Printing quality evaluation method based on machine vision

Abstract

The invention relates to the technical field of printing quality evaluation, and particularly discloses a printing quality evaluation method based on machine vision, which comprises the following steps: S1, acquiring a sample image of a printed matter sample, dividing the sample image into a plurality of pixels, and acquiring RGB values of the pixels to obtain an RGB region; obtaining an edge curve and a center coordinate according to the RGB region; s2, acquiring a shot image of the current printed matter, acquiring an RGB region in the shot image, and acquiring an RGB value, an edge curve and a center point coordinate of the RGB region; calculating a size index, a position index and a color difference index of the shot image according to the sample edge curve, the sample center coordinate and each RGB region in the shot image; and S3, obtaining the gray variance of the shot image, setting a standard size index, a standard position index, a standard chromatic aberration index and a standard gray variance index, and obtaining a quality index.

Description

Technical Field

[0001] The present invention relates to the technical field of printing quality assessment, and in particular to a printing quality assessment method based on machine vision. Background Art

[0002] During the printing process, printing quality assessment is a key link to ensure that the final product meets the expected standards and customer requirements; the content of quality assessment generally includes color reproduction, clarity, pattern position, etc.

[0003] Machine vision is the use of computer technology in artificial intelligence to extract information from images, process and understand them to achieve automated decision-making and control processes. It performs measurement and judgment tasks by simulating human visual functions and plays an important role in many fields.

[0004] In the existing technology, manual evaluation methods often rely on personal visual perception and aesthetic standards, which are highly subjective. This subjectivity leads to inconsistency in evaluation results, making it difficult to quantify and standardize printing quality, and the labor cost is high. In addition, printing quality is affected by many factors, and existing detection methods often only focus on the quality of one aspect of the printed product, such as color or image clarity, and lack a comprehensive evaluation of the overall quality of the printed product. Summary of the invention

[0005] The purpose of the present invention is to provide a printing quality assessment method based on machine vision to solve the above technical problems.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A printing quality assessment method based on machine vision comprises the following steps: Step S1: obtaining a sample image of a printed product sample, dividing the sample image into a number of pixels, and establishing a rectangular coordinate system on the sample image, where each pixel corresponds to a coordinate in the rectangular coordinate system; obtaining the RGB value of each pixel, and obtaining a closed area consisting of a number of pixels with the same RGB value, and recording each closed area as an RGB area; Obtain the outermost pixels of the RGB area and their corresponding coordinates to obtain an edge curve of the RGB area, recorded as a sample edge curve, and select m reference points on the sample edge curve; and obtain the coordinates of the center point of each RGB area, recorded as the sample center coordinate P0; Step S2: obtaining a photographed image of the current printed product, obtaining all RGB areas in the photographed image, and obtaining RGB values, edge curves and center point coordinates of each RGB area; According to the sample edge curve, the sample center coordinates and each RGB area in the captured image, the size index of the captured image is obtained. and position index , where D(P0 i , P i ) indicates P0 i With P i The Euclidean distance, P0 i is the sample center coordinate of the i-th RGB region, P i is the coordinate of the center point of the i-th RGB region in the captured image, n is the total number of RGB regions, F´ i (x) is the derivative value of the edge curve F(x) of the i-th RGB region in the captured image at the x-th reference point, f´ i (x) is the derivative value of the edge curve of the i-th sample at the x-th reference point, F i (x) is the coordinate of the xth reference point on the edge curve F(x) of the i-th RGB region in the captured image, i (x) is the coordinate corresponding to the xth reference point on the i-th sample edge curve F(x); Get the RGB value of the i-th RGB area in the captured image, denoted as R i , and obtain the RGB value of the i-th RGB area in the sample image, denoted as R0 i , then the color difference index of the image is obtained ; Step S3: Obtain the grayscale variance H of the captured image and set the standard size index QI side ´、Standard Position Index QI por ´、Standard color difference index QI color ´ and standard grayscale variance index H´, the quality index of the current print is obtained ; According to the quality index, the quality of the printed product is evaluated.

[0007] As a further solution of the present invention: the process of dividing the sample image into a number of pixels is based on rasterization processing.

[0008] As a further solution of the present invention: the reference point selection process includes: setting an interval threshold, and selecting m reference points at equal intervals on the sample edge curve according to the interval threshold.

[0009] As a further solution of the present invention: the process of obtaining the Euclidean distance includes: denoting the sample center coordinate as P0 i =(x0 i ,y0 i ), the corresponding center coordinates P in the captured image i =(x i ,y i ), then the Euclidean distance .

[0010] As a further solution of the present invention: the process of obtaining the grayscale variance includes: The average value of the RGB values ​​of each pixel in the captured image is obtained and recorded as RGB ave , then the grayscale variance of the captured image is obtained , where RGB k represents the kth pixel, and N is the total number of pixels in the captured image.

[0011] As a further solution of the present invention: the setting process of the standard size index, the standard position index, the standard color difference index and the standard grayscale variance index includes: Obtain historical data, the historical data including size index, position index, color difference index and grayscale variance index of historical qualified printed products; obtain the mean value of the size index in the historical data, recorded as the standard size index, obtain the mean value of the position index in the historical data, recorded as the standard position index, obtain the mean value of the color difference index in the historical data, recorded as the standard color difference index, obtain the mean value of the grayscale variance index in the historical data, recorded as the standard grayscale variance index.

[0012] As a further embodiment of the present invention: when QI por =0, directly record QI in the formula por ´ / QI por =QI por ´;When QI side =0, directly record QI in the formula side ´ / QI side =QI side ´;When QI color =0, directly record QI in the formula color ´ / QI color =QI color ´.

[0013] As a further solution of the present invention: the process of quality assessment of the printed product includes: A quality index threshold is set, denoted as μ; if QI ≥ μ, the quality of the current printed product is qualified; otherwise, the quality of the current printed product is unqualified.

[0014] Beneficial effects of the present invention: Compared with the prior art, the present invention can quickly and accurately evaluate the quality of printed products by automatically acquiring and processing captured images, avoiding the subjectivity and inefficiency of traditional manual inspection; by calculating RGB values ​​and grayscale variance, defects and differences in printed products can be accurately located and identified, thereby improving the accuracy of evaluation; the present invention not only takes into account the size and position deviation of printed products (calculated by edge curves and center point coordinates), but also combines color difference index and grayscale variance for comprehensive evaluation; this multi-dimensional evaluation method can comprehensively reflect the overall quality of printed products, help to discover potential problems and make improvements; this multi-dimensional evaluation method of the present invention can comprehensively reflect the overall quality of printed products, help to discover potential problems and make improvements; the standardized evaluation process makes the results more comparable and repeatable, and is convenient for long-term monitoring and quality management; the automated machine vision evaluation method in the present invention can significantly reduce the time and cost of manual inspection and improve the efficiency of the production line; timely discovery and correction of quality problems can reduce scrap rates, reduce production costs, and improve the economic benefits of the enterprise. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention will be further described below in conjunction with the accompanying drawings.

[0016] Figure 1 It is a flow chart of the printing quality evaluation method based on machine vision of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] See also Figure 1 As shown, the present invention is a printing quality assessment method based on machine vision, comprising the following steps: Step S1: obtaining a sample image of a printed product sample, dividing the sample image into a number of pixels, and establishing a rectangular coordinate system on the sample image, where each pixel corresponds to a coordinate in the rectangular coordinate system; obtaining the RGB value of each pixel, and obtaining a closed area consisting of a number of pixels with the same RGB value, and recording each closed area as an RGB area; Obtain the outermost pixels of the RGB area and their corresponding coordinates to obtain an edge curve of the RGB area, recorded as a sample edge curve, and select m reference points on the sample edge curve; and obtain the coordinates of the center point of each RGB area, recorded as the sample center coordinate P0; It is understandable that the sample image of the printed sample is usually completed by a high-resolution scanner or camera to ensure that the image quality is high enough; after the sample image is divided into a number of pixels, each pixel has its corresponding RGB value (red, green, blue) to represent the color information of the pixel; a rectangular coordinate system is established in the sample image, and the rectangular coordinate system can be a Cartesian coordinate system, so that each pixel corresponds to a unique coordinate, which is used to accurately locate the position of each pixel; a closed area composed of a number of pixels with the same RGB value is defined as an RGB area, which helps to identify the same color area in the image; the outermost pixels of the RGB area and their corresponding coordinates are obtained to form an edge curve of the RGB area, which describes the boundary shape of the RGB area; the center point is usually the average value of the coordinates of all pixels in the area, representing the position center of the area; As a preferred embodiment of the present invention, the process of dividing the sample image into a number of pixels is based on rasterization processing; It can be understood that the rasterization process is a process of image segmentation. Through the rasterization process, the image can be segmented into multiple regions, which are often rectangular, and each region contains a number of pixels; As a preferred embodiment of the present invention, the reference point selection process includes: setting an interval threshold, and selecting m reference points at equal intervals on the sample edge curve according to the interval threshold; It is understandable that an interval threshold needs to be set to determine the distance interval of selecting reference points on the sample edge curve; this threshold can be adjusted according to actual needs to ensure that the number and distribution of reference points can meet the requirements of the analysis; the interval threshold determines the distance between reference points, a smaller threshold will make the reference points more dense, and a larger threshold will make the reference points sparser; Step S2: obtaining a photographed image of the current printed product, obtaining all RGB areas in the photographed image, and obtaining RGB values, edge curves and center point coordinates of each RGB area; The size index of the captured image is obtained according to the sample edge curve, the sample center coordinates and each RGB area in the captured image. and position index , where D(P0 i , P i ) indicates P0 i With P i The Euclidean distance, P0 i is the sample center coordinate of the i-th RGB region, P i is the coordinate of the center point of the i-th RGB region in the captured image, n is the total number of RGB regions, F´ i(x) is the derivative value of the edge curve F(x) of the i-th RGB region in the captured image at the x-th reference point, f´ i (x) is the derivative value of the edge curve of the i-th sample at the x-th reference point, F i (x) is the coordinate of the xth reference point on the edge curve F(x) of the i-th RGB region in the captured image, i (x) is the coordinate corresponding to the xth reference point on the i-th sample edge curve F(x); Get the RGB value of the i-th RGB area in the captured image, denoted as R i , and obtain the RGB value of the i-th RGB area in the sample image, denoted as R0 i , then the color difference index of the image is obtained ; It can be understood that the processing of the captured image is consistent with the processing of the sample image in step S1, and the captured image is rasterized to divide the captured image into a number of pixels, and the RGB value of each pixel is obtained, and all pixels are divided into a number of RGB areas according to the RGB value, and each RGB area represents a same-color area in the captured image; It should be noted that the process of obtaining the size index is to determine whether the derivative values ​​of the edge curve of the RGB area with the same number and the sample edge curve at each reference point in the captured image and the sample image are the same, that is, whether the shape of the edge curve of the RGB area with the same number and the sample edge curve are consistent; the smaller the size index, the more consistent the shape; the process of obtaining the position index is to determine whether the center point coordinates of the RGB area with the same number and the sample center point coordinates are consistent in the captured image and the sample image, and to determine whether the distance from the center point to each reference point on the edge curve and the distance from the sample center point to each reference point on the sample edge curve are consistent; the smaller the position index, the more consistent the pattern position and shape of the captured image are with the sample print; the color difference index is the difference between the RGB values ​​of each RGB area, and the smaller the color difference index, the smaller the color difference between the captured image and the sample image; As a preferred embodiment of the present invention, the process of obtaining the Euclidean distance includes: denoting the sample center coordinate as P0 i =(x0 i ,y0 i ), the corresponding center coordinates P in the captured image i =(x i ,y i ), then the Euclidean distance ; Step S3: Obtain the grayscale variance H of the captured image and set the standard size index QI side ´、Standard Position Index QIpor ´、Standard color difference index QI color ´ and standard grayscale variance index H´, the quality index of the current print is obtained ; Performing a quality assessment on the printed product according to the quality index; It can be understood that the grayscale variance is a measure of the discrete degree of pixel value distribution in an image, reflects the contrast and detail richness of the image, and is used to evaluate the clarity and contrast of a printed pattern; the larger the grayscale variance of the captured image, the higher the clarity; As a preferred embodiment of the present invention, the process of obtaining the grayscale variance includes: The average value of the RGB values ​​of each pixel in the captured image is obtained and recorded as RGB ave , then the grayscale variance of the captured image is obtained , where RGB k represents the kth pixel, and N is the total number of pixels in the captured image; As a preferred embodiment of the present invention, the setting process of the standard size index, the standard position index, the standard color difference index and the standard grayscale variance index includes: Acquire historical data, the historical data including size index, position index, color difference index and grayscale variance index of historical qualified printed products; acquire the mean value of the size index in the historical data, recorded as the standard size index, acquire the mean value of the position index in the historical data, recorded as the standard position index, acquire the mean value of the color difference index in the historical data, recorded as the standard color difference index, acquire the mean value of the grayscale variance index in the historical data, recorded as the standard grayscale variance index; It is worth noting that the standard grayscale variance is often affected by the acquisition device for capturing the image, and is related to the capture clarity of the acquisition device, and is also affected by the printing accuracy of the printing device; As a preferred embodiment of the present invention, when QI por =0, directly record QI in the formula por ´ / QI por =QI por ´;When QI side =0, directly record QI in the formula side ´ / QI side =QI side ´;When QI color =0, directly record QI in the formula color ´ / QI color =QI color ´; It is understood that in the calculation formula of the quality index, QI por ,QI side,QI color All are numerators and cannot be 0; As a preferred embodiment of the present invention, the process of evaluating the quality of the printed product includes: Set a quality index threshold, denoted as μ; if QI ≥ μ, the quality of the current printed product is qualified; otherwise, the quality of the current printed product is unqualified; It is worth noting that the quality index is only used to judge whether the quality of the current printed product is qualified, so that there is a unified standard for detecting whether the printed product is qualified, and the effect of comparing the quality between printed products is relatively limited.

[0019] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A printing quality assessment method based on machine vision, characterized in that: The following steps are involved: Step S1: obtaining a sample image of a printed product sample, dividing the sample image into a number of pixels, and establishing a rectangular coordinate system on the sample image, where each pixel corresponds to a coordinate in the rectangular coordinate system; obtaining the RGB value of each pixel, and obtaining a closed area consisting of a number of pixels with the same RGB value, and recording each closed area as an RGB area; Obtain the outermost pixels of the RGB area and their corresponding coordinates to obtain an edge curve of the RGB area, recorded as a sample edge curve, and select m reference points on the sample edge curve; and obtain the coordinates of the center point of each RGB area, recorded as the sample center coordinate P0; Step S2: obtaining a photographed image of the current printed product, obtaining all RGB areas in the photographed image, and obtaining RGB values, edge curves and center point coordinates of each RGB area; The size index of the captured image is obtained according to the sample edge curve, the sample center coordinates and each RGB area in the captured image. and position index , where D(P0 i , P i ) indicates P0 i With P i The Euclidean distance, P0 i is the sample center coordinate of the i-th RGB region, P i is the coordinate of the center point of the i-th RGB region in the captured image, n is the total number of RGB regions, F´ i (x) is the derivative value of the edge curve F(x) of the i-th RGB region in the captured image at the x-th reference point, f´ i (x) is the derivative value of the edge curve of the i-th sample at the x-th reference point, F i (x) is the coordinate of the xth reference point on the edge curve F(x) of the i-th RGB region in the captured image, i (x) is the coordinate corresponding to the xth reference point on the i-th sample edge curve F(x); Get the RGB value of the i-th RGB area in the captured image, denoted as R i , and obtain the RGB value of the i-th RGB area in the sample image, denoted as R0 i , then the color difference index of the image is obtained ; Step S3: Obtain the grayscale variance H of the captured image and set the standard size index QI side ´、Standard Position Index QI por ´、Standard color difference index QI color ´ and standard grayscale variance index H´, the quality index of the current print is obtained ; According to the quality index, the quality of the printed product is evaluated.

2. The printing quality assessment method based on machine vision according to claim 1, characterized in that: In step S1 , the process of dividing the sample image into a number of pixels is based on a rasterization process.

3. The printing quality assessment method based on machine vision according to claim 1, characterized in that: In step S1, the reference point selection process includes: setting an interval threshold, and selecting m reference points at equal intervals on the sample edge curve according to the interval threshold.

4. The printing quality assessment method based on machine vision according to claim 1, characterized in that: In step S2, the process of obtaining the Euclidean distance includes: The sample center coordinate is denoted as P0 i =(x0 i ,y0 i ), the corresponding center coordinates P in the captured image i =(x i ,y i ), then the Euclidean distance .

5. The printing quality assessment method based on machine vision according to claim 1, characterized in that: In step S3, the grayscale variance obtaining process includes: The average value of the RGB values ​​of each pixel in the captured image is obtained and recorded as RGB ave , then the grayscale variance of the captured image is obtained , where RGB k represents the kth pixel, and N is the total number of pixels in the captured image.

6. The printing quality assessment method based on machine vision according to claim 1, characterized in that: In step S3, the process of setting the standard size index, the standard position index, the standard color difference index and the standard grayscale variance index includes: Obtain historical data, the historical data including size index, position index, color difference index and grayscale variance index of historical qualified printed products; obtain the mean value of the size index in the historical data, recorded as the standard size index, obtain the mean value of the position index in the historical data, recorded as the standard position index, obtain the mean value of the color difference index in the historical data, recorded as the standard color difference index, obtain the mean value of the grayscale variance index in the historical data, recorded as the standard grayscale variance index.

7. The printing quality assessment method based on machine vision according to claim 1, characterized in that: In step S3, when QI por =0, directly record QI in the formula por ´ / QI por =QI por ´;When QI side =0, directly record QI in the formula side ´ / QI side =QI side ´;When QI color =0, directly record QI in the formula color ´ / QI color =QI color ´.

8. The printing quality assessment method based on machine vision according to claim 1, characterized in that: In step S3, the process of evaluating the quality of the printed product includes: A quality index threshold is set, denoted as μ; if QI ≥ μ, the quality of the current printed product is qualified; otherwise, the quality of the current printed product is unqualified.

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