Manufacturing method of fuzzy bar code

Abstract

The invention discloses a method for manufacturing a fuzzy bar code. The method at least comprises the following steps: acquiring a standard bar code picture; pre-processing and positioning the bar code picture; inputting parameters of the fuzzy bar code; carrying out fuzzy processing on the bar code picture; and storing and outputting the blurred bar code. According to the method disclosed by the invention, the bar codes with different fuzzy degrees can be quantitatively manufactured by setting different fuzzy parameters. The problem of randomness of fuzzy bar code parameters in the prior art is solved, and controllable ambiguity manufacturing is realized.

Description

Technical Field

[0001] The invention is applied to the field of barcode reading, and specifically is a method for making a fuzzy barcode. Background Art

[0002] With the advancement of barcode reading technology, the market has stricter testing requirements for barcode reading equipment. Especially in the industrial field or extreme environments, barcodes may become blurred due to wear, stains, low contrast, etc. Therefore, researching and developing a barcode production method that can simulate different degrees of blur will not only help improve the error correction ability of the equipment, but also provide standardized testing tools for barcode reading equipment manufacturers, thereby improving the market competitiveness of products.

[0003] The existing methods have the following major drawbacks:

[0004] (1) It is impossible to quantify. The fuzzy barcode parameters obtained by the existing method are random, and it is difficult to obtain fuzzy barcodes of different degrees. However, the fuzzy barcode production method can accurately produce fuzzy barcodes according to the set parameter values.

[0005] (2) Poor repeatability. It is difficult to repeat the fuzzy barcodes produced by the existing methods. However, the fuzzy barcode production method does not have this limitation and can produce an unlimited number of fuzzy barcodes of the same specifications.

[0006] (3) It is impossible to create a screen barcode with a blurred barcode. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a method for making a fuzzy barcode in view of the deficiencies of the prior art.

[0008] In order to solve the above technical problems, a method for making a fuzzy barcode of the present invention at least comprises the following steps:

[0009] Get the standard barcode image;

[0010] Preprocess and locate the barcode image;

[0011] Enter the parameters of the blurred barcode;

[0012] Blurring the barcode image;

[0013] Save and output the blurred barcode.

[0014] As a possible implementation manner, further, the barcode image includes a one-dimensional barcode image and a two-dimensional barcode image;

[0015] The one-dimensional barcode code system includes at least: Code39, Code93, Code128, Codabar, EAN-13, EAN-8, UPC-A, ISBN-13, GS1-128, ITF-14; the two-dimensional barcode code system includes at least: PDF417, QR, Data Matrix, Grid Matrix, and Hanxin Code.

[0016] As a possible implementation, further, the preprocessing of the barcode image specifically includes:

[0017] Binarize the barcode image, scan the entire image, and use the grayscale formula: gray = 0.2126*r + 0.7152*g + 0.0722*b to calculate the grayscale value of each pixel, compare it with the threshold 128, convert the pixels with gray>128 to white and assign a value of 255, and convert the pixels with gray<=128 to black and assign a value of 0.

[0018] As a possible implementation mode, further, the positioning of the barcode image specifically includes:

[0019] Locate the barcode position in the image, scan the entire image, and determine each black pixel that appears. When the vertical coordinate is the smallest, it is the lower boundary, and when the horizontal coordinate is the largest, it is the upper boundary. When the horizontal coordinate is the smallest, it is the left boundary, and when the horizontal coordinate is the largest, it is the right boundary.

[0020] As a possible implementation manner, further, the fuzzy parameters in the step of inputting the parameters of the fuzzy barcode include at least a fuzzy radius r.

[0021] As a possible implementation, further, the step of blurring the barcode image specifically includes:

[0022] Select a square area with a radius of r and a pixel to be processed as the center point: Assume that the blur radius r is 2 and the pixel to be processed is the center position;

[0023] Find the sum and average of all pixel values ​​in the area:

[0024] After rounding off Average, determine the new value;

[0025] Traverse each pixel of the image and perform the above steps;

[0026] If the pixel to be processed is at the boundary of the image and there are not enough pixels around it, the existing pixels are copied to the corresponding positions on the other side to simulate a complete matrix.

[0027] A fuzzy barcode production system, comprising:

[0028] The barcode image acquisition module generates standard barcode images by scanning, taking photos, or using online open source barcode generation software;

[0029] The barcode image preprocessing module binarizes the barcode image and uses the grayscale formula: gray = 0.2126*r + 0.7152*g + 0.0722*b to calculate the grayscale value of each pixel, compares it with the threshold 128, converts the pixel points with gray>128 to white and assigns a value of 255, and converts the pixel points with gray<=128 to black and assigns a value of 0 to perform image preprocessing and positioning operations;

[0030] Fuzzy parameter input module;

[0031] Fuzzy processing module, used to perform fuzzy processing on barcode images;

[0032] The barcode output module saves and outputs the blurred barcode, including paper output and electronic display.

[0033] The present invention adopts the above technical solution and has the following beneficial effects:

[0034] The method of the present invention can quantitatively produce barcodes with different blurring degrees by setting different blurring parameters, thus overcoming the problem of randomness of blurring barcode parameters in the prior art and achieving controllable blurring degree production.

[0035] Different from the existing technology, the fuzzy barcodes of the same specifications can be repeatedly produced, which solves the problem of difficult reproducibility in traditional methods. This is conducive to maintaining consistency in different tests and ensuring the comparability of test results.

[0036] Barcodes with different resolutions and blur levels can be produced to meet the needs of different test scenarios, increasing the diversity of test barcodes. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0038] Figure 1 The figure is a schematic diagram of barcode fuzzy transformation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0040] Example 1

[0041] The present invention provides a method for producing a fuzzy barcode, which at least comprises the following steps:

[0042] Get the standard barcode image;

[0043] Preprocess and locate the barcode image;

[0044] Enter the parameters of the blurred barcode;

[0045] Blurring the barcode image;

[0046] Save and output the blurred barcode.

[0047] Among them, the barcode image includes a one-dimensional barcode image and a two-dimensional barcode image;

[0048] The one-dimensional barcode code system includes at least: Code39, Code93, Code128, Codabar, EAN-13, EAN-8, UPC-A, ISBN-13, GS1-128, ITF-14; the two-dimensional barcode code system includes at least: PDF417, QR, Data Matrix, Grid Matrix, and Hanxin Code.

[0049] Preprocessing of barcode images specifically includes:

[0050] Binarize the barcode image, scan the entire image, and use the grayscale formula: gray = 0.2126*r + 0.7152*g + 0.0722*b to calculate the grayscale value of each pixel, compare it with the threshold 128, convert the pixels with gray>128 to white and assign a value of 255, and convert the pixels with gray<=128 to black and assign a value of 0.

[0051] Positioning the barcode image specifically includes:

[0052] Locate the barcode position in the image, scan the entire image, and determine each black pixel that appears. When the vertical coordinate is the smallest, it is the lower boundary, and when the horizontal coordinate is the largest, it is the upper boundary. When the horizontal coordinate is the smallest, it is the left boundary, and when the horizontal coordinate is the largest, it is the right boundary.

[0053] The blur parameters in the step of inputting blur barcode parameters at least include blur radius r. The step of blurring the barcode image specifically includes:

[0054] Select a square area with a radius of r and a pixel to be processed as the center point: Assume that the blur radius r is 2 and the pixel to be processed is the center position;

[0055] Find the sum and average of all pixel values ​​in the area:

[0056] After rounding off Average, determine the new value;

[0057] Traverse each pixel of the image and perform the above steps;

[0058] If the pixel to be processed is at the boundary of the image and there are not enough pixels around it, the existing pixels are copied to the corresponding positions on the other side to simulate a complete matrix.

[0059] A fuzzy barcode production system, comprising:

[0060] The barcode image acquisition module generates standard barcode images by scanning, taking photos, or using online open source barcode generation software;

[0061] The barcode image preprocessing module binarizes the barcode image and uses the grayscale formula: gray = 0.2126*r + 0.7152*g + 0.0722*b to calculate the grayscale value of each pixel, compares it with the threshold 128, converts the pixel points with gray>128 to white and assigns a value of 255, and converts the pixel points with gray<=128 to black and assigns a value of 0 to perform image preprocessing and positioning operations;

[0062] Fuzzy parameter input module;

[0063] Fuzzy processing module, used to perform fuzzy processing on barcode images;

[0064] The barcode output module saves and outputs the blurred barcode, including paper output and electronic display.

[0065] Example 2

[0066] A method for generating a fuzzy barcode comprises the following steps:

[0067] Get the original image of the one-dimensional barcode or two-dimensional barcode to be processed, where the one-dimensional barcode code system may include: Code39, Code93, Code128, Codabar, EAN-13, EAN-8, UPC-A, ISBN-13, GS1-128, ITF-14; the two-dimensional barcode code system includes at least: PDF417, QR, Data Matrix, Grid Matrix, Han Xin Code, etc.

[0068] There are three main ways to obtain standard barcode images:

[0069] (a) Obtain a standard barcode image by scanning;

[0070] (b) Obtaining a standard barcode image by taking a photo;

[0071] (c) Generate standard barcode images through online open source barcode generation software;

[0072] The above pictures are preprocessed as follows:

[0073] Binarize the barcode image. Scan the entire image, use the grayscale formula: gray = 0.2126*r + 0.7152*g + 0.0722*b to calculate the grayscale value of each pixel, compare it with the threshold 128, and turn the pixels with gray>128 to white (assigned to 255), and turn the pixels with gray<=128 to black (assigned to 0).

[0074] Locate the barcode in the image. Since the barcode images in the database are relatively regular and irrelevant information has been removed, a simple method can be used for positioning: scan the entire image and determine each black pixel that appears. When the vertical coordinate is the smallest, it is the lower boundary, and when the horizontal coordinate is the largest, it is the upper boundary. When the horizontal coordinate is the smallest, it is the left boundary, and when the horizontal coordinate is the largest, it is the right boundary. In this way, the positions of the lower left corner (bcleft, bcbottom), lower right corner (bcright, bcbottom), upper left corner (bcleft, bctop), and upper right corner (bcright, bctop) of the barcode are marked.

[0075] Input the parameters of blur barcode, blur parameters include blur radius r.

[0076] Barcode fuzzy transformation steps:

[0077] Select a square area with a radius of r and a pixel to be processed as the center:

[0078] Assume that the blur radius r is 2, and the pixel to be processed is the position of the red frame. Figure 1 As shown;

[0079] Find the sum of all pixel values ​​in the area:

[0080] Sum=(0+1+1+0+0)+(0+0+1+1+1)+(0+1+1+0+0)+(0+1+0+1+1)+(1+1+1+0+0)=13

[0081] Find the average valueAverage=13 / 25=0.52

[0082] After rounding the Average, the new value is 1:

[0083] Traverse each pixel of the image and perform steps (1) to (4):

[0084] If the pixel to be processed is at the boundary of the image and there are not enough pixels around it, the existing pixels can be copied to the corresponding positions on the other side to simulate a complete matrix.

[0085] Different fuzzy radii r and different fuzzy rules can be set as needed, and fuzzy barcodes of different degrees can be quantitatively output to verify the error correction capability of the barcode reading equipment.

[0086] Save the blurred image.

[0087] Example 3

[0088] A method for producing fuzzy barcodes with different blur levels is provided. In order to evaluate the error correction capability of a barcode reading device under different blur levels, it is necessary to produce barcodes with different blur levels. This embodiment introduces in detail how to produce barcodes with blur levels of 1 to 10 by setting different blur parameters, and provides scientific and standardized test materials to evaluate the performance of barcode reading devices.

[0089] Step 1: Get the original barcode image

[0090] Method 1: Use a professional barcode scanning device to obtain the original image of the target barcode. Select different types of barcodes (such as one-dimensional barcode Code128 or two-dimensional barcode QR Code) as test samples.

[0091] Method 2: Use open source barcode generation software to generate original images of various standard barcodes to ensure that the original barcode images are of high quality and have complete information, which is convenient for subsequent blurring processing.

[0092] You need to ensure that the original barcode image format is a common high-resolution image format (such as PNG or TIFF) to maintain the clarity of the barcode.

[0093] Step 2: Preprocess the barcode image

[0094] Binarization processing: Convert the original barcode image into a grayscale image, and use the grayscale formula: gray = 0.2126×r + 0.7152×g + 0.0722×bgray = 0.2126×r + 0.7152×g + 0.0722×b to calculate the grayscale value of each pixel. The threshold is set to 128, and the pixels with grayscale values ​​greater than 128 are set to white (255), and the pixels with grayscale values ​​less than or equal to 128 are set to black (0), completing the binarization processing.

[0095] Locate the barcode area: Scan the binary barcode image to determine the boundary coordinates (left, right, top, bottom) of the barcode so that the blurred area can be accurately located later. The barcode position is determined by recording the horizontal and vertical coordinates of the minimum and maximum black pixel points.

[0096] Step 3: Set the blur parameters

[0097] Determine blur radius rr: Set the blur radius rr in the range from 1 to 10, representing the blur level 1 to 10 respectively. The larger the blur radius value, the higher the blur level.

[0098] Input fuzzy parameters: Input the preset fuzzy parameters into the fuzzy processing algorithm and set the corresponding fuzzy level for each barcode.

[0099] Step 4: Blur the barcode image

[0100] Traverse each pixel:

[0101] Each pixel to be processed is selected as the center and the blur radius rr is used as the side length to form a square area of ​​(2r+1)×(2r+1)(2r+1)×(2r+1).

[0102] Calculate the sum of the grayscale values ​​of all pixels in the area SumSum and find the average value AverageAverage: Average=Sum(2r+1)2Average=(2r+1)2Sum

[0103] The average value is rounded off and replaced with the gray value of the center pixel.

[0104] Edge processing: For pixels at the edge of the image, if there are not enough adjacent pixels, the mirror extension method is used to fill the missing pixels with the adjacent pixel values ​​to ensure that each pixel can be correctly blurred.

[0105] Step 5: Generate and save the blurred barcode

[0106] Generate fuzzy barcodes step by step: adjust the fuzzy radius rr from 1 to 10 in sequence to generate fuzzy barcodes with corresponding fuzziness levels 1 to 10.

[0107] Save barcode image: Save each level of blurred barcode image as a high-resolution PNG file with the file name format of "blurred barcode_level X.png", where X represents the blur level (for example, "blurred barcode_level 3.png").

[0108] Generate record table: Create a record table of fuzzy barcode levels to record the parameter information corresponding to each level of fuzzy barcode, including fuzzy radius rr, barcode type, production time and save path, etc., for future search and testing.

[0109] Also includes: Quality Check and Validation

[0110] Use professional barcode reading equipment to test the reading of fuzzy barcodes of various levels, record the barcode reading success rate of each level, and ensure that the fuzzy barcodes meet the expected blurriness and test standards.

[0111] Check the repeatability of blurred barcodes: Use the same blur parameters to make the same level of blurred barcodes multiple times and compare the results to ensure that the blurred barcode images produced each time are consistent.

[0112] This implementation scheme shows in detail how to produce barcodes with different blur levels in a quantitative manner to meet the testing requirements of various barcode reading devices.

[0113] It provides a repeatable and controllable fuzzy barcode production process to ensure the accuracy and reliability of test results, and helps equipment manufacturers and research institutions to scientifically evaluate and optimize reading equipment.

[0114] This detailed embodiment ensures that the produced fuzzy barcode is standardized and controllable, and can greatly improve the accuracy and efficiency of the barcode reading equipment test.

[0115] Example 4

[0116] A dual-mode fuzzy barcode test method for screen barcodes and paper barcodes; In actual application scenarios, barcode reading equipment needs to identify barcodes on various media, including paper and electronic screen barcodes. In order to comprehensively evaluate the error correction capability of the reading equipment under different media, it is necessary to produce fuzzy barcodes that can be displayed on the screen and paper for testing.

[0117] Detailed implementation steps:

[0118] Step 1: Get the original barcode image

[0119] Method 1: Use professional barcode scanning equipment to obtain high-resolution original barcode images. Ensure that the images include one-dimensional barcodes (such as Code128) and two-dimensional barcodes (such as QR Code) to provide the ability to test multiple barcode types.

[0120] Method 2: Use open source barcode generation software (such as Zxing, Online Barcode Generator, etc.) to generate original barcode images in different formats to ensure high image clarity and complete information for subsequent processing.

[0121] It is necessary to ensure that the original barcode image obtained has a resolution higher than 300 DPI and is saved in a lossless format (such as PNG or TIFF) to ensure that the details of the barcode are not lost during the blurring process.

[0122] Step 2: Preprocess the barcode image

[0123] Binarization processing: Convert the original barcode image into a grayscale image, use the grayscale formula: gray = 0.2126 × r + 0.7152 × g + 0.0722 × b gray = 0.2126 × r + 0.7152 × g + 0.0722 × b to calculate the grayscale value of each pixel, set the threshold to 128, set the pixel points with grayscale values ​​greater than 128 to white (255), and set the pixel points with grayscale values ​​less than or equal to 128 to black (0), and complete the binarization processing.

[0124] Locate the barcode area: Scan the binary barcode image to determine the four boundary coordinates (left, right, top, and bottom) of the barcode, so as to facilitate the subsequent fuzzy processing to accurately locate the barcode area.

[0125] Step 3: Set blur parameters and create blur barcode

[0126] Set blur parameters: According to the test requirements, set different blur levels. The blur radius rr ranges from 1 to 10, representing the blur level from mild to severe. Ensure that barcodes with different blur levels can be used to test the device's ability to read each blur level.

[0127] Perform blur processing: Apply the set blur parameters to the barcode image. Use the following steps:

[0128] For each pixel, a square area of ​​(2r+1)×(2r+1)(2r+1)×(2r+1) is constructed with rr as the radius, the grayscale average of all pixels in the area is calculated, and the grayscale value of the central pixel is replaced.

[0129] For pixels at the edge of the image, mirroring is used to expand and fill the missing area to ensure that each pixel can be processed correctly.

[0130] Step 4: Create a paper fuzzy barcode

[0131] Print settings: Import the barcode images with different blur levels into a high-quality printing device. Set the print resolution to 300 DPI or higher to ensure that the printed barcode is clear enough for testing.

[0132] Paper selection: Choose standard A4 paper or other specifications, and use high-contrast ink to ensure that the printed barcode is still clearly visible in low or high light environments.

[0133] Printout: Print different levels of blur barcodes (1 to 10) and mark the blur level of each barcode for easy identification in subsequent tests.

[0134] Step 5: Create a screen blur barcode

[0135] Screen display device selection: Select various types of screen devices, such as smartphones, tablets, LCD monitors, LED displays, etc., to ensure coverage of different resolutions, sizes and display technologies, and simulate a variety of actual application scenarios.

[0136] Screen Display Settings: Adjust the size of the barcode image to adapt it to different screen sizes and resolutions. Ensure that the original resolution of the image remains unchanged to avoid image distortion caused by scaling.

[0137] Display method: Display the blurred barcode image on different devices through screen display software (such as PPT, PDF Viewer, image browser, etc.), maintain the clarity of the barcode on the screen, and ensure that the real environment can be restored during testing.

[0138] Step 6: Testing and Evaluation

[0139] Test preparation: Place the paper barcode and screen barcode in front of the reading device respectively, ensure that the ambient light is stable, and simulate the actual application scenario.

[0140] Equipment reading test: Use different types of barcode reading devices (such as one-dimensional handheld scanners, two-dimensional scanners, fixed scanners, etc.) to test the reading of fuzzy barcodes, and record the reading success rate and error correction ability of each device under different blur levels and media.

[0141] Data recording and analysis: Conduct multiple tests on barcodes of each blur level, record data such as reading time, success rate, device error rate, and analyze the adaptability of different devices to blur levels and media.

[0142] Step 7: Record and report results

[0143] Test result record: Create a test result table to record the reading success rate, error correction capability and other indicators of each reading device for each blur level and medium (paper and screen).

[0144] Analysis and Reporting: Conduct statistical analysis on the test results, draw a graph showing the relationship between the reading success rate and the degree of fuzziness, and generate a test report to provide data support for performance optimization of the barcode reading equipment.

[0145] Provides fuzzy barcode testing methods for both paper and screen media, and comprehensively evaluates the barcode reading capabilities of barcode reading equipment under different blur levels and media.

[0146] Through standardized fuzzy barcode production and testing processes, the repeatability and reliability of test results are ensured, providing a reference for the equipment's error correction capabilities and performance optimization.

[0147] This detailed implementation step ensures that the process of making and testing dual-media fuzzy barcodes is complete and standardized, and can truly simulate the reading performance of the equipment in actual application scenarios, which helps reading equipment manufacturers improve product quality and reliability.

[0148] The above are embodiments of the present invention. For ordinary technicians in this field, according to the teachings of the present invention, all equivalent changes, modifications, substitutions and variations made within the scope of the patent application of the present invention without departing from the principles and spirit of the present invention should fall within the scope of the present invention.

Claims

1. A method for making a fuzzy barcode, characterized in that: At least the following steps are included: Get the standard barcode image; Preprocess and locate the barcode image; Enter the parameters of the blurred barcode; Blurring the barcode image; Save and output the blurred barcode.

2. The method for making a fuzzy barcode according to claim 1, characterized in that: The barcode image includes a one-dimensional barcode image and a two-dimensional barcode image; The one-dimensional barcode code system includes at least: Code39, Code93, Code128, Codabar, EAN-13, EAN-8, UPC-A, ISBN-13, GS1-128, ITF-14; the two-dimensional barcode code system includes at least: PDF417, QR, Data Matrix, Grid Matrix, and Hanxin Code.

3. The method for making a fuzzy barcode according to claim 1, characterized in that: The preprocessing of the barcode image specifically includes: Binarize the barcode image, scan the entire image, and use the grayscale formula: gray = 0.2126*r + 0.7152*g + 0.0722*b to calculate the grayscale value of each pixel, compare it with the threshold 128, convert the pixels with gray>128 to white and assign a value of 255, and convert the pixels with gray<=128 to black and assign a value of 0.

4. The method for making a fuzzy barcode according to claim 1, characterized in that: The positioning of the barcode image specifically includes: Locate the barcode position in the image, scan the entire image, and determine each black pixel that appears. When the vertical coordinate is the smallest, it is the lower boundary, and when the horizontal coordinate is the largest, it is the upper boundary. When the horizontal coordinate is the smallest, it is the left boundary, and when the horizontal coordinate is the largest, it is the right boundary.

5. The method for making a fuzzy barcode according to claim 1, characterized in that: The fuzzy parameters in the step of inputting the parameters of the fuzzy barcode at least include a fuzzy radius r.

6. The method for making a fuzzy barcode according to claim 1, characterized in that: The step of blurring the barcode image specifically includes: Select a square area with a radius of r and a pixel to be processed as the center point: Assume that the blur radius r is 2 and the pixel to be processed is the center position; Find the sum and average of all pixel values ​​in the area: After rounding off Average, determine the new value; Traverse each pixel of the image and perform the above steps; If the pixel to be processed is at the boundary of the image and there are not enough pixels around it, the existing pixels are copied to the corresponding positions on the other side to simulate a complete matrix.

7. A system for making fuzzy barcodes, characterized in that: include: The barcode image acquisition module generates standard barcode images by scanning, taking photos, or using online open source barcode generation software; The barcode image preprocessing module binarizes the barcode image and uses the grayscale formula: gray = 0.2126*r + 0.7152*g + 0.0722*b to calculate the grayscale value of each pixel, compares it with the threshold 128, converts the pixel points with gray>128 to white and assigns a value of 255, and converts the pixel points with gray<=128 to black and assigns a value of 0 to perform image preprocessing and positioning operations; Fuzzy parameter input module; Fuzzy processing module, used to perform fuzzy processing on barcode images; The barcode output module saves and outputs the blurred barcode, including paper output and electronic display.

Images