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improve doc.

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KUANG Fangjun
2017-10-30 17:13:59 +01:00
parent 22496742b4
commit 67acfc6e25
7 changed files with 28 additions and 28 deletions
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doc/tutorials/calib3d/real_time_pose/real_time_pose.markdown
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@@ -7,7 +7,7 @@ object in the case of computer vision area to do later some 3D rendering or in t
obtain an object pose in order to grasp it and do some manipulation. However, this is not a trivial
problem to solve due to the fact that the most common issue in image processing is the computational
cost of applying a lot of algorithms or mathematical operations for solving a problem which is basic
and immediateley for humans.
and immediately for humans.
Goal
----
@@ -63,7 +63,7 @@ The tutorial consists of two main programs:
-# **Model registration**
This applicaton is exclusive to whom don't have a 3D textured model of the object to be detected.
This application 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.
@@ -110,7 +110,7 @@ The tutorial consists of two main programs:
-c, --confidence (value:0.95)
RANSAC confidence
-e, --error (value:2.0)
RANSAC reprojection errror
RANSAC reprojection error
-f, --fast (value:true)
use of robust fast match
-h, --help (value:true)
@@ -269,7 +269,7 @@ Here is explained in detail the code for the real time application:
Firstly, we have to set which matcher we want to use. In this case is used
@ref cv::FlannBasedMatcher matcher which in terms of computational cost is faster than the
@ref cv::BFMatcher matcher as we increase the trained collectction of features. Then, for
@ref cv::BFMatcher matcher as we increase the trained collection 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.
@@ -381,12 +381,12 @@ Here is explained in detail the code for the real time application:
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*
approximate 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
attributes: 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 tutorial_camera_calibration_square_chess and @ref tutorial_camera_calibration tutorials.
@@ -406,7 +406,7 @@ Here is explained in detail the code for the real time application:
PnPProblem pnp_detection(params_WEBCAM); // instantiate PnPProblem class
@endcode
The following code is how the *PnPProblem class* initialises its atributes:
The following code is how the *PnPProblem class* initialises its attributes:
@code{.cpp}
// Custom constructor given the intrinsic camera parameters
@@ -431,13 +431,13 @@ Here is explained in detail the code for the real time application:
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
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
You can tune these parameters 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
will be unaccurate. Decreasing the confidence your algorithm will be faster, but the obtained
solution will be unaccurate.
The following parameters work for this application:
@@ -446,7 +446,7 @@ Here is explained in detail the code for the real time application:
int iterationsCount = 500; // number of Ransac iterations.
float reprojectionError = 2.0; // maximum allowed distance to consider it an inlier.
float confidence = 0.95; // ransac successful confidence.
float confidence = 0.95; // RANSAC successful confidence.
@endcode
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
@@ -458,7 +458,7 @@ Here is explained in detail the code for the real time application:
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
float reprojectionError, float confidence ) // Ransac parameters
float reprojectionError, float confidence ) // RANSAC parameters
{
cv::Mat distCoeffs = cv::Mat::zeros(4, 1, CV_64FC1); // vector of distortion coefficients
cv::Mat rvec = cv::Mat::zeros(3, 1, CV_64FC1); // output rotation vector
@@ -479,8 +479,8 @@ Here is explained in detail the code for the real time application:
}
@endcode
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
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 @ref cv::solvePnPRansac crashes due to any OpenCV *bug*.
@code{.cpp}
if(good_matches.size() > 0) // None matches, then RANSAC crashes
@@ -558,7 +558,7 @@ Here is explained in detail the code for the real time application:
\f[X = (x,y,z,\dot x,\dot y,\dot z,\ddot x,\ddot y,\ddot z,\psi,\theta,\phi,\dot \psi,\dot \theta,\dot \phi,\ddot \psi,\ddot \theta,\ddot \phi)^T\f]
Secondly, we have to define the number of measuremnts which will be 6: from \f$R\f$ and \f$t\f$ we can
Secondly, we have to define the number of measurements which will be 6: from \f$R\f$ and \f$t\f$ we can
extract \f$(x,y,z)\f$ and \f$(\psi,\theta,\phi)\f$. 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 \f$1/T\f$, where *T* is the frame rate of
doc/tutorials/calib3d/table_of_content_calib3d.markdown
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@@ -37,6 +37,6 @@ Although we get most of our images in a 2D format they do come from a 3D world.
*Author:* Vladislav Sovrasov
Camera calibration by using either the chessboard, chAruco, asymmetrical circle or dual asymmetrical circle
pattern. Calibration process is continious, so you can see results after each new pattern shot.
pattern. Calibration process is continuous, so you can see results after each new pattern shot.
As an output you get average reprojection error, intrinsic camera parameters, distortion coefficients and
confidence intervals for all of evaluated variables.