CN111539934A - Method for extracting line laser center - Google Patents

Method for extracting line laser center Download PDF

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CN111539934A
CN111539934A CN202010321479.4A CN202010321479A CN111539934A CN 111539934 A CN111539934 A CN 111539934A CN 202010321479 A CN202010321479 A CN 202010321479A CN 111539934 A CN111539934 A CN 111539934A
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line laser
extracting
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CN111539934B (en
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邢述达
汪雪林
顾庆毅
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Suzhou Zhongke Xingzhi Intelligent Technology Co ltd
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Abstract

The invention discloses a method for extracting a line laser center, and belongs to the technical field of industrial measurement. In the method, threshold processing is firstly carried out, most redundant calculation is removed, and the calculation speed and efficiency are improved. Then, calculating the direction of the target point by using a polynomial method; then recording the data of cache points in each k/2 pixel range at two sides of the target point according to the direction of the target point; finally, extracting coordinates of the center points of the sub-pixels of the line laser by adopting a one-dimensional Gaussian model; the method has the advantages of high calculation speed, interference resistance, convenience in hardware implementation and the like.

Description

Method for extracting line laser center
Technical Field
The invention belongs to the technical field of industrial measurement, and particularly relates to a method for extracting a line laser center.
Background
Line laser 3D measuring instruments are widely used in the field of industrial measurements, where the main method taken is triangulation. The structured light stripes are irradiated on the surface of an object, two-dimensional information of an image generated on the surface of the object is converted into three-dimensional information through the conversion of system coordinates, the outline of the surface of the object is obtained, and then subsequent point cloud operation, dimension measurement and the like are carried out, so that the extraction of the center of two-dimensional line laser is one of the most important components. The accuracy of a line laser gauge depends mainly on the relative position between the line laser and the CMOS camera and the depth of the object surface. To accurately obtain this information, the exact location of the center of the light stripe must first be obtained from the image containing the light stripe. And the extraction of the fast line laser center is an important guarantee for improving the detection speed of the line laser 3D measuring instrument.
In chinese patent application CN108986160A (a method for extracting laser center line of image containing interference of specular reflection light), the method for extracting center line includes the following steps: 1. preprocessing the image containing the specular reflection light to obtain a laser bright stripe area; 2. skeletonizing the obtained laser bright stripe areas to obtain N groups of floating skeletons; 3. comparing and eliminating the branched chains of the N groups of floating frameworks by adopting an internal elimination algorithm; 4. calculating the maximum response position P of each column in the image by adopting a Gaussian Laplacian LOG operator; 5. removing the disconnected and overlapped part of the skeleton by using an external removing algorithm according to the obtained simplified skeleton and the obtained maximum response position P; 6. and (5) performing sub-pixel position positioning near the reserved skeleton position by using a gravity center method, and outputting the final laser center line position. The method is more suitable for extracting the image laser center line containing the interference of the specular reflection light, so that the application range is narrower. In chinese patent application CN108921818A (a method for extracting a center line of a weld tracking laser), a method for positioning a line laser center is disclosed, which comprises the following steps: (1) acquiring the position of an initial laser center line, sampling and selecting a plurality of nodes to fit a non-uniform rational B spline curve and defining the nodes as the internal energy of an active contour model; (2) calculating a response value near each node in the step (1) by adopting a Gaussian Laplace LOG operator and defining external energy of the active contour model; (3) calculating and comparing response values of external energy by adopting a greedy algorithm to obtain a transformation position of the current iteration; (4) acquiring the sub-pixel precision position of the new node in the step (3) by utilizing a gray scale gravity center method; (5) and (4) fitting the new nodes of the sub-pixel precision positions obtained in the step (4) into a new non-uniform rational B-spline curve as the laser center line position of the current frame image. However, this method needs to first acquire the centerline position of the initial laser, and then can confirm the current laser centerline position based on the initial laser centerline position, and in the positioning process, it needs to calculate all points. Therefore, the method has limited application and complicated calculation process. In chinese patent application CN107203973A (a sub-pixel positioning method for line laser center in three-dimensional laser scanning system), the positioning method for line laser center includes: firstly, acquiring original image information scanned by laser from a camera, and preprocessing the original image to eliminate noise in the image; carrying out threshold segmentation on the image without the noise, and carrying out coarse extraction on the linear laser; eliminating the burr phenomenon of the line laser after the rough extraction, and further reducing the width of the line laser; and obtaining a sub-pixel level coordinate of the line laser center by using an improved gravity center method, and correcting a pseudo target pixel in the sub-pixel level coordinate by using Hough transformation. However, the method of combining the modified centroid method and the Hough transform in this method may reduce the robustness of the system, resulting in a decrease in the stability of the method for locating the line laser center.
Disclosure of Invention
The invention aims to solve the technical problems that in the method for extracting the line laser center in the prior art, the extraction method has poor robustness, low extraction speed, low precision, narrow application range and the like.
In order to solve the technical problem, the invention discloses a method for extracting a line laser center, which comprises the following steps:
(1) carrying out graying processing on an input image;
(2) scanning the image subjected to the graying processing point by point, and comparing the gray value of each point in the image with a given image gray threshold;
(3) if the gray value of the point in the image is smaller than the given image gray threshold, the calculation is not carried out, and the next point is continuously compared;
(4) if the gray value of the point in the image is larger than the given image gray threshold, the following operations are carried out:
(a) calculating the direction of the target point by using a polynomial method to obtain the position of the target point; because the line laser is positioned in the two-dimensional plane, the direction of the target point is determined, and the position of the target point is obtained.
(b) Recording data of cache points in each k/2 pixel range on two sides of the target point according to the direction of the target point;
(c) extracting coordinates of a line laser sub-pixel central point by adopting a one-dimensional Gaussian model;
automatically determining the image gray threshold, wherein the image gray threshold is equal to the gray value at the maximum gray value accumulation value in the gray histogram plus a variable, and the value range is 5-10; the value is selected based on experience in practical application, and the effect of the invention is best.
Where k is the desired width of the line laser beam that impinges on the surface of the test sample. According to the width of an incident line laser beam and a plane included angle alpha between the incident line laser beam and a test sample in an actual process, k is larger than or equal to the width/cos alpha of the incident line laser beam, a maximum effective angle alpha is specified, an ideal maximum line width can be calculated, the final designed line width is 160 through calculation and actual measurement of different samples, and k is 200 in order to increase the reliability of data.
Further, in the operation (a) of the step (4), the polynomial is calculated by an orthogonal decomposition method and a convolution method, so as to obtain the direction of the target point. In the process of direction calculation, a polynomial fitting calculation mode is adopted, and since the polynomial can be realized by an orthogonal decomposition method through a convolution mode, calculation can be accelerated and is convenient to realize.
Further, in operation (a) of step (4), for a one-dimensional case:
assume that a given set of orthogonal bases is: { P0(r),...PN-1(r)}；
Pn(r)＝rn+an-1rn-1+...+a1r+a0(1)
For each R ∈ R, each point d (R) can be obtained by equation (2):
Figure BDA0002461597320000031
the coefficients need only be obtained for the target equation (3) to obtain the minimum value:
Figure BDA0002461597320000032
the results obtained were:
Figure BDA0002461597320000033
for each d (r), the coefficients of equation (5) are multiplied:
Figure BDA0002461597320000034
further, in the operation (a) of the step (4), the operation may be performed for a two-dimensional case by decomposing the two-dimensional case into two one-dimensional cases:
assume class direction { P0(r),...PN-1(r) }, the row direction is { Q }0(c),...QN-1(c)}，
Figure BDA0002461597320000035
The polynomial is a second-order polynomial, as shown in equation (7):
g(x,y)＝k0+k1x+k2y+k3x2+k4xy+k5y2(7)
and (3) calculating the direction:
Figure BDA0002461597320000036
the direction vector corresponding to the maximum value of the eigenvector of the H matrix is the normal direction (n) of the target pointx，ny)。
Further, in the operation (b) of the step (4), after the target point direction is determined, the line laser may generate overexposure during the actual detection of the data with fixed length before and after the buffer direction, and the problem caused by the overexposure needs to be solved when the data is buffered. To solve this problem, if the gray value of a certain buffer point a appears to be equal to the gray value of the target point along the scanning direction, recording is performed in a way of a trip: recording the distance from the cache point A to the target point as traceB; on the other side of the target point, recording the distance from another cache point B which is symmetrical to the cache point A to the target point as traceE, wherein traceB is traceE; a point which is k/2 pixels away from the other side of the target point from the cache point A is taken as an end point locB, and a point which is k/2 pixels away from the other side of the target point from the cache point B is taken as an end point locE; the data to be cached is: the values of traceB and traceE, as well as data buffered at a point between point a to the endpoint locB, and data buffered at a point between point B to the endpoint locE.
Further, in the operation (c) of step (4), the method for extracting the coordinates of the center point of the sub-pixel by using the one-dimensional gaussian model includes:
let the Gaussian template be gn，gn′ and gn"its first and second derivatives, rn，rn′ and rn"convolution of a Gaussian template with each fixed length, respectively, Taylor expansion to obtain equation (9)
Figure BDA0002461597320000041
The center point coordinate thus obtained is at p' (x) ═ 0, and the resulting position is obtained from equation (10):
Figure BDA0002461597320000042
x is in the range of (-0.5, 0.5) to meet the requirement; the coordinates of the center point of the sub-pixels of the line laser can then be obtained from the orientation and origin coordinates as (Tx + x cos ψ, Ty + x sin ψ), where ψ is the normal angle calculated by the polynomial method. Because the Gaussian filtering template is related to the size in the executing process and the hardware implementation is difficult, the iterative Gaussian filtering is used for replacing the Gaussian filtering of the template convolution, and the purposes of acceleration and convenient hardware implementation can be achieved.
Furthermore, in the method for extracting the coordinates of the center points of the sub-pixels by adopting the one-dimensional Gaussian model, a Gaussian recursion method is selected to realize convolution. According to the idea of recursion, any convolution can be realized through recursion, the invention only selects one mode of Gaussian recursion, and further, the specific method of Gaussian recursion is as follows: assuming that an input image is I (n), Gaussian convolution of the image, and first order differential and second order differential respectively correspond to Tn，Tn′ and Tn", backward recursion On，On′ and On", then:
Figure BDA0002461597320000043
Figure BDA0002461597320000044
Figure BDA0002461597320000045
Figure BDA0002461597320000046
Figure BDA0002461597320000051
Figure BDA0002461597320000052
in the formula: coefficient c1、c2、c3Respectively as follows:
c1＝(2.44413q+2.85619q2+1.26662q3)/c0
c2＝-(1.4281q2+1.26661q3)/c0
c3＝0.422205q3/c0
B＝1-(c1+c2+c3)
wherein ,
Figure BDA0002461597320000053
c0＝1.57825+2.44413q+1.4281q2+0.422205q3；
σ is the gaussian distribution parameter of the gaussian function that determines the size of the convolution window.
The invention discloses a rapid line laser center detection method, which comprises the steps of firstly carrying out threshold processing to remove most redundant calculation and improve the calculation speed and efficiency. And then calculating the direction of the line, thereby realizing the random directional distribution of the laser lines. And finally, obtaining the sub-pixel center of the laser stripe by using an iterative Gaussian method. Therefore, the method has the advantages of high calculation speed, interference resistance, convenience in hardware implementation and the like.
Compared with the prior art, the line laser center extraction method has the following advantages:
(1) the robustness is good, the calculation speed is high, and the accuracy is high;
(2) the adaptability is high, and the repeatability precision is high;
(3) hardware implementation is facilitated.
Drawings
FIG. 1: a line laser image of the surface of the metal iron block;
FIG. 2: a line laser image of the screw surface;
FIG. 3: extracting a flow chart of a line laser central point;
FIG. 4: the line width of a target point cache during over-explosion;
FIG. 5: the method is adopted to obtain a line laser central point effect graph of the surface of the metal iron block;
FIG. 6: the method is adopted to obtain a partial enlarged view of the line laser central point effect graph of the surface of the metal iron block;
FIG. 7: obtaining a line laser central point effect graph of the surface of the metal iron block by adopting a two-dimensional Gaussian method;
FIG. 8: obtaining a local enlarged view of a line laser central point effect graph of the surface of the metal iron block by adopting a two-dimensional Gaussian method;
FIG. 9: the method is adopted to obtain a line laser center point effect graph of the surface of the screw;
FIG. 10: the method of the invention is adopted to obtain a partial enlarged view of the line laser center point effect graph of the screw surface.
Detailed Description
The technical solution of the present invention will be described in detail by the following specific examples.
The invention is mainly aimed at objects as shown in fig. 1 and 2, namely, the surface of a metal iron block and the surface of a screw. The illustrated line laser image has the conditions of non-concentrated reflection and non-uniform brightness in local areas, so that the requirement on the robustness of the algorithm is high, and for a real central point, mainly brightness information in the normal direction of a laser bar is fully utilized, and a proper model is selected to determine the coordinate of the central point. The main flow of the algorithm is shown in fig. 3, in order to increase the speed of the algorithm, the calculation is performed when the speed is greater than the threshold value T in the process of executing the algorithm, so that the number of points actually required to be calculated is very small. Since the background of the line laser is relatively uniform and is mostly a background region, the threshold T can be obtained by adding a small variation (value of 5-10) to the gray value at the maximum gray value accumulation value in the gray histogram. The value is selected based on experience in practical application, and the effect of the invention is best.
A method for extracting a line laser center, as shown in fig. 3, includes the following steps:
(1) carrying out graying processing on an input image;
(2) scanning the image subjected to the graying processing point by point, and comparing the gray value of each point in the image with a given image gray threshold;
(3) if the gray value of the point in the image is smaller than the given image gray threshold, the calculation is not carried out, and the next point is continuously compared;
(4) if the gray value of the point in the image is larger than the given image gray threshold, the following operations are carried out:
(a) calculating the direction of the target point by using a polynomial method to obtain the position of the target point; because the line laser is positioned in the two-dimensional plane, the direction of the target point is determined, and the position of the target point is obtained.
(b) Recording data of cache points in 100 pixel ranges on two sides of the target point according to the direction of the target point;
(c) extracting coordinates of a line laser sub-pixel central point by adopting a one-dimensional Gaussian model;
and automatically determining the image gray threshold, wherein the image gray threshold is equal to the gray value at the gray value accumulation maximum value in the gray histogram plus a variable, and the value range is 5-10.
In the operation (a) of step (4), the polynomial is calculated by orthogonal decomposition using gaussian convolution to obtain the direction of the target point. For the one-dimensional case:
assume that a given set of orthogonal bases is: { P0(r),...PN-1(r)}；
Pn(r)＝rn+an-1rn-1+...+a1r+a0(1)
For each R ∈ R, each point d (R) can be obtained by equation (2):
Figure BDA0002461597320000071
the coefficients need only be obtained for the target equation (3) to obtain the minimum value:
Figure BDA0002461597320000072
the results obtained were:
Figure BDA0002461597320000073
for each d (r), the coefficients of equation (5) are multiplied:
Figure BDA0002461597320000074
for the two-dimensional case, the calculation can be decomposed into two one-dimensional cases:
assume class direction { P0(r),...PN-1(r) }, the row direction is { Q }0(c),...QN-1(c)}，
Figure BDA0002461597320000075
The polynomial is a second-order polynomial, as shown in equation (7):
g(x,y)＝k0+k1x+k2y+k3x2+k4xy+k5y2(7)
and (3) calculating the direction:
Figure BDA0002461597320000076
the direction vector corresponding to the maximum value of the eigenvector of the H matrix is the normal direction (n) of the target pointx，ny)。
Then, in the operation (b) of step (4), after the target point direction is determined, the line laser may generate overexposure during the actual detection of the data with fixed length before and after the buffer direction, and the problem caused by the overexposure needs to be solved when the data is buffered. As shown in fig. 4, to solve this problem, if the gray value of a certain buffer point a appears to be equal to the gray value of the target point along the scanning direction, recording is performed in a run-length manner: recording the distance from the cache point A to the target point as traceB; on the other side of the target point, recording the distance from another cache point B which is symmetrical to the cache point A to the target point as traceE, wherein traceB is traceE; a point which has a distance m of 100 pixels from the cache point a to the other side of the target point is taken as an endpoint locB, and a point which has a distance m of 100 pixels from the cache point B to the other side of the target point is taken as an endpoint locE; the data to be cached is: the values of traceB and traceE, as well as data buffered at a point between point a to the endpoint locB, and data buffered at a point between point B to the endpoint locE.
Then, in operation (c) of step (4), the method for extracting the coordinates of the center point of the sub-pixel by using the one-dimensional gaussian model comprises:
let the Gaussian template be gn，gn′ and gn"its first and second derivatives, rn，rn′ and rn"convolution of a Gaussian template with each fixed length, respectively, Taylor expansion to obtain equation (9)
Figure BDA0002461597320000081
The center point coordinate thus obtained is at p' (x) ═ 0, and the resulting position is obtained from equation (10):
Figure BDA0002461597320000082
x is in the range of (-0.5, 0.5) to meet the requirement; the coordinates of the center point of the sub-pixels of the line laser can then be obtained from the orientation and origin coordinates as (Tx + x cos ψ, Ty + x sin ψ), where ψ is the normal angle calculated by the polynomial method.
Further, the gaussian convolution is implemented using a method of gaussian recursion. Further, the specific method of the gaussian recursion is as follows: assuming that an input image is I (n), Gaussian convolution of the image, and first order differential and second order differential respectively correspond to Tn，Tn′ and Tn", backward recursion On，On′ and On", then:
Figure BDA0002461597320000083
Figure BDA0002461597320000084
Figure BDA0002461597320000085
Figure BDA0002461597320000086
Figure BDA0002461597320000087
Figure BDA0002461597320000088
in the formula: coefficient c1、c2、c3Respectively as follows:
c1＝(2.44413q+2.85619q2+1.26662q3)/c0
c2＝-(1.4281q2+1.26661q3)/c0
c3＝0.422205q3/c0
B＝1-(c1+c2+c3)
wherein ,
Figure BDA0002461597320000091
c0＝1.57825+2.44413q+1.4281q2+0.422205q3；
σ is the gaussian distribution parameter of the gaussian function that determines the size of the convolution window.
The line laser center point effect graph and the local enlarged graph of the surface of the metal iron block obtained by the one-dimensional Gaussian method are shown in FIGS. 5 and 6, and the line laser center point effect graph and the local enlarged graph of the surface of the metal iron block obtained by the two-dimensional Gaussian method are shown in FIGS. 7 and 8. The stability of the two-dimensional Gaussian algorithm center line extraction algorithm depends on the filtering line width, the line width is not uniform, and the unstable condition can occur. As shown in fig. 9 and fig. 10, a line laser center point effect graph and a partial enlarged view of the screw surface obtained by the method of the present invention are respectively given, and it can be seen that the extracted line laser center line is uniform and stable in width and excellent in anti-interference performance. In the invention, the selected linear laser images on the surfaces of the metal iron block and the screw have the conditions of non-concentrated reflection and non-uniform brightness in local areas, but better linear laser center point coordinates can still be extracted by adopting the method of the invention, so that the method of the invention has the advantages of good stability, good anti-interference performance and excellent robustness.
Meanwhile, two-dimensional gaussians need to perform template convolution or global iteration in two directions, which is time-consuming operation. Under the specific operation test parameters shown in table 1, the extraction time of the two-dimensional gaussian algorithm is about 40ms, while the algorithm of the present invention only needs about 18ms, and the two-dimensional gaussian hardware is difficult to accelerate under the condition of large line width. In addition, the method has higher adaptability and repeatability precision for the areas with different overexposure line widths and widths.
Table 1: specific operational test parameters
Image parameters Computer parameters
Type (2): RGB image A processor: intel Core i7
Size: 2048*1536 CPU frequency: 3.7GHz
Meanwhile, in the specific calculation process, the method adopts an algorithm for reducing two dimensions into one dimension, so that the dimension reduction is realized, the calculation amount is reduced, and the complexity is reduced, thereby facilitating the realization of hardware.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, so any modifications, equivalents, improvements and the like made within the spirit of the present invention should be included in the scope of the present invention.

Claims (10)

1. A method for extracting a line laser center is characterized by comprising the following steps: the method comprises the following steps:
(1) carrying out graying processing on an input image;
(2) scanning the image subjected to the graying processing point by point, and comparing the gray value of each point in the image with a given image gray threshold;
(3) if the gray value of the point in the image is smaller than the given image gray threshold, the calculation is not carried out, and the next point is continuously compared;
(4) if the gray value of the point in the image is larger than the given image gray threshold, the following operations are carried out:
(a) calculating the direction of the target point by using a polynomial method to obtain the position of the target point;
(b) recording data of cache points in each k/2 pixel range on two sides of the target point according to the direction of the target point;
(c) extracting coordinates of a line laser sub-pixel central point by adopting a one-dimensional Gaussian model;
automatically determining the image gray threshold, wherein the image gray threshold is equal to the gray value at the maximum gray value accumulation value in the gray histogram plus a variable, and the value range is 5-10;
the k is the desired width of the line laser beam that impinges on the surface of the test sample.
2. The method of extracting a line laser center of claim 1, wherein: in the operation (a) of the step (4), the polynomial is calculated by an orthogonal decomposition method in a convolution mode, so as to obtain the direction of the target point.
3. The method of extracting a line laser center of claim 2, wherein: in operation (a) of the step (4), for a one-dimensional case:
assume that a given set of orthogonal bases is: { P0(r),...PN-1(r)}；
Pn(r)＝rn+an-1rn-1+...+a1r+a0(1)
For each R ∈ R, each point d (R) can be obtained by equation (2):
Figure FDA0002461597310000011
the coefficients need only be obtained for the target equation (3) to obtain the minimum value:
Figure FDA0002461597310000012
the results obtained were:
Figure FDA0002461597310000013
for each d (r), the coefficients of equation (5) are multiplied:
Figure FDA0002461597310000021
4. the method of extracting a line laser center of claim 3, wherein: in the operation (a) of the step (4), for the two-dimensional case, the calculation may be performed by decomposing into two one-dimensional cases:
assume class direction { P0(r),...PN-1(r) }, the row direction is { Q }0(c),...QN-1(c)}，
Figure FDA0002461597310000022
The polynomial is a second-order polynomial, as shown in equation (7):
g(x,y)＝k0+k1x+k2y+k3x2+k4xy+k5y2(7)
and (3) calculating the direction:
Figure FDA0002461597310000023
the direction vector corresponding to the maximum value of the eigenvector of the H matrix is the normal direction (n) of the target pointx，ny)。
5. The method of extracting a line laser center of claim 1, wherein: in operation (b) of the step (4), if the gray value of a certain buffer point a appears to be equal to the gray value of the target point along the scanning direction, recording in a way of a trip: recording the distance from the cache point A to the target point as traceB; on the other side of the target point, recording the distance from another cache point B which is symmetrical to the cache point A to the target point as traceE, wherein traceB is traceE; a point which is k/2 pixels away from the other side of the target point from the cache point A is taken as an end point locB, and a point which is k/2 pixels away from the other side of the target point from the cache point B is taken as an end point locE; the data to be cached is: the values of traceB and traceE, as well as data buffered at a point between point a to the endpoint locB, and data buffered at a point between point B to the endpoint locE.
6. The method of extracting a line laser center of claim 2, wherein: in the operation (c) of the step (4), the method for extracting the coordinates of the center point of the sub-pixel by adopting the one-dimensional Gaussian model comprises the following steps:
let the Gaussian template be gn，gn’ and gn"its first and second derivatives, rn，rn’ and rn"convolution of a Gaussian template and each fixed length, respectively, Taylor expansion to obtain equation (9)
Figure FDA0002461597310000024
The center point coordinate thus obtained is at p' (x) ═ 0, and the resulting position is obtained from equation (10):
Figure FDA0002461597310000031
x is in the range of (-0.5, 0.5) to meet the requirement; the coordinates of the center point of the sub-pixels of the line laser can then be obtained from the orientation and origin coordinates as (Tx + x cos ψ, Ty + x sin ψ), where ψ is the normal angle calculated by the polynomial method.
7. The method of extracting a line laser center of claim 6, wherein: in the method for extracting the coordinates of the center points of the sub-pixels by adopting the one-dimensional Gaussian model, a Gaussian recursion method is selected to realize convolution.
8. The method of extracting a line laser center of claim 7, wherein: the specific method of Gaussian recursion is as follows:
assuming that an input image is I (n), Gaussian convolution of the image, and first order differential and second order differential respectively correspond to Tn，Tn’ and Tn", backward recursion On，On’ and On", then:
Figure FDA0002461597310000032
Figure FDA0002461597310000033
Figure FDA0002461597310000034
Figure FDA0002461597310000035
Figure FDA0002461597310000036
Figure FDA0002461597310000037
in the formula: coefficient c1、c2、c3Respectively as follows:
c1＝(2.44413q+2.85619q2+1.26662q3)/c0
c2＝-(1.4281q2+1.26661q3)/c0
c3＝0.422205q3/c0
B＝1-(c1+c2+c3)
wherein ,
Figure FDA0002461597310000038
c0＝1.57825+2.44413q+1.4281q2+0.422205q3；
σ is a gaussian distribution parameter of a gaussian function.
9. The method of extracting a line laser center of claim 1, wherein: k is larger than or equal to the width/cos alpha of the incident line laser beam, wherein alpha is a plane included angle between the incident line laser beam and the test sample.
10. The method of extracting a line laser center of claim 9, wherein: k ≧ 160.
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