CN114518213A - Flow field measuring method, system and device based on skeleton line constraint and storage medium - Google Patents
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Abstract
The embodiment of the application discloses a flow field measuring method, a system and a device based on skeleton line constraint and a storage medium. The method comprises the following steps: acquiring two or more flow field images of the surface of a region to be detected of an object; extracting a flow field skeleton line of the surface of the object region to be measured based on at least one of the two or more flow field images; constraining and adjusting a cross-correlation window by the flow field skeleton line; and under the constraint of the flow field skeleton line, polling in at least two frames of flow field images in the two or more frames of flow field images through a cross-correlation window to determine the flow field vector of the surface of the object region to be detected.
Description
Technical Field
The present disclosure relates to the field of image processing technologies, and in particular, to a flow field measurement method, system, device and storage medium based on skeleton line constraint.
Background
Flow display technology is often applied to wind tunnel tests and is used for displaying the flow area and the structural form of the surface of an object (such as an object). It is an effective method for the study of complex flows, particularly for the study of various forms of separation and swirl flow. The flow condition near the object plane is the starting point of forming the whole space flow, and the development of the space flow can be further analyzed and understood on the basis of knowing the flow of the object plane.
The existing flow field measurement algorithm mainly takes an optical flow method and a cross-correlation method as main methods, and the two methods still have poor performance in particle velocity measurement.
Based on the problems, the application provides a flow field measurement technology based on skeleton line constraint.
Disclosure of Invention
One embodiment of the application provides a flow field measurement method based on skeleton line constraint. The measuring method comprises the following steps: acquiring two or more flow field images of the surface of a region to be detected of an object; extracting a flow field skeleton line of the surface of the object region to be measured based on at least one of the two or more flow field images; and restricting a cross-correlation window based on the flow field skeleton line, and patrolling at least two frames of flow field images in the two or more frames of flow field images through the cross-correlation window to determine the flow field vector of the surface of the to-be-detected area of the object.
One embodiment of the application provides a flow field measurement system based on skeleton line constraint, and the system comprises an acquisition module, a frame rate module and a frame rate module, wherein the acquisition module is used for acquiring two or more flow field images of the surface of an object to-be-measured area; a skeleton line extraction module for extracting a flow field skeleton line of the surface of the object region to be measured based on at least one of the two or more flow field images; the skeleton line constraint module is used for acquiring the flow field skeleton line and constraining a cross-correlation window through the flow field skeleton line; and the flow field vector calculation module is used for patrolling at least two frames of flow field images in the two or more frames of flow field images through the cross-correlation window to determine the flow field vector of the surface of the object region to be measured.
One of the embodiments of the present application provides a flow field measurement device based on skeleton line constraint, including at least one storage medium and at least one processor, where the at least one storage medium is used to store computer instructions; the at least one processor is configured to execute the computer instructions to implement the above-described skeleton line constraint-based flow field measurement method.
One of the embodiments of the present application provides a computer-readable storage medium, where after a computer reads a computer instruction in the storage medium, the computer runs the flow field measurement method based on skeleton line constraint.
Drawings
The present application will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
FIG. 1 is a one-frame flow field image of a surface of an object region of interest according to some embodiments of the present application;
FIG. 2 is another frame flow field image of the surface of an object region of interest according to some embodiments of the present application;
FIG. 3 is a block diagram of a system according to some embodiments of the present application;
FIG. 4 is an exemplary flow chart of a method according to some embodiments of the present application;
FIG. 5 is a fringe image obtained according to the method shown in some embodiments of the present application;
FIG. 6 is a skeletal line graph obtained according to a method shown in some embodiments of the present application;
FIG. 7 is a flow chart of a method according to some embodiments of the present application;
FIG. 8a is a flow field vector diagram obtained using optical flow methods according to some embodiments of the present application;
FIG. 8b is a flow diagram obtained using a prior art optical flow technique;
FIG. 8c is a thermodynamic diagram obtained using a prior art optical flow method technique;
FIG. 9a is a flow field vector diagram obtained using a prior art cross-correlation technique;
FIG. 9b is a flow chart obtained using a prior art cross-correlation technique;
FIG. 9c is a thermodynamic diagram obtained using a prior art cross-correlation technique;
FIG. 10a is a vector view of a flow field obtained according to the method shown in some embodiments of the present application;
FIG. 10b is a flow chart obtained by a method according to some embodiments of the present application; and
FIG. 10c is a thermodynamic diagram obtained by a method according to some embodiments of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below. It is obvious that the drawings in the following description are only examples or embodiments of the application, from which the application can also be applied to other similar scenarios without inventive effort for a person skilled in the art. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
It should be understood that "system" and/or "apparatus" as used herein is a method for distinguishing different components, elements, components, parts or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this application and the appended claims, the terms "a," "an," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used herein to illustrate operations performed by systems according to embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, certain steps may be processed in reverse order or concurrently. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
The surface flow display technology is widely applied to conventional wind tunnel flow display tests, and the flow characteristics of the surfaces of the to-be-tested areas of the test models and other objects in the flow field can be visually described through the surface flow display technology. For example, the oil solution is applied to the surface of the target region to be measured to form an oil film, or trace particles (such as a colored indicator or a reflective indicator) are mixed in the oil solution, the oil film flows along the surface of the target region to be measured by the airflow, different flow spectrums are formed on the surface of the target region to be measured, and the flow characteristics of the surface of the target region to be measured can be obtained by analyzing the flow condition of the oil solution on the surface of the target region to be measured. Some problems are faced when acquiring a corresponding flow field diagram (for example, a flow field vector diagram, a flow diagram, a thermodynamic diagram and the like) through a flow field image shot in an experiment at present. For example, the flow field vector calculation is inaccurate due to low inter-frame similarity of flow field images.
The flow field measuring system based on skeleton line constraint can be applied to the field of flow field display. For example, the pneumatic flow field measurement system based on skeleton line constraint may be applied to a wind tunnel test, and by acquiring two or more flow field images (see fig. 1 and 2) of the surface of the region to be measured of the object, and performing corresponding processing on the two or more acquired flow field images (e.g., acquiring corresponding average images and fringe images through the flow field images, extracting skeleton lines, sampling discrete points, calculating flow field vectors, etc.) to obtain flow field images (e.g., flow field vector diagrams, flow line diagrams, thermodynamic diagrams, etc.) of the surface of the region to be measured of the object, the flow area and the structural morphology of the surface of the region to be measured of the object may be displayed more accurately, so as to study complex flows (e.g., separation flows and vortex flows) of the surface of the region to be measured of the object.
It should be understood that the application scenarios of the flow field measurement system based on skeleton line constraint mentioned in this specification are only some examples or embodiments of this specification, and it is obvious for those skilled in the art that the flow field measurement system based on skeleton line constraint may also be applied to other similar scenarios without creative work, and this application is not limited thereto.
FIG. 1 is a one-frame flow field image of a surface of an object region of interest according to some embodiments of the present application; fig. 2 is another frame flow field image of a surface of a region under test of an object according to some embodiments of the present application. The images shown in fig. 1 and 2 may be captured continuous two-frame stream field images. In some embodiments, the flow condition of the surface of the region to be measured of the object can be directly observed through the flow trace curve in the flow field image by the flow field image shown in fig. 1 and 2. In other embodiments, the flow field vector between the target frame flow field image and at least one frame of adjacent frame flow field images may be determined by skeleton line constraints. Furthermore, a flow field vector diagram, a flow chart or a thermodynamic diagram and the like between the flow field image of the target frame and the flow field image of at least one other frame can be determined through the flow field vector, so that the flow condition of the surface of the region to be measured of the object can be further researched.
FIG. 3 is a block diagram of an exemplary flow field measurement system according to some embodiments of the present application. As shown in fig. 3, the flow field measurement system 300 may include an acquisition module 310, a skeleton line extraction module 320, a skeleton line constraint module 330, and a flow field vector calculation module 340.
The acquisition module 310 may be used to acquire two or more flow field images of the surface of the region under test of the object. Wherein, two or more stream field images can be continuous frame images, and can also be discontinuous frame images in some cases. In some embodiments, the obtaining module 310 may also perform pre-processing on two or more flow field images separately. In some embodiments, the pre-processing may include gray scale processing, and two or more flow field images may each be processed into a gray scale image. In some embodiments, the pre-processing may further include performing a brightness equalization process on two or more of the flow field images such that the bright and dark brightness ranges of the multi-frame flow field images remain substantially the same.
The skeleton line extracting module 320 may extract a flow field skeleton line of the surface of the object region to be measured based on at least one of the two or more flow field images. The skeleton line constraint module 330 may be configured to extract the flow field skeleton line, and constrain a cross-correlation window by the flow field skeleton line. The flow field vector calculation module 340 may be configured to determine the flow field vector of the surface of the region to be measured of the object by performing an inspection on at least two of the two or more flow field images through the cross-correlation window.
It should be understood that the system and its modules shown in FIG. 3 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, for example such code provided on a carrier medium such as a diskette, CD-or DVD-ROM, programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It should be noted that the above description of the system and its modules is merely for convenience of description and should not limit the present application to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the teachings of the present system, any combination of modules or sub-system configurations may be used to connect to other modules without departing from such teachings. For example, in some embodiments, for example, the obtaining module 310, the skeleton line extracting module 320, the skeleton line constraining module 330, and the flow field vector calculating module 340 disclosed in fig. 3 may be different modules in a system, or may be a module that implements the functions of two or more modules described above. Such variations are within the scope of the present application.
FIG. 4 is an exemplary flow chart of a flow field measurement method according to some embodiments of the present application. As shown in fig. 4, the flow field measurement method 400 may include the steps of:
In some embodiments, step 410 may be performed by acquisition module 310.
In some embodiments, the flow field image may refer to an image captured when a wind tunnel test is performed on the surface of the region to be detected of the object by using an oil flow method or trace particles. In some embodiments, two or more stream field pictures may refer to a consecutive multi-frame stream field picture or a non-consecutive multi-frame stream field picture in chronological order. By way of example only, the stream field images taken sequentially in chronological order have sequence number I1、I2、I3、I4、I5……InThe continuous multi-frame stream field image can be I1、I2、I3、I4The plurality of serial number continuous stream field images, but the non-continuous multi-frame stream field image can be I1、I3、I5、I6And waiting for a plurality of stream field pictures with discontinuous sequence numbers.
In some embodiments, the two or more flow field images may include a target frame flow field image and at least one pre-frame flow field image located at a time before the target frame flow field image and/or at least one post-frame flow field image located at a time after the target frame. In some embodiments, the target frame flow field image may refer to two or more flow field images as reference images in a process of extracting flow field skeleton lines and/or determining flow field vectors of the flow field images through a cross-correlation window, and may be specifically selected according to actual conditions, that is, a specific frame or a specific frame image, or may be a current frame image in real-time analysis processing. For example, in some embodiments, a target frame flow field image may be extracted as a reference image of a skeleton line and/or cross-correlation process from two or more flow field images captured by a camera, and one or more previous frame flow field images adjacent to the target frame flow field image may be subjected to skeleton line constraint-based flow field vector calculation. For another example, in some embodiments, one of two or more flow field images captured by the camera may be extracted as a target frame flow field image as a skeleton line and/or a reference image of a cross-correlation process, and one or a certain section of a subsequent frame flow field image adjacent to the target frame flow field image is subjected to skeleton line constraint-based flow field vector calculation. For another example, in some embodiments, one of two or more flow field images captured by the camera may be extracted as a target frame flow field image as a skeleton line and/or a reference image of a cross-correlation process, and one or a certain segment of a previous frame flow field image and one or a certain segment of a next frame flow field image adjacent to the target frame flow field image are subjected to skeleton line constraint-based flow field vector calculation.
In some embodiments, after two or more flow field images (e.g., a target frame flow field image, at least one previous frame flow field image, and at least one subsequent frame flow field image) are acquired, pre-processing may be performed, respectively, and the pre-processing may include performing brightness equalization processing on the two or more flow field images such that bright and dark brightness ranges of the two or more flow field images are substantially the same. The brightness ranges of the two or more flow field images are basically the same, which can be understood that the brightness ranges formed by the maximum brightness value and the minimum brightness value of each frame flow field image after brightness equalization processing are both within a preset range. For exemplary purposes only, the predetermined range is 60cd/m2-80cd/m2Wherein, the two or more flow field images may include a first flow field image and a second flow field image, and the brightness ranges of the first flow field image and the second flow field image which are not processed by brightness equalization may be 50cd/m2-90cd/m2And 52cd/m2-100cd/m2The brightness ranges of the first and second flow field images may be adjusted to 62cd/m respectively2-80cd/m2And 60cd/m2-75cd/m2Wherein, the brightness range of the first flow field image and the second flow field image after brightness equalization processing is within the preset range, thereby leading the brightness range of the two or more flow field images to be within the preset rangeThe circumference is basically the same. It should be noted that the predetermined range is not limited to 60cd/m2-80cd/m2And the adaptability adjustment can be carried out according to different scenes. In the process of shooting the flow field image, due to the change of an external environment (such as illumination intensity), the brightness and darkness of the shot flow field image are different, the similarity among multiple frames of flow field images is further reduced, and the flow field vector calculation is not facilitated. In order to reduce the influence of the bright and dark brightness of each flow field image on the subsequent flow field vector calculation, the bright and dark brightness range of each flow field image needs to be adjusted to be basically consistent. In some embodiments, the method of the luminance equalization process may include histogram equalization, mean, standard deviation, and the like, or any combination thereof. It should be noted that the preprocessing of two or more frames of the stream field image is not limited to the above-described luminance equalization processing.
In some embodiments, after two or more flow field images are acquired, preprocessing may be further performed, where the preprocessing performs gray scale processing on the two or more flow field images to obtain gray scale images of the two or more flow field images. The preprocessing of the two or more flow field images is not limited to the above grayscale processing, and may also be not limited to the above luminance equalization processing, and may also include, for example, a noise reduction processing of the flow field images, which is not further limited herein.
In some embodiments, the preprocessing process may perform gray scale processing on the two or more flow field images, and then perform brightness equalization processing on the gray scale images.
And step 420, extracting the flow field skeleton line of the surface of the object region to be measured based on at least one of the two or more flow field images.
In some embodiments, step 420 may be performed by the skeleton line extraction module 320, and the skeleton line extraction process may be performed using an edge detection algorithm. In some embodiments, the method of extracting the flow field skeleton line may include an euclidean distance algorithm, a ZhangSuen skeleton extraction algorithm, a DLMA skeleton extraction algorithm, or the like, or any combination thereof. In some embodiments, the flow field skeleton lines are thin curves that conform to the original shape connectivity and topology of the flow field image. Skeleton lines (see white lines in fig. 6) may be used to show the flow trace curves in the flow field image.
In some embodiments, in order to reduce or remove speckle noise at the flow trace in the flow field image and solve the problem of weak edge information of the flow trace, a fringe pattern processing process may be included before extracting the flow field skeleton line of the surface of the region to be measured of the object based on at least one of two or more flow field images, a fringe image of at least one of the two or more flow field images may be obtained through fringe pattern processing, and is counted as a first fringe image, and the flow field skeleton line of the surface of the region to be measured of the object is extracted based on the first fringe image. The at least one stream field picture may be a target stream field picture, a one/more previous frame stream field picture at a time before the target stream field picture, or a one/more next frame stream field picture at a time after the target stream field picture. In some embodiments, the first stripe image may refer to a flow trace map reflecting a certain moment at the surface of the object region to be measured. As shown in fig. 5, the white stripes in the figure are stripe images of the flow traces on the surface of the region to be measured of the object. In some embodiments, the fringe pattern processing may include, but is not limited to, a spin-filtering method. As an exemplary illustration, when a flow field image is processed by using a spin filtering method, a skeleton line extraction module 320 is used to generate a plurality of stripe gray contours in the flow field image, where the stripe gray contours may refer to curves formed by pixels with the same or similar gray levels, in an embodiment of the present description, each flow trace curve in the flow field image may be approximately regarded as a stripe gray contour, and the skeleton line extraction module 320 performs low-pass filtering along the direction of each stripe gray contour (or flow trace curve), so as to obtain a stripe image of the flow field image, that is, a first stripe image.
In some embodiments, before extracting the flow field skeleton line based on at least one of the at least two or more flow field images, an image averaging process may be further included, an average image of the at least two of the two or more flow field images is obtained, and the flow field skeleton line of the surface of the region to be measured of the object is extracted based on the average image. In some embodiments, the image averaging process may refer to calculating an average value of pixel values of pixels corresponding to each other in two or more flow field images. When the flow field image is also subjected to a gray scale process, the average value may be an average gray scale value. In the process of acquiring the flow field image, some interference or noise may be mixed into the flow field image due to various reasons (e.g., brightness variation). In some embodiments, the images participating in the averaging operation may include all or a portion of the two or more flow field images. In some embodiments, the average image may be obtained by the target frame stream field image and the multi-frame preceding frame stream field image, may be obtained by the target frame stream field image and the multi-frame succeeding frame stream field image, and may be obtained by a combination of the target frame stream field image and the multi-frame preceding and/or succeeding frame stream field images thereof. In some embodiments, the average image may be one or more.
In some embodiments, in order to reduce or remove speckle noise at the flow trace in the average image and solve the problem of weak information of the edge of the flow trace, extracting the flow field skeleton line based on the average image may include obtaining a fringe image of the average image through fringe image processing, counting as a second fringe image, and obtaining the flow field skeleton line based on the second fringe image.
In some embodiments, the step of obtaining the second stripe image based on the average image may be performed by the skeleton line extraction module 320.
In some embodiments, the first stripe image or the second stripe image may refer to a flow trace map reflecting a certain moment at the surface of the object region to be measured. As shown in fig. 5, the white stripes in the figure are stripe images of the flow traces on the surface of the region to be measured of the object. In some embodiments, the fringe pattern processing may include, but is not limited to, a spin filtering method. As an exemplary illustration, when the average image is processed by using a spin filtering method, the extraction module 320 generates a plurality of stripe gray contours in the average image, where the stripe gray contours may refer to curves formed by pixels with the same or similar gray levels, in an embodiment of the present specification, each flow trace curve in the average image may be approximately regarded as a stripe gray contour, and the extraction module 320 performs low-pass filtering along the direction of each stripe gray contour (or flow trace curve), respectively, so as to obtain a stripe image of the average image.
In some embodiments, the step of acquiring the flow field skeleton line based on the second stripe image may be performed by the skeleton line extraction module 320.
In some embodiments, after extracting the flow field skeleton line based on at least one of the at least two or more flow field images, an image averaging process may be further included, and after obtaining the flow field skeleton lines of the multiple frames of flow field images, the image averaging process is performed on the multiple frames of flow field skeleton lines to obtain an average skeleton line between adjacent frames of flow field images, and the average skeleton line is used to constrain the cross-correlation window. In some embodiments, the flow field skeleton line may be extracted by first obtaining an average image of at least two frames of flow field images and then extracting the flow field skeleton line based on the average image. In some embodiments, the flow field skeleton lines corresponding to at least two frames of flow field images may be extracted first, and then the multiple frames of flow field skeleton lines are subjected to average image processing to obtain average flow field skeleton lines.
In some embodiments, after extracting the flow field skeleton line of the surface of the object region-to-be-measured based on at least one of the two or more flow field images, the flow field skeleton line may be subjected to a curve fitting process. It will be appreciated that the flow field skeleton line obtained based on the foregoing method may be broken in some regions, and in order to obtain a continuous flow field skeleton line, the broken portions may be connected by means of curve fitting (or interpolation). In the embodiment of the present description, a relatively smooth and continuous flow field skeleton line curve can be obtained by performing curve fitting on the flow field skeleton line, which facilitates the subsequent steps. The algorithm for fitting the flow field skeleton line may include an OpenCV curve fitting method, a circle fitting method, a spline curve fitting method, a least square curve fitting method, a bezier curve fitting method, or the like, or any combination thereof.
In some embodiments, step 430 may be performed by the skeleton line constraint module 330.
In some embodiments, when the cross-correlation window is constrained based on the flow field skeleton line, discrete point sampling needs to be performed on the flow field skeleton line, and the motion of the cross-correlation window is constrained by the discrete points of the flow field skeleton line. In some embodiments, a cross-correlation window may be adaptively adjusted based on the flow field skeleton line. For example, the moving path of the cross-correlation window during sliding inspection can be constrained, and the window size of the cross-correlation window can also be constrained. Discrete points may refer to a corresponding number of pixels on the skeleton line. Wherein a discrete point corresponds to a pixel. Discrete point sampling may refer to establishing a two-dimensional spatial coordinate system based on a skeleton line, and sampling points (also referred to as pixels) on the skeleton line. In the embodiment of the present specification, in the process of performing discrete point sampling on a skeleton line, two-dimensional space coordinates of each discrete point in the skeleton line may be obtained, so that a computer can perform subsequent processing conveniently. In some embodiments, the discrete points may be uniformly spaced or non-uniformly spaced. In some embodiments, the discrete points may be randomly selected.
In some embodiments, discrete point sampling based on a skeleton line may include discrete point sampling of the skeleton line according to a sampling frequency, which may be positively correlated with a concentration of skeleton lines. The density may refer to the density of the skeleton lines in the image. In some embodiments, the greater the concentration of skeleton lines, meaning the denser the skeleton lines, the higher the sampling frequency may be at this time; conversely, the smaller the density of skeleton lines, the lower the sampling frequency. In other alternative embodiments, the sampling frequency may also be a preset fixed value.
In some embodiments, this step 440 may be performed by the flow calculation module 340.
In some embodiments, constraining a cross-correlation window based on a flow field skeleton line, and inspecting at least two of two or more flow field images through the cross-correlation window, determining a flow field vector of a surface of an object region to be inspected may include: sampling discrete points of the flow field skeleton line, performing sliding inspection in at least two frames of flow field images in the two or more frames of flow field images through a cross-correlation window under the constraint of the flow field skeleton line, respectively determining the corresponding discrete point positions in the cross-correlation window of the at least two frames of flow field images, calculating the flow field vector of each discrete point in the cross-correlation window based on the deviation of the corresponding discrete point positions, and determining the flow field vector of the surface of the area to be measured of the object after the sliding inspection is completed.
In some embodiments, the flow field skeleton line may be obtained by averaging two or more flow field images, and the flow field skeleton line may be approximately regarded as the skeleton line of the target frame flow field image and the at least one other flow field image. Because the flow field on the surface of the region to be measured of the object constantly moves in the wind tunnel test, the coordinate value of the discrete point of at least one other frame of flow field image changes relative to the coordinate value of the discrete point on the target frame of flow field image, namely the position of the discrete point of at least one frame of flow field image has deviation relative to the position of the discrete point on the target frame of flow field image. When the discrete point sampled on the flow field skeleton line is used as the initial point on the flow field image of the target frame, the discrete point moves along the flow field skeleton line or near the flow field skeleton line because the flow field skeleton line of the average image is used as the constraint, that is, the discrete point corresponding to the initial point in at least one other frame of flow field image can be on the flow field skeleton line or near the flow field skeleton line. In some embodiments, a flow field vector between the target frame flow field image and the other at least one flow field image may be determined based on the discrete point coordinate values of the other at least one flow field image of the two or more flow field images, the discrete point (or initial point) coordinate values on the target frame flow field image, and a flow field vector algorithm. In some embodiments, the flow field vector algorithm may include a cross-correlation method. In some embodiments, the cross-correlation algorithm may include a cross-correlation function algorithm, a Fast algorithm of discrete Fourier transform (FFT), or the like.
In some embodiments, when the flow field vector algorithm is a cross-correlation method, discrete points of flow field skeleton lines are used as initial points on the flow field image of the target frame, the flow field image of the target frame and at least one other flow field image of two or more frames of flow field images are processed by using the flow field vector algorithm, and the size of a cross-correlation window can be adjusted according to the density of the flow field skeleton lines when determining the flow field vector between the flow field image of the target frame and the at least one other flow field image. In the embodiments of the present specification, the cross-correlation window may be understood as a window respectively selected when the target stream field image and the at least one other stream field image are subjected to the cross-correlation operation. The cross-correlation window may be a region of the same position and same size in the target stream field image and the at least one other stream field image. In some embodiments, discrete points in the at least one other flow field image corresponding to the initial point of the target flow field image may be found through the cross-correlation window. It is understood that when at least one other frame of flow field image is located before the target frame of flow field image, the discrete point corresponding to the initial point of the target frame of flow field image can be regarded as the position before the initial point; when at least one other frame of flow field image is located behind the target frame of flow field image, the discrete point corresponding to the initial point of the target frame of flow field image can be regarded as the position where the initial point arrives at a subsequent time under the action of the airflow. The vector arrow connecting the initial point to the discrete point can be considered the flow field vector between the two points.
In some embodiments, a method of aerodynamic flow field measurement may further include determining a flow field image based on the flow field vector. The flow field image may be an image reflecting the distribution of the flow field. In some embodiments, the method of determining a flow field image based on a flow field vector may include a line convolution integration method. In some embodiments, the flow field image may include a flow field vector diagram, a flow field line diagram, a flow field thermodynamic diagram, or the like, or any combination thereof.
It should be noted that the above description related to the flow 400 is only for illustration and explanation, and does not limit the applicable scope of the present application. Various modifications and changes to flow 400 may occur to those skilled in the art in light of the teachings herein. However, such modifications and variations are intended to be within the scope of the present application.
FIG. 7 is a flow chart of a method for flow field measurement based on skeleton line constraints according to some embodiments described herein. As shown in fig. 7, in some embodiments, when the stream field image acquired by the acquiring module 310 is a multi-frame stream field image, for example, the multi-frame stream field image may be a target frame stream field image I respectivelyNBefore frame stream field picture IN-kAnd a post-frame stream field picture IN+kAnd the like, wherein I is an integer greater than 0 (e.g., I is 1, 2, 3, etc.), and k is an integer. It should be noted that the subscripts N, N-k and N + k indicate the flow field images at different time before and after the flow field image, and the values of N and k may be selected according to specific situations. In some embodiments, a target stream field picture I of a multi-frame stream field pictureNBefore frame stream field picture IN-k… post-frame stream field picture IN+kIt may be a continuous multi-frame stream field image or a non-continuous multi-frame stream field image.
In some embodiments, the target frame stream field image I in the multi-frame stream field image is subjected toNBefore frame stream field picture IN-k. . . Post-frame streaming field picture IN+kRespectively carrying out brightness equalization processing to make the target frame flow field image INBefore frame stream field picture IN-k. . . Post-frame streaming field picture IN+kThe bright and dark luminance ranges of (a) are substantially the same. The brightness ranges of the flow field images are basically the same, which can be understood as that the brightness ranges formed by the maximum brightness value and the minimum brightness value of each frame of flow field image after brightness equalization processing are both within a preset range. For details that are substantially the same with respect to the bright and dark luminance ranges, reference may be made to fig. 4 of the present application and its associated description.
In some embodiments, an average image is obtained based on the luminance equalized multiple frames of the streaming field image. In some embodiments, the average image may refer to an average gray value of corresponding pixels in two or more flow field images. In the process of acquiring the flow field image, some interference or noise may be mixed into the flow field image due to various reasons (e.g., brightness variation).
In some embodiments, the multi-frame flow field images may be further processed with gray scale respectively, and the multi-frame flow field images may be processed into gray scale images. The preprocessing of the multi-frame streaming field image is not limited to the above grayscale processing and luminance equalization processing, and may also include, for example, a noise reduction processing of the streaming field image, which is not further limited herein. In some embodiments, the multi-frame flow field image may be subjected to the gray scale processing first, and then the gray scale image may be subjected to the brightness equalization processing. In other embodiments, the luminance equalization process may be performed on the multi-frame flow field image first, and then the grayscale process may be performed on the grayscale image.
In some embodiments, a fringe image of the averaged image is acquired. The fringe pattern of the average image may refer to a flow trace pattern reflecting a certain moment in the flow process of the flow field at the surface of the region to be measured of the object. As shown in fig. 5, the white stripes in the figure are flow traces of the flow field flow. The average image of the flow field image can preliminarily display the flow trace at a certain moment in the flow process of the flow field on the surface of the region to be detected of the object, and in order to reduce or remove speckle noise at the flow trace in the fringe image and solve the problem of weak edge information of the flow trace, the average image of the flow field image can be processed, so that the fringe image of the average image is obtained. In some embodiments, the processing of the averaged image may include, but is not limited to, a spin filtering method. As an exemplary illustration, when the average image is processed by using a spin filtering method, the skeleton line extraction module 320 generates a plurality of stripe gray contours in the average image, where the stripe gray contours may refer to curves formed by pixels with the same or similar gray levels, in an embodiment of the present specification, each flow trace curve in the average image may be approximately regarded as a stripe gray contour, and the skeleton line extraction module 320 performs low-pass filtering along the direction of each stripe gray contour (or flow trace curve), so as to obtain a stripe image of the average image.
In some embodiments, the flow field skeleton line is extracted based on the fringe image. The flow field skeleton line may refer to a center line of a flow trace curve in the fringe image. Flow field skeleton lines (see white lines in fig. 6) can be used to show the flow trace curves in the flow field image. In some embodiments, the method of extracting the flow field skeleton line may include an euclidean distance algorithm, a ZhangSuen skeleton extraction algorithm, a DLMA skeleton extraction algorithm, or the like, or any combination thereof.
In some embodiments, the flow field skeleton lines are subjected to a curve fitting process. It will be appreciated that the flow field skeleton line obtained based on the foregoing method may be broken in some regions, and in order to obtain a continuous flow field skeleton line, the broken portions may be connected by means of curve fitting (or interpolation). In the embodiment of the present description, a relatively smooth and continuous flow field skeleton line curve can be obtained by fitting the flow field skeleton lines of the stripes, which facilitates the subsequent steps. The algorithm for fitting the flow field skeleton line of the stripe may include an OpenCV curve fitting method, a circle fitting method, a spline curve fitting method, a least square curve fitting method, a bezier curve fitting method, or the like, or any combination thereof.
In some embodiments, a discrete point may refer to a corresponding number of pixels on a skeletal line. Wherein a discrete point corresponds to a pixel. Discrete point sampling may refer to establishing a two-dimensional spatial coordinate system based on a flow field skeleton line and sampling points (also referred to as pixels) on the flow field skeleton line. In the embodiment of the present specification, in the process of sampling discrete points of a flow field skeleton line, two-dimensional space coordinates of each discrete point in the flow field skeleton line can be obtained, so that a computer can perform subsequent processing conveniently. In some embodiments, the discrete points may be uniformly spaced or non-uniformly spaced. In some embodiments, the discrete points may be randomly selected.
In some embodiments, discrete point sampling is performed based on a flow field skeleton line. In some embodiments, discretely point sampling based on the flow field skeleton line may include discretely point sampling the flow field skeleton line according to a sampling frequency, which may be positively correlated with a concentration of skeleton lines. The density may refer to the density of the flow field skeleton lines in the image. In some embodiments, the greater the density of the flow field skeleton lines, meaning the denser the flow field skeleton lines, the higher the sampling frequency may be at this time; conversely, the smaller the density of the flow field skeleton lines, the lower the sampling frequency. In other alternative embodiments, the sampling frequency may also be a preset fixed value.
In some embodiments, the size of the cross-correlation window may be adjusted according to the density of skeleton lines when determining the flow field vectors between the target frame flow field image and the at least one other frame flow field image (i.e., "cross-correlation window adaptation" shown in fig. 7). In the embodiments of the present specification, the cross-correlation window may be understood as a target frame stream field image INAnd at least one other stream field picture (e.g., previous stream field picture I)N-kAnd a post-frame stream field picture IN+k) Respectively selecting windows when performing cross-correlation operation. The cross-correlation window may be a target frame stream field image preceding frame stream field image INAnd a preceding frame stream field picture IN-kOr post-frame stream field picture IN+kThe middle areas have the same position and size. In some embodiments, the previous frame stream field image I may be found by cross-correlation windowN-kOr post-frame stream field picture IN+kIntermediate and target frame stream field image INThe initial point of (a) corresponds to a discrete point. It is understood that when the other at least one stream field image is located in the target stream field image INPrevious frame stream field picture IN-kAnd the target frame stream field image INThe discrete point corresponding to the initial point can be regarded as the position before the initial point; when at least one other stream field image is located in the target stream field image INSubsequent post-frame stream field picture IN+kAnd the target frame stream field image INThe discrete point corresponding to the initial point of (a) can be regarded as the position at which the initial point arrives at a subsequent time under the influence of the gas flow. The vector arrow connecting the initial point to the discrete point can be considered a flow field vector between the two points. In some embodiments, the size of the cross-correlation window is inversely related to the concentration of flow field skeleton lines. For example, the larger the density value of the flow field skeleton line, the smaller the cross-correlation window; the smaller the density value of the flow field skeleton line, the larger the cross-correlation window.
In some embodiments, a flow field vector between the target frame flow field image and the at least one other frame flow field image is determined. In some embodiments, the flow field skeleton line may be obtained by averaging two or more flow field images, and the flow field skeleton line may be approximately regarded as the skeleton line of the target frame flow field image and the at least one other flow field image. Because the flow field on the surface of the region to be measured of the object is in continuous motion in the wind tunnel test, the discrete point coordinate value of at least one other frame of flow field image is changed relative to the discrete point coordinate value on the target frame of flow field image. When the discrete point sampled on the flow field skeleton line is used as the initial point on the target frame flow field image, the discrete point moves along the skeleton line or near the skeleton line because the skeleton line of the average image is used as the constraint, that is, the discrete point corresponding to the initial point in at least one other frame flow field image can be on the flow field skeleton line or near the flow field skeleton line. The flow field vector between the target frame flow field image and the at least one other frame flow field image can be determined based on the coordinate values of the discrete points of the at least one other frame flow field image of the two or more frame flow field images, the coordinate values of the discrete points (or initial points) of the target frame flow field image, and the flow field vector algorithm. In some embodiments, the flow field vector algorithm may include a cross-correlation method. In some embodiments, the cross-correlation algorithm may include a cross-correlation function algorithm, a Fast Fourier Transform (FFT) algorithm, or the like.
In some embodiments, the method of pneumatic flow field measurement based on skeleton line constraints may further include determining a flow field map based on the flow field vectors. The flow field map may be an image reflecting the distribution of the flow field. In some embodiments, the method of determining a flow field map based on a flow field vector may include a line convolution integration method. In some embodiments, the flow field map may include a flow field vector map, a flow field flow map, a flow field thermodynamic map, or the like, or any combination thereof.
Regarding the effect of the pneumatic flow field measurement method provided by the embodiments of the present specification, the following analysis is performed:
FIG. 8a is a flow field vector diagram obtained using existing optical flow techniques; FIG. 8b is a flow chart obtained using existing optical flow techniques; FIG. 8c is a thermodynamic diagram obtained using a prior art optical flow method technique; FIG. 9a is a flow field vector diagram obtained using a prior art cross-correlation technique; FIG. 9b is a flow chart obtained using a prior art cross-correlation technique; FIG. 9c is a thermodynamic diagram obtained using a prior art cross-correlation technique; FIG. 10a is a vector view of a flow field obtained according to a flow field measurement method shown in some embodiments of the present application; FIG. 10b is a flow diagram obtained according to a flow field measurement method shown in some embodiments of the present application; FIG. 10c is a thermodynamic diagram obtained according to a flow field measurement method as shown in some embodiments of the present application.
The flow field vector diagram can display and indicate the flow field direction of each discrete point in the flow field, as shown in fig. 1, fig. 2, fig. 8a, fig. 9a, and fig. 10a, the flow field vector in fig. 10a is closer to the motion situation of the flow trace curve in fig. 1 and fig. 2 than the flow field vector in fig. 8a and fig. 9a, so that it can be seen that the flow field vector calculated by the flow field measurement method in this specification is more accurate. The flow chart can show and illustrate the overall movement trend of the flow field, as shown in fig. 1, fig. 2, fig. 8b, fig. 9b and fig. 10b, the flow field lines in fig. 10b are generally consistent with the movement situation of the flow trace curve in fig. 1 and fig. 2 relative to the flow field lines in fig. 8b and fig. 9b, and therefore, the flow chart calculated by the flow field measuring method in the specification is more accurate. The thermodynamic diagrams can more intuitively display and indicate abnormal points in the flow field, so that the turbulent structure in the flow field can be analyzed and judged conveniently, as shown in fig. 1, fig. 2, fig. 8c, fig. 9c and fig. 10c, the turbulent structure in fig. 1 and fig. 2 can be more accurately embodied in fig. 10c relative to fig. 8c and fig. 9c, and therefore, the thermodynamic diagrams calculated by the flow field measuring method in the specification are more accurate. Therefore, the flow field vector measured by the flow field measurement method based on skeleton line constraint provided by the embodiment of the specification is more accurate.
The embodiment of the specification also provides a flow field measuring device based on skeleton line constraint, and the device can comprise a processor and a memory; the memory is used for storing instructions, and when the instructions are executed by the processor, the device can realize the operation corresponding to the flow field measurement method.
The embodiments of the present disclosure also provide a computer-readable storage medium, where the storage medium stores computer instructions, and after the computer reads the computer instructions in the storage medium, the computer may execute the flow field measurement method.
The beneficial effects that may be brought by the embodiments of the present application include, but are not limited to: (1) when a flow field vector algorithm is used for calculating a flow field vector between two frames of flow field images, a flow field skeleton line based on an average image is taken as a constraint (namely, a point on the flow field skeleton line is extracted as an initial point of the flow field vector algorithm) so as to ensure that a flow field curve is kept relatively consistent with an actual flow field image and avoid the condition of large curve error; (2) the flow field has the property of relatively uniform flow velocity and direction within a certain time, and a flow field skeleton line obtained based on two or more adjacent flow field images is used as final constraint, so that a final flow field curve image is relatively smooth; (3) sampling discrete points according to the density of the flow field skeleton lines and performing cross-correlation calculation on the flow field vectors by adopting a cross-correlation window, so that the calculation result of the flow field vectors is more accurate and reasonable; (4) when a flow trace curve (flow field skeleton line) is extracted, the average image of the spin filtering method is processed, so that the noise and speckle characteristics of the average image can be effectively reduced. It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such alterations, modifications, and improvements are intended to be suggested herein and are intended to be within the spirit and scope of the exemplary embodiments of this application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereon. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the foregoing description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, and the like, cited in this application is hereby incorporated by reference in its entirety. Except where the application is filed in a manner inconsistent or contrary to the present disclosure, and except where the claim is filed in its broadest scope (whether present or later appended to the application) as well. It is noted that the descriptions, definitions and/or use of terms in this application shall control if they are inconsistent or contrary to the statements and/or uses of the present application in the material attached to this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
Claims (17)
1. The flow field measurement method based on skeleton line constraint is characterized by comprising the following steps of:
acquiring two or more flow field images of the surface of a region to be detected of an object;
extracting a flow field skeleton line of the surface of the object region to be measured based on at least one of the two or more flow field images;
and restricting a cross-correlation window based on the flow field skeleton line, and patrolling at least two frames of flow field images in the two or more frames of flow field images through the cross-correlation window to determine the flow field vector of the surface of the to-be-detected area of the object.
2. The flow field measurement method according to claim 1, wherein the extracting of the flow field skeleton line of the surface of the object region-to-be-measured based on at least one of the two or more flow field images comprises:
acquiring a first fringe image of at least one frame of flow field image in the two or more frames of flow field images through fringe image processing;
and extracting a flow field skeleton line of the surface of the to-be-detected region of the object based on the first stripe image.
3. The flow field measurement method according to claim 1, wherein the extracting of the flow field skeleton line of the surface of the object region-to-be-measured based on at least one of the two or more flow field images comprises:
acquiring an average image of at least two frames of flow field images in the two or more frames of flow field images;
and extracting a flow field skeleton line of the surface of the object region to be detected based on the average image.
4. The flow field measurement method according to claim 3, wherein the extracting a flow field skeleton line of the surface of the object region to be measured based on the average image comprises:
acquiring a second stripe image of the average image through stripe image processing;
and extracting a flow field skeleton line of the surface of the object to be detected based on the second stripe image.
5. The flow field measurement method according to claim 1, wherein the extracting of the flow field skeleton line of the surface of the object region-to-be-measured based on at least one of the two or more flow field images comprises:
respectively acquiring flow field skeleton lines corresponding to the surfaces of the to-be-detected areas of the objects in at least two frames of flow field images;
and obtaining the average flow field skeleton line of the surface of the object to be detected based on the multi-frame flow field skeleton line.
6. The flow field measurement method according to claim 1, wherein extracting the flow field skeleton line of the surface of the object region-to-be-measured based on at least one of the two or more flow field images further comprises:
and performing curve fitting treatment on the flow field skeleton line.
7. The flow field measurement method according to claim 1, further comprising performing a pre-processing on the two or more flow field images, respectively, the pre-processing comprising:
and carrying out gray level processing on the two or more flow field images to obtain gray level images of the two or more flow field images.
8. The flow field measurement method according to claim 1, further comprising preprocessing the two or more flow field images, respectively, the preprocessing comprising:
and performing brightness equalization processing on the two or more flow field images to ensure that the bright and dark brightness ranges of the multi-frame flow field images are basically the same.
9. The flow field measurement method of claim 1, wherein a cross-correlation window is constrained based on the flow field skeleton line; and patrolling at least two of the two or more flow field images through the cross-correlation window, and determining the flow field vector of the surface of the object region to be measured comprises:
sampling discrete points of the flow field skeleton line;
under the constraint of the flow field skeleton line, respectively determining the corresponding discrete point positions in the cross-correlation windows of the at least two frames of flow field images through the cross-correlation windows in the at least two frames of flow field images through sliding inspection in the two frames or more of flow field images, calculating the flow field vector of each discrete point in the cross-correlation windows based on the deviation of the corresponding discrete point positions, and determining the flow field vector of the surface of the region to be measured of the object after the sliding inspection is finished.
10. The flow field measurement method according to claim 9, wherein the flow field skeleton line is sampled at discrete points according to a sampling frequency, and the sampling frequency is positively correlated with the density of the flow field skeleton line.
11. The flow field measurement method according to claim 1, wherein said constraining a cross-correlation window based on the flow field skeleton line comprises: and self-adaptively adjusting a cross-correlation window based on the flow field skeleton line.
12. The aerodynamic flow field measurement method of claim 11, wherein said constraining a cross-correlation window based on said flow field skeleton line comprises: and self-adaptively and dynamically adjusting the size of a cross-correlation window according to the density of the flow field skeleton line.
13. The flow field measurement method of claim 12, wherein the size of the cross-correlation window is inversely related to the concentration of the flow field skeleton lines.
14. The flow field measurement method of any of claims 1-13, further comprising: and generating a flow field diagram based on the flow field vector of the surface of the region to be measured of the object, wherein the flow field diagram comprises a flow field vector diagram, a flow field diagram or a flow field thermodynamic diagram.
15. A flow field measurement system, characterized in that the system comprises:
the acquisition module is used for acquiring two or more flow field images of the surface of the region to be detected of the object;
a skeleton line extraction module for extracting a flow field skeleton line of the surface of the object region to be measured based on at least one of the two or more flow field images;
the skeleton line constraint module is used for acquiring the flow field skeleton line and constraining a cross-correlation window through the flow field skeleton line;
and the flow field vector calculation module is used for patrolling at least two frames of flow field images in the two or more frames of flow field images through the cross-correlation window to determine the flow field vector of the surface of the object region to be measured.
16. A flow field measurement device based on skeleton line constraints, the device comprising a processor and a memory; the memory is configured to store instructions, and when the instructions are executed by the processor, the apparatus implements operations corresponding to the flow field measurement method according to any one of claims 1 to 14.
17. A computer-readable storage medium, wherein the storage medium stores computer instructions, and when the computer instructions in the storage medium are read by a computer, the computer executes the flow field measurement method according to any one of claims 1 to 14.
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