Lens focusing sharpness evaluation method and system based on moire

Abstract

The application discloses a moire-based lens focusing sharpness evaluation method and system, and relates to the technical field of optical imaging. The method comprises the following steps: obtaining a moire original image, pre-processing the moire original image to obtain a moire processed image; analyzing the lens focusing state of the moire processed image by using a moire extraction algorithm; and controlling a motor to adjust the position of the lens thread according to the lens focusing state of the moire processed image until the focusing is sharp. The application adopts the moire-based evaluation method, which has strong anti-noise and anti-light change capabilities. Only the moire phenomenon needs to be detected, and the preset target value needs to be reached. The calculation amount is greatly reduced, so that the speed is improved. The application can be widely applied to the focusing debugging and production quality detection links of cameras, microscopes and other products.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of optical imaging technology, in particular to a lens focusing sharpness evaluation method and system based on moire. BACKGROUND

[0002] Lens focusing sharpness is one of the important indicators for measuring the imaging quality of a lens, which refers to the sharpness of lens imaging. The higher the lens focusing sharpness, the sharper the imaging, and the clearer the details.

[0003] There are mainly the following several kinds of traditional lens focusing sharpness evaluation methods:

[0004] Subjective evaluation method: the sharpness of the image is observed by the human eye, and the evaluation is carried out by scoring. This method has intuitiveness, but the evaluation result is subjective and difficult to repeat.

[0005] Objective evaluation method: the sharpness of the image is evaluated by measuring the gray level gradient, edge information and other characteristics of the image. This method has the advantages of objective evaluation result and repeatability, but the calculation complexity is high.

[0006] The prior art lacks an objective, quantitative and automatic lens focusing sharpness evaluation method, resulting in low evaluation efficiency and poor repeatability. SUMMARY

[0007] To solve the problems mentioned in the background art, the purpose of the present application is to provide a lens focusing sharpness evaluation method and system based on moire.

[0008] In a first aspect, the purpose of the present application can be achieved by the following technical solution: a lens focusing sharpness evaluation method based on moire, the method comprising the following steps:

[0009] Obtaining a moire original image, preprocessing the moire original image to obtain a moire processed image;

[0010] Using a moire extraction algorithm to analyze the lens focusing state of the moire processed image, and controlling the motor to adjust the lens thread position according to the lens focusing state of the moire processed image until the focusing is clear.

[0011] In combination with the first aspect, in some implementations of the first aspect, the method further comprises: obtaining the moire original image:

[0012] First, install the camera to be tested: install the camera to be focused on the automatic focusing machine, adjust the position to make the camera to be focused on the focusing plane of the industrial camera;

[0013] Then control the exposure parameters of the focusing camera, take the standard moire to obtain the original image as the moire original image.

[0014] With reference to the first aspect, in some implementations of the first aspect, the method further comprises: the pre-processing of the moire original image comprises denoising and enhancing contrast of the moire original image to obtain a moire processed image.

[0015] With reference to the first aspect, in some implementations of the first aspect, the method further comprises: the process of analyzing the lens focusing state of the moire processed image by using the moire extraction algorithm comprises the following steps:

[0016] performing edge detection on the moire processed image to extract bright spots in the moire in the moire processed image, calculating the number of bright spots, and marking the number of bright spots as D and setting a bright spot number threshold Y.

[0017] With reference to the first aspect, in some implementations of the first aspect, the method further comprises: the process of controlling the motor to adjust the lens thread position according to the lens focusing state of the moire processed image:

[0018] comparing the number of bright spots D with the bright spot number threshold Y: if D < Y, performing fixed angle compensation, the focusing is clear, and outputting the result, if D ≥ Y, determining that the focusing is poor, controlling the motor to adjust the lens position, and re-acquiring the number of bright spots in the moire until D < Y.

[0019] With reference to the first aspect, in some implementations of the first aspect, the method further comprises: the process of extracting bright spots in the moire in the moire processed image and calculating the number of bright spots:

[0020]

[0021] Wherein, L(p) is the label value of pixel p, p represents a pixel, and k represents the index of the connected component.

[0022] The second aspect, in order to achieve the above object, the application discloses a lens focusing clarity evaluation system based on moire, comprising:

[0023] An image processing module is configured to acquire a moire original image, pre-process the moire original image, and obtain a moire processed image.

[0024] A clear adjustment module is configured to analyze the lens focusing state of the moire processed image by using a moire extraction algorithm, control a motor to adjust the lens thread position according to the lens focusing state of the moire processed image, and achieve clear focusing.

[0025] With reference to the second aspect, in some implementations of the second aspect, the system further comprises: the acquisition of the moire original image in the image processing module:

[0026] Firstly, install the camera to be tested: install the camera to be focused on the auto-focus machine, and adjust the position to make the camera to be focused on the focusing plane of the industrial camera;

[0027] Then control the exposure parameters of the focusing camera, take the standard moire to obtain the original image, as the moire original image;

[0028] Or the pre-processing of the moire original image in the image processing module includes denoising and contrast enhancement of the moire original image, to obtain the moire processing image;

[0029] Preferably, the process of using the moire extraction algorithm to analyze the lens focusing state of the moire processing image in the clear adjustment module includes the following steps:

[0030] Edge detection is performed on the moire processing image to extract the bright spots in the moire in the moire processing image, the number of bright spots is calculated and marked as D, and a bright spot number threshold Y is set;

[0031] Preferably, the process of controlling the motor to adjust the lens thread position according to the lens focusing state of the moire processing image in the clear adjustment module:

[0032] The number of bright spots D is compared with the bright spot number threshold Y: if D < Y, fixed angle compensation is performed, the focus is clear, and the output result is output, if D ≥ Y, it is determined that the focus is poor, the motor is controlled to adjust the lens position, and the number of bright spots in the moire is acquired again until D < Y;

[0033] Preferably, the process of extracting the bright spots in the moire in the moire processing image and calculating the number of bright spots in the clear adjustment module:

[0034]

[0035] Wherein, L(p) is the label value of pixel p, p represents a pixel, and k represents the index of the connected component.

[0036] In another aspect of the present application, in order to achieve the above-mentioned purpose, a terminal device is disclosed, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, the memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the lens focusing clarity evaluation method based on moire is adopted as described above.

[0037] In still another aspect of the present application, in order to achieve the above-mentioned purpose, a computer readable storage medium is disclosed, the computer readable storage medium stores a computer program, and when the computer program is loaded and executed by the processor, the lens focusing clarity evaluation method based on moire is adopted as described above.

[0038] The beneficial effects of the present application are as follows:

[0039] The present application adopts a moire-based evaluation method, has strong anti-noise and anti-illumination change ability, only needs to detect the moire phenomenon and reach the preset target value, greatly reduces the calculation amount, thereby improving the speed, and can be widely applied to camera, microscope and other product focusing debugging and production quality detection links. BRIEF DESCRIPTION OF DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without any creative effort.

[0041] Figure 1 is a method flowchart of the present application;

[0042] Figure 2 is a flowchart of the algorithm of the present application;

[0043] Figure 3 is a schematic diagram of the focusing system of the present application;

[0044] Figure 4 is a schematic diagram of the algorithm recognition process of the present application;

[0045] Figure 5 is a schematic diagram of the system structure of the present application;

[0046] Figure 6 is a schematic diagram of the interface before running of the present application;

[0047] Figure 7 is a schematic diagram of the interface of the present application for real-time reading of camera lens pictures;

[0048] Figure 8 is a schematic diagram of the debugging software of the present application;

[0049] Figure 9 is a schematic diagram of the parameter setting of the camera of the present application;

[0050] Figure 10 is a schematic diagram of the experimental results of the present application. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort are within the scope of protection of the present application.

[0052] Embodiment one:

[0053] Next, the related terms involved in the embodiments of the present application are introduced:

[0054] Moiré, also known as Moire pattern or water ripples, is a visual phenomenon that occurs when two equal-amplitude sinusoidal waves with close frequencies are superimposed to produce interference fringes. In the field of photography and scanners, when the pixel spatial frequency of the photosensitive element is close to the spatial frequency of the stripes in the image, this interference fringe will appear on the image.

[0055] Moiré usually appears on objects with fine texture, such as fabrics in fashion photography, which are characterized by colorful high-frequency irregular stripes without obvious shape rules. These stripes can be exhibited by brightness or color.

[0056] To eliminate or reduce the impact of Moiré, an anti-aliasing filter (also known as a low-pass filter) is usually used in front of the sensor, but this will reduce the resolution of the lens. Therefore, a trade-off and compromise between Moiré and photo clarity needs to be made. The severity of the Moiré problem varies for different models of cameras, so the choice of solution will also vary.

[0057] The frequency of Moiré is the sum of the frequencies of the two textures in the image, so it has strong anti-noise and anti-illumination change capabilities. In addition, the clarity of Moiré is closely related to the focusing clarity of the lens, so the focusing clarity of the lens can be accurately evaluated using the clarity of Moiré.

[0058] As shown in Figure 1 the lens focusing clarity evaluation method based on Moiré, the method comprises the following steps:

[0059] Obtain a Moiré original image, preprocess the Moiré original image to obtain a Moiré processed image;

[0060] Wherein, the Moiré original image is obtained by:

[0061] First, install the camera to be tested: install the camera to be focused on the automatic focusing machine, adjust the position to make the camera to be focused on the focusing plane of the industrial camera;

[0062] Then control the exposure parameters of the focusing camera, shoot the standard Moiré to obtain the original image as the Moiré original image;

[0063] The preprocessing of the Moiré original image includes denoising and contrast enhancement of the Moiré original image to obtain the Moiré processed image;

[0064] The moire extraction algorithm is used to analyze the lens focusing state of the moire processed image, and the lens screw position is adjusted according to the lens focusing state of the moire processed image to achieve clear focusing.

[0065] The process of using the moire extraction algorithm to analyze the lens focusing state of the moire processed image includes the following steps:

[0066] Edge detection is performed on the moire processed image to extract bright spots in the moire in the moire processed image, the number of bright spots is calculated and marked as D, and a bright spot number threshold Y is set.

[0067] The process of adjusting the lens screw position according to the lens focusing state of the moire processed image:

[0068] The number of bright spots D is compared with the bright spot number threshold Y: if D < Y, fixed angle compensation is performed, the focusing is clear, and the output result is obtained, if D ≥ Y, it is determined that the focusing is poor, the motor is controlled to adjust the lens position, and the number of bright spots in the moire is obtained again until D < Y.

[0069] Where, Gaussian Blurring: the formula of Gaussian filter is: G(x,y) = (1 / (2πσ 2 ))*e ( -(x 2 +y 2 ) / (2*σ 2 ))

[0070] Where, σ is the standard deviation of the Gaussian kernel, which controls the degree of blurring.

[0071] Thresholding: the thresholding formula is:

[0072] Where, g(x,y) is the original pixel value, and T is the threshold.

[0073] Erosion and Dilation: the erosion operation formula is:

[0074]

[0075] The dilation operation formula is:

[0076]

[0077] Where, f is the original image, and b is the structure element.

[0078] The process of extracting bright spots in the moire in the moire processed image and calculating the number of bright spots:

[0079] The idea: This paper adopts the way of row scanning and 8-neighborhood. The first scan gives each pixel position a label. In the scanning process, the pixels in the same connected region may be given one or more different labels, so it is necessary to merge these labels belonging to the same connected region but with different values, that is, to record the equality relationship between them; the second scan is to merge the pixels marked by equal_labels into a connected region and give them the same label (usually this label is the minimum value in equal_labels).

[0080] The simple steps of the algorithm are as follows:

[0081] (1) First scan:

[0082] Visit the current pixel T(x, y), if T(x, y) == 1:

[0083] a. If the pixel values in the field of T(x, y) are all 0, give T(x, y) a new label:

[0084] label += 1, T(x, y) = label;

[0085] b. If there are pixels Neighbors with pixel values > 1 in the field of T(x, y):

[0086] 1) Assign the minimum value in Neighbors to T(x, y):

[0087] T(x, y) = min{Neighbors}

[0088] 2) Record the equality relationship between each value (label) in Neighbors, that is, these values (labels) belong to the same connected region;

[0089] labelSet[i] = {label_m,..,label_n}, all labels in labelSet[i] belong to the same connected region (Note: There are many ways to implement this, as long as the relationship between these labels with equal relationship can be recorded)

[0090] (2) Second scan:

[0091] Visit the current pixel T(x, y), if T(x, y) > 1:

[0092] a. Find a minimum label value with equal relationship with label = T(x, y), and give it to T(x, y);

[0093] After the scanning is completed, pixels with the same label value in the image form a same connected region

[0094]

[0095] Wherein, L(p) is a label value of pixel p, p represents a pixel, and k represents an index of a connected component.

[0096] Specifically, the scheme of the application is further described below through embodiments:

[0097] The device used in the application mainly consists of a server, an industrial camera, a zoom lens, and a point light source.

[0098] For the moire focusing mode, the moire phenomenon is first displayed on the industrial camera, at which time the lens of the industrial camera and the camera module are installed at a fixed distance and are ensured to be on the same vertical line. Meanwhile, the lens is equipped with a point light source to provide the accuracy of visual detection. When the threaded lens is rotating, the screen of the upper computer displays different phenomena. When the clear state is about to be reached, the moire phenomenon appears. At this time, the moire extraction algorithm of the application is used. The algorithm is an object detection and marking algorithm based on connected region analysis for identification / marking. Then, the lens is angle-compensated through experiments.

[0099] As shown in the process for identifying the moire and counting the moire, the process diagram is shown in FIG. 1. The original image obtained by shooting the lens is first segmented, and then the segmented image is converted into a gray scale and then into a binary image. The mask image is obtained through processing. On the basis of the mask image, the circle counting is performed to judge whether the preset standard value is reached, so as to proceed to the next step. Figure 4

[0100] As shown in the process for identifying the moire and counting the moire, the process diagram is shown in FIG. 1. The original image obtained by shooting the lens is first segmented, and then the segmented image is converted into a gray scale and then into a binary image. The mask image is obtained through processing. On the basis of the mask image, the circle counting is performed to judge whether the preset standard value is reached, so as to proceed to the next step. Figure 7 As shown in the process for identifying the moire and counting the moire, the process diagram is shown in FIG. 1. The original image obtained by shooting the lens is first segmented, and then the segmented image is converted into a gray scale and then into a binary image. The mask image is obtained through processing. On the basis of the mask image, the circle counting is performed to judge whether the preset standard value is reached, so as to proceed to the next step.

[0101] Figure 8 As shown in the process for identifying the moire and counting the moire, the process diagram is shown in FIG. 1. The original image obtained by shooting the lens is first segmented, and then the segmented image is converted into a gray scale and then into a binary image. The mask image is obtained through processing. On the basis of the mask image, the circle counting is performed to judge whether the preset standard value is reached, so as to proceed to the next step.

[0102] As shown in the process for identifying the moire and counting the moire, the process diagram is shown in FIG. 1. The original image obtained by shooting the lens is first segmented, and then the segmented image is converted into a gray scale and then into a binary image. The mask image is obtained through processing. On the basis of the mask image, the circle counting is performed to judge whether the preset standard value is reached, so as to proceed to the next step. Figure 9 As shown in the process for identifying the moire and counting the moire, the process diagram is shown in FIG. 1. The original image obtained by shooting the lens is first segmented, and then the segmented image is converted into a gray scale and then into a binary image. The mask image is obtained through processing. On the basis of the mask image, the circle counting is performed to judge whether the preset standard value is reached, so as to proceed to the next step.

[0103] Figure 10 As shown in the process for identifying the moire and counting the moire, the process diagram is shown in FIG. 1. The original image obtained by shooting the lens is first segmented, and then the segmented image is converted into a gray scale and then into a binary image. The mask image is obtained through processing. On the basis of the mask image, the circle counting is performed to judge whether the preset standard value is reached, so as to proceed to the next step.

[0104] ​​​Embodiment two: the second aspect, as shown in Figure 5 To achieve the above object, the application discloses a moire-based lens focusing sharpness evaluation system, which comprises:

[0105] An image processing module is configured to acquire a moire original image, pre-process the moire original image, and obtain a moire processed image.

[0106] A sharpness adjustment module is configured to analyze the lens focusing state of the moire processed image by using a moire extraction algorithm, control a motor to adjust the position of the lens thread according to the lens focusing state of the moire processed image, and achieve focusing sharpness.

[0107] In combination with the second aspect, in some implementations of the second aspect, the system further comprises: the acquisition of the moire original image in the image processing module:

[0108] First, install the camera to be tested: install the camera to be focused on the automatic focusing machine, adjust the position so that the camera to be focused is located on the focusing plane of the industrial camera;

[0109] Then control the exposure parameters of the focusing camera, take a standard moire to obtain an original image, which is used as the moire original image.

[0110] Or the pre-processing of the moire original image in the image processing module comprises denoising and contrast enhancement of the moire original image, and the moire processed image is obtained.

[0111] Preferably, the process of analyzing the lens focusing state of the moire processed image in the sharpness adjustment module by using the moire extraction algorithm comprises the following steps:

[0112] Edge detection is performed on the moire processed image to extract the bright spots in the moire in the moire processed image, the number of bright spots is calculated, and the number of bright spots is marked as D, and a bright spot number threshold Y is set.

[0113] Preferably, the process of controlling the motor to adjust the position of the lens thread according to the lens focusing state of the moire processed image in the sharpness adjustment module comprises:

[0114] The number of bright spots D is compared with the bright spot number threshold Y: if D < Y, fixed angle compensation is performed, the focusing is sharp, the output result is obtained, and if D ≥ Y, it is determined that the focusing is poor, the motor is controlled to adjust the position of the lens, the number of bright spots in the moire is acquired again, and D < Y.

[0115] Preferably, the process of extracting the bright spots in the moire in the moire processed image and calculating the number of bright spots in the sharpness adjustment module comprises:

[0116]

[0117] wherein L(p) is a label value of a pixel p, p represents a pixel, and k represents an index of a connected component.

[0118] Based on the same inventive concept, the present application further provides a computer device, comprising: one or more processors, and a memory for storing one or more computer programs; the program comprises program instructions, and the processor is configured to execute the program instructions stored in the memory. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are configured to implement one or more instructions, and are specifically configured to load and execute one or more instructions in the computer storage medium to implement the above method.

[0119] It needs to be further explained that, based on the same inventive concept, the present application further provides a computer storage medium, which stores a computer program, and the computer program is executed by a processor to perform the above method. The storage medium can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, be but is not limited to an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection with one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, the computer readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or component.

[0120] In the description of the specification, the description of the terms "one embodiment", "an example", "a specific example" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. In the specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Also, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in an appropriate manner.

[0121] The basic principles, main features and advantages of the present disclosure are shown and described above. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions in the specification are only to illustrate the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, various changes and improvements of the present disclosure can be made, which all fall within the scope of the claimed present disclosure.

Claims

1. A moire-based lens focus sharpness evaluation method, characterized by, The method comprises the following steps: The method comprises the following steps: The moire original image is acquired, and the moire original image is preprocessed to obtain a moire processed image; The moire processed image is analyzed by using a moire extraction algorithm to analyze the lens focusing state of the moire processed image, and the lens screw position is adjusted by controlling the motor according to the lens focusing state of the moire processed image until the focusing is clear; The process of analyzing the lens focusing state of the moire processed image by using the moire extraction algorithm comprises the following steps: The moire processed image is edge detected to extract the bright spots in the moire in the moire processed image, the number of bright spots is calculated, and the number of bright spots is marked as D, and a bright spot number threshold Y is set; The process of adjusting the lens screw position by controlling the motor according to the lens focusing state of the moire processed image in the clear adjustment module comprises the following steps: The number of bright spots D is compared with the bright spot number threshold Y: if D < Y, fixed angle compensation is performed, the focusing is clear, and the output result is obtained; if D >= Y, it is determined that the focusing is poor, the lens position is adjusted by controlling the motor, the number of bright spots in the moire is acquired again, and the process is repeated until D < Y; The process of extracting the bright spots in the moire in the moire processed image and calculating the number of bright spots comprises the following steps:

2. The moire-based lens focus sharpness evaluation method according to claim 1, characterized in that, Wherein, L(p) is the label value of pixel p, p represents a pixel, and k represents the index of a connected component. The acquisition of the moire original image comprises the following steps: First, install the camera to be focused: install the camera to be focused on the automatic focusing machine, and adjust the position to make the camera to be focused on the focusing plane of the industrial camera; 3. The moire-based lens focus sharpness evaluation method of claim 1, wherein, Then, control the exposure parameters of the focusing camera, shoot the standard moire to obtain the original image, and take the original image as the moire original image.

4. A moire-based lens focus sharpness evaluation system characterized by, The pre-processing of the moire original image comprises denoising and contrast enhancement of the moire original image to obtain the moire processed image. It comprises: An image processing module is configured to acquire a moire original image, pre-process the moire original image, and obtain a moire processed image; A clear adjustment module is configured to analyze the lens focusing state of the moire processed image by using a moire extraction algorithm, and adjust the lens screw position by controlling the motor according to the lens focusing state of the moire processed image until the focusing is clear; The process of analyzing the lens focusing state of the moire processed image by using the moire extraction algorithm in the clear adjustment module comprises the following steps: The moire processed image is edge detected to extract the bright spots in the moire in the moire processed image, the number of bright spots is calculated, and the number of bright spots is marked as D, and a bright spot number threshold Y is set; The process of adjusting the lens screw position by controlling the motor according to the lens focusing state of the moire processed image in the clear adjustment module comprises the following steps: The number of bright spots D is compared with the bright spot number threshold Y: if D < Y, fixed angle compensation is performed, the focusing is clear, and the output result is obtained; if D >= Y, it is determined that the focusing is poor, the lens position is adjusted by controlling the motor, the number of bright spots in the moire is acquired again, and the process is repeated until D < Y; The process of extracting the bright spots in the moire in the moire processed image and calculating the number of bright spots in the clear adjustment module comprises the following steps: Wherein, L(p) is the label value of pixel p, p represents a pixel, and k represents the index of a connected component.

5. The moire-based lens focus sharpness evaluation system according to claim 4, wherein, The image processing module acquires the moire fringe original image: First, install the camera to be tested: install the camera to be tested on the autofocus machine, adjust the position so that the camera to be tested is located on the focusing plane of the industrial camera; Then control the exposure parameters of the camera to be tested, take a standard moire fringe to obtain an original image, which is the moire fringe original image; Or the image processing module pre-processes the moire fringe original image, including denoising and enhancing the contrast of the moire fringe original image, to obtain a moire fringe processed image.

6. A terminal device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that, The memory stores a computer program capable of running on the processor, and when the processor loads and executes the computer program, the lens focusing sharpness evaluation method based on moire fringe in any one of claims 1-3 is adopted.

7. A computer-readable storage medium having stored therein a computer program, characterized in that, When the computer program is loaded and executed by the processor, the lens focusing sharpness evaluation method based on moire fringe in any one of claims 1-3 is adopted.

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