CN112926351A - Method and device for identifying graphic code and code scanning equipment - Google Patents
Method and device for identifying graphic code and code scanning equipment Download PDFInfo
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Abstract
The invention relates to a graphic code identification method, which comprises the steps of processing a graphic code image to be identified to obtain a fuzzy area in the graphic code image to be identified when a code scanning gun scans the graphic code image to be identified for the first time to obtain character string information in the graphic code image to be identified; inputting the fuzzy area in the graphic code image to be identified into a preset image enhancement network to obtain an enhanced image; the preset image enhancement network is used for carrying out bilateral grid sampling processing on the blurred image to obtain the enhanced image; and obtaining character string information in the graphic code image to be recognized according to the enhanced image and the graphic code image to be recognized. When code scanning fails, the image code image can still be identified, and the working efficiency is improved. The invention also relates to a device for identifying the graphic code and code scanning equipment.
Description
Technical Field
The invention relates to the technical field of code scanning, in particular to a method and a device for identifying a graphic code and code scanning equipment.
Background
In the prior art, a code scanning gun is usually used for scanning an image and then sending the image to a background server for image recognition, but due to the fact that an article attached to a graphic code image is irregular in shape or the graphic code image is polluted and the like, code scanning failure is caused, and working efficiency is reduced.
Disclosure of Invention
The invention aims to solve the technical problem of the prior art and provides a method and a device for identifying a graphic code and code scanning equipment.
The technical scheme for solving the technical problems is as follows:
a method for identifying a graphic code, which is used for a code scanning device, and comprises the following steps:
when a code scanning gun scans a graphic code image to be recognized for the first time and fails to obtain character string information in the graphic code image to be recognized, processing the graphic code image to be recognized to obtain a fuzzy area in the graphic code image to be recognized;
inputting the fuzzy area in the graphic code image to be identified into a preset image enhancement network to obtain an enhanced image; the preset image enhancement network is used for carrying out bilateral grid sampling processing on the blurred image to obtain the enhanced image;
and obtaining character string information in the graphic code image to be recognized according to the enhanced image and the graphic code image to be recognized.
The method has the beneficial effects that: the method is used for a code scanning device, when a code scanning gun scans a to-be-identified graphic code image for the first time to obtain character string information in the to-be-identified graphic code image, the to-be-identified graphic code image is processed to obtain a fuzzy area in the to-be-identified graphic code image; inputting the fuzzy area in the graphic code image to be identified into a preset image enhancement network to obtain an enhanced image; the preset image enhancement network is used for carrying out bilateral grid sampling processing on the blurred image to obtain the enhanced image; and obtaining character string information in the graphic code image to be recognized according to the enhanced image and the graphic code image to be recognized. When code scanning fails, the image code image can still be identified, and the working efficiency is improved.
On the basis of the technical scheme, the invention can be further improved as follows.
Further, the processing the to-be-identified pattern code image to obtain a fuzzy area in the to-be-identified pattern code image specifically includes the following steps:
carrying out gray level processing on the graphic code image to be identified to obtain a gray level image of the graphic code image to be identified;
obtaining an effective area based on the gray level image, and obtaining a fuzzy coefficient of each pixel in the graphic code image to be identified according to the effective area and the gray level image;
extracting pixel points of which the fuzzy coefficients are larger than the preset fuzzy threshold;
and determining a fuzzy area in the graphic code image to be identified based on the extracted pixel points of which the fuzzy coefficients are greater than the preset fuzzy threshold.
Further, the performing gray processing on the to-be-identified pattern code image to obtain a gray image of the to-be-identified pattern code image specifically includes:
performing convolution operation on the graphic code image to be identified by adopting a preset Gaussian convolution kernel to obtain a Gaussian blur image, and calculating the gradient and the direction of the Gaussian blur image;
and processing the gradient and the direction of the Gaussian blur image according to a non-maximum value inhibition method and a double-threshold detection method to obtain the gray image.
Further, the obtaining an effective area based on the grayscale image, and obtaining a blur coefficient of each pixel in the graphic code image to be recognized according to the effective area and the grayscale image includes:
carrying out contour detection on the gray level image to obtain the size and the position of all closed areas in the gray level image;
extracting a closed region which is larger than a preset threshold value in the gray-scale image, and taking the closed region which is larger than the preset threshold value as an effective region;
calculating the average gray value of a preset neighborhood range of each pixel point in the effective region;
calculating the variance of each pixel point in a preset neighborhood range based on the average gray value of each pixel point in the effective region in the preset neighborhood range and the gray image;
and obtaining the fuzzy coefficient of each pixel point according to the average gray value in the preset neighborhood range of each pixel point and the average gray value in the preset neighborhood range of each pixel point.
Further, the inputting the fuzzy area in the graphic code image to be recognized into a preset image enhancement network to obtain an enhanced image includes:
the preset image enhancement network comprises a prediction processing network, a sampling network and an enhancement processing network, the fuzzy area in the graphic code image to be identified is input into the preset image enhancement network to obtain an enhanced image, and the method comprises the following steps:
inputting the fuzzy area in the graphic code image to be identified into the prediction processing network to obtain a low-resolution illumination predicted image;
inputting the fuzzy area and the low-resolution illumination predicted image into the grid sampling network for sampling fusion processing to obtain a full-resolution illumination image;
and inputting the fuzzy area and the full-resolution illumination image into the enhancement processing network for pixel enhancement processing to obtain the enhanced image.
Further, the inputting the fuzzy area in the to-be-identified pattern code image into a prediction processing network to obtain a low-resolution illumination predicted image includes:
inputting the fuzzy area into a feature extraction sub-network for feature extraction to obtain global features and local features of the fuzzy area;
and inputting the global features and the local features into the feature fusion sub-network for feature fusion to obtain the low-resolution illumination predicted image.
Another technical solution of the present invention for solving the above technical problems is as follows:
an apparatus for pattern code recognition, the apparatus comprising:
the acquisition module is used for processing the graphic code image to be identified to obtain a fuzzy area in the graphic code image to be identified when the code scanning gun scans the graphic code image to be identified for the first time to obtain character string information in the graphic code image to be identified;
the enhancement module is used for inputting the fuzzy area in the graphic code image to be identified into a preset image enhancement network to obtain an enhanced image; the preset image enhancement network is used for carrying out bilateral grid sampling processing on the blurred image to obtain the enhanced image;
and the fusion module is used for obtaining the character string information in the graphic code image to be identified according to the enhanced image and the graphic code image to be identified.
The device has the beneficial effects that: the device for recognizing the graphic code is used for a code scanning device, when a code scanning gun scans a graphic code image to be recognized for the first time to obtain character string information in the graphic code image to be recognized, the graphic code image to be recognized is processed to obtain a fuzzy area in the graphic code image to be recognized; inputting the fuzzy area in the graphic code image to be identified into a preset image enhancement network to obtain an enhanced image; the preset image enhancement network is used for carrying out bilateral grid sampling processing on the blurred image to obtain the enhanced image; and obtaining character string information in the graphic code image to be recognized according to the enhanced image and the graphic code image to be recognized. When code scanning fails, the image code image can still be identified, and the working efficiency is improved.
Further, the obtaining module is specifically configured to perform gray processing on the to-be-identified pattern code image to obtain a gray image of the to-be-identified pattern code image;
obtaining an effective area based on the gray level image, and obtaining a fuzzy coefficient of each pixel in the graphic code image to be identified according to the effective area and the gray level image;
extracting pixel points of which the fuzzy coefficients are larger than the preset fuzzy threshold;
and determining a fuzzy area in the graphic code image to be identified based on the extracted pixel points of which the fuzzy coefficients are greater than the preset fuzzy threshold.
Further, the acquiring module is specifically configured to perform convolution operation on the to-be-identified pattern code image by using a preset gaussian convolution kernel to obtain a gaussian blurred image, and calculate a gradient and a direction of the gaussian blurred image;
and processing the gradient and the direction of the Gaussian blur image according to a non-maximum value inhibition method and a double-threshold detection method to obtain the gray image.
The invention also relates to a code scanning device, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the computer program, the graphic code identification method in any one of the above technical schemes is realized.
Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments of the present invention or in the description of the prior art will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for identifying a graphic code according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a device for identifying a graphic code according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
As shown in fig. 1, which is a schematic flow chart of a method for identifying a graphic code according to an embodiment of the present invention, the method for identifying a graphic code includes:
110. when the code scanning gun scans the graphic code image to be recognized for the first time and fails to obtain the character string information in the graphic code image to be recognized, processing the graphic code image to be recognized to obtain a fuzzy area in the graphic code image to be recognized.
120. Inputting the fuzzy area in the graphic code image to be identified into a preset image enhancement network to obtain an enhanced image; the preset image enhancement network is used for carrying out bilateral grid sampling processing on the blurred image to obtain the enhanced image.
130. And obtaining character string information in the graphic code image to be recognized according to the enhanced image and the graphic code image to be recognized.
It should be understood that when the code scanning gun scans the to-be-identified graphic code image for the first time to obtain the character string information in the to-be-identified graphic code image, specifically, the reason for the code scanning failure is many, and may be caused by irregular shapes of articles to which the graphic code image is attached, or may be caused by stains on the graphic code image to be partially covered, or may be caused by blurred graphic code image due to insufficient exposure and the like.
It should be understood that the acquired image of the to-be-identified pattern code may contain noise due to interference of an environment or equipment in the image capturing process, and in order to eliminate the interference of the noise, the to-be-identified pattern code image may be preprocessed, where the method for eliminating the interference of the image noise through preprocessing includes many methods, and is not limited in this embodiment.
It should be understood that there may be some color graphic codes, and there are many methods for converting a color image into a gray image, which are not limited in this embodiment, and may include: averaging, binary, reverse image, etc.
It should be understood that there are many methods for performing edge processing on a grayscale image, which are not limited in this embodiment, and the following methods may be preferably used to perform edge processing on a grayscale image:
performing convolution operation on the gray level image by adopting a preset Gaussian convolution kernel to obtain a Gaussian blur image; calculating the gradient and the direction of the Gaussian blur image; and processing the gradient and the direction of the Gaussian blur image based on a non-maximum suppression algorithm and a double-threshold detection method to obtain an edge image.
The graphic code identification method is used for a code scanning device, and when a code scanning gun scans a graphic code image to be identified for the first time and fails to obtain character string information in the graphic code image to be identified, the graphic code image to be identified is processed to obtain a fuzzy area in the graphic code image to be identified; inputting the fuzzy area in the graphic code image to be identified into a preset image enhancement network to obtain an enhanced image; the preset image enhancement network is used for carrying out bilateral grid sampling processing on the blurred image to obtain the enhanced image; and obtaining character string information in the graphic code image to be recognized according to the enhanced image and the graphic code image to be recognized. When code scanning fails, the image code image can still be identified, and the working efficiency is improved.
Based on the above embodiment, further, step 110 specifically includes the following steps:
111. and carrying out gray level processing on the graphic code image to be identified to obtain a gray level image of the graphic code image to be identified.
112. And obtaining an effective area based on the gray level image, and obtaining a fuzzy coefficient of each pixel in the graphic code image to be identified according to the effective area and the gray level image.
113. And extracting the pixel points of which the fuzzy coefficients are larger than the preset fuzzy threshold.
114. And determining a fuzzy area in the graphic code image to be identified based on the extracted pixel points of which the fuzzy coefficients are greater than the preset fuzzy threshold.
Further, step 111 specifically includes:
1111. and performing convolution operation on the graphic code image to be identified by adopting a preset Gaussian convolution kernel to obtain a Gaussian blur image, and calculating the gradient and the direction of the Gaussian blur image.
1112 processes the gradient and direction of the gaussian blurred image according to a non-maximum suppression method and a dual threshold detection method to obtain the gray image.
Further, step 112 includes:
1121, performing contour detection on the grayscale image to obtain the size and the position of all closed regions in the grayscale image.
1122. And extracting a closed region which is larger than a preset threshold value in the gray-scale image, and taking the closed region which is larger than the preset threshold value as an effective region.
1123. Calculating the average gray value of a preset neighborhood range of each pixel point in the effective region;
and calculating the variance in the preset neighborhood range of each pixel point based on the average gray value of the preset neighborhood range of each pixel point in the effective region and the gray image.
1124. And obtaining the fuzzy coefficient of each pixel point according to the average gray value in the preset neighborhood range of each pixel point and the average gray value in the preset neighborhood range of each pixel point.
Further, step 120 includes:
the preset image enhancement network comprises a prediction processing network, a sampling network and an enhancement processing network.
121. And inputting the fuzzy area in the graphic code image to be identified into the prediction processing network to obtain a low-resolution illumination predicted image.
122. And inputting the fuzzy area and the low-resolution illumination predicted image into the grid sampling network for sampling fusion processing to obtain a full-resolution illumination image.
123. And inputting the fuzzy area and the full-resolution illumination image into the enhancement processing network for pixel enhancement processing to obtain the enhanced image.
Further, step 121 includes:
1211. and inputting the fuzzy area into a feature extraction sub-network for feature extraction to obtain the global features and the local features of the fuzzy area.
1212. And inputting the global features and the local features into the feature fusion sub-network for feature fusion to obtain the low-resolution illumination predicted image.
It should be understood that the acquired image includes some invalid areas such as the screen frame and the area of the indicator light besides the valid area of the screen, and needs to be eliminated.
Carrying out contour detection on the edge image to obtain the size and the position of all closed areas in the edge image;
and extracting a closed region larger than a preset threshold value in the edge image, and taking the closed region larger than the preset threshold value as an effective region.
As shown in fig. 2, a schematic block diagram of a device for identifying a graphic code according to an embodiment of the present invention includes:
the acquisition module is used for processing the graphic code image to be identified to obtain a fuzzy area in the graphic code image to be identified when the code scanning gun scans the graphic code image to be identified for the first time to obtain character string information in the graphic code image to be identified;
the enhancement module is used for inputting the fuzzy area in the graphic code image to be identified into a preset image enhancement network to obtain an enhanced image; the preset image enhancement network is used for carrying out bilateral grid sampling processing on the blurred image to obtain the enhanced image;
and the fusion module is used for obtaining the character string information in the graphic code image to be identified according to the enhanced image and the graphic code image to be identified.
Further, the obtaining module is specifically configured to perform gray processing on the to-be-identified pattern code image to obtain a gray image of the to-be-identified pattern code image;
obtaining an effective area based on the gray level image, and obtaining a fuzzy coefficient of each pixel in the graphic code image to be identified according to the effective area and the gray level image;
extracting pixel points of which the fuzzy coefficients are larger than the preset fuzzy threshold;
and determining a fuzzy area in the graphic code image to be identified based on the extracted pixel points of which the fuzzy coefficients are greater than the preset fuzzy threshold.
Further, the acquiring module is specifically configured to perform convolution operation on the to-be-identified pattern code image by using a preset gaussian convolution kernel to obtain a gaussian blurred image, and calculate a gradient and a direction of the gaussian blurred image;
and processing the gradient and the direction of the Gaussian blur image according to a non-maximum value inhibition method and a double-threshold detection method to obtain the gray image.
The invention also relates to a code scanning device, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein when the processor executes the computer program, the graphic code identification method in any one of the above technical schemes is realized.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium.
Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for identifying a graphic code is used for code scanning equipment, and is characterized by comprising the following steps:
when a code scanning gun scans a graphic code image to be recognized for the first time and fails to obtain character string information in the graphic code image to be recognized, processing the graphic code image to be recognized to obtain a fuzzy area in the graphic code image to be recognized;
inputting the fuzzy area in the graphic code image to be identified into a preset image enhancement network to obtain an enhanced image; the preset image enhancement network is used for carrying out bilateral grid sampling processing on the blurred image to obtain the enhanced image;
and obtaining character string information in the graphic code image to be recognized according to the enhanced image and the graphic code image to be recognized.
2. The method for recognizing the graphic code according to claim 1, wherein the step of processing the graphic code image to be recognized to obtain the fuzzy area in the graphic code image to be recognized specifically comprises the following steps:
carrying out gray level processing on the graphic code image to be identified to obtain a gray level image of the graphic code image to be identified;
obtaining an effective area based on the gray level image, and obtaining a fuzzy coefficient of each pixel in the graphic code image to be identified according to the effective area and the gray level image;
extracting pixel points of which the fuzzy coefficients are larger than the preset fuzzy threshold;
and determining a fuzzy area in the graphic code image to be identified based on the extracted pixel points of which the fuzzy coefficients are greater than the preset fuzzy threshold.
3. The method for recognizing graphic codes according to claim 2, wherein the performing gray-scale processing on the graphic code image to be recognized to obtain a gray-scale image of the graphic code image to be recognized specifically comprises:
performing convolution operation on the graphic code image to be identified by adopting a preset Gaussian convolution kernel to obtain a Gaussian blur image, and calculating the gradient and the direction of the Gaussian blur image;
and processing the gradient and the direction of the Gaussian blur image according to a non-maximum value inhibition method and a double-threshold detection method to obtain the gray image.
4. The method for identifying graphic codes according to claim 2, wherein the obtaining an effective area based on the gray-scale image and obtaining a blurring coefficient of each pixel in the graphic code image to be identified according to the effective area and the gray-scale image comprises:
carrying out contour detection on the gray level image to obtain the size and the position of all closed areas in the gray level image;
extracting a closed region which is larger than a preset threshold value in the gray-scale image, and taking the closed region which is larger than the preset threshold value as an effective region;
calculating the average gray value of a preset neighborhood range of each pixel point in the effective region;
calculating the variance of each pixel point in a preset neighborhood range based on the average gray value of each pixel point in the effective region in the preset neighborhood range and the gray image;
and obtaining the fuzzy coefficient of each pixel point according to the average gray value in the preset neighborhood range of each pixel point and the average gray value in the preset neighborhood range of each pixel point.
5. The method for recognizing the graphic code according to claim 1, wherein the step of inputting the fuzzy area in the graphic code image to be recognized into a preset image enhancement network to obtain an enhanced image comprises:
the preset image enhancement network comprises a prediction processing network, a sampling network and an enhancement processing network;
inputting the fuzzy area in the graphic code image to be recognized into a preset image enhancement network to obtain an enhanced image, wherein the method comprises the following steps:
inputting the fuzzy area in the graphic code image to be identified into the prediction processing network to obtain a low-resolution illumination predicted image;
inputting the fuzzy area and the low-resolution illumination predicted image into the grid sampling network for sampling fusion processing to obtain a full-resolution illumination image;
and inputting the fuzzy area and the full-resolution illumination image into the enhancement processing network for pixel enhancement processing to obtain the enhanced image.
6. The method for identifying graphic codes according to claim 2, wherein the inputting the fuzzy area in the graphic code image to be identified into a prediction processing network to obtain a low-resolution illumination predicted image comprises:
inputting the fuzzy area into a feature extraction sub-network for feature extraction to obtain global features and local features of the fuzzy area;
and inputting the global features and the local features into the feature fusion sub-network for feature fusion to obtain the low-resolution illumination predicted image.
7. An apparatus for identifying a graphic code, the apparatus comprising:
the acquisition module is used for processing the graphic code image to be identified to obtain a fuzzy area in the graphic code image to be identified when the code scanning gun scans the graphic code image to be identified for the first time to obtain character string information in the graphic code image to be identified;
the enhancement module is used for inputting the fuzzy area in the graphic code image to be identified into a preset image enhancement network to obtain an enhanced image; the preset image enhancement network is used for carrying out bilateral grid sampling processing on the blurred image to obtain the enhanced image;
and the fusion module is used for obtaining the character string information in the graphic code image to be identified according to the enhanced image and the graphic code image to be identified.
8. The device for graphic code recognition according to claim 7,
the acquisition module is specifically used for carrying out gray level processing on the graphic code image to be identified to obtain a gray level image of the graphic code image to be identified;
obtaining an effective area based on the gray level image, and obtaining a fuzzy coefficient of each pixel in the graphic code image to be identified according to the effective area and the gray level image;
extracting pixel points of which the fuzzy coefficients are larger than the preset fuzzy threshold;
and determining a fuzzy area in the graphic code image to be identified based on the extracted pixel points of which the fuzzy coefficients are greater than the preset fuzzy threshold.
9. The device for graphic code recognition according to claim 8,
the acquisition module is specifically used for carrying out convolution operation on the graphic code image to be identified by adopting a preset Gaussian convolution kernel to obtain a Gaussian blur image and calculating the gradient and the direction of the Gaussian blur image;
and processing the gradient and the direction of the Gaussian blur image according to a non-maximum value inhibition method and a double-threshold detection method to obtain the gray image.
10. A code scanning device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for identifying a graphic code according to any one of claims 1 to 6 when executing the computer program.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115880300A (en) * | 2023-03-03 | 2023-03-31 | 北京网智易通科技有限公司 | Image blur detection method, image blur detection device, electronic equipment and storage medium |
| CN117787309A (en) * | 2023-12-13 | 2024-03-29 | 佛山鑫码电子科技有限公司 | Code scanning method and device based on double-color light source irradiation |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107609448A (en) * | 2017-08-28 | 2018-01-19 | 苏州佳世达电通有限公司 | Bar code decoding method and bar code decoding device |
| CN110570411A (en) * | 2019-09-05 | 2019-12-13 | 中国科学院长春光学精密机械与物理研究所 | mura detection method and device based on coefficient of variation |
| CN111583161A (en) * | 2020-06-17 | 2020-08-25 | 上海眼控科技股份有限公司 | Blurred image enhancement method, computer device and storage medium |
| CN112163443A (en) * | 2020-10-20 | 2021-01-01 | 汪金玲 | Code scanning method, code scanning device and mobile terminal |
-
2021
- 2021-03-17 CN CN202110286573.5A patent/CN112926351A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107609448A (en) * | 2017-08-28 | 2018-01-19 | 苏州佳世达电通有限公司 | Bar code decoding method and bar code decoding device |
| CN110570411A (en) * | 2019-09-05 | 2019-12-13 | 中国科学院长春光学精密机械与物理研究所 | mura detection method and device based on coefficient of variation |
| CN111583161A (en) * | 2020-06-17 | 2020-08-25 | 上海眼控科技股份有限公司 | Blurred image enhancement method, computer device and storage medium |
| CN112163443A (en) * | 2020-10-20 | 2021-01-01 | 汪金玲 | Code scanning method, code scanning device and mobile terminal |
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