CN112668574A - Parcel image processing method and device, computer equipment and storage medium - Google Patents
Parcel image processing method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a parcel image processing method, a parcel image processing device, a computer device and a storage medium. The method comprises the following steps: acquiring a parcel image; inputting the package image into an image segmentation model to obtain a pickup image containing pickup codes; inputting the pickup image into a character recognition model to obtain a pickup code in the pickup image; and taking the pickup code as the file name of the pickup image, and binding the pickup image and the corresponding user identifier according to the file name. By adopting the method, the package information query efficiency can be improved.
Description
Technical Field
The present application relates to the field of image processing technologies, and in particular, to a method and an apparatus for processing a package image, a computer device, and a storage medium.
Background
With the development of computer technology, Electronic Commerce (Electronic Commerce) technology has emerged, which utilizes computer technology, network technology and telecommunication technology to implement electronization, digitization and networking in the whole business (buying and selling) process. In the electronic commerce transaction process, often involve that the seller will pass on commodity to the customer hand through the mode of express delivery parcel, and after the express delivery parcel reached the express delivery post, need the customer to go to the express delivery post according to getting the piece code and get the express delivery parcel. At present, the express post commonly used delivery mode is to paste a delivery label on the package of each delivery, then put the goods in layers according to the shelf number on the delivery label, and then send the delivery code on the delivery label to the corresponding delivery client. When a customer takes a parcel, the corresponding goods shelf is found according to the parcel taking code, and the parcel taking code of each parcel on the goods shelf is sequentially compared to take out the express parcel.
However, in the current express parcel pickup mode, a customer only has a pickup code and does not have more express parcel information, the customer needs to compare the pickup codes with each other in a plurality of express parcels, and the time for finding the parcels is slow.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a package image processing method, device, computer device and storage medium capable of improving package information query efficiency.
A parcel image processing method, the method comprising:
acquiring a parcel image;
inputting the package image into an image segmentation model to obtain a pickup image containing pickup codes;
inputting the pickup image into a character recognition model to obtain a pickup code in the pickup image;
and taking the pickup code as the file name of the pickup image, and binding the pickup image with the corresponding user identifier according to the file name.
In one embodiment, the method further comprises:
receiving a target user identifier sent by a user terminal;
searching a target pickup image according to the target user identifier;
and sending the searched target pickup image to the user terminal.
In one embodiment, the method further comprises:
acquiring a sample parcel image;
determining key points in the sample parcel image, and performing feature labeling on the key points; the key point is a point containing coding information;
and training the image segmentation model through the sample package image after feature labeling.
In one embodiment, the method further comprises:
acquiring a sample pickup image;
determining a sample pickup code in the sample pickup image, and performing feature labeling on the sample pickup code;
and training the character recognition model through the sample pickup image after the characteristic marking.
In one embodiment, the inputting the package image into an image segmentation model to obtain a pickup image containing a pickup code includes:
inputting the package image to an image segmentation model;
extracting image features of the parcel image through the image segmentation model;
performing image segmentation on the package image according to the image characteristics to obtain a pickup image containing pickup codes; wherein, one pickup code corresponds to one pickup image.
In one embodiment, the inputting the pickup image into a character recognition model to obtain a pickup code in the pickup image includes:
inputting the pickup image to a character recognition model;
extracting character features of the pickup image through the character recognition model;
and performing character recognition on the pickup image according to the character features to obtain pickup codes in the pickup image.
In one embodiment, the binding the pickup image and the corresponding user identifier according to the file name includes:
acquiring a mapping relation table of the pickup code and the user identifier;
determining the corresponding relation between the user identification and the file name according to the mapping relation table;
and binding the pickup image with the corresponding user identifier according to the corresponding relation between the user identifier and the file name.
A parcel image processing apparatus, the apparatus comprising:
the acquisition module is used for acquiring a parcel image;
the segmentation module is used for inputting the parcel image into an image segmentation model to obtain a pickup image containing pickup codes;
the recognition module is used for inputting the pickup image into a character recognition model to obtain a pickup code in the pickup image;
and the binding module is used for taking the pickup code as the file name of the pickup image and binding the pickup image with the corresponding user identifier according to the file name.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring a parcel image;
inputting the package image into an image segmentation model to obtain a pickup image containing pickup codes;
inputting the pickup image into a character recognition model to obtain a pickup code in the pickup image;
and taking the pickup code as the file name of the pickup image, and binding the pickup image with the corresponding user identifier according to the file name.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
acquiring a parcel image;
inputting the package image into an image segmentation model to obtain a pickup image containing pickup codes;
inputting the pickup image into a character recognition model to obtain a pickup code in the pickup image;
and taking the pickup code as the file name of the pickup image, and binding the pickup image with the corresponding user identifier according to the file name.
According to the parcel image processing method, the parcel image processing device, the computer equipment and the storage medium, the parcel image is input into the image segmentation model by obtaining the parcel image, the pickup image containing the pickup code is obtained, the pickup image is input into the character recognition model, the pickup code in the pickup image is obtained, the pickup code is used as the file name of the pickup image, and the pickup image and the corresponding user identification are bound according to the file name. Therefore, the server can provide a function of quickly inquiring the package information because the binding relationship between the user identification and the corresponding pickup image is stored in the server. When finding the package, the pickup image corresponding to the package can be obtained only through the user identifier, and then the user can quickly find the package according to the package information in the pickup image.
Drawings
FIG. 1 is a diagram of an application scenario of a parcel image processing method in one embodiment;
FIG. 2 is a flow diagram illustrating a method for processing parcel images in one embodiment;
FIG. 3 is a schematic diagram of a parcel image in one embodiment;
FIG. 4 is a schematic illustration of a pickup image in one embodiment;
FIG. 5 is a flowchart of an application scenario of a parcel image processing method in one embodiment;
FIG. 6 is a flow diagram illustrating processing of a package image by the code recognition system in one embodiment;
FIG. 7 is a block diagram of the structure of a parcel image processing apparatus in one embodiment;
FIG. 8 is a block diagram showing the configuration of a parcel image processing apparatus in another embodiment;
FIG. 9 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The parcel image processing method provided by the application can be applied to the application environment shown in fig. 1. The application environment includes a terminal 102 and a server 104. The terminal 102 and the server 104 communicate via a network. The terminal 102 may specifically include a desktop terminal or a mobile terminal. The mobile terminal may specifically include at least one of a mobile phone, a tablet computer, a notebook computer, and the like. The server 104 may be implemented as a stand-alone server or as a server cluster comprised of multiple servers. Those skilled in the art will understand that the application environment shown in fig. 1 is only a part of the scenario related to the present application, and does not constitute a limitation to the application environment of the present application.
The server 104 acquires the parcel image from the terminal 102, inputs the parcel image into the image segmentation model, and acquires a pickup image containing a pickup code. The server 104 inputs the pickup image into the character recognition model, and a pickup code in the pickup image is obtained. The server 104 takes the pickup code as the file name of the pickup image, and binds the pickup image with the corresponding user identifier according to the file name.
In one embodiment, as shown in fig. 2, a parcel image processing method is provided, which is exemplified by the application of the method to the server 104 in fig. 1, and comprises the following steps:
s202, acquiring a package image.
Wherein the package image is an image comprising at least one package.
Specifically, the courier can place the parcels pasted with the delivery labels on the pickup shelf in sequence, and place the delivery labels with the side facing outward, wherein the delivery labels have corresponding pickup codes, and the pickup codes are codes for uniquely identifying the parcels. The courier can use a terminal, such as a mobile phone, an ipad or a camera, to take a picture of the package on the pickup shelf to obtain a package image. The terminal can upload the parcel image to the server, and the server can obtain the parcel image.
In one embodiment, the package images may be pre-stored in a third-party storage device, and the server may be communicatively coupled to the third-party storage device to obtain the package images directly from the third-party storage device.
In one embodiment, the package image, as shown in FIG. 3, is an image of the package placed on layer 2 of pick shelf 1. The parcel image comprises 7 parcels, each parcel is adhered with a corresponding parcel label, and each parcel label comprises a pickup code, such as 1-2-2007, and a two-dimensional code corresponding to the parcel.
And S204, inputting the package image into the image segmentation model to obtain a pickup image containing pickup codes.
The image segmentation model is used for segmenting the parcel image into pickup images. The pickup image is the image corresponding to each pickup package.
Specifically, the server may train an image segmentation model in advance, input the package image to the image segmentation model after acquiring the package image, and segment the package image through the image segmentation model to obtain a pickup image containing a pickup code.
For example, inputting the parcel image including 7 parcels shown in fig. 3 into the image segmentation model, 7 pickup images can be obtained, and each pickup image respectively includes a respective pickup code. For example, as shown in FIG. 4, the pick-up image containing the pick-up codes 1-2-2002 is obtained.
And S206, inputting the pickup image into the character recognition model to obtain a pickup code in the pickup image.
The character recognition model is a model for recognizing characters in the pickup image.
Specifically, the server may train a character recognition model in advance, after the pickup image is acquired, the server may input the pickup image to the character recognition model, and recognize characters in the pickup image through the character recognition model to obtain a pickup code in the pickup image.
In one embodiment, the text Recognition model may specifically be an OCR (Optical Character Recognition) text Recognition model.
And S208, taking the pickup code as the file name of the pickup image, and binding the pickup image with the corresponding user identifier according to the file name.
The file name of the pickup image is the file name which uniquely identifies the pickup image. The user identifier is a character string for uniquely identifying the user, and may include at least one of numbers, letters, special characters, and the like. The special number of words may be underline, asterisk key, number of wells key or percentile, etc.
Specifically, after the server identifies the corresponding pickup code from the pickup image, the corresponding pickup code may be used as the file name of the pickup image. The server can obtain a mapping relation table of the pickup code and the user identifier, and then bind the pickup image and the corresponding user identifier according to the file name and the mapping relation table.
In the package image processing method, a package image is acquired, the package image is input to an image segmentation model, a pickup image containing pickup codes is acquired, the pickup image is input to a character recognition model, pickup codes in the pickup image are acquired, the pickup codes are used as file names of the pickup images, and the pickup images and corresponding user identifications are bound according to the file names. Therefore, the server can provide a function of quickly inquiring the package information because the binding relationship between the user identification and the corresponding pickup image is stored in the server. When finding the package, the pickup image corresponding to the package can be obtained only through the user identifier, and then the user can quickly find the package according to the package information in the pickup image.
In one embodiment, the parcel image processing method further comprises: receiving a target user identifier sent by a user terminal; searching a target pickup image according to the target user identifier; and sending the searched target pickup image to a user terminal.
The target user identification is the user identification corresponding to the user searching the package. The target pickup image is a pickup image of a parcel sought by a user seeking the parcel.
Specifically, the pickup user may send the target user identifier to the server through a client application or applet in the user terminal. The server can receive the target user identification sent by the user terminal and search the target pickup image according to the target user identification. The server can send the searched target pickup image to the user terminal.
It will be appreciated that after the user terminal receives the target pickup image, the pickup user may obtain more package information from the target pickup image, such as the shape of the package, the color of the package, and the relative location where the package is placed. And then get a user and can be based on abundant parcel information, find oneself parcel from getting a goods shelves of placing a lot of parcels fast. Compared with the traditional method that only the pickup code is provided, the method has no more express package information, and can effectively shorten the time for a pickup user to find the package.
In the embodiment, the target pickup image is searched through the target user identifier, and the searched target pickup image is sent to the user terminal, so that the package information query efficiency is improved, and the user can acquire more package information from the target pickup image, so that the user can quickly find own packages from a pickup shelf on which a plurality of packages are placed, and the time for searching the packages is saved.
In one embodiment, the parcel image processing method further comprises: acquiring a sample parcel image; determining key points in the sample package image, and performing feature labeling on the key points; the key point is a point containing coded information; and training an image segmentation model through the sample package image after the characteristic marking.
The sample wrapping image is sample data used for training the image segmentation model.
Specifically, the server may obtain the sample package image, determine key points in the sample package image, and perform feature labeling on the key points. Wherein the key point is a point containing encoded information. The server can input the sample package image with the characteristic labeled into the image segmentation model to obtain an output sample pickup image, and then the server can adjust model parameters of the image segmentation model according to the sample pickup image and key points in the package image to finish training of the image segmentation model.
In one embodiment, the key points in the sample parcel image may specifically include a two-dimensional code on the parcel label of the parcel, and edge points of the parcel, etc.
In the embodiment, the image segmentation model is trained through the sample package image after the characteristic marking, so that the image segmentation model has the capability of accurately segmenting the package image, and the package information query efficiency is further improved.
In one embodiment, the parcel image processing method further comprises: acquiring a sample pickup image; determining a sample pickup code in the sample pickup image, and performing characteristic marking on the sample pickup code; and training a character recognition model through the sample pickup image after the characteristic marking.
And the sample pickup image is sample data used for training the character recognition model.
Specifically, the server may obtain a sample pickup image, determine a sample pickup code in the sample pickup image, and perform feature labeling on the sample pickup code. The server can input the sample pickup image with the characteristic mark into the character recognition model to obtain the output sample pickup code, and then the server can adjust the model parameters of the character recognition model according to the sample pickup code and the sample pickup code in the sample pickup image so as to complete the training of the character recognition model.
In the embodiment, the character recognition model is trained through the sample pickup image after the characteristic marking, so that the character recognition model has the capability of accurately recognizing pickup codes, and the package information query efficiency is further improved.
In one embodiment, step S202, namely inputting the parcel image into the image segmentation model, and obtaining a pickup image including a pickup code specifically includes: inputting the parcel image to an image segmentation model; extracting image characteristics of the package image through an image segmentation model; carrying out image segmentation on the package image according to the image characteristics to obtain a pickup image containing pickup codes; wherein, one pickup code corresponds to one pickup image.
In particular, the server may input the package image to the image segmentation model. For each parcel image, the parcel image comprises corresponding image features, and the server can extract the image features of the parcel image through an image segmentation model. Furthermore, the server can perform image segmentation on the package image according to the image characteristics to obtain a pickup image containing pickup codes. Wherein, one pickup code corresponds to one pickup image.
In the embodiment, the image features of the package image are extracted through the image segmentation model, and the package image is segmented according to the image features, so that the segmentation accuracy of the image segmentation model is further improved.
In an embodiment, step S206, namely inputting the pickup image to the character recognition model, and obtaining the pickup code in the pickup image specifically includes: inputting the pickup image to a character recognition model; extracting character features of the pickup image through a character recognition model; and performing character recognition on the pickup image according to the character characteristics to obtain a pickup code in the pickup image.
Specifically, the server may input the pickup image to the text recognition model. And aiming at each pickup image, the pickup image comprises corresponding character features, and the server can extract the character features of the pickup image through a character recognition model. Furthermore, the server can perform character recognition on the pickup image according to character features of the pickup image to obtain pickup codes in the pickup image.
In the above embodiment, the character features of the pickup image are extracted through the character recognition model, and the pickup image is subjected to character recognition according to the character features, so that the recognition accuracy of the character recognition model is further improved.
In an embodiment, the step of binding the pickup image and the corresponding user identifier according to the file name in step S20 specifically includes: acquiring a mapping relation table of pickup codes and user identifications; determining the corresponding relation between the user identification and the file name according to the mapping relation table; and binding the pickup image with the corresponding user identifier according to the corresponding relation between the user identifier and the file name.
Specifically, for each package, the package corresponds to a unique express waybill number, and the server can generate a pickup code according to the express waybill number and the corresponding user identifier, and generate a mapping relation table of the pickup code and the user identifier. The server can obtain a mapping relation table of the pickup code and the user identifier, and determine the corresponding relation between the user identifier and the file name according to the mapping relation table. The server can bind the pickup image with the corresponding user identifier according to the corresponding relation between the user identifier and the file name.
In the embodiment, the corresponding relation between the user identifier and the file name is accurately determined through the mapping relation table of the pickup code and the user identifier, and the binding accuracy of the pickup image and the corresponding user identifier is improved.
In one embodiment, as shown in fig. 5, the courier may print out a corresponding dispatch label according to the courier delivery order number of each package and attach the dispatch label to the corresponding package. The courier can put the packages on the corresponding goods taking shelf in sequence according to the package labels, and put the package labels outwards. The courier can take a picture of the package on the pickup shelf through a terminal, such as a mobile phone, obtain a package image and upload the package image to the server. And the server processes the parcel image through the code recognition system. As shown in fig. 6, the process of the server processing the package image through the code recognition system specifically includes: the server can preprocess the parcel image, and input the preprocessed parcel image into the image segmentation model to obtain a pickup image containing pickup codes. Furthermore, the server can input the pickup image into the OCR character recognition model, extract and obtain a pickup code in the pickup image, and use the pickup code as the file name of the pickup image. The server may bind the user identification with the corresponding pickup image. The server provides a function of quickly inquiring the package information because the binding relationship between the user identification and the corresponding pickup image is stored in the service. When the pickup user gets off the package, the user identification can be input at the user terminal, the user terminal can send the user identification to the server, and the server can locally inquire the pickup image corresponding to the user identification and send the pickup image to the user terminal. Because the pickup image contains rich package information, such as the shape of the package, the color of the package, the relative position of the package, and the like, the user can quickly find the package from the pickup shelf, and the time for finding the package is saved.
It should be understood that although the various steps of fig. 2 are shown in order, the steps are not necessarily performed in order. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in fig. 2 may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 7, there is provided a parcel image processing apparatus 700 comprising: an obtaining module 701, a splitting module 702, an identifying module 703 and a binding module 704, wherein:
an obtaining module 701, configured to obtain a parcel image.
And a segmentation module 702, configured to input the parcel image into an image segmentation model, and obtain a pickup image including a pickup code.
And the recognition module 703 is configured to input the pickup image to the character recognition model, and obtain a pickup code in the pickup image.
And the binding module 704 is configured to use the pickup code as a filename of the pickup image, and bind the pickup image with the corresponding user identifier according to the filename.
In one embodiment, the segmentation module 702 is further configured to input the package image to an image segmentation model; extracting image characteristics of the package image through an image segmentation model; carrying out image segmentation on the package image according to the image characteristics to obtain a pickup image containing pickup codes; wherein, one pickup code corresponds to one pickup image.
In one embodiment, the recognition module 703 is further configured to input the pickup image to a character recognition model; extracting character features of the pickup image through a character recognition model; and performing character recognition on the pickup image according to the character characteristics to obtain a pickup code in the pickup image.
In one embodiment, the binding module 704 is further configured to obtain a mapping relationship table between the pickup code and the user identifier; determining the corresponding relation between the user identification and the file name according to the mapping relation table; and binding the pickup image with the corresponding user identifier according to the corresponding relation between the user identifier and the file name.
Referring to fig. 8, in one embodiment, the parcel image processing apparatus 700 further comprises: a lookup module 705 and a training module 706, wherein:
the searching module 705 is configured to receive a target user identifier sent by a user terminal; searching a target pickup image according to the target user identifier; and sending the searched target pickup image to a user terminal.
A training module 706 for obtaining sample package images; determining key points in the sample package image, and performing feature labeling on the key points; the key point is a point containing coded information; and training an image segmentation model through the sample package image after the characteristic marking.
In one embodiment, the training module 706 is further configured to obtain a sample pickup image; determining a sample pickup code in the sample pickup image, and performing characteristic marking on the sample pickup code; and training a character recognition model through the sample pickup image after the characteristic marking.
According to the parcel image processing device, a parcel image is acquired, the parcel image is input into the image segmentation model, a pickup image containing a pickup code is acquired, the pickup image is input into the character recognition model, the pickup code in the pickup image is acquired, the pickup code is used as the file name of the pickup image, and the pickup image and the corresponding user identification are bound according to the file name. Therefore, the server can provide a function of quickly inquiring the package information because the binding relationship between the user identification and the corresponding pickup image is stored in the server. When finding the package, the pickup image corresponding to the package can be obtained only through the user identifier, and then the user can quickly find the package according to the package information in the pickup image.
For specific limitations of the parcel image processing apparatus, reference may be made to the above limitations of the parcel image processing method, which are not described herein again. The respective modules in the above-described parcel image processing apparatus may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be the server 104 in fig. 1, and its internal structure diagram may be as shown in fig. 9. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used to store package image processing data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a package image processing method.
Those skilled in the art will appreciate that the architecture shown in fig. 9 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory and a processor, the memory having a computer program stored therein, the processor implementing the following steps when executing the computer program:
acquiring a parcel image;
inputting the package image into an image segmentation model to obtain a pickup image containing pickup codes;
inputting the pickup image into a character recognition model to obtain a pickup code in the pickup image;
and taking the pickup code as the file name of the pickup image, and binding the pickup image and the corresponding user identifier according to the file name.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
receiving a target user identifier sent by a user terminal;
searching a target pickup image according to the target user identifier;
and sending the searched target pickup image to a user terminal.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring a sample parcel image;
determining key points in the sample package image, and performing feature labeling on the key points; the key point is a point containing coded information;
and training an image segmentation model through the sample package image after the characteristic marking.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring a sample pickup image;
determining a sample pickup code in the sample pickup image, and performing characteristic marking on the sample pickup code;
and training a character recognition model through the sample pickup image after the characteristic marking.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
inputting the parcel image to an image segmentation model;
extracting image characteristics of the package image through an image segmentation model;
carrying out image segmentation on the package image according to the image characteristics to obtain a pickup image containing pickup codes; wherein, one pickup code corresponds to one pickup image.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
inputting the pickup image to a character recognition model;
extracting character features of the pickup image through a character recognition model;
and performing character recognition on the pickup image according to the character characteristics to obtain a pickup code in the pickup image.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring a mapping relation table of pickup codes and user identifications;
determining the corresponding relation between the user identification and the file name according to the mapping relation table;
and binding the pickup image with the corresponding user identifier according to the corresponding relation between the user identifier and the file name.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a parcel image;
inputting the package image into an image segmentation model to obtain a pickup image containing pickup codes;
inputting the pickup image into a character recognition model to obtain a pickup code in the pickup image;
and taking the pickup code as the file name of the pickup image, and binding the pickup image and the corresponding user identifier according to the file name.
In one embodiment, the computer program when executed by the processor further performs the steps of:
receiving a target user identifier sent by a user terminal;
searching a target pickup image according to the target user identifier;
and sending the searched target pickup image to a user terminal.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a sample parcel image;
determining key points in the sample package image, and performing feature labeling on the key points; the key point is a point containing coded information;
and training an image segmentation model through the sample package image after the characteristic marking.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a sample pickup image;
determining a sample pickup code in the sample pickup image, and performing characteristic marking on the sample pickup code;
and training a character recognition model through the sample pickup image after the characteristic marking.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the parcel image to an image segmentation model;
extracting image characteristics of the package image through an image segmentation model;
carrying out image segmentation on the package image according to the image characteristics to obtain a pickup image containing pickup codes; wherein, one pickup code corresponds to one pickup image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
inputting the pickup image to a character recognition model;
extracting character features of the pickup image through a character recognition model;
and performing character recognition on the pickup image according to the character characteristics to obtain a pickup code in the pickup image.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a mapping relation table of pickup codes and user identifications;
determining the corresponding relation between the user identification and the file name according to the mapping relation table;
and binding the pickup image with the corresponding user identifier according to the corresponding relation between the user identifier and the file name.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of parcel image processing, the method comprising:
acquiring a parcel image;
inputting the package image into an image segmentation model to obtain a pickup image containing pickup codes;
inputting the pickup image into a character recognition model to obtain a pickup code in the pickup image;
and taking the pickup code as the file name of the pickup image, and binding the pickup image with the corresponding user identifier according to the file name.
2. The method of claim 1, further comprising:
receiving a target user identifier sent by a user terminal;
searching a target pickup image according to the target user identifier;
and sending the searched target pickup image to the user terminal.
3. The method of claim 1, further comprising:
acquiring a sample parcel image;
determining key points in the sample parcel image, and performing feature labeling on the key points; the key point is a point containing coding information;
and training the image segmentation model through the sample package image after feature labeling.
4. The method of claim 1, further comprising:
acquiring a sample pickup image;
determining a sample pickup code in the sample pickup image, and performing feature labeling on the sample pickup code;
and training the character recognition model through the sample pickup image after the characteristic marking.
5. The method of claim 1, wherein inputting the package image to an image segmentation model to obtain a pickup image containing a pickup code comprises:
inputting the package image to an image segmentation model;
extracting image features of the parcel image through the image segmentation model;
performing image segmentation on the package image according to the image characteristics to obtain a pickup image containing pickup codes; wherein, one pickup code corresponds to one pickup image.
6. The method of claim 1, wherein inputting the pickup image to a character recognition model to obtain a pickup code in the pickup image comprises:
inputting the pickup image to a character recognition model;
extracting character features of the pickup image through the character recognition model;
and performing character recognition on the pickup image according to the character features to obtain pickup codes in the pickup image.
7. The method according to any one of claims 1 to 6, wherein the binding the pickup image with the corresponding user identifier according to the file name comprises:
acquiring a mapping relation table of the pickup code and the user identifier;
determining the corresponding relation between the user identification and the file name according to the mapping relation table;
and binding the pickup image with the corresponding user identifier according to the corresponding relation between the user identifier and the file name.
8. A parcel image processing apparatus, characterized in that the apparatus comprises:
the acquisition module is used for acquiring a parcel image;
the segmentation module is used for inputting the parcel image into an image segmentation model to obtain a pickup image containing pickup codes;
the recognition module is used for inputting the pickup image into a character recognition model to obtain a pickup code in the pickup image;
and the binding module is used for taking the pickup code as the file name of the pickup image and binding the pickup image with the corresponding user identifier according to the file name.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method of any of claims 1 to 7 are implemented by the processor when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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