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Lens distortion correction division model 1p
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filters class AMI_DLL_H definition More...
#include <math.h>
#include <vector>
#include "../ami_image/image.h"
#include "../ami_utilities/utilities.h"
#include "../ami_primitives/subpixel_image_contours.h"
Go to the source code of this file.
Functions | |
template<class T , class U > | |
void | gauss_conv (ami::image< T > &img, ami::image< U > &img_conv, double sigma_x, double sigma_y, double precision) |
template<class T , class U > | |
void | grad (const ami::image< T > &img, ami::image< U > &grad_x, ami::image< U > &grad_y, const bool NeigborhoodType) |
float | ami_median_float (int k, int n, float *x) |
FUNCTION TO COMPUTE THE MEDIAN OF A VECTOR IN FLOAT PRECISION. More... | |
template<class T > | |
void | canny (ami::image< T > input, ami::image< T > &output, float *seno, float *coseno, int *x, int *y, float per_low, float per_high) |
template<class T > | |
ami::subpixel_image_contours | canny (ami::image< T > input, ami::image< T > &edges, float canny_low_threshold, float canny_high_threshold) |
filters class AMI_DLL_H definition
float ami_median_float | ( | int | k, |
int | n, | ||
float * | x | ||
) |
FUNCTION TO COMPUTE THE MEDIAN OF A VECTOR IN FLOAT PRECISION.
[in] | k | : the estimated median separates the k lower values of the data sample. |
[in] | n | : vector size |
[in] | x | : pointer to the vector |
void canny | ( | ami::image< T > | input, |
ami::image< T > & | output, | ||
float * | seno, | ||
float * | coseno, | ||
int * | x, | ||
int * | y, | ||
float | per_low, | ||
float | per_high | ||
) |
input | INPUT IMAGE (GRAY SCALE) |
output | OUTPUT IMAGE WITH THE EDGES |
seno | SINUS OF THE ORIENTATION |
coseno | COSINUS OF THE ORIENTATION |
x | COORDINATE X OF THE POSITION |
y | COORDINATE Y OF THE POSITION |
per_low | PERCENTAGE FOR THE LOW THRESHOLD (BETWEEN 0 AND 1) |
per_high | PERCENTAGE FOR THE HIGH THRESHOLD (BETWEEN 0 AND 1) |
ami::subpixel_image_contours canny | ( | ami::image< T > | input, |
ami::image< T > & | edges, | ||
float | canny_low_threshold, | ||
float | canny_high_threshold | ||
) |
input | INPUT IMAGE (GRAY SCALE) |
edges | OUTPUT IMAGE WITH THE EDGES |
canny_low_threshold | PERCENTAGE FOR THE LOW THRESHOLD (BETWEEN 0 AND 1) |
canny_high_threshold | PERCENTAGE FOR THE HIGH THRESHOLD (BETWEEN 0 AND 1) |
void gauss_conv | ( | ami::image< T > & | img, |
ami::image< U > & | img_conv, | ||
double | sigma_x, | ||
double | sigma_y, | ||
double | precision | ||
) |
img | INPUT IMAGE (IT CAN BE = OUTPUT IMAGE |
img_conv | OUTPUT IMAGE |
sigma_x | STANDARD DEVIATION IN THE x VARIABLE |
sigma_y | STANDARD DEVIATION IN THE y VARIABLE |
precision | PRECISION TO COMPUTE THE GAUSSIAN CONVOLUTION BIGGER IS THE PRECISION, MORE ITERATIONS TO COMPUTE THE CONVOLUTION |
void grad | ( | const ami::image< T > & | img, |
ami::image< U > & | grad_x, | ||
ami::image< U > & | grad_y, | ||
const bool | NeigborhoodType | ||
) |
img | INPUT IMAGE |
grad_x | OUTPUT x-GRADIENT IMAGE |
grad_y | OUTPUT y-GRADIENT IMAGE |
NeigborhoodType | =0 means 5 size neighborhood, =9 means 9 size neighborhood |