 | data2=pd.DataFrame(temp1) 381 | data2.to_csv(result_path+'/光束法平差理论精度.csv',mode='a',header=['dXs(mm)','dYs(mm)','dZs(mm)','dPhi(rad)','dOmega(rad)','dKappa(rad)']) 382 | print("【光束法平差理论精度】：中误差、所有点和外方位元素的理论精度") 383 | 384 | #实际精度 385 | def autual_eval(WuFang,pointXY): 386 | x1=np.array(WuFang).astype(float)[:,1:] 387 | x2=np.array(pointXY).astype(float).T[:,1:] 388 | x3=(x1-x2)**2 389 | ux=sum(x3[:,0])/np.shape(x3)[0] 390 | uy=sum(x3[:,1])/np.shape(x3)[0] 391 | uz=sum(x3[:,2])/np.shape(x3)[0] 392 | data3=pd.DataFrame(np.array([ux,uy,uz]).reshape(1,3)) 393 | data3.to_csv(result_path+'/光束法平差实际精度.csv',header=['ux(mm)','uy(mm)','uz(mm)'],index=False) 394 | print("【光束法平差实际精度】：所有点的实际精度") 395 | 396 | def get_AB(R,x,y,f): 397 | v=R[1,0]*x+R[1,1]*y-R[1,2]*f 398 | w=R[2,0]*x+R[2,1]*y-R[2,2]*f 399 | A=v*R[2,0]-w*R[1,0] 400 | B=w*R[1,1]-v*R[2,1] 401 | C=w*R[1,2]-v*R[2,2] 402 | A_=A/B 403 | B_=C/B 404 | x0=47.48571 405 | y0=12.02756 406 | f=4547.93519 407 | #从以x0,y0为原点的坐标系，移到以相片中心为原点的坐标系 408 | B_=B_*f+y0-A_*x0 409 | return A_,B_ 410 | 411 | #获得直线方程 412 | def img_line(A,B,x,style=0): 413 | if style==0: 414 | return A*x+B #以x为自变量 415 | else: 416 | return (x-B)/A #以y为自变量 417 | 418 | #画线（不包括点和标注） 419 | def draw_line(A,B,linefunction,img): 420 | height=img.shape[0] 421 | width=img.shape[1] 422 | point=[] 423 | #画第一个点 424 | x1=width/2 425 | y1=img_line(A,B,x1,0) 426 | if abs(y1)<=height/2: 427 | point.append([x1,y1]) 428 | 429 | x2=-(width/2) 430 | y2=img_line(A,B,x2,0) 431 | if abs(y2)<=height/2: 432 | point.append([x2,y2]) 433 | #画第二个点 434 | y3=height/2 435 | x3=img_line(A,B,y3,1) 436 | if abs(x3)<=width/2: 437 | point.append([x3,y3]) 438 | 439 | y4=-(height/2) 440 | x4=img_line(A,B,y4,1) 441 | if abs(x4)<=width/2: 442 | point.append([x4,y4]) 443 | 444 | point=np.array(point,dtype=np.float) 445 | point[:,1]=-point[:,1] 446 | point=point+np.array([width/2,height/2]) 447 | #从像主点坐标系转换到以左上角为原点的坐标系 448 | px1=int(round(point[0,0])) 449 | py1=int(round(point[0,1])) 450 | px2=int(round(point[1,0])) 451 | py2=int(round(point[1,1])) 452 | 453 | new_img=cv2.line(img,(px1,py1),(px2,py2),(0,0,255),4,4) 454 | return new_img 455 | 456 | 457 | #核线绘制（含点和标注） 458 | def Nuclear_line(img_file,points,Xs): 459 | img=cv2.imread(img_file) 460 | #读取002.jpg 461 | #读取以像主点为原点的坐标002和004 462 | for i in range(0,len(points[0])): 463 | if int(points[0][i])==4507: 464 | #获得左相片p点 以像主点为原点的坐标（mm） 465 | xp=points[1][i] 466 | yp=points[2][i] 467 | 468 | #旋转矩阵 469 | a1,a2,a3,b1,b2,b3,c1,c2,c3=calR(Xs[3:]) 470 | R1=np.array([[a1,a2,a3],[b1,b2,b3],[c1,c2,c3]]) 471 | #获得A、B矩阵 472 | L_A,L_B=get_AB(R1, xp, yp, f) 473 | #画经过4507点的核线 474 | new_img=draw_line(L_A, L_B, img_line, img) 475 | xp_pixel=int(round(xp*300/25.4+5344.0/2)) 476 | yp_pixel=int(round(4008.0/2-yp*300/25.4)) 477 | #标注4507号点 478 | new_img=cv2.circle(new_img,(xp_pixel,yp_pixel),16,(0,255,0),-1) 479 | new_img=cv2.putText(new_img, '4507', (xp_pixel+20,yp_pixel+20),cv2.FONT_HERSHEY_COMPLEX, 5, (255, 255, 255), 2) 480 | cv2.waitKey(30) 481 | cv2.imwrite(result_path+"/{}_result.JPG".format(img_file[-7:-4]), new_img) 482 | print("已绘制相片{}的核线".format(img_file[-7:-4])) 483 | 484 | 485 | # 控制点：2201、2209、4201、4211、4601、4509、2601、2609 486 | if __name__=='__main__': 487 | #已知条件 488 | f=4547.93519*25.4/300 489 | x0=47.48571*25.4/300 490 | y0=12.02756*25.4/300 491 | kzd=[2201,2209,4201,4211,4601,4509,2601,2609] 492 | 493 | #读取像点坐标和已知点物方坐标 494 | point1=read_xy('001.txt') 495 | point2=read_xy('002.txt') 496 | point3=read_xy('003.txt') 497 | point4=read_xy('004.txt') 498 | point5=read_xy('005.txt') 499 | point6=read_xy('006.txt') 500 | pointXY=read_XY('物点坐标.TXT') 501 | 502 | #后方交会 503 | Xs1,count=BundleAdjustment(point1, pointXY) 504 | print("相片001 后方交会 迭代了 {}次".format(count)) 505 | Xs2,count=BundleAdjustment(point2, pointXY) 506 | print("相片002 后方交会 迭代了 {}次".format(count)) 507 | Xs3,count=BundleAdjustment(point3, pointXY) 508 | print("相片003 后方交会 迭代了 {}次".format(count)) 509 | Xs4,count=BundleAdjustment(point4, pointXY) 510 | print("相片004 后方交会 迭代了 {}次".format(count)) 511 | Xs5,count=BundleAdjustment(point5, pointXY) 512 | print("相片005 后方交会 迭代了 {}次".format(count)) 513 | Xs6,count=BundleAdjustment(point6, pointXY) 514 | print("相片006 后方交会 迭代了 {}次".format(count)) 515 | 516 | nxy=[] 517 | #这里point1-6已经进行了像平面坐标的转化 518 | nxy.append(point1[:]) 519 | nxy.append(point2[:]) 520 | nxy.append(point3[:]) 521 | nxy.append(point4[:]) 522 | nxy.append(point5[:]) 523 | nxy.append(point6[:]) 524 | # nxy 表示点号、x、y 525 | # xy 表示坐标x、y 526 | xy=[i[1:] for i in nxy] 527 | allXs=[] 528 | allXs=[Xs1,Xs2,Xs3,Xs4,Xs5,Xs6] 529 | 530 | #前方交会 531 | WuFang=FrontAdjustment(xy,allXs) 532 | 533 | #光束法平差 534 | WuFang,allXs=BeamAdjustment(WuFang, nxy, allXs) 535 | 536 | #绘制002.JPG的核线 537 | img_file=path+'/002.JPG' 538 | points=point2 539 | Xs=Xs2 540 | Nuclear_line(img_file, points, Xs) 541 | 542 | #绘制004.JPG的核线 543 | img_file=path+'/004.JPG' 544 | points=point4 545 | Xs=Xs4 546 | Nuclear_line(img_file, points, Xs) 547 | 548 | 549 | 550 | 551 | 552 | -------------------------------------------------------------------------------- /Result/光束法平差结果.csv: -------------------------------------------------------------------------------- 1 | Point,X(mm),Y(mm),Z(mm),dX(mm),dY(mm),dZ(mm),dP(mm) 2 | 4201.0,599.3,1894.335,-4929.802,0.0,0.0,0.0,0.0 3 | 4202.0,597.4807127339438,1649.8187344649068,-4926.314898104122,0.1687127339438348,-0.1732655350931509,0.551101895877764,0.6018290719847165 4 | 4203.0,600.794020230566,1379.7254682836738,-4930.780164382091,0.06502023056600592,-0.29353171632624253,0.3018356179090915,0.42602023321768345 5 | 4204.0,600.9251193386392,930.8377561266486,-4930.067645958258,0.2711193386392097,-0.4582438733514209,0.4153540417419208,0.675286697069937 6 | 4205.0,599.9871630050367,474.7211012665536,-4929.884872837337,0.09516300503662478,-0.5528987334463977,-0.07787283733705408,0.5664072614022129 7 | 4206.0,600.0822645221818,191.69623714982131,-4928.903301049138,-0.0017354778181015718,-0.5747628501786721,0.12469895086269389,0.5881370369012858 8 | 4207.0,598.4953954816692,14.215080836992335,-4929.001820025329,-0.061604518330796054,-0.4119191630076653,0.23717997467065288,0.47929829325429113 9 | 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