CN112381814A - LabVIEW-based camera definition SFR (Small form-factor rating) measuring method - Google Patents

LabVIEW-based camera definition SFR (Small form-factor rating) measuring method Download PDF

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CN112381814A
CN112381814A CN202011343119.0A CN202011343119A CN112381814A CN 112381814 A CN112381814 A CN 112381814A CN 202011343119 A CN202011343119 A CN 202011343119A CN 112381814 A CN112381814 A CN 112381814A
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刘贝
欧阳鹏
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Huizhou Desay SV Automotive Co Ltd
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Abstract

The invention relates to the technical field of image processing, and provides a method for measuring the definition SFR of a camera based on LabVIEW, which sets a preset rule, and can automatically find out all preselected images from an image to be measured when the image to be measured is obtained; then calculating the edge contrast of the preselected image, and quickly judging whether the preselected image is the ROI image according to the digital data of the edge contrast, thereby automatically screening the ROI image in the image to be detected; based on LabVIEW graphical programming, the method can be developed secondarily and used integrally according to different devices, and further improves the compatibility and flexibility of the algorithm.

Description

LabVIEW-based camera definition SFR (Small form-factor rating) measuring method
Technical Field
The invention relates to the technical field of image processing, in particular to a method for measuring camera definition SFR based on LabVIEW.
Background
With the continuous development of automotive electronic technology and the gradual development and application of ADAS intelligent driving, more and more sensors are used in automobiles. The main sensors include ultrasonic radar, millimeter wave radar, camera, etc., and the camera is used as the main sensor, and the imaging definition of the camera is one of the very important indexes.
With the continuous development of the technology, the way of measuring the definition of the camera has been improved as follows: the sharpness is detected by means of the test equipment detecting the MTF (modulation transfer function). The equipment for measuring the MTF firstly needs a luminous body, images are formed through a camera, then a proper image acquisition card is selected to acquire images, the images are transmitted to a computer, the light intensity distribution is analyzed through special software, and finally the MTF is obtained through Fourier transform calculation.
In order to measure the definition of the camera, ISO12233-2014 defines a static picture definition measuring method sfr (slant Frequency response) and a method for measuring the bevel Frequency response, which belong to a commonly used method for measuring the definition of the camera. However, the conventional SFR measurement method has the following problems:
(1) the ROI is selected, common development tools such as Imatest, Quick MTF, SFRmat and the like need to be manually selected one by one in a frame mode, and the operation time is long;
(2) the bevel edge and the offset are acquired at least 2 times, so that the efficiency is low;
(3) common development tools are inconvenient to integrate with the device; standard program files used in the industry, such as Imatest, Quick MTF, SFRmat, etc., which are packaged into DLL or EXE, cannot be used integrally with an automated device, i.e., are not conducive to integration into an automated test system.
Disclosure of Invention
The invention provides a method for measuring camera definition SFR based on LabVIEW, which solves the technical problems of low efficiency, long time consumption, and poor compatibility and flexibility of the existing method for measuring the camera definition.
In order to solve the technical problems, the invention provides a method for measuring the definition SFR of a camera based on LabVIEW, which comprises the following steps:
s1, identifying the read image to be detected according to a preset rule, and acquiring a corresponding preselected image;
s2, calculating the edge contrast of the preselected image, and determining whether the preselected image is an ROI image according to the edge contrast;
s3, calculating the image offset of the ROI image according to a preset algorithm, and correcting the ROI image according to the image offset;
and S4, calculating to obtain a corresponding modulation transfer function according to the corrected ROI image, and outputting a corresponding measurement report according to the modulation transfer function.
The basic scheme is provided with a preset rule, and when the image to be detected is obtained, all preselected images can be automatically found from the image to be detected; then calculating the edge contrast of the preselected image, and quickly judging whether the preselected image is the ROI image according to the digital data of the edge contrast, thereby automatically screening the ROI image in the image to be detected; based on LabVIEW graphical programming, the method can be developed secondarily and used integrally according to different devices, and further improves the compatibility and flexibility of the algorithm.
In a further embodiment, the step S1 includes:
s11, reading the image to be detected, and identifying all edge areas in the image to be detected;
and S12, acquiring the image coordinate positions of all the edge areas, and selecting the corresponding preselected images according to the image coordinate positions and a preset frame selection frame.
The scheme utilizes an image recognition technology, the edge area is used as a target area in advance to carry out image recognition on the image to be detected, after the existence of the edge area is determined, the corresponding image coordinate position is further obtained and stored in the text file, and when the preselected image is obtained, the text file can be directly called to be intercepted from the image to be detected, so that the algorithm execution efficiency is greatly improved.
In a further embodiment, the step S2 includes:
s21, carrying out image processing on the preselected image to obtain a corresponding gray image;
s22, respectively calculating a first pixel sum of one side of the grayed image and a second pixel sum of the other side of the grayed image;
s23, comparing the first pixel with the second pixel, if the contrast of the first pixel and the second pixel is larger than a preset threshold, judging that the preselected image is an ROI image, and if not, ending the current measurement of the preselected image.
According to the scheme, a gray image is divided into two halves (one side and the other side of the gray image), corresponding pixel sums are respectively obtained to obtain a first pixel sum and a second pixel sum, and whether the edge line bevel edge exists in the preselected image or not can be judged by carrying out numerical comparison, namely whether the preselected image is an ROI image or not is judged; and a certain preset threshold value is reserved, so that the method can be compatible with error pixel points of the image, and the fault tolerance rate of ROI image judgment is improved.
In a further embodiment, the step S3 includes:
s31, calculating a windowing function of the ROI image according to the gray-scale image;
s32, performing convolution operation on the windowing function to obtain the coordinates of the center point of each line of pixels in the ROI image;
s33, substituting all the central point coordinates into a preset algorithm to obtain the slope and intercept of the edge line bevel edge in the ROI image;
and S34, correcting the ROI image according to the slope and the intercept.
The method adopts convolution operation, pertinently obtains the center point coordinates of each line of pixels in the ROI image, substitutes all the center point coordinates to obtain the slope and the intercept of the edge line bevel edge in the ROI image according to a preset algorithm, and can further improve the accuracy of the algorithm.
In a further embodiment, the step S34 includes:
s34a, calculating the inclination angle of the edge line bevel edge according to the slope;
and S34b, rotating the coordinate axis of the ROI image according to the inclination angle.
According to the scheme, the slope obtained by calculating the coordinates of the central point is used as basic data, the inclination angle of the edge line bevel edge is calculated, the amplitude can be adjusted and the ROI image can be corrected by directly using the inclination angle as a coordinate axis according to the consistency relation between the edge line bevel edge and the ROI image, the line and the plane are adopted, the algorithm is simple, and the image correction efficiency is high.
In a further embodiment, the step S4 includes:
s41, calculating an edge expansion function of the corrected ROI image;
s42, performing derivation on the edge expansion function, and calculating a corresponding line expansion function;
and S43, carrying out Fourier transform algorithm processing on the line spread function to obtain a modulation transfer function.
And S44, determining the imaging definition of the camera corresponding to the image to be measured according to the modulation transfer function, and outputting a corresponding measurement report.
In a further embodiment, the step S44 includes:
s44a, selecting a plurality of important parameter values from the modulation transfer function;
s44b, comparing the important parameter values with a preset standard table, and integrating the comparison result and the important parameter values to obtain a measurement report of the imaging definition of the camera corresponding to the image to be measured.
In further embodiments, the important parameter values include MTF50 values, MTF50P values, MTF30 values;
the preset standard table at least comprises industry division grades of imaging definition and corresponding numerical value ranges.
According to the scheme, an edge expansion function, a line expansion function and a modulation transfer function are calculated step by step according to the edge line bevel edge in the corrected ROI image, the contrast of the image is described from another angle in a spatial resolution mode, and therefore the modulation transfer function is used as a judgment standard of the imaging definition of a camera corresponding to the image to be detected;
the method includes the steps of directly setting a preset standard table, dividing clear imaging definition industry division levels and numerical value ranges corresponding to one, selecting a plurality of important parameter values (MTF50 values, MTF50P values and MTF30 values) from modulation transfer functions according to industry rules, comparing the important parameter values with the preset standard table, reflecting the imaging definition levels of corresponding cameras visually, and obtaining measurement results quickly.
In a further embodiment, the predetermined algorithm is a least squares method.
According to the scheme, the slope and the intercept of the edge line bevel edge in the ROI image are calculated by adopting a least square method, so that the calculation steps can be simplified, and the calculation efficiency can be improved.
Drawings
Fig. 1 is a work flow chart of a method for measuring camera definition SFR based on LabVIEW according to an embodiment of the present invention;
FIG. 2 is a block diagram of a preselected image provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating the calculation of edge contrast according to an embodiment of the present invention;
FIG. 4 is a graph illustrating the distribution of edge spreading functions provided by an embodiment of the present invention;
FIG. 5 is a graph illustrating a distribution of a line spread function according to an embodiment of the present invention;
fig. 6 is a graph illustrating a distribution of modulation transfer functions provided by an embodiment of the present invention.
Fig. 7 is a diagram illustrating measurement results of the prior art provided by an embodiment of the present invention.
Detailed Description
The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, which are given solely for the purpose of illustration and are not to be construed as limitations of the invention, including the drawings which are incorporated herein by reference and for illustration only and are not to be construed as limitations of the invention, since many variations thereof are possible without departing from the spirit and scope of the invention.
As shown in fig. 1, the method for measuring the resolution SFR of a camera based on LabVIEW in the embodiment of the present invention includes steps S1 to S4:
s1, identifying the read image to be detected according to a preset rule, and acquiring a corresponding preselected image, referring to FIG. 2, including steps S11-S12:
s11, reading the image to be detected, and identifying all edge areas in the image to be detected;
and S12, acquiring the image coordinate positions of all the edge areas, and selecting the corresponding preselected images according to the image coordinate positions and the preset frame selection frame.
The preset selection frame is a dotted line frame in fig. 2, the dotted line frame is a selection frame with a fixed size, for example, a square or rectangular selection frame, and the specific shape and size can be set according to the user requirement.
In the embodiment, the image recognition technology is utilized, the edge area is used as the target area in advance to perform image recognition on the image to be detected, the corresponding image coordinate position is further acquired after the existence of the edge area is determined and is stored in the text file, and the text file can be directly called to be intercepted from the image to be detected when the preselected image is acquired, so that the algorithm execution efficiency is greatly improved.
S2, calculating the edge contrast of the preselected image, and determining whether the preselected image is the ROI image according to the edge contrast, referring to FIG. 3, comprising the steps of S21-S23:
s21, carrying out image processing on the preselected image to obtain a corresponding gray image;
s22, calculating a first pixel sum of one side and a second pixel sum of the other side of the grayed image, respectively.
Specifically, when the framed grayed image includes 10 columns/row pixels, the first five columns of pixels on the left/top side and the second 5 columns of pixels on the right/bottom side as the first pixel sum may be acquired.
And S23, comparing the first pixel with the second pixel, if the contrast of the first pixel and the second pixel is larger than a preset threshold value, judging that the preselected image is the ROI image, and if not, finishing the measurement of the current preselected image.
As shown in fig. 3, in this embodiment, the preset threshold is set to 100, where the first pixel sum (the first five columns of pixel sums on the left, i.e., tleft in fig. 3) is 19967, and the second pixel sum (the last 5 columns of pixel sums on the right, i.e., right in fig. 3) is 38933, as can be seen from comparison, the pixel difference between the first pixel sum and the second pixel sum is much greater than 100, and therefore, the contrast between the two is obvious, and the preselected image is determined to be the ROI image.
In the embodiment, a gray image is divided into two halves (one side and the other side of the gray image), corresponding pixel sums are respectively obtained to obtain a first pixel sum and a second pixel sum, and whether the edge line bevel edge exists in the preselected image or not can be judged by performing numerical comparison, namely whether the preselected image is an ROI image or not is judged; and a certain preset threshold value is reserved, so that the method can be compatible with error pixel points of the image, and the fault tolerance rate of ROI image judgment is improved.
In the present embodiment, the division of the grayed-out image includes upper and lower division and left and right division.
S3, calculating the image shift amount of the ROI image according to a preset algorithm, and correcting the ROI image according to the image shift amount, wherein the method comprises the following steps of S31-S34:
s31, calculating a windowing function of the ROI image according to the gray-scale image;
s32, carrying out convolution operation on the windowing function to obtain the coordinates of the center point of each line of pixels in the ROI image;
s33, substituting all the center point coordinates into a preset algorithm to obtain the slope and intercept of the edge line bevel edge in the ROI image;
in the present embodiment, the predetermined algorithm is a least square method.
S34, correcting the ROI image according to the slope and the intercept, comprising the steps S34 a-S34 b:
s34a, calculating the inclination angle of the edge line bevel edge according to the slope;
s34b, rotating the coordinate axis of the ROI image according to the inclination angle.
In the embodiment, convolution operation is adopted, the center point coordinates of each row of pixels in the ROI image are obtained in a targeted manner, all the center point coordinates are substituted to obtain the slope and the intercept of the edge line bevel edge in the ROI image according to a preset algorithm, and the accuracy of the algorithm can be further improved;
the slope obtained by the coordinate calculation of the central point is used as basic data, the inclination angle of the edge line bevel edge is calculated, the ROI image can be corrected by directly adjusting the amplitude by taking the inclination angle as a coordinate axis according to the consistency relation between the edge line bevel edge and the ROI image, the line and the plane are adopted, the algorithm is simple, and the image correction efficiency is high;
in the embodiment, the slope and the intercept of the edge line bevel edge in the ROI image are calculated by adopting a least square method, so that the calculation steps can be simplified, and the calculation efficiency can be improved.
S4, calculating a corresponding modulation transfer function according to the corrected ROI image, and outputting a corresponding measurement report according to the modulation transfer function, which is shown in fig. 4 to 6 and includes S41 to S44:
s41, calculating an edge expansion function of the corrected ROI image;
s42, performing derivation on the edge expansion function, and calculating a corresponding line expansion function;
and S43, carrying out Fourier transform algorithm processing on the linear spread function to obtain a modulation transfer function.
S44, determining the imaging definition of the camera corresponding to the image to be measured according to the modulation transfer function, and outputting a corresponding measurement report, wherein the method comprises the following steps of S44 a-S44 b:
s44a, selecting a plurality of important parameter values from the modulation transfer function;
in the present embodiment, important parameter values include, but are not limited to, one or more of MTF50 values, MTF50P values, and MTF30 values.
S44b, comparing the important parameter values with a preset standard table, integrating the comparison result and the important parameter values, and obtaining a measurement report of the imaging definition of the camera corresponding to the image to be measured.
In this embodiment, the preset standard table at least includes industry classification levels of imaging sharpness and corresponding numerical ranges.
According to the embodiment, an edge expansion function, a line expansion function and a modulation transfer function are calculated step by step according to the edge line bevel edge in the corrected ROI image, the contrast of the image is described from another angle in a spatial resolution mode, and therefore the modulation transfer function is used as a judgment standard of the imaging definition of a camera corresponding to the image to be detected;
the method includes the steps of directly setting a preset standard table, dividing clear imaging definition industry division levels and numerical value ranges corresponding to one, selecting a plurality of important parameter values (MTF50 values, MTF50P values and MTF30 values) from modulation transfer functions according to industry rules, comparing the important parameter values with the preset standard table, reflecting the imaging definition levels of corresponding cameras visually, and obtaining measurement results quickly.
Referring to fig. 4, 6, and 7, fig. 7 is a schematic diagram of curves of a line spreading function and a modulation transfer function obtained by detecting the same image to be measured by the standard software Imatest, and the curves of the line spreading function and the modulation transfer function are substantially overlapped by comparing fig. 4, 6, and 7, that is, the method for measuring the definition SFR of the camera based on LabVIEW can achieve the same technical effect as the standard software Imatest and can perform a high-precision measurement on the definition of the camera by analyzing the image to be measured. However, the method for measuring the camera definition SFR provided by this embodiment is based on LabVIEW software, and can be developed and utilized for the second time by being integrated into a testing device, so as to better meet the use requirements of users.
The embodiment of the invention sets a preset rule, and when the image to be detected is obtained, all preselected images can be automatically found from the image to be detected; then calculating the edge contrast of the preselected image, and quickly judging whether the preselected image is the ROI image according to the digital data of the edge contrast, thereby automatically screening the ROI image in the image to be detected; based on LabVIEW graphical programming, the method can be developed secondarily and used integrally according to different devices, and further improves the compatibility and flexibility of the algorithm.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.

Claims (9)

1. A method for measuring the definition SFR of a camera based on LabVIEW is characterized by comprising the following steps:
s1, identifying the read image to be detected according to a preset rule, and acquiring a corresponding preselected image;
s2, calculating the edge contrast of the preselected image, and determining whether the preselected image is an ROI image according to the edge contrast;
s3, calculating the image offset of the ROI image according to a preset algorithm, and correcting the ROI image according to the image offset;
and S4, calculating to obtain a corresponding modulation transfer function according to the corrected ROI image, and outputting a corresponding measurement report according to the modulation transfer function.
2. The method for measuring LabVIEW-based camera definition SFR as claimed in claim 1, wherein the step S1 comprises:
s11, reading the image to be detected, and identifying all edge areas in the image to be detected;
and S12, acquiring the image coordinate positions of all the edge areas, and selecting the corresponding preselected images according to the image coordinate positions and a preset frame selection frame.
3. The method for measuring LabVIEW-based camera definition SFR as claimed in claim 1, wherein the step S2 comprises:
s21, carrying out image processing on the preselected image to obtain a corresponding gray image;
s22, respectively calculating a first pixel sum of one side of the grayed image and a second pixel sum of the other side of the grayed image;
s23, comparing the first pixel with the second pixel, if the contrast of the first pixel and the second pixel is larger than a preset threshold, judging that the preselected image is an ROI image, and if not, ending the current measurement of the preselected image.
4. The method for measuring LabVIEW-based camera definition SFR as claimed in claim 3, wherein the step S3 comprises:
s31, calculating a windowing function of the ROI image according to the gray-scale image;
s32, performing convolution operation on the windowing function to obtain the coordinates of the center point of each line of pixels in the ROI image;
s33, substituting all the central point coordinates into a preset algorithm to obtain the slope and intercept of the edge line bevel edge in the ROI image;
and S34, correcting the ROI image according to the slope.
5. The method for measuring LabVIEW-based camera definition SFR as claimed in claim 4, wherein the step S34 comprises:
s34a, calculating the inclination angle of the edge line bevel edge according to the slope;
and S34b, rotating the coordinate axis of the ROI image according to the inclination angle.
6. The method for measuring LabVIEW-based camera definition SFR as claimed in claim 1, wherein the step S4 comprises:
s41, calculating an edge expansion function of the corrected ROI image;
s42, performing derivation on the edge expansion function, and calculating a corresponding line expansion function;
and S43, carrying out Fourier transform algorithm processing on the line spread function to obtain a modulation transfer function.
And S44, determining the imaging definition of the camera corresponding to the image to be measured according to the modulation transfer function, and outputting a corresponding measurement report.
7. The method for measuring LabVIEW-based camera definition SFR as claimed in claim 6, wherein the step S44 comprises:
s44a, selecting a plurality of important parameter values from the modulation transfer function;
s44b, comparing the important parameter values with a preset standard table, and integrating the comparison result and the important parameter values to obtain a measurement report of the imaging definition of the camera corresponding to the image to be measured.
8. The method for measuring LabVIEW-based camera definition SFR as claimed in claim 7, wherein:
the important parameter values comprise MTF50 values, MTF50P values and MTF30 values;
the preset standard table at least comprises industry division grades of imaging definition and corresponding numerical value ranges.
9. The method for measuring LabVIEW-based camera definition SFR as claimed in claim 1, wherein: the preset algorithm is a least square method.
CN202011343119.0A 2020-11-26 2020-11-26 LabVIEW-based camera definition SFR (Small form-factor rating) measuring method Pending CN112381814A (en)

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