CN116681879B - Intelligent interpretation method for transition position of optical image boundary layer - Google Patents
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Abstract
The invention discloses an intelligent interpretation method for a transition position of an optical image boundary layer, which comprises the following steps: s1, unifying optical images under different imaging conditions by an image enhancement algorithm based on logarithmic transformation, and enhancing details in the optical images; s2, automatically determining a boundary layer region from the optical image based on a boundary layer region positioning method of morphological operation; s3, accurately positioning the boundary layer transition position of the boundary layer region in the optical image under unified standards by using the boundary layer transition position positioning method based on the linear detection. The method can be used for rapidly carrying out batch processing on the optical images, and positioning the boundary layer transition position in the optical images. Compared with manual interpretation, the processing results have repeatability, and the processing results for different images share the same standard and have comparability; compared with semi-automatic interpretation, the method can automatically position the region of interest near the boundary layer, and greatly improves the processing efficiency.
Description
Technical Field
The invention relates to the technical field of wind tunnel optical image interpretation, in particular to an intelligent interpretation method for a transition position of an optical image boundary layer.
Background
Optical technology is one of the most commonly used technologies in flow display, is an important hydrodynamic experimental technology for researching flow mechanism by reflecting the change of flow field density, and interpretation of optical images is an interpretation of optical technology measurement data, which is a necessary means for developing research data processing by utilizing optical images. The main development direction in the field of optical image interpretation is from qualitative to quantitative, and along with the development of related technologies such as ultra-low distortion lenses and ultra-high resolution cameras and transient optics, the description of flow field flow by an optical image has been provided with a high-precision quantitative measurement basis. The intelligent interpretation method of the optical image is based on the principle that the optical image is processed by the related means such as digital image analysis and the like combined with the judgment of the flow related research, and the interested target is directly extracted and positioned. The boundary layer transition position in the optical image refers to a position where flow starts to be changed from laminar flow to turbulent flow, and the transition is a process, so that a transition area is shown in the optical image, the traditional manual interpretation can only perform descriptive interpretation, the repeatability of interpretation results cannot be ensured, and the difficulty is brought to the comparative study of the interpretation results of a plurality of optical images. However, the position of the optical image, where boundary layer transition is performed, is interpreted by a digital image analysis method, and the interpretation result may be affected due to the difference of imaging conditions of the optical image.
The prior art mainly has two defects:
1. the manual interpretation depends on the relative experience of the judgment personnel, and the repeatability of the interpretation result cannot be ensured;
2. in order to eliminate interference of other areas in the image, the area to be interpreted needs to be manually specified in advance, generally, a rectangle frame which is as small as possible and contains a transition area is specified in the image, and the mode is in fact semi-automatic interpretation which depends on manual interpretation, so that the efficiency is low in multiple optical interpretations.
Disclosure of Invention
Aiming at the defects in the prior art, the intelligent interpretation method for the optical image boundary layer transition position solves the problem of low interpretation efficiency of the optical image boundary layer transition position.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: an intelligent interpretation method for a transition position of an optical image boundary layer comprises the following steps:
s1, unifying optical images under different imaging conditions by an image enhancement algorithm based on logarithmic transformation, and enhancing details in the optical images;
s2, automatically determining a boundary layer region from the optical image based on a boundary layer region positioning method of morphological operation;
s3, accurately positioning the boundary layer transition position of the boundary layer region in the optical image under unified standards by using the boundary layer transition position positioning method based on the linear detection.
Further: the image enhancement algorithm based on logarithmic transformation in the step S1 specifically comprises: and taking the logarithmic function as a mapping function, realizing nonlinear transformation of the image gray scale, stretching a low gray scale region in the image, and compressing a high gray scale region.
Further: the expression of the logarithmic function is:
in the above-mentioned method, the step of,representing pixel coordinates before logarithmic transformation>Pixel value of>Representing transformed pixel coordinatesPixel value of>、/>、/>Representing intermediate parameters introduced to adjust the curve position and shape of the logarithmic function.
Further: the step S2 specifically comprises the following steps: the method comprises the steps of performing morphological operation on an optical image to automatically position a region of interest, firstly, binarizing the optical image by drawing a histogram based on the light-tight property of a model in the optical image, dividing the model region from the optical image, performing expansion operation on the model region, removing the model region before the expansion operation from the model region after the expansion operation, and obtaining a boundary layer region, wherein the thickness of the boundary layer region is determined by the resolution of the optical image.
Further: the step S3 specifically comprises the following steps: firstly, extracting the edge of a boundary layer region by a Canny method, then, carrying out straight line detection on the extracted edge by Hough transformation, and determining the left side end point of the straight line as a boundary layer transition position.
Further: the straight line detection specifically comprises the following steps: and (3) corresponding each point in the edge of the boundary layer region to a polar coordinate parameter space to obtain curves corresponding to each point, finding out the intersection point of the curves, namely, corresponding to one straight line in the optical image, and obtaining a straight line detection result of the outer edge of the boundary layer by reasonably setting the threshold value of the number of intersection curves.
The beneficial effects of the invention are as follows: the method can be used for rapidly carrying out batch processing on the optical images, and positioning the boundary layer transition position in the optical images. Compared with manual interpretation, the processing results have repeatability, and the processing results for different images share the same standard and have comparability; compared with semi-automatic interpretation, the method can automatically position the region of interest near the boundary layer, and greatly improves the processing efficiency.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a mapping relationship diagram of image logarithmic transformation in the embodiment of the invention;
FIG. 3 is a representative optical image histogram in an embodiment of the invention;
FIG. 4 is a schematic illustration of locating boundary layer locations by an expansion operation in an embodiment of the present invention;
FIG. 5 is a graph of the results of linear detection of boundary layer regions of an optical image in an embodiment of the invention;
FIG. 6a is a graph showing the result of the optical image enhancement according to the embodiment of the present invention;
FIG. 6b is a graph showing the result of optical image enhancement in accordance with an embodiment of the present invention;
FIG. 7a is a schematic view of a mold area in an embodiment of the invention;
FIG. 7b is a schematic illustration of the post-mold expansion region in an embodiment of the invention;
FIG. 7c is a schematic illustration of boundary layer regions in an embodiment of the invention;
FIG. 8a is a schematic diagram of a boundary layer region linear detection result in an embodiment of the present invention;
fig. 8b is a schematic diagram of a result of the linear detection result superimposed on the original optical image according to an embodiment of the present invention.
Reference numerals: 1-corresponds to the model area in the schlieren image.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, an intelligent interpretation method for a transition position of an optical image boundary layer includes the following steps:
s1, unifying optical images under different imaging conditions by an image enhancement algorithm based on logarithmic transformation, and enhancing details in the optical images;
as shown in fig. 2, the image enhancement algorithm based on logarithmic transformation in the step S1 specifically includes: and taking the logarithmic function as a mapping function, realizing nonlinear transformation of the image gray scale, stretching a low gray scale region in the image, and compressing a high gray scale region.
The expression of the logarithmic function is:
in the above-mentioned method, the step of,representing pixel coordinates before logarithmic transformation>Pixel value of>Representing transformed pixel coordinatesPixel value of>、/>、/>Representing intermediate parameters introduced to adjust the curve position and shape of the logarithmic function.
S2, automatically determining a boundary layer region from the optical image based on a boundary layer region positioning method of morphological operation;
the method comprises the steps of performing morphological operation on an optical image to automatically position a region of interest, firstly, binarizing the optical image by drawing a histogram based on the light-tight property of a model in the optical image, and dividing the model region from the optical image, namely, a model region 1 in a corresponding schlieren image in fig. 3; as shown in fig. 4, the model region is subjected to an expansion operation, and the model region before the expansion operation is removed from the model region after the expansion operation, so that a boundary layer region is obtained, and the thickness of the boundary layer region is determined by the resolution of the optical image.
S3, accurately positioning the boundary layer transition position of the boundary layer region in the optical image under unified standards by using the boundary layer transition position positioning method based on the linear detection.
Only the boundary layer region is extracted in a straight line, so that the interference and influence caused by other target features in the optical image can be avoided. Firstly, extracting the edge of a boundary layer region by a Canny method, and then carrying out linear detection on the extracted edge by Hough transformation, wherein the principle is that the linear detection problem is converted into the extremum problem of searching the intersection point number statistics in a parameter space. One line in the image space corresponds to a point in the polar parameter space, all straight lines passing through a certain point in the image space are curves in the polar parameter space, each point in the edge corresponds to the polar parameter space, a series of curves are obtained, and the intersection point of the curves is found, namely the straight line in the image space is correspondingly. The linear detection result of the boundary layer outer edge can be obtained by reasonably setting the threshold value of the number of the intersecting curves. As shown in FIG. 5, a straight line segment can be obtained from the final result, and the direction of the incoming flow, i.e. the left end point of the straight line segment, can be judged according to the distance from the straight line detection result to the model, and is determined as the transition position of the boundary layer.
Example 1:
(1) the obtained optical image is firstly input, the optical image is processed through an image enhancement algorithm based on logarithmic transformation, the image before processing is shown in fig. 6a, the image after processing is shown in fig. 6b, the details of the image after processing are more abundant, and the pixel value of the image is normalized.
(2) And then the normalized image in the step (1) is processed by a boundary layer region positioning method based on morphological operation, and the interested part of the boundary layer region can be obtained after the processing, wherein fig. 7a is a model region, fig. 7b is a region after model expansion, and fig. 7c is a boundary layer region.
(3) And finally, carrying out straight line detection on the boundary layer region of interest in the step (2), judging according to the distance between the end points of the straight line detection result and the model, wherein the end points on the left side of the straight line segment are boundary layer transition positions, fig. 8a is the straight line detection result of the boundary layer region, and fig. 8b is the result of superposition of the straight line detection result into the original optical image.
The method can be used for rapidly carrying out batch processing on the optical images, and positioning the boundary layer transition position in the optical images. Compared with manual interpretation, the processing results have repeatability, and the processing results for different images share the same standard and have comparability; compared with semi-automatic interpretation, the method can automatically position the region of interest near the boundary layer, and greatly improves the processing efficiency.
Claims (2)
1. An intelligent interpretation method for a transition position of an optical image boundary layer is characterized by comprising the following steps:
s1, unifying optical images under different imaging conditions by an image enhancement algorithm based on logarithmic transformation, and enhancing details in the optical images;
s2, automatically determining a boundary layer region from the optical image based on a boundary layer region positioning method of morphological operation;
s3, accurately positioning the boundary layer transition position of the boundary layer region in the optical image under unified standards by using a boundary layer transition position positioning method based on linear detection;
the image enhancement algorithm based on logarithmic transformation in the step S1 specifically comprises: taking the logarithmic function as a mapping function to realize nonlinear transformation of the image gray scale, stretching a low gray scale region in the image, and compressing a high gray scale region;
the step S2 specifically comprises the following steps: the method comprises the steps of performing morphological operation on an optical image to automatically position a region of interest, firstly, binarizing the optical image by drawing a histogram based on the light-tight property of a model in the optical image, dividing the model region from the optical image, performing expansion operation on the model region, removing the model region before the expansion operation from the model region after the expansion operation to obtain a boundary layer region, wherein the thickness of the boundary layer region is determined by the resolution of the optical image;
the step S3 specifically comprises the following steps: firstly, extracting the edge of a boundary layer region by a Canny method, then carrying out straight line detection on the extracted edge by Hough transformation, and determining the left side end point of the straight line as a boundary layer transition position;
the straight line detection specifically comprises the following steps: and (3) corresponding each point in the edge of the boundary layer region to a polar coordinate parameter space to obtain curves corresponding to each point, finding out the intersection point of the curves, namely, corresponding to one straight line in the optical image, and obtaining a straight line detection result of the outer edge of the boundary layer by reasonably setting the threshold value of the number of intersection curves.
2. The intelligent interpretation method of the transition position of the optical image boundary layer according to claim 1, wherein the expression of the logarithmic function is:
in the above-mentioned method, the step of,representing pixel coordinates before logarithmic transformation>Pixel value of>Representing transformed pixel coordinates +.>Pixel value of>、/>、/>Representing intermediate parameters introduced to adjust the curve position and shape of the logarithmic function.
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