CN112001991B - High-speed wind tunnel dynamic oil flow map image processing method - Google Patents
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Abstract
The invention discloses a high-speed wind tunnel dynamic oil flow map image processing method, which comprises the following steps: the method for processing the high-speed wind tunnel dynamic oil flow map image by using the optical flow method with the direction constraint specifically comprises the following steps: s1, obtaining an average graph by a sliding window; s2, extracting a directional diagram of the average graph; s3, generating a stable directional diagram; s4, calculating the velocity vector of the flow field of the adjacent frame based on the stable directional diagram as a constrained optical flow method; and S5, calculating a streamline chart and a vorticity chart by using the speed vector, and drawing the particle animation. Compared with the classical optical flow method, the method has the advantage that the matching of the flow chart calculated by the optical flow method with the directional constraint and the actual distribution is obviously improved.
Description
Technical Field
The invention relates to the technical field of oil flow maps, in particular to a dynamic oil flow map image processing method for a high-speed wind tunnel.
Background
The surface flow characteristic is one of main contents for developing the research on the aerodynamic characteristics of the aircraft, the test model generally utilizes an oil flow test to obtain the surface flow information of the model in the high-speed wind tunnel test process, and the method is simple and practical. However, the conventional recording method of the stop-shooting or discrete oil flow map is limited to obtain the flow information of the model surface, and the recorded oil flow map is only a flow picture or a flow trace of the model surface and is difficult to reflect the surface vector field flow field structure.
Aiming at the characteristics of the oil flow test, the dynamic oil flow map is calculated and processed by using an image processing technology, so that not only can a clear surface flow picture be obtained, but also richer surface flow field information such as a direction field, a speed field and the like can be obtained, and the effect of displaying more complex flow characteristics is obvious. Therefore, the dynamic oil flow map image processing and analyzing technology is developed, and the method has strong practicability for improving the quality and effect of the surface flow displayed by the oil flow method. At present, a classical optical flow method can be adopted to perform high-speed wind tunnel dynamic oil flow map image processing, but the obtained flow map is not highly matched with actual distribution, and even after the fine adjustment of the Liu-Shen algorithm, the detection result is still not ideal.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the existing problems, a high-speed wind tunnel dynamic oil flow map image processing method is provided.
The invention adopts a high-speed wind tunnel dynamic oil flow map image processing method, which is to process the high-speed wind tunnel dynamic oil flow map image by using an optical flow method with direction constraint and comprises the following steps:
s1, obtaining an average graph by a sliding window;
s2, extracting a directional diagram of the average graph;
s3, generating a stable directional diagram;
s4, calculating the velocity vector of the flow field of the adjacent frame based on the stable directional diagram as a constrained optical flow method;
and S5, calculating a streamline chart and a vorticity chart by using the speed vector, and drawing the particle animation.
Further, the method for obtaining the average map by sliding the window in step S1 includes: setting a sliding window with a radius of k, calculating an average image I for every 2k +1 imagesm。
Further, step S2 includes the following sub-steps:
s21, average chart ImPerforming Contextual filtering to complete image enhancement;
s22, calculating an average image I after image enhancement by using sobel operatormObtaining the point direction of each pixel point, then counting all the point directions in the neighborhood of the fixed window of the pixel point to obtain the block direction of the pixel point, wherein the block direction of all the pixel points is the average graph ImThe directional pattern of (a).
Further, the method for generating a stable directional diagram in step S3 includes: and for each pixel point, taking the block direction median value in the time sequence as the direction of the pixel point, and expressing the direction by adopting radians with the ranges of [ -pi/2, pi/2 ] to obtain a stable directional diagram.
Further, step S4 includes the following sub-steps:
s41, performing brightness equalization on the adjacent frame images;
s42, performing Gaussian low-pass filtering on the adjacent frame images;
s43, calculating the velocity vector of the flow field of the adjacent frame by using a stable directional diagram as a constrained optical flow method;
s44, taking the velocity vector as input, and performing iterative fine adjustment by utilizing a Liu-Shen algorithm;
s45, carrying out exception handling on the speed vector after the iterative fine adjustment;
and S46, performing median calculation on the velocity vector after the abnormal processing to obtain a stable velocity vector of the adjacent frame flow field.
Further, step S43 includes the following sub-steps:
s431, based on the stable directional diagram, using an angle for the direction of each pixel point on the adjacent frame imageθ(x,y) Expressing, establishing a direction constraint equation:
s432, establishing a new minimization equation for all pixels according to the optical flow basic constraint equation, the speed smooth constraint equation and the direction constraint equation, wherein the new minimization equation comprises the following steps:
wherein the content of the first and second substances,ε(u,v) Is the total error of the minimization problem,λandωis a weighting coefficient;
the optical flow basic constraint equation is as follows: i is x u+I y v+I t = 0; the light intensity of a pixel point in the current frame is I (x,y,t)，(x,y) Which represents the coordinates of the image and which,tis time;
the velocity smoothing constraint equation is:
；
and
are respectively velocity vectorsu,vMean values in the neighborhood;
s433, total error for minimizing problemε(u,v) At minimum, thenεAre respectively pairedu,vThe partial derivative is calculated and made equal to zero, resulting in the following two equations:
s434, combining the two equations to obtain a velocity vectoru,vExpression:
wherein the content of the first and second substances,
s435, solving the velocity vector by adopting an iterative methodu,vExpression to obtain velocity vectoru,v：
Wherein the content of the first and second substances,nrepresenting the number of iterations;
initialization orderu (0)=0，v (0)(= 0) whenu n()-u n(-1)|≤ε 1，|v n()-v n(-1)|≤ε 2And then, the iteration is stopped,u n(),v n()for the determined velocity vectoru,v。
Further, step S45 includes the following sub-steps:
s451, searching a speed abnormal area;
s452, the speed of the speed abnormal region is reversed.
Furthermore, if the flow field of the adjacent frame image is too high or the calculation speed is increased, the adjacent frame image is scaled by a certain proportion and then the speed vector is calculated.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. compared with the classical optical flow method, the method has the advantage that the matching of the flow chart calculated by the optical flow method with the directional constraint and the actual distribution is obviously improved.
2. The optical flow method with the directional constraint has good stability.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1a is a flow chart at a certain time calculated by a classical optical flow method.
FIG. 1b is a flow chart of FIG. 1a after the Liu-Shen algorithm is refined.
FIG. 2 is a block diagram of the flow process of the high-speed wind tunnel dynamic oil flow map image processing by using the optical flow method with directional constraint according to the present invention.
Fig. 3a is a first image enhancement effect diagram according to the present invention.
Fig. 3b is a second image enhancement effect diagram according to the present invention.
FIG. 4 is an average graph I of the present inventionmThe directional pattern of (a).
Fig. 5 is a steady pattern of the present invention.
FIG. 6 is a block diagram of the flow of calculating the velocity vector of the flow field of the adjacent frame based on the stable directional diagram as the optical flow method of the constraint of the present invention.
FIG. 7 is a schematic diagram of velocity vector anomaly analysis according to the present invention.
FIG. 8 is a velocity vector diagram of an adjacent frame flow field calculated by the optical flow method based on the stable directional diagram as a constraint.
Fig. 9 is a flow chart at a certain time calculated using the velocity vector calculated by the present invention.
Fig. 10 is a diagram of the eddy current at a certain time calculated using the velocity vector calculated by the present invention.
FIG. 11 is a particle animation generated using the velocity vectors calculated by the present invention.
FIG. 12a is a schematic view ofω=λThe velocity vector calculated by the present invention is used to calculate a flow chart at a certain time.
FIG. 12b isω=10λThe velocity vector calculated by the present invention is used to calculate a flow chart at a certain time.
FIG. 12c isω=λAt/10, a flow chart at a certain time calculated using the velocity vector calculated by the present invention.
Detailed Description
Description of classical optical flow techniques:
the classical optical flow methods are classified into two types, sparse optical flow represented by Lucas-Kanade (LK) optical flow and dense optical flow represented by Horn-Schunck (HS) optical flow. Flow field analysis typically employs a dense flow of HS light. Whatever the optical flow method, it is based on two basic assumptions:
(1) the brightness is constant. I.e. the brightness of the same object does not change when it moves between different frames. This is an assumption of basic optical flow (all optical flow variants must be satisfied) for obtaining the basic equations of optical flow;
(2) temporal continuity or motion is "small motion". I.e. the temporal variation does not cause a drastic change in the target position, the displacement between adjacent frames is relatively small.
Consider a pixel with a light intensity of I (in) for the current framex,y,t)（(x,y) Which represents the coordinates of the image and which,tis time (i.e. the light intensity of the current frame is I: (x,y,t) At the instant), the light intensity, i.e., the image gray value), which has been shifted by (a)dx,dy) To the next frame, usedtTime (i.e. thetTime (x,y) The pixel point of the position ist+dtMoved to the position (at any moment of time)x+dx,y+ dy)). Because the pixel point is the same pixel point, the light intensity of the pixel point before and after the movement is considered to be unchanged according to the first assumption mentioned above, namely:
carrying out Taylor expansion on the right end of the formula (1) to obtain:
wherein the content of the first and second substances,εrepresenting a second-order infinite small term which can be ignored, and then the formula (2) is substituted into the formula (1) and then is divideddtThe following can be obtained:
is provided withu,vVelocity vectors of the light stream along the X-axis and Y-axis, respectively, havingdx=udt，dy=vdtAnd making the partial derivative of the gray level of a pixel point in the image along the X, Y and T directions as follows:
then equation (3) can be written as:
I x u+I y v+I t =0 （5）
wherein, I x ,I y ,I z Can be obtained by calculating the gray values of the sequence of images, andu,v) I.e. the velocity vector of the optical flow sought.
The constraint equation has only one, and the unknowns of the constraint equation have two, in which case it is impossible to obtainu,vThe exact value of (c). Additional constraints need to be introduced at this point. The Horn-Schunck algorithm adds an assumption of continuous smooth optical flow field on the basis of the basic constraint equation of optical flow, assuming that the change of optical flow is smooth over the whole image, i.e. the motion vector of the object is smooth or only slowly changing. The constraint equation is expressed as:
wherein the content of the first and second substances,
and
are respectively velocity vectorsu,vMean value in the neighborhood.
By combining the equations (5) and (6) and establishing a minimization equation for all pixels, it is possible to obtain:
wherein the content of the first and second substances,ε(u,v) Is the total error of the minimization problem,λare weighting coefficients. When the image data itself contains large noise, it needs to be enlargedλCan be reduced, and vice versaλThe value of (c).
To make the total errorε(u,v) At minimum, thenεAre respectively pairedu,vThe partial derivative is calculated and made equal to zero, resulting in the following two equations:
the velocity vector is obtained by combining the formula (8) and the formula (9)u,vExpression:
the iterative method can be adopted to solve the formula (10) and the formula (11) to obtain the velocity vectoru,v：
Wherein the content of the first and second substances,nthe number of iterations is indicated.
Initialization orderu (0)=0，v (0)(= 0) whenu n()-u n(-1)|≤ε 1，|v n()-v n(-1)|≤ε 2And then, the iteration is stopped,u n(),v n()for the determined velocity vectoru,v。
A flow chart of a flow field generated by a motion field calculated by using a Horn-Schunck algorithm in two adjacent frames at a certain time is shown in fig. 1 a. It can be seen that the matching of the flow chart and the actual distribution is not high, and even after the Liu-Shen algorithm is fine-tuned, as shown in FIG. 1b, the detection result is still not ideal. Therefore, the invention provides a high-speed wind tunnel dynamic oil flow map image processing method, which comprises the following steps: the method utilizes an optical flow method with direction constraint to perform high-speed wind tunnel dynamic oil flow map image processing so as to solve the problem of performing high-speed wind tunnel dynamic oil flow map image processing by adopting a classical optical flow method.
The features and properties of the present invention are described in further detail below with reference to examples.
As shown in fig. 2, the method comprises the steps of:
s1, sliding window average: setting a sliding window with a radius of k, calculating an average image I for every 2k +1 imagesmTherefore, the influence of random noise on subsequent optical flow calculation in the shooting process is eliminated.
S2, extracting a directional diagram of the average graph:
s21, average chart ImPerforming context filtering to complete image enhancement, as shown in fig. 3a and 3 b;
s22, the invention uses block-wise pattern dot direction for pattern stability. Firstly, using sobel operator to calculate average image I after image enhancementmObtaining the point direction of each pixel point, then counting all the point directions in the neighborhood of the fixed window of the pixel point to obtain the block direction of the pixel point, wherein the block direction of all the pixel points is the average graph ImThe directional pattern of (a). The generated average graph ImThe pattern of the oil flow is shown in fig. 4, and it can be seen that the pattern and the oil flow trace almost coincide.
And S3, generating a stable directional diagram: since it is assumed that the field structure does not change in a certain time, the directional field should be stable in a certain time, and noise interference has randomness, so that for each pixel point, the block direction median value in the time sequence is taken as the direction of the pixel point, and the direction is expressed by radians in the range of [ -pi/2, pi/2 ], so as to obtain a stable directional diagram, as shown in fig. 5. Further, since the processing of each pattern requires about 1.4s, in order to increase the processing speed, the inter-frame interval when the pattern is processed and the number of frames at which a stable pattern is finally generated may be set.
S4, calculating the velocity vector of the flow field of the adjacent frame based on the stable directional diagram as the constrained optical flow method, as shown in fig. 6, including the following sub-steps:
s41, performing luminance equalization on the adjacent frame image: because the brightness of the optical flow method is set to be constant, namely, the brightness of the same object does not change when the same object moves among different frames. Therefore, brightness equalization is carried out on the adjacent frame images, the light and shade brightness ranges of the adjacent frame images are basically kept consistent, and the influence caused by illumination change in the shooting process is eliminated.
And S42, performing Gaussian low-pass filtering on the adjacent frame images to further eliminate the influence of random noise.
It should be noted that, if the flow rate of the flow field of the adjacent frame image is too fast or the calculation speed is increased, the speed vector is calculated in step S43 after the adjacent frame image is scaled by a certain ratio.
S43, calculating the velocity vector of the flow field of the adjacent frame by using a stable directional diagram as a constrained optical flow method;
s431, based on the stable directional diagram, using an angle for the direction of each pixel point on the adjacent frame imageθ(x,y) Expressing, establishing a direction constraint equation:
s432, based on the optical flow basic constraint equation (5)), the velocity smoothing constraint equation (6)), and the direction constraint equation (14)), a new minimization equation is established for all pixels as follows:
wherein the content of the first and second substances,ε(u,v) Is the total error of the minimization problem,λandωis a weighting coefficient;
the optical flow basic constraint equation is as follows: i is x u+I y v+I t = 0; as in the classical optical flow method, the light intensity of a pixel point in the current frame is I (x,y,t)，(x,y) Which represents the coordinates of the image and which,tis time;
the velocity smoothing constraint equation is:
(ii) a As in the case of the classical optical flow method,
and
are respectively velocity vectorsu,vMean value in the neighborhood.
S433, making the electrodeMinimizing the total error of the problemε(u,v) At minimum, thenεAre respectively pairedu,vThe partial derivative is calculated and made equal to zero, resulting in the following two equations:
s434, combining the above two equations (equation (16) and (17)) to obtain a velocity vectoru,vExpression:
wherein the content of the first and second substances,
s435, solving the velocity vector by adopting an iterative methodu,vExpression to obtain velocity vectoru,v：
Wherein the content of the first and second substances,nrepresenting the number of iterations;
initialization orderu (0)=0，v (0)(= 0) whenu n()-u n(-1)|≤ε 1，|v n()-v n(-1)|≤ε 2And then, the iteration is stopped,u n(),v n()for the determined velocity vectoru,v。
S44, taking the velocity vector as input, and performing iterative fine adjustment by utilizing a Liu-Shen algorithm; this process is prior art and will not be described herein.
S45, carrying out exception processing on the velocity vector after iterative fine adjustment: inevitably, local abnormality may occur to the velocity vector between adjacent frame images, as shown in fig. 7, in order to observe the abnormality more simply and intuitively, the background is the velocity direction field (0 to 2 pi) obtained from the velocity vector field, and the foreground draws the generated flow chart. It is clear that the anomaly in the velocity field is due to a sudden reversal of velocity. Therefore, the method for exception handling of the iteratively refined velocity vector comprises:
s451, searching a speed abnormal area;
s452, the speed of the speed abnormal region is reversed.
And S46, performing median calculation on the velocity vector after the abnormal processing to obtain a stable velocity vector of the adjacent frame flow field. The method of this step is the same as the way of calculating the steady direction diagram, and is obtained by performing median calculation.
The velocity vector required by the present invention is calculated through the above steps S41 to S46, and the velocity vector diagram is shown in fig. 8.
And S5, calculating a streamline chart and a vorticity chart by using the velocity vector, and drawing a particle animation:
in the particle animation demonstration, the motion trail of the particle is required to be obtained first, and the motion trail of the particle is a streamline. A streamline is a curve of different fluid particles at the same time, which gives the velocity direction of the different fluid particles at that time. When calculating the streamline graph, firstly determining the differential equation of the streamline as follows:
dr×v(r,t)=0 （23）
in the formula (I), the compound is shown in the specification,v(r,t) Anddrare respectively the speedDegree vectors and arc element vectors (corresponding to the foregoing pixel point directions are expressed by radians),ttime, the integration time is regarded as constant.
The expression of the differential equation of the streamline in a rectangular coordinate system is as follows:
since in the two-dimensional plane, the differential equation of the streamline can be expressed as:
and (4) integrating the equation (25) by using the solved velocity vector, solving a streamline equation, and drawing a streamline diagram and a particle animation according to the streamline equation. The method for calculating the vortex map according to the velocity vector is the prior art and is not described herein again. A flow chart plotted from the velocity vector shown in fig. 8 is shown in fig. 9, vorticity is shown in fig. 10, and a particle animation is shown in fig. 11. As can be seen from the flow chart shown in fig. 9, the matching between the flow chart calculated by the optical flow method with directional constraint and the actual distribution is significantly improved in the present invention compared with the classical optical flow method.
In addition, separately takeω=λ、ω=10λAndω=λthe/10 time calculated flow diagrams are shown in fig. 12a, 12b and 12c, respectively. It can be seen that no matter the weighting coefficientsλAndωthe ratio of (1: 10) to (1: 1) or (10: 1), and the calculated flow chart shows strong similarity, so that the optical flow method with the directional constraint has good stability.
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, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (5)
1. A high-speed wind tunnel dynamic oil flow map image processing method is characterized in that the method is used for carrying out high-speed wind tunnel dynamic oil flow map image processing by using an optical flow method with direction constraint, and comprises the following steps:
s1, obtaining an average graph by a sliding window;
s2, extracting a directional diagram of the average graph;
s3, generating a stable directional diagram;
s4, calculating the velocity vector of the flow field of the adjacent frame based on the stable directional diagram as a constrained optical flow method;
s5, calculating a streamline chart and a vorticity chart by using the velocity vector, and drawing a particle animation;
the method for obtaining the average map by the sliding window in step S1 includes: setting a sliding window with a radius of k, calculating an average image I for every 2k +1 imagesm；
Step S2 includes the following sub-steps:
s21, average chart ImPerforming Contextual filtering to complete image enhancement;
s22, calculating an average image I after image enhancement by using sobel operatormObtaining the point direction of each pixel point, then counting all the point directions in the neighborhood of the fixed window of the pixel point to obtain the block direction of the pixel point, wherein the block direction of all the pixel points is the average graph ImThe directional pattern of (a);
the method for generating the stable directional diagram in step S3 includes: and for each pixel point, taking the block direction median value in the time sequence as the direction of the pixel point, and expressing the direction by adopting radians with the ranges of [ -pi/2, pi/2 ] to obtain a stable directional diagram.
2. The high-speed wind tunnel dynamic oil flow map image processing method according to claim 1, wherein the step S4 comprises the following sub-steps:
s41, performing brightness equalization on the adjacent frame images;
s42, performing Gaussian low-pass filtering on the adjacent frame images;
s43, calculating the velocity vector of the flow field of the adjacent frame by using a stable directional diagram as a constrained optical flow method;
s44, taking the velocity vector as input, and performing iterative fine adjustment by utilizing a Liu-Shen algorithm;
s45, carrying out exception handling on the speed vector after the iterative fine adjustment;
and S46, performing median calculation on the velocity vector after the abnormal processing to obtain a stable velocity vector of the adjacent frame flow field.
3. The high-speed wind tunnel dynamic oil flow map image processing method according to claim 2, wherein the step S43 comprises the following sub-steps:
s431, based on the stable directional diagram, using an angle for the direction of each pixel point on the adjacent frame imageθ(x,y) Expressing, establishing a direction constraint equation:
s432, establishing a new minimization equation for all pixels according to the optical flow basic constraint equation, the speed smooth constraint equation and the direction constraint equation, wherein the new minimization equation comprises the following steps:
wherein the content of the first and second substances,ε(u,v) Is the total error of the minimization problem,λandωis a weighting coefficient;
the optical flow basic constraint equation is as follows: i is x u+I y v+I t = 0; the light intensity of a pixel point in the current frame is I (x,y,t)，(x,y) Which represents the coordinates of the image and which,tis time;
the velocity smoothing constraint equation is:
；
and
are respectively velocity vectorsu,vMean values in the neighborhood;
s433, total error for minimizing problemε(u,v) At minimum, thenεAre respectively pairedu,vThe partial derivative is calculated and made equal to zero, resulting in the following two equations:
s434, combining the two equations to obtain a velocity vectoru,vExpression:
wherein the content of the first and second substances,
s435, solving the velocity vector by adopting an iterative methodu,vExpression to obtain velocity vectoru,v：
Wherein the content of the first and second substances,nrepresenting the number of iterations;
initialization orderu (0)=0，v (0)(= 0) whenu n()-u n(-1)|≤ε 1，|v n()-v n(-1)|≤ε 2And then, the iteration is stopped,u n(),v n()for the determined velocity vectoru,v。
4. The high-speed wind tunnel dynamic oil flow map image processing method according to claim 3, wherein the step S45 comprises the following sub-steps:
s451, searching a speed abnormal area;
s452, the speed of the speed abnormal region is reversed.
5. The high-speed wind tunnel dynamic oil flow map image processing method according to any one of claims 2 to 4, wherein if the flow field of the adjacent frame image is too fast or the calculation speed is increased, the speed vector is calculated after scaling the adjacent frame image by a certain proportion.
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