Image processing method, apparatus and device

Abstract

This application discloses an image processing method, apparatus, and device, belonging to the field of electronic equipment technology. The image processing method includes: receiving a first input to a first table; displaying a first interface in response to the first input; wherein the first interface includes at least one image; receiving a second input to the first interface; wherein the second input is used to select a first image from at least one image; determining, in response to the second input, fields and / or field values ​​associated with the first image based on the first image; and inserting the fields and / or field values ​​associated with the first image into the first table to obtain a second table.

Description

Technical Field

[0001] This application belongs to the field of electronic equipment technology, and specifically relates to an image processing method, apparatus, and device. Background Technology

[0002] With the development of electronic devices, there are situations where tables are generated based on images during the user's use of electronic devices.

[0003] In related technologies, when generating tables based on images, users need to manually edit the tables and add image-related content. However, generating tables in this way is inefficient. Summary of the Invention

[0004] The purpose of this application is to provide an image processing method, apparatus, and device that can solve the problem of poor efficiency in generating tables.

[0005] In a first aspect, embodiments of this application provide an image processing method, including: Receive the first input to the first form; In response to a first input, a first interface is displayed; wherein the first interface includes at least one image; Receive a second input to the first interface; wherein the second input is used to select a first image from at least one image; In response to the second input, based on the first image, determine the fields and / or field values ​​associated with the first image; Insert the fields and / or field values ​​associated with the first image into the first table to obtain the second table.

[0006] Secondly, embodiments of this application provide an image processing apparatus, including: The first receiving module is used to receive the first input to the first table; A first display module is configured to display a first interface in response to a first input; wherein the first interface includes at least one image; The second receiving module is used to receive a second input to the first interface; wherein the second input is used to select a first image from at least one image; A determination module is configured to, in response to a second input, determine, based on a first image, fields and / or field values ​​associated with the first image; The insertion module is used to insert fields and / or field values ​​associated with the first image into the first table to obtain the second table.

[0007] Thirdly, embodiments of this application provide an electronic device, which includes a processor and a memory. The memory stores programs or instructions that can run on the processor, and when the programs or instructions are executed by the processor, they implement the steps of the image processing method provided in embodiments of this application.

[0008] Fourthly, embodiments of this application provide a readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the image processing method provided in embodiments of this application.

[0009] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the steps of the image processing method provided in embodiments of this application.

[0010] Sixthly, embodiments of this application provide a computer program product, which is stored in a storage medium and executed by at least one processor to implement the steps of the image processing method provided in embodiments of this application.

[0011] In this embodiment, a second table is obtained by receiving a first input to a first table; displaying a first interface in response to the first input; wherein the first interface includes at least one image; receiving a second input to the first interface; wherein the second input is used to select a first image from the at least one image; determining fields and / or field values ​​associated with the first image based on the first image in response to the second input; and inserting the fields and / or field values ​​associated with the first image into the first table. In this way, based on the image selected by the user, the fields and / or field values ​​associated with the user-selected image can be automatically determined, and the fields and / or field values ​​associated with the user-selected image can be inserted into the table, thereby improving table generation efficiency. Attached Figure Description

[0012] Figure 1 This is a schematic flowchart of an image processing method provided in some embodiments of this application; Figure 2 This is a schematic diagram of the first input provided in some embodiments of this application; Figure 3 This is a first schematic diagram of a first image provided in some embodiments of this application; Figure 4 This is a first schematic diagram of a second table provided in some embodiments of this application; Figure 5 This is a schematic diagram of a third interface provided in some embodiments of this application; Figure 6These are schematic diagrams illustrating the display of first prompt information provided in some embodiments of this application; Figure 7 This is a second schematic diagram of a second table provided in some embodiments of this application; Figure 8 These are schematic diagrams illustrating the deletion of fields and / or field values ​​in a table according to some embodiments of this application; Figure 9 These are schematic diagrams illustrating the deletion of human figures according to some embodiments of this application; Figure 10 This is a second schematic diagram of the first image provided in some embodiments of this application; Figure 11 This is a third schematic diagram of the second table provided in some embodiments of this application; Figure 12 These are schematic diagrams of the structure of an image processing apparatus provided in some embodiments of this application; Figure 13 These are schematic diagrams of the structure of electronic devices provided in some embodiments of this application; Figure 14 These are schematic diagrams of the hardware structure of electronic devices according to some embodiments of this application. Detailed Implementation

[0013] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0014] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0015] It should be noted that the acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0016] The terminology used in the implementation section of this application is only for explaining specific embodiments of this application and is not intended to limit this application. The terminology involved in the embodiments of this application is explained below.

[0017] The interface is the medium for interaction and information exchange between the system and the user. It realizes the conversion between the internal form of information and the form that humans can accept.

[0018] A control (also called a part, component, widget, or control) is a graphical user interface element and a basic building block of the user interface. It is a window or text box that is displayed in the program interface of any application. Controls can be buttons, text boxes, labels, etc., and are used to control all the data processed by each application and the interactive operations on that data.

[0019] The image processing methods, apparatus, and devices provided in this application will be described in detail below with reference to the accompanying drawings and through specific embodiments and application scenarios.

[0020] It should be noted that the image processing method provided in this application can be executed by electronic devices such as mobile phones, tablets, laptops, PDAs, and in-vehicle electronic devices. Some embodiments of this application use electronic devices as the executing entity to illustrate the image processing method provided in this application.

[0021] The image processing method provided in this application can be applied to scenarios where tables need to be generated based on images.

[0022] Figure 1 This is a schematic flowchart illustrating an image processing method provided in some embodiments of this application. The image processing method may include: Step 101: Receive the first input to the first table; In some embodiments of this application, the first table in these embodiments can be an empty table. The first input in these embodiments is an input used to trigger the generation of table content based on an image. The first input in these embodiments includes, but is not limited to, touch input by the user using a finger or stylus to interact with the control used to trigger the generation of table content based on an image, voice command input, specific gesture input, etc.; wherein, touch input includes, but is not limited to, click input, swipe input, etc. Click input can be a single click, double click, or any number of clicks, and can also be a long press or short press; specific gesture input can be any one of a single click gesture, swipe gesture, drag gesture, long press gesture, area change gesture, double press gesture, or double click gesture. The first input in these embodiments can be set and modified adaptively according to actual needs.

[0023] In some embodiments of this application, the control used to trigger the generation of table content based on the image can be a "Add image to complete information insertion" button.

[0024] For example, such as Figure 2 As shown, Figure 2 This is a schematic diagram of the first input provided in some embodiments of this application. Figure 2 In the middle, the first input is the user's click. Figure 2 The input is the "Add Image to Complete Information Insertion" button 201 in the interface shown.

[0025] Step 102: In response to the first input, display the first interface; In some embodiments of this application, the first interface in the embodiments of this application may be an interface for selecting an image, and the first interface includes at least one image.

[0026] Step 103: Receive the second input to the first interface; In some embodiments of this application, the second input is used to select a first image from at least one image.

[0027] Step 104: In response to the second input, determine the fields and / or field values ​​associated with the first image based on the first image; In some embodiments of this application, the fields and / or field values ​​in these embodiments are associated with a first image, including at least one of the following: The fields and / or field values ​​include at least one of the following: personal identification information, personal communication information, work-related information, physiological data information, and image data information of the person in the first image; The fields and / or field values ​​include at least one of the following: numerical information contained in the text paragraphs of the first image, object information represented by the numerical information, text information, and statistical information or analytical data obtained based on the numerical information contained in the text paragraphs; The fields and / or field values ​​include at least one of the following: landscape type, location information, time information, and itinerary information and / or schedule information planned based on the location of the landscape pattern in the first image; Fields and / or field values ​​include pre-stored information associated with the objects included in the first image; Fields and / or field values ​​include custom information associated with the objects included in the first image; Fields and / or field values ​​include information obtained through AI identification or analysis of objects included in the first image.

[0028] In some embodiments of this application, the personal identification information includes, but is not limited to, name, age, and gender; personal communication information includes, but is not limited to, telephone number and email address; work-related information includes, but is not limited to, workplace, workplace address, employee number, and job title; physiological data information includes, but is not limited to, heart rate and blood pressure; image data information includes, but is not limited to, images of people; object information represented by digital information includes, but is not limited to, year, sales revenue, and growth rate; statistical information includes, but is not limited to, annual sales revenue and total sales revenue; landscape types include, but are not limited to, mountains, seas, grasslands, and deserts; location information includes, but is not limited to, the geographical location of the electronic device when the first image was captured; time information includes, but is not limited to, the time when the first image was captured; travel information includes, but is not limited to, departure point, destination, transit points, estimated number of days, and budget; schedule information includes, but is not limited to, time, content, and location; pre-stored information associated with the objects included in the first image includes, but is not limited to, name, age, and height; custom information associated with the objects included in the first image includes, but is not limited to, name, age, and height; and information obtained by AI recognition or analysis of the objects included in the first image includes, but is not limited to, the position of the object in the image and the height of the object in the image.

[0029] In some embodiments of this application, when determining fields and / or field values ​​associated with a first image based on the first image, at least one of the following can be determined as fields and / or field values ​​associated with the first image: personal identification information, personal communication information, work-related information, physiological data information, and image data information of the person in the first image; at least one of the following can also be determined as fields and / or field values ​​associated with the first image: digital information contained in a text segment of the first image, object information represented by the digital information, text information, and statistical information or analytical data obtained based on the digital information contained in the text segment; at least one of the following can also be determined as fields and / or field values ​​associated with the first image: At least one of the following is determined as a field and / or field value associated with the first image: the landscape type, location information, time information, and itinerary information and / or schedule information planned based on the location of the landscape pattern in the image; pre-stored information associated with objects included in the first image may also be determined as a field and / or field value associated with the first image; custom information associated with objects included in the first image may also be determined as a field and / or field value associated with the first image; information obtained by AI identification or analysis of objects included in the first image may also be determined as a field and / or field value associated with the first image.

[0030] Step 105: Insert the fields and / or field values ​​associated with the first image into the first table to obtain the second table.

[0031] For example, the first interface includes four images: image A, image B, image C, and image D. When the user selects image A from the first interface, the fields and / or field values ​​associated with image A are determined, and then the fields and / or field values ​​associated with image A are inserted into the first table to obtain the second table.

[0032] In this embodiment, a second table is obtained by receiving a first input to a first table; displaying a first interface in response to the first input; wherein the first interface includes at least one image; receiving a second input to the first interface; wherein the second input is used to select a first image from the at least one image; determining fields and / or field values ​​associated with the first image based on the first image in response to the second input; and inserting the fields and field values ​​associated with the first image into the first table. In this way, fields and / or field values ​​associated with the user-selected image can be automatically determined based on the image selected by the user, and the fields and / or field values ​​associated with the user-selected image can be inserted into the table, thereby improving table generation efficiency.

[0033] In some embodiments of this application, before step 104, the image processing method provided in this application may further include: displaying a second interface; wherein the second interface includes at least one application identifier; receiving a third input to the second interface; wherein the third input is used to select a first application identifier from at least one application identifier; correspondingly, step 104 may include: in response to the third input, extracting attribute information and attribute values ​​of objects included in the first image from the application corresponding to the first application identifier; using the attribute information as a field and the attribute values ​​as field values.

[0034] In some embodiments of this application, the second interface can be an interface for selecting an application identifier, and the second interface includes at least one application identifier. A user can select one or more application identifiers from the second interface, and the electronic device extracts attribute information and attribute values ​​of the objects included in the first image from the application corresponding to the user-selected application identifier; the attribute information is used as a field, and the attribute values ​​are used as field values.

[0035] In some embodiments of this application, at least one application identifier in the embodiments of this application may be the identifier of at least one instant messaging application.

[0036] In some embodiments of this application, when extracting attribute information and attribute values ​​of objects included in the first image from the application corresponding to the first application identifier, the feature information of objects included in the first image can be matched with the feature information of objects included in the application corresponding to the first application identifier, and the attribute information and attribute values ​​of objects whose feature information matches the feature information of objects included in the first image can be extracted from the application corresponding to the first application identifier.

[0037] For example, the first image is shown in 3. Figure 3 This is a first schematic diagram of a first image provided by some embodiments of this application. Figure 3 The first image includes six people. From the application identified by the user, the attribute information of these six people, including name, gender, and corresponding attribute values, is extracted. Name and gender are used as fields, and the attribute values ​​corresponding to name, gender, and age are used as field values. The fields and field values ​​are inserted into the first table to obtain the second table. The resulting second table is shown below. Figure 4 As shown, Figure 4 This is a first schematic diagram of a second table provided in some embodiments of this application.

[0038] exist Figure 4 In the second table, the fields include name and gender. (Extracted from the application) Figure 3 The names of the six people shown are Zhao Yi, Qian Er, Sun San, Li Si, Zhou Wu, and Wu Liu; their genders are male, female, female, female, male, and female, respectively.

[0039] In some embodiments of this application, before extracting the attribute information and attribute values ​​of the objects included in the first image from the application corresponding to the first application identifier, the image processing method provided in this application embodiment may further include: displaying a third interface; wherein the third interface includes at least one attribute information of the object in the first application. Correspondingly, extracting the attribute information and attribute values ​​of the objects included in the first image from the application corresponding to the first application identifier may include: receiving a fourth input to the third interface; wherein the fourth input is used to select attribute information from at least one attribute information; in response to the fourth input, extracting the first attribute information selected by the fourth input; and extracting the attribute value corresponding to the first attribute information from the first application.

[0040] For example, as shown in 5, Figure 5 This is a schematic diagram of a third interface provided in some embodiments of this application. Figure 5 In the third interface, the attribute information includes name, gender, contact information, address, and hobbies. Users can select from... Figure 5 The system selects the five attributes shown: name, gender, contact information, address, and hobbies. Then, it extracts the attribute information selected by the user and extracts the corresponding attribute values ​​from the first application.

[0041] In some embodiments of this application, the second input is further used to select a second image from at least one image. The image processing method provided in the embodiments of this application may further include: in response to the second input, determining fields and / or field values ​​associated with the second image based on the second image; accordingly, step 105 may include: inserting the fields and / or field values ​​associated with the first image and the fields and / or field values ​​associated with the second image into a first table to obtain a second table.

[0042] In some embodiments of this application, the process of determining fields and / or field values ​​associated with the second image based on the second image is similar to the process of determining fields and / or field values ​​associated with the first image based on the first image; the process of inserting the fields and / or field values ​​associated with the first image and the fields and / or field values ​​associated with the second image into the first table to obtain the second table is similar to the process of inserting the fields and / or field values ​​associated with the first image into the first table to obtain the second table. The embodiments of this application will not be described in detail here. Specific processes can be referred to the descriptions in the above embodiments.

[0043] In this embodiment of the application, the user can select multiple images, and the fields and / or field values ​​associated with the multiple images selected by the user are automatically determined. The fields and / or field values ​​associated with the multiple images selected by the user are then inserted into the table, which can improve the efficiency of table generation.

[0044] In some embodiments of this application, the first image includes at least one human figure pattern; accordingly, step 104 may include: in response to the second input, determining fields and / or field values ​​associated with the human figure pattern based on the human figure pattern in the first image.

[0045] In some embodiments of this application, fields and / or field values ​​are associated with a person image, including at least one of the following: Fields and / or field values ​​include personally identifiable information about the person in the image; Fields and / or field values ​​include personal communication information of the person in the image; Fields and / or field values ​​include job-related information about the person image; Fields and / or field values ​​include physiological data information of the human figure pattern; Fields and / or field values ​​include image data information of the person's image; Fields and / or field values ​​include pre-stored information related to the character image; Fields and / or field values ​​include custom information related to the character image; Fields and / or field values ​​include information obtained through AI-based recognition of human figures.

[0046] In some embodiments of this application, the personal identification information includes, but is not limited to, name, age, and gender; personal communication information includes, but is not limited to, telephone number and email address; work-related information includes, but is not limited to, workplace, workplace address, employee number, and job title; physiological data information includes, but is not limited to, heart rate and blood pressure; image data information includes, but is not limited to, the position of the person in the image, the height of the person in the image, the person, the person's head, and the person's photo; pre-stored information related to the person in the image includes, but is not limited to, name, age, and height; custom information related to the person in the image includes, but is not limited to, name, age, and height; and information obtained by AI recognition of the person in the image includes, but is not limited to, the position of the person in the image and the height of the person in the image.

[0047] When determining the fields and / or field values ​​associated with the human figure pattern based on the human figure pattern in the first image, the personal identity information, personal communication information, work-related information, physiological data information, and image data information of the human figure pattern can be determined as the fields and / or field values ​​associated with the human figure pattern; pre-stored information related to the human figure pattern can also be determined as the fields and / or field values ​​associated with the human figure pattern; custom information related to the human figure pattern can also be determined as the fields and / or field values ​​associated with the human figure pattern; and information obtained by recognizing the human figure pattern through AI can also be determined as the fields and / or field values ​​associated with the human figure pattern.

[0048] In some embodiments of this application, before determining the information obtained by recognizing a person pattern through AI as fields and / or field values ​​associated with the person pattern, the image processing method provided in this application may further include: displaying a first prompt message, wherein the first prompt message includes information obtained by recognizing a person pattern through AI.

[0049] For example, such as Figure 6 As shown, Figure 6 These are schematic diagrams illustrating the display of first prompt information provided in some embodiments of this application. Figure 6 The first prompt asks, "Insert the person's position into the table?" The person's position is determined by AI recognizing the image. When the user confirms, the person's position is used as a field, and the coordinates of each recognized person's position are used as field values. The person's position and coordinates are then inserted into the first table, resulting in a second table. The second table is shown below. Figure 7 As shown, Figure 7 This is a second schematic diagram of a second table provided in some embodiments of this application.

[0050] In some embodiments of this application, the first image includes multiple human figures; accordingly, step 104 may include: in response to the second input, determining multiple sets of fields and / or field values ​​associated with the human figures based on the multiple human figures in the first image; step 105 may include: inserting the multiple sets of fields and / or field values ​​into a first table according to the positional order of the multiple human figures in the first image to obtain a second table; the arrangement order of the multiple sets of fields and / or field values ​​in the second table is the same as or the opposite of the positional order of the multiple human figures in the first image.

[0051] In some embodiments of this application, when the first image includes multiple character patterns, the fields and / or field values ​​associated with each character pattern can be grouped together; according to the positional order of the multiple character patterns in the first image, multiple groups of fields and / or field values ​​are inserted into the first table to obtain the second table.

[0052] For example, suppose the first image includes three figures, A, B, and C, with figure A above figure B and figure C below figure B. Then, the fields and / or field values ​​associated with figures A, B, and C are inserted into the first table to obtain the second table.

[0053] In the second table, the fields and / or field values ​​associated with person image A are above the fields and / or field values ​​associated with person image B, and the fields and / or field values ​​associated with person image B are above the fields and / or field values ​​associated with person image C. Alternatively, in the second table, the fields and / or field values ​​associated with person image A are below the fields and / or field values ​​associated with person image B, and the fields and / or field values ​​associated with person image B are below the fields and / or field values ​​associated with person image C.

[0054] In some embodiments of this application, the image processing method provided in this application may further include: modifying the fields and / or field values ​​associated with the human figure pattern in the second table to adjust the human figure pattern in the first image accordingly; and / or updating the human figure pattern in the first image to modify the fields and / or field values ​​associated with the human figure pattern in the second table.

[0055] In some embodiments of this application, modifying the fields and / or field values ​​associated with the human figure in the second table to adjust the human figure in the first image accordingly includes at least one of the following: Add or delete fields and / or field values ​​associated with the first figure image in the second table, thereby adding or deleting the first figure image in the first image accordingly; In the second table, adjust the row or column order of the first field associated with the first figure image and the second field associated with the second figure image, thereby adjusting the position order of the first figure image and the second figure image in the first image, so that the position order of the first figure image and the second figure image in the first image is the same as or the opposite of the position order of the first field and the second field in the second table. In the second table, adjust the row or column order of the first field value associated with the first figure pattern and the second field value associated with the second figure pattern, thereby adjusting the position order of the first figure pattern and the second figure pattern in the first image, so that the position order of the first figure pattern and the second figure pattern in the first image is the same as or the opposite of the position order of the first field value and the second field value in the second table. Modify the fields and / or field values ​​in the second table that include physiological data or image data information of the first person's pattern, thereby adjusting the local image of the first person's pattern in the first image accordingly.

[0056] For example, suppose the first image includes six figures, three of which are Zhao Yi, Qian Er, Sun San, Li Si, Zhou Wu, and Wu Liu; the second table includes fields and / or field values ​​associated with Zhao Yi, Qian Er, Sun San, Li Si, Zhou Wu, and Wu Liu respectively.

[0057] When a user deletes a field and / or field value associated with Zhao Yi in the second table, Zhao Yi is deleted from the first image. For example, as shown... Figure 8 and Figure 9 As shown, Figure 8 These are schematic diagrams illustrating the deletion of fields and / or field values ​​in a table according to some embodiments of this application; Figure 9 These are schematic diagrams illustrating the deletion of human figures as provided in some embodiments of this application.

[0058] For example, suppose the first image includes three figures, namely figure A, figure B, and figure C, where figure A is above figure B and figure C is below figure B. The second table includes fields and / or field values ​​associated with figure A, figure B, and figure C, respectively; wherein the fields and / or field values ​​associated with figure A are above the fields and / or field values ​​associated with figure B, and the fields and / or field values ​​associated with figure B are above the fields and / or field values ​​associated with figure C.

[0059] When the user adjusts the field and / or field value corresponding to person pattern B in the second table to the row above the field and / or field value corresponding to person pattern A, person pattern B will be moved above person pattern A in the first image.

[0060] For example, suppose the first image includes three figures, namely figure A, figure B, and figure C. The second table includes the position information of figure A, figure B, and figure C in the first image.

[0061] When the user modifies the position information of the figure B in the second table in the first image, for example, by adjusting the position information of the figure B in the first image from [x1,y1,x2,y2] to [x3,y3,x4,y4], the position of the figure B in the first image is adjusted from [x1,y1,x2,y2] to [x3,y3,x4,y4].

[0062] In some embodiments of this application, updating the person image in the first image to modify the fields and / or field values ​​associated with the person image in the second table includes at least one of the following: Adding or deleting a first person image in the first image corresponds to adding or deleting fields and / or field values ​​associated with the first person image in the second table. Adjusting the positional order of the first and second character patterns in the first image corresponds to adjusting the row or column order of the first field associated with the first character pattern and the second field associated with the second character pattern in the second table, so that the positional order of the first and second fields in the second table is the same as or opposite to the positional order of the first and second character patterns in the first image; or, correspondingly adjusting the row or column order of the first field value associated with the first character pattern and the second field value associated with the second character pattern in the second table, so that the positional order of the first field value and the second field value in the second table is the same as or opposite to the positional order of the first and second character patterns in the first image. Adjust a local image of the first person pattern in the first image, thereby modifying the corresponding fields and / or field values ​​in the second table that include the physiological data information or image data information of the first person pattern.

[0063] For example, suppose the first image includes three figures, namely figure A, figure B, and figure C. The second table includes fields and / or field values ​​associated with figure A, figure B, and figure C, respectively.

[0064] When a user deletes person pattern B in the first image, delete the fields and / or field values ​​associated with person pattern B in the second table.

[0065] For example, suppose the first image includes three figures, namely figure A, figure B, and figure C, where figure A is above figure B and figure C is below figure B. The second table includes fields and / or field values ​​associated with figure A, figure B, and figure C, respectively; wherein the fields and / or field values ​​associated with figure A are above the fields and / or field values ​​associated with figure B, and the fields and / or field values ​​associated with figure B are above the fields and / or field values ​​associated with figure C.

[0066] When the user moves the person pattern B in the first image above the person pattern A, the field and / or field value corresponding to person pattern B in the second table is moved to the row above the field and / or field value corresponding to person pattern A.

[0067] For example, suppose the first image includes three figures, namely figure A, figure B, and figure C. The second table includes the position information of figure A, figure B, and figure C in the first image.

[0068] When the user adjusts the position of the figure pattern B in the first image from [x1,y1,x2,y2] to [x3,y3,x4,y4], the position information of the figure pattern B in the second table in the first image is changed from [x1,y1,x2,y2] to [x3,y3,x4,y4].

[0069] In some embodiments of this application, the first image may include at least one text paragraph; accordingly, step 104 may include: in response to the second input, determining fields and / or field values ​​associated with the text paragraph based on the text paragraph in the first image.

[0070] In some embodiments of this application, fields and / or field values ​​are associated with text paragraphs, including at least one of the following: Fields and / or field values ​​include numeric information contained in a text paragraph and / or object information represented by numeric information; Fields and / or field values ​​include portions of text information contained within a text paragraph; Fields and / or field values ​​include statistical information or analytical data obtained based on numerical information contained in text paragraphs; Fields and / or field values ​​include pre-stored information related to textual information; Fields and / or field values ​​include custom information related to text. Fields and / or field values ​​include information obtained through AI analysis of text information.

[0071] In some embodiments of this application, the object information represented by the digital information may include, but is not limited to, year, sales amount, growth rate, etc.; statistical information may include, but is not limited to, annual sales amount, total sales amount, etc.; pre-stored information related to text information, customized information related to text information, and information obtained by AI analysis of text information include, but are not limited to, text summary information.

[0072] In some embodiments of this application, when determining the fields and / or field values ​​associated with a text segment based on the text segment in the first image, the numerical information contained in the text segment and / or the object information represented by the numerical information can be determined as the fields and / or field values ​​associated with the text segment; the partial text information contained in the text segment can also be determined as the fields and / or field values ​​associated with the text segment; the statistical information or analytical data obtained based on the numerical information contained in the text segment can also be determined as the fields and / or field values ​​associated with the text segment; the pre-stored information related to the text information can also be determined as the fields and / or field values ​​associated with the text segment; the customized information related to the text information can also be determined as the fields and / or field values ​​associated with the text segment; and the information obtained by analyzing the text information through AI can also be determined as the fields and / or field values ​​associated with the text segment.

[0073] In some embodiments of this application, at least one text segment includes multiple sets of numerical information representing the same object; accordingly, step 104 may include: in response to the second input, determining multiple sets of third fields and / or third field values ​​associated with the numerical information representing the same object based on the multiple sets of numerical information representing the same object in the at least one text segment; accordingly, step 105 may include: inserting multiple sets of third fields and / or third field values ​​into a first table according to the size relationship of the multiple sets of numerical information representing the same object, to obtain a second table; the multiple sets of third fields and / or third field values ​​are arranged in the second table in descending order or ascending order according to the size relationship of the multiple sets of numerical information representing the same object.

[0074] In some embodiments of this application, when the first image includes at least one text paragraph, and the at least one text paragraph includes multiple numerical information representing the same object, the fields and / or field values ​​associated with each numerical information representing the same object can be grouped together; according to the size relationship of the multiple numerical information representing the same object, multiple groups of fields and / or field values ​​are inserted into the first table to obtain the second table.

[0075] For example, suppose the text paragraph is "In 2025, the sales revenue of product A was 10 million, a year-on-year decrease of 10%; in 2025, the sales revenue of product B was 12 million, a year-on-year increase of 20%"; where "In 2025, the sales revenue of product A was 10 million, a year-on-year decrease of 10%" represents one object, and "In 2025, the sales revenue of product B was 12 million, a year-on-year increase of 20%" represents another object. The sales revenue of product B is greater than that of product A. Then, in the second table, the fields and / or field values ​​associated with the numerical information of the object represented by "In 2025, the sales revenue of product B was 12 million, a year-on-year increase of 20%" are located below the fields and / or field values ​​associated with the numerical information of the object represented by "In 2025, the sales revenue of product A was 10 million, a year-on-year decrease of 10%". Figure 10 and Figure 11 As shown, Figure 10 This is a second schematic diagram of the first image provided in some embodiments of this application; Figure 11 This is a third schematic diagram of a second table provided in some embodiments of this application.

[0076] In some embodiments of this application, the image processing method provided in this application further includes: modifying fields and / or field values ​​associated with text paragraphs in a second table to adjust the text paragraphs in the first image accordingly; and / or updating the text paragraphs in the first image to modify fields and / or field values ​​associated with text paragraphs in the second table.

[0077] In some embodiments of this application, modifying the fields and / or field values ​​associated with the text paragraphs in the second table to adjust the text paragraphs in the first image accordingly includes at least one of the following: Add or delete a fourth field and / or a fourth field value associated with a text paragraph in the second table, thereby correspondingly adding or deleting text information containing the fourth field and / or the fourth field value in the first image; The fourth and fifth field values ​​include numerical information contained in the text paragraphs, and the objects represented by the numerical information in the fourth and fifth field values ​​are the same. The row order or column order of the fourth and fifth field values ​​in the second table is adjusted, thereby correspondingly adjusting the positional order of the text information containing the fourth field value and the text information containing the fifth field value in the first image, so that the positional order of the text information containing the fourth field value and the text information containing the fifth field value in at least one text paragraph is the same as or the opposite of the positional order of the fourth and fifth field values ​​in the second table. Based on the analysis and calculation of multiple sets of fields and / or field values ​​containing numerical information in the second table, new result fields and / or result field values ​​are generated in the second table; thereby, text information including the result fields and / or result field values ​​is added to the first image accordingly.

[0078] For example, the user deletes the above Figure 11 The fields and / or field values ​​"2025, A, 10 million, -10%" associated with the numerical information of the object represented by "Sales of Product A in 2025 were 10 million, a year-on-year decrease of 10%" in the second table shown above will be used to... Figure 10 The phrase "Sales of product A in 2025 will be 10 million, a year-on-year decrease of 10%" in the first image shown should be deleted.

[0079] For example, the user in the above Figure 11 Adding the field and / or field value "2025, C, 9.8 million, 1%" to the numerical information of the object represented by "Sales of Product A in 2025 was 10 million, a year-on-year decrease of 10%" to the second table shown will then... Figure 10 In the first image shown, the text "Product C sales reached 9.8 million in 2025, a year-on-year increase of 1%" is added below "Product B sales reached 12 million in 2025, a year-on-year increase of 20%".

[0080] For example, the user will use the above Figure 11 In the second table shown, the fields and / or field values ​​"2025, A, 10 million, -10%" associated with the numerical information of the object represented by "2025 sales of product A were 10 million, a year-on-year decrease of 10%" should be moved below the fields and / or field values ​​"2025, B, 12 million, 20%" associated with the numerical information of the object represented by "2025 sales of product B were 12 million, a year-on-year increase of 20%". This will be in accordance with the above... Figure 10 In the first image shown, the text "Sales of product A in 2025 were 10 million, a year-on-year decrease of 10%" is moved below the text "Sales of product B in 2025 were 12 million, a year-on-year increase of 20%".

[0081] For example, the user on the above Figure 11 The sales figures in the second table shown are summed, generating a result field value of "22 million" for both the "Total Sales" and "Total Sales" fields. Figure 10 The first image shown has the added text information "The total sales revenue of Product A and Product B in 2025 will be 22 million".

[0082] In some embodiments of this application, updating a text paragraph in a first image to modify the field and / or field values ​​associated with the text paragraph in a second table includes at least one of the following: Add or delete text information containing a fourth field and / or a fourth field value in the first image, thereby correspondingly adding or deleting a fourth field and / or a fourth field value associated with a text paragraph in the second table; The fourth and fifth field values ​​include numerical information contained in the text paragraphs, and the objects represented by the numerical information in the fourth and fifth field values ​​are the same. The positional order of the text information containing the fourth and fifth field values ​​in the first image is adjusted, thereby correspondingly adjusting the row or column order of the fourth and fifth field values ​​in the second table, so that the positional order of the fourth and fifth field values ​​in the second table is the same as or the opposite of the positional order of the text information containing the fourth and fifth field values ​​in at least one text paragraph.

[0083] For example, the user in the above Figure 10 In the first image shown, below the statement "Product B sales reached 12 million in 2025, a year-on-year increase of 20%", add the text "Product C sales reached 9.8 million in 2025, a year-on-year increase of 1%". Then, in the above... Figure 11 Add the field value "2025, Product C, 9.8 million, 1%" to the second table shown.

[0084] For example, the user will use the above Figure 10 The phrase "Sales of product A in 2025 was 10 million, a year-on-year decrease of 10%" in the first image shown should be deleted. Figure 11 The fields and / or field values ​​associated with the numerical information of the object represented by “Sales of Product A in 2025 was 10 million, a year-on-year decrease of 10%” in the second table shown are “2025, A, 10 million, -10%”.

[0085] For example, the user will use the above Figure 10 If the text "Sales of product A in 2025 were 10 million, a year-on-year decrease of 10%" in the first image is moved below the text "Sales of product B in 2025 were 12 million, a year-on-year increase of 20%", then the above will be... Figure 11 In the second table shown, the fields and / or field values ​​associated with the numerical information of the object represented by "Sales of Product A in 2025 were 10 million, a year-on-year decrease of 10%" are adjusted to be below the fields and / or field values ​​associated with the numerical information of the object represented by "Sales of Product B in 2025 were 12 million, a year-on-year increase of 20%" and "2025, B, 12 million, 20%".

[0086] In some embodiments of this application, the first image includes a landscape pattern; accordingly, step 104 may include: in response to a second input, determining fields and / or field values ​​associated with the landscape pattern based on the landscape pattern in the first image.

[0087] In some embodiments of this application, fields and / or field values ​​are associated with landscape patterns, including at least one of the following: Fields and / or field values ​​include the landscape type of the landscape pattern; Fields and / or field values ​​include location information for the landscape image; Fields and / or field values ​​include time information for the landscape image; Fields and / or field values ​​include itinerary information and / or schedule information planned based on the location of the landscape image; Fields and / or field values ​​include pre-stored information related to the landscape pattern; Fields and / or field values ​​include custom information related to the landscape pattern; Fields and / or field values ​​include information obtained through AI-based recognition of landscape patterns.

[0088] In some embodiments of this application, the landscape type includes, but is not limited to, mountains, seas, grasslands, deserts, etc.; the location information includes, but is not limited to, the geographical location of the electronic device when the first image was captured; the time information includes, but is not limited to, the time when the first image was captured; the itinerary information includes, but is not limited to, departure point, destination, transit points, estimated number of days, budget, etc.; the schedule information includes, but is not limited to, time, content, location, etc.; the pre-stored information related to the landscape pattern, the customized information related to the landscape pattern, and the information obtained by AI recognizing the landscape pattern include, but is not limited to, landscape type, lighting information, etc.

[0089] In some embodiments of this application, when determining fields and / or field values ​​associated with a landscape pattern based on the landscape pattern in the first image, the landscape type of the landscape pattern can be determined as a field and / or field value associated with the landscape pattern; the location information of the landscape pattern can also be determined as a field and / or field value associated with the landscape pattern; the time information of the landscape pattern can also be determined as a field and / or field value associated with the landscape pattern; the itinerary information and / or schedule information planned based on the location of the landscape pattern can also be determined as a field and / or field value associated with the landscape pattern; pre-stored information related to the landscape pattern can also be determined as a field and / or field value associated with the landscape pattern; customized information related to the landscape pattern can also be determined as a field and / or field value associated with the landscape pattern; and information obtained by recognizing the landscape pattern through AI can also be determined as a field and / or field value associated with the landscape pattern.

[0090] In some embodiments of this application, the first image includes a first landscape pattern; the second input is further used to select a second image from at least one image, the second image including the second landscape pattern; the image processing method provided in the embodiments of this application further includes: in response to the second input, determining fields and / or field values ​​associated with the second landscape pattern based on the second image; accordingly, step 105 may include: inserting the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern into a first table to obtain a second table.

[0091] In some embodiments of this application, the process of determining fields and / or field values ​​associated with the second landscape pattern based on the second image is similar to the process of determining fields and / or field values ​​associated with the landscape pattern in the first image based on the first image; the process of inserting the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern into the first table to obtain the second table is similar to the process of inserting the fields and / or field values ​​associated with the landscape pattern in the first image into the first table to obtain the second table. The embodiments of this application will not be described in detail here. Specific processes can be referred to the descriptions in the above embodiments.

[0092] In some embodiments of this application, the first table includes multiple preset fields, and the preset fields include landscape types; accordingly, step 105 may include: in response to the second input, identifying the landscape type to which the landscape pattern included in the first image belongs; determining the fields and / or field values ​​associated with the first image based on the comparison result between the landscape type included in the preset fields and the landscape type of the landscape pattern included in the first image; the fields associated with the first image include the landscape type of the landscape pattern; the field values ​​associated with the first image include the comparison result between the landscape type included in the preset fields and the landscape type of the landscape pattern included in the first image.

[0093] For example, the preset fields include mountains, seas, grasslands, and deserts. When the landscape pattern included in the first image is identified as belonging to the landscape type of mountains, the landscape types "mountains, seas, grasslands, and deserts" included in the preset fields are compared with the landscape type "mountains" of the landscape pattern included in the first image. Then, mountains are determined as the field associated with the first image. The comparison results of the landscape types "mountains, seas, grasslands, and deserts" included in the preset fields with the landscape type "mountains" of the landscape pattern included in the first image are used as the field values ​​for "mountains, seas, grasslands, and deserts," respectively. The generated second table is shown in Table 1 below: Table 1

[0094] In Table 1, the comparison result is yes when the landscape type contained in the preset field is the same as the landscape type of the landscape pattern included in the first image; the comparison result is no when the landscape type contained in the preset field is the same as the landscape type of the landscape pattern included in the first image.

[0095] In some embodiments of this application, the first image includes a first landscape pattern; the second input is further used to select a second image from at least one image, the second image including the second landscape pattern; the image processing method provided in this application further includes: in response to the second input, determining fields and / or field values ​​associated with the second landscape pattern according to the second image; correspondingly, step 105 may include: inserting the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern into a first table to obtain a second table; the image processing method provided in this application further includes: based on a first comparison result of the landscape types contained in multiple preset fields and the landscape types of the first landscape pattern included in the first image, and a second comparison result of the landscape types contained in multiple preset fields and the landscape types of the second landscape pattern included in the second image, determining at least one of the following: the positional order of the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern in the second table; determining one of the first landscape pattern and the second landscape pattern as the target landscape pattern.

[0096] In some embodiments of this application, the process of determining fields and / or field values ​​associated with the second landscape pattern based on the second image is similar to the process of determining fields and / or field values ​​associated with the landscape pattern in the first image based on the first image; the process of inserting the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern into the first table to obtain the second table is similar to the process of inserting the fields and / or field values ​​associated with the landscape pattern in the first image into the first table to obtain the second table. The embodiments of this application will not be described in detail here. Specific processes can be referred to the descriptions in the above embodiments.

[0097] In some embodiments of this application, after inserting fields and / or field values ​​associated with the first landscape pattern and fields and / or field values ​​associated with the second landscape pattern into a first table to obtain a second table, the landscape types contained in multiple preset fields can be compared with the landscape types of the first landscape pattern to obtain a first comparison result of the landscape types contained in multiple preset fields and the landscape types of the first landscape pattern, and the landscape types contained in multiple preset fields can be compared with the landscape types of the second landscape pattern to obtain a second comparison result of the landscape types contained in multiple preset fields and the landscape types of the second landscape pattern; based on the first comparison result... Based on the first and second comparison results, determine the positional order of the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern in the second table; or, based on the first and second comparison results, determine one of the landscape patterns in the first and second landscape patterns as the target landscape pattern; or, based on the first and second comparison results, determine the positional order of the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern in the second table and determine one of the landscape patterns in the first and second landscape patterns as the target landscape pattern.

[0098] In some embodiments of this application, when determining the positional order of fields and / or field values ​​associated with the first scenic pattern and those associated with the second scenic pattern in a second table based on the first comparison result and the second comparison result, priority scores can be pre-set for each of a plurality of preset fields. Based on the first comparison result and the priority scores, a first comprehensive score corresponding to the first scenic pattern is determined; based on the second comparison result and the priority scores, a second comprehensive score corresponding to the second scenic pattern is determined. The positional order of fields and / or field values ​​associated with the first scenic pattern and those associated with the second scenic pattern in the second table is determined based on the first and second comprehensive scores. For example, if the first comprehensive score is greater than the second comprehensive score, then the position of the fields and / or field values ​​associated with the first scenic pattern in the second table is above the position of the fields and / or field values ​​associated with the second scenic pattern in the second table; that is, the fields and / or field values ​​associated with the first and second scenic patterns are sorted in descending order of comprehensive scores.

[0099] In some embodiments of this application, when determining one of the first and second landscape patterns as the target landscape pattern based on the first comparison result and the second comparison result, priority scores can be pre-set for each of the multiple preset fields. Based on the first comparison result and the priority scores, a first comprehensive score corresponding to the first landscape pattern is determined, and based on the second comparison result and the priority scores, a second comprehensive score corresponding to the second landscape pattern is determined. The landscape pattern with the largest comprehensive score among the first and second comprehensive scores is determined as the target landscape pattern.

[0100] In some embodiments of this application, the image processing method provided in this application can be used in a first electronic device. The image processing method provided in this application may further include: receiving a fifth input to a first image; wherein the fifth input is used to configure the attributes of the first image; in response to the fifth input, adding a table attribute to the attributes of the first image, and configuring the attribute value of the table attribute as a second table; receiving a sixth input to the first image; wherein the sixth input is used to share the first image; and in response to the sixth input, sending the first image to a second electronic device so that the second electronic device can display the second table through the attributes of the first image.

[0101] For example, users in such Figure 3 The attributes of the first image shown are given a table attribute, and the attribute value of the table attribute is configured as follows: Figure 4 The second table shown; upon receiving a send input from the user to send the first image to another electronic device, the first image is sent to that other electronic device. The other electronic device can receive a view input from the user to view the attributes of the first image, and display the attributes of the first image. The attributes of the first image include table attributes. When the user clicks the view control corresponding to a table attribute, the following is displayed: Figure 4 The second table shown.

[0102] The image processing method provided in this application can be executed by an image processing device. This application uses an image processing device executing the image processing method as an example to illustrate the image processing device provided in this application.

[0103] Figure 12 This is a schematic diagram of the structure of an image processing apparatus provided in some embodiments of this application. The image processing apparatus 1200 may include: The first receiving module 1201 is used to receive the first input to the first table; The first display module 1202 is configured to display a first interface in response to a first input; wherein the first interface includes at least one image; The second receiving module 1203 is used to receive a second input to the first interface; wherein the second input is used to select a first image from at least one image; The determination module 1204 is configured to, in response to a second input, determine, based on a first image, fields and / or field values ​​associated with the first image; Insertion module 1205 is used to insert fields and / or field values ​​associated with the first image into the first table to obtain the second table.

[0104] In some embodiments of this application, the image processing apparatus provided in this application further includes: The second display module is used to display a second interface; wherein the second interface includes at least one application identifier; The third receiving module is used to receive a third input to the second interface; wherein the third input is used to select a first application identifier from at least one application identifier; Accordingly, module 1204 includes: An extraction submodule is used to extract attribute information and attribute values ​​of objects included in the first image from the first application corresponding to the first application identifier in response to a third input. The first determination submodule is used to treat attribute information as fields and attribute values ​​as field values.

[0105] In some embodiments of this application, the image processing apparatus provided in this application further includes: The second display module is used to display the third interface; wherein the third interface includes at least one attribute information of an object in the first application; The fourth receiving module is used to receive a fourth input to the third interface; wherein the fourth input is used to select attribute information from at least one attribute information. Accordingly, the extraction submodule is specifically used to: extract the first attribute information selected by the fourth input in response to the fourth input; and extract the attribute value corresponding to the first attribute information from the first application.

[0106] In some embodiments of this application, the second input is further configured to select a second image from at least one image; accordingly, the determining module 1204 is further configured to: in response to the second input, determine, based on the second image, fields and / or field values ​​associated with the second image; Accordingly, the insertion module 1205 is specifically used for: The fields and / or field values ​​associated with the first image and the fields and / or field values ​​associated with the second image are inserted into the first table to obtain the second table.

[0107] In some embodiments of this application, fields and / or field values ​​are associated with a first image, including at least one of the following: The fields and / or field values ​​include at least one of the following: personal identification information, personal communication information, work-related information, physiological data information, and image data information of the person in the first image; The fields and / or field values ​​include at least one of the following: numerical information contained in the text paragraphs of the first image, object information represented by the numerical information, text information, and statistical information or analytical data obtained based on the numerical information contained in the text paragraphs; The fields and / or field values ​​include at least one of the following: landscape type, location information, time information, and itinerary information and / or schedule information planned based on the location of the landscape pattern in the first image; Fields and / or field values ​​include pre-stored information associated with the objects included in the first image; Fields and / or field values ​​include custom information associated with the objects included in the first image; Fields and / or field values ​​include information obtained through AI identification or analysis of objects included in the first image.

[0108] In some embodiments of this application, the first image includes at least one human figure pattern; Accordingly, module 1204 is specifically used for: In response to the second input, based on the human figure pattern in the first image, determine the fields and / or field values ​​associated with the human figure pattern; In some embodiments of this application, fields and / or field values ​​are associated with a person image, including at least one of the following: Fields and / or field values ​​include personally identifiable information about the person in the image; Fields and / or field values ​​include personal communication information of the person in the image; Fields and / or field values ​​include job-related information about the person image; Fields and / or field values ​​include physiological data information of the human figure pattern; Fields and / or field values ​​include image data information of the person's image; Fields and / or field values ​​include pre-stored information related to the character image; Fields and / or field values ​​include custom information related to the character image; Fields and / or field values ​​include information obtained through AI-based recognition of human figures.

[0109] In some embodiments of this application, the first image includes multiple human figures; Accordingly, module 1204 is specifically used for: In response to the second input, based on the multiple human figures in the first image, multiple sets of fields and / or field values ​​associated with the human figures are determined respectively; Insertion module 1205 is specifically used for: Based on the positional order of multiple figures in the first image, multiple sets of fields and / or field values ​​are inserted into the first table to obtain the second table; the order of the multiple sets of fields and / or field values ​​in the second table is the same as or the opposite of the positional order of the multiple figures in the first image.

[0110] In some embodiments of this application, the image processing apparatus provided in this application further includes: The first modification module is used to modify the fields and / or field values ​​in the second table associated with the figure image, so as to adjust the figure image in the first image accordingly; and / or, The first update module is used to update the figure pattern in the first image to modify the field and / or field value associated with the figure pattern in the second table.

[0111] In some embodiments of this application, the first modification module is specifically used for at least one of the following: Add or delete fields and / or field values ​​associated with the first figure image in the second table, thereby adding or deleting the first figure image in the first image accordingly; In the second table, adjust the row or column order of the first field associated with the first figure image and the second field associated with the second figure image, thereby adjusting the position order of the first figure image and the second figure image in the first image, so that the position order of the first figure image and the second figure image in the first image is the same as or the opposite of the position order of the first field and the second field in the second table. In the second table, adjust the row or column order of the first field value associated with the first figure pattern and the second field value associated with the second figure pattern, thereby adjusting the position order of the first figure pattern and the second figure pattern in the first image, so that the position order of the first figure pattern and the second figure pattern in the first image is the same as or the opposite of the position order of the first field value and the second field value in the second table. Modify the fields and / or field values ​​in the second table that include physiological data or image data information of the first person's pattern, thereby adjusting the local image of the first person's pattern in the first image accordingly.

[0112] In some embodiments of this application, the first update module is specifically used for at least one of the following: Adding or deleting a first person image in the first image corresponds to adding or deleting fields and / or field values ​​associated with the first person image in the second table. Adjusting the positional order of the first and second character patterns in the first image corresponds to adjusting the row or column order of the first field associated with the first character pattern and the second field associated with the second character pattern in the second table, so that the positional order of the first and second fields in the second table is the same as or opposite to the positional order of the first and second character patterns in the first image; or, correspondingly adjusting the row or column order of the first field value associated with the first character pattern and the second field value associated with the second character pattern in the second table, so that the positional order of the first field value and the second field value in the second table is the same as or opposite to the positional order of the first and second character patterns in the first image. Adjust a local image of the first person pattern in the first image, thereby modifying the corresponding fields and / or field values ​​in the second table that include the physiological data information or image data information of the first person pattern.

[0113] In some embodiments of this application, the first image includes at least one paragraph of text; Accordingly, module 1204 is specifically used for: In response to the second input, based on the text paragraph in the first image, determine the fields and / or field values ​​associated with the text paragraph.

[0114] In some embodiments of this application, fields and / or field values ​​are associated with text paragraphs, including at least one of the following: Fields and / or field values ​​include numeric information contained in a text paragraph and / or object information represented by numeric information; Fields and / or field values ​​include portions of text information contained within a text paragraph; Fields and / or field values ​​include statistical information or analytical data obtained based on numerical information contained in text paragraphs; Fields and / or field values ​​include pre-stored information related to textual information; Fields and / or field values ​​include custom information related to text. Fields and / or field values ​​include information obtained through AI analysis of text information.

[0115] In some embodiments of this application, at least one text paragraph includes multiple numerical information representing the same object; Accordingly, module 1204 is specifically used for: In response to the second input, based on the same numerical information of multiple representation objects in at least one text paragraph, determine multiple sets of third field and / or third field values ​​associated with the same numerical information of representation objects; Insertion module 1205 is specifically used for: Based on the size relationship of the same numerical information of multiple representation objects, multiple sets of third fields and / or third field values ​​are inserted into the first table to obtain the second table; the multiple sets of third fields and / or third field values ​​are arranged in the second table in descending order or ascending order according to the size relationship of the same numerical information of multiple representation objects.

[0116] In some embodiments of this application, the image processing apparatus provided in this application further includes: The second modification module is used to modify the fields and / or field values ​​associated with the text paragraphs in the second table to adjust the text paragraphs in the first image accordingly; and / or, The second update module is used to update the text paragraph in the first image to modify the field and / or field values ​​associated with the text paragraph in the second table.

[0117] In some embodiments of this application, the second modification module is specifically used for at least one of the following: Add or delete a fourth field and / or a fourth field value associated with a text paragraph in the second table, thereby correspondingly adding or deleting text information containing the fourth field and / or the fourth field value in the first image; The fourth and fifth field values ​​include numerical information contained in the text paragraphs, and the objects represented by the numerical information in the fourth and fifth field values ​​are the same. The row order or column order of the fourth and fifth field values ​​in the second table is adjusted, thereby correspondingly adjusting the positional order of the text information containing the fourth field value and the text information containing the fifth field value in the first image, so that the positional order of the text information containing the fourth field value and the text information containing the fifth field value in at least one text paragraph is the same as or the opposite of the positional order of the fourth and fifth field values ​​in the second table. Based on the analysis and calculation of multiple sets of fields and / or field values ​​containing numerical information in the second table, new result fields and / or result field values ​​are generated in the second table; thereby, text information including the result fields and / or result field values ​​is added to the first image accordingly.

[0118] In some embodiments of this application, the second update module is specifically used for at least one of the following: Add or delete text information containing a fourth field and / or a fourth field value in the first image, thereby correspondingly adding or deleting a fourth field and / or a fourth field value associated with a text paragraph in the second table; The fourth and fifth field values ​​include numerical information contained in the text paragraphs, and the objects represented by the numerical information in the fourth and fifth field values ​​are the same. The positional order of the text information containing the fourth and fifth field values ​​in the first image is adjusted, thereby correspondingly adjusting the row or column order of the fourth and fifth field values ​​in the second table, so that the positional order of the fourth and fifth field values ​​in the second table is the same as or the opposite of the positional order of the text information containing the fourth and fifth field values ​​in at least one text paragraph.

[0119] In some embodiments of this application, the first image includes a landscape pattern; Accordingly, module 1204 is specifically used for: In response to the second input, based on the landscape pattern in the first image, determine the fields and / or field values ​​associated with the landscape pattern.

[0120] In some embodiments of this application, fields and / or field values ​​are associated with landscape patterns, including at least one of the following: Fields and / or field values ​​include the landscape type of the landscape pattern; Fields and / or field values ​​include location information for the landscape image; Fields and / or field values ​​include time information for the landscape image; Fields and / or field values ​​include itinerary information and / or schedule information planned based on the location of the landscape image; Fields and / or field values ​​include pre-stored information related to the landscape pattern; Fields and / or field values ​​include custom information related to the landscape pattern; Fields and / or field values ​​include information obtained through AI-based recognition of landscape patterns.

[0121] In some embodiments of this application, the first image includes a first landscape pattern; the second input is further configured to select a second image from at least one image, the second image including the second landscape pattern; correspondingly, the determining module 1204 is further configured to: In response to the second input, based on the second image, determine the fields and / or field values ​​associated with the second landscape pattern; Insertion module 1205 is also used for: Insert the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern into the first table to obtain the second table.

[0122] In some embodiments of this application, the first table includes multiple preset fields, and the preset fields include landscape type; accordingly, the determining module 1204 is specifically used for: In response to the second input, the landscape type to which the landscape pattern included in the first image belongs is identified; Based on the comparison results between the landscape types contained in the preset fields and the landscape types of the landscape patterns included in the first image, fields and / or field values ​​associated with the first image are determined; the fields associated with the first image include the landscape types of the landscape patterns; the field values ​​associated with the first image include the comparison results between the landscape types contained in the preset fields and the landscape types of the landscape patterns included in the first image.

[0123] In some embodiments of this application, the first image includes a first landscape pattern; the second input is further configured to select a second image from at least one image, the second image including the second landscape pattern; correspondingly, the determining module 1204 is further configured to: In response to the second input, based on the second image, determine the fields and / or field values ​​associated with the second landscape pattern; Insertion module 1205 is also used for: Insert the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern into the first table to obtain the second table; Module 1204 is also used for: Based on the first comparison result of the landscape types contained in multiple preset fields with the landscape types of the first landscape pattern included in the first image, and the second comparison result of the landscape types contained in multiple preset fields with the landscape types of the second landscape pattern included in the second image, at least one of the following is determined: The order of the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern in the second table; Choose one of the first and second landscape patterns as the target landscape pattern.

[0124] In some embodiments of this application, the image processing apparatus provided in this application is applied to a first electronic device; the image processing apparatus provided in this application further includes: The fifth receiving module is used to receive a fifth input to the first image; wherein the fifth input is used to configure the attributes of the first image; An additional module is added to the properties of the first image in response to the fifth input, and the attribute values ​​of the table attribute are configured as the second table. The sixth receiving module is used to receive a sixth input to the first image; wherein the sixth input is used to share the first image; A sending module is configured to send a first image to a second electronic device in response to a sixth input, so that the second electronic device can display a second table based on the attributes of the first image.

[0125] The image processing device in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the device.

[0126] The image processing device in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system used.

[0127] The image processing apparatus provided in this application embodiment can achieve... Figures 1 to 11 The various processes implemented in the image processing method embodiment will not be described again here to avoid repetition.

[0128] Optionally, such as Figure 13 As shown, this application embodiment also provides an electronic device 1300, including a processor 1301 and a memory 1302. The memory 1302 stores a program or instructions that can run on the processor 1301. When the program or instructions are executed by the processor 1301, they implement the various steps of the image processing method embodiment provided in this application embodiment and can achieve the same technical effect. To avoid repetition, they will not be described again here.

[0129] Figure 14 These are schematic diagrams of the hardware structure of electronic devices according to some embodiments of this application.

[0130] The electronic device 1400 includes, but is not limited to, components such as: radio frequency unit 1401, network module 1402, audio output unit 1403, input unit 1404, sensor 1405, display unit 1406, user input unit 1407, interface unit 1408, memory 1409, and processor 1410.

[0131] Those skilled in the art will understand that the electronic device 1400 may also include a power supply (such as a battery) for supplying power to various components. The power supply may be logically connected to the processor 1410 through a power management system, thereby enabling functions such as managing charging, discharging, and power consumption through the power management system. Figure 14 The electronic device structure shown does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown, or combine certain components, or have different component arrangements, which will not be elaborated here.

[0132] The user input unit 1407 is used to: receive a first input to the first table; The display unit 1406 is configured to: display a first interface in response to a first input; wherein the first interface includes at least one image; The user input unit 1407 is further configured to: receive a second input to the first interface; wherein the second input is used to select a first image from at least one image; The processor 1410 is configured to: in response to a second input, determine, based on a first image, fields and / or field values ​​associated with the first image; insert the fields and / or field values ​​associated with the first image into a first table to obtain a second table.

[0133] In some embodiments of this application, the display unit 1406 is further configured to: display a second interface; wherein the second interface includes at least one application identifier; The user input unit 1407 is further configured to: receive a third input to the second interface; wherein the third input is used to select a first application identifier from at least one application identifier; Accordingly, the processor 1410 is specifically configured to: in response to the third input, extract attribute information and attribute values ​​of objects included in the first image from the first application corresponding to the first application identifier; use the attribute information as a field and the attribute values ​​as field values.

[0134] In some embodiments of this application, the display unit 1406 is further configured to: display a third interface; wherein the third interface includes at least one attribute information of an object in the first application; The user input unit 1407 is further configured to: receive a fourth input to the third interface; wherein the fourth input is used to select attribute information from at least one attribute information; Accordingly, the processor 1410 is specifically configured to: extract first attribute information selected by the fourth input in response to the fourth input; and extract the attribute value corresponding to the first attribute information from the first application.

[0135] In some embodiments of this application, the second input is further configured to select a second image from at least one image; accordingly, the processor 1410 is further configured to: in response to the second input, determine fields and / or field values ​​associated with the second image based on the second image; insert the fields and / or field values ​​associated with the first image and the fields and / or field values ​​associated with the second image into a first table to obtain a second table.

[0136] In some embodiments of this application, fields and / or field values ​​are associated with a first image, including at least one of the following: The fields and / or field values ​​include at least one of the following: personal identification information, personal communication information, work-related information, physiological data information, and image data information of the person in the first image; The fields and / or field values ​​include at least one of the following: numerical information contained in the text paragraphs of the first image, object information represented by the numerical information, text information, and statistical information or analytical data obtained based on the numerical information contained in the text paragraphs; The fields and / or field values ​​include at least one of the following: landscape type, location information, time information, and itinerary information and / or schedule information planned based on the location of the landscape pattern in the first image; Fields and / or field values ​​include pre-stored information associated with the objects included in the first image; Fields and / or field values ​​include custom information associated with the objects included in the first image; Fields and / or field values ​​include information obtained through AI identification or analysis of objects included in the first image.

[0137] In some embodiments of this application, the first image includes at least one human figure pattern; Accordingly, processor 1410 is specifically used for: In response to the second input, based on the human figure pattern in the first image, determine the fields and / or field values ​​associated with the human figure pattern.

[0138] In some embodiments of this application, fields and / or field values ​​are associated with a person image, including at least one of the following: Fields and / or field values ​​include personally identifiable information about the person in the image; Fields and / or field values ​​include personal communication information of the person in the image; Fields and / or field values ​​include job-related information about the person illustrations; Fields and / or field values ​​include physiological data information of the human figure pattern; Fields and / or field values ​​include image data information of the person's image; Fields and / or field values ​​include pre-stored information related to the character image; Fields and / or field values ​​include custom information related to the character image; Fields and / or field values ​​include information obtained through AI-based recognition of human figures.

[0139] In some embodiments of this application, the first image includes multiple human figures; Accordingly, processor 1410 is specifically used for: In response to the second input, based on the multiple figures in the first image, multiple sets of fields and / or field values ​​associated with the figures are determined respectively; based on the positional order of the multiple figures in the first image, the multiple sets of fields and / or field values ​​are inserted into a first table to obtain a second table; the arrangement order of the multiple sets of fields and / or field values ​​in the second table is the same as or the opposite of the positional order of the multiple figures in the first image.

[0140] In some embodiments of this application, the processor 1410 is further configured to: modify the field and / or field values ​​associated with the figure pattern in the second table to adjust the figure pattern in the first image accordingly; and / or, Update the figure pattern in the first image to modify the field and / or field values ​​associated with the figure pattern in the second table.

[0141] In some embodiments of this application, the processor 1410 is specifically used for at least one of the following: Add or delete fields and / or field values ​​associated with the first figure image in the second table, thereby adding or deleting the first figure image in the first image accordingly; In the second table, adjust the row or column order of the first field associated with the first figure image and the second field associated with the second figure image, thereby adjusting the position order of the first figure image and the second figure image in the first image, so that the position order of the first figure image and the second figure image in the first image is the same as or the opposite of the position order of the first field and the second field in the second table. In the second table, adjust the row or column order of the first field value associated with the first figure pattern and the second field value associated with the second figure pattern, thereby adjusting the position order of the first figure pattern and the second figure pattern in the first image, so that the position order of the first figure pattern and the second figure pattern in the first image is the same as or the opposite of the position order of the first field value and the second field value in the second table. Modify the fields and / or field values ​​in the second table that include physiological data or image data information of the first person's pattern, thereby adjusting the local image of the first person's pattern in the first image accordingly.

[0142] In some embodiments of this application, the processor 1410 is specifically used for at least one of the following: Adding or deleting a first person image in the first image corresponds to adding or deleting fields and / or field values ​​associated with the first person image in the second table. Adjusting the positional order of the first and second character patterns in the first image corresponds to adjusting the row or column order of the first field associated with the first character pattern and the second field associated with the second character pattern in the second table, so that the positional order of the first and second fields in the second table is the same as or opposite to the positional order of the first and second character patterns in the first image; or, correspondingly adjusting the row or column order of the first field value associated with the first character pattern and the second field value associated with the second character pattern in the second table, so that the positional order of the first field value and the second field value in the second table is the same as or opposite to the positional order of the first and second character patterns in the first image. Adjust a local image of the first person pattern in the first image, thereby modifying the corresponding fields and / or field values ​​in the second table that include the physiological data information or image data information of the first person pattern.

[0143] In some embodiments of this application, the first image includes at least one paragraph of text; Accordingly, processor 1410 is specifically used for: In response to the second input, based on the text paragraph in the first image, determine the fields and / or field values ​​associated with the text paragraph.

[0144] In some embodiments of this application, fields and / or field values ​​are associated with text paragraphs, including at least one of the following: Fields and / or field values ​​include numeric information contained in a text paragraph and / or object information represented by numeric information; Fields and / or field values ​​include portions of text information contained within a text paragraph; Fields and / or field values ​​include statistical information or analytical data obtained based on numerical information contained in text paragraphs; Fields and / or field values ​​include pre-stored information related to textual information; Fields and / or field values ​​include custom information related to text. Fields and / or field values ​​include information obtained through AI analysis of text information.

[0145] In some embodiments of this application, at least one text paragraph includes multiple numerical information representing the same object; Accordingly, processor 1410 is specifically used for: In response to the second input, based on the numerical information that is the same for multiple representation objects in at least one text paragraph, determine multiple sets of third fields and / or third field values ​​associated with the numerical information that is the same for multiple representation objects; insert the multiple sets of third fields and / or third field values ​​into the first table according to the size relationship of the numerical information that is the same for multiple representation objects to obtain the second table; arrange the multiple sets of third fields and / or third field values ​​in the second table in descending order or ascending order according to the size relationship of the numerical information that is the same for multiple representation objects.

[0146] In some embodiments of this application, the processor 1410 is also used for: Modify the fields and / or field values ​​associated with the text paragraphs in the second table to adjust the text paragraphs in the first image accordingly; and / or, Update the text paragraph in the first image to modify the field and / or field values ​​associated with the text paragraph in the second table.

[0147] In some embodiments of this application, the processor 1410 is specifically used for at least one of the following: Add or delete a fourth field and / or a fourth field value associated with a text paragraph in the second table, thereby correspondingly adding or deleting text information containing the fourth field and / or the fourth field value in the first image; The fourth and fifth field values ​​include numerical information contained in the text paragraphs, and the objects represented by the numerical information in the fourth and fifth field values ​​are the same. The row order or column order of the fourth and fifth field values ​​in the second table is adjusted, thereby correspondingly adjusting the positional order of the text information containing the fourth field value and the text information containing the fifth field value in the first image, so that the positional order of the text information containing the fourth field value and the text information containing the fifth field value in at least one text paragraph is the same as or the opposite of the positional order of the fourth and fifth field values ​​in the second table. Based on the analysis and calculation of multiple sets of fields and / or field values ​​containing numerical information in the second table, new result fields and / or result field values ​​are generated in the second table; thereby, text information including the result fields and / or result field values ​​is added to the first image accordingly.

[0148] In some embodiments of this application, the processor 1410 is specifically used for at least one of the following: Add or delete text information containing a fourth field and / or a fourth field value in the first image, thereby correspondingly adding or deleting a fourth field and / or a fourth field value associated with a text paragraph in the second table; The fourth and fifth field values ​​include numerical information contained in the text paragraphs, and the objects represented by the numerical information in the fourth and fifth field values ​​are the same. The positional order of the text information containing the fourth and fifth field values ​​in the first image is adjusted, thereby correspondingly adjusting the row or column order of the fourth and fifth field values ​​in the second table, so that the positional order of the fourth and fifth field values ​​in the second table is the same as or the opposite of the positional order of the text information containing the fourth and fifth field values ​​in at least one text paragraph.

[0149] In some embodiments of this application, the first image includes a landscape pattern; Accordingly, processor 1410 is specifically used for: In response to the second input, based on the landscape pattern in the first image, determine the fields and / or field values ​​associated with the landscape pattern.

[0150] In some embodiments of this application, fields and / or field values ​​are associated with landscape patterns, including at least one of the following: Fields and / or field values ​​include the landscape type of the landscape pattern; Fields and / or field values ​​include location information for the landscape image; Fields and / or field values ​​include time information for the landscape image; Fields and / or field values ​​include itinerary information and / or schedule information planned based on the location of the landscape image; Fields and / or field values ​​include pre-stored information related to the landscape pattern; Fields and / or field values ​​include custom information related to the landscape pattern; Fields and / or field values ​​include information obtained through AI-based recognition of landscape patterns.

[0151] In some embodiments of this application, the first image includes a first landscape pattern; the second input is further configured to select a second image from at least one image, the second image including the second landscape pattern; correspondingly, the processor 1410 is further configured to: In response to the second input, based on the second image, determine the fields and / or field values ​​associated with the second landscape pattern; insert the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern into the first table to obtain the second table.

[0152] In some embodiments of this application, the first table includes multiple preset fields, the preset fields including landscape type; correspondingly, the processor 1410 is specifically used for: In response to the second input, the landscape type to which the landscape pattern included in the first image belongs is identified; Based on the comparison results between the landscape types contained in the preset fields and the landscape types of the landscape patterns included in the first image, fields and / or field values ​​associated with the first image are determined; the fields associated with the first image include the landscape types of the landscape patterns; the field values ​​associated with the first image include the comparison results between the landscape types contained in the preset fields and the landscape types of the landscape patterns included in the first image.

[0153] In some embodiments of this application, the first image includes a first landscape pattern; the second input is further configured to select a second image from at least one image, the second image including the second landscape pattern; accordingly, the processor 1410 is further configured to: in response to the second input, determine, based on the second image, fields and / or field values ​​associated with the second landscape pattern; insert the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern into a first table to obtain a second table; and determine at least one of the following based on a first comparison result of the landscape types contained in a plurality of preset fields with the landscape types of the first landscape pattern included in the first image, and a second comparison result of the landscape types contained in a plurality of preset fields with the landscape types of the second landscape pattern included in the second image: The order of the fields and / or field values ​​associated with the first landscape pattern and the fields and / or field values ​​associated with the second landscape pattern in the second table; Choose one of the first and second landscape patterns as the target landscape pattern.

[0154] In some embodiments of this application, the user input unit 1407 is further configured to: receive a fifth input to the first image; wherein the fifth input is used to configure the attributes of the first image; The processor 1410 is also configured to: in response to the fifth input, add a table attribute to the attributes of the first image, and configure the attribute value of the table attribute as a second table; The user input unit 1407 is further configured to: receive a sixth input to the first image; wherein the sixth input is used to share the first image; The processor 1410 is also configured to: in response to the sixth input, send a first image to the second electronic device for the second electronic device to display a second table based on the attributes of the first image.

[0155] It should be understood that, in this embodiment, the input unit 1404 may include a graphics processing unit (GPU) 14041 and a microphone 14042. The GPU 14041 processes image data of still images or videos obtained by an image capture device (such as a camera) in video capture mode or image capture mode. The display unit 1406 may include a display panel 14061, which may be configured in the form of a liquid crystal display, an organic light-emitting diode, or the like. The user input unit 1407 includes at least one of a touch panel 14071 and other input devices 14072. The touch panel 14071 is also called a touch screen. The touch panel 14071 may include a touch detection device and a touch controller. Other input devices 14072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, power buttons, etc.), trackballs, mice, and joysticks, which will not be described in detail here.

[0156] The memory 1409 can be used to store software programs and various data. The memory 1409 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, application programs or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory 1409 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory 1409 in this embodiment includes, but is not limited to, these and any other suitable types of memory.

[0157] Processor 1410 may include one or more processing units; optionally, processor 1410 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into processor 1410.

[0158] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the image processing method provided in this application and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0159] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0160] This application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor. The processor is used to run programs or instructions to implement the various processes of the image processing method provided in this application and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0161] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0162] This application also provides a computer program product, which is stored in a storage medium and executed by at least one processor to implement the various processes of the image processing method embodiment provided in this application, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0163] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0164] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a computer software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the image processing methods provided in the various embodiments of this application.

[0165] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. An image processing method, characterized in that, The method includes: Receive the first input to the first form; In response to the first input, a first interface is displayed; wherein the first interface includes at least one image; Receive a second input to the first interface; wherein the second input is used to select a first image from the at least one image; In response to the second input, based on the first image, determine the fields and / or field values ​​associated with the first image; Insert the field and / or the field value into the first table to obtain the second table.

2. The method according to claim 1, characterized in that, Before determining the fields and / or field values ​​associated with the first image based on the first image, the method further includes: Display a second interface; wherein the second interface includes at least one application identifier; Receive a third input to the second interface; wherein the third input is used to select a first application identifier from the at least one application identifier; The step of determining the fields and / or field values ​​associated with the first image based on the first image includes: In response to the third input, attribute information and attribute values ​​of the objects included in the first image are extracted from the first application corresponding to the first application identifier; The attribute information is used as the field, and the attribute value is used as the field value.

3. The method according to claim 2, characterized in that, Before extracting the attribute information and attribute values ​​of the objects included in the first image from the first application corresponding to the first application identifier, the method further includes: A third interface is displayed; wherein the third interface includes at least one attribute information of the object in the first application; The step of extracting attribute information and attribute values ​​of objects included in the first image from the first application corresponding to the first application identifier includes: Receive a fourth input to the third interface; wherein the fourth input is used to select attribute information from the at least one attribute information; In response to the fourth input, the first attribute information selected by the fourth input is extracted; Extract the attribute value corresponding to the first attribute information from the first application.

4. The method according to claim 1, characterized in that, The second input is further used to select a second image from the at least one image, and the method further includes: In response to the second input, based on the second image, determine the fields and / or field values ​​associated with the second image; The step of inserting the field and / or the field value into the first table to obtain the second table includes: The fields and / or field values ​​associated with the first image and the fields and / or field values ​​associated with the second image are inserted into the first table to obtain the second table.

5. The method according to claim 1, characterized in that, The field and / or field value are associated with the first image, including at least one of the following: The fields and / or field values ​​include at least one of the following: personal identification information, personal communication information, work-related information, physiological data information, and image data information of the person in the first image; The field and / or field value includes at least one of the following: numerical information contained in the text paragraph in the first image, object information represented by the numerical information, text information, and statistical information or analytical data obtained based on the numerical information contained in the text paragraph; The fields and / or field values ​​include at least one of the following: landscape type, location information, time information, and itinerary information and / or schedule information planned based on the location of the landscape pattern in the first image; The fields and / or field values ​​include pre-stored information associated with objects included in the first image; The fields and / or field values ​​include custom information associated with the objects included in the first image; The fields and / or field values ​​include information obtained by AI identification or analysis of objects included in the first image.

6. The method according to claim 1, characterized in that, The first image includes at least one human figure; In response to the second input, determining fields and / or field values ​​associated with the first image based on the first image includes: In response to the second input, based on the human figure pattern in the first image, determine the fields and / or field values ​​associated with the human figure pattern; Wherein, the field and / or field value associated with the figure image includes at least one of the following: The fields and / or field values ​​include the personal identification information of the person image; The fields and / or field values ​​include the personal communication information of the person image; The fields and / or field values ​​include work-related information related to the figure image; The fields and / or field values ​​include physiological data information of the human figure pattern; The field and / or field value includes image data information of the figure pattern; The fields and / or field values ​​include pre-stored information related to the character image; The fields and / or field values ​​include custom information related to the character image; The fields and / or field values ​​include information obtained by AI recognition of the human figure pattern.

7. The method according to claim 6, characterized in that, The first image includes multiple figures; In response to the second input, determining fields and / or field values ​​associated with the first image based on the first image includes: In response to the second input, based on the multiple character patterns in the first image, a plurality of sets of fields and / or field values ​​associated with the character patterns are determined respectively; The step of inserting the field and / or the field value into the first table to obtain the second table includes: Based on the positional order of the multiple figures in the first image, multiple sets of fields and / or field values ​​are inserted into the first table to obtain the second table; the arrangement order of the multiple sets of fields and / or field values ​​in the second table is the same as or the opposite of the positional order of the multiple figures in the first image.

8. The method according to claim 6, characterized in that, The method further includes: Modify the fields and / or field values ​​in the second table associated with the figure pattern to adjust the figure pattern in the first image accordingly; and / or, Update the figure pattern in the first image to modify the field and / or field value associated with the figure pattern in the second table.

9. The method according to claim 1, characterized in that, The first image includes at least one paragraph of text; In response to the second input, determining fields and / or field values ​​associated with the first image based on the first image includes: In response to the second input, based on the text paragraph in the first image, determine the fields and / or field values ​​associated with the text paragraph; Wherein, the field and / or field value associated with the text paragraph includes at least one of the following: The fields and / or field values ​​include numerical information contained in the text paragraph and / or object information represented by the numerical information; The field and / or field value include some text information contained in the text paragraph; The fields and / or field values ​​include statistical information or analytical data obtained based on the numerical information contained in the text paragraph; The fields and / or field values ​​include pre-stored information related to the text information; The fields and / or field values ​​include custom information related to the text information; The fields and / or field values ​​include information obtained through AI analysis of the text information.

10. The method according to claim 9, characterized in that, The at least one text paragraph includes multiple numerical information representing the same objects; In response to the second input, determining the fields and / or field values ​​associated with the text paragraph based on the text paragraph in the first image includes: In response to the second input, based on the same numerical information of multiple representation objects in the at least one text paragraph, determine multiple sets of third field and / or third field values ​​associated with the same numerical information of the representation objects; The step of inserting the field and / or the field value into the first table to obtain the second table includes: Based on the size relationship of the same numerical information of the plurality of representation objects, multiple sets of the third field and / or the third field values ​​are inserted into the first table to obtain the second table; the multiple sets of the third field and / or the third field values ​​are arranged in the second table in descending order or ascending order according to the size relationship of the same numerical information of the plurality of representation objects.

11. The method according to claim 9, characterized in that, The method further includes: Modify the fields and / or field values ​​in the second table associated with the text paragraph to adjust the text paragraph in the first image accordingly; and / or, Update the text paragraph in the first image to modify the field and / or field value associated with the text paragraph in the second table.

12. The method according to claim 1, characterized in that, The first image includes a landscape pattern; In response to the second input, determining fields and / or field values ​​associated with the first image based on the first image includes: In response to the second input, based on the landscape pattern in the first image, determine the fields and / or field values ​​associated with the landscape pattern; Wherein, the field and / or field value associated with the landscape pattern includes at least one of the following: The field and / or field value include the landscape type of the landscape pattern; The field and / or field value include the location information of the landscape pattern; The field and / or field value includes the time information of the landscape pattern; The fields and / or field values ​​include itinerary information and / or schedule information planned based on the location of the landscape pattern; The fields and / or field values ​​include pre-stored information related to the landscape pattern; The fields and / or field values ​​include custom information related to the landscape pattern; The fields and / or field values ​​include information obtained by AI-based recognition of the landscape patterns.

13. The method according to claim 12, characterized in that, The first table includes multiple preset fields, and the preset fields include landscape type; In response to the second input, determining fields and / or field values ​​associated with the first image based on the first image includes: In response to the second input, the landscape type to which the landscape pattern included in the first image belongs is identified; Based on the comparison result between the landscape type contained in the preset field and the landscape type of the landscape pattern included in the first image, fields and / or field values ​​associated with the first image are determined; the fields associated with the first image include the landscape type of the landscape pattern; the field values ​​associated with the first image include the comparison result between the landscape type contained in the preset field and the landscape type of the landscape pattern included in the first image.

14. The method according to claim 1, characterized in that, The method is applied to a first electronic device; the method further includes: Receive a fifth input to the first image; wherein the fifth input is used to configure the attributes of the first image; In response to the fifth input, a table attribute is added to the attributes of the first image, and the attribute value of the table attribute is configured as the second table; Receive a sixth input to the first image; wherein the sixth input is used to share the first image; In response to the sixth input, the first image is sent to the second electronic device for the second electronic device to display the second table based on the attributes of the first image.

15. An image processing apparatus, characterized in that, The device includes: The first receiving module is used to receive the first input to the first table; A first display module is configured to display a first interface in response to the first input; wherein the first interface includes at least one image; The second receiving module is configured to receive a second input to the first interface; wherein the second input is used to select a first image from the at least one image; A determining module is configured to, in response to the second input, determine, based on the first image, fields and / or field values ​​associated with the first image; An insertion module is used to insert the field and / or the field value into the first table to obtain a second table.

16. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the steps of the image processing method as described in any one of claims 1-14.

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