Visual SLAM講座14 ～ch7の実践操作とピット回避～
1. 実運用前の準備
- ターミナルでch7フォルダに入り、以下のコマンドを実行してコンパイルします。
mkdir build
cd build
cmake ..
//注意，j8还是其他主要看自己的电脑情况
make -j8
- ビルドファイル内で実行します。
注:メイクプロセス中にいくつかの警告が表示される場合がありますが、終了には影響しません。
2. 練習プロセス
2.1 特徴抽出とマッチング
1. OpenCV の ORB 機能
ビルド内のステートメントを実行します: ./orb_cv /home/fighter/slam/slambook2/ch7/1.png /home/fighter/slam/slambook2/ch7/2.png (注: 不要なものを避けるために絶対パスを直接使用することをお勧めします)
ここでの画像パラメーターはターミナルで渡されますが、渡したくない場合は、コードを変更してコードから直接取得することもできます。
実行結果:
ウィンドウが表示されている間、ターミナルは次の情報を出力します。
extract ORB cost = 0.194423 seconds.
match ORB cost = 0.03702 seconds.
-- Max dist : 94.000000
-- Min dist : 4.000000
2. 手書き ORB 機能
ビルド内でステートメントを実行します: ./orb_self (注: 操作が間違っていてピクチャが見つからない場合は、忘れずに orb_self.cpp ファイル内のピクチャ パスを変更してください)
実行結果:
同時に、端末はいくつかの機能の一致した情報を出力します:
bad/total: 43/638
bad/total: 8/595
extract ORB cost = 0.0047491 seconds.
match ORB cost = 0.0013752 seconds.
matches: 41
done.
特徴抽出には 4.7 ミリ秒しかかからず、マッチングには 1.3 ミリ秒しかかからなかったことがわかります。
2.2 エピポーラ幾何学
ビルドでステートメントを実行します: ./pose_estimation_2d2d /home/fighter/slam/slambook2/ch7/1.png /home/fighter/slam/slambook2/ch7/2.png
実行結果:
-- Max dist : 94.000000
-- Min dist : 4.000000
一共找到了79组匹配点
fundamental_matrix is
[4.54443750398184e-06, 0.0001333855576992603, -0.01798499246479044;
-0.0001275657012964255, 2.266794804645652e-05, -0.01416678429206633;
0.01814994639971766, 0.004146055870980492, 1]
essential_matrix is
[-0.008455114492964278, 0.05451570701059781, 0.1546375809484052;
-0.008287154708445212, 0.03351311565984172, -0.6896472136971504;
-0.1153993974485718, 0.6945899967012867, 0.02159624094256633]
homography_matrix is
[0.9261214237658335, -0.1445322040802305, 33.26921164265664;
0.04535424230636757, 0.9386696658342905, 8.570980713233848;
-1.006198269424755e-05, -3.008140685985328e-05, 1]
R is
[0.9956584940813579, -0.05615340406690447, 0.07423582945816433;
0.05268846331440004, 0.9974645001566195, 0.04783823534446425;
-0.07673388428334535, -0.0437191735855581, 0.9960926386957119]
t is
[-0.9726703113454949;
-0.2153829834753195;
0.08673313009645391]
t^R=
[0.01195733758736675, -0.07709685221674556, -0.2186905642298021;
0.01171980658216709, -0.04739470268352609, 0.9753084428633267;
0.1631993929614534, -0.9822985936236425, -0.03054169683725466]
epipolar constraint = [-0.0005617285518606241]
epipolar constraint = [0.002891683190146016]
epipolar constraint = [-0.0001941259398173245]
epipolar constraint = [0.003462947761727536]
epipolar constraint = [8.120001470268701e-06]
epipolar constraint = [0.002710644239222917]
epipolar constraint = [-0.001869251694575136]
epipolar constraint = [0.00139456385994044]
epipolar constraint = [0.001761227647336161]
epipolar constraint = [1.869571731462349e-06]
epipolar constraint = [-0.004004668139513667]
epipolar constraint = [0.002638369853227809]
epipolar constraint = [4.71865360582302e-06]
epipolar constraint = [0.002768559038208648]
epipolar constraint = [0.001230260274886132]
epipolar constraint = [-9.947439380264544e-07]
epipolar constraint = [-0.0002841959014567297]
epipolar constraint = [0.001139813094577335]
epipolar constraint = [0.002209250772744531]
epipolar constraint = [0.002850233510438394]
epipolar constraint = [-0.0009898447085951168]
epipolar constraint = [-5.94020961628694e-05]
epipolar constraint = [0.001896654223267416]
epipolar constraint = [0.001705775496444906]
epipolar constraint = [7.876819478169761e-06]
epipolar constraint = [-0.0001806518038049848]
epipolar constraint = [0.003204291493829357]
epipolar constraint = [-0.0006467971601622075]
epipolar constraint = [-0.0008445761906836491]
epipolar constraint = [-0.0002847958262359729]
epipolar constraint = [0.001126554642501526]
epipolar constraint = [0.001484320538271348]
epipolar constraint = [-0.002115865517619359]
epipolar constraint = [0.004033028487439499]
epipolar constraint = [0.000665497431603157]
epipolar constraint = [0.0005557219318915591]
epipolar constraint = [0.0007972861772887335]
epipolar constraint = [-0.001440067047765337]
epipolar constraint = [0.0003964624006285444]
epipolar constraint = [-0.0003556864447603059]
epipolar constraint = [0.001175277405117692]
epipolar constraint = [-0.001470809843158782]
epipolar constraint = [-0.0007155680024874544]
epipolar constraint = [0.003041807422365872]
epipolar constraint = [-0.000355171981926361]
epipolar constraint = [-7.065871935545143e-05]
epipolar constraint = [0.001191022361631378]
epipolar constraint = [-0.0007055167484525393]
epipolar constraint = [0.0004088281809971096]
epipolar constraint = [-0.000892980090616162]
epipolar constraint = [0.001026346981193374]
epipolar constraint = [0.001502989651308746]
epipolar constraint = [-0.001131199458784926]
epipolar constraint = [-0.003495250951856245]
epipolar constraint = [-0.0002070785253527191]
epipolar constraint = [0.0004211619083240026]
epipolar constraint = [0.004030229295353918]
epipolar constraint = [0.002423184222031846]
epipolar constraint = [-0.001799922916276332]
epipolar constraint = [0.00214236066535746]
epipolar constraint = [-0.001604766538417207]
epipolar constraint = [-0.00156708990112403]
epipolar constraint = [-0.002429487992740217]
epipolar constraint = [0.000401189111909464]
epipolar constraint = [-0.001494836576249617]
epipolar constraint = [-0.0003175435711454538]
epipolar constraint = [-0.007289352381657122]
epipolar constraint = [-0.003396636093576003]
epipolar constraint = [-0.004063091392346646]
epipolar constraint = [-0.00269429995497647]
epipolar constraint = [-0.003170213468765316]
epipolar constraint = [0.001227259432891176]
epipolar constraint = [-0.001403642253683501]
epipolar constraint = [0.006666696972492035]
epipolar constraint = [0.005653889777384447]
epipolar constraint = [0.0008830143247820065]
epipolar constraint = [-0.001103292290051336]
epipolar constraint = [-0.003982708195313309]
epipolar constraint = [-0.0053874915375101]
2.3 三角測量
ビルドでステートメントを実行します: ./triangulation /home/fighter/slam/slambook2/ch7/1.png /home/fighter/slam/slambook2/ch7/2.png
実行結果:
実行中、ターミナルは次のように出力します。
-- Max dist : 94.000000
-- Min dist : 4.000000
一共找到了79组匹配点
depth: 8.95119
depth: 8.06918
depth: 10.3294
depth: 8.6317
depth: 8.17645
depth: 7.37573
depth: 11.8396
depth: 8.05226
depth: 8.08888
depth: 8.92732
depth: 8.17315
depth: 7.03846
depth: 8.17768
depth: 7.93303
depth: 9.0064
depth: 10.0725
depth: 12.2046
depth: 12.348
depth: 7.31126
depth: 8.03819
depth: 8.11742
depth: 8.80592
depth: 7.29207
depth: 10.3618
depth: 8.15175
depth: 8.25446
depth: 9.02341
depth: 7.1761
depth: 10.0498
depth: 10.0789
depth: 8.20269
depth: 8.74983
depth: 8.19139
depth: 8.55992
depth: 8.14039
depth: 7.13569
depth: 7.10316
depth: 11.8327
depth: 8.07921
depth: 10.1274
depth: 10.2183
depth: 11.9348
depth: 8.01726
depth: 8.23094
depth: 8.88643
depth: 8.25303
depth: 8.73167
depth: 8.71261
depth: 9.22772
depth: 8.76106
depth: 8.31465
depth: 8.92808
depth: 10.2404
depth: 8.44863
depth: 9.06756
depth: 8.10639
depth: 8.40526
depth: 8.74884
depth: 8.92165
depth: 9.13693
depth: 7.0544
depth: 8.8007
depth: 7.85402
depth: 8.72166
depth: 9.82223
depth: 10.5516
depth: 6.73889
depth: 8.60173
depth: 6.64584
depth: 6.66798
depth: 8.73605
depth: 8.18344
depth: 9.19246
depth: 8.94078
depth: 9.11707
depth: 8.76513
depth: 8.22164
depth: 7.17891
depth: 8.70631
2.4 PnP の解決
ビルドでステートメントを実行します: ./pose_estimation_3d2d /home/fighter/slam/slambook2/ch7/1.png /home/fighter/slam/slambook2/ch7/2.png /home/fighter/slam/slambook2/ch7/1_ Depth。 png /home/fighter/slam/slambook2/ch7/2_ Depth.png (注: ここでのパラメータは 4 つ、2 つの画像、2 つの対応する深度画像です)
実行結果:
-- Max dist : 94.000000
-- Min dist : 4.000000
一共找到了79组匹配点
3d-2d pairs: 75
solve pnp in opencv cost time: 0.0552358 seconds.
R=
[0.9979059095501289, -0.05091940089111062, 0.03988747043647115;
0.04981866254254162, 0.9983623157438141, 0.02812094175381178;
-0.04125404886071617, -0.02607491352889358, 0.9988083912027663]
t=
[-0.1267821389556796;
-0.008439496817594587;
0.06034935748886031]
calling bundle adjustment by gauss newton
iteration 0 cost=40517.7576706
iteration 1 cost=410.547029116
iteration 2 cost=299.76468142
iteration 3 cost=299.763574327
pose by g-n:
0.997905909549 -0.0509194008562 0.0398874705187 -0.126782139096
0.049818662505 0.998362315745 0.0281209417649 -0.00843949683874
-0.0412540489424 -0.0260749135374 0.998808391199 0.0603493575229
0 0 0 1
solve pnp by gauss newton cost time: 0.0001665 seconds.
calling bundle adjustment by g2o
iteration= 0 chi2= 410.547029 time= 2.15e-05 cumTime= 2.15e-05 edges= 75 schur= 0
iteration= 1 chi2= 299.764681 time= 1.26e-05 cumTime= 3.41e-05 edges= 75 schur= 0
iteration= 2 chi2= 299.763574 time= 1.19e-05 cumTime= 4.6e-05 edges= 75 schur= 0
iteration= 3 chi2= 299.763574 time= 1.14e-05 cumTime= 5.74e-05 edges= 75 schur= 0
iteration= 4 chi2= 299.763574 time= 1.23e-05 cumTime= 6.97e-05 edges= 75 schur= 0
iteration= 5 chi2= 299.763574 time= 1.36e-05 cumTime= 8.33e-05 edges= 75 schur= 0
iteration= 6 chi2= 299.763574 time= 1.13e-05 cumTime= 9.46e-05 edges= 75 schur= 0
iteration= 7 chi2= 299.763574 time= 1.23e-05 cumTime= 0.0001069 edges= 75 schur= 0
iteration= 8 chi2= 299.763574 time= 1.14e-05 cumTime= 0.0001183 edges= 75 schur= 0
iteration= 9 chi2= 299.763574 time= 1.28e-05 cumTime= 0.0001311 edges= 75 schur= 0
optimization costs time: 0.001120401 seconds.
pose estimated by g2o =
0.99790590955 -0.0509194008911 0.0398874704367 -0.126782138956
0.0498186625425 0.998362315744 0.0281209417542 -0.00843949681823
-0.0412540488609 -0.0260749135293 0.998808391203 0.0603493574888
0 0 0 1
solve pnp by g2o cost time: 0.001277401 seconds.
2.5 ICPを解く
ビルドでステートメントを実行します: ./pose_estimation_3d3d /home/fighter/slam/slambook2/ch7/1.png /home/fighter/slam/slambook2/ch7/2.png /home/fighter/slam/slambook2/ch7/1_ Depth。 png /home/fighter/slam/
slambook2/ch7/2_ Depth.png 実行結果:
- 呼び出しバンドル調整を境界として、呼び出しバンドル調整より上の演算結果は SVD 法によって解決され、呼び出しバンドル調整の次の部分は非線形最適化メソッドです。
-- Max dist : 94.000000
-- Min dist : 4.000000
一共找到了79组匹配点
3d-3d pairs: 72
W= 10.871 -1.01948 2.54771
-2.16033 3.85307 -5.77742
3.94738 -5.79979 9.62203
U= 0.558087 -0.829399 -0.0252034
-0.428009 -0.313755 0.847565
0.710878 0.462228 0.530093
V= 0.617887 -0.784771 -0.0484806
-0.399894 -0.366747 0.839989
0.676979 0.499631 0.540434
ICP via SVD results:
R = [0.9969452351705235, 0.0598334759429696, -0.05020112774999549;
-0.05932607556034211, 0.9981719680327525, 0.01153858709846634;
0.05079975225724825, -0.008525103530306, 0.9986724727258676]
t = [0.1441598281917405;
-0.06667849447794799;
-0.03009747343724256]
R_inv = [0.9969452351705235, -0.05932607556034211, 0.05079975225724825;
0.0598334759429696, 0.9981719680327525, -0.008525103530306;
-0.05020112774999549, 0.01153858709846634, 0.9986724727258676]
t_inv = [-0.1461462830262246;
0.0576744363694081;
0.03806387978797152]
calling bundle adjustment
iteration= 0 chi2= 1.816112 time= 2.33e-05 cumTime= 2.33e-05 edges= 72 schur= 0 lambda= 0.000758 levenbergIter= 1
iteration= 1 chi2= 1.815514 time= 2.02e-05 cumTime= 4.35e-05 edges= 72 schur= 0 lambda= 0.000505 levenbergIter= 1
iteration= 2 chi2= 1.815514 time= 1.24e-05 cumTime= 5.59e-05 edges= 72 schur= 0 lambda= 0.000337 levenbergIter= 1
iteration= 3 chi2= 1.815514 time= 1.09e-05 cumTime= 6.68e-05 edges= 72 schur= 0 lambda= 0.000225 levenbergIter= 1
iteration= 4 chi2= 1.815514 time= 2.02e-05 cumTime= 8.7e-05 edges= 72 schur= 0 lambda= 0.000150 levenbergIter= 1
iteration= 5 chi2= 1.815514 time= 1.91e-05 cumTime= 0.0001061 edges= 72 schur= 0 lambda= 0.000299 levenbergIter= 1
optimization costs time: 0.000488299 seconds.
after optimization:
T=
0.996945 0.0598335 -0.0502011 0.14416
-0.0593261 0.998172 0.0115386 -0.0666785
0.0507998 -0.0085251 0.998672 -0.0300979
0 0 0 1
p1 = [-0.243698, -0.117719, 1.5848]
p2 = [-0.297211, -0.0956614, 1.6558]
(R*p2+t) = [-0.2409901495364605;
-0.1254270500587826;
1.609221205029395]
p1 = [0.402045, -0.341821, 2.2068]
p2 = [0.378811, -0.262859, 2.2196]
(R*p2+t) = [0.3946591022539743;
-0.3259188829495218;
2.20803983035825]
p1 = [-0.522843, -0.214436, 1.4956]
p2 = [-0.58581, -0.208584, 1.6052]
(R*p2+t) = [-0.532923946912698;
-0.2216052393093164;
1.54499035805527]
p1 = [-0.627753, 0.160186, 1.3396]
p2 = [-0.709645, 0.159033, 1.4212]
(R*p2+t) = [-0.6251478068660965;
0.1505624195985039;
1.351809862638435]
p1 = [0.594266, -0.0256024, 1.5332]
p2 = [0.514795, 0.0391393, 1.5332]
(R*p2+t) = [0.582755696243957;
-0.04046060384335358;
1.526884519595548]
3. 発生した問題
3.1 準備作業で発生した問題
- 問題: cmake... プロセス中に次のエラーが発生しました:
CMake Error at CMakeLists.txt:9 (find_package):
Could not find a configuration file for package "OpenCV" that is compatible
with requested version "3".
The following configuration files were considered but not accepted:
/usr/local/lib/cmake/opencv4/OpenCVConfig.cmake, version: 4.5.0
/usr/lib/x86_64-linux-gnu/cmake/opencv4/OpenCVConfig.cmake, version: 4.2.0
/lib/x86_64-linux-gnu/cmake/opencv4/OpenCVConfig.cmake, version: 4.2.0
解決策:
この種の問題は主に、コード内の opencv のバージョンが現在インストールされているバージョンと異なるために発生します。CMakeLists.txt ファイル内の opencv のバージョンを変更するだけです。
//更改前：
find_package(OpenCV 3 REQUIRED)
//更改后：
find_package(OpenCV REQUIRED)
- 問題: make -j8 を実行すると次のエラーが表示されます。
/home/fighter/slam/slambook2/ch7/pose_estimation_2d2d.cpp: In function ‘int main(int, char**)’:
/home/fighter/slam/slambook2/ch7/pose_estimation_2d2d.cpp:36:31: error: ‘CV_LOAD_IMAGE_COLOR’ was not declared in this scope
36 | Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR);
| ^~~~~~~~~~~~~~~~~~~
/home/fighter/slam/slambook2/ch7/pose_estimation_2d2d.cpp: In function ‘void pose_estimation_2d2d(std::vector<cv::KeyPoint>, std::vector<cv::KeyPoint>, std::vector<cv::DMatch>, cv::Mat&, cv::Mat&)’:
/home/fighter/slam/slambook2/ch7/pose_estimation_2d2d.cpp:143:61: error: ‘CV_FM_8POINT’ was not declared in this scope
143 | fundamental_matrix = findFundamentalMat(points1, points2, CV_FM_8POINT);
| ^~~~~~~~~~~~
make[2]: *** [CMakeFiles/pose_estimation_2d2d.dir/build.make:63: CMakeFiles/pose_estimation_2d2d.dir/pose_estimation_2d2d.cpp.o] Error 1
make[1]: *** [CMakeFiles/Makefile2:140: CMakeFiles/pose_estimation_2d2d.dir/all] Error 2
make[1]: *** Waiting for unfinished jobs....
/home/fighter/slam/slambook2/ch7/triangulation.cpp: In function ‘int main(int, char**)’:
/home/fighter/slam/slambook2/ch7/triangulation.cpp:44:31: error: ‘CV_LOAD_IMAGE_COLOR’ was not declared in this scope
44 | Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR);
| ^~~~~~~~~~~~~~~~~~~
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp: In member function ‘virtual bool VertexPose::read(std::istream&)’:
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp:59:44: warning: no return statement in function returning non-void [-Wreturn-type]
59 | virtual bool read(istream &in) override {
}
| ^
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp: In member function ‘virtual bool VertexPose::write(std::ostream&) const’:
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp:61:52: warning: no return statement in function returning non-void [-Wreturn-type]
61 | virtual bool write(ostream &out) const override {
}
| ^
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp: In function ‘int main(int, char**)’:
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp:54:31: error: ‘CV_LOAD_IMAGE_COLOR’ was not declared in this scope
54 | Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR);
| ^~~~~~~~~~~~~~~~~~~
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp:64:28: error: ‘CV_LOAD_IMAGE_UNCHANGED’ was not declared in this scope
64 | Mat d1 = imread(argv[3], CV_LOAD_IMAGE_UNCHANGED); // 深度图为16位无符号数，单通道图像
| ^~~~~~~~~~~~~~~~~~~~~~~
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp: In member function ‘virtual bool EdgeProjectXYZRGBDPoseOnly::read(std::istream&)’:
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp:84:27: warning: no return statement in function returning non-void [-Wreturn-type]
84 | bool read(istream &in) {
}
| ^
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp: In member function ‘virtual bool EdgeProjectXYZRGBDPoseOnly::write(std::ostream&) const’:
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp:86:35: warning: no return statement in function returning non-void [-Wreturn-type]
86 | bool write(ostream &out) const {
}
| ^
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp: In function ‘int main(int, char**)’:
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp:98:31: error: ‘CV_LOAD_IMAGE_COLOR’ was not declared in this scope
98 | Mat img_1 = imread(argv[1], CV_LOAD_IMAGE_COLOR);
| ^~~~~~~~~~~~~~~~~~~
/home/fighter/slam/slambook2/ch7/pose_estimation_3d3d.cpp:107:32: error: ‘CV_LOAD_IMAGE_UNCHANGED’ was not declared in this scope
107 | Mat depth1 = imread(argv[3], CV_LOAD_IMAGE_UNCHANGED); // 深度图为16位无符号数，单通道图像
| ^~~~~~~~~~~~~~~~~~~~~~~
make[2]: *** [CMakeFiles/triangulation.dir/build.make:63: CMakeFiles/triangulation.dir/triangulation.cpp.o] Error 1
make[1]: *** [CMakeFiles/Makefile2:113: CMakeFiles/triangulation.dir/all] Error 2
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp: In member function ‘virtual bool VertexPose::read(std::istream&)’:
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp:262:44: warning: no return statement in function returning non-void [-Wreturn-type]
262 | virtual bool read(istream &in) override {
}
| ^
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp: In member function ‘virtual bool VertexPose::write(std::ostream&) const’:
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp:264:52: warning: no return statement in function returning non-void [-Wreturn-type]
264 | virtual bool write(ostream &out) const override {
}
| ^
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp: In member function ‘virtual bool EdgeProjection::read(std::istream&)’:
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp:298:44: warning: no return statement in function returning non-void [-Wreturn-type]
298 | virtual bool read(istream &in) override {
}
| ^
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp: In member function ‘virtual bool EdgeProjection::write(std::ostream&) const’:
/home/fighter/slam/slambook2/ch7/pose_estimation_3d2d.cpp:300:52: warning: no return statement in function returning non-void [-Wreturn-type]
300 | virtual bool write(ostream &out) const override {
}
| ^
make[2]: *** [CMakeFiles/pose_estimation_3d3d.dir/build.make:63: CMakeFiles/pose_estimation_3d3d.dir/pose_estimation_3d3d.cpp.o] Error 1
make[1]: *** [CMakeFiles/Makefile2:167: CMakeFiles/pose_estimation_3d3d.dir/all] Error 2
make[2]: *** [CMakeFiles/pose_estimation_3d2d.dir/build.make:63: CMakeFiles/pose_estimation_3d2d.dir/pose_estimation_3d2d.cpp.o] Error 1
make[1]: *** [CMakeFiles/Makefile2:86: CMakeFiles/pose_estimation_3d2d.dir/all] Error 2
それを以下の図に示します。
問題の原因:この種の問題の主な理由は、CV_LOAD_IMAGE_COLOR が OpenCV 2.x バージョンで使用されている古い定数であることです。
解決策:主な変更方法は 2 つあり、1 つはヘッダー ファイルを追加する方法、もう 1 つはコードを変更する方法です。
方法 1: ヘッダー ファイルを追加します: #include <opencv2/imgcodecs/imgcodecs_c.h>それでも機能しない場合は、これを試してください ( #include “opencv2/imgcodecs/legacy/constants_c.h” )
方法 2: OpenCV 3 の場合.x 以降 それ以降のバージョンでは、代わりに IMREAD_COLOR 定数を使用する必要があります。
注 1: opencv エラーの変更の詳細については、次の記事を参照してください: https://blog.csdn.net/qq_44164791/article/details/131210608?spm=1001.2014.3001.5501
注 2:すべてのエラーを報告するファイルです。！！