CN112949667A - Image recognition method, system, electronic device and storage medium - Google Patents
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Abstract
The invention discloses an image recognition method, an image recognition system, electronic equipment and a storage medium. The method comprises the following steps: the method comprises the steps that a server side receives an image sent by a browser client side or an application client side, the image is input into a preset classifier to be classified, and an identification result of the image is obtained.
Description
Technical Field
The present invention relates to the field of image recognition technologies, and in particular, to an image recognition method, an image recognition system, an electronic device, and a storage medium.
Background
At present, when a certain part of a body of a person is abnormal, for example, when a finger part is abnormal, the person usually needs to register in a hospital to see a doctor to determine whether the abnormality is caused by some diseases or normal, so that the phenomenon that some patients without actual diseases also see the doctor in the hospital occurs, and medical resources are occupied. In the prior art, there are some medical platforms, which allow a user to upload a photograph of a body part, such as a finger photograph, which is considered to be abnormal, and a doctor registered on the medical platform can preliminarily determine whether the user needs to go to a hospital for further medical treatment according to the photograph uploaded by the user. Therefore, there is a need to provide a technical solution that can automatically complete a preliminary abnormal judgment according to an image uploaded by a user to prompt the user whether to need further medical treatment.
Disclosure of Invention
The invention aims to provide an image identification method, an image identification system and an image identification device, which are used for avoiding the situation that the abnormal finger part cannot be found in time due to inaccurate identification of an abnormal image caused by insufficient experience by using manual judgment.
According to a first aspect of the present invention, there is provided an image recognition method, executed on a server side, the method comprising:
receiving an image to be identified;
identifying whether the image is a target image with the characteristics of the target finger part or not, and obtaining an identification result;
and feeding back the identification result to the account sending the image.
Optionally, receiving the image to be recognized includes:
and receiving an image sent by a browser client or an application client which is installed on the terminal equipment and logs in an account.
Optionally, the target finger feature comprises at least one of: the base angle formed by the skin on the back of the finger tip and the nail is equal to or greater than 180 °; the first knuckle is widened relative to the second knuckle; the first knuckle is thickened directly relative to the second knuckle; and, the nail is arched from root to tip.
Optionally, after receiving the image to be recognized, the method further includes:
preprocessing an image, wherein the preprocessing comprises at least one of replacing the background of the image with a set background, cutting the image according to a set size and carrying out gray processing on the image;
identifying whether the image is a target image having target finger features, comprising:
and identifying whether the preprocessed image is a target image with the target finger characteristics.
Optionally, the identifying whether the image is a target image with target finger features includes:
and loading a preset classifier scanning image for identifying the target finger characteristics to identify whether the image is the target image.
Optionally, the method further includes a step of generating a classifier, including:
acquiring a finger image with target finger features as a positive sample;
acquiring a finger image without the target finger feature as a negative sample;
and training the positive sample and the negative sample to obtain the classifier.
According to a second aspect of the present invention, an image recognition method, performed at a terminal device, includes:
acquiring an image to be identified, and sending the image to a server to identify whether the image is a target image with target finger characteristics;
and receiving and outputting the identification result fed back by the server after identification.
Optionally, the method further includes:
and responding to the operation of uploading the image, entering an image acquisition interface, and providing an image acquisition frame with a set size on the image acquisition interface.
According to a third aspect of the present invention, there is provided a server comprising:
a processor and a memory for storing executable instructions for controlling the processor to perform the image recognition method provided according to the third aspect of the invention.
According to a fourth aspect of the present invention, there is provided a terminal device comprising:
a processor and a memory for storing executable instructions for controlling the processor to perform the image recognition method provided according to the second aspect.
According to a fifth aspect of the present invention, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the image recognition method provided according to the first or second aspect of the present invention.
According to the embodiment of the invention, the image is received at the browser client or the application client, and the image is input into the preset classifier to be classified, so that the recognition result of the image is obtained, and because the preset classifier comprises the finger part characteristics in each stage, the recognition result can be directly obtained as long as a user uploads the image according to the requirement, whether the finger part image is an abnormal image or not is known, the operation is simple, the recognition is fast, and the situation that the finger part is not found in time due to inaccurate recognition of the abnormal image caused by insufficient experience is avoided.
Other features of the present invention and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a hardware configuration diagram of a system that can be used to implement the image recognition method according to an embodiment of the present invention.
FIG. 2 is a flowchart illustrating steps of an image recognition method according to a first embodiment of the present invention.
FIG. 3 is a flowchart illustrating a method for image recognition according to a second embodiment of the present invention.
Fig. 4 is a block diagram of a server structure according to a first embodiment of the present invention.
Fig. 5 is a block diagram of a terminal device according to a second embodiment of the present invention.
Fig. 6 is a diagram showing a hardware configuration structure of a server according to a first embodiment of the present invention.
Fig. 7 is a diagram of a hardware configuration structure of a terminal device according to a second embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
< hardware configuration >
Fig. 1 is a schematic diagram showing a configuration of a system in which the image recognition method according to the embodiment of the present invention can be implemented.
As shown in fig. 1, the image recognition system 1000 of the present embodiment includes a server 1100, a terminal apparatus 1200, and a network 1300.
The server 1100 may be, for example, a blade server, a rack server, or the like, and the server 1100 may also be a server cluster deployed in a cloud, which is not limited herein. The server may be a server providing an online transaction platform service party, or a server of the above administrative function department, which is not limited herein.
As shown in FIG. 1, server 1100 may include a processor 1110, a memory 1120, an interface device 1130, a communication device 1140, a display device 1150, and an input device 1160. Processor 1110 is configured to execute computer programs. The computer program may be written in an instruction set of an architecture such as x86, Arm, RISC, MIPS, SSE, etc. The memory 1120 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1130 includes, for example, a USB interface, a serial interface, and the like. The communication device 1140 is capable of wired or wireless communication, for example. The display device 1150 is, for example, a liquid crystal display panel. Input devices 1160 may include, for example, a touch screen, a keyboard, and the like.
In this embodiment, the server 1100 may be used to participate in implementing an image recognition method according to any embodiment of the present invention.
In any embodiment of the present invention, the memory 1120 of the server 1100 is configured to store instructions for controlling the processor 1110 to operate so as to support the implementation of the image recognition method according to any embodiment of the present invention. The skilled person can design the instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
Those skilled in the art will appreciate that although a number of devices are shown in FIG. 1 for server 1100, server 1100 of embodiments of the present invention may refer to only some of the devices therein, such as only processor 1110 and memory 1120.
As shown in fig. 1, the terminal apparatus 1200 may include a processor 1210, a memory 1220, an interface device 1230, a communication device 1240, a display device 1250, an input device 1260, an audio output device 1270, an audio input device 1280, and the like. The processor 1210 may be a central processing unit CPU, a microprocessor MCU, or the like, and the processor 1210 is configured to execute a computer program. The computer program may be written in an instruction set of an architecture such as x86, Arm, RISC, MIPS, SSE, etc. The memory 1220 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1230 includes, for example, a USB interface, a headphone interface, and the like. The communication device 1240 can perform wired or wireless communication, for example. The display device 1250 is, for example, a liquid crystal display, a touch display, or the like. The input device 1260 may include, for example, a touch screen, a keyboard, and the like. The terminal apparatus 1200 may output the audio information through the audio output device 1270, the audio output device 1270 including a speaker, for example. The terminal apparatus 1200 may pick up voice information input by the user through the audio pickup device 1280, and the audio pickup device 1280 includes, for example, a microphone.
The terminal device 1200 may be any device that can support the use of an e-commerce platform application, such as a smart phone, a laptop, a desktop computer, a tablet computer, and the like.
In this embodiment, the terminal device 1200 may be configured to, during image recognition, acquire the image and send the image to the server 1100, so that the server 1100 may recognize whether the image is an abnormal image according to the finger features of the image.
In an embodiment of the present invention, the memory 1220 of the terminal device 1200 is configured to store instructions for controlling the processor 1210 to operate so as to support implementation of an image recognition method according to any embodiment of the present invention. The skilled person can design the instructions according to the disclosed solution. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
It should be understood by those skilled in the art that although a plurality of devices of the terminal apparatus 1200 are shown in fig. 1, the terminal apparatus 1200 of the embodiment of the present invention may only relate to some of the devices, for example, only relate to the processor 1210, the memory 1220, the display device 1250, the input device 1260 and the like.
The communication network 1300 may be a wireless network or a wired network, and may be a local area network or a wide area network. The terminal apparatus 1200 can communicate with the server 1100 through the communication network 1300.
The system 1000 shown in FIG. 1 is illustrative only and is not intended to limit the invention, its application, or uses in any way. For example, although fig. 1 shows only one server 1100 and one terminal apparatus 1200, it is not meant to limit the respective numbers, and multiple servers 1100 and/or multiple terminal apparatuses 1200 may be included in the system 1000.
< method embodiment I >
In an embodiment of the present invention, an image recognition method is provided, please refer to fig. 2, which is a flowchart of the image recognition method according to the embodiment of the present invention.
The image recognition method of the present embodiment may be implemented by a server, which may be, for example, the server 1100 shown in fig. 1.
As shown in fig. 2, the image recognition method according to the embodiment of the present invention may include the following steps:
And 104, identifying whether the image is a target image with the target finger characteristics or not, and obtaining an identification result.
And 106, feeding back the identification result to the account sending the image.
Taking the image recognition system shown in fig. 1 as an example, in step 102, when there is a recognition request, the terminal device 1200 uploads a required image according to a request and sends the image to the server 1100, and after receiving the recognition request, the server 1100 starts to receive the image and processes the image to complete a subsequent recognition operation.
Optionally, the image to be identified may be obtained by: for convenience of use, a user can download a client for image recognition on the terminal device 1200, register the account of the user, log in the system, and perform related operations according to prompts, and acquire images through a camera on the client first, and then upload images meeting the recognition requirements of the system.
Or the user can upload the image on the terminal device 1200 of the browser without downloading the client, the image is collected by using the camera on the terminal device 1200 of the browser, and the user can use the image only by registering the account and logging in the system. The client of the terminal device 1200 or the client of the browser can visually display the recognition result for the user, so that the user can conveniently check the recognition result.
Optionally, after receiving the image sent by the terminal device 1200, the server 1100 needs to perform preprocessing on the image, where the purpose of the preprocessing is to enable the image to be recognized to meet the recognition requirement, and facilitate comparison between the image to be recognized and the sample in the classifier.
The pre-processing includes at least one of replacing a background of the image with a set background, cropping the image in a set size, and performing a grayscale processing on the image. When a user collects an image, the background is not particularly limited, so that the image uploaded by the terminal device 1200 contains background information of the image, the background information also exists as a feature value and affects the comparison of the system to a target object, so that in order to improve the comparison efficiency and accuracy, the background of the image needs to be replaced, the background is selected to be replaced according to the background of a training sample in the classifier, and the effect of being consistent with the background of the training sample can be achieved, so that the recognition rate of the image features is improved.
After the background processing of the image is completed, the method further comprises the step of processing the size of the image, for example, the image is cut, and in the cutting process, the length-width ratio of the image is ensured to be consistent with that of the training sample, so that subsequent zooming, rotation and feature reading are facilitated. Or when the image is collected, an image collecting frame with a set size is provided for the user, so that the image uploaded by the terminal device 1200 meets the required size requirement, the server 1100 is not required to process the image in the aspect of image size after receiving the image, and the image processing speed is improved.
And carrying out gray level processing on the image by using a component method, a maximum value method, an average value method or a weighted average method, wherein the purpose of carrying out gray level processing on the image is to reduce the original data volume of the image, so that the calculation amount is less during subsequent processing.
In the process of practical application, if a user uploads more images, the images can be encoded, the encoding can compress the information amount of the images, but the quality of the images is not changed, and for example, an analog processing technology can be adopted, and then the encoding or digital encoding of the images is obtained through analog-digital conversion, so as to meet the requirements on transmission and storage of the images.
If the definition of the image acquired by the user is not high and is not beneficial to subsequent identification, the server 1100 may further perform enhancement restoration processing on the image after receiving the image sent by the terminal device 1200, so as to improve the quality of the image and improve the identification rate of the image. For example, to enhance and restore images by increasing contrast, removing blur and noise, correcting geometric distortion, and the like.
In step 104, after receiving the identification request of the terminal device 1200 and receiving the image to be identified, the server 1100 starts the classifier generated by the training of the server 1100 to identify the image, identifies whether the image is a target image with the target finger feature, and obtains the identification result.
Optionally, the target finger features are finger features of pestle-shaped fingers, the pestle-shaped fingers are also called drum hammer fingers, and the pestle-shaped fingers are characterized in that the tail ends of fingers or toes are thickened and expanded in a pestle shape, and the main features of the pestle-shaped fingers include at least one of the following: the base angle formed by the skin on the back of the finger end of the clubbed finger and the nail is equal to or larger than 180 degrees; the first knuckle of any one finger of the clubroot finger is widened relative to the second knuckle, wherein the thumb and middle finger are most obvious in performance; the first knuckle of any finger is thickened relative to the second knuckle; and, pestle means that the nail of any one finger is arched from the root to the tip. The characteristics are the standard for judging the difference between the clubbed fingers and normal fingers, the identification rate of the clubbed finger images can be improved by identifying the characteristics of the images according to the finger characteristics, and once the system identifies that one or more characteristics are contained in the images sent by the terminal equipment, the server returns a positive identification result to the terminal equipment.
The target finger characteristics are the same as the finger characteristics collected when the positive samples are collected in the classifier, and after the terminal device 1200 sends the image to the server 1100, the server 1100 compares the received image with the positive samples and the negative samples in the classifier, outputs the image with the highest similarity as a recognition result and sends the recognition result to the terminal device 1200, so that the user can conveniently check the image.
The classifier in the application is obtained according to the following steps:
an image of the finger having the target finger feature is acquired as a positive sample.
An image of the finger without the target finger feature is acquired as a negative example.
And training the positive sample and the negative sample to obtain the classifier.
The technical scheme provided by the application is that whether the image provided by the user has the clubbed finger features or not is recognized, and the recognition process is realized by using python + openCV 2. In the application, all images required by a positive sample are provided by the user, all the images are processed by using a tool and put into an OpenCV binary format for training. In order to obtain a robust model, the positive sample needs to cover various varieties, so characteristic images of various stages of clubbing are collected, and each stage comprises an image of an early clubbing first knuckle, an image of a medium clubbing first knuckle and an image of a late clubbing first knuckle. The reason why the image of the first knuckle of the finger or toe is collected is that the image of the first knuckle can obviously reflect the characteristics of the clubbed finger, has larger difference with the image of the first knuckle of the normal finger or toe, and has high recognition rate. The number of collections is greater than 1000, which may make the identification more accurate.
After the image is obtained, preprocessing is needed, and the preprocessing process comprises the following steps:
the background in the image is changed into a uniform background, or the image is collected in the uniform background when the image is collected, for example, when the image is shot, a white background is selected, so that the time for image preprocessing can be saved.
And then, the size of the image is cut, and in the cutting process, the length-width ratios of all the images are ensured to be consistent, so that the subsequent zooming, rotation and feature reading are facilitated. Or when the image is acquired, the image acquisition frame with the set size is provided, so that the uploaded image meets the required size requirement, and the image rate is improved.
And then, carrying out gray level processing on the image by using the cvtColor function of the openCV to obtain a binary image, wherein the purpose of carrying out the binary processing on the image is to conveniently extract information in the image and increase the efficiency of computer identification.
The positive sample creation flow may be as follows:
the input image is randomly rotated along three axes.
The angle of rotation is defined by-maxx \ y \ zangle, and then the luminance value of the pixel is located at [ bg _ color-bg _ color _ threshold; pixels in the bg _ color + bg _ color _ threshold range are set as transparent pixels, and white noise is added to the foreground image.
If-inv is specified, the color of the foreground image will be flipped. If-randnv is specified, the program will randomly choose whether to flip the color.
And (4) optionally selecting a background image, putting the obtained foreground image on the background image, and adjusting the image to the sizes specified by-w and-h. Finally, the image is stored in the vec file, and the vec file name is specified by a command line parameter-vec.
Positive samples may also be created from a series of previously marked images. The tag information may be stored in a text file, similar to the background description file. Each line in the file corresponds to an image file. The first element of each line is the image file name, followed by the number of objects, and finally a description of the object's position and size (x, y, width, height).
And (3) creating negative samples after the creation of the positive samples is finished, wherein the negative samples do not contain the target of the positive samples, each negative sample needs to be different to ensure the diversity of the negative samples, the images collected by the negative samples are the images of the first knuckle of the normal finger or toe in each period, and the collection number is more than 1000. And then, processing the negative sample by steps such as positive sample image processing to obtain the negative sample. And finally, training the positive sample and the negative sample by using an opencv _ traincacade command to obtain a classifier.
In step 106, after the image uploaded by the terminal device 1200 is sent to the server 1100, the server 1100 starts a classifier to recognize the image, and according to the preset information, the probability of the incoming image is 70% similar to the image in the positive sample, and the server 1100 returns a yes recognition result to the terminal device 1200, otherwise, a no recognition result is returned.
According to the method provided by the embodiment of the invention, the images are received at the browser terminal equipment or the application terminal equipment, and are input into the preset classifier for classification, so that the identification result of the images is obtained, and because the preset classifier comprises the finger part characteristics of each stage, the user can directly obtain the identification result as long as uploading the images according to the requirement, and know whether the own finger part images are abnormal images, the operation is simple, the identification is fast, the manual judgment is avoided, and the condition that the abnormal images are not accurately identified due to insufficient experience, so that the abnormal finger parts cannot be found in time is avoided.
< method example 2>
In yet another embodiment of the present invention, an image recognition method is provided, please refer to fig. 3, which is a flowchart of the image recognition method according to the embodiment of the present invention.
The image recognition method of the present embodiment may be implemented by a terminal device, which may be, for example, the terminal device 1200 shown in fig. 1.
As shown in fig. 3, the image recognition method according to the embodiment of the present invention may include the following steps:
In step 202, an image sent by the browser client or the application client, which is installed on the terminal device 1200 and logs in the account, is sent to the server 1100, so that the client can upload the image conveniently, the recognition result can be displayed for the user visually, and the user can check the image conveniently, regardless of the terminal device 1200 or the browser client.
In step 204, after the image uploaded by the terminal device 1200 is sent to the server 1100, the server 1100 starts a classifier to identify the image, and according to the preset information, the incoming image has a probability of 70% similar to the image in the positive sample, and the server 1100 returns a yes identification result to the terminal device 1200, otherwise, returns a no identification result.
Optionally, the method further includes:
and responding to the operation of uploading the image, entering an image acquisition interface, and providing an image acquisition frame with a set size on the image acquisition interface. After receiving an image uploading instruction sent by the server 1100, the user acquires an image on an image acquisition interface, waits for verification after acquisition is finished, acquires a qualified image, and needs to acquire the image again if the system returns to be unqualified until uploading is successful, and stops an acquisition task.
According to the method provided by the embodiment of the invention, the images are received at the browser terminal equipment or the application terminal equipment, and are input into the preset classifier for classification, so that the identification result of the images is obtained, and because the preset classifier comprises the finger part characteristics of each stage, the user can directly obtain the identification result as long as uploading the images according to the requirement, and know whether the own finger part images are abnormal images, the operation is simple, the identification is fast, the manual judgment is avoided, and the condition that the abnormal images are not accurately identified due to insufficient experience, so that the abnormal finger parts cannot be found in time is avoided.
< first embodiment of the apparatus >
In another embodiment of the present invention, a server 300 is provided, please refer to fig. 4, which is a block diagram illustrating a structure of the server 300 according to the embodiment of the present invention. The server 300 may include a receiving module 302, an identifying module 304, and a sending module 306.
The receiving module 302 is used for receiving an image to be recognized.
The recognition module 304 is used to recognize whether the image is a target image with target finger features.
The sending module 306 is configured to feed back a recognition result to the account sending the image.
In one embodiment, the server 300 may further include a preprocessing module, configured to preprocess the image and send the preprocessed image to the recognition module 304, so that the recognition module 304 recognizes whether the image is a target image with the target finger feature. The pre-processing may include at least one of replacing a background of the image with a set background, cropping the image at a set size, and grayscale processing the image.
In one embodiment, the recognition module 304 is configured to load a preset classifier scan image for recognizing the target finger feature to recognize whether the image is the target image when recognizing whether the image is the target image having the target finger feature.
In one embodiment, the server 300 may further include a classifier generation module, which when generating the classifier, may be configured to: acquiring a finger image with the target finger feature as a positive sample; acquiring a finger image without the target finger feature as a negative sample; and training the positive sample and the negative sample to obtain the classifier.
Referring to fig. 6, in another embodiment, the server 300 may include a processor 308 and a memory 310, where the memory 310 is used for storing executable instructions for controlling the processor 308 to execute the image recognition method according to the first embodiment of the present invention.
The server 300 in this embodiment may be, for example, the server 1100 in fig. 1, or may be a server having another configuration.
The various modules of server 300 in the above embodiments may be implemented by processor 308.
According to the server 300 provided by the embodiment of the invention, the image is received at the server 300 and is input into the preset classifier for classification, so that the identification result of the image is obtained, and because the preset classifier comprises the finger part characteristics in each stage, the identification result can be directly obtained as long as a user uploads the image according to the requirement, whether the finger part image is an abnormal image or not is known, the operation is simple, the identification is fast, and the situation that the finger part is not found in time due to inaccurate identification of the abnormal image caused by insufficient experience by manual judgment is avoided.
< example II of the apparatus >
In an embodiment of the present invention, a terminal device 400 is further provided, please refer to fig. 5, which is a block diagram illustrating a structure of the terminal device 400 according to the embodiment of the present invention. The terminal device 400 includes an acquisition module 402 and an output module 404.
The obtaining module 402 is configured to obtain an image to be identified, and send the image to a server to identify whether the image is a target image with a target finger feature.
The output module 404 is configured to receive and output an identification result fed back by the server after identification.
The terminal device 400 further includes a response module, where the response module is configured to respond to an operation of uploading an image, enter an image capturing interface, and provide an image capturing frame with a set size on the image capturing interface.
Referring to fig. 7, in another embodiment, the terminal device 400 may further include a processor 406 and a memory 408, where the memory 408 is used for storing executable instructions for controlling the processor 406 to execute the image recognition method according to the second embodiment of the present invention.
The terminal device 400 in this embodiment may be, for example, the terminal device 1200 in fig. 1, or may be a terminal device with another structure.
The respective modules of the terminal device 400 in the above embodiments may be implemented by the processor 406.
According to the terminal device 400 provided by the embodiment of the invention, the image is acquired on the terminal device 400, and the image is input into the preset classifier for classification, so that the identification result of the image is obtained, because the preset classifier comprises the finger part characteristics in each stage, the user can directly acquire the identification result as long as uploading the image according to the requirement, and know whether the finger part image is an abnormal image, the operation is simple, the identification is fast, the situation that the finger part is not found in time due to inaccurate identification of the abnormal image caused by insufficient experience is avoided by manual judgment.
< computer-readable storage Medium embodiment >
According to a fifth embodiment of the present invention, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the data reading method according to any of the embodiments of the present invention.
The present invention may be a system, method and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied therewith for causing a processor to implement various aspects of the present invention.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present invention may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present invention are implemented by personalizing an electronic circuit, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA), with state information of computer-readable program instructions, which can execute the computer-readable program instructions.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are equivalent.
Having described embodiments of the present invention, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the invention is defined by the appended claims.
Claims (11)
1. An image recognition method, executed on a server side, the method comprising:
receiving an image to be identified;
identifying whether the image is a target image with target finger characteristics or not, and obtaining an identification result;
and feeding back the identification result to the account sending the image.
2. The method of claim 1, wherein the receiving an image to be identified comprises:
and receiving the image sent by a browser client or an application client which is installed on the terminal equipment and logs in the account.
3. The method of claim 1, wherein the target finger feature comprises at least one of: the base angle formed by the skin on the back of the finger tip and the nail is equal to or greater than 180 °; the first knuckle is widened relative to the second knuckle; the first knuckle is thickened relative to the second knuckle; and, the nail is arched from root to tip.
4. The method of claim 1, wherein the method further comprises, after receiving the image to be identified:
preprocessing the image, wherein the preprocessing comprises at least one of replacing a background of the image with a set background, cropping the image according to a set size, and performing grayscale processing on the image;
the identifying whether the image is a target image with target finger features comprises:
and identifying whether the preprocessed image is a target image with the target finger characteristics.
5. The method of claim 1, wherein said identifying whether the image is a target image with target finger features comprises:
and loading a preset classifier for identifying the target finger characteristics to scan the image so as to identify whether the image is the target image.
6. The method of claim 5, wherein the method further comprises the step of generating the classifier comprising:
acquiring a finger image with the target finger feature as a positive sample;
acquiring a finger image without the target finger feature as a negative sample;
and training the positive sample and the negative sample to obtain the classifier.
7. An image recognition method, which is executed on a terminal device, comprises the following steps:
acquiring an image to be identified, and sending the image to a server to identify whether the image is a target image with target finger characteristics;
and receiving and outputting the identification result fed back by the server after the identification.
8. The method of claim 7, wherein the method further comprises:
and responding to the operation of uploading the image, entering an image acquisition interface, and providing an image acquisition frame with a set size on the image acquisition interface.
9. A server comprising a processor and a memory for storing executable instructions for controlling the processor to perform the image recognition method of any one of claims 1 to 6.
10. A terminal device comprising a processor and a memory for storing executable instructions for controlling the processor to perform the image recognition method of any one of claims 7 to 8.
11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out an image recognition method according to any one of claims 1 to 8.
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