CN113705749A - Two-dimensional code identification method, device and equipment based on deep learning and storage medium - Google Patents
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Abstract
The embodiment of the invention relates to the field of artificial intelligence, and discloses a two-dimension code identification method, a device, equipment and a storage medium based on deep learning, wherein the method comprises the following steps: constructing a sample image set; adding labels to one or more two-dimensional codes of each sample training image in the sample image set, and clustering each sample training image added with the labels according to the position coordinates of the position area of each two-dimensional code; inputting the clustered training images of each sample into a deep learning network model for training to obtain a two-dimensional code detection model; inputting an image to be tested into a two-dimensional code detection model to obtain position information of one or more two-dimensional codes; one or more two-dimensional codes in the image to be tested are identified according to the position information, so that the identification of the one or more two-dimensional codes in the image is realized, and the accuracy and the efficiency of identifying the one or more two-dimensional codes in the image are improved. The present invention relates to blockchain techniques, such as images can be written into blockchains for use in scenarios such as data forensics.
Description
Technical Field
The invention relates to the field of artificial intelligence, in particular to a two-dimension code identification method, a two-dimension code identification device, two-dimension code identification equipment and a storage medium based on deep learning.
Background
Two-dimensional codes are an encoding mode which is super popular on mobile equipment in recent years, and can store more information and represent more data types than traditional bar codes.
At present, in the two-dimensional code identification industry, the identification is performed by adopting methods such as gray processing and binarization in an OpenCV method, and the characteristic area of the two-dimensional code is found for positioning. However, in general, white edges, black edges, edges between black and white, patterns, and the like may occur around the two-dimensional code in this method, which may affect the recognition result of the two-dimensional code, and may even fail to recognize the two-dimensional code, or even fail to recognize a plurality of two-dimensional codes. Therefore, how to identify the two-dimensional code more effectively becomes a focus of research.
Disclosure of Invention
The embodiment of the invention provides a two-dimension code identification method, a two-dimension code identification device, two-dimension code identification equipment and a storage medium based on deep learning, which can realize the identification of one or more two-dimension codes in an image and improve the accuracy and efficiency of the identification of one or more two-dimension codes in the image.
In a first aspect, an embodiment of the present invention provides a two-dimensional code identification method based on deep learning, including:
constructing a sample image set, wherein the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimensional codes;
adding labels to one or more two-dimensional codes of each sample training image in the sample image set, and clustering each sample training image in the sample image set after the labels are added according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes;
inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model;
inputting an image to be tested into the two-dimensional code detection model to obtain position information of one or more two-dimensional codes in the image to be tested;
and identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested.
Further, the adding labels to one or more two-dimensional codes of training images of each sample in the sample image set includes:
determining a position area corresponding to each two-dimensional code in each sample training image and an area object name corresponding to each two-dimensional code;
and adding a first label to the position coordinates of the position area corresponding to each two-dimensional code in each sample training image by using a specified marking tool, adding a second label to the area object name corresponding to each two-dimensional code in each sample training image, and storing each sample training image added with the first label and the second label in a specified file.
Further, the clustering, according to the position coordinates of the position areas corresponding to the two-dimensional codes in the one or more two-dimensional codes, the sample training images in the sample image set to which the labels are added, includes:
determining the height and width of each sample training image according to the position coordinates of the position area corresponding to each two-dimensional code;
and clustering the sample training images in the sample image set added with the labels according to the height and the width of each sample training image.
Further, the determining the height and the width of each sample training image according to the position coordinates of the position area corresponding to each two-dimensional code includes:
determining the minimum position coordinate and the maximum position coordinate of the position area corresponding to each two-dimensional code according to the position coordinate of the position area corresponding to each two-dimensional code;
and inputting the minimum position coordinate and the maximum position coordinate into a specified clustering algorithm model to obtain the height and the width of each sample training image.
Further, before each sample training image in the clustered sample image set is input into a preset deep learning network model for training to obtain a two-dimensional code detection model, the method further includes:
acquiring the size of a video memory, and adjusting the size of each sample training image in the clustered sample image set according to the size of the video memory;
inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model, wherein the method comprises the following steps:
inputting each sample training image after the size is adjusted into a preset deep learning network model to obtain a loss function value;
when the loss function value does not meet a preset condition, adjusting model parameters of the preset deep learning network model according to the loss function value, and inputting each sample training image with the adjusted size into the deep learning network model with the adjusted model parameters for iterative training;
and when the loss function value obtained after the iterative training meets the preset condition, determining that the two-dimensional code detection model is obtained through training.
Further, the inputting the image to be tested into the two-dimensional code detection model to obtain the position information of one or more two-dimensional codes in the image to be tested includes:
inputting the image to be tested into the two-dimensional code detection model to obtain a prediction position label and a prediction area object name of the two-dimensional code of the image to be tested;
and determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label and the predicted area object name of the two-dimensional code of the image to be tested.
Further, the determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label and the predicted area object name of the two-dimensional code of the image to be tested includes:
determining the confidence coefficient of the two-dimensional code in the image to be tested according to the predicted position label and the predicted region object name of the two-dimensional code of the image to be tested;
and when the confidence coefficient is greater than a preset confidence coefficient threshold value, determining that the two-dimensional codes exist in the image to be tested, and determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the position labels, the area object names and the confidence coefficient of the two-dimensional codes of the image to be tested.
In a second aspect, an embodiment of the present invention provides a two-dimensional code recognition apparatus based on deep learning, including:
the image processing device comprises a construction unit, a processing unit and a processing unit, wherein the construction unit is used for constructing a sample image set, the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimensional codes;
the labeling unit is used for adding labels to one or more two-dimensional codes of each sample training image in the sample image set and clustering each sample training image in the sample image set added with labels according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes;
the training unit is used for inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model;
the determining unit is used for inputting the image to be tested into the two-dimensional code detection model to obtain the position information of one or more two-dimensional codes in the image to be tested;
and the identification unit is used for identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested.
In a third aspect, an embodiment of the present invention provides a computer device, including a processor and a memory, where the memory is used to store a computer program, and the computer program includes a program, and the processor is configured to call the computer program to execute the method of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium, which stores a computer program, where the computer program is executed by a processor to implement the method of the first aspect.
According to the embodiment of the invention, a sample image set can be constructed, wherein the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimensional codes; adding labels to one or more two-dimensional codes of each sample training image in the sample image set, and clustering each sample training image in the sample image set after the labels are added according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes; inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model; inputting an image to be tested into the two-dimensional code detection model to obtain position information of one or more two-dimensional codes in the image to be tested; and identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested. By the method, the one or more two-dimensional codes in the image can be identified, and the accuracy and efficiency of identifying the one or more two-dimensional codes in the image are improved.
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 description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a two-dimensional code identification method based on deep learning according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a two-dimensional code recognition apparatus based on deep learning according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of a computer device provided by 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The two-dimensional code identification method based on deep learning provided by the embodiment of the invention can be applied to a two-dimensional code identification device based on deep learning. In certain embodiments, the computer device includes, but is not limited to, one or more of a smartphone, tablet, laptop, and the like.
According to the embodiment of the invention, a sample image set can be constructed, wherein the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimensional codes; adding labels to one or more two-dimensional codes of each sample training image in the sample image set, and clustering each sample training image in the sample image set after the labels are added according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes; inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model; inputting an image to be tested into the two-dimensional code detection model to obtain position information of one or more two-dimensional codes in the image to be tested; and identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested. The embodiment of the invention can realize the identification of one or more two-dimensional codes in the image in such a way, and improves the accuracy and efficiency of the identification of one or more two-dimensional codes in the image.
The embodiment of the application can acquire and process related data (such as a sample training image and an image to be tested) based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The two-dimensional code recognition method based on deep learning provided by the embodiment of the invention is schematically described below with reference to fig. 1.
Referring to fig. 1, fig. 1 is a schematic flow chart of a two-dimensional code recognition method based on deep learning according to an embodiment of the present invention, and as shown in fig. 1, the method may be executed by a two-dimensional code recognition apparatus based on deep learning, where the two-dimensional code recognition apparatus based on deep learning is disposed in a computer device. Specifically, the method of the embodiment of the present invention includes the following steps.
S101: constructing a sample image set, wherein the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimensional codes.
In the embodiment of the invention, a two-dimension code recognition device based on deep learning can construct a sample image set, wherein the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimension codes.
In one embodiment, when a sample image set is constructed, the two-dimensional code identification device based on deep learning may collect image data, and store the collected image data as sample training images in the same folder to construct the sample image set, where each image in the collected image data includes one or more two-dimensional codes. In some embodiments, images including one or more two-dimensional codes may be acquired from different galleries as the image data is acquired.
In one embodiment, the sample image set may further include a sample image set and a test image set, where the sample image set is used for training a deep learning network model to obtain a two-dimensional code detection model; and the test image set is used for verifying the two-dimensional code detection model obtained by training. When a two-dimensional code recognition device based on deep learning constructs a sample image set, image data can be collected, and the image data is divided according to a preset proportion to obtain the sample image set and a test image set. For example, assuming a preset scale of 9:1 for the sample image set and the test image set, the acquired image data may be divided into the sample image set and the test image set at a scale of 9: 1.
In one embodiment, after determining the sample image set and the test image set, the image file name may be randomly extracted from a folder storing the sample image set and/or the test image set, and the suffix such as jpg may be removed and saved as an index into a txt file, and saved in a single line, that is, finally containing the text.
S102: adding labels to one or more two-dimensional codes of each sample training image in the sample image set, and clustering each sample training image in the sample image set after the labels are added according to the position coordinates of the position areas corresponding to each two-dimensional code in the one or more two-dimensional codes.
In the embodiment of the invention, the two-dimension code recognition device based on deep learning can add labels to one or more two-dimension codes of each sample training image in the sample image set, and cluster each sample training image in the sample image set after adding labels according to the position coordinates of the position area corresponding to each two-dimension code in the one or more two-dimension codes.
In one embodiment, when adding labels to one or more two-dimensional codes of each sample training image in the sample image set, the two-dimensional code recognition device based on deep learning may determine a position area corresponding to each two-dimensional code in each sample training image and an area object name corresponding to each two-dimensional code; and adding a first label to the position coordinates of the position area corresponding to each two-dimensional code in each sample training image by using a specified marking tool, adding a second label to the area object name corresponding to each two-dimensional code in each sample training image, and storing each sample training image added with the first label and the second label in a specified file. In certain embodiments, the designated marking tool includes, but is not limited to, labelImg.
In some embodiments, the designated file includes an image object name, an image file path, an image size, position coordinates of a position region corresponding to the two-dimensional code, a region object name corresponding to the two-dimensional code, and the like of each sample training image to which the first annotation and the second annotation are added.
In one embodiment, when adding labels to one or more two-dimensional codes of each sample training image in the sample image set, the two-dimensional code recognition apparatus based on deep learning may add labels to each two-dimensional code in each sample training image by manually framing a position area (e.g., a rectangle) corresponding to each two-dimensional code in each sample training image and an area object name (e.g., qrcode) corresponding to the two-dimensional code.
In one embodiment, when the two-dimensional code recognition device based on deep learning clusters the sample training images in the sample image set to which labels are added according to the position coordinates of the position areas corresponding to the two-dimensional codes in the one or more two-dimensional codes, the height and the width of each sample training image can be determined according to the position coordinates of the position areas corresponding to the two-dimensional codes; and clustering the sample training images in the sample image set added with the labels according to the height and the width of each sample training image.
In one embodiment, when determining the height and the width of each sample training image according to the position coordinates of the position region corresponding to each two-dimensional code, the two-dimensional code recognition device based on deep learning may determine the minimum position coordinates and the maximum position coordinates of the position region corresponding to each two-dimensional code according to the position coordinates of the position region corresponding to each two-dimensional code; and inputting the minimum position coordinate and the maximum position coordinate into a specified clustering algorithm model to obtain the height and the width of each sample training image. In certain embodiments, the minimum location coordinate comprises a minimum abscissa and a minimum ordinate, and the maximum location coordinate comprises a maximum abscissa and a maximum ordinate.
In certain embodiments, the specified clustering algorithm includes, but is not limited to, such as: k-means clustering algorithm, Hierarchical clustering algorithm, Partition-based methods Partition clustering algorithm, etc.
In one embodiment, when clustering is performed on each sample training image in a sample image set added with labels, a two-dimensional code recognition device based on deep learning can acquire a sample data set, wherein the sample data set comprises a plurality of sample data, and the sample data is image data of labeled classes; randomly selecting K sample data from the sample data set, and calculating the distance between the characteristic information of each sample training image and each sample data in the K sample data, wherein K is a positive integer greater than or equal to 1; and determining the class marked by the sample data corresponding to the minimum distance as the class of the corresponding sample training image. In some embodiments, the feature information may be vector data, and the sample data may be vector data labeled with a two-dimensional code category.
In an embodiment, the specified clustering algorithm may be a hierarchical clustering algorithm, when the two-dimensional code recognition device based on deep learning performs clustering on each sample training image in the sample image set to which the label is added, the distance between the feature information of each sample training image and each sample data may be calculated, the sample data with the minimum distance and the feature information are merged and determined to be the same category, the distance between the merged data and each sample data is recalculated, the sample data with the minimum distance and the merged data are merged and determined to be the same category, and the same category to which all the sample data obtained by final merging belong is determined to be the category of the corresponding sample training image through the loop calculation.
S103: and inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model.
In the embodiment of the invention, the two-dimension code recognition device based on deep learning can input each sample training image in the clustered sample image set into the preset deep learning network model for training to obtain the two-dimension code detection model.
In some embodiments, the preset deep learning network model may adopt a YOLO framework basis, where YOLO is an object recognition and positioning algorithm based on a deep neural network. The backbone network is configured by adding CSPs to each large residual block of Darknet53, and corresponding to layers 0 to layer104, i.e., layers 0 to 104. And increasing the receptive field of the network by increasing spatial pyramid pooling, realizing the maximal pooling of 5 × 5, 9 × 9 and 13 × 13 for layer107 to obtain layer108, layer110 and layer112 respectively, after the pooling is completed, connecting the layers into a feature map layer114, and reducing the dimension to 512 channels by 1 × 1.
In an embodiment, the two-dimensional code recognition device based on deep learning can acquire a video memory size before inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain the two-dimensional code detection model, and adjust the size of each sample training image in the clustered sample image set according to the video memory size.
In one example, when the size of each sample training image in the clustered sample image set is adjusted according to the video memory size, V100 four-card training may be adopted, and the image size 416 × 416 of the input depth network is adjusted according to video memory size adaptation.
In one embodiment, when the two-dimensional code recognition device based on deep learning inputs each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain the two-dimensional code detection model, each sample training image after size adjustment can be input into the preset deep learning network model to obtain a loss function value; when the loss function value does not meet a preset condition, adjusting model parameters of the preset deep learning network model according to the loss function value, and inputting each sample training image with the adjusted size into the deep learning network model with the adjusted model parameters for iterative training; and when the loss function value obtained after the iterative training meets the preset condition, determining that the two-dimensional code detection model is obtained through training.
S104: and inputting the image to be tested into the two-dimensional code detection model to obtain the position information of one or more two-dimensional codes in the image to be tested.
In the embodiment of the invention, the two-dimension code recognition device based on deep learning can input the image to be tested into the two-dimension code detection model to obtain the position information of one or more two-dimension codes in the image to be tested.
S105: and identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested.
In the embodiment of the invention, the two-dimension code recognition device based on deep learning can recognize one or more two-dimension codes in the image to be tested according to the position information of the one or more two-dimension codes in the image to be tested.
In one embodiment, when an image to be tested is input into the two-dimensional code detection model to obtain position information of one or more two-dimensional codes in the image to be tested, the two-dimensional code recognition device based on deep learning can input the image to be tested into the two-dimensional code detection model to obtain a predicted position label and a predicted region object name of the two-dimensional code of the image to be tested; and determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label and the predicted area object name of the two-dimensional code of the image to be tested.
In one embodiment, when the two-dimensional code recognition device based on deep learning determines the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label and the predicted area object name of the two-dimensional code of the image to be tested, the confidence coefficient of the two-dimensional code existing in the image to be tested can be determined according to the predicted position label and the predicted area object name of the two-dimensional code of the image to be tested; and when the confidence coefficient is greater than a preset confidence coefficient threshold value, determining that the two-dimensional codes exist in the image to be tested, and determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the position labels, the area object names and the confidence coefficient of the two-dimensional codes of the image to be tested.
In some embodiments, the confidence level is the probability that a location area (e.g., bounding box) corresponding to a two-dimensional code contains an object and the accuracy of the location (i.e., whether the two-dimensional code is wrapped exactly).
In one embodiment, after determining the confidence that the two-dimensional code exists in the test image, a category probability that the category exists as the two-dimensional code in the test image at the confidence may also be determined, and a predicted value of the two-dimensional code in the image to be tested is determined according to the confidence and the category probability, that is, the predicted value is the confidence and the category probability; and sequencing the predicted values under each category according to the size of the predicted values under each category. And performing primary screening by the class probability being larger than the preset parameters, and then determining that the two-dimensional codes exist in the to-be-tested image corresponding to the predicted value within the preset threshold range according to the sorted predicted values.
In one embodiment, when the number of the two-dimensional codes in the image to be tested is determined according to the position label, the area object name and the confidence coefficient of the two-dimensional codes of the image to be tested, if the number of the two-dimensional codes in the image to be tested is determined to be 0, it is determined that no two-dimensional codes exist in the image to be tested, and if the number of the two-dimensional codes in the image to be tested is determined to be N, it is determined that the number of the two-dimensional codes in the image to be tested is N.
In the embodiment of the invention, a two-dimension code recognition device based on deep learning can construct a sample image set, wherein the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimension codes; adding labels to one or more two-dimensional codes of each sample training image in the sample image set, and clustering each sample training image in the sample image set after the labels are added according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes; inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model; inputting an image to be tested into the two-dimensional code detection model to obtain position information of one or more two-dimensional codes in the image to be tested; and identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested. By the method, the one or more two-dimensional codes in the image can be identified, and the accuracy and efficiency of identifying the one or more two-dimensional codes in the image are improved.
The embodiment of the invention also provides a two-dimensional code recognition device based on deep learning, which is used for executing the unit of any one of the methods. Specifically, referring to fig. 2, fig. 2 is a schematic block diagram of a two-dimensional code recognition apparatus based on deep learning according to an embodiment of the present invention. The two-dimensional code recognition device based on deep learning of this embodiment includes: a construction unit 201, an annotation unit 202, a training unit 203, a determination unit 204 and a recognition unit 205.
A constructing unit 201, configured to construct a sample image set, where the sample image set includes a plurality of sample training images, and each sample training image includes one or more two-dimensional codes;
the labeling unit 202 is configured to add labels to one or more two-dimensional codes of each sample training image in the sample image set, and cluster the sample training images in the sample image set to which labels are added according to position coordinates of position areas corresponding to each two-dimensional code in the one or more two-dimensional codes;
the training unit 203 is configured to input each sample training image in the clustered sample image set into a preset deep learning network model for training, so as to obtain a two-dimensional code detection model;
the determining unit 204 is configured to input an image to be tested into the two-dimensional code detection model, so as to obtain position information of one or more two-dimensional codes in the image to be tested;
the identifying unit 205 is configured to identify one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested.
Further, when the labeling unit 202 adds a label to one or more two-dimensional codes of each sample training image in the sample image set, the labeling unit is specifically configured to:
determining a position area corresponding to each two-dimensional code in each sample training image and an area object name corresponding to each two-dimensional code;
and adding a first label to the position coordinates of the position area corresponding to each two-dimensional code in each sample training image by using a specified marking tool, adding a second label to the area object name corresponding to each two-dimensional code in each sample training image, and storing each sample training image added with the first label and the second label in a specified file.
Further, when the labeling unit 202 clusters the sample training images in the sample image set to which the label is added according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes, specifically:
determining the height and width of each sample training image according to the position coordinates of the position area corresponding to each two-dimensional code;
and clustering the sample training images in the sample image set added with the labels according to the height and the width of each sample training image.
Further, when the labeling unit 202 determines the height and the width of each sample training image according to the position coordinates of the position area corresponding to each two-dimensional code, it is specifically configured to:
determining the minimum position coordinate and the maximum position coordinate of the position area corresponding to each two-dimensional code according to the position coordinate of the position area corresponding to each two-dimensional code;
and inputting the minimum position coordinate and the maximum position coordinate into a specified clustering algorithm model to obtain the height and the width of each sample training image.
Further, the training unit 203 inputs each sample training image in the clustered sample image set into a preset deep learning network model for training, and before obtaining the two-dimensional code detection model, is further configured to:
acquiring the size of a video memory, and adjusting the size of each sample training image in the clustered sample image set according to the size of the video memory;
the training unit 203 inputs each sample training image in the clustered sample image set into a preset deep learning network model for training, and when a two-dimensional code detection model is obtained, the training unit is specifically used for:
inputting each sample training image after the size is adjusted into a preset deep learning network model to obtain a loss function value;
when the loss function value does not meet a preset condition, adjusting model parameters of the preset deep learning network model according to the loss function value, and inputting each sample training image with the adjusted size into the deep learning network model with the adjusted model parameters for iterative training;
and when the loss function value obtained after the iterative training meets the preset condition, determining that the two-dimensional code detection model is obtained through training.
Further, when the determining unit 204 inputs the image to be tested into the two-dimensional code detection model to obtain the position information of one or more two-dimensional codes in the image to be tested, the determining unit is specifically configured to:
inputting the image to be tested into the two-dimensional code detection model to obtain a prediction position label and a prediction area object name of the two-dimensional code of the image to be tested;
and determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label and the predicted area object name of the two-dimensional code of the image to be tested.
Further, when the determining unit 204 determines the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label and the predicted region object name of the two-dimensional code of the image to be tested, the determining unit is specifically configured to:
determining the confidence coefficient of the two-dimensional code in the image to be tested according to the predicted position label and the predicted region object name of the two-dimensional code of the image to be tested;
and when the confidence coefficient is greater than a preset confidence coefficient threshold value, determining that the two-dimensional codes exist in the image to be tested, and determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the position labels, the area object names and the confidence coefficient of the two-dimensional codes of the image to be tested.
In the embodiment of the invention, a two-dimension code recognition device based on deep learning can construct a sample image set, wherein the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimension codes; adding labels to one or more two-dimensional codes of each sample training image in the sample image set, and clustering each sample training image in the sample image set after the labels are added according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes; inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model; inputting an image to be tested into the two-dimensional code detection model to obtain position information of one or more two-dimensional codes in the image to be tested; and identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested. By the method, the one or more two-dimensional codes in the image can be identified, and the accuracy and efficiency of identifying the one or more two-dimensional codes in the image are improved.
Referring to fig. 3, fig. 3 is a schematic block diagram of a computer device provided in an embodiment of the present invention, and in some embodiments, the computer device in the embodiment shown in fig. 3 may include: one or more processors 301; one or more input devices 302, one or more output devices 303, and memory 304. The processor 301, the input device 302, the output device 303, and the memory 304 are connected by a bus 305. The memory 304 is used for storing computer programs, including programs, and the processor 301 is used for executing the programs stored in the memory 304. Wherein the processor 301 is configured to invoke the program to perform:
constructing a sample image set, wherein the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimensional codes;
adding labels to one or more two-dimensional codes of each sample training image in the sample image set, and clustering each sample training image in the sample image set after the labels are added according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes;
inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model;
inputting an image to be tested into the two-dimensional code detection model to obtain position information of one or more two-dimensional codes in the image to be tested;
and identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested.
Further, when the processor 301 adds a label to one or more two-dimensional codes of each sample training image in the sample image set, the method is specifically configured to:
determining a position area corresponding to each two-dimensional code in each sample training image and an area object name corresponding to each two-dimensional code;
and adding a first label to the position coordinates of the position area corresponding to each two-dimensional code in each sample training image by using a specified marking tool, adding a second label to the area object name corresponding to each two-dimensional code in each sample training image, and storing each sample training image added with the first label and the second label in a specified file.
Further, when the processor 301 performs clustering on each sample training image in the sample image set to which the label is added according to the position coordinate of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes, specifically, the processor is configured to:
determining the height and width of each sample training image according to the position coordinates of the position area corresponding to each two-dimensional code;
and clustering the sample training images in the sample image set added with the labels according to the height and the width of each sample training image.
Further, when the processor 301 determines the height and the width of each sample training image according to the position coordinates of the position area corresponding to each two-dimensional code, it is specifically configured to:
determining the minimum position coordinate and the maximum position coordinate of the position area corresponding to each two-dimensional code according to the position coordinate of the position area corresponding to each two-dimensional code;
and inputting the minimum position coordinate and the maximum position coordinate into a specified clustering algorithm model to obtain the height and the width of each sample training image.
Further, the processor 301 inputs each sample training image in the clustered sample image set into a preset deep learning network model for training, and before obtaining the two-dimensional code detection model, is further configured to:
acquiring the size of a video memory, and adjusting the size of each sample training image in the clustered sample image set according to the size of the video memory;
the processor 301 inputs each sample training image in the clustered sample image set into a preset deep learning network model for training, and when a two-dimensional code detection model is obtained, the processor is specifically configured to:
inputting each sample training image after the size is adjusted into a preset deep learning network model to obtain a loss function value;
when the loss function value does not meet a preset condition, adjusting model parameters of the preset deep learning network model according to the loss function value, and inputting each sample training image with the adjusted size into the deep learning network model with the adjusted model parameters for iterative training;
and when the loss function value obtained after the iterative training meets the preset condition, determining that the two-dimensional code detection model is obtained through training.
Further, when the processor 301 inputs the image to be tested into the two-dimensional code detection model to obtain the position information of one or more two-dimensional codes in the image to be tested, the processor is specifically configured to:
inputting the image to be tested into the two-dimensional code detection model to obtain a prediction position label and a prediction area object name of the two-dimensional code of the image to be tested;
and determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label and the predicted area object name of the two-dimensional code of the image to be tested.
Further, when the processor 301 determines the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label and the predicted area object name of the two-dimensional code of the image to be tested, the processor is specifically configured to:
determining the confidence coefficient of the two-dimensional code in the image to be tested according to the predicted position label and the predicted region object name of the two-dimensional code of the image to be tested;
and when the confidence coefficient is greater than a preset confidence coefficient threshold value, determining that the two-dimensional codes exist in the image to be tested, and determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the position labels, the area object names and the confidence coefficient of the two-dimensional codes of the image to be tested.
In an embodiment of the present invention, a computer device may construct a sample image set, where the sample image set includes a plurality of sample training images, and each sample training image includes one or more two-dimensional codes; adding labels to one or more two-dimensional codes of each sample training image in the sample image set, and clustering each sample training image in the sample image set after the labels are added according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes; inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model; inputting an image to be tested into the two-dimensional code detection model to obtain position information of one or more two-dimensional codes in the image to be tested; and identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested. By the method, the one or more two-dimensional codes in the image can be identified, and the accuracy and efficiency of identifying the one or more two-dimensional codes in the image are improved.
It should be understood that, in the embodiment of the present invention, the Processor 301 may be a Central Processing Unit (CPU), and the Processor may also be other general processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable gate arrays (FPGAs) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 302 may include a touch pad, a microphone, etc., and the output device 303 may include a display (LCD, etc.), a speaker, etc.
The memory 304 may include a read-only memory and a random access memory, and provides instructions and data to the processor 301. A portion of the memory 304 may also include non-volatile random access memory. For example, the memory 304 may also store device type information.
In specific implementation, the processor 301, the input device 302, and the output device 303 described in this embodiment of the present invention may execute the implementation described in the method embodiment shown in fig. 1 provided in this embodiment of the present invention, and may also execute the implementation of the two-dimensional code recognition apparatus based on deep learning described in fig. 2 in this embodiment of the present invention, which is not described herein again.
The embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the two-dimensional code recognition method based on deep learning described in the embodiment corresponding to fig. 1 is implemented, and the two-dimensional code recognition device based on deep learning in the embodiment corresponding to fig. 2 of the present invention may also be implemented, which is not described herein again.
The computer readable storage medium may be an internal storage unit of the deep learning based two-dimensional code recognition device according to any of the foregoing embodiments, for example, a hard disk or a memory of the deep learning based two-dimensional code recognition device. The computer readable storage medium may also be an external storage device of the deep learning based two-dimensional code recognition device, such as a plug-in hard disk, a Smart Memory Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the deep learning based two-dimensional code recognition device. Further, the computer-readable storage medium may further include both an internal storage unit and an external storage device of the deep learning-based two-dimensional code recognition device. The computer-readable storage medium is used for storing the computer program and other programs and data required by the deep learning-based two-dimensional code recognition device. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a computer-readable storage medium, which includes several instructions for causing a computer device (which may be a personal computer, a terminal, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned computer-readable storage media comprise: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. The computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.
It is emphasized that the data may also be stored in a node of a blockchain in order to further ensure the privacy and security of the data. The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The above description is only a part of the embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the present invention, and these modifications or substitutions should be covered within the scope of the present invention.
Claims (10)
1. A two-dimension code identification method based on deep learning is characterized by comprising the following steps:
constructing a sample image set, wherein the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimensional codes;
adding labels to one or more two-dimensional codes of each sample training image in the sample image set, and clustering each sample training image in the sample image set after the labels are added according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes;
inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model;
inputting an image to be tested into the two-dimensional code detection model to obtain position information of one or more two-dimensional codes in the image to be tested;
and identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested.
2. The method of claim 1, wherein the adding labels to the one or more two-dimensional codes of the respective sample training images in the sample image set comprises:
determining a position area corresponding to each two-dimensional code in each sample training image and an area object name corresponding to each two-dimensional code;
and adding a first label to the position coordinates of the position area corresponding to each two-dimensional code in each sample training image by using a specified marking tool, adding a second label to the area object name corresponding to each two-dimensional code in each sample training image, and storing each sample training image added with the first label and the second label in a specified file.
3. The method according to claim 1, wherein the clustering the sample training images in the labeled sample image set according to the position coordinates of the position areas corresponding to the two-dimensional codes comprises:
determining the height and width of each sample training image according to the position coordinates of the position area corresponding to each two-dimensional code;
and clustering the sample training images in the sample image set added with the labels according to the height and the width of each sample training image.
4. The method according to claim 3, wherein the determining the height and the width of each sample training image according to the position coordinates of the position area corresponding to each two-dimensional code comprises:
determining the minimum position coordinate and the maximum position coordinate of the position area corresponding to each two-dimensional code according to the position coordinate of the position area corresponding to each two-dimensional code;
and inputting the minimum position coordinate and the maximum position coordinate into a specified clustering algorithm model to obtain the height and the width of each sample training image.
5. The method according to claim 1, wherein before inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model, the method further comprises:
acquiring the size of a video memory, and adjusting the size of each sample training image in the clustered sample image set according to the size of the video memory;
inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model, wherein the method comprises the following steps:
inputting each sample training image after the size is adjusted into a preset deep learning network model to obtain a loss function value;
when the loss function value does not meet a preset condition, adjusting model parameters of the preset deep learning network model according to the loss function value, and inputting each sample training image with the adjusted size into the deep learning network model with the adjusted model parameters for iterative training;
and when the loss function value obtained after the iterative training meets the preset condition, determining that the two-dimensional code detection model is obtained through training.
6. The method of claim 5, wherein inputting the image to be tested into the two-dimensional code detection model to obtain the position information of one or more two-dimensional codes in the image to be tested comprises:
inputting the image to be tested into the two-dimensional code detection model to obtain a prediction position label and a prediction area object name of the two-dimensional code of the image to be tested;
and determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label and the predicted area object name of the two-dimensional code of the image to be tested.
7. The method of claim 6, wherein the determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label and the predicted area object name of the two-dimensional code of the image to be tested comprises:
determining the confidence coefficient of the two-dimensional code in the image to be tested according to the predicted position label and the predicted region object name of the two-dimensional code of the image to be tested;
and when the confidence coefficient is greater than a preset confidence coefficient threshold value, determining that the two-dimensional codes exist in the image to be tested, and determining the number of the two-dimensional codes in the image to be tested and the position information of each two-dimensional code according to the predicted position label, the predicted region object name and the confidence coefficient of the two-dimensional codes of the image to be tested.
8. The utility model provides a two-dimensional code recognition device based on degree of depth study which characterized in that includes:
the image processing device comprises a construction unit, a processing unit and a processing unit, wherein the construction unit is used for constructing a sample image set, the sample image set comprises a plurality of sample training images, and each sample training image comprises one or more two-dimensional codes;
the labeling unit is used for adding labels to one or more two-dimensional codes of each sample training image in the sample image set and clustering each sample training image in the sample image set added with labels according to the position coordinates of the position area corresponding to each two-dimensional code in the one or more two-dimensional codes;
the training unit is used for inputting each sample training image in the clustered sample image set into a preset deep learning network model for training to obtain a two-dimensional code detection model;
the determining unit is used for inputting the image to be tested into the two-dimensional code detection model to obtain the position information of one or more two-dimensional codes in the image to be tested;
and the identification unit is used for identifying the one or more two-dimensional codes in the image to be tested according to the position information of the one or more two-dimensional codes in the image to be tested.
9. A computer device comprising a processor and a memory, wherein the memory is configured to store a computer program and the processor is configured to invoke the computer program to perform the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which is executed by a processor to implement the method of any one of claims 1-7.
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