CN118196792A - Labeling method, labeling device, labeling equipment and storage medium - Google Patents

Labeling method, labeling device, labeling equipment and storage medium Download PDF

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CN118196792A
CN118196792A CN202410232537.4A CN202410232537A CN118196792A CN 118196792 A CN118196792 A CN 118196792A CN 202410232537 A CN202410232537 A CN 202410232537A CN 118196792 A CN118196792 A CN 118196792A
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image
data file
labeling
target object
client
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万明曦
于博
胡洪
余丹曦
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Streamax Technology Co Ltd
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Streamax Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/70—Labelling scene content, e.g. deriving syntactic or semantic representations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G06V10/774—Generating sets of training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/94—Hardware or software architectures specially adapted for image or video understanding
    • G06V10/95—Hardware or software architectures specially adapted for image or video understanding structured as a network, e.g. client-server architectures

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Abstract

According to the labeling method, the device, the equipment and the storage medium, the server is used for dividing the image to be labeled based on the image division model to generate the predicted data file, the predicted data file is sent to the client, and the client can directly use the predicted data file when labeling.

Description

Labeling method, labeling device, labeling equipment and storage medium
Technical Field
The application belongs to the technical field of image labeling, and particularly relates to a labeling method, a labeling device, labeling equipment and a storage medium.
Background
The existing AI image recognition algorithm model training relies on a large amount of manual data labeling, for example, a polygonal frame for a vehicle in a picture is given out, and corresponding coordinate data is used for model training. In the labeling of complex type labeling data, for example, labeling of the outer frame of a cyclist, the labeling time is relatively consumed, auxiliary labeling is performed through a SAM large model in the related art, because the reasoning capacity of the SAM large model cannot respond to the front end in real time, an operation user using the SAM to perform auxiliary labeling needs to wait for generating prediction data for a long time, the labeling efficiency is greatly influenced, in addition, in the manual labeling process, when the labeling such as the frame size and the edge part smoothness degree are required to be modified for a plurality of times, the SAM is required to be called for a plurality of times, interaction is performed with the SAM model for a plurality of times, the interaction time is seriously consumed, and in addition, the calculation resource is also increased.
Disclosure of Invention
Aiming at the problems, the embodiment of the application provides a labeling method, a labeling device, labeling equipment and a storage medium, which can reduce the waiting time of a user during image labeling and improve the labeling efficiency.
The embodiment of the application provides a labeling method, which is applied to a client and comprises the following steps:
under the condition that the preset operation for the target object in the image to be marked is obtained, determining the coordinates of the target object;
Labeling the target object based on the coordinates and a pre-stored prediction data file, wherein the prediction data file is transmitted to a client by a server, and the prediction data file is generated after the image to be labeled is segmented by the server based on an image segmentation model.
In some embodiments, the labeling the target object based on the coordinates and a pre-stored prediction data file includes:
Inputting the coordinates and the prediction data file into a prestored neural network model, and determining a polygonal outer frame of the target object so as to mark the target object, wherein the prediction data file comprises the following components: polygonal outer frames of the respective target objects.
In some embodiments, the method further comprises:
the image to be marked is sent to the server, so that the server divides the image to be marked based on an image division model, and a prediction data file is generated;
and acquiring a predicted data file sent by a server, and storing the predicted data file.
In some embodiments, the method further comprises:
Acquiring the demand time set by a user;
And sending the required time to the server so that the server determines the idle time before the required time, and dividing the image to be marked by using the image division model based on the idle time.
The embodiment of the application provides a labeling method, which is characterized by comprising the following steps:
Acquiring an image to be marked sent by a client;
inputting the image to be annotated into an image segmentation model, and determining a predicted data file of each object;
and sending the predicted data file to a client so that the client determines the coordinates of the target object in the image to be marked under the condition of acquiring the preset operation for the target object, and marking the target object based on the coordinates and the predicted data file.
In some embodiments, the method further comprises:
Under the condition that the demand time set by the user is acquired, determining the idle time before the demand time;
The step of inputting the image to be annotated into an image segmentation model to determine the predicted data file of each object comprises the following steps:
And inputting the image to be annotated into the image segmentation model in idle time, and determining a predicted data file of each object.
The embodiment of the application provides a labeling device, which is applied to a client and comprises:
The first determining module is used for determining coordinates of a target object in an image to be marked under the condition that a preset operation for the target object in the image to be marked is obtained;
The labeling module is used for labeling the target object based on the coordinates and a prestored prediction data file, wherein the prediction data file is sent to the client by the server, and the prediction data file is generated after the image to be labeled is segmented by the server based on an image segmentation model.
The embodiment of the application provides a labeling device, which is applied to a server and comprises the following components:
the first acquisition module is used for acquiring an image to be marked sent by the client;
The second determining module is used for inputting the image to be annotated into the image segmentation model and determining the predicted data file of each object;
And the sending module is used for sending the predicted data file to the client so that the client can determine the coordinates of the target object under the condition that the client obtains the preset operation for the target object in the image to be marked, and mark the target object based on the coordinates and the predicted data file.
An embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the method of any one of the above when executing the computer program.
Embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements a method as described in any one of the above.
Embodiments of the present application provide a computer program product for causing an electronic device to perform any one of the methods described above when the computer program product is run on a terminal device.
According to the labeling method, the device, the equipment and the storage medium, the server is used for dividing the image to be labeled based on the image division model to generate the predicted data file, the predicted data file is sent to the client, and the client can directly use the predicted data file when labeling.
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The application will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings.
FIG. 1 is a schematic diagram of an implementation flow of a labeling method according to the present application;
FIG. 2 is a schematic diagram of an implementation flow of a labeling method according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an implementation flow of a labeling method according to an embodiment of the present application;
FIG. 4 is a specific example of a labeling method according to an embodiment of the present application;
FIG. 5 is a schematic structural diagram of a labeling device according to an embodiment of the present application;
fig. 6 is a schematic diagram of a composition structure of an electronic device according to an embodiment of the present application.
In the drawings, like elements are numbered alike and are not drawn to scale.
Detailed Description
The present application will be further described in detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present application more apparent, and the described embodiments should not be construed as limiting the present application, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.
If a similar description of "first\second\third" appears in the application document, the following description is added, in which the terms "first\second\third" are merely distinguishing between similar objects and do not represent a particular ordering of the objects, it being understood that the "first\second\third" may be interchanged in a particular order or precedence, where allowed, to enable embodiments of the application described herein to be practiced in an order other than that illustrated or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the application only and is not intended to be limiting of the application.
Based on the problems existing in the related art, the embodiment of the application provides a labeling method, which is applied to a labeling system, wherein the labeling system comprises: the system comprises a client and a server, wherein the client is in communication connection with the server, the client provides an interactive interface to acquire the operation of a user, and the client can also determine the coordinates of a target object in an image to be marked under the condition of acquiring the preset operation of the target object; labeling the target object based on the coordinates and a pre-stored prediction data file, wherein the server side is used for acquiring an image to be labeled sent by the client side; inputting the image to be annotated into an image segmentation model, and determining a predicted data file of each object; and sending the predicted data file to a client.
In the embodiment of the present application, the communication connection between the client and the server may include:
Communication connection based on TCP/IP protocol, which is one of the basic protocols of the Internet, the client and the server can communicate using the TCP/IP protocol. The TCP/IP protocol provides reliable, connection-oriented communications, suitable for applications where data integrity and reliability need to be ensured;
Communication connections based on the HTTP protocol, which is a protocol used for communication over the Web, can be used by clients and servers for communication. The HTTP protocol is stateless, i.e., each request and response is independent, and is applicable to communications between a Web browser and a server;
The labeling method provided by the embodiment of the application can be applied to electronic equipment such as mobile phones, tablet computers, wearable equipment, vehicle-mounted equipment, augmented reality (augmented reality, AR)/Virtual Reality (VR) equipment, notebook computers, ultra-mobile personal computer (UMPC), netbooks, personal digital assistants (personal DIGITAL ASSISTANT, PDA) and the like, and the electronic equipment can be a client. The embodiment of the application does not limit the specific type of the electronic equipment. The functions implemented by the labeling method provided by the embodiment of the application can be implemented by calling program codes by a processor of the electronic device, wherein the program codes can be stored in a computer storage medium.
An embodiment of the present application provides a labeling method, and fig. 1 is a schematic implementation flow diagram of the labeling method provided by the embodiment of the present application, as shown in fig. 1, including:
step S101, determining coordinates of a target object in an image to be annotated when a preset operation for the target object is acquired.
In the embodiment of the application, the image to be marked is an image to be marked, and the target object can be any object in the image, for example, the object can be a vehicle, a person and the like.
In the embodiment of the present application, the preset operation may be configured, and the preset operation may include, for example: the clicking operation and the box selection operation can be a combination of the clicking operation and the box selection operation.
For example, the user may select a preset area in the image to be annotated by a frame, and then click on an object in the preset area, so that the client obtains a preset operation for a target in the image to be annotated.
In the embodiment of the application, the user can open the displayed image to be marked through the client, the image to be marked is displayed on the display device of the client, and the user can operate the image to be marked through a mouse and the like, so that the client can acquire the preset operation aiming at the target object in the image to be marked.
In the embodiment of the application, the coordinates of the target object can be determined by the following modes:
The electronic device may analyze the preset operation to obtain information about the target object, such as a category, number, location, and the like of the target object. The image to be marked is processed, and an image processing or computer vision algorithm can be used for determining the position of the target object, and the coordinates of the target object are determined according to the processing result.
And step S102, marking the target object based on the coordinates and a pre-stored prediction data file, wherein the prediction data file is transmitted to a client by a server, and the prediction data file is generated after the image to be marked is segmented by the server based on an image segmentation model.
In the embodiment of the application, a client receives a predicted data file sent by a server, the file is a result generated by the server after dividing an image to be marked based on an image dividing model, then the predicted data file is stored, and under the condition that coordinates are obtained, the coordinates and a pre-stored preset data file can be output to a pre-stored neural network model to mark a target object.
In an embodiment of the present application, a predicted data file includes: the positions of the objects and the polygonal outer frames can be marked on the target object when marking is carried out.
In the embodiment of the application, the labeling result can be displayed to the user, and the user can edit and adjust the labeling result further. In editing and adjustment, the labels are also made by the predictive data files stored in advance.
In the embodiment of the application, the prestored prediction data file is called to mark when the modification or editing is carried out, so that frequent interaction with the server is avoided, the user interactivity can be improved, and the blocking and reasoning time consumption can be reduced.
In some embodiments, the coordinates may be passed to a script, which loads a data file, and the script invokes a neural network model to predict the polygon outline corresponding to the coordinates, thereby labeling the target object.
According to the method provided by the embodiment of the application, the predicted data file is generated after the image to be marked is segmented based on the image segmentation model by the server, and is sent to the client, and the client can directly use the predicted data file when marking.
In some embodiments, prior to step S101, the method further comprises:
And step S1011, transmitting the image to be annotated to the server, so that the server segments the image to be annotated based on an image segmentation model, and generates a prediction data file.
In the embodiment of the application, the image segmentation model may be a segment-rendering (SAM) model. The server can input the image to be marked into the SAM model, so that the image to be marked is segmented, and each segmentation result is obtained, wherein the segmentation result comprises: the position of each object and the polygon outline, thereby generating a prediction data file based on the segmentation result.
Step S1012, obtaining a predicted data file sent by the server, and storing the predicted data file.
In the embodiment of the application, the server can send a plurality of predicted data files to a plurality of clients, the clients can store the predicted data files, and then the clients can carry out multi-person labeling based on the predicted data files.
In some embodiments, while step S1011 is being performed, the method may further include:
in step S1013, the demand time set by the user is acquired.
In the embodiment of the application, the user can input the required time through the input device.
Step S1014, transmitting the required time to the server, so that the server determines an idle time before the required time, and segments the image to be annotated using the image segmentation model based on the idle time.
In the embodiment of the application, the idle time can be night time, and the idle time can be regarded as a period of less time used by the server.
In the embodiment of the application, an interface or a form can be arranged on the client side, so that a user is allowed to input or select the required time. May be a date and time selector or a text field, and the user may manually enter the time or select the time from predefined options. When the user has completed entering or selecting the desired time, the selected time is sent to the server as part of the request. The request may be sent using a network request library or API and the data for the selected demand time is included in the request. After receiving the request, the server analyzes the demand time data. The server may determine the idle period of time before the demand time by querying its schedule or task management system. In some embodiments, it may be an idle schedule pre-stored in the server or an idle time dynamically calculated using a scheduling algorithm. And selecting an idle time period by the server, and segmenting the image to be annotated by using the image segmentation model. The server sends the segmentation result back to the client so that the user can perform further labeling operation or result display.
Based on the foregoing embodiments, the embodiment of the present application further provides a labeling method, which is applied to a server, and fig. 2 is a schematic implementation flow chart of the labeling method provided by the embodiment of the present application, as shown in fig. 2, where the method includes:
Step S201, an image to be annotated sent by a client is obtained.
In the embodiment of the application, the client can use the network request library or the API to send the image data to the server.
Step S202, inputting the image to be annotated into an image segmentation model, and determining a prediction data file of each object.
In the embodiment of the application, after receiving the image to be marked, the server inputs the image to the image segmentation model for processing. The model may be a SAM model by which image segmentation may be performed to determine the position of individual objects in the image and the polygonal outer frame. And after the model is used for dividing the image to be marked, generating a prediction data file.
In the embodiment of the present application, the image segmentation model may be a SAM model.
In some embodiments, inputting the image to be annotated into an image segmentation model, determining a prediction data file for each object, including:
And inputting the image to be annotated into the image segmentation model in idle time, and determining a predicted data file of each object.
In the embodiment of the application, the idle time can be determined by the server based on the demand time set by the user. For example, the image to be annotated is subjected to a segmentation process by the image segmentation model at night.
Step S203, sending the predicted data file to the client, so that the client determines coordinates of the target object in the image to be annotated if a preset operation for the target object is acquired, and annotates the target object based on the coordinates and the predicted data file.
In the embodiment of the application, the server can send the predicted data file to the client through a network request or other communication modes.
In the embodiment of the application, after receiving the predicted data file, the client determines the coordinates of the target object according to the obtained preset operation. The client may annotate the target object based on the determined coordinates of the target object and the prediction data file. A labeling tool or a graphical interface may be used to draw a polygonal outline of the target object, etc., on the image to be labeled.
According to the method provided by the embodiment of the application, the image to be marked sent by the client is obtained; inputting the image to be annotated into an image segmentation model, and determining a predicted data file of each object; and sending the predicted data file to the client so that the client can determine the coordinates of the target object under the condition that the client obtains the preset operation for the target object in the image to be marked, and mark the target object based on the coordinates and the predicted data file, so that the real-time occupation of a server by a user during marking can be reduced.
Based on the foregoing embodiments, the embodiment of the present application further provides a labeling method, which is applied to a labeling system, and fig. 3 is a schematic implementation flow chart of the labeling method provided by the embodiment of the present application, as shown in fig. 3, where the labeling method includes:
Step S301, the client sends the image to be annotated to the server.
In step S302, the server inputs the image to be annotated into the image segmentation model, and determines a prediction data file of each object.
In the embodiment of the present application, the image segmentation model may be a SAM model, and the prediction data file includes: the object corresponds to the position and the polygonal outer frame.
In step S303, the server sends the predicted data file to the client.
In step S304, the client acquires the predicted data file sent by the server, and stores the predicted data file.
In the embodiment of the application, the predicted data file is stored, so that communication with a server can be omitted during manual annotation.
In step S305, the client determines coordinates of the target object in the image to be annotated when acquiring a preset operation for the target object.
In the embodiment of the application, the preset operation can be to select a preset area in the image to be marked in a frame mode and then click the image of the preset area.
In step S306, the client annotates the target object based on the coordinates and a pre-stored prediction data file.
In the embodiment of the application, the client can call the neural network model stored by the client to finish the labeling.
The method provided by the embodiment of the application can fully utilize the capability of the large model to improve the quality and efficiency of manual labeling, the server is used for dividing, the algorithm in the client side is used for realizing labeling, the server can use the GPU to divide and process the label in advance, and the client side can realize noninductive interaction in the labeling process.
According to the embodiment of the application, the problem that server resources are increased when auxiliary labels need to be modified for many times can be solved, SAM preprocessing is carried out once, and the subsequent modification and interaction for many times can be carried out by only calling the preprocessed predicted data file by the client, so that algorithm decoding operation can be realized, and labels can be completed.
The method provided by the embodiment of the application can support the simultaneous marking requirements of multiple users and multiple terminals, the preprocessed prediction marking files can be obtained by the multiple users or the multiple terminals through network requests, each client decodes and infers to realize marking, the calculation force is further dispersed, and the problems of inference resources and network delay blocking are solved.
Fig. 4 is a specific example of a labeling method provided by an embodiment of the present application, as shown in fig. 4, including:
And the server calls the SAM model by using Django, traverses the pictures to be marked, and generates a predicted data file.
Browser page access is provided using Django services. The browser loads the pre-generated predicted data file through the script, and calls onnx a component for analysis.
And the user performs frame selection on the marked object by using a rectangular frame in advance through a web interface, clicks the target object to be marked in the picture, transmits coordinates to the script, and the script of the client calls onnx a component to predict and returns the polygonal frame coordinate point of the predicted target object.
The embodiment of the application further provides an application scene example, in the labeling company, a plurality of labeling clients are arranged, a user can completely send images to be labeled to the server through one client, the server performs segmentation prediction on the images to be labeled through the SAM model to obtain a prediction generation data file, and the server sends the prediction generation file to the plurality of labeling clients, so that each client can label the images to be labeled.
In the embodiment of the application, under the application scene, the simultaneous marking requirements of multiple users and multiple terminals can be supported, the preprocessed prediction marking files can be obtained by the multiple users or the multiple terminals of the prediction marking files through network requests, and each client decodes and infers to realize marking, so that the calculation force is further dispersed, and the problems of inference resources and network delay blocking are solved.
Based on the foregoing embodiments, the embodiments of the present application provide a labeling apparatus, where each module included in the labeling apparatus, and each unit included in each module may be implemented by a processor in a computer device; of course, the method can also be realized by a specific logic circuit; in an implementation, the processor may be a central Processing unit (CPU, central Processing Unit), a microprocessor (MPU, microprocessor Unit), a digital signal processor (DSP, digital Signal Processing), a field programmable gate array (FPGA, field Programmable GATE ARRAY), or the like.
An embodiment of the present application provides a labeling device, which is applied to a client, and fig. 5 is a schematic structural diagram of the labeling device provided by the embodiment of the present application, as shown in fig. 5, a labeling device 500 includes:
A first determining module 501, configured to determine coordinates of a target object in an image to be annotated when a preset operation for the target object is acquired;
The labeling module 502 is configured to label the target object based on the coordinates and a pre-stored prediction data file, where the prediction data file is sent to a client by a server, and the prediction data file is generated after the server segments an image to be labeled based on an image segmentation model.
In some embodiments, the labeling module 502 is specifically configured to:
Inputting the coordinates and the prediction data file into a prestored neural network model, and determining a polygonal outer frame of the target object so as to mark the target object, wherein the prediction data file comprises the following components: polygonal outer frames of the respective objects.
In some embodiments, the labeling apparatus further comprises:
The second sending module is used for sending the image to be marked to the server so that the server can divide the image to be marked based on an image division model and generate a prediction data file;
And the storage module is used for acquiring the predicted data file sent by the server and storing the predicted data file.
In some embodiments, the labeling apparatus further comprises:
The second acquisition module is used for acquiring the demand time set by the user;
And the third sending module is used for sending the required time to the server so that the server can determine the idle time before the required time and divide the image to be marked by using the image division model based on the idle time.
Based on the foregoing embodiments, the embodiment of the present application further provides an labeling device, which is applied to a server, and includes:
the first acquisition module is used for acquiring an image to be marked sent by the client;
The second determining module is used for inputting the image to be annotated into the image segmentation model and determining the predicted data file of each object;
The first sending module is used for sending the predicted data file to the client so that the client can determine the coordinates of the target object under the condition that the client obtains the preset operation for the target object in the image to be marked, and marking the target object based on the coordinates and the predicted data file.
In some embodiments, the labeling apparatus further comprises:
The third determining module is used for determining the idle time before the demand time under the condition that the demand time set by the user is acquired;
the second determining module is specifically configured to:
And inputting the image to be annotated into the image segmentation model in idle time, and determining a predicted data file of each object.
The embodiment of the application provides electronic equipment which can be a client or a server; fig. 6 is a schematic diagram of a composition structure of an electronic device according to an embodiment of the present application, as shown in fig. 6, the electronic device 1500 includes: a processor 1501, at least one communication bus 1502, a user interface 1503, at least one external communication interface 1504, and a memory 1505. Wherein communication bus 1502 is configured to enable connected communication between these components. The user interface 1503 may include a display screen, and the external communication interface 1504 may include a standard wired interface and a wireless interface, among others. The processor 1501 is configured to execute a program of the labeling method stored in the memory to implement the steps in the labeling method provided in the above-described embodiment.
The embodiment of the application provides a labeling system, which comprises: the system comprises a client and a server, wherein the client and the server are in communication connection.
In the embodiment of the present application, if the labeling method is implemented in the form of a software functional module and sold or used as a separate product, the labeling method may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes. Thus, embodiments of the application are not limited to any specific combination of hardware and software.
Accordingly, an embodiment of the present application provides a storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the labeling method provided in the above embodiment.
Embodiments of the present application further provide a computer program product, which when run on a terminal device, causes an electronic device to perform the labeling method described in any of the above.
The description of the electronic device and the storage medium embodiments above is similar to that of the method embodiments described above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the computer apparatus and the storage medium of the present application, please refer to the description of the method embodiment of the present application.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application. The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read Only Memory (ROM), a magnetic disk or an optical disk, or the like, which can store program codes.
Or the above-described integrated units of the application may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied essentially or in part in the form of a software product stored in a storage medium, including instructions for causing a controller to perform all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.
The foregoing is merely an embodiment of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (10)

1. The labeling method is characterized by being applied to a client and comprising the following steps of:
under the condition that the preset operation for the target object in the image to be marked is obtained, determining the coordinates of the target object;
Labeling the target object based on the coordinates and a pre-stored prediction data file, wherein the prediction data file is transmitted to a client by a server, and the prediction data file is generated after the image to be labeled is segmented by the server based on an image segmentation model.
2. The method of claim 1, wherein the labeling the target object based on the coordinates and a pre-stored predictive data file comprises:
Inputting the coordinates and the prediction data file into a prestored neural network model, and determining a polygonal outer frame of the target object so as to mark the target object, wherein the prediction data file comprises the following components: polygonal outer frames of the respective objects.
3. The method according to claim 1, wherein the method further comprises:
the image to be marked is sent to the server, so that the server divides the image to be marked based on an image division model, and a prediction data file is generated;
and acquiring a predicted data file sent by a server, and storing the predicted data file.
4. A method according to claim 3, characterized in that the method further comprises:
Acquiring the demand time set by a user;
And sending the required time to the server so that the server determines the idle time before the required time, and dividing the image to be marked by using the image division model based on the idle time.
5. A method of labeling, applied to a server, the method comprising:
Acquiring an image to be marked sent by a client;
inputting the image to be annotated into an image segmentation model, and determining a predicted data file of each object;
and sending the predicted data file to a client so that the client determines the coordinates of the target object in the image to be marked under the condition of acquiring the preset operation for the target object, and marking the target object based on the coordinates and the predicted data file.
6. The labeling method of claim 5, wherein the method further comprises:
Under the condition that the demand time set by the user is acquired, determining the idle time before the demand time;
The step of inputting the image to be annotated into an image segmentation model to determine the predicted data file of each object comprises the following steps:
And inputting the image to be annotated into the image segmentation model in idle time, and determining a predicted data file of each object.
7. An labeling apparatus, applied to a client, comprising:
The first determining module is used for determining coordinates of a target object in an image to be marked under the condition that a preset operation for the target object in the image to be marked is obtained;
The labeling module is used for labeling the target object based on the coordinates and a prestored prediction data file, wherein the prediction data file is sent to the client by the server, and the prediction data file is generated after the image to be labeled is segmented by the server based on an image segmentation model.
8. An annotating device, characterized in that it is applied to a server, comprising:
the first acquisition module is used for acquiring an image to be marked sent by the client;
The second determining module is used for inputting the image to be annotated into the image segmentation model and determining the predicted data file of each object;
And the sending module is used for sending the predicted data file to the client so that the client can determine the coordinates of the target object under the condition that the client obtains the preset operation for the target object in the image to be marked, and mark the target object based on the coordinates and the predicted data file.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the labeling method according to any of claims 1-5 or 6-7 when the computer program is executed.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the labeling method of any of claims 1-5 or 6-7.
CN202410232537.4A 2024-02-29 2024-02-29 Labeling method, labeling device, labeling equipment and storage medium Pending CN118196792A (en)

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Application Number Priority Date Filing Date Title
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Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202410232537.4A CN118196792A (en) 2024-02-29 2024-02-29 Labeling method, labeling device, labeling equipment and storage medium

Publications (1)

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CN118196792A true CN118196792A (en) 2024-06-14

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