CN111402307B - Method for processing electric water level image - Google Patents
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Abstract
The application discloses processing method of electricity level image, which is characterized by comprising the following steps: acquiring a preset number of original electric water level images in front of and behind the rotating shaft; determining an x coordinate system, a y coordinate system and the number of image spots to be searched according to the original electric level image; carrying out median filtering processing on the obtained original electric level image to obtain a de-noised electric level image; searching the determined number of image spots in the de-noised electric level image according to the gray value, and respectively determining the central positions of the searched image spots; sequencing and numbering the de-noised electric quasi-images according to the coordinates of the central positions of the image spots in an x coordinate system and a y coordinate system; and (4) sorting the data columns of the coordinate values of the x axis and the y axis of the image spots with the same number on the de-noised electric level image, performing statistical analysis, and judging whether the image spot points with abnormal data exist. One technical effect of the present invention is that the image processing result can obtain a relatively stable processing result without adjusting the optical path and the mechanical structure.
Description
Technical Field
The application belongs to the field of astronomical instrument detection, and particularly relates to a method for processing an electric water level image.
Background
The multifunctional astronomical theodolite is a celestial body measuring instrument, and in order to obtain a high-precision measurement result, the influence of various instrument errors needs to be measured and deducted. The level difference is one of the main instrument errors, which mainly result from the deflection of the instrument base pier and the manufacturing errors and installation errors of the instrument. The level difference is constantly changing due to changes in ambient temperature and thermal deformation of the instrument. Therefore, how to measure the level difference of the celestial body measuring instrument in real time is the key to improve the observation precision of the instrument. In order to measure the change of the level difference in real time, the multifunctional astronomical theodolite adopts an electric water level detector to measure the level difference. The main principle is as follows: an analog CCD camera is used for acquiring electric collimation images in front of and behind the rotating shaft of the instrument by using a mercury surface as a reference reflecting surface in an auto-collimation mode, and the images generally comprise a 3 x 3 image spot array (see fig. 9 and 10).
However, due to the influence of optical axis offset of the electric water quasi-auto-collimation optical path, the oxidation of the mercury reflecting surface and the quantum efficiency of the configured analog CCD camera are low, the background noise is high, the signal-to-noise ratio of the obtained electric water quasi-image is low, the brightness of the image spot is uneven, the image is poor, and the stable processing result is not obtained easily. Some stress changes of the optical-mechanical structure and the unsatisfactory adjustment result of the optical path can cause optical path offset, so that the obtained electric level image has inconsistent image spot brightness (as shown in fig. 1-8), and the signal-to-noise ratio of a certain image spot is too low to cause recognition error (fig. 10), thereby affecting the final centering result.
Therefore, it is necessary to provide a method for processing an electrical level image.
Disclosure of Invention
The invention aims to provide a novel technical scheme of an electric level image processing method.
According to one aspect of the present invention, the present invention provides a method for processing an electrical level image, comprising the following steps:
acquiring a preset number of original electric water level images in front of and behind the rotating shaft;
determining an x coordinate system, a y coordinate system and the number of image spots to be searched according to the original electric level image;
carrying out median filtering processing on the obtained original electric level image to obtain a de-noised electric level image; de-noising electric level images corresponding to a preset number of original electric level images in front of the rotating shaft are set A; b, taking the denoising electric water level images corresponding to the original electric water level images with the preset number behind the rotating shaft as a group B;
searching the determined number of image spots in the de-noised electric level image according to the gray value, and respectively determining the central positions of the searched image spots;
sequencing and numbering the de-noised electric quasi-images according to the coordinates of the central positions of the image spots in an x coordinate system and a y coordinate system;
respectively carrying out statistical analysis on data columns of coordinate values of an x axis and a y axis of image spots with the same number on the de-noised electric level images in the group A and the group B, and judging whether image spot points with abnormal data exist or not; if the image spot exists, the x-axis coordinate value data array and the y-axis coordinate value data array of the image spot are removed, and the central position positioning data of the residual image spots participate in the subsequent data processing work; if not, the central position positioning data of all the image spots participate in the subsequent data processing work.
Optionally, the specific method for finding the determined number of image patches in the denoised electrical level image is as follows: finding out the point with the maximum gray value in the de-noised electric level image, selecting the area of a centering region according to the size of the image spot, if the average value of the region is larger than the background value of the image, considering the point as a target image spot, centering the image spot based on x and y coordinate coefficients of the original electric level image, assigning 0 to the pixel value of the image spot position of the filtered image, and then searching the next image spot until the determined number is reached.
Optionally, the central position of the image spot is determined according to the following formula,
wherein:
x and y are x and y coordinates on the image, respectively 0 、y 0 The coordinate values of the center after centering processing, I' (x, y) is the corrected pixel brightness value, I (x, y) is the original pixel brightness value, and T is the threshold value.
Optionally, the method for sequencing and numbering image patches includes: sorting according to the x coordinate values from small to large, sorting the y values along with the x values, and determining the number of columns; and sorting the image spots in each row from small to large according to the number of the image spots in each row.
Optionally, the method for determining whether there is an image spot with data abnormality includes:
eigenvalue m = [ std _ n _ x _ flag)] 2 +[data_n_v_flag)] 2 ；
Wherein std () is standard deviation solution, and flag is data before the rotating shaft or data identification after the rotating shaft;
the symbol meaning in data _ n _ x _ flag is: data _ n _ x represents an x coordinate column of the center point of the nth image spot;
the symbol meaning in data _ n _ y _ flag is: data _ n _ y represents the y coordinate column of the center point of the nth image spot;
determining a threshold value n by combining conventional abnormal-free data;
threshold n =2 × (avg (m) 1～k )+3×std(m 1～k ))；
Wherein avg () is the mean solution, std () is the standard deviation solution, k is the number of outliers, m 1～k A characteristic value column without abnormal points;
when m is larger than or equal to n, the data point data is abnormal, the data point data is removed, and subsequent data processing work is not involved;
when m is less than n, the data point is a valid point and is reserved and participates in the subsequent data processing work.
One technical effect of the present invention is that the image processing result can obtain a relatively stable processing result without adjusting the optical path and the mechanical structure.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is an example 1 of the luminance distribution unevenness of an obtained image spot caused by the optical path offset;
FIG. 2 is an example 2 of the uneven brightness distribution of the acquired image spot due to the optical path offset;
fig. 3 is an example 3 of the luminance distribution unevenness of the acquired image spot caused by the optical path offset;
FIG. 4 is an example 4 of the uneven brightness distribution of the acquired image spot due to the optical path offset;
fig. 5 is an example 5 of the luminance distribution unevenness of the obtained image spot caused by the optical path offset;
fig. 6 is an example 6 of the luminance distribution unevenness of the acquired image spot due to the optical path offset;
FIG. 7 is an example 7 of the luminance distribution unevenness of the obtained image spot caused by the optical path offset;
FIG. 8 is an example 8 of the uneven brightness distribution of the acquired image spot due to the optical path offset;
FIG. 9 is an example of electrical level image correct recognition;
FIG. 10 is an example of poor level quasi-image error identification;
FIG. 11 is a partial electrical level image data column for target number 0221 acquired on 27/12/2018;
FIG. 12 shows electrical level data results for 7 points selected in some embodiments;
FIG. 13 is a result of electrical level data for 6 points selected in some embodiments.
Detailed Description
The embodiments of the present application will be described in detail with reference to the drawings and examples, so that how to implement the technical means for solving the technical problems and achieving the technical effects of the present application can be fully understood and implemented.
The invention provides a processing method of an electric level image, which is applied to an electric level meter to measure level difference, aiming at an electric level image sequence shot by CCD or CMOS detectors at electric level system terminals before and after an instrument rotating shaft, the brightness distribution of an image spot lattice is uneven due to light path offset, and part of image spots are poor-quality electric level images which are difficult to identify due to low signal-to-noise ratio. In some embodiments, the method comprises the steps of:
reading image data of a single original electrical level image, determining an x coordinate system and a y coordinate system, and determining the number of image spots to be searched through visual inspection. Taking the 3 × 3 image patch array shown in fig. 9 as an example, only 8 brighter image patches among the 9 image patches are searched.
The method comprises the steps of obtaining a preset number of original electric level images in front of a rotating shaft and behind the rotating shaft, namely continuously obtaining a preset number of original electric level images before the rotating shaft rotates an astronomical instrument such as a multifunctional astronomical theodolite, and then continuously obtaining a preset number of original electric level images after the rotating shaft rotates. The denoising electric level images corresponding to the original electric level images with the preset number in front of the rotating shaft are A groups; and B groups of de-noised electric water level images corresponding to the original electric water level images with the preset number behind the rotating shaft. In normal operation, 100 original electrical level images are taken before and after the axis of rotation, respectively, for a total of 200 original electrical level images.
And searching the determined number of image spots in each de-noised electric level image according to the gray value, and respectively determining the central positions of the searched image spots. The specific method for searching the determined number of image spots in the de-noised electric level image comprises the following steps: finding out the point with the maximum gray value in the de-noised electric level image, selecting the area of a centering region according to the size of the image spot, if the average value of the region is larger than the background value of the image, considering the point as a target image spot, centering the image spot based on x and y coordinate coefficients of the original electric level image, assigning 0 to the pixel value of the image spot position of the filtered image, and then searching the next image spot until the determined number is reached. The central position of the image spot is determined according to the following formula,
wherein:
x and y are x and y coordinates on the image, respectively 0 、y 0 The values of the center coordinates after centering, I' (x, y) are the corrected pixel brightness values, I (x, y) are the original pixel brightness values, and T is the threshold value.
And sequencing and numbering the de-noised electric quasi-images according to the coordinates of the central positions of the image spots in an x coordinate system and a y coordinate system. The image spot sequencing and numbering method comprises the following steps: sorting according to the x coordinate values from small to large, sorting the y values along with the x values, and determining the number of columns; and sorting the image spots in each row from small to large according to the number of the image spots in each row. Taking a 3 × 3 image spot array as an example, only 8 brighter image spots among the 9 image spots are found, firstly, the image spots are sorted from small to large by the x coordinate value, and the y value is sorted along with the x value. The resulting ordering of the image patches is thus: a first column; a second column; a third column; then, the number of the image spots contained in each column of the three columns is determined as follows: 3,3,2. And sorting the image spots in each row from small to large according to the number of the image spots in each row. The distribution of the finally obtained central points is orderly corresponding to the corresponding positions in 3 multiplied by 3 in the original image.
Respectively carrying out statistical analysis on data columns of coordinate values of an x axis and a y axis of image spots with the same number on the de-noised electric level images in the group A and the group B, and judging whether image spot points with abnormal data exist or not; if the image spot exists, the x-axis coordinate value data array and the y-axis coordinate value data array of the image spot are removed, and the central position positioning data of the residual image spots participate in the subsequent data processing work; if not, the central position positioning data of all the image spots participate in the subsequent data processing work.
The method for judging whether the image spots with abnormal data exist comprises the following steps:
characteristic value m = [ std (data _ n _ x _ flag)] 2 +[std(data_n_y_flag)] 2 ；
Wherein std () is the standard deviation solution, and flag is the data before the rotating shaft or the data mark after the rotating shaft, for example, the data mark before the rotating shaft is zq, and the data mark after the rotating shaft is zh; the symbol meaning in data _ n _ x _ flag is:
data _ n _ x represents an x coordinate column of the center point of the nth image spot; the symbol meaning in data _ n _ y _ flag is:
data _ n _ y represents the y coordinate column of the center point of the nth image spot;
determining a threshold value n by combining conventional abnormal-free data;
threshold n =2 × (avg (m) 1～k )+3×std(m 1～k ))；
Wherein avg () is the mean solution, std () is the standard deviation solution, k is the number of outliers, m 1～k The characteristic value column is abnormal point-free;
when m is larger than or equal to n, the data point data is abnormal, the data point data is removed, and subsequent data processing work is not involved;
when m is less than n, the data point is a valid point and is reserved and participates in the subsequent data processing work.
In other subsequent operations, those skilled in the art select the desired data according to the image condition and the statistical analysis of the speckle data points.
The method can reduce the recognition error caused by the over-low signal-to-noise ratio of a certain image spot, thereby influencing the final centering result, reducing the influence of uneven brightness of the image spot caused by light path offset on the data processing result, and obtaining a more stable processing result under the condition of not adjusting a light path and a mechanical structure.
In a specific embodiment 1, referring to fig. 11, the electrical level data of target number 0221 acquired by the multifunctional astronomical theodolite in Yunnan astronomical stage at 12 and 27 months in 2018 is processed by the processing method proposed by the present invention (only 19 original electrical level images before and after the rotating shaft are shown, and the actual data are 100 original electrical level images before and after the co-rotating shaft), the threshold n is determined to be 30 according to the conventional no-abnormal data, as shown in table 1,
TABLE 1 statistics of electric level data characteristic values for target # 0221 at 12 months, 27 days in 2018
Calculating the characteristic value [ std (data _7_x_zq) in front of the rotation axis of the obtained data point 7] 2 +[std(data_7_y_zq)] 2 5434.9 is much larger than the threshold 30. The point value is used for subsequently calculating the variation of the position of the image spot before and after the rotating shaft, as shown in a result obtained by selecting 7 points of data (data point 1 to data point 7) in fig. 12, it can be seen that the variation of the measured coordinate position before and after (Y-axis direction) the rotating shaft of the electric quasi-optical path image spot obtained by calculation has large dispersion and shows obvious jump, the point can be judged to be an abnormal data point, and the change of the horizontal difference before and after the rotating shaft of the multifunctional astronomical theodolite can not be really reflected, so the point data needs to be removed, as shown in fig. 13, the variation of the coordinate values of the x axis and the Y axis before and after the rotating shaft is calculated by adopting 6 points of data (data point 1 to data point 6) after the removal, a calculation result is shown in fig. 13, the result shows that the dispersion of the variation of the x-axis and the Y-axis directions is small, and the variation of the horizontal difference of the multifunctional astronomical theodolite can be more accurately reflected, and the calculation processing result can further participate in subsequent data processing.
As used in the specification and claims, certain terms are used to refer to particular components or methods. As one skilled in the art will appreciate, different regions may refer to a component by different names. The present specification and claims do not intend to distinguish between components that differ in name but not in name. In the following description and in the claims, the terms "include" and "comprise" are used in an open-ended fashion, and thus should be interpreted to mean "include, but not limited to. "substantially" means within an acceptable error range, and a person skilled in the art can solve the technical problem within a certain error range to substantially achieve the technical effect. The description which follows is a preferred embodiment of the present application, but is made for the purpose of illustrating the general principles of the application and not for the purpose of limiting the scope of the application. The protection scope of the present application shall be subject to the definitions of the appended claims.
It is also noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a good or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such good or system. Without further limitation, an element defined by the phrases "comprising one of ..." does not exclude the presence of additional like elements in an article or system comprising the element.
While the foregoing description shows and describes several preferred embodiments of the invention, it is to be understood, as noted above, that the invention is not limited to the forms disclosed herein, but is not to be construed as excluding other embodiments and is capable of use in various other combinations, modifications, and environments and is capable of changes within the scope of the inventive concept as expressed herein, commensurate with the above teachings, or the skill or knowledge of the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (1)
1. A method for processing an electric level image is characterized by comprising the following steps:
acquiring a preset number of original electric water level images in front of and behind the rotating shaft;
determining an x coordinate system, a y coordinate system and the number of image spots to be searched according to the original electric level image;
carrying out median filtering processing on the obtained original electric level image to obtain a de-noised electric level image; the denoising electric level images corresponding to the original electric level images with the preset number in front of the rotating shaft are A groups; b, taking the denoising electric water level images corresponding to the original electric water level images with the preset number behind the rotating shaft as a group B;
searching the determined number of image spots in the de-noising electric level image according to the gray value, and respectively determining the central positions of the searched image spots;
sequencing and numbering the de-noised electric quasi-images according to the coordinates of the central positions of the image spots in an x coordinate system and a y coordinate system;
respectively carrying out statistical analysis on data columns of coordinate values of an x axis and a y axis of the image spots with the same number on the de-noised electric quasi-images in the group A and the group B, and judging whether image spot points with abnormal data exist or not; if the image spot exists, the x-axis coordinate value data array and the y-axis coordinate value data array of the image spot are removed, and the central position positioning data of the residual image spots participate in the subsequent data processing work; if not, the central position positioning data of all the image spots participate in the subsequent data processing work;
the specific method for finding the determined number of image spots in the denoised electrical level image is as follows: finding a point with the maximum gray value in the de-noised electric level image, selecting the area of a centering area according to the size of an image spot, if the average value of the area is larger than the background value of the image, regarding the point as a target image spot, centering the image spot based on x and y coordinate coefficients of the original electric level image, assigning 0 to the pixel value of the image spot position of the filtered image, and then searching the next image spot until the determined number is reached;
the central position of the image spot is determined according to the following formula,
wherein:
x and y are x and y coordinates on the image, respectively 0 、y 0 Respectively, the central coordinate values after centeringI' (x, y) is the modified pixel luminance value, I (x, y) is the original pixel luminance value, T is the threshold taken;
the image spot sequencing and numbering method comprises the following steps: sorting according to the x coordinate values from small to large, sorting the y values along with the x values, and determining the number of columns; sorting the image spots in each row from small to large according to the number of the image spots in each row;
the method for judging whether the image spots with abnormal data exist comprises the following steps:
characteristic value m- [ std (data _ n _ x _ flag)] 2 +[std(data_n_y_flag)] 2 ；
Wherein std () is standard deviation solution, and flag is data before the rotating shaft or data identification after the rotating shaft; the symbol meaning in data _ n _ x _ flag is: data _ n _ x represents an x coordinate column of the center point of the nth image spot; the symbol meaning in data _ n _ y _ flag is: data _ n _ y represents the y coordinate column of the center point of the nth image spot;
determining a threshold value n by combining conventional abnormal-free data;
threshold n =2 × (avg (m) 1～k )+3×std(m 1～k ))；
Wherein avg () is the mean solution, std () is the standard deviation solution, k is the number of outliers, m 1～k A characteristic value column without abnormal points;
when m is larger than or equal to n, the data point data is abnormal, the data point data is removed, and subsequent data processing work is not involved;
when m is less than n, the data point is a valid point and is reserved and participates in the subsequent data processing work.
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