CN110766033A - Image processing method, image processing device, electronic equipment and storage medium - Google Patents
Image processing method, image processing device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides an image processing method, an image processing device, electronic equipment and a storage medium, which relate to the technical field of image processing, and the method comprises the following steps: acquiring and obtaining an image to be audited, wherein the image to be audited comprises information to be audited; verifying whether the image to be verified meets the verification condition by adopting at least one verification model corresponding to a preset verification rule, and acquiring a verification result; and displaying the auditing result. According to the method, the combination of the audit models is carried out through the multiple audit models on the terminal according to the preset rule and the information of the image to be audited, the acquired image to be audited is audited, and the audit result is fed back to the user, so that the image audit time is greatly saved, and the image audit efficiency is improved to a certain extent.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to an image processing method and apparatus, an electronic device, and a storage medium.
Background
With the rapid development of mobile terminals and the convenience brought by applications, more and more people are involved in information registration, identity authentication, and the like on Applications (APPs). Especially, in the APP which needs to verify identity information and the like, it often involves collecting image information such as certificates or bank cards.
In the prior art, generally, an image is shot through a terminal, then a solidified model is loaded through a software or hardware chip algorithm to extract content of the shot image, and the extracted image content is sent to a server to perform image quality detection and content authentication.
However, in the prior art, when the collected picture is sent to the server and the server fails to audit, the audit failure information is fed back to the terminal, and the user needs to shoot the picture again at the terminal and then report the picture again, so that the time of the whole audit process is long, and the image audit efficiency is reduced.
Disclosure of Invention
In view of the above, an object of the embodiments of the present application is to provide an image processing method, an image processing apparatus, an electronic device, and a storage medium, which are used to solve the problem of low image review efficiency in the prior art.
In a first aspect, an embodiment of the present application provides an image processing method, including:
acquiring and obtaining an image to be audited, wherein the image to be audited comprises information to be audited;
verifying whether the image to be verified meets the verification condition by adopting at least one verification model corresponding to a preset verification rule, and acquiring a verification result;
and displaying the auditing result.
Optionally, the verifying whether the image to be audited meets the audit condition by using at least one audit model corresponding to a preset audit rule, and obtaining the audit result includes:
verifying whether the image to be checked is a preset type image or not by adopting a type verification model;
and if the image to be audited is the preset type image, verifying whether the image quality of the image to be audited meets the preset requirement by adopting a quality verification model, and obtaining the audit result.
Optionally, the verifying, by using a type verification model, whether the image to be checked is a preset type image includes:
judging whether the shooting parameters of the image to be audited meet preset regulations or not by adopting a preset position frame;
and if the shooting parameters of the image to be checked meet preset regulations, verifying whether the image to be checked is a preset type image.
Optionally, the method further comprises:
acquiring a first sample image and classifying the first sample image;
respectively labeling the type information and shooting parameters of the classified first sample images to obtain a plurality of groups of labeled first sample images to be trained;
and training to obtain the type verification model by adopting the marked multiple groups of first sample images to be trained.
Optionally, the method further comprises:
acquiring a second sample image, and grouping according to the quality of each image in the second sample image;
respectively labeling the quality attribute information of the grouped second sample images, and acquiring a plurality of groups of labeled second sample images to be trained;
and training to obtain the quality verification model by adopting the marked multiple groups of second sample images to be trained.
Optionally, the verifying whether the image to be audited meets the audit condition by using at least one audit model corresponding to a preset audit rule, and after obtaining the audit result, the method further includes:
if the audit result marks that the image to be audited meets the audit condition, adopting a preset identification model to identify and obtain target content in the image to be audited;
and sending an audit notification to a server, wherein the audit notification comprises: user identification, the auditing result and the target content.
Optionally, the method further comprises:
acquiring a third sample image, the third sample image comprising: the marked target content;
and training to obtain the preset recognition model by adopting the third sample image.
Optionally, the verifying whether the image to be audited meets the audit condition by using at least one audit model corresponding to a preset audit rule, and after obtaining the audit result, the method further includes:
if the audit result marks that the image to be audited meets the audit condition, sending an audit instruction to a server, wherein the audit instruction is used for indicating the server to extract the target content of the image to be audited, and the audit instruction comprises: and the user identification, the auditing result and the image to be audited.
In a second aspect, an embodiment of the present application provides an image processing apparatus, including: the device comprises an acquisition module, a verification module and a display module;
the acquisition module is used for acquiring and acquiring an image to be audited, wherein the image to be audited comprises information to be audited; the verification module is used for verifying whether the image to be verified meets the verification condition by adopting at least one verification model corresponding to a preset verification rule, and obtaining a verification result; and the display module is used for displaying the auditing result.
Optionally, the verification module is specifically configured to verify whether the image to be checked is a preset type image by using a type verification model; and if the image to be audited is the preset type image, verifying whether the image quality of the image to be audited meets the preset requirement by adopting a quality verification model, and obtaining the audit result.
Optionally, the verification module is specifically configured to determine, by using a preset position frame, whether the shooting parameter of the image to be audited meets a preset specification; and if the shooting parameters of the image to be checked meet preset regulations, verifying whether the image to be checked is a preset type image.
Optionally, the method further comprises: the system comprises a classification module, a first labeling module and a first training module;
the classification module is used for acquiring a first sample image and classifying the first sample image; the first labeling module is used for labeling the type information and the shooting parameters of the classified first sample image respectively to obtain a plurality of groups of labeled first sample images to be trained; and the first training module is used for training and acquiring the type verification model by adopting the marked multiple groups of first sample images to be trained.
Optionally, the method further comprises: the system comprises a grouping module, a second labeling module and a second training module;
the grouping module is used for acquiring a second sample image and grouping according to the quality of each image in the second sample image; the second labeling module is used for labeling the quality attribute information of the grouped second sample images respectively and acquiring a plurality of groups of labeled second sample images to be trained; and the second training module is used for training and acquiring the quality verification model by adopting the marked multiple groups of second sample images to be trained.
Optionally, the method further comprises: the system comprises an identification module and a first sending module;
the identification module is used for identifying and acquiring target content in the image to be audited by adopting a preset identification model if the audit result marks that the image to be audited meets the audit condition; the first sending module is configured to send an audit notification to a server, where the audit notification includes: user identification, the auditing result and the target content.
Optionally, the method further comprises: an acquisition module and a third training module;
the acquiring module is configured to acquire a third sample image, where the third sample image includes: the marked target content; and the third training module is used for training to obtain the preset recognition model by adopting the third sample image.
Optionally, the system further comprises a second sending module; the second sending module is configured to send an audit instruction to a server if the audit result identifies that the image to be audited meets the audit condition, where the audit instruction is used to instruct the server to extract target content of the image to be audited, and the audit instruction includes: and the user identification, the auditing result and the image to be audited.
In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operated, the processor executing the machine-readable instructions to perform the steps of the image processing method as provided in the first aspect when executed.
In a fourth aspect, the present application provides a storage medium having a computer program stored thereon, where the computer program is executed by a processor to perform the steps of the image processing method as provided in the first aspect.
According to the image processing method and device, the electronic equipment and the storage medium, the combination of the audit models is carried out through the audit models on the terminal according to the preset rules and the information of the image to be audited, the acquired image to be audited is audited, and the audit result is fed back to the user, so that the image audit time is greatly saved, and the image audit efficiency is improved to a certain extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
FIG. 1 is a block diagram of an image processing system of some embodiments of the present application;
FIG. 2 illustrates a schematic diagram of exemplary hardware and software components of an electronic device of some embodiments of the present application;
FIG. 3 is a flow chart of an image processing method provided by an embodiment of the present application;
FIG. 4 is a flow chart of another image processing method provided in the embodiment of the present application;
FIG. 5 is a flow chart illustrating a further image processing method provided by an embodiment of the present application;
FIG. 6 is a flow chart of another image processing method provided in the embodiments of the present application;
FIG. 7 is a flow chart illustrating a further image processing method provided by an embodiment of the present application;
FIG. 8 is a flow chart of another image processing method provided in the embodiments of the present application;
FIG. 9 is a flow chart of another image processing method provided in the embodiments of the present application;
FIG. 10 is a schematic diagram illustrating an image processing apparatus according to an embodiment of the present application;
fig. 11 is a schematic structural diagram illustrating another image processing apparatus provided in an embodiment of the present application;
FIG. 12 is a schematic structural diagram of another image processing apparatus provided in an embodiment of the present application;
FIG. 13 is a schematic structural diagram of another image processing apparatus provided in an embodiment of the present application;
fig. 14 is a schematic structural diagram illustrating another image processing apparatus provided in an embodiment of the present application;
fig. 15 is a schematic structural diagram illustrating another image processing apparatus provided in an embodiment of the present application;
fig. 16 shows a schematic structural diagram of another image processing apparatus provided in the embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
In order to enable a person skilled in the art to use the present disclosure, the following embodiments are given in connection with a specific application scenario "authentication". It will be apparent to those skilled in the art that the general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the application. Although the present application is primarily described in the context of identity authentication, it should be understood that this is merely one exemplary embodiment. The present application may be applied to any other scenario. For example, the present application can be applied to information registration, account activation, and the like.
It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.
FIG. 1 is a block diagram of an image processing system of some embodiments of the present application. For example, the image processing system may be a platform for applications such as identity authentication, information registration, account activation, and the like.
The image processing system may include one or more of a server 110, a network 120, a terminal 130, and a database 140, and the server 110 may include a processor therein that performs operations of instructions.
In some embodiments, the server 110 may be a single server or a group of servers. The set of servers can be centralized or distributed (e.g., the servers 110 can be a distributed system). In some embodiments, the server 110 may be local or remote to the terminal. For example, server 110 may access information and/or data stored in terminal 130 or database 140, or any combination thereof, via network 120. As another example, server 110 may be directly connected to at least one of terminal 130 and database 140 to access stored information and/or data. In some embodiments, the server 110 may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (community cloud), a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof. In some embodiments, the server 110 may be implemented on an electronic device 200 having one or more of the components shown in FIG. 2 in the present application.
In some embodiments, the server 110 may include a processor. In some embodiments, a processor may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a reduced Instruction Set computer (reduced Instruction Set computer), a microprocessor, or the like, or any combination thereof.
In some embodiments, a database 140 may be connected to network 120 to communicate with one or more components in an image processing system (e.g., server 110, terminal 130, etc.). One or more components in the image processing system may access data or instructions stored in database 140 via network 120. In some embodiments, the database 140 may be directly connected to one or more components in the image processing system (e.g., the server 110, the terminal 130, etc.); alternatively, in some embodiments, database 140 may also be part of server 110.
Fig. 2 illustrates a schematic diagram of exemplary hardware and software components of an electronic device of some embodiments of the present application.
For example, a processor may be used on the electronic device 200 and to perform the functions herein.
The electronic device 200 may be a general purpose computer or a special purpose computer, both of which may be used to implement the resource handling methods of the present application. Although only a single computer is shown, for convenience, the functions described herein may be implemented in a distributed fashion across multiple similar platforms to balance processing loads.
For example, the electronic device 200 may include a network port 210 connected to a network, one or more processors 220 for executing program instructions, a communication bus 230, and a different form of storage medium 240, such as a disk, ROM, or RAM, or any combination thereof. Illustratively, the computer platform may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented in accordance with these program instructions. The electronic device 200 also includes an Input/Output (I/O) interface 250 between the computer and other Input/Output devices (e.g., keyboard, display screen).
For ease of illustration, only one processor is depicted in the electronic device 200. However, it should be noted that the electronic device 200 in the present application may also comprise a plurality of processors, and thus the steps performed by one processor described in the present application may also be performed by a plurality of processors in combination or individually. For example, if the processor of the electronic device 200 executes steps a and B, it should be understood that steps a and B may also be executed by two different processors together or separately in one processor. For example, a first processor performs step a and a second processor performs step B, or the first processor and the second processor perform steps a and B together.
For convenience of description, in the embodiment of the present application, an example of a resource processing method based on a block chain is provided only by taking an application program sharing an automobile service as an example, and in practical applications, the embodiment of the present application does not limit the method.
In addition, it should be noted that the resource processing method based on the blockchain effectively utilizes the blockchain technology and the attribute that data on the blockchain is not changeable, and can effectively avoid the possibility that the pre-stored resources of the user are manually operated when the user uses the shared product, so that the user resource use is transparent, and the resource state can be checked at any time, thereby ensuring the benefit of the user and improving the user experience.
Fig. 3 shows a schematic flowchart of an image processing method provided in an embodiment of the present application, where an execution subject of the embodiment may be a terminal, or may be a device with a processing function, such as a server and a processor, as shown in fig. 3, the method includes:
s101, acquiring an image to be checked, wherein the image to be checked comprises information to be checked.
Generally, when a user registers software or applies for application access authorization, a portrait of the user or a related certificate photo of the user needs to be collected and matched with related information of the user stored in a background database of a server for identity verification.
Optionally, an image to be checked is obtained, where the image to be checked may be a user head portrait shot in real time, or an identification card image, a bank card image, or a driver license image of the user. The terminal can be used for carrying out image acquisition by the camera, or the terminal can be used for carrying out external camera, and image acquisition is carried out, and the terminal is not limited specifically.
The acquired image to be audited includes information to be audited, for example: when the collected image is a face image of a user, the corresponding information to be audited can be the characteristics of the five sense organs and the like; when the acquired identity card image is acquired, the corresponding information to be audited may include: identity card number, user name, identity card portrait page, identity card national emblem page, etc.; when the collected image is a bank card image, the corresponding information to be audited may include: bank card number, bank name, etc. Specifically, the acquired information to be audited is different according to different image types.
S102, verifying whether the image to be verified meets the verification condition by adopting at least one verification model corresponding to the preset verification rule, and obtaining a verification result.
In step S101, to-be-audited information of the to-be-audited image is acquired, where the to-be-audited information is also image feature information included in the to-be-audited image. The terminal can audit the acquired information to be audited according to an audit model which is trained in advance and packaged in the terminal, and whether the acquired image to be audited meets the requirements or not is verified.
It should be noted that, in the present application, different models need to be provided for different audit information, for example, a model for processing a human face is different from a model for processing a vehicle. Correspondingly, a uniform model call interface may be configured in the client, and the types of audit information that may be needed for different functions that are enabled by the user at the client are also different, for example: starting a user identity auditing function, wherein the corresponding acquired image to be audited can be a face image, an identity card image and the like; the client side can call one or more corresponding models in a self-adaptive mode through the model call interface to perform operations such as image detection and identification without additionally changing specific contents of the client side or the terminal.
Optionally, according to the information to be audited of different images to be audited, different audit models can be correspondingly selected for image auditing, for example: for the image text information, an image type classification model, a quality classification model or a text content classification model and the like can be adopted for auditing; for the audio and video images, a sound quality classification model, a video target detection model or a video fluency detection model and the like can be adopted for auditing.
In some embodiments, the audit models correspond to preset audit rules, and the preset audit rules of the audit models are adopted to audit the information to be audited. For example: for the identity card image, the image classification can be carried out according to the rules of the image position frame or the certificate number length and the like in the image type classification model. Usually, the number of the identification card is 18 bits, the number of the bank card is 19 bits, and then whether the type of the identification card is correct or not can be determined according to the length of the target number, in addition, the size of the identification card is different from that of the bank card, and auxiliary judgment and the like can be carried out according to the size of the set position frame.
S103, displaying the auditing result.
Optionally, the image review may be performed through a plurality of review models integrated on the terminal, and the terminal may obtain a review result and feed the review result back to the user. Optionally, the display may be performed through a terminal display screen, and the result display may also be performed through an indicator light, for example: and if the audit is passed, the terminal indicator light is green, and if the audit is not passed, the indicator light is red, and the like. The embodiment is not particularly limited with respect to the display manner of the result.
In summary, according to the image processing method provided by the embodiment of the application, the combination of the audit models is performed according to the preset rules and the information of the image to be audited through the multiple audit models on the terminal, the acquired image to be audited is audited, and the audit result is fed back to the user, so that the image audit time is greatly saved, and the image audit efficiency is improved to a certain extent.
Fig. 4 shows a schematic flow chart of another image processing method provided in an embodiment of the present application, and further, as shown in fig. 4, verifying whether an image to be audited meets an audit condition by using at least one audit model corresponding to a preset audit rule, and obtaining an audit result includes:
s201, verifying whether the image to be checked is a preset type image or not by adopting a type verification model.
It should be noted that the type verification model is used for judging the type of the acquired image to be audited, that is, performing preliminary image judgment, and if the type of the acquired image is incorrect, the subsequent auditing process is directly terminated to improve the image auditing efficiency.
The types of images to be audited may include: for the identity card image, the driving license image, the bank card and the like, the contents of the front and back images of the identity card image are different from those of the driving license image and the front and back images of the bank card, and the image type can be judged by adopting a type verification model according to the characteristic information and the preset type image information.
S202, if the image to be checked is the preset type image, a quality verification model is adopted to verify whether the image quality of the image to be checked meets the preset requirement, and a checking result is obtained.
And for the verification result obtained by the type verification model, if the verification result is that the image to be verified is the preset type image, the quality verification model can be used for further verifying whether the quality of the obtained image meets the requirement.
Generally, when identity information verification is performed or information registration is performed, the quality requirement of acquired image information is relatively high, whether the image is clear or not determines whether specific content information in the image can be correctly extracted to a great extent, and for the image to be checked meeting the type requirement, checking can be further performed through a quality verification model, and similarly, a checking result is obtained.
Fig. 5 shows a schematic flowchart of another image processing method provided in an embodiment of the present application, and further, as shown in fig. 5, verifying whether an image to be checked is a preset type image by using a type verification model includes:
s301, judging whether the shooting parameters of the image to be audited meet preset regulations or not by adopting a preset position frame.
It should be noted that, before the image type is checked, whether the angle, position, distance, etc. of the acquired image meet the requirements or not can be judged through the preset position frame in the image acquisition process, for the different types of images, the sizes of the corresponding position frames can also be different, and when the image is acquired and the image to be checked is located in the position frame, the image to be checked meets the preset rules; when a portion of the image is outside the frame, the captured image is incomplete and type verification is also not possible.
S302, if the shooting parameters of the image to be checked meet preset regulations, verifying whether the image to be checked is a preset type image.
After the parameter verification is performed according to the preset position frame, if the shooting parameters meet the preset regulations, the type verification model can be further adopted to perform the image type verification, and a verification result is obtained.
Fig. 6 shows a schematic flowchart of another image processing method provided in the embodiment of the present application, and further, as shown in fig. 6, the method further includes:
s401, acquiring a first sample image and classifying the first sample image.
Before image review is performed using the review model, model training is also required. Optionally, a first sample image is obtained, wherein the first sample image may include multiple types of images to be audited, such as: an identification card image, a bank card image, a driver's license image, etc., which are classified.
Alternatively, the classification may be performed manually, or may be performed by an image classification algorithm. In some embodiments, a Network detection model (e.g., YOLO2, young Only live one) for extracting features by using a convolutional Network (e.g., VGG (Visual Geometry Group, deep convolutional Network), resource Network (Residual Network), etc.) may be used as the feature extraction method. Specifically, the method is not limited to the above method, and other image feature extraction and classification algorithms may be adopted, for example: wavelet transform, SVM (Support Vector Machine), and the like.
S402, respectively labeling the type information and shooting parameters of the classified first sample images, and obtaining a plurality of groups of labeled first sample images to be trained.
After the obtained first sample image is classified, a plurality of groups of training samples are obtained, and the obtained plurality of groups of training sample images are labeled, for example: and for the identity card image class, marking all images contained in the identity card image class as the identity card, and for the bank card image class, marking all images contained in the bank card image class as the bank card and the like. Thereby obtaining a plurality of training samples.
And S403, training to obtain a type verification model by adopting the marked multiple groups of first sample images to be trained.
It should be noted that, labeling is performed on the classified multiple groups of images, so that the labeled images can be conveniently input into a computer, and the computer trains and acquires the type verification model according to the labeling information.
Fig. 7 shows a schematic flowchart of another image processing method provided in an embodiment of the present application, and further, as shown in fig. 7, the method further includes:
s501, second sample images are obtained and grouped according to the quality of each image in the second sample images.
Likewise, as in step S401 above, the quality verification model also needs to be trained and acquired. Optionally, a second sample image is acquired, wherein an image in the second sample image comprises: and the second sample images are grouped according to various quality conditions in the second sample images, such as sharp images, blurred images or reflected images. Wherein, the clear images are in a group, the fuzzy images are in a group, the reflective images are in a group, and the like. Alternatively, the image quality is not limited to sharpness, blur, and reflection, but may be a defect, wrinkle, or the like.
And S502, respectively labeling the quality attribute information of the grouped second sample images, and acquiring a plurality of groups of labeled second sample images to be trained.
Similarly, as in step S402, labeling the grouped plurality of sample information to be trained, and acquiring a plurality of groups of second sample images to be trained after labeling.
Optionally, in some embodiments, an SVM support vector machine may also be used for image classification, and a Res18(Residual Network-18) deep Network classification model may also be used for image classification.
And S503, training and obtaining a quality verification model by adopting the marked multiple groups of second sample images to be trained.
Similarly, the labeled multiple groups of second sample images to be trained can be used as the input of the computer, and the quality verification model can be obtained according to the training of the image classification algorithm.
Fig. 8 shows a schematic flow chart of another image processing method provided in the embodiment of the present application, further, as shown in fig. 8, verifying whether an image to be audited meets an audit condition by using at least one audit model corresponding to a preset audit rule, and after obtaining an audit result, the method further includes:
s601, if the audit result marks that the image to be audited meets the audit condition, identifying and obtaining the target content in the image to be audited by adopting a preset identification model.
The acquired information of the image to be audited is preliminarily audited through the type verification model and the quality verification model respectively, and only whether the type and the quality of the required image are qualified is judged, but whether the required image is the certificate image of the user cannot be judged, so that the specific text content information in the image to be audited is usually extracted to carry out accurate verification, and the certificate information of the user A is prevented from being audited as the certificate information of the user B by mistake.
Optionally, after the image to be checked passes the verification of the type verification model and the quality verification model, the preset identification model may be further adopted to verify the target content in the image to be checked.
S602, sending an audit notice to the server, wherein the audit notice comprises: user identification, an audit result and target content.
Optionally, the preset identification model may be integrated on a terminal, and the terminal performs the audit on the image to be audited that passes the preliminary audit by using the preset identification model. And sending the audit result to a server, specifically, sending the target content in the image to be audited to the server, so that the server can store the content information of the image to be audited corresponding to the user identifier.
Fig. 9 shows a schematic flowchart of another image processing method provided in the embodiment of the present application, and further, the method further includes:
s701, obtaining a third sample image, wherein the third sample image comprises: the marked target content.
Similarly, a preset recognition model needs to be trained. Optionally, a third sample image is obtained, in particular, the annotated target content included in the third sample image is obtained. It should be noted that the preset recognition model may be formed by performing character labeling training on key fields in the image. For example: for the identity card image, the corresponding target content can be information such as an identity card number, a name, a birthday and the like; for the bank card image, the corresponding target content may be a bank card number, a bank name, and the like.
And S702, training and obtaining a preset recognition model by adopting a third sample image.
Optionally, the preset recognition model may be obtained by training through the labeled third sample image and by using a corresponding recognition method.
In some embodiments, text localization, rule segmentation, and CNN convolutional neural network algorithms may be used for training. The model training may also be performed by using methods such as CTPN (natural scene Text detection), CRNN (Convolutional Neural Network), or densneet (Dense Convolutional Network) with CTC (sequential temporal classification algorithm), and the like, and specifically, is not limited to the methods listed herein.
Further, at least one auditing model corresponding to a preset auditing rule is adopted to verify whether the image to be audited meets the auditing condition, and after the auditing result is obtained, the method further comprises the following steps:
if the image to be audited meets the audit condition, an audit instruction is sent to the server, wherein the audit instruction is used for indicating the server to extract the target content of the image to be audited, and the audit instruction comprises the following steps: user identification, an audit result and an image to be audited.
Optionally, the image type and quality can be checked at the terminal, and the checking result is sent to the server, and the server can extract the target content in the sent image to be checked, and check the extracted target content according to the preset identification model.
Optionally, the user identifier may be a user terminal number, a mobile phone number of the user, or the like. In addition, the server can also match the target content in the image to be audited corresponding to the extracted user identification through the user information stored in the server background so as to audit the content.
It should be noted that, when the audit is passed, the server stores the audit information, and when the audit is not passed, the image to be audited may be obtained again through the terminal until the audit is passed.
According to the image processing method provided by the embodiment of the application, the combination of the audit models is carried out according to the preset rules and the information of the images to be audited through the plurality of audit models on the terminal, the acquired images to be audited are audited, the audit results are fed back to the user, meanwhile, the audit results can be fed back in time when the audit is not passed, the images are acquired again for auditing again, the image audit time is greatly saved, and the image audit efficiency is improved to a certain extent.
Fig. 10 is a schematic structural diagram of an image processing apparatus provided in an embodiment of the present application, and as shown in fig. 10, the apparatus includes: an acquisition module 801, a verification module 802 and a display module 803;
the acquisition module 801 is used for acquiring and acquiring an image to be audited, wherein the image to be audited includes information to be audited; the verification module 802 is configured to verify whether the image to be verified meets a verification condition by using at least one verification model corresponding to a preset verification rule, and obtain a verification result; and the display module 803 is used for displaying the auditing result.
Further, the verification module 802 is specifically configured to verify whether the image to be checked is a preset type image by using a type verification model; and if the image to be audited is the preset type image, adopting a quality verification model to verify whether the image quality of the image to be audited meets the preset requirement, and obtaining an audit result.
Further, the verification module 802 is specifically configured to determine whether the shooting parameters of the image to be checked meet preset regulations by using a preset position frame; and if the shooting parameters of the image to be checked conform to the preset regulations, verifying whether the image to be checked is a preset type image.
Fig. 11 shows a schematic structural diagram of another image processing apparatus provided in an embodiment of the present application, and as shown in fig. 11, the apparatus further includes: a classification module 804, a first labeling module 805, and a first training module 806;
a classification module 804, configured to obtain a first sample image and classify the first sample image; a first labeling module 805, configured to label type information and shooting parameters of the classified first sample image respectively, and obtain a plurality of groups of labeled first sample images to be trained; the first training module 806 is configured to train and obtain a type verification model by using the labeled groups of first to-be-trained sample images.
Fig. 12 is a schematic structural diagram of another image processing apparatus provided in an embodiment of the present application, and as shown in fig. 12, the apparatus further includes: a grouping module 807, a second labeling module 808, and a second training module 809;
a grouping module 807 for obtaining the second sample image and grouping according to the quality of each image in the second sample image; a second labeling module 808, configured to label quality attribute information of the grouped second sample images respectively, and obtain a plurality of groups of labeled second sample images to be trained; and the second training module 809 is configured to train to obtain a quality verification model by using the labeled multiple groups of second sample images to be trained.
Fig. 13 is a schematic structural diagram of another image processing apparatus provided in an embodiment of the present application, and as shown in fig. 13, the image processing apparatus further includes: an identification module 810 and a first transmission module 811;
the identification module 810 is configured to identify and obtain target content in the image to be audited by using a preset identification model if the audit result identifies that the image to be audited meets the audit condition; a first sending module 811, configured to send an audit notification to the server, where the audit notification includes: user identification, an audit result and target content.
Fig. 14 shows a schematic structural diagram of another image processing apparatus provided in an embodiment of the present application, as shown in fig. 14, further includes: an acquisition module 812 and a third training module 813;
an obtaining module 812 configured to obtain a third sample image, where the third sample image includes: the marked target content; and a third training module 813, configured to train and obtain the preset recognition model by using the third sample image.
Fig. 15 is a schematic structural diagram of another image processing apparatus according to an embodiment of the present application, as shown in fig. 15, further including a second sending module 814; a second sending module 814, configured to send an audit instruction to the server if the audit result identifies that the image to be audited meets the audit condition, where the audit instruction is used to instruct the server to extract the target content of the image to be audited, and the audit instruction includes: user identification, an audit result and an image to be audited.
The apparatus may be configured to execute the method provided by the method embodiment, and the specific implementation manner and the technical effect are similar and will not be described herein again.
Fig. 16 is a schematic structural diagram of another image processing apparatus provided in an embodiment of the present application, and as shown in fig. 16, the apparatus includes: a processor 901 and a memory 902, wherein: the memory 902 is used for storing programs, and the processor 901 calls the programs stored in the memory 902 to execute the above method embodiments. The specific implementation and technical effects are similar, and are not described herein again.
The apparatus may be integrated in a device such as a terminal or a server, and is not limited in this application.
Optionally, the invention also provides a program product, for example a computer-readable storage medium, comprising a program which, when being executed by a processor, is adapted to carry out the above-mentioned method embodiments.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to corresponding processes in the method embodiments, and are not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules 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 of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 application 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 functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (18)
1. An image processing method, comprising:
acquiring and obtaining an image to be audited, wherein the image to be audited comprises information to be audited;
verifying whether the image to be verified meets the verification condition by adopting at least one verification model corresponding to a preset verification rule, and acquiring a verification result;
and displaying the auditing result.
2. The method according to claim 1, wherein the verifying whether the image to be audited meets the audit condition by using at least one audit model corresponding to a preset audit rule, and obtaining the audit result, comprises:
verifying whether the image to be checked is a preset type image or not by adopting a type verification model;
and if the image to be audited is the preset type image, verifying whether the image quality of the image to be audited meets the preset requirement by adopting a quality verification model, and obtaining the audit result.
3. The method according to claim 2, wherein the verifying whether the image to be checked is a preset type image by using a type verification model comprises:
judging whether the shooting parameters of the image to be audited meet preset regulations or not by adopting a preset position frame;
and if the shooting parameters of the image to be checked meet preset regulations, verifying whether the image to be checked is a preset type image.
4. The method of claim 3, further comprising:
acquiring a first sample image and classifying the first sample image;
respectively labeling the type information and shooting parameters of the classified first sample images to obtain a plurality of groups of labeled first sample images to be trained;
and training to obtain the type verification model by adopting the marked multiple groups of first sample images to be trained.
5. The method of claim 2, further comprising:
acquiring a second sample image, and grouping according to the quality of each image in the second sample image;
respectively labeling the quality attribute information of the grouped second sample images, and acquiring a plurality of groups of labeled second sample images to be trained;
and training to obtain the quality verification model by adopting the marked multiple groups of second sample images to be trained.
6. The method according to claim 1, wherein the verifying whether the image to be audited meets the audit condition by using at least one audit model corresponding to a preset audit rule, and after obtaining the audit result, further comprising:
if the audit result marks that the image to be audited meets the audit condition, adopting a preset identification model to identify and obtain target content in the image to be audited;
and sending an audit notification to a server, wherein the audit notification comprises: user identification, the auditing result and the target content.
7. The method of claim 6, further comprising:
acquiring a third sample image, the third sample image comprising: the marked target content;
and training to obtain the preset recognition model by adopting the third sample image.
8. The method according to claim 1, wherein the verifying whether the image to be audited meets the audit condition by using at least one audit model corresponding to a preset audit rule, and after obtaining the audit result, further comprising:
if the audit result marks that the image to be audited meets the audit condition, sending an audit instruction to a server, wherein the audit instruction is used for indicating the server to extract the target content of the image to be audited, and the audit instruction comprises: and the user identification, the auditing result and the image to be audited.
9. An image processing apparatus characterized by comprising: the device comprises an acquisition module, a verification module and a display module;
the acquisition module is used for acquiring and acquiring an image to be audited, wherein the image to be audited comprises information to be audited;
the verification module is used for verifying whether the image to be verified meets the verification condition by adopting at least one verification model corresponding to a preset verification rule, and obtaining a verification result;
and the display module is used for displaying the auditing result.
10. The apparatus according to claim 9, wherein the verification module is specifically configured to verify whether the image to be checked is a preset type image by using a type verification model; and if the image to be audited is the preset type image, verifying whether the image quality of the image to be audited meets the preset requirement by adopting a quality verification model, and obtaining the audit result.
11. The apparatus according to claim 10, wherein the verification module is specifically configured to determine whether the shooting parameters of the image to be reviewed meet preset regulations by using a preset position frame; and if the shooting parameters of the image to be checked meet preset regulations, verifying whether the image to be checked is a preset type image.
12. The apparatus of claim 11, further comprising: the system comprises a classification module, a first labeling module and a first training module;
the classification module is used for acquiring a first sample image and classifying the first sample image;
the first labeling module is used for labeling the type information and the shooting parameters of the classified first sample image respectively to obtain a plurality of groups of labeled first sample images to be trained;
and the first training module is used for training and acquiring the type verification model by adopting the marked multiple groups of first sample images to be trained.
13. The apparatus of claim 10, further comprising: the system comprises a grouping module, a second labeling module and a second training module;
the grouping module is used for acquiring a second sample image and grouping according to the quality of each image in the second sample image;
the second labeling module is used for labeling the quality attribute information of the grouped second sample images respectively and acquiring a plurality of groups of labeled second sample images to be trained;
and the second training module is used for training and acquiring the quality verification model by adopting the marked multiple groups of second sample images to be trained.
14. The apparatus of claim 9, further comprising: the system comprises an identification module and a first sending module;
the identification module is used for identifying and acquiring target content in the image to be audited by adopting a preset identification model if the audit result marks that the image to be audited meets the audit condition;
the first sending module is configured to send an audit notification to a server, where the audit notification includes: user identification, the auditing result and the target content.
15. The apparatus of claim 14, further comprising: an acquisition module and a third training module;
the acquiring module is configured to acquire a third sample image, where the third sample image includes: the marked target content;
and the third training module is used for training to obtain the preset recognition model by adopting the third sample image.
16. The apparatus of claim 9, further comprising a second sending module; the second sending module is configured to send an audit instruction to a server if the audit result identifies that the image to be audited meets the audit condition, where the audit instruction is used to instruct the server to extract target content of the image to be audited, and the audit instruction includes: and the user identification, the auditing result and the image to be audited.
17. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the image processing method according to any one of claims 1 to 8.
18. A storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, performs the steps of the image processing method according to any one of claims 1 to 8.
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| CN113010721B (en) * | 2021-03-17 | 2023-10-10 | 北京百度网讯科技有限公司 | Picture auditing method and device, electronic equipment and storage medium |
| CN113011989B (en) * | 2021-03-23 | 2024-04-23 | 腾讯科技（深圳）有限公司 | Object verification method, device, equipment and storage medium |
| CN113011989A (en) * | 2021-03-23 | 2021-06-22 | 腾讯科技（深圳）有限公司 | Object verification method, device, equipment and storage medium |
| CN113486793A (en) * | 2021-07-05 | 2021-10-08 | 广州智选网络科技有限公司 | Shopping feedback method based on big data |
| CN117011616B (en) * | 2023-10-07 | 2024-01-26 | 腾讯科技（深圳）有限公司 | Image content auditing method and device, storage medium and electronic equipment |
| CN117011616A (en) * | 2023-10-07 | 2023-11-07 | 腾讯科技（深圳）有限公司 | Image content auditing method and device, storage medium and electronic equipment |
| CN118262155A (en) * | 2024-03-26 | 2024-06-28 | 北京数美时代科技有限公司 | Wind control image content security auditing method, system, equipment and medium |
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| CN110766033B (en) | 2021-02-23 |
| CN112906741A (en) | 2021-06-04 |
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