CN114383668A - Variable background-based flow field measuring device and method - Google Patents
Variable background-based flow field measuring device and method Download PDFInfo
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Abstract
The application provides a flow field measuring device and method based on variable background, relates to schlieren quantitative measurement technical field, includes: the device comprises an LED parallel backlight source, a liquid crystal panel, a camera, an image processing module and a background pattern generating module; the LED parallel backlight source is used for emitting parallel light sources to the liquid crystal panel; the background pattern generation module is used for generating an optimal background pattern according to the resolution and the view field of the camera based on a preset background pattern generation algorithm and sending the optimal background pattern to the liquid crystal panel for displaying; the camera is used for respectively imaging the flow field to be detected which is not placed in front of the optimal background pattern and the flow field to be detected which is placed in front of the optimal background pattern to obtain a first image and a second image; and the image processing module is used for processing the first image and the second image according to a preset algorithm to obtain the density and the temperature of the flow field to be measured. According to the method and the device, the size and the style of the background pattern are changed according to the measurement requirement, and the measurement precision of the flow field to be measured is improved.
Description
Technical Field
The application relates to the technical field of schlieren quantitative measurement, in particular to a flow field measuring device and method based on a variable background.
Background
In quantitative measurements using background schlieren, dynamic processes are usually captured, so the light source is required to have sufficient intensity to support high frame rate measurements. When the background pattern is large, the light source needs to be distributed over a larger area, and thus the light intensity is limited.
In the existing background schlieren method, if a large field of view is measured, the time resolution is sacrificed, for example, a halogen lamp is adopted to illuminate a large background pattern; if the flow field to be measured is reconstructed with high time resolution, the size of the field of view is sacrificed, for example, a small background pattern is illuminated by high-intensity laser. Both of these methods have certain limitations, and the background pattern is usually printed and cannot be replaced according to actual requirements. Therefore, how to establish a measuring device which can ensure that the field of view is large enough and can quickly change the background pattern is one of the problems faced by the current background schlieren method.
Disclosure of Invention
In view of this, the present application provides a flow field measurement apparatus and method based on a variable background, which can solve the technical problems in the prior art that a background pattern cannot be changed and a sampling frame rate is insufficient.
In one aspect, an embodiment of the present application provides a flow field measurement apparatus based on a variable background, including: the device comprises an LED parallel backlight source, a liquid crystal panel, a camera, an image processing module and a background pattern generating module;
the LED parallel backlight source is used for emitting parallel light sources to the liquid crystal panel;
the background pattern generation module is used for executing the following steps:
step S1: acquiring the resolution of a camera and the size of a background area shot by the camera;
step S2: calculating the actual size of a background pattern area corresponding to one pixel of the camera, thereby obtaining the actual side length of the pixel;
step S3: setting the side length a of a black square sampling point to be 2 times or 3 times of the actual side length of the pixel, and setting the initial value of the sampling radius R to be 1.5 times of the side length a of the square;
step S4: randomly generating a plurality of black square sampling points on a white background by using a Poisson disc sampling method by taking the side length a and the sampling radius R as parameters, and taking the black square sampling points as background patterns;
step S5, generating a histogram of the background pattern, thereby calculating a gray level average value of the background pattern;
step S6, judging whether the absolute value of the difference between the gray average value and 0.5 is larger than a threshold value, if so, reducing the sampling radius R if the gray average value is larger than 0.5, and increasing the sampling radius R if the gray average value is smaller than 0.5, and entering step S4; otherwise, go to step S7;
step S7: taking the generated background pattern as an optimal background pattern, and sending the optimal background pattern to a liquid crystal panel;
the liquid crystal panel is used for displaying an optimal background pattern;
the camera is used for respectively imaging the flow field to be detected which is not placed in front of the optimal background pattern and the flow field to be detected which is placed in front of the optimal background pattern to obtain a first image and a second image;
and the image processing module is used for processing the first image and the second image according to a preset algorithm to obtain the density and the temperature of the flow field to be measured.
Further, a parallel light film is arranged on the LED parallel backlight source.
Furthermore, the sizes of the LED parallel backlight source and the liquid crystal panel are determined according to the size of a flow field to be detected.
Further, the image processing module is specifically configured to:
acquiring a first image and a second image;
respectively preprocessing the first image and the second image; the pretreatment comprises the following steps: removing noise interference of the image and smoothing the gray level histogram of the image;
selecting a cross-correlation window, and performing cross-correlation processing on the preprocessed first image and the preprocessed second image to obtain the offset of each pixel of the background pattern of the whole flow field area to be detected, so as to obtain the offset distribution of the background pattern of the whole flow field area to be detected;
obtaining the relative refractive index distribution of the flow field to be detected according to the offset distribution of the background pattern of the whole flow field area to be detected;
calculating the refractive index distribution of the flow field to be detected according to the environment refractive index and the relative refractive index distribution of the flow field to be detected;
calculating the density distribution of the flow field to be detected according to the relative refractive index distribution of the flow field to be detected;
and calculating the temperature distribution of the flow field to be measured according to the density distribution of the flow field to be measured.
Further, after obtaining the offset of each pixel of the background pattern of the whole flow field area to be measured, the method includes:
selecting an attention area needing parameter processing or an attention area with axial symmetry from a background pattern of a flow field area to be detected, wherein the number of rows of the attention area is t, and the number of columns of the attention area is q;
extracting the maximum value and the position of the minimum value of the offset in the first row of data from the first row of the attention area, calculating the horizontal coordinate average value of the two positions, and taking the integral part of the horizontal coordinate average value as the initial value m of the symmetry axis of the first row of data;
determining the traversal range of the symmetry axis as [ m-b, m + b ], wherein b is a traversal radius;
forming a set B by the absolute values of q offsets in the first line of the attention area, and calculating the cross correlation coefficients of data on the left side and the right side of a symmetry axis through the data of the set B for each possible symmetry axis in the [ m-B, m + B ] interval to obtain 2B +1 cross correlation coefficients in total;
comparing the sizes of 2b +1 cross correlation coefficients, and taking the symmetry axis coordinate corresponding to the maximum cross correlation coefficient
As a symmetry axis position of the first row;
traversing other t-1 lines of the attention area according to the steps to obtain the symmetric axis coordinate of the t-1 line
；
Computing
Is taken as the integer part of the averageAnd the horizontal coordinate is taken as the symmetrical axis of the whole area of the flow field to be measured.
On the other hand, an embodiment of the present application provides a flow field measurement method based on a variable background, which is applied to a flow field measurement device based on a variable background in an embodiment of the present application, and includes:
the background pattern generation module performs the steps of:
step S1: acquiring the resolution of a camera and the size of a background area shot by the camera;
step S2: calculating the actual size of a background pattern area corresponding to one pixel of the camera, thereby obtaining the actual side length of the pixel;
step S3: setting the side length a of a black square sampling point to be 2 times or 3 times of the actual side length of the pixel, and setting the initial value of the sampling radius R to be 1.5 times of the side length a of the square;
step S4: randomly generating a plurality of black square sampling points on a white background by using a Poisson disc sampling method by taking the side length a and the sampling radius R as parameters, and taking the black square sampling points as background patterns;
step S5, generating a histogram of the background pattern, thereby calculating a gray level average value of the background pattern;
step S6, judging whether the absolute value of the difference between the gray average value and 0.5 is larger than a threshold value, if so, reducing the sampling radius R if the gray average value is larger than 0.5, and increasing the sampling radius R if the gray average value is smaller than 0.5, and entering step S4; otherwise, go to step S7;
step S7: taking the generated background pattern as an optimal background pattern, and sending the optimal background pattern to a liquid crystal panel;
imaging the optimal background pattern displayed by the liquid crystal panel by using a camera to obtain a first image;
placing the flow field to be detected between the liquid crystal panel and the camera, and imaging the flow field to be detected and the optimal background pattern by using the camera to obtain a second image;
and the image processing module processes the first image and the second image according to a preset algorithm to obtain the density and the temperature of the flow field to be measured.
Further, the method further comprises:
setting the resolution and the frame frequency of acquisition of a camera;
and inputting the resolution of the camera and the size of the flow field to be detected into a background pattern generation module.
Further, before imaging the optimal background pattern displayed by the liquid crystal panel using the camera, the method comprises:
adjusting the LED parallel backlight source to the maximum brightness, and projecting light rays on the liquid crystal panel;
and adjusting the height and the level of the camera to enable the visual field of the camera to completely cover the area of the flow field to be detected and the optimal background pattern behind the area of the flow field to be detected.
Further, the image processing module processes the first image and the second image according to a preset algorithm to obtain the density and the temperature of the flow field to be measured: the method comprises the following steps:
acquiring a first image and a second image;
respectively preprocessing the first image and the second image; the pretreatment comprises the following steps: removing noise interference of the image and smoothing the gray level histogram of the image;
selecting a cross-correlation window, and performing cross-correlation processing on the preprocessed first image and the preprocessed second image to obtain the offset of each pixel of the background pattern of the whole flow field area to be detected, so as to obtain the offset distribution of the background pattern of the whole flow field area to be detected;
obtaining the relative refractive index distribution of the flow field to be detected according to the offset distribution of the background pattern of the whole flow field area to be detected;
calculating the refractive index distribution of the flow field to be detected according to the environment refractive index and the relative refractive index distribution of the flow field to be detected;
calculating the density distribution of the flow field to be detected according to the relative refractive index distribution of the flow field to be detected;
and calculating the temperature distribution of the flow field to be measured according to the density distribution of the flow field to be measured.
Further, after obtaining the offset of each pixel of the background pattern of the whole flow field area to be measured, the method includes:
selecting an attention area needing parameter processing or an attention area with axial symmetry from a background pattern of a flow field area to be detected, wherein the number of rows of the attention area is t, and the number of columns of the attention area is q;
extracting the maximum value and the position of the minimum value of the offset in the first row of data from the first row of the attention area, calculating the horizontal coordinate average value of the two positions, and taking the integral part of the horizontal coordinate average value as the initial value m of the symmetry axis of the first row of data;
determining the traversal range of the symmetry axis as [ m-b, m + b ], wherein b is a traversal radius;
forming a set B by the absolute values of q offsets in the first line of the attention area, and calculating the cross correlation coefficients of data on the left side and the right side of a symmetry axis through the data of the set B for each possible symmetry axis in the [ m-B, m + B ] interval to obtain 2B +1 cross correlation coefficients in total;
comparing the sizes of 2b +1 cross correlation coefficients, and taking the symmetry axis coordinate corresponding to the maximum cross correlation coefficient
As a symmetry axis position of the first row;
traversing other t-1 lines of the attention area according to the steps to obtain the symmetric axis coordinate of the t-1 line
Computing
And taking an integral part of the average value as an abscissa of a symmetry axis of the whole flow field area to be measured.
According to the embodiment of the application, the side length of a black square sampling point in the background pattern is determined according to the resolution of the camera and the size of the background area shot by the camera, and the sampling radius of the Poisson disc sampling method is determined according to the gray average value of the background pattern, so that the size and the style of the background pattern can be changed according to the measurement requirement, and the measurement precision of the flow field to be measured is improved.
Drawings
In order to more clearly illustrate the detailed description of the present application or the technical solutions in the prior art, the drawings needed to be used in the detailed description of the present application or the prior art description will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a structural composition diagram of a variable background-based flow field measurement device provided in an embodiment of the present application;
fig. 2 is a flowchart of a background pattern generation module generating an optimal background pattern according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a flow field to be measured being a propane-air premixed flame;
FIG. 4 is a schematic diagram of an optimal background pattern provided by an embodiment of the present application;
fig. 5 is a schematic diagram of a specific implementation process of an image processing module according to an embodiment of the present application;
FIG. 6 is a graph of the background pattern of the entire flow field area plotted against the offset profile of the background pattern according to an embodiment of the present disclosure;
FIG. 7 is a cloud of flame temperature profiles plotted according to an embodiment of the present disclosure;
fig. 8 is a flowchart of a flow field measurement method based on a variable background according to an embodiment of the present application.
Icon: 101-LED parallel backlight; 102-a liquid crystal panel; 103-a flow field to be measured; 104-an imaging lens; 105-an image sensor; 106-image processing means; 107-background Pattern Generation means.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
First, the design idea of the embodiment of the present application is briefly introduced.
The background schlieren method determines the parameters of the flow field to be measured based on the deflection angle of the light after passing through the flow field to be measured: the specific background pattern is placed behind the flow field to be detected, two background patterns of the flow field to be detected and the flow field not to be detected are shot by a camera, and the background offset is extracted by utilizing a cross-correlation algorithm so as to solve the deflection angle of the light after passing through the flow field to be detected. Compared with the traditional schlieren method, the method has the advantages that the measuring visual field is not limited any more, as long as the size of the background pattern is selected reasonably, the measuring visual field can be infinite, and even the shock waves generated by the supersonic aircraft can be qualitatively captured by taking the sand stones and trees in the nature as the background.
In the existing background schlieren method, if a large field of view is measured, the time resolution is sacrificed, for example, a halogen lamp is adopted to illuminate a large background pattern; if the flow field to be measured is reconstructed with high time resolution, the size of the field of view is sacrificed, for example, a small background pattern is illuminated by high-intensity laser. Both of these methods have certain limitations, and the background pattern is usually printed and cannot be replaced according to actual requirements. Therefore, how to establish a measuring device which can ensure that the field of view is large enough and can quickly change the background pattern is one of the problems faced by the current background schlieren method.
In order to solve the above technical problems, an embodiment of the present application provides a flow field measurement device based on a variable background, based on a principle of reconstructing a density field by using a refractive index gradient in a background schlieren method, an LED parallel backlight source with a parallel light film is used as a parallel light source, a liquid crystal panel is used as a background pattern display device, and a high-speed camera is used as an image acquisition device, which can perform high spatial and temporal resolution measurement on an area with a large field of view, and provide a background pattern generation algorithm, which can generate an optimal background pattern by combining camera parameters and a field of view to be measured, and should present a uniform distribution of gray scale as a whole.
The measuring device provided by the embodiment of the application can be used for carrying out instantaneous temperature measurement on a flow field to be measured on a millisecond time scale, and can be used for changing the size of the LED parallel backlight source and the size and the pattern of the background pattern according to the measurement requirement so as to ensure the optimal spatial resolution and the measurement precision. The method overcomes the defects that the original experimental device can not change the background pattern in real time and the sampling frame rate is insufficient while the measurement precision is not reduced. And the light path arrangement is shortened through the LED parallel backlight source, and the space is saved.
After introducing the application scenario and the design concept of the embodiment of the present application, the following describes a technical solution provided by the embodiment of the present application.
As shown in fig. 1, an embodiment of the present application provides a flow field measurement device based on a variable background, including: the device comprises an LED parallel backlight 101, a liquid crystal panel 102, an imaging lens 104, an image sensor 105, an image processing device 106 and a background pattern generating device 107, wherein the imaging lens 104 and the image sensor 105 form a camera. The flow field 103 to be measured is placed between the liquid crystal panel 102 and the imaging lens 104, and meanwhile, the centers of the LED parallel backlight 101, the liquid crystal panel 102, the flow field 103 to be measured and the camera are ensured to be on the same horizontal plane. The image processing device 106 and the camera are connected by wire, and the liquid crystal panel 102 and the background pattern generation device 107 are connected by wire. Meanwhile, the image processing device 106 and the background pattern generation device 107 are ensured to have enough distance with the optical path system, and the heat dissipation of the device does not influence the optical path system.
Preferably, the surface of the LED parallel backlight 101 is covered with a parallel optical film; the background pattern generation device 107 is an electronic device which is provided with a background pattern generation module and can be connected with the liquid crystal panel 102; the imaging lens 104 is a 105mm macro lens; the image sensor 105 employs a high-speed camera sensor; the image processing apparatus 106 is an electronic device configured with a background pattern generation module. The LED parallel backlight source 101, the liquid crystal panel 102 and the flow field 103 to be measured are supported by an optical platform, and the imaging lens 104 and the image sensor 105 are supported by a tripod.
An LED parallel backlight 101 for emitting parallel light sources to the liquid crystal panel; the background pattern generation module is used for generating an optimal background pattern according to the resolution and the view field of the camera based on a preset background pattern generation algorithm and sending the optimal background pattern to the liquid crystal panel; a liquid crystal panel 102 for displaying an optimal background pattern; the camera is used for respectively imaging the flow field to be detected which is not placed in front of the optimal background pattern and the flow field to be detected which is placed in front of the optimal background pattern to obtain a first image and a second image; the image processing module is used for processing the first image and the second image according to a preset algorithm to obtain the density and the temperature of the flow field to be measured.
The sizes of the LED parallel backlight 101 and the liquid crystal panel 102 are determined according to the size of the flow field to be measured.
Because the adopted liquid crystal panel 102 can change the background pattern in real time and has higher resolution, the adopted liquid crystal panel can provide the background pattern with high precision for identification compared with the traditional background schlieren device. The background pattern should have sufficient characteristics locally and should exhibit a uniform distribution of gray scale as a whole.
As shown in fig. 2, the background pattern generation module generates the optimal background pattern as follows:
step S1: acquiring the resolution of a camera and the size of a background area shot by the camera;
step S2: calculating the actual size of a background pattern area corresponding to one pixel of the camera, thereby obtaining the actual side length of the pixel;
step S3: setting the side length a of a black square sampling point to be 2 times or 3 times of the actual side length of the pixel, and setting the initial value of the sampling radius R to be 1.5 times of the side length of the square;
step S4: randomly generating a plurality of black square sampling points on a white background by using a Poisson disc sampling method by taking the side length a and the sampling radius R as parameters, and taking the black square sampling points as background patterns;
the Poisson disc sampling method is used for generating a plurality of Poisson disc sampling points and meets the following conditions:
the distance between any two sampling points is larger than a given sampling radius R;
the sampling area is completely covered by all sampling disks, wherein only one sampling point is arranged in one sampling disk; the radius of the sampling disc is the sampling radius R.
Step S5: generating a histogram of the background pattern, thereby calculating a gray-scale average value of the background pattern;
step S6: judging whether the absolute value of the difference between the gray average value and 0.5 is greater than a threshold value, if so, reducing the sampling radius R if the gray average value is greater than 0.5, and increasing the sampling radius R if the gray average value is less than 0.5, and entering step S4; otherwise, go to step S7;
wherein, the threshold is a very small number and can be defined by the convergence speed; the aim is to generate a background pattern with a gray level average close to 0.5.
Step S7: and taking the generated background pattern as an optimal background pattern, and sending the optimal background pattern to the liquid crystal panel.
As an application example, the flow field to be measured is a propane-air premixed flame, as shown in fig. 3, and is placed in the area of the flow field to be measured 103 in fig. 1. At the start of the measurement, it is placed in the measurement position. The optimal background pattern generated by the background pattern generation module is shown in fig. 4, and the pattern has obvious local features and uniform overall gray scale.
As a possible implementation manner, fig. 5 is an image processing process executed by the image processing module, and specifically, when the flow field to be measured is an axisymmetric flow field, the image processing module is specifically configured to:
step A1: acquiring a first image and a second image;
step A2: respectively preprocessing the first image and the second image; the pretreatment comprises the following steps: removing noise interference of the image and smoothing the gray level histogram of the image;
step A3: selecting a cross-correlation window, and performing cross-correlation processing on the preprocessed first image and the preprocessed second image to obtain the offset of each pixel of the background pattern of the whole flow field area to be detected, so as to obtain the offset distribution of the background pattern of the whole flow field area to be detected;
wherein the cross-correlation window is a small square that can cover pixels in p x p images. When the flow field to be measured is a flame, the offset distribution is as shown in fig. 6.
This step is followed by:
the measurement of the centrosymmetric flow field requires selecting the symmetry axis of the whole flow field area to be measured, so as to perform subsequent chromatographic reconstruction, and the method specifically comprises the following steps:
selecting an attention area needing parameter processing or an attention area with axial symmetry from a background pattern of a flow field area to be detected, wherein the number of rows of the attention area is t, and the number of columns of the attention area is q;
starting from the first row of the attention area, extracting the positions of the maximum value and the minimum value of the offset in the first row of data because the offsets on the two sides of the symmetry axis are opposite numbers, calculating the average value of the abscissa of the two positions, and taking the integer part m as the initial value of the symmetry axis of the first row of data;
determining the traversal range of the symmetry axis as [ m-b, m + b ], wherein b is a traversal radius, and the selection is determined according to the spatial resolution of the processed data;
the absolute values of the q offsets in the first row of the region of interest are grouped into set B, for [ m-B, m + B]Calculating the cross correlation coefficient of the data on the left side and the right side of the symmetry axis through the data of the set B for each possible symmetry axis in the interval to obtain the total cross correlation coefficient
A cross-correlation coefficient;
comparing the sizes of 2b +1 cross correlation coefficients, and taking the symmetry axis coordinate corresponding to the maximum cross correlation coefficient
As a symmetry axis position of the first row;
traversing other t-1 lines of the attention area according to the steps to obtain the symmetric axis coordinate of the t-1 line
And taking an integral part of the average value as an abscissa of a symmetry axis of the whole flow field area to be measured.
Step A4: calculating light deflection angle distribution according to the deflection distribution of the background pattern of the whole flow field area to be measured, and integrating the light deflection angle distribution along the direction of the ordinate axis to obtain the relative refractive index distribution of the flow field to be measured;
step A5: calculating the refractive index distribution of the flow field to be detected according to the environment refractive index and the relative refractive index distribution of the flow field to be detected;
step A6: calculating the density distribution of the flow field to be detected according to the refractive index distribution of the flow field to be detected and the Glassy-Dalton coefficient;
step A7: calculating the temperature distribution of the flow field to be measured based on the density distribution of the flow field to be measured and an ideal gas state equation;
step A8: drawing a temperature distribution cloud chart of the flow field to be detected according to the temperature distribution of the flow field to be detected;
when the flow field to be measured is a flame, the temperature distribution cloud chart is shown in fig. 7.
Based on the measurement device provided by the embodiment of the present application, as shown in fig. 8, the embodiment of the present application provides a flow field measurement method based on a variable background, including the following steps:
step 201: the background pattern generation module generates an optimal background pattern according to the resolution and the view field of the camera based on a preset background pattern generation algorithm and sends the optimal background pattern to the liquid crystal panel;
step 202: imaging the optimal background pattern displayed by the liquid crystal panel by using a camera to obtain a first image;
step 203: placing the flow field to be detected between the liquid crystal panel and the camera, and imaging the flow field to be detected and the optimal background pattern by using the camera to obtain a second image;
step 204: and the image processing module processes the first image and the second image according to a preset algorithm to obtain the density and the temperature of the flow field to be measured.
As a possible implementation manner, before step 201, the method further includes:
setting the resolution and the frame frequency of acquisition of a camera;
and inputting the resolution of the camera and the size of the flow field to be detected into a background pattern generation module.
As a possible implementation manner, the background pattern generation module generates an optimal background pattern according to the resolution and the view field of the camera based on a preset background pattern generation algorithm; the method comprises the following steps:
step S1: acquiring the resolution of a camera and the size of a background area shot by the camera;
step S2: calculating the actual size of an actual background pattern area corresponding to one pixel of the camera, thereby obtaining the actual side length of the pixel;
step S3: setting the side length a of a black square sampling point to be 2 times or 3 times of the actual side length of the pixel, and setting the initial value of the sampling radius R to be 1.5 times of the side length of the square;
step S4: randomly generating a plurality of black square sampling points on a white background by using a Poisson disc sampling method by taking the side length a and the sampling radius R as parameters, and taking the black square sampling points as background patterns;
step S5, generating a histogram of the background pattern, thereby calculating a gray level average value of the background pattern;
step S6, judging whether the absolute value of the difference between the gray average value and 0.5 is larger than a threshold value, if so, reducing the sampling radius R if the gray average value is larger than 0.5, and increasing the sampling radius R if the gray average value is smaller than 0.5, and entering step S4; otherwise, go to step S7;
step S7: and taking the generated background pattern as an optimal background pattern, and sending the optimal background pattern to the liquid crystal panel.
As a possible implementation manner, step 202 further includes:
adjusting the LED parallel backlight source to the maximum brightness, and projecting light rays on the liquid crystal panel;
and adjusting the height and the level of the camera to enable the visual field of the camera to completely cover the area of the flow field to be detected and the optimal background pattern behind the area to be detected.
As a possible implementation manner, the image processing module processes the first image and the second image according to a preset algorithm to obtain the density and the temperature of the flow field to be measured: the method comprises the following steps:
acquiring a first image and a second image;
respectively preprocessing the first image and the second image; the pretreatment comprises the following steps: removing noise interference of the image and smoothing the gray level histogram of the image;
selecting a cross-correlation window, and performing cross-correlation processing on the preprocessed first image and the preprocessed second image to obtain the offset distribution of the background pattern of the whole flow field area;
obtaining the relative refractive index distribution of the flow field to be detected according to the offset distribution of the background pattern of the whole flow field area;
calculating the refractive index distribution of the flow field to be detected according to the environment refractive index and the relative refractive index distribution of the flow field to be detected;
calculating the density distribution of the flow field to be detected according to the relative refractive index distribution of the flow field to be detected;
and calculating the temperature distribution of the flow field to be measured according to the density distribution of the flow field to be measured.
As a possible implementation, after obtaining the offset of each pixel of the background pattern of the whole flow field area to be measured, the method includes:
selecting an attention area needing parameter processing or an attention area with axial symmetry from a background pattern of a flow field area to be detected, wherein the number of rows of the attention area is t, and the number of columns of the attention area is q;
extracting the maximum value and the position of the minimum value of the offset in the first row of data from the first row of the attention area, calculating the horizontal coordinate average value of the two positions, and taking the integral part of the horizontal coordinate average value as the initial value m of the symmetry axis of the first row of data;
determining the traversal range of the symmetry axis as [ m-b, m + b ], wherein b is a traversal radius;
forming a set B by the absolute values of q offsets in the first line of the attention area, and calculating the cross correlation coefficients of data on the left side and the right side of a symmetry axis through the data of the set B for each possible symmetry axis in the [ m-B, m + B ] interval to obtain 2B +1 cross correlation coefficients in total;
comparing the sizes of 2b +1 cross correlation coefficients, and taking the symmetry axis coordinate corresponding to the maximum cross correlation coefficient
As axis of symmetry of the first rowPlacing;
traversing other t-1 lines of the attention area according to the steps to obtain the symmetric axis coordinate of the t-1 line
；
Computing
And taking an integral part of the average value as an abscissa of a symmetry axis of the whole flow field area to be measured.
It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more units described above may be embodied in one unit, according to embodiments of the application. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.
Further, while the operations of the methods of the present application are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. A variable background-based flow field measurement device, comprising: the device comprises an LED parallel backlight source, a liquid crystal panel, a camera, an image processing module and a background pattern generating module;
the LED parallel backlight source is used for emitting parallel light sources to the liquid crystal panel;
the background pattern generation module is used for executing the following steps:
step S1: acquiring the resolution of a camera and the size of a background area shot by the camera;
step S2: calculating the actual size of a background pattern area corresponding to one pixel of the camera, thereby obtaining the actual side length of the pixel;
step S3: setting the side length a of a black square sampling point to be 2 times or 3 times of the actual side length of the pixel, and setting the initial value of the sampling radius R to be 1.5 times of the side length a;
step S4: randomly generating a plurality of black square sampling points on a white background by using a Poisson disc sampling method by taking the side length a and the sampling radius R as parameters, and taking the black square sampling points as background patterns;
step S5, generating a histogram of the background pattern, thereby calculating a gray level average value of the background pattern;
step S6, judging whether the absolute value of the difference between the gray average value and 0.5 is larger than a threshold value, if so, reducing the sampling radius R if the gray average value is larger than 0.5, and increasing the sampling radius R if the gray average value is smaller than 0.5, and entering step S4; otherwise, go to step S7;
step S7: taking the generated background pattern as an optimal background pattern, and sending the optimal background pattern to a liquid crystal panel;
the liquid crystal panel is used for displaying an optimal background pattern;
the camera is used for respectively imaging the flow field to be detected which is not placed in front of the optimal background pattern and the flow field to be detected which is placed in front of the optimal background pattern to obtain a first image and a second image;
and the image processing module is used for processing the first image and the second image according to a preset algorithm to obtain the density and the temperature of the flow field to be measured.
2. The variable background-based flow field measurement device according to claim 1, wherein a parallel light film is disposed on the LED parallel backlight.
3. The variable background-based flow field measurement device according to claim 1, wherein the sizes of the LED parallel backlight source and the liquid crystal panel are determined according to the size of the flow field to be measured.
4. The variable background-based flow field measurement device according to claim 1, wherein the image processing module is specifically configured to:
acquiring a first image and a second image;
respectively preprocessing the first image and the second image; the pretreatment comprises the following steps: removing noise interference of the image and smoothing the gray level histogram of the image;
selecting a cross-correlation window, and performing cross-correlation processing on the preprocessed first image and the preprocessed second image to obtain the offset of each pixel of the background pattern of the whole flow field area to be detected, so as to obtain the offset distribution of the background pattern of the whole flow field area to be detected;
obtaining the relative refractive index distribution of the flow field to be detected according to the offset distribution of the background pattern of the whole flow field area to be detected;
calculating the refractive index distribution of the flow field to be detected according to the environment refractive index and the relative refractive index distribution of the flow field to be detected;
calculating the density distribution of the flow field to be detected according to the relative refractive index distribution of the flow field to be detected;
and calculating the temperature distribution of the flow field to be measured according to the density distribution of the flow field to be measured.
5. The variable background-based flow field measurement device according to claim 4, comprising, after obtaining the offset of each pixel of the background pattern of the entire flow field area to be measured:
selecting an attention area needing parameter processing or an attention area with axial symmetry from a background pattern of a flow field area to be detected, wherein the number of rows of the attention area is t, and the number of columns of the attention area is q;
extracting the maximum value and the position of the minimum value of the offset in the first row of data from the first row of the attention area, calculating the horizontal coordinate average value of the two positions, and taking the integral part of the horizontal coordinate average value as the initial value m of the symmetry axis of the first row of data;
determining the traversal range of the symmetry axis as [ m-b, m + b ], wherein b is a traversal radius;
forming a set B by the absolute values of q offsets in the first line of the attention area, and calculating the cross correlation coefficients of data on the left side and the right side of a symmetry axis through the data of the set B for each possible symmetry axis in the [ m-B, m + B ] interval to obtain 2B +1 cross correlation coefficients in total;
comparing the sizes of 2b +1 cross correlation coefficients, and taking the symmetry axis coordinate corresponding to the maximum cross correlation coefficient
As a symmetry axis position of the first row;
traversing other t-1 lines of the attention area according to the steps to obtain the symmetric axis coordinate of the t-1 line
；
Computing
And taking an integral part of the average value as an abscissa of a symmetry axis of the whole flow field area to be measured.
6. A variable background-based flow field measurement method applied to the variable background-based flow field measurement device according to any one of claims 1 to 5, comprising:
the background pattern generation module performs the steps of:
step S1: acquiring the resolution of a camera and the size of a background area shot by the camera;
step S2: calculating the actual size of a background pattern area corresponding to one pixel of the camera, thereby obtaining the actual side length of the pixel;
step S3: setting the side length a of a black square sampling point to be 2 times or 3 times of the actual side length of the pixel, and setting the initial value of the sampling radius R to be 1.5 times of the side length a;
step S4: randomly generating a plurality of black square sampling points on a white background by using a Poisson disc sampling method by taking the side length a and the sampling radius R as parameters, and taking the black square sampling points as background patterns;
step S5, generating a histogram of the background pattern, thereby calculating a gray level average value of the background pattern;
step S6, judging whether the absolute value of the difference between the gray average value and 0.5 is larger than a threshold value, if so, reducing the sampling radius R if the gray average value is larger than 0.5, and increasing the sampling radius R if the gray average value is smaller than 0.5, and entering step S4; otherwise, go to step S7;
step S7: taking the generated background pattern as an optimal background pattern, and sending the optimal background pattern to a liquid crystal panel;
imaging the optimal background pattern displayed by the liquid crystal panel by using a camera to obtain a first image;
placing the flow field to be detected between the liquid crystal panel and the camera, and imaging the flow field to be detected and the optimal background pattern by using the camera to obtain a second image;
and the image processing module processes the first image and the second image according to a preset algorithm to obtain the density and the temperature of the flow field to be measured.
7. The variable background-based flow field measurement method according to claim 6, further comprising:
setting the resolution and the frame frequency of acquisition of a camera;
and inputting the resolution of the camera and the size of the flow field to be detected into a background pattern generation module.
8. The variable background-based flow field measurement method according to claim 6, wherein before imaging the optimal background pattern displayed by the liquid crystal panel using the camera, the method comprises:
adjusting the LED parallel backlight source to the maximum brightness, and projecting light rays on the liquid crystal panel;
and adjusting the height and the level of the camera to enable the visual field of the camera to completely cover the area of the flow field to be detected and the optimal background pattern behind the area of the flow field to be detected.
9. The variable background-based flow field measurement method according to claim 6, wherein the image processing module processes the first image and the second image according to a preset algorithm to obtain the density and the temperature of the flow field to be measured, and the method comprises:
acquiring a first image and a second image;
respectively preprocessing the first image and the second image; the pretreatment comprises the following steps: removing noise interference of the image and smoothing the gray level histogram of the image;
selecting a cross-correlation window, and performing cross-correlation processing on the preprocessed first image and the preprocessed second image to obtain the offset distribution of the background pattern of the whole flow field area;
obtaining the relative refractive index distribution of the flow field to be detected according to the offset distribution of the background pattern of the whole flow field area to be detected;
calculating the refractive index distribution of the flow field to be detected according to the environment refractive index and the relative refractive index distribution of the flow field to be detected;
calculating the density distribution of the flow field to be detected according to the relative refractive index distribution of the flow field to be detected;
and calculating the temperature distribution of the flow field to be measured according to the density distribution of the flow field to be measured.
10. The variable background-based flow field measurement method according to claim 9, comprising, after obtaining the offset of each pixel of the background pattern of the entire flow field area to be measured:
selecting an attention area needing parameter processing or an attention area with axial symmetry from a background pattern of a flow field area to be detected, wherein the number of rows of the attention area is t, and the number of columns of the attention area is q;
extracting the maximum value and the position of the minimum value of the offset in the first row of data from the first row of the attention area, calculating the horizontal coordinate average value of the two positions, and taking the integral part of the horizontal coordinate average value as the initial value m of the symmetry axis of the first row of data;
determining the traversal range of the symmetry axis as [ m-b, m + b ], wherein b is a traversal radius;
forming a set B by the absolute values of q offsets in the first line of the attention area, and calculating the cross correlation coefficients of data on the left side and the right side of a symmetry axis through the data of the set B for each possible symmetry axis in the [ m-B, m + B ] interval to obtain 2B +1 cross correlation coefficients in total;
comparing the sizes of 2b +1 cross correlation coefficients, and taking the symmetry axis coordinate corresponding to the maximum cross correlation coefficient
As a symmetry axis position of the first row;
traversing other t-1 lines of the attention area according to the steps to obtain the symmetric axis coordinate of the t-1 line
；
Computing
And taking an integral part of the average value as an abscissa of a symmetry axis of the whole flow field area to be measured.
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Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5515158A (en) * | 1994-02-01 | 1996-05-07 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Retroreflection focusing schlieren system |
| CN1823241A (en) * | 2003-06-16 | 2006-08-23 | 威力沃格勒公司 | Method for monitoring an oil and gas lubricating device with the aid of a striae sensor |
| CN101040246A (en) * | 2006-04-27 | 2007-09-19 | 埃派克森微电子有限公司 | Optical motion sensing method |
| JP2009014359A (en) * | 2007-06-29 | 2009-01-22 | Niigata Univ | Three-dimensional noncontact temperature measuring instrument, and three-dimensional noncontact temperature measuring method |
| CN101739550A (en) * | 2009-02-11 | 2010-06-16 | 北京智安邦科技有限公司 | Method and system for detecting moving objects |
| CN102142128A (en) * | 2010-10-12 | 2011-08-03 | 华为技术有限公司 | Method and device for computing description vectors of interest points of image |
| CN102147861A (en) * | 2011-05-17 | 2011-08-10 | 北京邮电大学 | Moving target detection method for carrying out Bayes judgment based on color-texture dual characteristic vectors |
| CN103679758A (en) * | 2012-08-29 | 2014-03-26 | 通用电气航空系统有限责任公司 | Method of controlling the resolution of a hyperspectral image |
| CN103940850A (en) * | 2014-04-30 | 2014-07-23 | 武汉理工大学 | Test device for dust explosion experiment |
| CN106679929A (en) * | 2016-11-28 | 2017-05-17 | 南京航空航天大学 | Positioning method for shock train separation point |
| CN106682278A (en) * | 2016-12-06 | 2017-05-17 | 西安交通大学 | Supersonic flow field predicting accuracy determination device and method based on image processing |
| CN212459371U (en) * | 2020-07-01 | 2021-02-02 | 四川物科光学精密机械有限公司 | Resonant cavity type high-sensitivity schlieren instrument |
| CN112525905A (en) * | 2020-11-23 | 2021-03-19 | 西安建筑科技大学 | Background schlieren imaging based polluted air flow intensity identification and emission control method and system |
| CN112629679A (en) * | 2020-12-02 | 2021-04-09 | 中国人民解放军国防科技大学 | High-precision measurement method suitable for background schlieren, electronic equipment and medium |
| CN112902037A (en) * | 2021-02-01 | 2021-06-04 | 中国兵器装备集团自动化研究所有限公司 | Schlieren imaging system based on cutting light source |
| CN113570639A (en) * | 2021-09-24 | 2021-10-29 | 北京理工大学 | Moving target schlieren detection system based on stripe correction correlation |
| CN113567296A (en) * | 2021-06-11 | 2021-10-29 | 北京航空航天大学 | Projector-based quantitative background schlieren method temperature measuring device and method |
| CN114170201A (en) * | 2021-12-08 | 2022-03-11 | 山东大学 | Non-contact respiration rate detection method and system based on edge optical flow information |
-
2022
- 2022-03-24 CN CN202210292219.8A patent/CN114383668B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5515158A (en) * | 1994-02-01 | 1996-05-07 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | Retroreflection focusing schlieren system |
| CN1823241A (en) * | 2003-06-16 | 2006-08-23 | 威力沃格勒公司 | Method for monitoring an oil and gas lubricating device with the aid of a striae sensor |
| CN101040246A (en) * | 2006-04-27 | 2007-09-19 | 埃派克森微电子有限公司 | Optical motion sensing method |
| JP2009014359A (en) * | 2007-06-29 | 2009-01-22 | Niigata Univ | Three-dimensional noncontact temperature measuring instrument, and three-dimensional noncontact temperature measuring method |
| CN101739550A (en) * | 2009-02-11 | 2010-06-16 | 北京智安邦科技有限公司 | Method and system for detecting moving objects |
| CN102142128A (en) * | 2010-10-12 | 2011-08-03 | 华为技术有限公司 | Method and device for computing description vectors of interest points of image |
| CN102147861A (en) * | 2011-05-17 | 2011-08-10 | 北京邮电大学 | Moving target detection method for carrying out Bayes judgment based on color-texture dual characteristic vectors |
| CN103679758A (en) * | 2012-08-29 | 2014-03-26 | 通用电气航空系统有限责任公司 | Method of controlling the resolution of a hyperspectral image |
| CN103940850A (en) * | 2014-04-30 | 2014-07-23 | 武汉理工大学 | Test device for dust explosion experiment |
| CN106679929A (en) * | 2016-11-28 | 2017-05-17 | 南京航空航天大学 | Positioning method for shock train separation point |
| CN106682278A (en) * | 2016-12-06 | 2017-05-17 | 西安交通大学 | Supersonic flow field predicting accuracy determination device and method based on image processing |
| CN212459371U (en) * | 2020-07-01 | 2021-02-02 | 四川物科光学精密机械有限公司 | Resonant cavity type high-sensitivity schlieren instrument |
| CN112525905A (en) * | 2020-11-23 | 2021-03-19 | 西安建筑科技大学 | Background schlieren imaging based polluted air flow intensity identification and emission control method and system |
| CN112629679A (en) * | 2020-12-02 | 2021-04-09 | 中国人民解放军国防科技大学 | High-precision measurement method suitable for background schlieren, electronic equipment and medium |
| CN112902037A (en) * | 2021-02-01 | 2021-06-04 | 中国兵器装备集团自动化研究所有限公司 | Schlieren imaging system based on cutting light source |
| CN113567296A (en) * | 2021-06-11 | 2021-10-29 | 北京航空航天大学 | Projector-based quantitative background schlieren method temperature measuring device and method |
| CN113570639A (en) * | 2021-09-24 | 2021-10-29 | 北京理工大学 | Moving target schlieren detection system based on stripe correction correlation |
| CN114170201A (en) * | 2021-12-08 | 2022-03-11 | 山东大学 | Non-contact respiration rate detection method and system based on edge optical flow information |
Non-Patent Citations (3)
| Title |
|---|
| JIAQI NAN 等: "Analytical solutions for the three-dimensional acoustic field in a rectangular duct with temperature gradient and mean flow", 《AEROSPACE SCIENCE AND TECHNOLOGY》 * |
| 徐翔等: "聚焦纹影显示技术在激波风洞的初步应用", 《实验流体力学》 * |
| 田立丰等: "基于BOS的气动光学波前测量技术研究及其应用", 《科学通报》 * |
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