A stria shadow wave front automatic identification tracking method

Abstract

The present application relates to image data processing, in particular to a kind of schlieren wave front automatic identification tracking method, for solving the existing schlieren wave front identification and tracking method in the complex situation such as weak target of wave front relative to background, several concentric and similar wave fronts in wave front vicinity, several different wave fronts will cross and overlap, cannot exclude the influence of interference wave front, it is difficult to achieve the identification result of each wave front to meet the result of human eye observation, the deficiency of poor robustness.The schlieren wave front automatic identification tracking method is finally obtained by setting identification parameter, removing image background, image addition, image power transformation, image median filtering etc. such as step, the change trend chart of explosive wave front profile movement process and wave front radius.

Description

TECHNICAL FIELD

[0001] The present application relates to image data processing, in particular to a schlieren wave front automatic recognition and tracking method. BACKGROUND

[0002] Schlieren method is to convert the density gradient change in flow into the relative change of light intensity in image, that is, to reveal the process of shock wave and thermal turbulence through the different gray scale of image, according to the principle that the refractive index gradient of light in the measured fluid is proportional to the flow density. In high-speed schlieren system, the wave front of explosion shock wave needs to be profiled and recognized and tracked to calculate the speed of explosion shock wave. The manual profile extraction method not only cannot meet the accuracy requirement, but also is time-consuming and laborious, and cannot realize the processing requirement of a large amount of data.

[0003] In view of the above problems, Chinese patent CN112085651A discloses a shock wave automatic detection and tracking algorithm based on image adaptive threshold and feature extraction, which extracts the feature profile of binary schlieren image by background image subtraction, image filtering, image enhancement and sub-pixel interpolation, and combines with automatic threshold algorithm. The document "Li G, Agir M B, Kontis K, et al. Image Processing Techniques for Shock Wave Detection and Tracking in High Speed Schlieren and Shadowgraph Systems [J]. Journal of Physics: Conference Series, 2019, 1215(1): 012021(9pp)." uses the method of background subtraction, filtering, thresholding and edge detection to detect and track the shock wave.

[0004] The above two methods can identify the profile of shock wave, but are only applicable to wave front with low complexity. In the shock wave schlieren image taken by actual high-speed schlieren instrument, various complex situations will be encountered, mainly divided into the following three situations: (1) the wave front is a weak target relative to the background, which cannot be identified by a single method; (2) several concentric and similar wave fronts near the wave front are easily identified as interference wave fronts in the tracking process; (3) several different concentric wave fronts will cross and overlap, which are also easily disturbed by interference wave fronts. For the above three situations, the adaptive threshold and filtering method cannot exclude the influence of interference wave fronts, and it is difficult to make the identification result of each wave front meet the result observed by human eye, and the robustness is poor. SUMMARY

[0005] The present application aims to solve the problems of the prior art, i.e. the prior shadow wave front recognition and tracking method cannot eliminate the influence of interference wave fronts, and it is difficult to make the recognition result of each wave front consistent with the result of human eye observation, and the robustness is poor, and the present application provides a shadow wave front automatic recognition and tracking method.

[0006] In order to solve the problems of the prior art, the present application provides the following technical solutions.

[0007] A shadow wave front automatic recognition and tracking method, which is characterized in that it comprises the following steps:

[0008] Step 1: introducing a series of to-be-recognized images obtained by a high-speed camera into image processing software, wherein the series of to-be-recognized images are images arranged in time sequence in the same visual area;

[0009] Step 2: setting recognition parameters;

[0010] The recognition parameters comprise a recognition type, an ROI area, calibration data and interval data; the recognition type is a circular arc or a straight line; the calibration data is an actual distance represented by each pixel; and the interval data is a time interval between adjacent two images;

[0011] Step 3: subtracting a background image from the series of to-be-recognized images introduced in step 1 to obtain background-eliminated images corresponding to the to-be-recognized images, wherein the background image is an image of the visual area of the series of to-be-recognized images before the arrival of the shadow wave front;

[0012] Step 4: sequentially processing the background-eliminated images through image addition, first image power transformation, image median filtering, and then second image power transformation enhancement to obtain enhanced images corresponding to the to-be-recognized images;

[0013] Step 5: setting the search area of the wave front contour as a circular ring or a rectangle according to the recognition type and the ROI area set in step 2, and finally obtaining a trend chart of the movement process of the explosion wave front contour and the change trend of the wave front radius according to the set calibration data and interval data.

[0014] Further, in step 2, the ROI area is specifically set as follows:

[0015] When the recognition type is a circular arc, the ROI area is a series of circular ring areas with a manually selected point (X0, Y0) as the center, wherein the outer diameter R 外 is a fixed value, and the inner diameter R 内 is a series of increasing arithmetic sequences, and the outer diameter R外 Initial value, inner diameter R 内 The initial value is manually set after estimating the target region range;

[0016] When the recognition type is a straight line, the ROI region is a series of rectangular regions, the lower right corner coordinates (Xmax, Ymax) of the series of rectangular regions are fixed, the upper left corner coordinates (Xmin, Ymin) are a series of increasing arithmetic sequences, and the initial value of the lower right corner coordinates (Xmax, Ymax) and the initial value of the upper left corner coordinates (Xmin, Ymin) are both manually set after estimating the target region range.

[0017] Further, in step 4, the quantification method of the transformation coefficient P of the first image power transformation and the second image power transformation is as follows:

[0018] Select a to-be-recognized image located at the middle position of the time axis in the series of to-be-recognized images in step 1, select a straight line located in the wavefront propagation direction in the image, obtain the maximum gray value Gmax and the minimum gray value Gmin, calculate the gray value difference AG of the maximum value and the minimum value, and determine the transformation coefficient P according to the range of AG according to the following formula:

[0019]

[0020] Further, according to the recognition type set in step 2, the search region of the wavefront profile is set to be a circular ring or a rectangle, and specifically:

[0021] When the recognition type is a circular arc, the search region is set to be a circular ring, a reference search radius is first set, and on the basis of the reference search radius, the inner diameter and the outer diameter of the search region are set, the outer diameter of the search region is a fixed value, and the inner diameter is a series of increasing arithmetic sequences.

[0022] When the recognition type is a straight line, the search region is set to be a rectangle, the lower right corner coordinates and the upper left corner coordinates are first set, and in the search process, the lower right corner coordinates are fixed, and the upper left corner coordinates are a series of increasing arithmetic sequences.

[0023] Further, in step 4, the image addition refers to adding each background-eliminated image to itself.

[0024] Compared with the prior art, the present application has the following beneficial effects:

[0025] The present application is a schlieren wave front automatic identification and tracking method, through setting identification parameters, removing image background, image addition, image power transformation, image median filtering and other steps, finally obtaining the trend chart of the explosion wave front profile movement process and the wave front radius change; the present application can complete the identification and tracking of the following three kinds of complex shock wave wave fronts: first, the wave front is a weak target relative to the background; second, several concentric and similar wave fronts will appear near the wave front; third, several non-concentric wave fronts will cross and overlap; the present application can draw the wave front position curve according to the automatic identification and tracking result, and further calculate the explosion shock wave speed, having certain engineering application value. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 It is a flowchart of the schlieren wave front automatic identification and tracking method of the present application;

[0027] Figure 2 It is a schematic diagram of setting identification parameters in step 2 of the embodiment of the present application;

[0028] Figure 3 It is a schematic diagram of selecting a straight line in the wave front propagation direction in step 4 of the embodiment of the present application;

[0029] Figure 4 It is a gray value information schematic diagram of the selected straight line in step 4 of the embodiment of the present application;

[0030] Figure 5 It is the to-be-identified image obtained after step 4 in the embodiment of the present application;

[0031] Figure 6 It is the wave front radius change trend chart obtained in step 5 of the embodiment of the present application. DETAILED DESCRIPTION

[0032] The present application will be further described below in combination with the drawings and exemplary embodiments.

[0033] Reference Figure 1 A schlieren wave front automatic identification and tracking method, comprising the following steps:

[0034] Step 1, importing a series of to-be-identified images obtained by a high-speed camera into LabVIEW software, the series of to-be-identified images being images arranged in time sequence in the same visual area;

[0035] Step 2, setting identification parameters;

[0036] The identification parameters include identification type, ROI area, calibration data and interval data;

[0037] The identification type is a circular arc or a straight line;

[0038] Referring to Figure 2 , when the recognition type is a circular arc, the ROI region is a series of annular regions with a manually selected point (X0, Y0) as the center, the outer diameter R 外 is a fixed value, and the inner diameter R 内 is a series of increasing arithmetic sequences, the initial value of the outer diameter R 外 , the initial value of the inner diameter R 内 are manually set after estimating the target region range;

[0039] When the recognition type is a straight line, the ROI region is a series of rectangular regions, the lower right corner coordinates (Xmax, Ymax) of the series of rectangular regions are fixed, the upper left corner coordinates (Xmin, Ymin) are a series of increasing arithmetic sequences, and the initial value of the lower right corner coordinates (Xmax, Ymax) and the initial value of the upper left corner coordinates (Xmin, Ymin) are manually set after estimating the target region range;

[0040] The calibration data is how many actual distances each pixel represents, with the unit of mm, and the interval data is the time interval between adjacent two images, with the unit of μs;

[0041] Step 3, subtracting a series of to-be-recognized images and a background image introduced in step 1 to obtain background-eliminated images corresponding to each to-be-recognized image, the background image being an image of a visible region of the series of to-be-recognized images before the arrival of the speckle wavefront;

[0042] Step 4, referring to Figures 3 to 5 , adding a series of to-be-recognized images processed in step 3 to themselves, performing a first image power transformation on each to-be-recognized image after the addition; performing median filtering on each to-be-recognized image, with the neighborhood size being set to 7x7, so that the edge features can be preserved without being blurred while the noise is filtered out, and then performing a second image power transformation on each to-be-recognized image to obtain enhanced images corresponding to each to-be-recognized image;

[0043] The quantization method of the transformation coefficient P of the first image power transformation and the second image power transformation is as follows:

[0044] Selecting a to-be-recognized image located at the middle position of the time axis in the series of to-be-recognized images in step 1, selecting a straight line in the image in the direction of wavefront propagation, obtaining the maximum gray value Gmax and the minimum gray value Gmin, calculating the gray value difference AG of the maximum value and the minimum value, and determining the transformation coefficient P according to the range of AG;

[0045]

[0046] Step 5, referring to Figure 6According to the identification type and the ROI region set in step 2, the search region of the wave front profile is set as a circular ring or a rectangle, and according to the set calibration data and interval data, the moving process of the blast wave front profile and the change trend of the wave front radius are finally obtained, which are specifically as follows:

[0047] When the identification type is a circular arc, the search region is set as a circular ring, a reference search radius is first set, and on the basis of the reference search radius, the inner diameter and the outer diameter of the search region are set, the outer diameter of the search region is a fixed value, and the inner diameter is a series of increasing arithmetic sequences;

[0048] When the identification type is a straight line, the search region is set as a rectangle, the lower right corner coordinate and the upper left corner coordinate are first set, and in the search process, the lower right corner coordinate is fixed, and the upper left corner coordinate is a series of increasing arithmetic sequences.

[0049] The above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same, and for the ordinary skilled in the art, the specific technical solutions recorded in the above embodiments can be modified, or some technical features can be replaced by the equivalent, and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions protected by the present application.

Claims

1. A striae wavefront automatic recognition tracking method, characterized in that, It comprises the following steps: Step 1, a series of images to be identified obtained by a high-speed camera are imported into image processing software, the series of images to be identified are images of the same visual area arranged in time sequence; Step 2, identification parameters are set; The identification parameters comprise identification type, ROI area, calibration data and interval data; the identification type is a circular arc or a straight line; the calibration data is actual distance represented by each pixel, and the interval data is time interval between adjacent two images; The ROI area is specifically set as follows: When the identification type is an arc, the ROI region is a series of annular regions centered at the manually selected point (X0, Y0), and the outer diameter R of the series of annular regions is... 外 For a fixed value, the inner diameter R 内 Given a series of increasing arithmetic sequences, the outer diameter R... 外 Initial value, inner diameter R 内 The initial values ​​were all set manually after estimating the target area. When the identification type is a straight line, the ROI area is a series of rectangular areas, the lower right corner coordinates (Xmax, Ymax) of the series of rectangular areas are fixed, the upper left corner coordinates (Xmin, Ymin) are a series of increasing arithmetic sequences, and the initial value of the lower right corner coordinates (Xmax, Ymax) and the initial value of the upper left corner coordinates (Xmin, Ymin) are manually set after estimating the target area range; Step 3, the series of images to be identified imported in step 1 and a background image are subtracted to obtain background-eliminated images corresponding to the images to be identified; the background image is an image of the visual area of the series of images to be identified before the arrival of the interference wave; Step 4, the background-eliminated images are sequentially subjected to image addition, first image power transformation, image median filtering, second image power transformation and enhancement to obtain enhanced images corresponding to the images to be identified; The quantification method of the transformation coefficient P of the first image power transformation and the second image power transformation is as follows: A to-be-identified image located at the middle position of the time axis in the series of to-be-identified images in step 1 is selected, a straight line located in the direction of wave propagation is selected in the image, the maximum gray value Gmax and the minimum gray value Gmin of the straight line are obtained, the gray value difference ΔG of the maximum gray value and the minimum gray value is calculated, and the transformation coefficient P is determined according to the range of ΔG according to the following formula: ； Step 5, according to the identification type and the ROI area set in step 2, the search area of the wave front profile is set as a circular ring or a rectangle, and according to the set calibration data and interval data, the moving process of the explosion wave front profile and the change trend of the wave front radius are finally obtained.

2. The method of claim 1, wherein, According to the identification type set in step 2, the search area of the wave front profile is set as a circular ring or a rectangle, which is specifically as follows: When the identification type is a circular arc, the search area is set as a circular ring, a reference search radius is first set, and then the inner diameter and the outer diameter of the search area are set, the outer diameter of the search area is a fixed value, and the inner diameter is a series of increasing arithmetic sequences; When the identification type is a straight line, the search area is set as a rectangle, the lower right corner coordinates and the upper left corner coordinates are first set, and during the search process, the lower right corner coordinates are fixed, and the upper left corner coordinates are a series of increasing arithmetic sequences.

3. The method of claim 2, wherein: In step 4, the image addition refers to adding the background-eliminated images to themselves.

Images