CN107102165A - A kind of surface flow field measuring method based on particle image velocimetry - Google Patents
A kind of surface flow field measuring method based on particle image velocimetry Download PDFInfo
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- G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
- G01P5/00—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft
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- G01P5/20—Measuring speed of fluids, e.g. of air stream; Measuring speed of bodies relative to fluids, e.g. of ship, of aircraft by measuring the time taken to traverse a fixed distance using particles entrained by a fluid stream
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Abstract
The invention discloses a kind of surface flow field measuring method based on particle image velocimetry, it is included in flow field face to be measured and dispenses the trace particle for not limiting material and size；At least one visual field is set in the flow field to be measured, video image sampling is carried out to visual field, and to setting up corresponding birds-eye view after video image progress visual field correction；Each birds-eye view is divided into multiple sub-grids, and the flow velocity of respective subnet lattice in all birds-eye views is averaged, the flow velocity of visual field is obtained；Data splicing and visualization processing are carried out successively to the flow velocity of multiple visual fields, the steps such as the visualization flow velocity image of flow field face to be measured is obtained, the measurement of surface flow field flow velocity is successfully realized using PIV technologies.Its remarkable result is：Video capture device visual angle is free, and complicated landform and ambient light, which are shone, has preferable robustness, can meet different test environment demands；Support any type of trace particle；Analyze speed is fast, and measurement accuracy is high.
Description
Technical field
It is a kind of surface stream based on particle image velocimetry specifically the present invention relates to water conservancy measurement technology field
Field measurement method.
Background technology
It is the important means of river model analysis that surface flow field, which tests the speed, is also the basic technology that water conservancy measures field.At present,
The single-point type flow rate measuring device generally used in the industry, the device not only stream field formation interference in measurement, causes measurement to miss
Difference；And because measurement efficiency is too low, it is difficult to flow velocity field analysis and unsteady flow field analysis applied to large-scale river model.
Moreover, being primarily present following shortcoming in existing surface velocity measuring method：(1) video capture device is necessary
The angle perpendicular to flow velocity surface is operated in, illumination condition requires high to external world, and operating process is complicated；(2) multichannel data point
Analyse speed slow, measurement accuracy is not high enough；(3) trace particle cost used in most of flow field velocity-measuring systems is higher.
The content of the invention
In view of the shortcomings of the prior art, it is an object of the invention to provide a kind of surface flow field survey based on particle image velocimetry
The visual angle of video capture device is free in amount method, this method, with good robustness, convenience, can meet different surveys
Try environmental demand；Also support any type of trace particle；Analyze speed is fast, and measurement accuracy is high.
To reach above-mentioned purpose, the technical solution adopted by the present invention is as follows：
A kind of surface flow field measuring method based on particle image velocimetry, its key is to follow the steps below：
Step 1：The trace particle for not limiting material and size is dispensed in flow field face to be measured；
Step 2：At least one visual field is set in the flow field to be measured, and adopts the stream for obtaining each visual field with the following method
Speed：
Step 2-1：Multiple video image sampling is carried out to the visual field, visual field correction is then carried out, sets up corresponding bird
Look down from a height figure；
Step 2-2：Each birds-eye view is divided into multiple sub-grids, passes through pyramid Lucas-Kanade optical flow algorithm meters
The velocity of trace particle in each sub-grid is calculated, and as the flow velocity of the sub-grid center；
Step 2-3：The flow velocity of respective subnet lattice in all birds-eye views is averaged, obtained flow velocity matrix is as the visual field
Flow velocity；
Step 3：Data splicing and visualization processing are carried out successively to the flow velocity of multiple visual fields, flow field face to be measured is obtained
Visualize flow velocity image.
Further, video image is acquired by video capture device described in step 2-1, the video capture device
Acquisition angles and decorating position it is any.
Further, concretely comprising the following steps for birds-eye view is corrected and set up in visual field in step 2-1：
Step 2-1-1：Distortion correction processing is carried out to raw video image；
Step 2-1-2：Four pixels are chosen on image after distortion correction, are constituted using four pixels as summit
Rectangle measured zone R, then obtains the corresponding practical flow field coordinate points of four pixels, and with four practical flow field coordinate points structures
It is R into practical flow field measured zone0；
Step 2-1-3：Calculate rectangle measured zone R width and the ratio T of length；Structure width and the ratio of length are
T arbitrary size gets a bird's eye view plan R1；
Step 2-1-4：According to rectangle measured zone R with getting a bird's eye view plan R1Vertex correspondence relation, calculate distortion correction
The pixel coordinate of image afterwards perspective transformation matrix H corresponding with birds-eye view coordinate；
Step 2-1-5：According to perspective transformation matrix H, birds-eye view is set up.
Further, practical flow field measured zone R is obtained according to rectangle measured zone R pixel coordinate in step 2-1-20
The actual coordinates of middle corresponding points is concretely comprised the following steps：
Step s1：Four points of known actual coordinate are marked in practical flow field；
Step s2：The corresponding pixel coordinate of aforementioned four point is obtained in image after distortion correction；
Step s3：According to the proportionate relationship of the pixel coordinate of image after distortion correction, practical flow field correspondence position is determined
Coordinate.
Further, in step 3 to flow velocity carry out data splicing the step of be：
A1：Lateral coordinates to flow speed data are matched with longitudinal coordinate；
A2：Average is asked for when same coordinate points have multigroup flow speed data, and as the flow velocity number of the coordinate points
According to；In the absence of when then directly will calculate obtain flow speed data as the coordinate points flow speed data.
Further, it is the step of flow field face flow velocity visualization processing in step 3：
B1：Create the virtual flow field of a gridding with flow field to be measured in proportion；
B2：Flow speed data is answered into relationship map into the virtual flow field according to coordinate pair；
B3：According to obtained flow speed data, the visualization flow velocity image of flow field face to be measured is drawn by the virtual flow field.
The video image of acquisition is carried out distortion correction by this programme first, sets up corresponding birds-eye view；Secondly, by each bird
Figure of looking down from a height is divided into multiple sub-grids, and trace particle in each sub-grid is calculated by pyramid Lucas-Kanade optical flow algorithms
Velocity, and as the flow velocity of the sub-grid center；Again, to the flow velocity of respective subnet lattice in all birds-eye views
Average, obtained flow velocity matrix as the visual field flow velocity；Finally, the flow velocitys of multiple visual fields is carried out successively data splicing and
Visualization processing, obtains the visualization flow velocity image of flow field face to be measured.
The present invention remarkable result be：
(1) velocity vector is obtained using PIV Particle Image Velocimetries, can more quickly and accurately realizes that flow velocity is surveyed
Amount.
(2) visual field correction is carried out to the video collected from algorithm and birds-eye view is rebuild, it would be preferable to support video acquisition is set
Standby primary optical axis into any angle, that is, realizes visual angle freedom with flow field face, manually installed and debugging is eliminated, to intricately
Shape and ambient light, which are shone, has preferable robustness.
(3) any type of trace particle is supported, while systematic survey cost is reduced, the convenient of system is added
Property and application.
(4) any type of lighting source and any number of video capture device are supported, different test wrappers can be met
The demand in border.
Brief description of the drawings
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is raw video image；
Fig. 3 is the image after distortion correction；
Fig. 4 is the rectangle measured zone R schematic diagrames chosen；
Fig. 5 is the birds-eye view set up；
Fig. 6 is flow velocity visual image；
Fig. 7 is measurement accuracy comparison diagram.
Embodiment
The embodiment to the present invention is described in further detail below in conjunction with the accompanying drawings.
As shown in figure 1, a kind of surface flow field measuring method based on particle image velocimetry, is concretely comprised the following steps：
Step 1：The trace particle for not limiting material and size is dispensed in flow field face to be measured；
In the specific implementation, this example test uses the shredded paper of trace particle to consider to be worth doing, and is illuminated using natural light, almost
Zero cost and easy to operate.
Step 2：At least one visual field is set in the flow field to be measured, and adopts the stream for obtaining each visual field with the following method
Speed：
Step 2-1：Multiple video image sampling is carried out to the visual field, visual field correction is then carried out, sets up corresponding bird
Look down from a height figure；
In this example, the video image is acquired by video capture device, and video capture device uses Haikang prestige
Depending on (HIKVISION) DS-2CD3410FD-IW video cameras.The resolution ratio of the video camera be 1920 × 1080, frame per second be 25~
30fps.It it is 17 DEG C in temperature, relative humidity is under 66% test environment conditions, enters to scope for 1.7m × 2.5m flow field
Row surface velocity is measured.
Concretely comprising the following steps for birds-eye view is corrected and set up in described visual field：
Step 2-1-1：As shown in Fig. 2 raw video image be unfavorable for due to the presence of distortion image identification, analysis and
Judge.Accordingly, it would be desirable to carry out distortion correction to raw video image, the image after distortion correction is as shown in Figure 3；
Step 2-1-2：Four pixels are chosen on image after distortion correction, this four points are the rectangle that summit is constituted
Measured zone is defined as R, rectangle frame region as shown in Figure 4.Then, this corresponding practical flow field coordinate points of four points is obtained,
The flow field survey region that this four practical flow field coordinate points are constituted is defined as R0。
In this example, tetra- summits of rectangle measured zone R are defined as A, B, C, D, obtain A, B, C, D pixel coordinate, and ask
Go out the corresponding practical flow field coordinate A of A, B, C, D0、B0、C0、D0.Specific method is as follows：
Step s1：Four points of known actual coordinate are marked in practical flow field；
Step s2：The corresponding pixel coordinate of aforementioned four point is obtained in image after distortion correction；
Step s3：According to the proportionate relationship of the pixel coordinate of image after distortion correction, practical flow field correspondence position is determined
Coordinate.
Step 2-1-3：Rectangle measured zone R width and the ratio T of length are calculated, the ratio for building width and length is
T arbitrary size gets a bird's eye view plan R1, this gets a bird's eye view plan R1Four summits be defined as A1、B1、C1、D1.Known A0、B0、
C0、D0A can be obtained with ratio T1、B1、C1、D1；
Step 2-1-4：According to rectangle measured zone R with getting a bird's eye view plan R1Vertex correspondence relation, i.e., according to A, B, C,
D、A1、B1、C1、D1Corresponding relation between this 8 coordinate points, calculates the pixel coordinate and birds-eye view of the image after distortion correction
The corresponding perspective transformation matrix H of coordinate；
In the present embodiment, perspective transformation matrix H is calculated using homogeneous coordinate system.
Perspective projection transformation can specifically be described with following 3x3 matrix forms, i.e.,：
Then perspective transformation matrix H is expressed as：
Wherein (x, y, w) is rectangle measured zone R summit pixel coordinate, and (x ', y ', w ') is that the summit of birds-eye view is sat
Mark.Several above-mentioned shown corresponding relations will be similarly obtained, perspective transformation matrix H can be drawn by being decomposed by SVD.
Step 2-1-5：According to perspective transformation matrix H, birds-eye view is set up.
The process for setting up birds-eye view is exactly, using perspective transformation matrix H, the image (Fig. 3) after distortion correction to be converted into bird
Look down from a height figure (Fig. 5) process.Known perspective transformation matrix H and pixel coordinate, calculate that each pixel coordinate is corresponding to be got a bird's eye view
Figure coordinate, calculation is as described in step 2-1-4, to visual angle figure to the conversion of birds-eye view before completing.
Visual field distortion correction has been carried out to video frame images by above-mentioned steps, corresponding birds-eye view has been set up, realizes and regard
The angle and decorating position of frequency collecting device can unrestricted choice be that visual angle is free, eliminate manually installed and debugging, so not only
Make the selection of measured zone more flexible, and sunlight, light-illuminating can be reduced by changing the direction of video capture device
The reflective influence produced, to complicated landform and ambient light according to preferable robustness is provided with, improves system for illumination variation
Adaptability.
Step 2-2：Each birds-eye view is divided into multiple sub-grids, passes through pyramid Lucas-Kanade optical flow algorithm meters
The velocity of trace particle in each sub-grid is calculated, and as the flow velocity of the sub-grid center；
The specific calculation procedure of trace particle velocity is as follows：
I, the coordinate u=[u for extracting trace particle central pointx uy]T。
II, found in the second two field picture J using pyramid Lucas-Kanade (Lucas-Kanade, LK) optical flow algorithms
One point z=[zx zy]TSo that z=u+d=[ux+dx uy+dy]T.Vectorial d=[dx dy]TExactly point u light stream.
For pyramid LK optical flow algorithms, main step has three steps：Set up pyramid, pyramid signature tracking,
Iterative process.
S1：Set up image I and image J pyramid：With
In the present embodiment, pyramid is set up using a kind of recursive fashion：Utilize I0(raw video image) calculates I1, so
I is utilized afterwards1Calculate I2, wherein L is the pyramid number of plies, then ILRepresent L layers of image I picture, JLRepresent L layers of image J figure
Piece；LmRepresent pyramidal height.According to the definition of image pyramid, if image I size is nx×ny, then ILLayer figure
The size of piece is (nx/2L)×(ny/2L)。
S2：Pyramid signature tracking；
It is described in detail below：
1) first, in top Lm=3, according toCalculate initialization
Pyramidal light stream estimatorAnd by minimizing erroneous matching functionTo obtain remaining light stream estimation
Amount
Minimize erroneous matching functionCalculation formula it is as follows：
Wherein, uxWith uyIt is grid element center point u coordinate.wxAnd wyIt is two integers, general value is 2,3,4,5,6,7
Individual pixel；
2) then, by top Lm=3 result of calculation is as initial value, according to formulaPass to next layer i.e. Lm- 1=2 tomographic images, LmBase of -1 tomographic image in this initial value
On plinth, the transmission initialization light stream estimator for obtaining this layer is calculatedAnd by minimizing erroneous matching functionTo obtain remaining light stream estimator
3) again by Lm- 1=2 layers of transmission initialization estimatorWith remaining light stream estimatorTo be initial
Value, passes to next layer i.e. Lm- 2=1 tomographic images, until passing to last layer, i.e. raw video image layer, according toCalculate Lm- 2 layers of light stream estimatorAnd by minimizing mistake
Adaptation functionTo obtainRemaining light stream estimator
4) by Lm- 2=1 layers of result of calculation passes to next layer i.e. Lm- 3=0 layers, calculated using same computational methods
Obtain Lm- 3 layers of light stream estimator g0=2 (g1+d1), and minimize erroneous matching function of ε0(d0) obtain d0, last light
Stream result d is obtained by optical flow computation：
D=g0+d0
S3：Iterative process
In pyramidal each layer, target is to calculate light stream d to minimize erroneous matching function of εLIt is minimum.Due to
Each layer of iterative process is identical, so this example only describes the iterative process from one layer to next layer.
If k is iteration index, 1 is initialized as when starting.The solution of minimum can be obtained by calculating LK light streams
Arrive：
Wherein,It is image mismatch vector, G is spatial gradient matrix.Last light stream vectors are：
Wherein, K is the iterations carried out when reaching convergence.
Step 2-3：The flow velocity of respective subnet lattice in all birds-eye views is averaged, obtained flow velocity matrix is as the visual field
Flow velocity；
Step 3：Data splicing and visualization processing are carried out successively to the flow velocity of multiple visual fields, flow field face to be measured is obtained
Visualize flow velocity image：
(1) to flow velocity carry out data splicing the step of be：
A1：Lateral coordinates to flow speed data are matched with longitudinal coordinate；
A2：Because the video image that video capture device is collected there may exist overlapping situation, for overlapping stream
Field measurement region, same coordinate position has multigroup flow speed data.Therefore, in this case, to multigroup flow velocity of same coordinate points
Data ask for average as the flow speed data of the coordinate position.If visual field overlapping phenomenon is not present in the coordinate points, without place
Reason, will directly calculate the flow speed data of acquisition as the flow speed data of the coordinate position.
(2) visible processing method is：
B1：Create the virtual flow field of a gridding with flow field to be measured in proportion；
B2：Flow speed data is answered into relationship map into the virtual flow field according to coordinate pair；
B3：The visualization flow velocity image of flow field face to be measured, gained visualization flow velocity image are drawn by the virtual flow field
As shown in Figure 6.
Using the above method, suspect system is measured to 425 test points in measurement flow field simultaneously, obtains 425 speed
Spend measured value (unit：m/s).And using the actual value obtained by acoustic Doppler velocimeter measurement as reference value, both are right
Than result as shown in fig. 7, curve L-AOM ordinate represents the measurement average value and ginseng for 425 test points that the system is obtained
The absolute value (absolute error) of the difference of value is examined, slope represents relative error.L-PA and L-PB ordinate is that system is randomly selected
Two single test point measured values and reference value difference absolute value.
By the statistics to overall flow velocity measurement result and single measurement point measurement result, tested the speed by acoustic Doppler
The measurement result of instrument is as reference value, and relative error is less than 10% in the range of [0.01~0.05] m/s, (0.05~1.5] m/s models
Interior relative error is enclosed less than 5%, and in whole test scope, flow velocity measurement angular deviation is respectively less than 0.5 °.Therefore deduce that, this
Invention improves the adaptability for illumination variation, and good realizes the measurement of surface flow field flow velocity, and measurement accuracy is high.
Claims (6)
1. a kind of surface flow field measuring method based on particle image velocimetry, it is characterised in that follow the steps below：
Step 1：The trace particle for not limiting material and size is dispensed in flow field face to be measured；
Step 2：At least one visual field is set in the flow field to be measured, and adopts the flow velocity for obtaining each visual field with the following method：
Step 2-1：Multiple video image sampling is carried out to the visual field, visual field correction is then carried out, sets up corresponding birds-eye view；
Step 2-2：Each birds-eye view is divided into multiple sub-grids, calculates every by pyramid Lucas-Kanade optical flow algorithms
The velocity of trace particle in individual sub-grid, and as the flow velocity of the sub-grid center；
Step 2-3：The flow velocity of respective subnet lattice in all birds-eye views is averaged, obtained flow velocity matrix as the visual field stream
Speed；
Step 3：Data splicing and visualization processing are carried out successively to the flow velocity of multiple visual fields, the visual of flow field face to be measured is obtained
Change flow velocity image.
2. the surface flow field measuring method according to claim 1 based on particle image velocimetry, it is characterised in that：Step 2-
Video image is acquired by video capture device described in 1, and acquisition angles and the decorating position of the video capture device are appointed
Meaning.
3. the surface flow field measuring method according to claim 1 or 2 based on particle image velocimetry, it is characterised in that：Step
Concretely comprising the following steps for birds-eye view is corrected and set up in visual field in rapid 2-1：
Step 2-1-1：Distortion correction processing is carried out to raw video image；
Step 2-1-2：Four pixels are chosen on image after distortion correction, rectangle is constituted using four pixels as summit
Measured zone R, then obtains the corresponding practical flow field coordinate points of four pixels, and constituted in fact with four practical flow field coordinate points
Border flow field survey region is R0；
Step 2-1-3：Rectangle measured zone R width and the ratio T of length are calculated, it is T's to build the ratio of width and length
Arbitrary size gets a bird's eye view plan R1；
Step 2-1-4：According to rectangle measured zone R with getting a bird's eye view plan R1Vertex correspondence relation, calculate distortion correction after figure
The pixel coordinate of picture perspective transformation matrix H corresponding with birds-eye view coordinate；
Step 2-1-5：According to perspective transformation matrix H, birds-eye view is set up.
4. the surface flow field measuring method according to claim 3 based on particle image velocimetry, it is characterised in that：Step 2-
Practical flow field measured zone R is obtained according to rectangle measured zone R pixel coordinate in 1-20The tool of the actual coordinate of middle corresponding points
Body step is：
Step s1：Four points of known actual coordinate are marked in practical flow field；
Step s2：The corresponding pixel coordinate of aforementioned four point is obtained in image after distortion correction；
Step s3：According to the proportionate relationship of the pixel coordinate of image after distortion correction, the coordinate of practical flow field correspondence position is determined.
5. the surface flow field measuring method according to claim 1 based on particle image velocimetry, it is characterised in that：Step 3
In to flow velocity carry out data splicing the step of be：
A1：Lateral coordinates to flow speed data are matched with longitudinal coordinate；
A2：Average is asked for when same coordinate points have multigroup flow speed data, and as the flow speed data of the coordinate points；No
In the presence of then directly will calculate obtain flow speed data as the coordinate points flow speed data.
6. the surface flow field measuring method based on particle image velocimetry according to claim 1 or 5, it is characterised in that：Step
It is the step of flow field face flow velocity visualization processing in rapid 3：
B1：Create the virtual flow field of a gridding with flow field to be measured in proportion；
B2：Flow speed data is answered into relationship map into the virtual flow field according to coordinate pair；
B3：According to obtained flow speed data, the visualization flow velocity image of flow field face to be measured is drawn by the virtual flow field.
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| CN112884806A (en) * | 2021-01-12 | 2021-06-01 | 昆明理工大学 | Video stream measuring method and device based on combination of block matching and intensive reverse search |
| CN112884806B (en) * | 2021-01-12 | 2022-09-02 | 昆明理工大学 | Video stream measuring method and device based on combination of block matching and intensive reverse search |
| CN117952173A (en) * | 2024-03-26 | 2024-04-30 | 浙江大学 | PIV and velocity field data set construction method and device for deep learning model training |
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