Image acquisition

Abstract

One or more embodiments of the present description disclose an image acquisition method and apparatus, and an electronic device. The method comprises: acquiring an image captured for a target object; then when the image does not satisfy a preset image quality condition, inputting the image into a pre-trained reason generation model, determining a location, which does not satisfy the preset image quality condition, in the image by means of the reason generation model, and generating image recapture guide information for the determined location, wherein the reason generation model is a model used for detecting a reason causing the image not to satisfy the preset image quality condition, and providing a corresponding improvement suggestion for a detection result; and finally performing image capture on the target object again on the basis of the image recapture guide information.

Description

Image acquisition Technical Field

[0001] This document relates to the field of image recognition technology, and in particular to an image acquisition method, device and electronic equipment. Background Art

[0002] In the field of image recognition, when capturing an image of a target subject, it often happens that the image obtained in a single shot fails to meet user satisfaction. Take the eKYC (electronic Know Your Customer) process as an example. In the eKYC process, users are required to take photos of their ID and face separately to complete identity verification. If the quality of the image captured by the user is low (e.g., blurry, with high light levels), it will affect the accuracy of ID and face recognition, resulting in the user being unable to successfully complete account opening. Therefore, how to capture images to obtain high-quality images that meet user satisfaction is a key issue in improving user experience.

[0003] Currently, when the image obtained by a single shot does not meet the user's satisfaction, the method usually adopted is to continue shooting until the image obtained meets the user's needs. Therefore, it is necessary to provide a more efficient image acquisition method that can reduce the number of repeated shots.

[0004] Summary of the Invention

[0005] On the one hand, one or more embodiments of the present specification provide an image acquisition method, comprising: acquiring an image taken of a target object; when the image does not meet a preset image quality condition, inputting the image into a pre-trained cause generation model, determining the position in the image that does not meet the preset image quality condition through the cause generation model, and generating image reshooting guidance information for the determined position, wherein the cause generation model is a model for detecting the cause of the image not meeting the preset image quality condition and providing corresponding improvement suggestions for the detection result; and re-taking the image of the target object according to the image reshooting guidance information.

[0006] On the other hand, one or more embodiments of the present specification provide an image acquisition device, comprising: an image acquisition module, which acquires an image taken of a target object; an image reshooting guidance information generation module, which, when the image does not meet the preset image quality conditions, inputs the image into a pre-trained cause generation model, determines the position in the image that does not meet the preset image quality conditions through the cause generation model, and generates image reshooting guidance information for the determined position, wherein the cause generation model is a model for detecting the cause that causes the image to not meet the preset image quality conditions and providing corresponding improvement suggestions for the detection results; and a control module, which re-takes an image of the target object according to the image reshooting guidance information.

[0007] On the other hand, one or more embodiments of the present specification provide an electronic device, comprising: a processor; and a memory arranged to store computer-executable instructions, which, when the executable instructions are executed, enable the processor to: obtain an image taken of a target object; when the image does not meet a preset image quality condition, input the image into a pre-trained cause generation model, determine the position in the image that does not meet the preset image quality condition through the cause generation model, and generate image reshooting guidance information for the determined position, wherein the cause generation model is a model for detecting the cause of the image not meeting the preset image quality condition and providing corresponding improvement suggestions for the detection result; and re-take an image of the target object according to the image reshooting guidance information.

[0008] On the other hand, one or more embodiments of the present specification provide a storage medium for storing computer-executable instructions, which implement the following process when executed by a processor: obtaining an image taken of a target object; when the image does not meet a preset image quality condition, inputting the image into a pre-trained cause generation model, determining the position in the image that does not meet the preset image quality condition through the cause generation model, and generating image reshooting guidance information for the determined position, wherein the cause generation model is a model for detecting the cause of the image not meeting the preset image quality condition and providing corresponding improvement suggestions for the detection result; and re-taking the image of the target object according to the image reshooting guidance information. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate one or more embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in one or more embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0010] FIG1 is a schematic flow chart of an image acquisition method according to an embodiment of the present specification;

[0011] FIG2 is a schematic flow chart of an image acquisition method according to another embodiment of this specification;

[0012] FIG3 is a schematic flow chart of an image acquisition method according to another embodiment of this specification;

[0013] FIG4 is a schematic diagram of an image acquisition method according to an embodiment of the present specification;

[0014] FIG5 is a schematic block diagram of an image acquisition device according to an embodiment of this specification;

[0015] FIG6 is a schematic block diagram of an electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0016] One or more embodiments of this specification provide an image acquisition method, device, and electronic device to solve current problems.

[0017] In order to enable those skilled in the art to better understand the technical solutions in one or more embodiments of this specification, the technical solutions in one or more embodiments of this specification will be clearly and completely described below in conjunction with the drawings in one or more embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on one or more embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this document.

[0018] As shown in Figure 1, an embodiment of this specification provides an image acquisition method. The method can be performed by a terminal device, such as a mobile phone, a tablet computer, or a computer device such as a laptop or desktop computer, or an IoT device (such as a smartwatch or in-vehicle device). The method may include the following steps.

[0019] In step S102 , an image captured of a target object is acquired.

[0020] The methods of one or more embodiments of this specification can be applied to scenarios where certain quality requirements are placed on the captured images, such as capturing images of scenery, faces, or ID cards using a mobile phone or smart camera. In one embodiment of this specification, the target object can be an ID card, a face, or a scene. In actual applications, the target object can also include multiple objects, for example, two target objects, namely, an ID card and a face.

[0021] Taking the eKYC process as an example, the user needs to take photos of the ID and face respectively to complete the identity verification. According to the above step S102, the target object may include the ID and the face. The ID and the face can be photographed separately by the camera component of the terminal device to obtain the corresponding ID image and face image, or the ID and the face can be captured by the camera component of the terminal device to obtain an image containing both the ID and the face.

[0022] In step S104, when the image does not meet the preset image quality conditions, the image is input into a pre-trained cause generation model, and the cause generation model is used to determine the position in the image that does not meet the preset image quality conditions, and generate image reshooting guidance information for the determined position. The cause generation model is a model used to detect the reasons why the image does not meet the preset image quality conditions and provide corresponding improvement suggestions for the detection results.

[0023] Among them, the preset image quality conditions are determined based on the user's shooting requirements for the target object. For example, when shooting a document or a face in the eKYC process, the preset image quality conditions may include one or more of the following: the shot image reaches a preset pixel size, the shot image reaches a preset resolution, and the shot image reaches a set exposure parameter. Accordingly, images that do not meet the preset image quality conditions generally include: images with overexposed highlights (i.e., the exposure of the highlight area is higher than the preset exposure threshold), images that are too dark (i.e., the image brightness is lower than the preset brightness threshold and / or the image contrast is lower than the preset contrast threshold, etc.), blurred images (i.e., the pixels are lower than the preset pixel threshold, etc.), etc.

[0024] It should be noted that the image obtained through step S102 may or may not meet the preset image quality conditions. There are many ways to determine whether the image meets the preset image quality conditions, such as: pre-setting multiple image quality conditions, and determining whether each image quality condition is met by comparing one by one, thereby determining whether the image meets the preset drawing quality conditions. Alternatively, based on the image quality conditions, a corresponding algorithm (such as a classification algorithm or a random forest algorithm, etc.) can be selected, and a corresponding model can be constructed using the selected algorithm. The model can be used to determine whether the captured image meets the preset image quality conditions, etc.

[0025] In one embodiment of the present specification, a cause generation model is pre-trained. The cause generation model determines the specific location where the quality problem occurs in the image taken last time, detects the cause of the quality problem, and finally generates image reshoot guidance information for the specific location, thereby providing targeted improvement suggestions for the next image shooting. The input data of the cause generation model is an image that does not meet the preset image quality conditions, which may specifically include the image, the information contained in the image, etc. The output result of the cause generation model is the image reshoot guidance information, which can be presented in text form, for example: "The highlight in the upper left corner of the document is overexposed, please adjust the highlight area in the upper left corner when shooting", "The overall image is blurred, please keep the phone still when shooting", etc. The image reshoot guidance information can also be played in the form of voice broadcast.

[0026] In one embodiment, the image reshooting guidance information may include at least: specific locations of quality issues on corresponding shooting objects and shooting suggestions, and may also include reasons for unqualified quality.

[0027] In one embodiment of this specification, the pre-trained cause generation model can use an image-to-text training model. This training model can be constructed based on a neural network, such as a convolutional neural network or a transformer. The loss function for training the cause generation model can use a standard loss function used in text generation.

[0028] In one embodiment of the present specification, a method for determining the positions in an image that do not meet preset image quality conditions is provided. The reason generation model may adopt a method of overall comparison of the captured image with a standard image, or an image segmentation method, that is, a segmentation algorithm is used to determine which pixel points in the captured image do not meet the preset pixel size.

[0029] In step S106 , the target object is re-imaged according to the image re-shooting guidance information.

[0030] After the image reshooting guidance information is generated in step S104, the generated image reshooting guidance information can be used to guide the user to reshoot the target object in a targeted manner, thereby completing the image acquisition operation with fewer shots, which is conducive to improving image shooting efficiency.

[0031] The embodiments of the present specification provide an image acquisition method, which first acquires an image captured of a target object, then determines whether the image meets a preset image quality condition, and when the image does not meet the preset image quality condition, inputs the image into a pre-trained cause generation model, determines the location in the image that does not meet the preset image quality condition through the cause generation model, and generates image retake guidance information for the determined location, and finally, based on the generated image retake guidance information, retakes the target object, thereby completing image acquisition. When the image does not meet the preset image quality condition, the pre-trained cause generation model can detect the cause of the image not meeting the preset image quality condition during the previous capture process, obtain the specific cause of the quality problem, and locate the location in the image that did not meet the preset image quality condition in the previous capture, obtain the specific location of the quality problem, and finally generate image retake guidance information for the determined location and feedback it to the user, thereby providing targeted improvement suggestions for the next capture, which can effectively reduce the number of repeated captures, improve image capture efficiency, and thus improve user experience.

[0032] Furthermore, there are many ways to train the cause generation model in the above step S102. An optional processing method is provided below. For details, please refer to the following steps S1022-S1024.

[0033] In step S1022 , a plurality of image samples captured for different shooting objects, a reason why each image sample does not meet a preset image quality condition, and a position in each image sample that does not meet the preset image quality condition are obtained.

[0034] In implementation, the multiple image samples may include one or more of the following images: historical images of different subjects, original images of each subject that do not meet preset image quality conditions, and images simulated based on the original images of each subject.

[0035] In one embodiment, the multiple image samples obtained for different shooting objects, the reasons why each image sample does not meet the preset image quality conditions, and the positions in each image sample that do not meet the preset image quality conditions can be directly input into the cause generation model as input data for model training.

[0036] In another embodiment, the process of the above step S1022 can be performed as the following steps A1 and A2.

[0037] Step A1: Acquire multiple image samples taken for different objects.

[0038] Step A2: respectively determining the reason why each image sample does not meet the preset image quality condition and the position of each image sample that does not meet the preset image quality condition.

[0039] It can be seen from the above steps A1 and A2 that in another embodiment, multiple image samples taken for different shooting objects can be first obtained, and the multiple image samples can be used as input data for model training. Then, the cause generation model determines the reason why each image sample does not meet the preset image quality conditions and the position of each image sample that does not meet the preset image quality conditions based on the multiple image samples through the corresponding network layer set therein. Then, the network layer set in the cause generation model for generating reason text is used to generate corresponding image reshooting guidance information based on the determined reason why each image sample does not meet the preset image quality conditions and the position of each image sample that does not meet the preset image quality conditions.

[0040] In another embodiment, the reasons why the image sample does not meet the preset image quality conditions include: a first quality problem and a second quality problem, and the first quality problem is an image quality problem in which the image granularity is greater than or equal to a preset image granularity threshold (or called a large-granularity quality problem), and the second quality problem is an image quality problem in which the image granularity is less than a preset image granularity threshold (or called a fine-granularity quality problem). The processing of obtaining the position in each image sample that does not meet the preset image quality conditions in the above step S1022 can be executed as the following steps B1 and B2.

[0041] Step B1: When the reason why the image sample does not meet the preset image quality condition is the first quality problem, the position of each image sample that does not meet the preset image quality condition is determined based on the target detection algorithm.

[0042] Step B2: When the reason why the image sample does not meet the preset image quality condition is the second quality problem, a location in each image sample that does not meet the preset image quality condition is identified based on a semantic segmentation algorithm.

[0043] Among them, the target detection algorithm is an algorithm that finds the target of interest from the image and classifies and locates the target of interest found. The target detection algorithm can roughly select the target of interest. The embodiment of this specification does not limit the specific target detection algorithm. In step B1, the TwoStage detection algorithm or the OneStage detection algorithm based on deep learning can be used. The semantic segmentation algorithm is an algorithm that classifies each pixel in the image, thereby dividing the image into multiple regions containing different categories of information. The semantic segmentation algorithm can accurately depict the outline of the target and provide coordinate information and classification information in each pixel. The semantic segmentation algorithm can not only predict the position and category of the target object in the image, but also depict the boundaries between target objects of different categories. The embodiment of this specification does not limit the specific semantic segmentation algorithm. In step B2, the FCN (Fully Convolutional Network) algorithm, the CRF (Conditional Random Field algorithm) algorithm, etc. can be used.

[0044] From the above steps B1 and B2, it can be seen that when the reason why the image sample does not meet the preset image quality conditions is a large-scale quality problem, the position in the image sample that does not meet the preset image quality conditions can be determined based on the result determined by the target detection algorithm; when the reason why the image sample does not meet the preset image quality conditions is a fine-grained quality problem, the position in the image sample that does not meet the preset image quality conditions can be identified by identifying which pixel points in the image sample do not meet the requirements based on the semantic segmentation algorithm. According to the different reasons why the image sample does not meet the preset image quality conditions, different algorithms can be used to determine the position in each image sample that does not meet the preset image quality conditions, making the positioning method for image quality problems more flexible, which can not only effectively save resources, but also efficiently and accurately obtain positioning results that meet the requirements.

[0045] In step S1024, based on multiple images, the reasons why each image sample does not meet the preset image quality conditions, and the positions in each image sample that do not meet the preset image quality conditions, the cause generation model is trained using a preset first loss function to obtain a trained cause generation model.

[0046] In one embodiment, the preset first loss function adopts the cross entropy loss function. The preset first loss function is used for cause generation model training, which belongs to a supervised training method. It can not only improve the efficiency of model training, but also improve the accuracy of model training output results.

[0047] Furthermore, as shown in FIG2 , when the image acquisition method in the embodiment of this specification is used in a blockchain system based on electronic identity authentication information, the image acquisition method may include the following steps S202 - S208 .

[0048] S202: Collect an image of the target user's designated ID and / or an image of the target user taken for the target user to access the blockchain system based on electronic identity authentication information, and use the image of the designated ID and / or the image of the target user taken as the image taken of the target object.

[0049] During implementation, in order to access the blockchain system based on electronic identity authentication information, the target user needs to take a corresponding image (which may be an image of a designated document, an image of the target user, or an image of a designated document and an image of the target user), collect the above image, and use the above image as the image taken of the target object.

[0050] S204: When the image does not meet the preset image quality conditions, the image is input into a pre-trained cause generation model, the cause generation model is used to determine the position in the image that does not meet the preset image quality conditions, and generate image reshooting guidance information for the determined position. The cause generation model is a model for detecting the cause of the image not meeting the preset image quality conditions and providing corresponding improvement suggestions for the detection results.

[0051] S206: Retake an image of the target object according to the image retake guidance information.

[0052] The specific processing procedures of the above steps S204-S206 can refer to the relevant contents of the above steps S104-S106, which will not be repeated here.

[0053] S208: If the image meets the preset image quality condition, the image and / or the information contained in the image is stored in the blockchain system.

[0054] When an image meets the preset image quality conditions, by storing the image and / or the information contained in the image in the blockchain system, the image information capacity in the blockchain system can be increased while ensuring the security and reliability of the image information.

[0055] Furthermore, as shown in FIG3 , there may be multiple ways to determine whether the image captured of the target object meets the preset image quality conditions in the embodiments of this specification. Determining whether the captured image meets the preset image quality conditions through a quality model is an optional processing method. When determining whether the captured image meets the preset image quality conditions through a quality model, the image acquisition method of the embodiments of this specification may include the following steps S302-S308.

[0056] Step S302: Acquire an image captured of the target object.

[0057] The specific processing process of step S302 can refer to the relevant content of the above step S102, which will not be repeated here.

[0058] Step S304: input the image into a pre-trained quality model to obtain a quality assessment result of the image.

[0059] The image quality assessment results include: unqualified quality (i.e., the image does not meet the preset image quality conditions) and qualified quality (i.e., the image meets the preset image quality conditions). The quality model is used to determine whether the image meets the preset image quality conditions based on an image classification method, and the quality model is trained using image samples of different photographic objects and a preset second loss function. The input data of the quality model are image samples of different photographic objects, i.e., the images obtained in step S302 above. The output of the quality model is an image with quality assessment results, including qualified quality images and unqualified quality images.

[0060] By training a quality model to obtain the quality assessment results of the image, it is possible to more quickly and accurately determine whether the current image meets the preset image quality conditions, thereby improving image acquisition efficiency.

[0061] In one embodiment, the quality model can be constructed using a binary classification algorithm, and the images taken of the target object can be divided into qualified quality images (or high-quality images) and unqualified quality images (or low-quality images) through the quality model.

[0062] In one embodiment, the second loss function may adopt a classification model loss function, specifically a cross entropy loss function, or a BCE-loss (binary cross entropy loss function).

[0063] If the quality assessment result indicates that the image meets the preset image quality conditions, the user continues to perform subsequent processes based on the acquired image.

[0064] Step S306: If the quality assessment result indicates that the image does not meet the preset image quality conditions, the image is input into a pre-trained cause generation model, and the cause generation model is used to determine the position in the image that does not meet the preset image quality conditions, and generate image reshooting guidance information for the determined position. The cause generation model is a model used to detect the reasons why the image does not meet the preset image quality conditions and provide corresponding improvement suggestions for the detection results.

[0065] Step S308: re-photographing the target object according to the image re-photographing guidance information.

[0066] The specific processing procedures of the above steps S306-S308 can refer to the relevant contents of the above steps S104-S106, which will not be repeated here.

[0067] For example, FIG4 takes the eKYC process as an example, showing a schematic principle diagram of an image acquisition method according to an embodiment of this specification. As can be seen from FIG4 , the process of an image acquisition method provided by one or more embodiments of this specification in actual application is as follows.

[0068] 1) During the eKYC process, the user completes the photo taking of their ID or face.

[0069] 2) After obtaining the image taken by the user, a pre-trained quality model based on image classification is used to perform a binary quality classification on the image taken by the user to determine whether the image taken by the user is a high-quality image or a low-quality image.

[0070] 3) If the classification result is a high-quality image, the user continues to execute the subsequent process; if the classification result is a low-quality image, the image is input into a pre-trained reason generation model.

[0071] 4) In the cause generation model, the causes of quality problems in low-quality images are subdivided. The causes of quality problems may include: highlights, low light, blur, etc. At the same time, the specific locations of quality problems in low-quality images are located in the cause generation model.

[0072] 5) After obtaining the detailed causes and specific locations of the quality issues, the cause generation model is used to generate text to obtain guidance for retaking the image, such as "The highlight in the upper left corner of the ID is overexposed" or "The overall image is blurry, please keep the phone still when taking the photo." This guidance is then fed back to the user.

[0073] 6) The user retakes the image according to the obtained image retake guidance information and continues the eKYC process.

[0074] The embodiments of the present specification provide an image acquisition method, which first acquires an image captured of a target object, then determines whether the image meets a preset image quality condition, and when the image does not meet the preset image quality condition, inputs the image into a pre-trained cause generation model, determines the location in the image that does not meet the preset image quality condition through the cause generation model, and generates image retake guidance information for the determined location, and finally, based on the generated image retake guidance information, retakes the target object, thereby completing image acquisition. When the image does not meet the preset image quality condition, the pre-trained cause generation model can detect the cause of the image not meeting the preset image quality condition during the previous capture process, obtain the specific cause of the quality problem, and locate the location in the image that did not meet the preset image quality condition in the previous capture, obtain the specific location of the quality problem, and finally generate image retake guidance information for the determined location and feedback it to the user, thereby providing targeted improvement suggestions for the next capture, which can effectively reduce the number of repeated captures, improve image capture efficiency, and thus improve user experience.

[0075] In summary, specific embodiments of the present subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing may be advantageous.

[0076] The above is an image acquisition method provided in one or more embodiments of this specification. Based on the same idea, one or more embodiments of this specification also provide an image acquisition device, as shown in FIG5 .

[0077] The image acquisition device includes: an image acquisition module 410 , an image reshooting guidance information generation module 420 and a control module 430 .

[0078] The image acquisition module 410 acquires an image captured of a target object.

[0079] The image reshooting guidance information generation module 420 inputs the image into a pre-trained cause generation model when the image does not meet the preset image quality conditions. The cause generation model is used to determine the position in the image that does not meet the preset image quality conditions, and generate image reshooting guidance information for the determined position. The cause generation model is a model for detecting the cause of the image not meeting the preset image quality conditions and providing corresponding improvement suggestions for the detection results.

[0080] The control module 430 retakes an image of the target object according to the image retake guidance information.

[0081] In one embodiment, the image acquisition module 410 is further used to capture an image of the target user's designated ID and / or an image of the target user taken for the target user to access the blockchain system based on electronic identity authentication information, and use the captured image of the designated ID and / or the image of the target user as the image captured of the target object; the image acquisition device may further include: a storage module, which stores the image and / or the information contained in the image in the blockchain system if the image meets a preset image quality condition.

[0082] In one embodiment, the image acquisition device may also include: a quality assessment result acquisition module, which inputs the image into a pre-trained quality model to obtain a quality assessment result of the image; an image reshooting guidance information generation module 420, which is also used to input the image into a pre-trained cause generation model if the quality assessment result indicates that the image does not meet the preset image quality conditions, determine the position in the image that does not meet the preset image quality conditions through the cause generation model, and generate image reshooting guidance information for the determined position.

[0083] The embodiments of the present specification provide an image acquisition device, which first acquires an image captured of a target object through an image acquisition module. Then, when the image does not meet a preset image quality condition, the image retake guidance information generation module inputs the image into a pre-trained cause generation model. The cause generation model determines the location in the image that does not meet the preset image quality condition, and generates image retake guidance information for the determined location. Finally, a control module retakes the image of the target object based on the generated image retake guidance information, thereby completing image acquisition. When the image does not meet the preset image quality condition, the pre-trained cause generation model can detect the cause of the image not meeting the preset image quality condition during the previous shooting process, obtain the specific cause of the quality problem, and locate the location in the image that does not meet the preset image quality condition in the previous shooting process, obtain the specific location of the quality problem, and finally generate image retake guidance information for the determined location and feedback it to the user, thereby providing targeted improvement suggestions for the next shooting, which can effectively reduce the number of repeated shooting, improve image shooting efficiency, and thus improve user experience.

[0084] Those skilled in the art should understand that the above-mentioned image acquisition device can be used to implement the image acquisition method described above, and the detailed description should be similar to the description of the method part above. To avoid redundancy, it will not be repeated here.

[0085] Based on the same idea, one or more embodiments of this specification also provide an electronic device, as shown in Figure 6. Electronic devices may have relatively large differences due to different configurations or performances, and may include one or more processors 501 and memory 502, and the memory 502 may store one or more storage applications or data. Among them, the memory 502 can be a temporary storage or a persistent storage. The application stored in the memory 502 may include one or more modules (not shown in the figure), and each module may include a series of computer-executable instructions in the electronic device. Furthermore, the processor 501 can be configured to communicate with the memory 502 and execute a series of computer-executable instructions in the memory 502 on the electronic device. The electronic device may also include one or more power supplies 503, one or more wired or wireless network interfaces 504, one or more input and output interfaces 505, and one or more keyboards 506.

[0086] Specifically in this embodiment, the electronic device includes a memory and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs may include one or more modules, and each module may include a series of computer-executable instructions in the electronic device, and is configured to be executed by one or more processors. The one or more programs include instructions for performing the following computer-executable instructions: obtaining an image taken of a target object; when the image does not meet a preset image quality condition, inputting the image into a pre-trained cause generation model, determining the position in the image that does not meet the preset image quality condition through the cause generation model, and generating image reshooting guidance information for the determined position, the cause generation model is a model for detecting the cause of the image not meeting the preset image quality condition and providing corresponding improvement suggestions for the detection result; and re-taking the image of the target object according to the image reshooting guidance information.

[0087] One or more embodiments of the present specification also propose a storage medium for storing computer-executable instructions, which implement the following process when executed by a processor: obtaining an image taken of a target object; when the image does not meet the preset image quality conditions, inputting the image into a pre-trained cause generation model, determining the position in the image that does not meet the preset image quality conditions through the cause generation model, and generating image reshooting guidance information for the determined position, the cause generation model is a model for detecting the cause of the image not meeting the preset image quality conditions and providing corresponding improvement suggestions for the detection results; and re-taking the image of the target object according to the image reshooting guidance information.

[0088] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0089] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing one or more embodiments of this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0090] It will be understood by those skilled in the art that one or more embodiments of this specification may be provided as a method, system, or computer program product. Thus, one or more embodiments of this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] One or more embodiments of this specification are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device produce a device for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0092] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more processes in the flowchart and / or one or more boxes in the block diagram.

[0094] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0095] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0096] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0097] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0098] One or more embodiments of this specification may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In distributed computing environments, program modules may be located in local and remote computer storage media, including storage devices.

[0099] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

[0100] The foregoing is merely one or more embodiments of this specification and is not intended to limit this application. It will be apparent to those skilled in the art that various modifications and variations may be made to one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included within the scope of the claims of one or more embodiments of this specification.

Claims

1. An image acquisition method, comprising: Acquire an image taken of a target object; When the image does not meet the preset image quality condition, the image is input into a pre-trained cause generation model, the position of the image that does not meet the preset image quality condition is determined by the cause generation model, and image reshoot guidance information for the determined position is generated, wherein the cause generation model is a model for detecting the cause of the image not meeting the preset image quality condition and providing corresponding improvement suggestions for the detection result; The target object is imaged again according to the image reshooting guidance information.

2. According to the method of claim 1, the training method of the cause generation model comprises: Acquire multiple image samples taken for different photographed objects, the reason why each image sample does not meet the preset image quality condition, and the position of each image sample that does not meet the preset image quality condition; Based on the multiple image samples, the reason why each image sample does not meet the preset image quality conditions, and the position in each image sample that does not meet the preset image quality conditions, the cause generation model is trained using a preset first loss function to obtain a trained cause generation model.

3. The method according to claim 2, wherein the acquiring of a plurality of image samples taken for different photographed objects, the reason why each image sample does not meet the preset image quality condition, and the position in each image sample that does not meet the preset image quality condition comprises: Acquire multiple image samples taken for different objects; The reason why each image sample does not meet the preset image quality condition and the position of each image sample that does not meet the preset image quality condition are determined respectively.

4. According to the method of claim 2, the reason why the image sample does not meet the preset image quality condition includes: A first quality problem and a second quality problem, wherein the first quality problem is an image quality problem in which the image granularity is greater than or equal to a preset image granularity threshold, and the second quality problem is an image quality problem in which the image granularity is less than the preset image granularity threshold; Determining the position of each image sample that does not meet the preset image quality condition includes: When the reason why the image samples do not meet the preset image quality condition is a first quality problem, determining a position in each image sample that does not meet the preset image quality condition based on a target detection algorithm; When the reason why the image samples do not meet the preset image quality condition is the second quality problem, a position in each image sample that does not meet the preset image quality condition is identified based on a semantic segmentation algorithm.

5. The method according to claim 1, wherein acquiring the image taken of the target object comprises: The target user is the target user photographed by the blockchain system based on electronic identity authentication information. The image of the designated certificate and / or the image of the target user is used as the image taken of the target object; The method further comprises: If the image meets the preset image quality condition, the image and / or the information contained in the image is stored in the blockchain system.

6. According to the method of claim 2, the preset first loss function adopts a cross entropy loss function.

7. The method according to claim 1, further comprising: Inputting the image into a pre-trained quality model to obtain a quality assessment result of the image; When the image does not meet the preset image quality condition, the image is input into a pre-trained cause generation model, a position in the image that does not meet the preset image quality condition is determined by the cause generation model, and image reshooting guidance information for the determined position is generated, including: If the quality assessment result indicates that the image does not meet the preset image quality conditions, the image is input into a pre-trained cause generation model, the position of the image that does not meet the preset image quality conditions is determined by the cause generation model, and image reshoot guidance information is generated for the determined position.

8. According to the method of claim 7, the quality model is a model constructed using a binary classification algorithm.

9. An image acquisition device, comprising: An image acquisition module, which acquires images taken of a target object; an image retake guidance information generating module, which, when the image does not meet the preset image quality condition, inputs the image into a pre-trained cause generation model, determines the position of the image that does not meet the preset image quality condition through the cause generation model, and generates image retake guidance information for the determined position, wherein the cause generation model is a model for detecting the cause causing the image to not meet the preset image quality condition and providing corresponding improvement suggestions for the detection result; A control module retakes an image of the target object according to the image retake guidance information.

10. An electronic device, comprising: processor; as well as a memory arranged to store computer executable instructions which, when executed, cause the processor to: Acquire an image taken of a target object; When the image does not meet the preset image quality condition, the image is input into a pre-trained cause generation model, the position of the image that does not meet the preset image quality condition is determined by the cause generation model, and image reshoot guidance information for the determined position is generated, wherein the cause generation model is used to determine the cause of the image not meeting the preset image quality condition. A model that detects the causes of preset image quality conditions and provides corresponding improvement suggestions for the detection results; The target object is imaged again according to the image reshooting guidance information.

Images