CN114972084A - Image focusing accuracy evaluation method and system - Google Patents
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- CN114972084A CN114972084A CN202210522635.2A CN202210522635A CN114972084A CN 114972084 A CN114972084 A CN 114972084A CN 202210522635 A CN202210522635 A CN 202210522635A CN 114972084 A CN114972084 A CN 114972084A
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
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- H—ELECTRICITY
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- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/67—Focus control based on electronic image sensor signals
Abstract
The invention discloses an image focusing accuracy evaluation method and system, which comprises the following steps: s1, acquiring an image to be evaluated and preprocessing the image to be evaluated to obtain a gray level image to be evaluated; s2, extracting the edge line of the gray level image to be evaluated and the gradient information of each pixel on the edge line; s3, setting a sampling ratio or sampling point number, and uniformly sampling the edge line to obtain a sampling point; s4, acquiring the detection frame of each sampling point according to the gradient information; s5, calculating the edge definition of each sampling point detection frame; and S6, calculating the edge definition of the whole image according to the edge definition of the detection frame of each sampling point, and evaluating the focusing effect according to the edge definition of the whole image. The invention realizes the rapid and accurate detection and evaluation of the image focusing accuracy, is suitable for various detection scenes and can guide the focusing process in real time.
Description
Technical Field
The invention relates to the field of machine vision, in particular to an image focusing accuracy evaluation method and system.
Background
In machine vision-aided industrial part measurement, the captured image is required to be sufficiently clear. For the object to be observed, the imaging surface of the object does not necessarily coincide with the photosensitive surface of the camera, and in order to obtain a clear image, the position of the imaging surface needs to be adjusted to coincide with the photosensitive surface, that is, focusing is performed. The better the focusing pair is, the clearer the edge of the target object image is, the better the consistency with the edge position of the actual object is, and the more accurate the result is when the target object image is further used for measurement, edge detection and other applications; otherwise, the edge of the object of the formed image will be blurred to some extent, and the edge of the target object cannot be accurately positioned, thereby affecting the machine vision detection precision.
In the existing image detection technology, most of the methods are to manually judge whether focusing is accurate or not by naked eyes by amplifying edge positions in an image, so that quantitative data is lacked.
Disclosure of Invention
The present invention is directed to a solution to the problems of the prior art.
In one aspect, embodiments of the present invention provide a method,
further, in the above-mentioned case,
on the other hand, the embodiment of the invention also provides a method,
the invention has the beneficial effects that: the image focusing accuracy evaluation method provided by the invention calculates the definition of the image to be detected based on edge detection, gradient change and gray change sharpness, and improves the accuracy of image evaluation; by means of edge detection and uniform sampling on edge lines, the total calculation amount in the detection process can be reduced, the calculation speed is increased, and the focusing process is guided in real time; a certain range is selected around the sampling point according to the gradient frame to serve as a detection frame, the statistic of the gray level in the detection frame is calculated, the interference of noise points can be effectively eliminated, the detection result is more reliable, and the method can be suitable for detection under various scenes.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
fig. 1 is a schematic overall flow chart of an image focusing accuracy evaluation method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of edge point sampling locations according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the distribution of the gray level of the pixels in the detection frame projected in the gradient direction according to the embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a distribution of gray level projection of pixels in a detection frame in a gradient direction under an ideal condition in the embodiment of the present invention;
fig. 5 is a schematic diagram of an overall structure of the image focusing accuracy evaluation system according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention are further described in detail below with reference to the accompanying drawings. The exemplary embodiments and descriptions of the present invention are provided to explain the present invention, but not to limit the present invention.
In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are used in an open-ended fashion, i.e., to mean including, but not limited to. Reference to the description of the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the embodiments is for illustrative purposes to illustrate the implementation of the present application, and the sequence of steps is not limited and can be adjusted as needed.
The embodiment of the invention provides an image focusing accuracy evaluation method, fig. 1 is an overall flow schematic diagram of the image focusing accuracy evaluation method, and as shown in fig. 1, the method specifically comprises the following steps:
s1, acquiring an image to be evaluated of the target object and preprocessing the image to be evaluated to obtain a gray level image to be evaluated;
the image to be evaluated is obtained by shooting a target object through a camera, and if the shot image to be evaluated is a color image, the color image is converted into a gray-scale image through preprocessing.
Specifically, the camera used in this embodiment is a CCD camera; the preprocessing method includes a weighting method and an averaging method, and the weighting method is preferably used in this embodiment.
S2, extracting the edge line of the gray level image to be evaluated and the gradient information of each pixel on the edge line;
whether the image focusing is accurate or not is mainly reflected on the edge of the object image, and the image definition can be better reflected due to larger gray scale change at the edge; and the gray level change of the area of the image surface block is smooth, so that the reference value in the definition evaluation is small. Therefore, the invention adopts the edge detection algorithm to extract the edge information of the image to be evaluated, and the obtained result can greatly reduce the image data amount needing to be processed, thereby filtering a large amount of unnecessary information and reducing the calculated amount in the image accuracy detection process.
Specifically, S2 further includes the following steps:
s2-1, extracting the edge line of the gray level image to be evaluated obtained in the S1 based on the edge detection operator;
in this embodiment, the edge detection operator used for extracting the edge line of the gray scale image to be evaluated is a Canny operator.
S2-2, carrying out noise screening treatment on the edge line to obtain the edge of the target object;
according to the characteristics of the target object, screening the edges of the target object from the edges obtained in the step S2-1, for example, obtaining various closed contours in the whole image through connectivity analysis, and performing noise point screening; wherein, the noise screening treatment comprises breakpoint connection and length filtering and screening.
S2-3, calculating change gradients DX and DY of each pixel of the gray image to be evaluated in the horizontal direction and the vertical direction based on an edge detection operator;
in this embodiment, the edge detection operator used for calculating the variation gradient of each pixel of the grayscale image to be evaluated is a Sobel operator, that is, two sets of 3 × 3 masks are used, which are in the horizontal and vertical directions, and are respectively subjected to plane convolution with the image, so that the variation gradients in the horizontal and vertical directions can be obtained.
And S2-4, calculating the gradient direction of each pixel on the edge line by combining the edge of the target object obtained by S2-2 and the change gradients DX and DY obtained by S2-3.
Specifically, the gradient direction is a direction perpendicular to the tangent of the edge line, and the gradient direction calculation formula is as follows:
wherein G is i Representing the gradient direction of the ith pixel, DX i And DY i Respectively representing the horizontal direction variation gradient and the vertical direction variation gradient of the ith pixel.
S3, setting a sampling ratio or sampling point number, and uniformly sampling the edge line to obtain a sampling point;
specifically, the edge line of the gray level image to be evaluated obtained in S2-1 is sampled according to a set sampling ratio or the number of sampling points, so as to obtain N sampling points.
In this embodiment, a sampling ratio or the number of sampling points can be selected and set, and the value range of the sampling ratio is 0.01 to 0.3, preferably 0.05; the number of sampling points satisfies: the number of sampling points is equal to the sampling ratio multiplied by the number of edge points, and the number of sampling points is more than 3.
S4, acquiring the detection frame of each sampling point according to the gradient information;
s4 further includes: according to the edge line and the gradient direction thereof obtained in the step S2, L pixel points are expanded inward and outward of the sampling point along the gradient direction, and W pixel points are expanded to both sides of the gradient line in the direction perpendicular to the gradient direction, so as to construct a detection frame consisting of W × L pixel points. The gradient line is a line segment which takes edge line sampling points as middle points and has the length of L pixel points along the gradient direction.
Specifically, referring to the illustration of fig. 3, the gradient direction is perpendicular to the tangential direction of the edge line, and is directed from dark to light, i.e., from the side where the gradation is small to the side where the gradation is large.
It should be understood by those skilled in the art that the size of the constructed detection frame may be adjusted according to the size of the image, specifically, the length L should be satisfied, and the value of L is greater than 3 and less than 1/4 of the length of the short side of the image; the width W should be an odd number, and the value of W is more than 3, and W is less than 1/sampling ratio.
Further, the length L of the pixel is preferably 12, the width W is preferably 5, and fig. 3 is a detection frame formed by 12 × 5 pixels when the length L of the pixel is 12 and the width W is 5.
S5, calculating the edge definition of each sampling point detection frame;
specifically, S5 further includes the following steps:
s5-1, obtaining the maximum value V of each pixel gray scale in the detection frame max And a minimum value V min And is combined withCalculating the gray difference H, G ═ V of the detection frame max -V min ；
S5-2, calculating the gray level middle value in the detection frame
S5-3, calculating the offset delta V between the gray level of each pixel in the detection frame and the intermediate value j The calculation formula is:
wherein, is Δ V j Denotes the offset, V, of the jth pixel j Representing the gray value of the jth pixel;
s5-4, synthesizing the offsets and performing normalization processing to obtain the edge sharpness D of each detection frame, where the offsets of each pixel should be H/2 under the ideal sharpness condition, that is, when the sharpness D is equal to 1, in this embodiment, H/2 is used to perform normalization processing, and the calculation formula of the edge sharpness D is:
s6, calculating the edge definition of the whole image according to the edge definition of each sampling point detection frame, and evaluating the focusing effect according to the edge definition of the whole image;
specifically, the step of calculating the edge sharpness of the full graph in S6 includes: calculating the average value of the edge definition of each sampling point detection frame as the edge definition of the whole image, wherein the calculation formula is as follows:
wherein the content of the first and second substances,
for the definition of the whole image edge, N is the number of sampling points, D i And indicating the edge definition of the detection frame corresponding to the ith sampling point.
Further, the edge definition of the whole image is calculated
In the range of [0,1]And according to the edge definition of the whole picture
The size of the image is judged to evaluate the focusing effect, in this embodiment, the definition of the whole image edge
The closer to 1 the value of (b) indicates that the clearer the captured image, the better the focusing effect.
In particular, when the whole image is edge-defined
When the value of (A) is greater than 0.8, the image is clear and the focusing effect is good;
when the edge of the whole image is clear
When the value of (1) is 0.5-0.8, the image definition and the focusing effect are considered to be common, and the image should be collected again at the moment;
when the edge of the whole image is clear
When the value of (A) is less than 0.5, it means that the detection process is erroneous and should be performed again.
As shown in fig. 5, the present invention further provides an image focusing accuracy evaluation system, through which the image focusing accuracy evaluation method can be implemented, specifically, the system includes an acquisition module, a gradient extraction module, a sampling module, a detection frame acquisition module, a calculation module, and an evaluation module;
the system comprises an acquisition module, a pre-processing module and a display module, wherein the acquisition module is used for acquiring and pre-processing images of a target object so as to obtain a gray level image to be evaluated; the gradient extraction module is used for acquiring the gradient direction of each pixel on the edge line of the image to be evaluated; the sampling module can sample the edge line and obtain a sampling point; the detection frame acquisition module can combine the sampling points and the gradient information to obtain the detection frame of each sampling point; the computing module is used for computing the edge definition of each sampling point detection frame and the edge definition of the whole image; the evaluation module can judge the focusing effect of the collected image according to the edge definition of the whole image.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. An image focusing accuracy evaluation method is characterized by comprising the following steps:
s1, acquiring an image to be evaluated and preprocessing the image to be evaluated to obtain a gray level image to be evaluated;
s2, extracting the edge line of the gray level image to be evaluated and the gradient information of each pixel on the edge line;
s3, setting a sampling ratio or sampling point number, and uniformly sampling the edge line to obtain a sampling point;
s4, acquiring the detection frame of each sampling point according to the gradient information;
s5, calculating the edge definition of each sampling point detection frame;
and S6, calculating the edge definition of the whole image according to the edge definition of the detection frame of each sampling point, and evaluating the focusing effect according to the edge definition of the whole image.
2. The method for evaluating the image focusing accuracy of claim 1, wherein in S1, the image to be evaluated is obtained by photographing the object with a camera, and if the photographed image to be evaluated is a color image, the color image is converted into a gray scale image by preprocessing.
3. The image focusing accuracy evaluation method according to claim 1 or 2, wherein said S2 further comprises the steps of:
s2-1, extracting the edge line of the gray level image to be evaluated obtained in the S1 based on the edge detection operator;
s2-2, carrying out noise screening treatment on the edge line to obtain the edge of the target object;
s2-3, calculating change gradients DX and DY of each pixel of the gray image to be evaluated in the horizontal direction and the vertical direction based on an edge detection operator;
and S2-4, calculating the gradient direction of each pixel on the edge line by combining the edge of the object obtained in S2-2 and the change gradients DX and DY obtained in S2-3.
4. The image focusing accuracy evaluation method according to claim 3, wherein in S2-1, the edge detection operator used is a Canny operator; in the step S2-3, the adopted edge detection operator is a Sobel operator.
5. The method for evaluating the image focusing accuracy of claim 4, wherein in the step S3, the sampling ratio ranges from 0.01 to 0.3; the number of sampling points satisfies: the number of sampling points is equal to the sampling ratio multiplied by the number of edge points, and the number of sampling points is more than 3.
6. The image focusing accuracy evaluation method according to claim 5, wherein said S4 further comprises: according to the edge line and the gradient direction thereof obtained in the step S2, L pixel points are extended inward and outward of the sampling point along the gradient direction, and W pixel points are extended to both sides of the gradient line in the direction perpendicular to the gradient direction, so as to construct a detection frame composed of W × L pixel points, wherein the length of the detection frame is L, and the width of the detection frame is W.
7. The method for evaluating the image focusing accuracy according to claim 6, wherein the length L is set to 3 or more, and L is 1/4 or less of the length of the image short side; the width W is greater than 3 and less than 1/sample ratio.
8. The image focusing accuracy evaluation method according to claim 6 or 7, wherein said S5 further comprises the steps of:
s5-1, obtaining the maximum value V of the gray scale of each pixel in the detection frame max And a minimum value V min And calculating the gray difference H, H ═ V of the detection frame max -V min ；
S5-2, calculating the gray level middle value in the detection frame
S5-3, calculating the offset delta V between the gray level of each pixel in the detection frame and the intermediate value j The calculation formula is:
wherein, is Δ V j Denotes the offset, V, of the jth pixel j Representing the gray value of the jth pixel;
s5-4, synthesizing the offset and carrying out normalization processing to obtain the edge definition D of each detection frame, wherein the calculation formula is as follows:
9. the image focusing accuracy evaluating method according to claim 8, wherein the calculating of the full-image edge sharpness in S6 includes: calculating the average value of the edge definition of each sampling point detection frame as the edge definition of the whole image, wherein the calculation formula is as follows:
wherein the content of the first and second substances,
for full picture edge definition, N is the number of sampling points, D i And indicating the edge definition of the detection frame corresponding to the ith sampling point.
10. An image focusing accuracy evaluation system is characterized by comprising an acquisition module, a gradient extraction module, a sampling module, a detection frame acquisition module, a calculation module and an evaluation module;
the system comprises an acquisition module, a pre-processing module and a display module, wherein the acquisition module is used for acquiring and pre-processing images of a target object so as to obtain a gray level image to be evaluated; the gradient extraction module is used for acquiring the gradient direction of each pixel on the edge line of the image to be evaluated; the sampling module can sample the edge line and obtain a sampling point; the detection frame acquisition module can combine the sampling points and the gradient information to obtain the detection frame of each sampling point; the computing module is used for computing the edge definition of each sampling point detection frame and the edge definition of the whole image; the evaluation module can judge the focusing effect of the collected image according to the edge definition of the whole image.
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| CN115937324B (en) * | 2022-09-09 | 2024-03-26 | 郑州思昆生物工程有限公司 | Assembly quality evaluation method, device, equipment and storage medium |
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