CN103868460B - Binocular stereo vision method for automatic measurement based on parallax optimized algorithm - Google Patents
Binocular stereo vision method for automatic measurement based on parallax optimized algorithm Download PDFInfo
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Abstract
The invention discloses the binocular stereo vision method for automatic measurement based on parallax optimized algorithm, 1) the binocular view after being corrected；2) utilize Stereo Matching Algorithm to mate for base figure with left view, obtain preliminary disparity map；3) to the left view after correction, target object region is colour original, and other nontarget areas are all black；4) according to target object region, it is thus achieved that the complete disparity map in target object region；5) to complete disparity map, three-dimensional point cloud is obtained according to projection model；6) to three-dimensional point cloud, enter row-coordinate re-projection, synthesize coordinate associated pixel figure；7) morphologic method is utilized, it is achieved automatically measure the length and width of target object.This invention simplifies binocular measurement procedure；Decrease mirror-reflection, foreshortening, perspective distortion, the low texture of smooth surface and repeat texture effects；Achieve automation and intelligentification measurement, extend the range of application of binocular measurement, provide technical support for follow-up Robot Binocular Vision.
Description
Technical field
The present invention relates to the fields of measurement of binocular stereo vision, specifically automatically survey based on the binocular stereo vision of parallax optimized algorithm
Metering method.
Background technology
In recent years, the fields such as robot self-aiming, unmanned, hidden military surveillance, medical diagnosis and industrial detection should
With increasingly extensively, binocular stereo vision is increasingly becoming in Robotics research and enlivens one of branch the most, is also that subsequent technology is sent out
The important foundation of exhibition.It is a kind of efficient untouchable advanced detection, measurement based on the measurement technology of binocular stereo vision, identifies
Technology, development prospect is considerable, has wide range of applications.
Currently, the application of Binocular vision photogrammetry is largely focused on measurement distance, has certain limitation；And at measurement space thing
The size dimension application aspect of body, needs point take target pixel points or arrange special characteristic point, it is impossible to realize the automatic of object
Extract, automatically measure, so hinder industrial intellectuality, mechanization；And the artificial behaviour in traditional measuring method
Make step, not only increase labour cost and make step more complicated；The qualified detection of part realizing in terms of industrial detection,
Also it is the detection under fixed scene, it is impossible to realize the automatic measurement of relative motion dimension of object size；And the object to motion,
It is very inaccurate that manual point takes object pixel, can not realize real-time.The more important thing is, due to the minute surface of smooth surface
The impact on binocular ranging such as reflection, foreshortening, perspective distortion, low texture and repetition texture, causes manually putting the target taking
Pixel not necessarily effective three-dimensional information.
Content of the invention
It is an object of the invention to, for overcoming the deficiencies in the prior art, provide a kind of binocular stereo vision based on parallax optimized algorithm certainly
Dynamic measuring method, this method not only simplify the operating procedure of binocular measurement, more accurately can automatically obtain target object
Metrical information, and be optimized the parallax producing after its coupling, reduces the mirror-reflection of smooth surface, foreshortening, thoroughly
Depending on ectocines such as distortion, low texture and repetition textures, the three-dimensional point cloud information making target object is more complete, it is achieved automation
Intelligent measuring, extends the range of application of binocular measurement greatly, provides technical support for follow-up Robot Binocular Vision.
The technical scheme realizing the object of the invention is:
Based on the binocular stereo vision method for automatic measurement of parallax optimized algorithm, comprise the steps:
1) obtained two-way RGB color image by binocular camera simultaneously, utilize chessboard calibration method to carry out binocular camera camera
Binocular calibration, it is thus achieved that the inside and outside parameter of two binocular camera cameras, carries out binocular correction according to these parameters to binocular view,
Remove fisheye effect and site error impact, the binocular view after being corrected of camera lens；
2) to the binocular view after correction, utilize Stereo Matching Algorithm to mate for base figure with left view, obtain preliminary parallax
Figure；
3) to the left view after correction, utilizing background subtracting method to extract target object, target object region is colour original, other
Nontarget area is all black；
4) according to target object region, utilize based on foreground detection parallax optimized algorithm to preliminary disparity map local no parallax region
It is filled with, it is thus achieved that the complete disparity map in target object region；
5) to complete disparity map, three-dimensional point cloud is obtained according to projection model；
6) to three-dimensional point cloud, enter row-coordinate re-projection, three-dimensional coordinate is projected to two dimensional surface, and by the method for image procossing,
The world coordinates of two dimensional surface is associated with the pixel coordinate of picture, with gray scale picture concrete demonstrate two after re-projection
Dimension coordinate generates coordinate associated pixel figure；
7) to coordinate associated pixel figure, utilize morphologic method, use minimum enclosed rectangle to frame the profile in pixel picture,
According to the relation of world coordinates and picture pixels, the pixel value of its length of side is converted to real length information, and automatically demonstrates
Come, it is achieved automatically measure the length and width of target object.
Described binocular camera is that the double-channel collection video combined by two eyes of the camera simulation people of two same specifications sets
Standby, obtain picture pixels and be set to 640x480 pixel, keep original RGB picture.
Described chessboard calibration method is the calibration algorithm utilizing specific black and white gridiron pattern to realize, demarcates binocular camera, obtains
Obtain the inside and outside parameter of two cameras, such as: the parameters such as focal length, distortion parameter, translation vector, spin matrix；Use chessboard mark
The focal length determining two cameras during method keeps consistent, and chessboard uses the black and white lattice of 9x6 lattice, unit square length of side 30mm, gathers picture
15-20 opens；Binocular correction zoom factor is set to 0；Image after correction zooms in and out and translates so that correction chart picture only shows
Effect pixel i.e. removes irregular corner areas.
Described binocular correction refers to that binocular camera calibration not only to draw the inner parameter of each camera, in addition it is also necessary to by mark
The fixed relative position measured between two cameras, i.e. right camera is relative to the vectorial t of the D translation of left camera and rotation
Matrix R；The parallax that impact point to be calculated is formed on left and right two views, first has to this point on left and right view two
Corresponding Pixel matching gets up, but it is very time-consuming for mating corresponding points on two-dimensional space, in order to reduce coupling hunting zone,
We utilize epipolar-line constraint to make the coupling of corresponding points be reduced to linear search by two-dimensional search；The effect of binocular correction seeks to handle and disappears
Except the two width images strictly row correspondence after distortion so that two width images to polar curve just in the same horizontal line, such width
On image, any point and its corresponding points on another piece image just necessarily have identical line number, only need to carry out one-dimensional at this row
Search can match corresponding points.
Described Stereo Matching Algorithm is BM (Boyer-Moore) algorithm (local matching algorithm), or SGBM algorithm (half
Global registration algorithm), BM algorithm state parameter is respectively as follows: the cutoff value (preFilterCap)=31 of pre-processing filter；SAD
Window size (SADWinSiz)=19；Minimum parallax (minDisp)=0；Parallax window (numberOfDisparities)=64；
The judgment threshold (textureThreshold)=10 of low texture region, if the x of all neighbor pixel points leads in current SAD window
Number absolute value sum is less than specifying threshold value, then the parallax value of the corresponding pixel of this window is 0；Parallax uniqueness percentage
(uniquenessRatio)=25, when in parallax window ranges, lowest costs is secondary low-cost (1+uniquenessRatio/100) times,
The corresponding parallax value of lowest costs is only the parallax of this pixel, and otherwise the parallax of this pixel is 0；Parallax change threshold
(speckleRange)=32, when in window, parallax change is more than threshold value, the parallax in this window resets.
The state parameter of SGBM algorithm from unlike BM algorithm: SAD window size=7, permissible range is [1,11]；Phase
Penalty coefficient P1 during adjacent pixel parallax add drop 1；Penalty coefficient when neighbor pixel parallax changing value is more than 1 is P2,
P2 is more than P1；FullDP is set to FALSE.
Described background subtracting method, is frame difference method, and i.e. under simple background, the difference such as foreground and background brightness and color is brighter
Aobvious, deduct that frame only comprising background by a later frame comprising prospect, it is thus achieved that complete foreground area and background frames can be at any time
Choosing, the target object extracting is colored original image, and other nontarget areas are all black.
Described extraction target object is to extract automatically to scratch the form of figure, and the foreground target object extracting is original RGB figure
Picture, other background parts are completely black, i.e. the coloured image of a display foreground object.
Described parallax optimized algorithm is in foreground area, detects effective parallax and invalid parallax, and record accessing by row,
Effective parallax is the known conditions of least square method, carries out fitting a straight line to invalid parallax part, and effective parallax keeps constant,
Reaching whole target object region has complete disparity map, and isolates the disparity map in this region.
Described coordinate associated pixel figure is the method utilizing space projection, will with the left optical center of binocular camera as initial point, eyeglass
Plane is that the three-dimensional coordinate in the world coordinate system of X/Y plane projects to X/Y plane, it is achieved the three-dimensional conversion to two dimension；Then
With the corresponding 5cm of a pixel in binaryzation picture, coordinate can be believed by this respective value 5cm according to the scope modification of measurement
Breath shows with the form of binaryzation pixel so that it is visualization.
The above-mentioned binocular stereo vision method for automatic measurement based on parallax optimized algorithm, is applied in the coordinate associated pixel figure generating, from
The data that dynamic measurement obtains, as the mobile tracking of target object shows.
This advantage based on the binocular stereo vision method for automatic measurement of parallax optimized algorithm is:
The steps such as Utilization prospects detection, spatial alternation, Morphological scale-space realize automatically obtaining exactly the metrical information of target object；
Simplify binocular measurement procedure, expanded its application prospect, and the parallax producing after its coupling is optimized, subtracted
Lack mirror-reflection, foreshortening, perspective distortion, the low texture of smooth surface and repeated the ectocines such as texture, having made object
The three-dimensional point cloud information of body is more complete, it is achieved that automation and intelligentification is measured, and extends the application of binocular measurement greatly
Scope, provides technical support for follow-up Robot Binocular Vision.
Brief description
Fig. 1 is binocular stereo imaging principle schematic in embodiment.
Detailed description of the invention
With embodiment, present invention is further elaborated below in conjunction with the accompanying drawings, but is not limitation of the invention.
Embodiment:
Based on the binocular stereo vision method for automatic measurement of parallax optimized algorithm, comprise the following steps:
1) obtained two-way RGB color image by binocular camera simultaneously, utilize chessboard calibration method to carry out binocular camera camera
Binocular calibration, it is thus achieved that the inside and outside parameter of two binocular camera cameras, carries out binocular correction according to these parameters to binocular view,
Remove fisheye effect and site error impact, the binocular view after being corrected of camera lens；
Described binocular camera is that the double-channel collection video combined by two eyes of the camera simulation people of two same specifications sets
Standby, obtain picture pixels and be set to 640x480 pixel, keep original RGB picture；
After adjusting binocular camera and collecting two-way RGB image, utilizing black and white lattice chessboard to demarcate it, camera is due to optics
The characteristic of lens makes imaging there is radial distortion:
xp=x (1+k1r2+k2r4+k3r6)
yp=y (1+k1r2+k2r4+k3r6) (1)
Parameter k in formula (1)1,k2,k3For radial distortion parameter, (x y) is original coordinates, (xp,yp) it is the new coordinate after correcting；
R is radius；
Error in terms of due to assembling, is not substantially parallel between sensor and optical lens, and therefore imaging exists tangential distortion:
xp=x+ [2p1y+p2(r2+2x2)]
yp=y+ [p1(r2+2y2)+2p2x)] (2)
Parameter p in formula (2)1,p2For tangential distortion parameter；The calibration of single camera mainly calculates the interior of camera
Ginseng, i.e. focal length f, imaging initial point cx、cyAnd five distortion parameters, and join outward, i.e. demarcate the world coordinates of thing.Binocular
Camera calibration not only to draw the inner parameter of each camera, in addition it is also necessary to by demarcating the phase measuring between two cameras
To position, i.e. right camera is relative to the D translation vector T of left camera and spin matrix R；Impact point to be calculated left,
The parallax being formed on right two views, first has to this point two corresponding Pixel matchings on left and right view.But,
It is very time-consuming for mating corresponding points on two-dimensional space, and in order to reduce coupling hunting zone, we can utilize epipolar-line constraint to make
The coupling obtaining corresponding points is reduced to linear search by two-dimensional search；And the effect of binocular correction seeks to two width figures after elimination is distorted
As strictly row correspondence so that two width images to polar curve just in the same horizontal line, on such piece image any point with
Its corresponding points on another piece image just necessarily have identical line number, only need to this row carry out linear search can match right
Ying Dian；
Then binocular correction is according to the monocular internal reference data (focal length, imaging initial point, distortion factor) obtaining after camera calibration
With binocular relative position relation (spin matrix and translation vector), carry out to left and right view respectively eliminating distortion and row alignment, make
The imaging origin of left and right view is consistent, two camera optical axis imaging planes parallel, left and right are coplanar, to polar curve row pair
Together；
2) to the binocular view after correction, utilize Stereo Matching Algorithm to mate for base figure with left view, obtain preliminary parallax
Figure, this disparity map is due to mirror-reflection, foreshortening, perspective distortion, low texture and repeats the impact generation sudden change parallaxes such as texture
Or local no parallax；
Stereo Matching Algorithm includes following step: first carry out Matching power flow calculating,
C (p, d)=C (x, y, d)=max{0, IL(pLi)-I'max,I'min-IL(pLi)} (3)
In equation (3), C is Matching power flow function, and d is parallax, pLiRepresent the ith pixel point p of Zuo TuLi(x,y).Left view
In figure, the gray-scale intensity of i-th is expressed as IL(pLi), I'maxAnd I'minRepresenting minimax intensity level respectively, L represents left.
Then Matching power flow superposition:
In formula (4), E is heat-supplied function, and D is whole disparity map, DPAnd DqRepresent that pixel p and q is corresponding respectively to regard
Difference, NPFor the field of pixel p, right side Section 1 represents the Matching power flow sum of all pixels.For pixel p with
Its adjacent pixel depth difference has small change and two kinds of situations of large change, and Section 2 and Section 3 use FACTOR P respectively1And P2
Being punished, function T [] is that 1 and if only if that its parameter is true here, is otherwise 0.Substantially, Section 2 and Section 3
Being smoothness constraint mentioned above, they require that the depth value of adjacent pixel is consistent as much as possible, i.e. keep smooth, aobvious
So, P1＜ P2；
Then direct original match cost is processed, to heat-supplied function (4), try to achieve energy by different optimized algorithms
Minimum of a value, simultaneously each point parallax value also just calculate.Some measures are used to enter parallax after initial parallax obtains
Row refinement.
3) to the left view after correction, utilizing background subtracting method to extract target object, target object region is colour original, other
Nontarget area is all black；
After left view correction, utilizing frame difference method to extract target object, its principle is:
| i (t)-i (t-1) | ＜ T background
| i (t)-i (t-1) | >=T prospect
Wherein i (t), i (t-1) are respectively t, and the pixel value of t-1 moment corresponding pixel points, T is threshold value.Frame difference method is in background subtracting method
One, only frame difference method does not needs modeling, because its background model is exactly the figure of previous frame, so speed is very fast,
Other frame difference method is not very sensitive to the illumination of slow conversion, and its shortcoming is quite a few certainly, easily occurs that " slur " and " empty " is existing
As so next needing to be effectively improved slur and cavitation by morphology largest contours method, finally with flooding modulus method (mask)
Pluck out target object.
4) according to target object region, utilize based on foreground detection parallax optimized algorithm to preliminary disparity map local no parallax region
It is filled with, it is thus achieved that the complete disparity map in target object region；
Specifically, by row scanning binaryzation foreground picture, it is all that record continuously uninterrupted white pixel is counted more than n (threshold value)
The pixel coordinate of continuity point, obtains coordinate set V.This part is to process foreground picture, obtains the pixel coordinate of area-of-interest
Collection V, wherein V simply records the coordinate value of each pixel of area-of-interest, due to left view, disparity map and foreground picture
In area-of-interest be the same, so V is also the same.Followed by the V above obtaining, preliminary disparity map is carried out
Optimization process.Again by row scanning disparity map, obtain the pixel coordinate with a line (ordinate is identical) in pixel coordinate collection V corresponding
Parallax value, detect whether as virtual value, utilize least square line matching by row, parallax optimization is carried out to foreground area,
Obtain the complete disparity map in target object region.
5) to complete disparity map, three-dimensional point cloud is obtained according to projection model.Three-dimensional point cloud is to utilize binocular stereo imaging principle, i.e.
Projection model (relation between world coordinate system, image coordinate system and camera coordinate system) combines disparity map and generates with binocular phase
Machine left optical center point is the three dimensional space coordinate of initial point, it is thus achieved that three-dimensional point cloud comprise the tables such as three-dimensional coordinate a little, characteristic information
Show the much information between midpoint, space.
As it is shown in figure 1, left camera optical axis is parallel with right camera optical axis, baseline distance B be the projection centre line of two video cameras away from
From；Camera focus is f.
The image of existing two video cameras is in approximately the same plane, then the image coordinate Y coordinate of characteristic point P is identical, i.e. Yletf=Yright=Y
Then obtained by triangle geometrical relationship:
Then parallax is: Disparity=Xleft-Xright, i.e. the difference of the ordinate of the ordinate of left view and right view.
Thus can calculate three-dimensional coordinate under camera coordinates system for characteristic point P is:
Therefore,
On view, institute is a little simply by the presence of corresponding match point, by above-mentioned computing, just can obtain its corresponding three-dimensional coordinate.
6) to three-dimensional point cloud, enter row-coordinate re-projection, three-dimensional coordinate is projected to two dimensional surface, and by the method for image procossing,
The world coordinates of two dimensional surface is associated with the pixel coordinate of picture, with gray scale picture concrete demonstrate two after re-projection
Dimension coordinate generates coordinate associated pixel figure, and its pixel wide, length will directly reflect the actual width of object and length.
7) to coordinate associated pixel figure, utilize morphologic method, use minimum enclosed rectangle to frame the profile in pixel picture,
According to the relation of world coordinates and picture pixels, the pixel value of its length of side is converted to real length information, and automatically demonstrates
Come, it is achieved automatically measure the length and width of target object.
Described chessboard calibration method is the calibration algorithm utilizing specific black and white gridiron pattern to realize, demarcates binocular camera, it is thus achieved that two
The inside and outside parameter of individual camera, such as: the parameters such as focal length, distortion parameter, translation vector, spin matrix；Use chessboard calibration method
When two cameras focal length at same plane, chessboard uses the black and white lattice of 9x6 lattice, unit square length of side 30mm, gathers picture 15-20
?；Binocular correction zoom factor is set to 0；Image after correction zooms in and out and translates so that correction chart picture only shows effective picture
Element i.e. removes irregular corner areas.
Described binocular correction refers to that binocular camera calibration not only to draw the inner parameter of each camera, in addition it is also necessary to by demarcating
Measuring the relative position between two cameras, i.e. right camera is relative to the vectorial t of the D translation of left camera and spin matrix
R；The parallax that impact point to be calculated is formed on the view of two, left and right, first have to this point on left and right view two corresponding
Pixel matching gets up, but it is very time-consuming for mating corresponding points on two-dimensional space, in order to reduce coupling hunting zone, we
Epipolar-line constraint is utilized to make the coupling of corresponding points be reduced to linear search by two-dimensional search；The effect of binocular correction seeks to eliminating abnormal
The strictly row correspondence of two width images after change so that two width images to polar curve just in the same horizontal line, such piece image
Upper any point and its corresponding points on another piece image just necessarily have identical line number, only need to carry out linear search at this row
Corresponding points can be matched.
Described Stereo Matching Algorithm is BM algorithm or SGBM algorithm, and BM algorithm state parameter is respectively as follows: pretreatment filtering
The cutoff value (preFilterCap)=31 of device；SAD window size (SADWinSiz)=19；Minimum parallax (minDisp)
=0；Parallax window (numberOfDisparities)=64；The judgment threshold (textureThreshold)=10 of low texture region, if
In current SAD window, the x derivative absolute value sum of all neighbor pixel points is less than appointment threshold value, the then corresponding pixel of this window
Parallax value be 0；Parallax uniqueness percentage (uniquenessRatio)=25, in parallax window ranges, lowest costs is time low generation
During (1+uniquenessRatio/100) times of valency, the corresponding parallax value of lowest costs is only the parallax of this pixel, otherwise this pixel
The parallax of point is 0；Parallax change threshold (speckleRange)=32, when in window, parallax change is more than threshold value, in this window
Parallax reset；
The state parameter of SGBM algorithm from unlike BM algorithm: SAD window size=7, permissible range is [1,11]；Phase
Penalty coefficient P1 during adjacent pixel parallax add drop 1；Penalty coefficient when neighbor pixel parallax changing value is more than 1 is P2,
P2 is more than P1；FullDP is set to FALSE.
Described background subtracting method, is frame difference method, and i.e. under simple background, the difference such as foreground and background brightness and color is obvious,
Deduct that frame only comprising background by a later frame comprising prospect, it is thus achieved that complete foreground area and background frames can be chosen at any time,
The target object extracting is colored original image, and other nontarget areas are all black.
Described extraction target object is to extract automatically to scratch the form of figure, and the foreground target object extracting is original RGB figure
Picture, other background parts are completely black, i.e. the coloured image of a display foreground object.
Described parallax optimized algorithm is in foreground area, detects effective parallax and invalid parallax, and record accessing by row,
Effective parallax is the known conditions of least square method, carries out fitting a straight line to invalid parallax part, and effective parallax keeps constant,
Reaching whole target object region has complete disparity map, and isolates the disparity map in this region.
Described coordinate associated pixel figure is the method utilizing space projection, will with the left optical center of binocular camera as initial point, eyeglass
Plane is that the three-dimensional coordinate in the world coordinate system of X/Y plane projects to X/Y plane, it is achieved the three-dimensional conversion to two dimension；Then
With the corresponding 5cm of a pixel in binaryzation picture, coordinate can be believed by this respective value 5cm according to the scope modification of measurement
Breath shows with the form of binaryzation pixel so that it is visualization.
This binocular stereo vision method for automatic measurement based on parallax optimized algorithm, is applied in the coordinate associated pixel figure generating, from
The data that dynamic measurement obtains, as the mobile tracking of target object shows.
The above-mentioned binocular stereo vision method for automatic measurement based on parallax optimized algorithm, only uses or binocular camera quilt first
Adjust out-of-date just needs to demarcate, as long as the binocular parameter during binocular ranging is arranged once, need not measure every time and all arrange,
And background frames can be changed according to application scenarios at any time；When target object enters view, will be identified, and pluck out mesh
Mark object is at corresponding binaryzation view interface meeting tracing detection object and completes automatically to measure, the metrical information of display target object.
Claims (2)
1. the binocular stereo vision method for automatic measurement based on parallax optimized algorithm, is characterized in that, comprise the steps:
1) obtained two-way RGB color image by binocular camera simultaneously, chessboard calibration method is utilized to carry out binocular calibration to binocular camera camera, obtain the inside and outside parameter of two binocular camera cameras, according to these parameters, binocular correction is carried out to binocular view, remove fisheye effect and site error impact, the binocular view after being corrected of camera lens；
2) to the binocular view after correction, utilize Stereo Matching Algorithm to mate for base figure with left view, obtain preliminary disparity map；
3) to the left view after correction, utilizing background subtracting method to extract target object, target object region is colour original, and other nontarget areas are all black；
4) according to target object region, the parallax optimized algorithm based on foreground detection is utilized to be filled with no parallax region, preliminary disparity map local, it is thus achieved that the complete disparity map in target object region；
5) to complete disparity map, three-dimensional point cloud is obtained according to projection model；
6) to three-dimensional point cloud, enter row-coordinate re-projection, three-dimensional coordinate is projected to two dimensional surface, and by the method for image procossing, the world coordinates of two dimensional surface is associated with the pixel coordinate of picture, and with concrete the demonstrating of gray scale picture, the two-dimensional coordinate after re-projection generates coordinate associated pixel figure；
7) to coordinate associated pixel figure, utilize morphologic method, minimum enclosed rectangle is used to frame the profile in pixel picture, relation according to world coordinates and picture pixels, the pixel value of its length of side is converted to real length information, and automatically show, it is achieved automatically measure the length and width of target object；
Binocular camera obtains picture pixels and is set to 640x480 pixel, keeps original RGB picture；
When using chessboard calibration method, the focal length of two cameras keeps consistent, and chessboard uses the black and white lattice of 9x6 lattice, unit square length of side 30mm, gathers picture 15-20 and opens；Binocular correction zoom factor is set to 0；
Described Stereo Matching Algorithm is BM algorithm or SGBM algorithm, and BM algorithm state parameter is respectively as follows: cutoff value=31 of pre-processing filter；SAD window size=19；Minimum parallax=0；Parallax window=64；Judgment threshold=10 of low texture region, if the x derivative absolute value sum of all neighbor pixel points is less than specifying threshold value in current SAD window, then the parallax value of the corresponding pixel of this window is 0；Parallax uniqueness percentage=25, when in parallax window ranges, lowest costs is secondary low-cost (1+uniquenessRatio/100) times, the corresponding parallax value of lowest costs is only the parallax of this pixel, and otherwise the parallax of this pixel is 0；Parallax change threshold=32, when in window, parallax change is more than threshold value, the parallax in this window resets；
The state parameter of SGBM algorithm from unlike BM algorithm: SAD window size=7, permissible range is [1,11]；Penalty coefficient P1 during neighbor pixel parallax add drop 1；Penalty coefficient when neighbor pixel parallax changing value is more than 1 is P2, and P2 is more than P1；FullDP is set to FALSE；
Described parallax optimized algorithm is in foreground area, detect effective parallax and invalid parallax by row, and record accessing, effective parallax is the known conditions of least square method, fitting a straight line is carried out to invalid parallax part, and effectively parallax keeps constant, reaching whole target object region has complete disparity map, and isolates the disparity map in this region.
2. the binocular stereo vision method for automatic measurement based on parallax optimized algorithm according to claim 1, it is characterized in that, described coordinate associated pixel figure is the method utilizing space projection, three-dimensional coordinate with the left optical center of binocular camera as initial point, in the world coordinate system as X/Y plane for the lens plane is projected to X/Y plane, it is achieved the three-dimensional conversion to two dimension；Then with the corresponding 5cm of a pixel in binaryzation picture, coordinate information can be showed with the form of binaryzation pixel so that it is visualization by this respective value 5cm according to the scope modification of measurement.
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