Method and device for quantifying color appearance uniformity of tobacco sheets

Abstract

The invention provides a tobacco sheet color appearance uniformity quantification method and device, and the method comprises the steps: S1, obtaining a first tobacco sheet image of a to-be-detected tobacco sheet, and carrying out the image binarization processing of the first tobacco sheet image, and obtaining a second tobacco sheet image; s2, based on the second tobacco sheet image, respectively carrying out proportion calculation processing and relative standard deviation calculation processing on pixel points to obtain pixel point proportion information and relative standard deviation information; and S3, quantifying the color appearance uniformity of the tobacco sheet to be detected according to the pixel point proportion information and the relative standard deviation information.

Description

Technical Field

[0001] This application belongs to the field of tobacco detection, and particularly relates to a quantification method for the color appearance uniformity of tobacco sheet and a quantification device for the color appearance uniformity of tobacco sheet. Background Art

[0002] In the tobacco processing industry, the color appearance uniformity of tobacco sheet is one of the important factors affecting its quality. As an important raw material for cigarettes, the color uniformity of tobacco sheet not only affects the visual effect of cigarettes, but also indirectly reflects the consistency of its internal components and the stability of the processing process. Therefore, accurate detection of the color appearance uniformity of tobacco sheet is of great significance.

[0003] Currently, the detection methods for the color appearance uniformity of tobacco sheet mainly rely on manual visual inspection and traditional color difference meter measurement. However, these detection methods have many deficiencies in practical applications, resulting in poor detection effects.

[0004] Although manual visual inspection is intuitive, it is affected by the subjective judgment and experience level of inspectors, and the detection results often have large errors. In addition, manual inspection is time-consuming and laborious, and cannot meet the rapid detection requirements of large-scale production lines. At the same time, for small color differences, it may be difficult for manual inspection to accurately identify and quantify.

[0005] Although traditional color difference meter measurement can provide relatively accurate color data, its operation is cumbersome and requires professional measurement skills and experience. In addition, color difference meter measurement usually can only measure a single sample and cannot comprehensively reflect the color uniformity of the whole batch of tobacco sheet. During the measurement process, the sample also needs to be pre-treated, such as balancing temperature and humidity, which further increases the complexity and time consumption of the measurement.

[0006] Chinese Patent CN202410008723.X proposes an innovative detection method for the color appearance stability of tobacco sheet. This method converts the collected image into an image in the Lab color space, performs equal division slicing processing, and then calculates the standard deviation of each slice in the three color channels of L (brightness), a (red-green value), and b (yellow-blue value), and uses this as an index to evaluate the color appearance stability of tobacco sheet.

[0007] However, in practical applications, testers found that this method has certain limitations. Especially when facing different batches or types of tobacco sheet, its detection effect is insufficient. Specifically, although the Lab values of different tobacco sheet may have significant differences, when their standard deviation values are the same, the conclusions of color stability obtained according to this method are consistent. However, by directly observing the original pictures of these tobacco sheet, testers found that there are actually obvious differences in their colors. Summary of the Invention

[0008] In view of this, the purpose of this application is to provide a method and device for quantifying the color appearance uniformity of tobacco sheets to solve the above problems.

[0009] To solve the above technical problems, this application adopts the following technical solutions:

[0010] This application provides a method for quantifying the color appearance uniformity of tobacco sheets. The quantification method includes the following steps: Step S1: Obtain the first tobacco sheet image of the tobacco sheet to be detected, and perform image binarization processing on the first tobacco sheet image to obtain the second tobacco sheet image; Step S2: Based on the second tobacco sheet image, perform proportion calculation processing and relative standard deviation calculation processing on pixel points respectively to obtain pixel point proportion information and relative standard deviation information; Step S3: Quantify the color appearance uniformity of the tobacco sheet to be detected according to the pixel point proportion information and the relative standard deviation information.

[0011] Further, Step S1 includes: Step S11: Obtain the original image of the tobacco sheet to be detected; Step S12: Perform image grayscale processing on the original image to obtain the grayscale image of the tobacco sheet to be detected; Step S13: Perform image noise reduction processing on the grayscale image to obtain the noise-reduced image of the tobacco sheet to be detected; Step S14: Perform image contrast enhancement processing on the noise-reduced image to obtain the first tobacco sheet image.

[0012] Further, the image grayscale processing is one of the maximum value method, the average value method or the weighted average method; the image noise reduction processing is one of mean filtering, median filtering or Gaussian filtering; the image contrast enhancement processing is global histogram equalization or local histogram equalization.

[0013] Further, the image binarization processing is global threshold binarization or adaptive threshold binarization.

[0014] Further, Step S2 includes: Step A21: Analyze the second tobacco sheet image to obtain the color difference image of the tobacco sheet to be detected. The color difference image is characterized by the white pixel point part in the second tobacco sheet image; Step A22: Calculate the proportion of white pixel points in the second tobacco sheet image to obtain the pixel point proportion information.

[0015] Further, Step S2 includes: Step B21: Perform image equal division processing on the second tobacco sheet image to obtain an image slice group corresponding to the second tobacco sheet image. The image slice group includes multiple slice images; Step B22: Statistically analyze the pixel point information of each slice image in the image slice group; Step B23: Determine the relative standard deviation information according to the pixel point information.

[0016] In a second aspect, the present application also provides a quantification device for the color appearance uniformity of tobacco sheet. The quantification device includes: an acquisition module, a processing module, and a quantification module. The acquisition module is configured to acquire a first tobacco sheet image of the tobacco sheet to be detected, and perform image binarization processing on the first tobacco sheet image to obtain a second tobacco sheet image; the processing module is configured to perform proportion calculation processing and relative standard deviation calculation processing on pixel points respectively based on the second tobacco sheet image to obtain pixel point proportion information and relative standard deviation information; the quantification module is configured to quantify the color appearance uniformity of the tobacco sheet to be detected according to the pixel point proportion information and the relative standard deviation information.

[0017] In a third aspect, the present application also provides a computer system, which includes a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the quantification method for the color appearance uniformity of the tobacco sheet in the first aspect.

[0018] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program / instructions are stored. When the computer program / instructions are executed by a processor, the quantification method for the color appearance uniformity of the tobacco sheet in the first aspect is implemented.

[0019] In a fifth aspect, the present application also provides a computer program product, which includes computer program / instructions. When the computer program / instructions are executed by a processor, the quantification method for the color appearance uniformity of the tobacco sheet in the first aspect is implemented.

[0020] As can be seen from the above technical solutions, the advantages and positive effects of the quantification method and device for the color appearance uniformity of the tobacco sheet proposed by the present application are as follows:

[0021] Compared with the traditional solution that relies on manual visual inspection and is easily affected by subjective factors, the present application realizes the quantitative evaluation of the color appearance quality of the tobacco sheet through image processing technology, reduces human errors, and improves the objectivity and accuracy of the evaluation. Moreover, the method of the present application is suitable for computer operation, can realize automatic acquisition, processing, and analysis of images, reduces manual intervention, and improves the detection efficiency, especially suitable for large-scale production environments.

[0022] Compared with other existing technologies, the present application ingeniously combines the calculation of the pixel proportion of the tobacco color difference area and the relative standard deviation of pixel points, realizes the multi-dimensional quantitative evaluation of the color appearance uniformity of the tobacco sheet, and this innovative method also greatly enhances the detection accuracy and effect in quantifying the color appearance uniformity of the tobacco sheet. Description of the Drawings

[0023] The above content of this application and the following specific embodiments will be better understood when read in conjunction with the accompanying drawings. It should be noted that the drawings are only examples of the claimed technical solutions.

[0024] Figure 1 is a flowchart of the method for quantifying the color appearance uniformity of tobacco sheet in this application;

[0025] Figure 2 is the original image of the tobacco sheet to be detected in this application;

[0026] Figure 3 is the grayscale image of the tobacco sheet to be detected in this application;

[0027] Figure 4 is the denoised image of the tobacco sheet to be detected in this application;

[0028] Figure 5 is the first tobacco sheet image of the tobacco sheet to be detected in this application;

[0029] Figure 6 is the second tobacco sheet image of the tobacco sheet to be detected in this application;

[0030] Figure 7 is an example diagram of the color difference part of the second tobacco sheet image in this application;

[0031] Figure 8 is a comparison diagram of the color difference part of the second tobacco sheet image in this application. Specific Embodiments

[0032] The following details the detailed features and advantages of this application in the specific embodiments. The content is sufficient for any person skilled in the art to understand the technical content of this application and implement it accordingly. And according to the specification, claims and drawings disclosed in this specification, those skilled in the art can easily understand the related purposes and advantages of this application.

[0033] It should be noted that in this specification, similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0034] In the description of this embodiment, it should be noted that the orientation or positional relationship indicated by the terms "upper", "lower", "inner", "bottom", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product is usually placed during use. It is only for the convenience of describing this application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this application.

[0035] To make the objectives, technical solutions, and advantages of this application clearer, the following will further describe the implementation manners of this application in detail with reference to the accompanying drawings.

[0036] Please refer to Figure 1 , this application provides a quantification method for improving the color appearance uniformity of tobacco sheet. The specific steps of this quantification method are as follows:

[0037] Step S1: Obtain the first tobacco sheet image of the tobacco sheet to be detected, and perform image binarization processing on the first tobacco sheet image to obtain the second tobacco sheet image.

[0038] Step S2: Based on the second tobacco sheet image, perform proportion calculation processing and relative standard deviation calculation processing on pixel points respectively to obtain pixel point proportion information and relative standard deviation information.

[0039] Step S3: Quantify the color appearance uniformity of the tobacco sheet to be detected according to the pixel point proportion information and the relative standard deviation information.

[0040] Among them, the steps for obtaining the first tobacco sheet image of the tobacco sheet to be detected include:

[0041] Step S11: Obtain the original image of the tobacco sheet to be detected.

[0042] The original image can be obtained through the synchronous control technology of the industrial camera and the conveyor belt speed.

[0043] Specifically, during the process of the industrial camera collecting the original image after drying, the acquisition frequency of the camera needs to match the speed of the conveyor belt to avoid collecting duplicate parts, so as to ensure that the acquisition of the original image is without repetition and full coverage.

[0044] Step S12: Perform image grayscale processing on the original image to obtain the grayscale image of the tobacco sheet to be detected.

[0045] Exemplarily, the image grayscale processing can select one of the maximum value method, the average value method, and the weighted average method.

[0046] Such as Figure 2 and 3 shown, after the original image is grayscale processed, the grayscale image of the tobacco sheet to be detected only contains brightness information, and the data dimension is reduced from three-dimensional (red, green, and blue color channels) to one-dimensional, which greatly simplifies the computational amount of image processing, and can remove the color information in the image to reduce noise interference and improve the accuracy and robustness of the algorithm.

[0047] Step S13: Perform image noise reduction processing on the grayscale image to obtain the noise reduction image of the tobacco sheet to be detected.

[0048] Exemplarily, for image denoising processing, filtering denoising can be selected. Among them, filtering denoising can be mean filtering, median filtering, and Gaussian filtering.

[0049] As Figure 4 shown, filtering denoising can effectively remove the noise in the grayscale image and avoid interference from other impurities adhering to the surface of the thin sheet to the detection result. After removing the noise, the clarity, contrast, and detail performance of the denoised image will all be improved.

[0050] It can be understood that although grayscale images lose color information, they can still retain the key features of the image, such as edges, textures, and shapes, etc. While filtering denoising processing removes noise, it can maximize the retention of these key information, facilitating subsequent image processing.

[0051] Step S14: Perform image contrast enhancement processing on the denoised image to obtain the first tobacco thin sheet image.

[0052] Exemplarily, for image contrast enhancement processing, one of global histogram equalization and local histogram equalization can be selected.

[0053] It can be understood that global histogram equalization adjusts the grayscale distribution of the denoised image, making the brightness of the denoised image better distributed on the histogram, thereby enhancing the global contrast of the denoised image, which is particularly effective for images with a relatively uniform overall brightness distribution.

[0054] While local histogram equalization can adjust the local area of the denoised image, thereby better enhancing the local contrast of the denoised image, which is particularly effective for areas with large brightness differences in the denoised image.

[0055] As Figure 5 shown, enhancing the denoised image can highlight the information in the specific required image while weakening some unnecessary information, and can make the subsequent segmentation processing results more accurate.

[0056] After enhancing the denoised image, the first tobacco thin sheet image is obtained, and the first tobacco thin sheet image is subjected to image binarization processing to obtain the second tobacco thin sheet image as Figure 6 shown.

[0057] Exemplarily, image binarization processing can be one of global threshold binarization and adaptive threshold binarization.

[0058] It can be understood that global threshold binarization is applicable to images with uniform illumination and high contrast. By setting a fixed threshold, the pixel values of the image are divided into two categories: 0 and 255. The processing process is simple and fast, and can achieve a good segmentation effect, clearly separating the target area and the background area.

[0059] Adaptive threshold binarization can be adjusted according to different requirements. It automatically adjusts the threshold based on the local features of the image, enabling better adaptation to local changes in the image. This also makes it perform excellently when processing images with different lighting conditions, contrasts, and textures.

[0060] The steps for calculating the pixel point ratio information include:

[0061] Step A21: Analyze the second tobacco sheet image to obtain the color difference image of the tobacco sheet to be detected. The color difference image is characterized by the white pixel part in the second tobacco sheet image.

[0062] Step A22: Calculate the proportion of white pixels in the second tobacco sheet image to obtain pixel proportion information.

[0063] Please refer to Figure 7 , the binarized white part in the second tobacco sheet image is the color difference part, and the binarized black part in the second tobacco sheet image is the uniform part.

[0064] By calculating the proportion of the binarized white part in the second tobacco sheet image in the entire image, the pixel proportion information is determined. The pixel proportion information includes the proportion information of white pixels and the proportion information of black pixels.

[0065] It can be understood that the pixel proportion information can only partially quantify the color appearance quality of the tobacco sheet. It is also necessary to cooperate with the relative standard deviation information to better achieve the quantification of the color appearance uniformity of the tobacco sheet to be detected.

[0066] Specifically, please refer to Figure 7 and Figure 8 , Figure 7 and Figure 8 The proportion of the white part in both is 3.125%, but Figure 7 compared with Figure 8 there are obvious non-uniform phenomena. That is to say, if only looking at the pixel proportion information, it cannot fully explain the color appearance quality of the tobacco sheet.

[0067] The steps for calculating the relative standard deviation information include:

[0068] Step B21: Perform image equal division processing on the second tobacco sheet image to obtain an image slice group corresponding to the second tobacco sheet image. The image slice group includes multiple slice images.

[0069] Step B22: Statistically analyze the pixel information of each slice image in the image slice group.

[0070] Step B23: Determine the relative standard deviation information based on the pixel information.

[0071] Specifically, in the first embodiment provided by the present application, the second tobacco sheet image can be equally divided according to a preset number of portions. Exemplarily, according to the preset number of equal divisions in rows and columns, for example, 4 rows and 5 columns, the second tobacco sheet image can be equally divided into an image slice group, and the image slice group contains 4 * 5 = 20 equal division slice images.

[0072] Then, according to the number of equal divisions, calculate the size of each slice image. After that, traverse each slice image, count the number of pixel points corresponding to the color difference part (i.e., the white pixel points in the slice image) in each slice image. After the traversal is completed, 20 data corresponding to the number of equal divisions are obtained, and calculate the relative standard deviation of these 20 data.

[0073] In the second embodiment provided by the present application, the second tobacco sheet image can be equally divided according to a preset equal division size. Exemplarily, preset an equal division size, for example, 15 * 15. Then, determine whether the size of the second tobacco sheet image is an integer multiple of the equal division size:

[0074] If it is, divide the second tobacco sheet image according to the specified equal division size. After that, traverse each slice image, count the number of pixel points corresponding to the color difference part (i.e., the white pixel points in the slice image) in each slice image. For example, if the size of the second tobacco sheet image is 150 * 300 and it is equally divided into 200 slice images according to 15 * 15, 200 data corresponding to the number of slice images are counted, and calculate the relative standard deviation of these data;

[0075] If not, first equally divide the second tobacco sheet image into the maximum number of portions that do not exceed the boundary of the second tobacco sheet image according to the specified slice size, and take the remaining part that is not enough for the specified size as one slice image. After that, traverse each slice image after equal division, count the number of pixel points corresponding to the color difference part (i.e., the white pixel points) it contains, and calculate the relative standard deviation of these data.

[0076] It can be understood that the formula for the relative standard deviation is: RSD = σ / μ × 100%, where σ is the standard deviation and μ is the average value.

[0077] The formula for calculating the average value is: μ = (Σxi) / N,

[0078] The formula for calculating the standard deviation is: σ = √[(Σ(xi - μ)2) / (N - 1)].

[0079] Among them, xi in the average value calculation formula and the standard deviation calculation formula is the number of white pixel points in the i-th slice image, and N is the total number of slice images.

[0080] According to the pixel ratio information and relative standard deviation information, the color appearance uniformity of the tobacco sheet to be detected can be quantified, and the Figure 6 index parameters for quantifying the color appearance uniformity of the tobacco sheet to be detected in Figure 6 are respectively: the proportion of color difference area: 41% (usually ≤10% is good, 10% - 30% is medium, >35% is poor), and the relative standard deviation (RSD): 0.29 (usually ≤0.1 is good, 0.1 - 0.2 is medium, >0.2 is poor).

[0081] Based on the same inventive concept, the present application also provides a device for quantifying the color appearance uniformity of a tobacco sheet. The quantification device includes an acquisition module, a processing module, and a quantification module.

[0082] Among them, the acquisition module is used to acquire a first tobacco sheet image of the tobacco sheet to be detected, and perform image binarization processing on the first tobacco sheet image to obtain a second tobacco sheet image.

[0083] The processing module is used to perform ratio calculation processing and relative standard deviation calculation processing on the pixel points respectively based on the second tobacco sheet image to obtain pixel ratio information and relative standard deviation information.

[0084] The quantification module is used to quantify the color appearance uniformity of the tobacco sheet to be detected according to the pixel ratio information and relative standard deviation information.

[0085] Specifically, the acquisition module is further used to acquire the original image of the tobacco sheet to be detected; perform image graying processing on the original image to obtain the gray image of the tobacco sheet to be detected; perform image noise reduction processing on the gray image to obtain the noise-reduced image of the tobacco sheet to be detected; perform image contrast enhancement processing on the noise-reduced image to obtain the first tobacco sheet image.

[0086] The processing module is further used to analyze the second tobacco sheet image to obtain the color difference image of the tobacco sheet to be detected, and the color difference image is characterized as the white pixel part in the second tobacco sheet image; calculate the proportion of white pixels in the second tobacco sheet image to obtain the pixel ratio information.

[0087] The processing module is further used to perform image equal division processing on the second tobacco sheet image to obtain an image slice group corresponding to the second tobacco sheet image, and the image slice group includes a plurality of slice images; count the pixel point information of each slice image in the image slice group; determine the relative standard deviation information according to the pixel point information.

[0088] It can be understood that the device for quantifying the color appearance uniformity of the tobacco sheet provided in the present application corresponds to the method for quantifying the color appearance uniformity of the tobacco sheet provided in the present application. To make the description in the specification concise, the same or similar parts can refer to the content of the part about the method for quantifying the color appearance uniformity of the tobacco sheet, and will not be repeated here.

[0089] Each module in the above-mentioned quantification device for the color appearance uniformity of the tobacco sheet can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor in the server in hardware form or be independent of the processor, or can be stored in the memory in the server in software form, so that the processor can call and execute the operations corresponding to each of the above modules. The processor can be a central processing unit (CPU), a microprocessor, a single-chip microcomputer, etc.

[0090] The above-mentioned quantification method for the color appearance uniformity of the tobacco sheet and / or the quantification device for the color appearance uniformity of the tobacco sheet can be implemented in the form of a computer-readable instruction, and the computer-readable instruction can run on a computer system.

[0091] An embodiment of the present application also provides a computer system, which includes a memory, a processor, and computer-readable instructions stored on the memory and executable on the processor. When the processor executes the program, it implements the above-mentioned quantification method for the color appearance uniformity of the tobacco sheet.

[0092] The computer system includes a processor, a non-volatile storage medium, an internal memory, an input device, a display screen, and a network interface connected through a system bus. Among them, the non-volatile storage medium of the computer system can store an operating system and computer-readable instructions. When the computer-readable instructions are executed, the processor can execute a quantification method for the color appearance uniformity of the tobacco sheet in each embodiment of the present application. The specific implementation process of this method can refer to Figure 1 the specific content and will not be elaborated here.

[0093] The processor of the computer system is used to provide computing and control capabilities to support the operation of the entire computer system. The internal memory can store computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor can execute a quantification method for the color appearance uniformity of the tobacco sheet. The input device of the computer system is used for inputting various parameters, the display screen of the computer system is used for display, and the network interface of the computer system is used for network communication.

[0094] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium, on which computer-readable instructions are stored. When the program is executed by the processor, it implements the steps in the above-mentioned quantification method for the color appearance uniformity of the tobacco sheet.

[0095] The memory in the embodiments of the present invention may be a volatile memory, a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchlink dynamic random access memory (SLDRAM), and direct rambus random access memory (DR RAM).

[0096] The above embodiments may be implemented in whole or in part by software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wired (such as infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that the computer can access or a data storage device such as a server or data center that contains one or more collections of available media. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state drive.

[0097] It should be understood that the term "and / or" in this text is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, in this text, the character " / " generally indicates an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship. Specifically, it can be understood by referring to the context before and after.

[0098] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c can be single or multiple.

[0099] It should be understood that in various embodiments of the present invention, the magnitude of the sequence numbers of the above processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0100] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this text can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0101] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0102] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be in an electrical, mechanical, or other form.

[0103] The unit described as a separate component may or may not be physically separated, and the component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional unit in various embodiments of the present invention may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0105] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0106] The terms and expressions used here are only for description, and this application should not be limited to these terms and expressions. Using these terms and expressions does not mean excluding any equivalent features of the illustration and description (or parts thereof). It should be recognized that various modifications that may exist should also be included within the scope of the claims. Other modifications, variations, and substitutions may also exist. Correspondingly, the claims should be regarded as covering all such equivalents.

[0107] Similarly, it should be noted that although this application has been described with reference to current specific embodiments, those of ordinary skill in the art in this technical field should recognize that the above embodiments are only used to illustrate this application, and various equivalent changes or substitutions can be made without departing from the spirit of the present invention. Therefore, as long as the changes and modifications to the above embodiments are within the scope of the spirit of this application, they will fall within the scope of the claims of this application.

Claims

1. A method for quantifying the color appearance uniformity of tobacco sheet, characterized in that, The quantization method includes: Step S1: Obtain a first tobacco sheet image of the tobacco sheet to be detected, and perform image binarization processing on the first tobacco sheet image to obtain a second tobacco sheet image; Step S2: Based on the second tobacco sheet image, perform proportion calculation processing and relative standard deviation calculation processing on pixel points respectively to obtain pixel point proportion information and relative standard deviation information; Step S3: Quantify the color appearance uniformity of the tobacco sheet to be detected according to the pixel point proportion information and the relative standard deviation information.

2. The quantization method according to claim 1, wherein The step S1 includes: Step S11: Obtain the original image of the tobacco sheet to be detected; Step S12: Perform image grayscale processing on the original image to obtain the grayscale image of the tobacco sheet to be detected; Step S13: Perform image noise reduction processing on the grayscale image to obtain the noise-reduced image of the tobacco sheet to be detected; Step S14: Perform image contrast enhancement processing on the noise-reduced image to obtain the first tobacco sheet image.

3. The quantization method according to claim 2, wherein The image grayscale processing is one of the maximum value method, the average value method or the weighted average method; The image noise reduction processing is one of mean filtering, median filtering or Gaussian filtering; The image contrast enhancement processing is global histogram equalization or local histogram equalization.

4. The quantization method according to claim 1, characterized in that, The image binarization processing is global threshold binarization or adaptive threshold binarization.

5. The quantization method according to claim 1, characterized in that, The step S2 includes: Step A21: Analyze the second tobacco sheet image to obtain the color difference image of the tobacco sheet to be detected, and the color difference image is characterized by the white pixel point part in the second tobacco sheet image; Step A22: Calculate the proportion of the white pixel points in the second tobacco sheet image to obtain the pixel point proportion information.

6. The quantization method according to claim 1, wherein The step S2 includes: Step B21: Perform image equal division processing on the second tobacco sheet image to obtain an image slice group corresponding to the second tobacco sheet image, and the image slice group includes a plurality of slice images; Step B22: Statistically analyze the pixel point information of each slice image in the image slice group; Step B23: Determine the relative standard deviation information according to the pixel point information.

7. A quantification device for the color appearance uniformity of a tobacco sheet, characterized in that, The quantization device includes: an acquisition module, a processing module and a quantization module, The acquisition module is used to obtain a first tobacco sheet image of the tobacco sheet to be detected, and perform image binarization processing on the first tobacco sheet image to obtain a second tobacco sheet image; The processing module is used to perform pixel point proportion calculation processing and relative standard deviation calculation processing respectively based on the second tobacco sheet image to obtain pixel point proportion information and relative standard deviation information; The quantization module is used to quantify the color appearance uniformity of the tobacco sheet to be detected according to the pixel point proportion information and the relative standard deviation information.

8. A computer system, comprising a memory, a processor, and a computer program stored on the memory, characterized in that, The processor executes the computer program to implement the quantization method for the color appearance uniformity of the tobacco sheet as claimed in claim 1.

9. A computer-readable storage medium having computer programs / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, the quantization method for the color appearance uniformity of the tobacco sheet as claimed in claim 1 is implemented.

10. A computer program product, the computer program product comprising computer programs / instructions, characterized in that, When the computer program / instructions are executed by a processor, the method for quantifying the color appearance uniformity of the tobacco sheet described in claim 1 is implemented.

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