CN115203458A - Food image processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
本公开涉及一种食物图像的处理方法、装置、电子设备及存储介质，所述处理方法包括：获取食物图像、目标账户；确定所述食物图像与预设图像是否匹配失败；在确定所述食物图像与预设图像匹配失败的情况下，获取所述食物图像对应的食物信息；将所述食物图像、所述食物图像对应的食物信息，保存至目标账户对应的私有图像库。本公开实施例可提高了目标账户下次使用食物图像检索功能时匹配成功的几率，有利于提高食物图像检索功能的个性化程度。此外，由于本公开实施例提供的处理方法将食物图像及食物信息存储至目标账户对应的私有图像库，故其不会影响其他用户的食物检索流程，有利于保持食物图像检索功能的整体稳定性。
The present disclosure relates to a food image processing method, device, electronic device and storage medium. The processing method includes: acquiring a food image and a target account; determining whether the food image fails to match a preset image; If the image fails to match the preset image, obtain the food information corresponding to the food image; save the food image and the food information corresponding to the food image to a private image library corresponding to the target account. The embodiment of the present disclosure can improve the probability of successful matching when the target account uses the food image retrieval function next time, which is beneficial to improve the personalization degree of the food image retrieval function. In addition, since the processing method provided by the embodiment of the present disclosure stores the food image and food information in the private image library corresponding to the target account, it will not affect the food retrieval process of other users, which is beneficial to maintain the overall stability of the food image retrieval function .
Description
Technical Field
The present disclosure relates to the field of information processing technologies, and in particular, to a method and an apparatus for processing a food image, an electronic device, and a storage medium.
Background
The food image retrieval has wide application scenes in production and life, such as: dietary recommendations, food nutrient composition queries, etc. However, generally, the retrieval of the food images is limited, so that the retrieval accuracy is low, which makes the food image retrieval function difficult to be popularized, and is not beneficial to the practical application deployment of the food image retrieval function.
Disclosure of Invention
The disclosure provides a technical scheme for processing food images.
According to an aspect of the present disclosure, there is provided a processing method of a food image, the processing method including: acquiring a food image and a target account; determining whether the food image fails to be matched with a preset image; under the condition that the food image is determined to be unsuccessfully matched with a preset image, food information corresponding to the food image is obtained; and storing the food image and the food information corresponding to the food image to a private image library corresponding to the target account.
In a possible embodiment, the determining whether the food image fails to match with a preset image includes: determining whether the food image fails to be matched with a preset image in a public image library under the condition that the food image is determined to fail to be matched with the preset image in the private image library; the account number corresponding to the public image library is greater than the account number corresponding to the private image library; and under the condition that the food image is determined to fail to be matched with a preset image in the public image library, determining that the food image fails to be matched with the preset image.
In one possible embodiment, the acquiring the food image includes: acquiring an image to be detected; determining at least one food area image including food objects in the image to be detected as the food image.
In a possible embodiment, the acquiring food information corresponding to the food image includes: marking each food image which fails to be matched in the image to be detected; and responding to the selection of any food image which fails to be matched and the text content input aiming at the food image which fails to be matched, and taking the text content as the food information corresponding to the food image which fails to be matched.
In one possible implementation, the processing method further includes: under the condition that the food image is successfully matched with a preset image, marking each successfully matched food image in the images to be detected; acquiring food information corresponding to each successfully matched food image; and displaying the food information corresponding to each successfully matched food image in the image to be detected according to the position of each successfully matched food image in the image to be detected.
In a possible embodiment, the determining whether the food image fails to match with a preset image includes: determining the highest similarity between the food image and a preset image; determining that the food image is successfully matched with a preset image under the condition that the highest similarity is determined to be greater than or equal to the preset similarity; the marking of each successfully matched food image in the to-be-detected image comprises the following steps: for any successfully matched food image, determining a difference value between the highest similarity corresponding to the successfully matched food image and the preset similarity; determining an identifier corresponding to any successfully matched food image according to the size relation between the difference and a preset difference; and marking any successfully matched food image in the images to be detected according to the mark corresponding to any successfully matched food image.
In one possible implementation, the processing method further includes: responding to the selection of any food image and the text content input aiming at the food image, and taking the text content as new food information corresponding to the food image; and storing the new food information corresponding to any food image to a private image library corresponding to the target account.
In one possible embodiment, the food information includes at least one food sub-information; the step of taking the text content as new food information corresponding to any food image in response to the selection of any food image and the text content input for any food image comprises: responding to the food sub-information corresponding to the selected any food image and the text content input aiming at the food sub-information, and taking the text content as new food sub-information corresponding to the any food image; storing the new food information corresponding to any food image to a private image library corresponding to a target account, wherein the steps of: and storing the new food sub-information corresponding to any food image to a private image library corresponding to the target account.
In one possible implementation, the processing method further includes: determining the total heat of the food according to the food information corresponding to all the successfully matched food images; and generating prompt information under the condition that the total heat of the food is determined to be larger than the preset heat.
According to an aspect of the present disclosure, there is provided a processing apparatus of food images, the processing apparatus including: the image information acquisition module is used for acquiring a food image and a target account; the image matching module is used for determining whether the food image is unsuccessfully matched with a preset image; the food information acquisition module is used for acquiring food information corresponding to the food image under the condition that the food image is determined to be failed to be matched with a preset image; and the information storage module is used for storing the food images and the food information corresponding to the food images to a private image library corresponding to the target account.
According to an aspect of the present disclosure, there is provided an electronic device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
According to an aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.
In the embodiment of the disclosure, a food image and a target account can be acquired, then whether the food image fails to be matched with a preset image or not is determined, under the condition that the food image fails to be matched with the preset image, food information corresponding to the food image is acquired, and finally the food image and the food information corresponding to the food image are stored in a private image library corresponding to the target account. According to the method and the device for searching the food images, different private image libraries can be established for different accounts or account groups, so that the images which are failed to be matched can be stored in the private image library corresponding to the target account, the matching success rate of the target account when the food image searching function is used next time is improved, and the improvement of the individuation degree of the food image searching function is facilitated. In addition, the processing method provided by the embodiment of the disclosure stores the food image and the food information in the private image library corresponding to the target account, so that the food retrieval process of other users is not affected, and the overall stability of the food image retrieval function is favorably maintained.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
Fig. 1 shows a flowchart of a processing method of a food image provided according to an embodiment of the present disclosure.
Fig. 2 shows a reference schematic diagram of a processing method of a food image provided according to an embodiment of the present disclosure.
Fig. 3 shows a reference schematic diagram of a processing method of a food image provided according to an embodiment of the present disclosure.
Fig. 4 shows a block diagram of a processing device of food images provided according to an embodiment of the present disclosure.
Fig. 5 shows a block diagram of an electronic device provided in accordance with an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of a, B, and C, and may mean including any one or more elements selected from the group consisting of a, B, and C.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present disclosure.
In the related art, the food image retrieval relies on a public image library, which is maintained by developers and provided to all users for inquiry, so as to realize the food image retrieval function. However, such an arrangement is liable to cause the following problems: 1. the public image library is limited to be established, the stored preset images are usually maintained by developers, and the selection of the preset images tends to be in a certain field, such as: the convenience store food, fast food field, etc., i.e., when the public image library is applied to search in other fields, the accuracy is generally low. For example: the public image library stores more preset images in the fast food field, so that the public image library is easy to have the condition of low precision or failed matching when searching food images in the fruit field. 2. Although the public image library meets the requirements of most users, the personalization degree of the public image library is low, and the public image library is not beneficial to providing food image retrieval services adapted to different users.
In view of this, the embodiment of the present disclosure provides a method for processing a food image, which may acquire a food image and a target account, determine whether the food image fails to match a preset image, acquire food information corresponding to the food image when it is determined that the food image fails to match the preset image, and finally store the food image and the food information corresponding to the food image in a private image library corresponding to the target account. According to the method and the device for searching the food images, different private image libraries can be established for different accounts or account groups, so that the images which are failed to be matched can be stored in the private image library corresponding to the target account, the matching success rate of the target account when the food image searching function is used next time is improved, and the improvement of the individuation degree of the food image searching function is facilitated. In addition, the processing method provided by the embodiment of the disclosure stores the food image and the food information in the private image library corresponding to the target account, so that the food retrieval process of other users is not affected, and the overall stability of the food image retrieval function is favorably maintained.
In a possible implementation manner, the processing method may be executed by an electronic device such as a terminal device or a server, the terminal device may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or the like, and the method may be implemented by a manner in which a processor calls a computer-readable instruction stored in a memory. Alternatively, the method may be performed by a server. In combination with an actual application scene, a user can acquire food images through a camera on the terminal device, then the food images are retrieved and matched by the terminal device or uploaded to the server, and after the retrieval and matching are failed, the user can input food information corresponding to the food images. And finally, the terminal equipment stores the food images and the corresponding food information into a private image library in the terminal equipment or uploads the food images and the corresponding food information to a private image library in a server, so that the matching success rate of the user when the user uses the food image retrieval function next time is improved.
Referring to fig. 1, fig. 1 is a flowchart illustrating a processing method of a food image according to an embodiment of the present disclosure, where as shown in fig. 1, the processing method includes:
and step S100, acquiring a food image and a target account. Illustratively, the food image includes at least one food object, which may be any kind of food. The target account is an account that the user logs in when using the food image search function.
In one possible implementation, step S100 may include: acquiring an image to be detected, and then determining that the image to be detected comprises at least one food area image of the food object and using the image as the food image. For example, the image to be detected may be acquired by a camera of the terminal device, or may be directly obtained from a database storing captured images, and the embodiment of the present disclosure is not limited herein. In one example, the image to be detected may be input to a food object detection model (e.g., an artificial intelligence model such as fasternn, retinaNet) in the related art, and then the region where the food object is located in the image to be detected is determined by the model and is used as the food image. The image to be detected may also be processed by other images, such as: image sharpness processing, image size processing, etc. to facilitate processing by the food object detection model, and embodiments of the present disclosure are not limited herein. In one example, the image to be detected may include one or more food images, that is, after the image to be detected is input to the food object detection model, the model may output the position information of each food object in the image to be detected, where the position information may be represented by Φ i (x i ,y i ,w i ,h i ) Wherein phi i As coordinate information of the ith food object, x i The initial coordinate of the ith food object on the x-axis of the image to be detected (e.g. the x-axis coordinate of the top left corner vertex of the detection frame corresponding to the food object), y i The initial coordinate of the ith food object on the y-axis of the image to be detected (e.g. the y-axis coordinate of the top left corner vertex of the detection frame corresponding to the food object), w i Width, h, of the ith food object based on the starting coordinate on the x-axis i The height of the ith food object based on the starting coordinate on the y-axis is represented, so that the coverage area of each detection frame with the food object can be determined, and a plurality of food area images can be obtained. The embodiment of the disclosure can allow a user to shoot a plurality of food objects at one time and allow a developer not to set a view finder by using a food object detection model, i.e., the user does not need to adjust the shooting angle of the user according to the position of the view finder, thereby simplifying the matching process.
Step S200, determining whether the food image fails to be matched with a preset image. For example, the preset images may include preset images in a public image library maintained by a developer, and preset images in a private image library maintained by a user having a target account. In one example, whether the matching between the food image and the preset image fails may be determined by comparing the similarity between the food image and the preset image (for example, the preset image may be a preset image in a public image library or a private image library, and the comparison rules and the preset values of the two image libraries may be the same or different, which is not limited herein). For example, the ith food image may be input to a feature extraction model in the related art to obtain image features, and then the image features are compared with the image features of the preset image, so that the similarity between the two images can be determined. The above similarity can be represented by cosine similarity in the related art, and the embodiments of the present disclosure are not described herein again. If the similarity between the food image and a preset image is higher than a preset value (the developer can flexibly set according to actual conditions), the preset image can be directly used as a successful matching image of the food image, so as to reduce time consumption of a retrieval process, which is not limited herein.
In one possible implementation, step S200 may include: determining the highest similarity between the food image and a preset image, and determining that the food image is successfully matched with the preset image under the condition that the highest similarity is determined to be greater than or equal to the preset similarity. According to the embodiment of the disclosure, the preset images comprise a plurality of images, the food images can be compared with each preset image respectively to determine the similarity between the food images and each preset image, and then the highest similarity is obtained, namely the highest similarity is determined in a one-by-one comparison mode, so that the retrieval accuracy of the food images is improved. The preset similarity may be set by a developer or a user, and the embodiment of the disclosure is not limited herein.
In one possible implementation, step S200 may include: determining whether the food image fails to be matched with a preset image in a public image library under the condition that the food image is determined to fail to be matched with the preset image in the private image library. And the account number corresponding to the public image library is greater than the account number corresponding to the private image library. For example, the public image library may correspond to all accounts using the food image search function, that is, the public image library provides the food image search function for all accounts. The private image library may correspond to a target account or a target account group, that is, the private image library only provides a food image retrieval function for the target account or the target account group. For example: the queried users in the food field that are close to each other may associate the accounts with each other to form a target account group, and then use and maintain the same private image library together to improve the data breadth of the preset images in the private image library. And then determining that the food image fails to be matched with a preset image in the public image library under the condition that the food image fails to be matched with the preset image in the public image library. In the embodiment of the disclosure, the electronic device preferentially performs image matching in the private image library, and because the private image library is established by a user who has a target account, the preset image in the private image library is more adapted to the familiar scene of the user, which is beneficial to improving the matching success rate and the matching efficiency. For example: the preset image in the private image library of the target account and the food image are usually shot by the same camera, namely the resolution of the preset image and the resolution of the food image are usually the same, which is beneficial to improving the matching precision. For another example: the eating habits of the target account are not changed usually, so that the matching probability of the preset images in the private image library is higher, namely, the matching efficiency is improved.
Continuing to refer to fig. 1, in step S300, in case that it is determined that the food image fails to match the preset image, food information corresponding to the food image is acquired. Illustratively, the food information may include: food type, nutritional content, food calories, etc., embodiments of the present disclosure are not limited herein.
In one possible implementation, step S300 may include: marking each food image which fails in matching in the images to be detected, and then responding to any selected food image which fails in matching and the text content input aiming at any food image which fails in matching, and taking the text content as the food information corresponding to any food image which fails in matching. Illustratively, the terminal device can display the marked image to be detected, and then the user can select the food image with failed matching in the image to be detected through an input module of the terminal device and input the text content.
And S400, storing the food image and the food information corresponding to the food image into a private image library corresponding to the target account. In one example, the electronic device can also perform expansion of food information according to food information entered by the user. For example: if the food information entered by the user is mango (that is, the food category), the electronic device may query the nutritional content, the food calorie, and the like of mango in a network or in a public image library (for example, the cyan mango and the yellow mango may fail to match, but the food information of the mango may still be queried in the public image library), and then store the above three information as new food information in a private image library, which is not limited in this disclosure.
Referring to fig. 2, fig. 2 is a reference schematic diagram of a food image processing method according to an embodiment of the present disclosure, and as shown in fig. 2, an image to be detected passes through a food object detection model to obtain a plurality of food images (i.e., in-picture detection process), and then a matching process (i.e., in-picture retrieval process) with a preset image (e.g., the preset image may be a preset image in a public image library or a private image library) is performed. In the matching process, each food image needs to be matched with a preset image, if the similarity between the two food images is higher than the preset similarity (i.e. 0.5 in the figure), the matching is successful (i.e. the output in the figure), in one example, the preset similarities in different image libraries may be the same or different, the embodiment of the disclosure is not limited herein, and if not, the food image is used as a new preset image, and is stored in the private image library after the corresponding food information is obtained. By the arrangement, the matching success rate of the target user when the target user uses the food image retrieval function next time can be improved, and the individuation degree of the food image retrieval function is favorably improved.
In a possible implementation, the processing method may further include: and marking each successfully matched food image in the images to be detected under the condition that the food images are successfully matched with the preset images. For example: the food image may be marked by a detection box. And then acquiring food information corresponding to each successfully matched food image, and displaying the food information corresponding to each successfully matched food image in the image to be detected according to the position of each successfully matched food image in the image to be detected. The food information may be stored in association with the preset image in a public image repository or a private image repository. Referring to fig. 3, fig. 3 is a schematic diagram illustrating a processing method of food images according to an embodiment of the present disclosure, as shown in fig. 3, the image to be detected includes five food images, the electronic device may mark the position of each food image in the image to be detected through a box (i.e., the above-mentioned detection frame), and then display the corresponding food information (i.e., food category, food, etc., in the figure, 65kcal/100g, 160kcal/100g, etc.), similarity (i.e., score:0.93, score:0.92, etc.) and the like of each food image in or near the marked position of each food image.
In a possible embodiment, the marking of each successfully matched food image in the to-be-detected image may include: and determining the difference value between the highest similarity corresponding to any successfully matched food image and the preset similarity aiming at any successfully matched food image. And determining the identifier corresponding to any successfully matched food image according to the size relationship between the difference and a preset difference. For example, if the difference is greater than or equal to the preset difference, the electronic device may consider that the matching reliability of the food image is high, and if the difference is smaller than the preset difference, the electronic device may consider that the matching reliability of the food image is low, that is, the electronic device may generate different identifiers according to the matching reliability of each food image, so as to prompt the user of possible matching errors. For example: the difference value is larger than or equal to the preset difference value and can be marked by a green marking frame, and the difference value is smaller than the preset difference value and can be marked by a red marking frame. And then marking any successfully matched food image in the images to be detected according to the mark corresponding to any successfully matched food image.
In a possible implementation, the processing method further includes: and in response to the selection of any food image and the text content input aiming at the food image, taking the text content as new food information corresponding to the food image. And then, storing the new food information corresponding to any food image into a private image library corresponding to the target account. Illustratively, if one food image is successfully matched, but actually, a matching error still exists, the user can select any food image in the images to be detected through an input module of the terminal equipment, and input the text content into the private image library. In one example, if the matching process is ended when the food image is successfully matched with the preset image in the private image library, the matching accuracy of the food image of the same food category is effectively improved when the food image of the same food category is detected again. In other words, the food image can no longer be compared with the preset image with the matching error in the previous time, so that the probability of the matching error is reduced.
In a possible embodiment, the food information includes at least one food sub-information, and the taking the text content as the new food information corresponding to any food image in response to selecting any food image and the text content input for any food image includes: and in response to the selection of the food sub-information corresponding to any food image and the text content input aiming at the food sub-information, taking the text content as the new food sub-information corresponding to any food image. The storing of the new food information corresponding to any food image to the private image library corresponding to the target account includes: and storing the new food sub-information corresponding to any food image to a private image library corresponding to the target account. In combination with the practical application scenario, the food sub-information may be the above food category, nutrient content, food calorie, etc. When the user finds that any item of food sub-information has an error or is wrong, the food sub-information with the error or the mistake can be modified in a targeted manner, and then the electronic equipment automatically stores the food sub-information into the private image library corresponding to the target account, so that the food information can be modified in a targeted manner.
In a possible implementation, the processing method further includes: and determining the total heat of the food according to the food information corresponding to all the successfully matched food images. And generating prompt information under the condition that the total heat of the food is determined to be larger than the preset heat. Illustratively, after determining the successfully matched food image, the unit calorie corresponding to each food object in the food image can be obtained, such as: 160kcal/100g, and then an estimate of the weight of the food objects can be made by the electronic device (e.g., determining the weight of the food objects based on the size and weight of the fiduciary object in an image to be detected), or the weight of each food object can be manually entered by the user, which is not limited in this disclosure. And multiplying the weight corresponding to each food object by the corresponding unit calorie to obtain a first calorie corresponding to each food object, and finally adding the first calories to obtain the total calorie of the food. The preset amount of heat may be set by a developer or a user, and the embodiments of the disclosure are not limited herein. The prompt message can be a voice prompt or a text prompt, and is used for prompting the user that the total food calorie exceeds the preset calorie, namely the possibility that the user does not conform to the diet plan exists. In combination with the practical application scene, the user can shoot the image to be detected before each meal, so as to automatically determine whether the meal meets the self diet plan.
It is understood that the above-mentioned embodiments of the method of the present disclosure can be combined with each other to form a combined embodiment without departing from the principle logic, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.
In addition, the present disclosure also provides a processing apparatus, an electronic device, a computer-readable storage medium, and a program for food images, which can be used to implement any one of the processing methods for food images provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are not repeated.
Fig. 4 shows a block diagram of a processing apparatus of food images according to an embodiment of the present disclosure, and as shown in fig. 4, the processing apparatus 100 includes: an image information obtaining module 110, configured to obtain a food image and a target account; an image matching module 120 for determining whether the matching of the food image and a preset image fails; a food information obtaining module 130, configured to obtain food information corresponding to the food image when it is determined that the food image fails to match a preset image; the information saving module 140 is configured to save the food image and the food information corresponding to the food image to a private image library corresponding to the target account.
In a possible embodiment, the determining whether the food image fails to match with a preset image includes: under the condition that the food image is determined to be failed to be matched with a preset image in the private image library, determining whether the food image is failed to be matched with a preset image in a public image library; the account number corresponding to the public image library is larger than the account number corresponding to the private image library; and under the condition that the food image is determined to be failed to be matched with a preset image in the public image library, determining that the food image is failed to be matched with the preset image.
In one possible embodiment, the acquiring the food image includes: acquiring an image to be detected; determining at least one food area image including food objects in the image to be detected as the food image.
In a possible embodiment, the acquiring food information corresponding to the food image includes: marking each food image which fails in matching in the images to be detected; and responding to the selection of any food image which fails to be matched and the text content input aiming at the food image which fails to be matched, and taking the text content as the food information corresponding to the food image which fails to be matched.
In a possible implementation, the processing device further includes: a food information display module to perform any one of: under the condition that the food images are successfully matched with preset images, marking each successfully matched food image in the images to be detected; acquiring food information corresponding to each successfully matched food image; and displaying the food information corresponding to each successfully matched food image in the image to be detected according to the position of each successfully matched food image in the image to be detected.
In a possible embodiment, the determining whether the food image fails to match with a preset image includes: determining the highest similarity between the food image and a preset image; determining that the food image is successfully matched with a preset image under the condition that the highest similarity is determined to be greater than or equal to the preset similarity; the marking of each successfully matched food image in the to-be-detected image comprises the following steps: for any successfully matched food image, determining a difference value between the highest similarity corresponding to the successfully matched food image and the preset similarity; determining an identifier corresponding to any successfully matched food image according to the size relation between the difference and a preset difference; and marking any successfully matched food image in the images to be detected according to the mark corresponding to any successfully matched food image.
In one possible implementation, the processing device further includes: a food information update module to perform any one of: responding to the selection of any food image and the text content input aiming at the food image, and taking the text content as new food information corresponding to the food image; and storing the new food information corresponding to any food image to a private image library corresponding to the target account.
In one possible embodiment, the food information includes at least one food sub-information; the step of taking the text content as new food information corresponding to any food image in response to the selection of any food image and the text content input for any food image comprises: responding to the food sub-information corresponding to the selected any food image and the text content input aiming at the food sub-information, and taking the text content as new food sub-information corresponding to the any food image; storing the new food information corresponding to any food image to a private image library corresponding to a target account, wherein the steps of: and storing the new food sub-information corresponding to any food image to a private image library corresponding to the target account.
In a possible implementation, the processing device further includes: a prompt information generation module for executing any one of the following: determining the total heat of the food according to the food information corresponding to all the successfully matched food images; and generating prompt information under the condition that the total heat of the food is determined to be larger than the preset heat.
The method has specific technical relevance with the internal structure of the computer system, and can solve the technical problems of how to improve the hardware operation efficiency or the execution effect (including reducing data storage capacity, reducing data transmission capacity, improving hardware processing speed and the like), thereby obtaining the technical effect of improving the internal performance of the computer system according with the natural law.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a volatile or non-volatile computer readable storage medium.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
The disclosed embodiments also provide a computer program product comprising computer readable code or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, the processor in the electronic device performs the above method.
The electronic device may be provided as a server or other modality of device.
Fig. 5 illustrates a block diagram of an electronic device 1900 provided in accordance with an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server or terminal device. Referring to fig. 5, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
The electronic device 1900 may further include a power component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input output (I/O) interface 1958. The electronic device 1900 may operate based on an operating system, such as the Microsoft Server operating system (Windows Server), stored in the memory 1932 TM ) Apple Inc. of a graphical user interface based operating system (Mac OS X) TM ) Multi-user, multi-process computer operating system (Unix) TM ) Free and open native code Unix-like operating System (Linux) TM ) Open native code Unix-like operating System (FreeBSD) TM ) Or the like.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as a memory 1932, is also provided that includes computer program instructions executable by a processing component 1922 of an electronic device 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer-readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
The foregoing description of the various embodiments is intended to highlight various differences between the embodiments, and the same or similar parts may be referred to each other, and for brevity, will not be described again herein.
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
If the technical scheme of the application relates to personal information, a product applying the technical scheme of the application clearly informs personal information processing rules before processing the personal information, and obtains personal independent consent. If the technical scheme of the application relates to sensitive personal information, a product applying the technical scheme of the application obtains individual consent before processing the sensitive personal information, and simultaneously meets the requirement of 'express consent'. For example, at a personal information collection device such as a camera, a clear and significant identifier is set to inform that the personal information collection range is entered, the personal information is collected, and if the person voluntarily enters the collection range, the person is regarded as agreeing to collect the personal information; or on the device for processing the personal information, under the condition of informing the personal information processing rule by using obvious identification/information, obtaining personal authorization by modes of popping window information or asking a person to upload personal information of the person by himself, and the like; the personal information processing rule may include information such as a personal information processor, a personal information processing purpose, a processing method, and a type of personal information to be processed.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (12)
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