@ -26,7 +26,7 @@ The application will have the followings parts:
Theory
======
In computer vision estimate the camera pose from *n* 3D-to-2D point correspondences is a fundamental and well understood problem. The most general version of the problem requires estimating the six degrees of freedom of the pose and five calibration parameters: focal length, principal point, aspect ratio and skew. It could be established with a minimum of 6 correspondences, using the well known Direct Linear Transform (DLT) algorithm. There are, though, several simplifications to the problem which turn into an extensive list of different algorithms that improve the accuracy of the DLT.
In computer vision estimate the camera pose from *n* 3D-to-2D point correspondences is a fundamental and well understood problem. The most general version of the problem requires estimating the six degrees of freedom of the pose and five calibration parameters: focal length, principal point, aspect ratio and skew. It could be established with a minimum of 6 correspondences, using the well known Direct Linear Transform (DLT) algorithm. There are, though, several simplifications to the problem which turn into an extensive list of different algorithms that improve the accuracy of the DLT.
The most common simplification is to assume known calibration parameters which is the so-called Perspective-*n*-Point problem:
@ -49,9 +49,9 @@ Source code
You can find the source code of this tutorial in the :file:`samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/` folder of the OpenCV source library.
The tutorial consists of two main programs:
The tutorial consists of two main programs:
**1. Model registration**
1. **Model registration**
This applicaton is exclusive to whom don't have a 3D textured model of the object to be detected. You can use this program to create your own textured 3D model. This program only works for planar objects, then if you want to model an object with complex shape you should use a sophisticated software to create it.
@ -64,9 +64,9 @@ The application starts up extracting the ORB features and descriptors from the i
:align:center
**2. Model detection**
#. **Model detection**
The aim of this application is estimate in real time the object pose given its 3D textured model.
The aim of this application is estimate in real time the object pose given its 3D textured model.
The application starts up loading the 3D textured model in YAML file format with the same structure explained in the model registration program. From the scene the ORB features and descriptors are detected and extracted. Then, is used :flann_based_matcher:`FlannBasedMatcher<>` with :flann:`LshIndexParams <flann-index-t-index>` to do the matching between the scene descriptors and the model descriptors. Using the found matches along with :calib3d:`solvePnPRansac <solvepnpransac>` function the :math:`R` and :math:`t` of the camera are computed. Finally, a :video:`KalmanFilter<kalmanfilter>` is applied in order to reject bad poses.
@ -76,33 +76,33 @@ Explanation
Here is explained in detail the code for the real time application:
**1. Read 3D textured object model and object mesh.**
1. **Read 3D textured object model and object mesh.**
In order to load the textured model I implemented the *class***Model** which has the function *load()* that opens a YAML file and take the stored 3D points with its corresponding descriptors. You can find an example of a 3D textured model in :file:`samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/Data/cookies_ORB.yml`.
In the main program the model is loaded as follows:
..code-block:: cpp
Model model; // instantiate Model object
model.load(yml_read_path); // load a 3D textured object model
Model model; // instantiate Model object
model.load(yml_read_path); // load a 3D textured object model
@ -110,100 +110,100 @@ In order to read the model mesh I implemented a *class* **Mesh** which has a fun
..code-block:: cpp
/** Load a CSV with *.ply format **/
void Mesh::load(const std::string path)
{
/** Load a CSV with *.ply format **/
void Mesh::load(const std::string path)
{
// Create the reader
CsvReader csvReader(path);
// Create the reader
CsvReader csvReader(path);
// Clear previous data
list_vertex_.clear();
list_triangles_.clear();
// Clear previous data
list_vertex_.clear();
list_triangles_.clear();
// Read from .ply file
csvReader.readPLY(list_vertex_, list_triangles_);
// Read from .ply file
csvReader.readPLY(list_vertex_, list_triangles_);
// Update mesh attributes
num_vertexs_ = list_vertex_.size();
num_triangles_ = list_triangles_.size();
// Update mesh attributes
num_vertexs_ = list_vertex_.size();
num_triangles_ = list_triangles_.size();
}
}
In the main program the mesh is loaded as follows:
..code-block:: cpp
Mesh mesh; // instantiate Mesh object
mesh.load(ply_read_path); // load an object mesh
Mesh mesh; // instantiate Mesh object
mesh.load(ply_read_path); // load an object mesh
**2. Take input from Camera or Video**
#. **Take input from Camera or Video**
To detect is necessary capture video. It's done loading a recorded video by passing the absolute path where it is located in your machine or using the default camera device. In order to test the application you can find a recorded video in :file:`samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/Data/box.mp4`.
..code-block:: cpp
cv::VideoCapture cap; // instantiate VideoCapture
(argc < 2) ? cap.open(0) : cap.open(argv[1]); // open the default camera device
// or a recorder video
cv::VideoCapture cap; // instantiate VideoCapture
(argc < 2) ? cap.open(0) : cap.open(argv[1]); // open the default camera device
// or a recorder video
if(!cap.isOpened()) // check if we succeeded
{
std::cout << "Could not open the camera device" << std::endl;
return -1;
}
if(!cap.isOpened()) // check if we succeeded
{
std::cout << "Could not open the camera device" << std::endl;
return -1;
}
Then the algorithm is computed frame per frame:
..code-block:: cpp
cv::Mat frame, frame_vis;
cv::Mat frame, frame_vis;
while(cap.read(frame) && cv::waitKey(30) != 27) // capture frame until ESC is pressed
{
while(cap.read(frame) && cv::waitKey(30) != 27) // capture frame until ESC is pressed
**3. Extract ORB features and descriptors from the scene**
#. **Extract ORB features and descriptors from the scene**
The next step is to detect the scene features and extract it descriptors. For this task I implemented a *class***RobustMatcher** which has a function for keypoints detection and features extraction. You can find it in :file:`samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/RobusMatcher.cpp`. In your *RobusMatch* object you can use any of the 2D features detectors of OpenCV. In this case I used :feature_detection_and_description:`ORB<orb>` features because is based on :feature_detection_and_description:`FAST<fast>` to detect the keypoints and :descriptor_extractor:`BRIEF<briefdescriptorextractor>` to extract the descriptors which means that is fast and robust to rotations. You can find more detailed information about *ORB* in the documentation.
The next step is to detect the scene features and extract it descriptors. For this task I implemented a *class***RobustMatcher** which has a function for keypoints detection and features extraction. You can find it in :file:`samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/RobusMatcher.cpp`. In your *RobusMatch* object you can use any of the 2D features detectors of OpenCV. In this case I used :feature_detection_and_description:`ORB<orb>` features because is based on :feature_detection_and_description:`FAST<fast>` to detect the keypoints and :descriptor_extractor:`BRIEF<briefdescriptorextractor>` to extract the descriptors which means that is fast and robust to rotations. You can find more detailed information about *ORB* in the documentation.
The following code is how to instantiate and set the features detector and the descriptors extractor:
rmatcher.setFeatureDetector(detector); // set feature detector
rmatcher.setDescriptorExtractor(extractor); // set descriptor extractor
rmatcher.setFeatureDetector(detector); // set feature detector
rmatcher.setDescriptorExtractor(extractor); // set descriptor extractor
The features and descriptors will be computed by the *RobustMatcher* inside the matching function.
**4. Match scene descriptors with model descriptors using Flann matcher**
#. **Match scene descriptors with model descriptors using Flann matcher**
It is the first step in our detection algorithm. The main idea is to match the scene descriptors with our model descriptors in order to know the 3D coordinates of the found features into the current scene.
Firstly, we have to set which matcher we want to use. In this case is used :flann_based_matcher:`FlannBasedMatcher<>` matcher which in terms of computational cost is faster than the :brute_force_matcher:`BruteForceMatcher<bfmatcher>` matcher as we increase the trained collectction of features. Then, for FlannBased matcher the index created is *Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search* due to *ORB* descriptors are binary.
Firstly, we have to set which matcher we want to use. In this case is used :flann_based_matcher:`FlannBasedMatcher<>` matcher which in terms of computational cost is faster than the :brute_force_matcher:`BruteForceMatcher<bfmatcher>` matcher as we increase the trained collectction of features. Then, for FlannBased matcher the index created is *Multi-Probe LSH: Efficient Indexing for High-Dimensional Similarity Search* due to *ORB* descriptors are binary.
You can tune the *LSH* and search parameters to improve the matching efficiency:
rmatcher.setDescriptorMatcher(matcher); // set matcher
Secondly, we have to call the matcher by using *robustMatch()* or *fastRobustMatch()* function. The difference of using this two functions is its computational cost. The first method is slower but more robust at filtering good matches because uses two ratio test and a symmetry test. In contrast, the second method is faster but less robust because only applies a single ratio test to the matches.
@ -212,225 +212,225 @@ The following code is to get the model 3D points and its descriptors and then ca
..code-block:: cpp
// Get the MODEL INFO
// Get the MODEL INFO
std::vector<cv::Point3f> list_points3d_model = model.get_points3d(); // list with model 3D coordinates
cv::Mat descriptors_model = model.get_descriptors(); // list with descriptors of each 3D coordinate
std::vector<cv::Point3f> list_points3d_model = model.get_points3d(); // list with model 3D coordinates
cv::Mat descriptors_model = model.get_descriptors(); // list with descriptors of each 3D coordinate
..code-block:: cpp
// -- Step 1: Robust matching between model descriptors and scene descriptors
// -- Step 1: Robust matching between model descriptors and scene descriptors
std::vector<cv::DMatch> good_matches; // to obtain the model 3D points in the scene
std::vector<cv::KeyPoint> keypoints_scene; // to obtain the 2D points of the scene
The following code corresponds to the *robustMatch()* function which belongs to the *RobustMatcher* class. This function uses the given image to detect the keypoints and extract the descriptors, match using *two Nearest Neighbour* the extracted descriptors with the given model descriptors and vice versa. Then, a ratio test is applied to the two direction matches in order to remove these matches which its distance ratio between the first and second best match is larger than a given threshold. Finally, a symmetry test is applied in order the remove non symmetrical matches.
// 3. Remove matches for which NN ratio is > than threshold
// clean image 1 -> image 2 matches
int removed1 = ratioTest(matches12);
// clean image 2 -> image 1 matches
int removed2 = ratioTest(matches21);
// 3. Remove matches for which NN ratio is > than threshold
// clean image 1 -> image 2 matches
int removed1 = ratioTest(matches12);
// clean image 2 -> image 1 matches
int removed2 = ratioTest(matches21);
// 4. Remove non-symmetrical matches
symmetryTest(matches12, matches21, good_matches);
// 4. Remove non-symmetrical matches
symmetryTest(matches12, matches21, good_matches);
}
}
After the matches filtering we have to subtract the 2D and 3D correspondences from the found scene keypoints and our 3D model using the obtained *DMatches* vector. For more information about :basicstructures:`DMatch <dmatch>` check the documentation.
..code-block:: cpp
// -- Step 2: Find out the 2D/3D correspondences
// -- Step 2: Find out the 2D/3D correspondences
std::vector<cv::Point3f> list_points3d_model_match; // container for the model 3D coordinates found in the scene
std::vector<cv::Point2f> list_points2d_scene_match; // container for the model 2D coordinates found in the scene
std::vector<cv::Point3f> list_points3d_model_match; // container for the model 3D coordinates found in the scene
std::vector<cv::Point2f> list_points2d_scene_match; // container for the model 2D coordinates found in the scene
for(unsigned int match_index = 0; match_index < good_matches.size(); ++match_index)
{
cv::Point3f point3d_model = list_points3d_model[ good_matches[match_index].trainIdx ]; // 3D point from model
cv::Point2f point2d_scene = keypoints_scene[ good_matches[match_index].queryIdx ].pt; // 2D point from the scene
list_points3d_model_match.push_back(point3d_model); // add 3D point
list_points2d_scene_match.push_back(point2d_scene); // add 2D point
}
for(unsigned int match_index = 0; match_index < good_matches.size(); ++match_index)
{
cv::Point3f point3d_model = list_points3d_model[ good_matches[match_index].trainIdx ]; // 3D point from model
cv::Point2f point2d_scene = keypoints_scene[ good_matches[match_index].queryIdx ].pt; // 2D point from the scene
list_points3d_model_match.push_back(point3d_model); // add 3D point
list_points2d_scene_match.push_back(point2d_scene); // add 2D point
}
**5. Pose estimation using PnP + Ransac**
#. **Pose estimation using PnP + Ransac**
Once with the 2D and 3D correspondences we have to apply a PnP algorithm in order to estimate the camera pose. The reason why we have to use :calib3d:`solvePnPRansac <solvepnpransac>` instead of :calib3d:`solvePnP <solvepnp>` is due to the fact that after the matching not all the found correspondences are correct and, as like as not, there are false correspondences or also called *outliers*. The `Random Sample Consensus <http://en.wikipedia.org/wiki/RANSAC>`_ or *Ransac* is a non-deterministic iterative method which estimate parameters of a mathematical model from observed data producing an aproximate result as the number of iterations increase. After appyling *Ransac* all the *outliers* will be eliminated to then estimate the camera pose with a certain probability to obtain a good solution.
For the camera pose estimation I have implemented a *class***PnPProblem**. This *class* has 4 atributes: a given calibration matrix, the rotation matrix, the translation matrix and the rotation-translation matrix. The intrinsic calibration parameters of the camera which you are using to estimate the pose are necessary. In order to obtain the parameters you can check :ref:`CameraCalibrationSquareChessBoardTutorial` and :ref:`cameraCalibrationOpenCV` tutorials.
For the camera pose estimation I have implemented a *class***PnPProblem**. This *class* has 4 atributes: a given calibration matrix, the rotation matrix, the translation matrix and the rotation-translation matrix. The intrinsic calibration parameters of the camera which you are using to estimate the pose are necessary. In order to obtain the parameters you can check :ref:`CameraCalibrationSquareChessBoardTutorial` and :ref:`cameraCalibrationOpenCV` tutorials.
The following code is how to declare the *PnPProblem class* in the main program:
..code-block:: cpp
/*
* Set up the intrinsic camera parameters: UVC WEBCAM
*/
/*
* Set up the intrinsic camera parameters: UVC WEBCAM
*/
double f = 55; // focal length in mm
double sx = 22.3, sy = 14.9; // sensor size
double width = 640, height = 480; // image size
double f = 55; // focal length in mm
double sx = 22.3, sy = 14.9; // sensor size
double width = 640, height = 480; // image size
double params_WEBCAM[] = { width*f/sx, // fx
height*f/sy, // fy
width/2, // cx
height/2}; // cy
double params_WEBCAM[] = { width*f/sx, // fx
height*f/sy, // fy
width/2, // cx
height/2}; // cy
PnPProblem pnp_detection(params_WEBCAM); // instantiate PnPProblem class
PnPProblem pnp_detection(params_WEBCAM); // instantiate PnPProblem class
The following code is how the *PnPProblem class* initialises its atributes:
..code-block:: cpp
// Custom constructor given the intrinsic camera parameters
// Custom constructor given the intrinsic camera parameters
PnPProblem::PnPProblem(const double params[])
{
_A_matrix = cv::Mat::zeros(3, 3, CV_64FC1); // intrinsic camera parameters
_A_matrix.at<double>(0, 0) = params[0]; // [ fx 0 cx ]
OpenCV provides four PnP methods: ITERATIVE, EPNP, P3P and DLS. Depending on the application type, the estimation method will be different. In the case that we want to make a real time application, the more suitable methods are EPNP and P3P due to that are faster than ITERATIVE and DLS at finding an optimal solution. However, EPNP and P3P are not especially robust in front of planar surfaces and sometimes the pose estimation seems to have a mirror effect. Therefore, in this this tutorial is used ITERATIVE method due to the object to be detected has planar surfaces.
The OpenCV Ransac implementation wants you to provide three parameters: the maximum number of iterations until stop the algorithm, the maximum allowed distance between the observed and computed point projections to consider it an inlier and the confidence to obtain a good result. You can tune these paramaters in order to improve your algorithm performance. Increasing the number of iterations you will have a more accurate solution, but will take more time to find a solution. Increasing the reprojection error will reduce the computation time, but your solution will be unaccurate. Decreasing the confidence your arlgorithm will be faster, but the obtained solution will be unaccurate.
The following parameters work for this application:
..code-block:: cpp
// RANSAC parameters
int iterationsCount = 500; // number of Ransac iterations.
float reprojectionError = 2.0; // maximum allowed distance to consider it an inlier.
The following code corresponds to the *estimatePoseRANSAC()* function which belongs to the *PnPProblem class*. This function estimates the rotation and translation matrix given a set of 2D/3D correspondences, the desired PnP method to use, the output inliers container and the Ransac parameters:
..code-block:: cpp
// Estimate the pose given a list of 2D/3D correspondences with RANSAC and the method to use
// Estimate the pose given a list of 2D/3D correspondences with RANSAC and the method to use
void PnPProblem::estimatePoseRANSAC( const std::vector<cv::Point3f> &list_points3d, // list with model 3D coordinates
const std::vector<cv::Point2f> &list_points2d, // list with scene 2D coordinates
int flags, cv::Mat &inliers, int iterationsCount, // PnP method; inliers container
Rodrigues(rvec,_R_matrix); // converts Rotation Vector to Matrix
_t_matrix = tvec; // set translation matrix
Rodrigues(rvec,_R_matrix); // converts Rotation Vector to Matrix
_t_matrix = tvec; // set translation matrix
this->set_P_matrix(_R_matrix, _t_matrix); // set rotation-translation matrix
this->set_P_matrix(_R_matrix, _t_matrix); // set rotation-translation matrix
}
}
In the following code are the 3th and 4th steps of the main algorithm. The first, calling the above function and the second taking the output inliers vector from Ransac to get the 2D scene points for drawing purpose. As seen in the code we must be sure to apply Ransac if we have matches, in the other case, the function :calib3d:`solvePnPRansac <solvepnpransac>` crashes due to any OpenCV *bug*.
..code-block:: cpp
if(good_matches.size() > 0) // None matches, then RANSAC crashes
{
if(good_matches.size() > 0) // None matches, then RANSAC crashes
{
// -- Step 3: Estimate the pose using RANSAC approach
int n = inliers_idx.at<int>(inliers_index); // i-inlier
cv::Point2f point2d = list_points2d_scene_match[n]; // i-inlier point 2D
list_points2d_inliers.push_back(point2d); // add i-inlier to list
}
Finally, once the camera pose has been estimated we can use the :math:`R` and :math:`t` in order to compute the 2D projection onto the image of a given 3D point expressed in a world reference frame using the showed formula on *Theory*.
Finally, once the camera pose has been estimated we can use the :math:`R` and :math:`t` in order to compute the 2D projection onto the image of a given 3D point expressed in a world reference frame using the showed formula on *Theory*.
The following code corresponds to the *backproject3DPoint()* function which belongs to the *PnPProblem class*. The function backproject a given 3D point expressed in a world reference frame onto a 2D image:
..code-block:: cpp
// Backproject a 3D point to 2D using the estimated pose parameters
// Backproject a 3D point to 2D using the estimated pose parameters
The above function is used to compute all the 3D points of the object *Mesh* to show the pose of the object.
**6. Linear Kalman Filter for bad poses rejection**
#. **Linear Kalman Filter for bad poses rejection**
Is it common in computer vision or robotics fields that after applying detection or tracking techniques, bad results are obtained due to some sensor errors. In order to avoid these bad detections in this tutorial is explained how to implement a Linear Kalman Filter. The Kalman Filter will be applied after detected a given number of inliers.
@ -440,210 +440,210 @@ Firstly, we have to define our state vector which will have 18 states: the posit
Secondly, we have to define the number of measuremnts which will be 6: from :math:`R` and :math:`t` we can extract :math:`(x,y,z)` and :math:`(\psi,\theta,\phi)`. In addition, we have to define the number of control actions to apply to the system which in this case will be *zero*. Finally, we have to define the differential time between measurements which in this case is :math:`1/T`, where *T* is the frame rate of the video.
..code-block:: cpp
cv::KalmanFilter KF; // instantiate Kalman Filter
cv::KalmanFilter KF; // instantiate Kalman Filter
int nStates = 18; // the number of states
int nMeasurements = 6; // the number of measured states
int nInputs = 0; // the number of action control
int nStates = 18; // the number of states
int nMeasurements = 6; // the number of measured states
int nInputs = 0; // the number of action control
double dt = 0.125; // time between measurements (1/FPS)
double dt = 0.125; // time between measurements (1/FPS)
initKalmanFilter(KF, nStates, nMeasurements, nInputs, dt); // init function
initKalmanFilter(KF, nStates, nMeasurements, nInputs, dt); // init function
The following code corresponds to the *Kalman Filter* initialisation. Firstly, is set the process noise, the measurement noise and the error covariance matrix. Secondly, are set the transition matrix which is the dynamic model and finally the measurement matrix, which is the measurement model.
The following code corresponds to the *Kalman Filter* initialisation. Firstly, is set the process noise, the measurement noise and the error covariance matrix. Secondly, are set the transition matrix which is the dynamic model and finally the measurement matrix, which is the measurement model.
You can tune the process and measurement noise to improve the *Kalman Filter* performance. As the measurement noise is reduced the faster will converge doing the algorithm sensitive in front of bad measurements.
..code-block:: cpp
void initKalmanFilter(cv::KalmanFilter &KF, int nStates, int nMeasurements, int nInputs, double dt)
In the following code is the 5th step of the main algorithm. When the obtained number of inliers after *Ransac* is over the threshold, the measurements matrix is filled and then the *Kalman Filter* is updated:
// update the Kalman filter with good measurements
updateKalmanFilter( KF, measurements,
translation_estimated, rotation_estimated);
// update the Kalman filter with good measurements
updateKalmanFilter( KF, measurements,
translation_estimated, rotation_estimated);
The following code corresponds to the *fillMeasurements()* function which converts the measured `Rotation Matrix to Eulers angles <http://euclideanspace.com/maths/geometry/rotations/conversions/matrixToEuler/index.htm>`_ and fill the measurements matrix along with the measured translation vector:
The following code corresponds to the *updateKalmanFilter()* function which update the Kalman Filter and set the estimated Rotation Matrix and translation vector. The estimated Rotation Matrix comes from the estimated `Euler angles to Rotation Matrix <http://euclideanspace.com/maths/geometry/rotations/conversions/eulerToMatrix/index.htm>`_.
edgarriba
11 years ago