WO2019128507A1

WO2019128507A1 - Image processing method and apparatus, storage medium and electronic device

Info

Publication number: WO2019128507A1
Application number: PCT/CN2018/115467
Authority: WO
Inventors: 陈岩; 刘耀勇
Original assignee: Oppo广东移动通信有限公司
Priority date: 2017-12-28
Filing date: 2018-11-14
Publication date: 2019-07-04
Also published as: CN109977739A

Abstract

Disclosed are an image processing method and apparatus, a storage medium and an electronic device. The image processing method comprises: acquiring posture information of a facial image in a target picture; acquiring a target sample facial image from a pre-set facial image set according to the posture information; extracting an expression feature of the facial image, and adjusting the target sample facial image according to the expression feature to obtain a target facial image; and processing the facial image in the target picture based on the target facial image.

Description

Image processing method, device, storage medium and electronic device

This application claims the priority of the Chinese patent application filed on December 28, 2017, the Chinese Patent Office, the application number is 201711466330.X, and the invention name is "image processing method, device, storage medium and electronic device". The citations are incorporated herein by reference.

Technical field

The present application relates to the field of image processing technologies, and in particular, to an image processing method, apparatus, storage medium, and electronic device.

Background technique

With the development of the Internet and the development of mobile communication networks, and with the rapid development of processing capabilities and storage capabilities of electronic devices, massive applications have been rapidly spread and used. The existing electronic devices generally have a photographing function, and the light intensity at the time of photographing has a certain influence on the photographs taken. For example, in the case of low light at night, the noise of the photos taken by the camera of the electronic device is more serious, and the picture quality is poor. When the light is ideal, the noise is weaker in the photos taken during the day, and the picture quality is better. it is good.

Summary of the invention

The embodiment of the present application provides an image processing method, device, storage medium, and electronic device, which can reduce noise and improve image quality.

In a first aspect, an embodiment of the present application provides an image processing method, including:

Obtaining posture information of a face image in the target screen;

Obtaining a target sample face image from the preset face image set according to the posture information;

Extracting an expression feature of the face image, and adjusting the target sample face image according to the expression feature to obtain a target face image;

The face image in the target picture is processed based on the target face image.

In a second aspect, an embodiment of the present application provides an image processing apparatus, including:

An information acquiring module, configured to acquire posture information of a face image in a target image;

An image obtaining module, configured to acquire a target sample face image from the preset face image set according to the posture information;

An adjustment module, configured to extract an expression feature of the face image, and adjust a target sample face image according to the expression feature to obtain a target face image;

And a processing module, configured to process the face image in the target image based on the target face image.

In a third aspect, the embodiment of the present application further provides a storage medium, where the storage medium stores a plurality of instructions, and the instructions are adapted to be loaded by a processor to perform the following steps:

Obtaining posture information of a face image in the target screen;

In a fourth aspect, an embodiment of the present application further provides an electronic device, including a processor and a memory, where the processor is electrically connected to the memory, the memory is used to store instructions and data, and the processor is configured to perform the following steps. :

Obtaining posture information of a face image in the target screen;

DRAWINGS

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the present application. Other drawings can also be obtained from those skilled in the art based on these drawings without paying any creative effort.

FIG. 1 is a schematic diagram of a scenario structure of an electronic device for implementing deep learning according to an embodiment of the present application.

FIG. 2 is a schematic flowchart of an image processing method provided by an embodiment of the present application.

FIG. 3 is a schematic diagram of an application scenario of an image processing method provided by an embodiment of the present application.

FIG. 4 is another application scenario diagram of an image processing method provided by an embodiment of the present application.

[Correct according to Rule 91 08.01.2019]
FIG. 5 is still another application scenario diagram of an image processing method provided by an embodiment of the present application.

FIG. 6 is another schematic flowchart of an image processing apparatus according to an embodiment of the present application.

FIG. 7 is a schematic structural diagram of an image processing apparatus according to an embodiment of the present application.

FIG. 8 is another schematic structural diagram of an image processing apparatus according to an embodiment of the present application.

FIG. 9 is a schematic diagram of still another structure of an image processing apparatus according to an embodiment of the present application.

FIG. 10 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

FIG. 11 is another schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed ways

The technical solutions in the embodiments of the present application are clearly and completely described in the following with reference to the drawings in the embodiments of the present application. It is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments of the present application without creative efforts are within the scope of the present application.

The embodiment of the present application provides an image processing method, device, storage medium, and electronic device. The details will be described separately below.

Referring to FIG. 1 , FIG. 1 is a schematic diagram of a scenario in which an electronic device implements deep learning according to an embodiment of the present disclosure.

When the user processes the image through the image processing function in the electronic device, the electronic device can record the input and output data during the processing. The electronic device may include a data collection and statistics system and a prediction system with feedback adjustment. The electronic device can acquire a large amount of image classification result data of the user through the data acquisition system, and make corresponding statistics, and extract image features of the image, and analyze and process the extracted image features based on machine depth learning. When an image is input, the electronic device predicts the classification result of the image through the prediction system. After the user makes the final selection behavior, the prediction system reversely reciprocates the weights of the weighting items according to the final result of the user behavior. After a number of iterative corrections, the weights of the weighting items of the prediction system are finally converged to form a learned database.

The electronic device may be a mobile terminal, such as a mobile phone, a tablet computer, or the like, or may be a conventional PC (Personal Computer), etc., which is not limited in this embodiment of the present application.

An embodiment of the present application provides an image processing method, including:

Obtaining posture information of a face image in the target screen;

In some embodiments, the step of determining posture information of the face image in the target image includes:

Determining facial feature points of the face image in the target picture;

Generating a facial feature vector according to the facial feature point;

Obtaining a difference value between the facial feature vector and the preset facial feature vector;

Obtaining the posture information of the face image according to the difference value.

In some embodiments, the posture information includes a deflection angle; and the step of acquiring a corresponding sample facial image from the preset facial image set according to the posture information comprises:

Obtaining a sample deflection angle corresponding to each sample face image in the preset face image set, to obtain a plurality of sample deflection angles;

Selecting a target sample deflection angle that is the smallest difference from the deflection angle from a plurality of sample deflection angles;

The sample face image corresponding to the target sample deflection angle is used as the target sample face image.

In some embodiments, the step of extracting the expression feature of the face image and adjusting the target sample face image according to the expression feature includes:

Extracting an expression feature of the face image;

The expression feature is processed based on a preset algorithm model to obtain an expression feature parameter;

The target sample face image is adjusted according to the expression feature parameter.

In some embodiments, the step of processing the face image in the target picture based on the target face image includes:

Performing edge feature point detection on the face image, and acquiring location information of the edge feature point;

The face image in the target picture is processed based on the position information and the target face image.

In some embodiments, the step of processing the face image in the target picture based on the location information and the target face image includes:

Generating a face mask according to the location information;

Processing the target face image based on the face mask to obtain the processed target face image;

The face image in the target screen is replaced with the processed target face image.

In some embodiments, after replacing the face image in the target picture with the processed target face image, the method further includes:

Obtaining color information of the original face image before the face image is replaced;

Generating a color adjustment parameter according to the color information;

The color of the current face image is adjusted based on the color adjustment parameter.

In an embodiment, an image processing method is provided. As shown in FIG. 2, the flow may be as follows:

101. Acquire posture information of a face image in a target screen.

In some embodiments, the target screen may specifically be that the electronic device collects the target image through the camera. The camera can be a digital camera or an analog camera. The digital camera converts the analog image signal generated by the image acquisition device into a digital signal, which is then stored in a computer. The image signal captured by the analog camera must be converted to a digital mode by a specific image capture card and compressed before being converted to a computer for use. The digital camera captures the image directly and transmits it to the computer via a serial, parallel or USB interface. In the embodiment of the present application, the electronic device generally adopts a digital camera to convert the collected image into data in real time and display it on the display interface of the electronic device (ie, the preview frame of the camera).

The image processing method provided by the embodiment of the present application is mainly applied to a scene in which noise is affected during nighttime image capturing. Especially in the case of self-timer, taking the mobile phone as an example, the pixels of the front camera are generally poor, and shooting at night with insufficient light is more likely to cause serious noise effects. The target picture includes one or more person images, and at least one recognizable face image exists. In addition, the target image may further include a scene image such as a building, an animal or a plant.

In the embodiment of the present application, the target image is tracked and analyzed in real time, the face image is recognized based on the image recognition technology, and the key points in the face image are detected to determine the posture of the face. That is, in some embodiments, the step of "determining the pose information of the face image in the target screen" may include the following process:

Determining facial feature points of the face image in the target picture;

Generating a facial feature vector from the facial feature points;

The facial feature point may specifically be a feature point obtained according to "two eyes + mouth" or "two eyes + nose" in the face image. The preset facial feature vector may be a feature vector when the face pose is positive. The gesture information may be a gesture relative to the front side. For example, referring to FIG. 3, A is an eyebrow, an eye B1 (left), an eye B2 (right), a nose C, a mouth D, an ear E, and a face F, wherein two eyes B1, B2, and a nose C are used as features, further Feature points are selected from the features as facial feature points. A vector formed by a positional relationship between feature points is used as a feature vector.

The feature points selected in Fig. 3 are the inner corner of the eye B1, the inner corner of the eye B2, and the tip of the nose C (because the eyeball is rotated, it is possible to select a marker point with a fixed position) as the facial feature point, and Three vectors constituting a triangular region are formed, and these three vectors can be used as preset facial feature vectors. In practical applications, once the pose changes, the size and/or direction of the three vectors will also change. Therefore, the facial feature vector of the real-time face can be detected and compared with the preset facial feature vector. Then, the posture information of the current face image can be determined according to the difference value between the two calculations, such as the left side, the right side, the upper side, the lower side, and the upper right side. Left and right, etc.

102. Acquire a target sample face image from the preset face image set according to the posture information.

In the embodiment of the present application, it is necessary to construct a face image set in advance. It should be noted that the face image set includes sample face images of a plurality of different postures of the same person, and the sample image is an expressionless image, that is, a face image that does not express emotions. The embodiment of the present application is mainly directed to the noise influence problem of nighttime image capturing. Therefore, the sample face images in the constructed face image set are images with higher image quality. In practice, these high-resolution sample face images can be taken by the user during daylight hours.

When constructing a preset face image collection, the first is to collect a plurality of photos of different postures, specifically to obtain photos of different angles. The angle of deflection of the face relative to the plane of the camera lens can then be analyzed by the camera's shooting parameters or the positional relationship between the lens and the subject. Finally, using the collected face image as the sample face image and the corresponding deflection angle as the sample deflection angle, and establishing a mapping relationship between the captured face image and the deflection angle, the sample face image, The sample deflection angle and the mapping relationship between the two are added to the preset face image set to complete the construction of the set.

That is, in some embodiments the pose information includes a deflection angle. Then, the step of “acquiring the corresponding sample face image from the preset face image set according to the posture information” may include the following processes:

Selecting, from a plurality of sample deflection angles, a target sample deflection angle that is the smallest difference from the deflection angle;

Wherein, the deflection angle may be a deflection angle in six degrees of freedom. In order to improve the matching degree between the face image and the sample face image, a large number of face images of different postures can be obtained to increase the density of the deflection angle in the sample face image and reduce the interval value between the deflection angles.

103. Extract an expression feature of the face image, and adjust the target sample face image according to the expression feature to obtain the target face image.

In this embodiment, the expression of the face image in the target picture needs to be migrated to the expressionless face image in real time. In a specific implementation, the deep learning technology of the electronic device may be used to dynamically migrate the expression in the face image to the unrecognized sample face image in the preset face image set, thereby better retaining the original image expression information and the high frequency information.

That is, in some embodiments, the step of "extracting the expression features of the face image and adjusting the target sample face image according to the expression features" may include the following process:

Extracting facial expression features of the face image;

In some embodiments, the expression features are extracted, specifically, the color features, the texture features, the shape features, and the spatial relationship features in the face image are extracted, thereby identifying the facial features of the face according to the extracted image features, such as eyes and mouths. , nose, eyebrows, ears. If the recognition accuracy is to be improved, the electronic device can be trained based on the machine's deep learning technology to obtain a high-precision algorithm model, and the expression features are analyzed and processed to obtain accurate expression feature parameters.

Then, the expression of the target sample face image may be adjusted according to the obtained expression feature parameter, and the expression of the target sample face image is adjusted to be consistent with the original face image expression in the target image to achieve the target image. The expression of the face image in the migration migrates to the target sample face image.

A color feature is a global feature that describes the surface properties of a scene corresponding to an image or image area. A texture feature is also a global feature that also describes the surface properties of a scene corresponding to an image or image region. The shape feature is a local feature. There are two types of representation methods, one is the contour feature, which is mainly for the outer boundary of the object, and the other is the regional feature, which is related to the entire shape region. The spatial relationship feature refers to the mutual spatial position or relative direction relationship between multiple objects segmented in the image. These relationships can also be divided into connection/adjacency relationship, overlap/overlap relationship, and inclusion/inclusion relationship.

Image feature extraction is the use of a computer to extract image information to determine whether a point of each image belongs to an image feature. The result of feature extraction is to divide the points on the image into different subsets, which tend to belong to isolated points, continuous curves, or continuous regions. Features are the starting point for many computer image analysis algorithms. One of the most important features of feature extraction is "reproducibility," meaning that features extracted from different images of the same scene should be identical.

In the specific implementation process, the image features of the face image can be extracted by using Fourier transform method, window Fourier transform method, wavelet transform method, least square method, boundary direction histogram method, and texture feature extraction based on Tamura texture feature.

104. Process the face image in the target image based on the target face image.

In some embodiments, the step of "processing the face image in the target image based on the target face image" may include the following process:

Performing edge feature point detection on the face image, and acquiring position information of the edge feature points;

The edge feature points of the face image in the target picture can be referred to as shown in FIG. 4 . Obtain relative position information of these edge feature points relative to each other.

In some embodiments, the step of "processing the face image in the target image based on the location information and the target face image" may include the following process:

Generating a face mask based on the location information;

The target face image is processed based on the face mask to obtain the processed target face image;

With continued reference to FIG. 4, the right image in FIG. 4 is a human face mask generated based on the position information of the edge feature points in the left figure. The face mask of the high-definition is exchanged with the face mask to the face image with a more serious noise influence on the target image. The specific method may be: overlaying the face mask on the high-definition target face image, and extracting an overlapping area image of the target face image overlapping the face mask area as the processed target face image. For example, referring to FIG. 5, the left image is a face image in the target picture, and the right is a face image in the target picture after replacing the face.

When the face image in the target picture is replaced with the processed target face image, the processed target face image may be merged with the target image based on the Poisson fusion technique to cover the original face image in the target image. Thereby, the face image in the target picture is replaced with the processed target face image. Among them, Poisson fusion technology can better eliminate the boundary between the target face image and the target image, making the picture more natural and unobtrusive, achieving seamless splicing.

As can be seen from the above, an embodiment of the present application provides an image processing method for acquiring posture information of a face image in a target image, acquiring a target sample face image from a preset face image set according to the posture information, and extracting a face image. The expression features are adjusted according to the expression features to obtain the target face image; the face image in the target image is processed based on the target face image. This scheme can replace the face image in the more serious picture with the pre-stored high-definition face image, which can reduce the influence of noise and improve the image quality.

In an embodiment, another image processing method is also provided. As shown in FIG. 6, the flow may be as follows:

201. Construct a face image database.

202. Acquire posture information of a face image in the target image, and obtain a target sample face image from the preset face image set according to the posture information.

The image processing method provided by the embodiment of the present application is mainly applied to a scene in which noise is affected during nighttime image capturing. The target screen may specifically be that the electronic device collects the target image through the camera. The target picture includes one or more person images, and at least one recognizable face image exists. In addition, the target image may further include a scene image such as a building, an animal or a plant.

In the embodiment of the present application, the target image is tracked and analyzed in real time, the face image is recognized based on the image recognition technology, and the key points in the face image are detected to determine the posture of the face.

In some embodiments the attitude information includes a deflection angle. The sample deflection angle corresponding to each sample face image in the preset face image set may be obtained, and a plurality of sample deflection angles are obtained, and then the target sample deflection with the smallest difference between the deflection angles and the deflection angle is selected from the plurality of sample deflection angles. Angle, the sample face image corresponding to the target sample deflection angle is used as the target sample face image.

203. Extract an expression feature of the face image, and process the expression feature based on the preset algorithm model to obtain an expression feature parameter.

204. Adjust the target sample face image according to the expression feature parameter to obtain the target human sample image.

Specifically, the expression of the target sample face image may be adjusted according to the obtained expression feature parameter, and the expression of the target sample face image is adjusted to be consistent with the original face image expression in the target image to achieve the target. The expression of the face image in the picture is moved to the target sample face image.

205. Perform edge feature point detection on the face image in the target image to position information of the edge feature point.

The acquired location information is relative location information between the edge feature points.

206. Generate a face mask according to the location information, and process the target face image based on the face mask.

A face mask generated based on position information of edge feature points in the left figure. The face mask of the high-definition is exchanged with the face mask to the face image with a more serious noise influence on the target image. The specific method may be: overlaying the face mask on the high-definition target face image, and extracting an overlapping area image of the target face image overlapping the face mask area.

207. Replace the face image in the target screen with the processed target face image.

Specifically, when the face image in the target image is replaced with the processed target face image, the processed target face image may be merged with the target image based on the Poisson fusion technique to cover the original person in the target image. The face image, thereby replacing the face image in the target picture with the processed target face image. Among them, Poisson fusion technology can better eliminate the boundary between the target face image and the target image, making the picture more natural and unobtrusive, achieving seamless splicing.

208. Acquire color information of the original face image before replacing the face image, and generate a color adjustment parameter according to the color information.

The acquired color information may include various colors such as color temperature, hue, brightness, saturation, and the like. Specifically, the acquired color information may be analyzed and processed based on a correlation algorithm to obtain a target color parameter. Then, the color information of the target face image is obtained, and the acquired color information is also analyzed and processed to obtain the target color parameter. Finally, the difference parameter value between the target color parameter and the target color parameter is obtained, and the difference parameter value is used as the final color adjustment parameter.

209. Adjust the color of the current face image based on the color adjustment parameter.

Specifically, after the color adjustment parameter is acquired, the color of the current face image is adjusted according to the color adjustment parameter, so that the face image parameter makes the face light color more natural and closer to the real scene.

It can be seen from the above that the image processing method provided by the embodiment of the present application constructs a face image database having a high-resolution sample face image, and matches the target sample face image according to the posture information of the face image in the target image, and the target image is obtained. The expression of the face image in the migration migrates to the sample face image to obtain the target face image. Then, the edge feature points are detected on the face image in the target image, the face mask is generated according to the position information of the edge feature points, and the target face image is processed based on the face mask, and the face image in the target image is replaced. For the processed target face image, finally, the color information of the original face image before the face image replacement is acquired, the color adjustment parameter is generated according to the color information, and the color of the replaced face image is adjusted based on the color adjustment parameter. The scheme can replace the face image in the more severe picture with the pre-stored high-definition face image, which can reduce the influence of noise and improve the image quality.

In another embodiment of the present application, an image processing apparatus is further provided, which may be integrated in an electronic device in the form of software or hardware, and the electronic device may specifically include a mobile phone, a tablet computer, a notebook computer, and the like. As shown in FIG. 7, the image processing apparatus 30 may include an information acquisition module 31, an image acquisition module 32, an adjustment module 33, and a processing module 34, wherein:

The information acquiring module 31 is configured to acquire posture information of a face image in the target image;

The image obtaining module 32 is configured to obtain a target sample face image from the preset face image set according to the posture information;

The adjusting module 33 is configured to extract an expression feature of the face image, and adjust the target sample face image according to the expression feature to obtain the target face image;

The processing module 34 is configured to process the face image in the target image based on the target face image.

In some embodiments, referring to FIG. 8, the adjustment module 33 can include:

An extraction sub-module 331, configured to extract an expression feature of the face image;

The first processing sub-module 332 is configured to process the expression feature based on the preset algorithm model to obtain an expression feature parameter;

The adjustment sub-module 333 is configured to adjust the target sample face image according to the expression feature parameter.

In some embodiments, referring to FIG. 9, the processing module 34 can include:

The obtaining sub-module 341 is configured to perform edge feature point detection on the face image, and acquire location information of the edge feature point;

The second processing sub-module 342 is configured to process the facial image in the target image based on the location information and the target facial image.

In some embodiments, the information acquisition module 31 can be used to:

Determining facial feature points of the face image in the target picture;

Generating a facial feature vector according to the facial feature point;

In some embodiments, the attitude information includes a deflection angle, and the image acquisition module 32 can be configured to:

It can be seen that the image processing apparatus provided by the embodiment of the present application acquires the posture information of the face image in the target image; acquires the target sample face image from the preset face image set according to the posture information; and extracts the expression of the face image. Feature, and adjusting the target sample face image according to the expression feature to obtain the target face image; and processing the face image in the target image based on the target face image. The scheme can replace the face image in the more severe picture with the pre-stored high-definition face image, which can reduce the influence of noise and improve the image quality.

In another embodiment of the present application, an electronic device is further provided, and the electronic device may be a device such as a smart phone or a tablet computer. As shown in FIG. 10, the electronic device 400 includes a processor 401 and a memory 402. The processor 401 is electrically connected to the memory 402.

The processor 401 is a control center of the electronic device 400, which connects various parts of the entire electronic device using various interfaces and lines, executes the electronic by running or loading an application stored in the memory 402, and calling data stored in the memory 402. The various functions and processing data of the device enable overall monitoring of the electronic device.

In this embodiment, the processor 401 in the electronic device 400 loads the instructions corresponding to the process of one or more applications into the memory 402 according to the following steps, and is stored and stored in the memory 402 by the processor 401. In the application, thus implementing various functions:

Obtaining posture information of a face image in the target screen;

Extracting the expression features of the face image, and adjusting the target sample face image according to the expression features to obtain the target face image;

In some embodiments, the processor 401 is configured to perform the following steps:

Determining facial feature points of the face image in the target picture;

Generating a facial feature vector from the facial feature points;

In some embodiments, the pose information includes a deflection angle; the processor 401 also performs the following steps:

Selecting a target sample deflection angle that is the smallest difference from the deflection angle from the plurality of sample deflection angles;

In some embodiments, processor 401 also performs the following steps:

Extracting facial expression features of the face image;

In some embodiments, the processor 401 further performs the following steps: performing edge feature point detection on the face image, and acquiring location information of the edge feature points; and processing the face image in the target image based on the location information and the target face image .

In some embodiments, processor 401 also performs the following steps:

Generating a face mask based on the location information;

In some embodiments, after replacing the face image in the target picture with the processed target face image, the processor 401 further performs the following steps:

Generating a color adjustment parameter according to the color information;

Memory 402 can be used to store applications and data. The application stored in the memory 402 contains instructions that are executable in the processor. Applications can form various functional modules. The processor 401 executes various functional applications and data processing by running an application stored in the memory 402.

In some embodiments, as shown in FIG. 11, the electronic device 400 further includes a display screen 403, a control circuit 404, a radio frequency circuit 405, an input unit 406, an audio circuit 407, a sensor 408, and a power source 409. The processor 401 is electrically connected to the display screen 403, the control circuit 404, the radio frequency circuit 405, the input unit 406, the audio circuit 407, the sensor 408, and the power source 409, respectively.

The display screen 403 can be used to display information entered by the user or information provided to the user as well as various graphical user interfaces of the electronic device, which can be composed of images, text, icons, video, and any combination thereof. The display screen 403 can be used as a screen in the embodiment of the present application for displaying information.

The control circuit 404 is electrically connected to the display screen 403 for controlling the display screen 403 to display information.

The radio frequency circuit 405 is configured to transmit and receive radio frequency signals to establish wireless communication with network devices or other electronic devices through wireless communication, and to transmit and receive signals with network devices or other electronic devices.

The input unit 406 can be configured to receive input digits, character information, or user characteristic information (eg, fingerprints), and to generate keyboard, mouse, joystick, optical, or trackball signal inputs related to user settings and function controls. The input unit 406 can include a fingerprint identification module.

The audio circuit 407 can provide an audio interface between the user and the electronic device through a speaker and a microphone.

Sensor 408 is used to collect external environmental information. Sensor 408 can include ambient brightness sensors, acceleration sensors, light sensors, motion sensors, and other sensors.

Power source 409 is used to power various components of electronic device 400. In some embodiments, the power supply 409 can be logically coupled to the processor 401 through a power management system to enable functions such as managing charging, discharging, and power management through the power management system.

The camera 410 is used for collecting external images, and can be a digital camera or an analog camera. In some embodiments, camera 410 may convert the acquired external picture into data for transmission to processor 401 to perform image processing operations.

Although not shown in FIG. 11, the electronic device 400 may further include a Bluetooth module or the like, and details are not described herein again.

It can be seen that the electronic device provided by the embodiment of the present application acquires the posture information of the face image in the target image; acquires the target sample face image from the preset face image set according to the posture information; and extracts the expression feature of the face image. And adjusting the target sample face image according to the expression feature to obtain the target face image; and processing the face image in the target image based on the target face image. The scheme can replace the face image in the more severe picture with the pre-stored high-definition face image, which can reduce the influence of noise and improve the image quality.

A further embodiment of the present application further provides a storage medium having stored therein a plurality of instructions adapted to be loaded by a processor to perform the steps of any of the image processing methods described above.

A person skilled in the art may understand that all or part of the various steps of the foregoing embodiments may be performed by a program to instruct related hardware. The program may be stored in a computer readable storage medium, and the storage medium may include: Read Only Memory (ROM), Random Access Memory (RAM), disk or optical disk.

The terms "a", "an", "the", and "the" In addition, unless otherwise stated herein, the recitation of numerical ranges herein is merely referring to each of the individual These values are stated separately in this article. In addition, the steps of all methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise indicated. Changes to the application are not limited to the sequence of steps described. The use of any and all examples or exemplary language, such as "a" Numerous modifications and adaptations will be apparent to those skilled in the art without departing from the scope of the invention.

The image processing method, the device, the storage medium and the electronic device provided by the embodiments of the present application are described in detail. In the present application, the principle and the implementation manner of the application are described in the specific examples, and the description of the above embodiments is provided. It is only used to help understand the method of the present application and its core ideas; at the same time, for those skilled in the art, according to the idea of the present application, there will be changes in the scope of specific implementations and applications, in summary, The contents of this specification are not to be construed as limiting the application.

Claims

An image processing method, comprising:

Obtaining posture information of a face image in the target screen;

Obtaining a target sample face image from the preset face image set according to the posture information;

Extracting an expression feature of the face image, and adjusting the target sample face image according to the expression feature to obtain a target face image;

The face image in the target picture is processed based on the target face image.
The image processing method according to claim 1, wherein the determining the posture information of the face image in the target image comprises:

Determining facial feature points of the face image in the target picture;

Generating a facial feature vector according to the facial feature point;

Obtaining a difference value between the facial feature vector and the preset facial feature vector;

Obtaining the posture information of the face image according to the difference value.
The image processing method according to claim 1, wherein the posture information comprises a deflection angle; and the step of acquiring a corresponding sample face image from the preset face image set according to the posture information comprises:

Obtaining a sample deflection angle corresponding to each sample face image in the preset face image set, to obtain a plurality of sample deflection angles;

Selecting a target sample deflection angle that is the smallest difference from the deflection angle from a plurality of sample deflection angles;

The sample face image corresponding to the target sample deflection angle is used as the target sample face image.
The image processing method according to claim 1, wherein the step of extracting the expression feature of the face image and adjusting the target sample face image according to the expression feature comprises:

Extracting an expression feature of the face image;

The expression feature is processed based on a preset algorithm model to obtain an expression feature parameter;

The target sample face image is adjusted according to the expression feature parameter.
The image processing method according to claim 1, wherein the step of processing the face image in the target image based on the target face image comprises:

Performing edge feature point detection on the face image, and acquiring location information of the edge feature point;

The face image in the target picture is processed based on the position information and the target face image.
The image processing method according to claim 5, wherein the step of processing the face image in the target image based on the position information and the target face image comprises:

Generating a face mask according to the location information;

Processing the target face image based on the face mask to obtain the processed target face image;

The face image in the target screen is replaced with the processed target face image.
The image processing method according to claim 6, wherein after the face image in the target image is replaced with the processed target face image, the method further includes:

Obtaining color information of the original face image before the face image is replaced;

Generating a color adjustment parameter according to the color information;

The color of the current face image is adjusted based on the color adjustment parameter.
An image processing apparatus, comprising:

An information acquiring module, configured to acquire posture information of a face image in a target image;

An image obtaining module, configured to acquire a target sample face image from the preset face image set according to the posture information;

An adjustment module, configured to extract an expression feature of the face image, and adjust a target sample face image according to the expression feature to obtain a target face image;

And a processing module, configured to process the face image in the target image based on the target face image.
The image processing device according to claim 8, wherein said information acquisition module is configured to:

Determining facial feature points of the face image in the target picture;

Generating a facial feature vector according to the facial feature point;

Obtaining a difference value between the facial feature vector and the preset facial feature vector;

Obtaining the posture information of the face image according to the difference value.
The image processing device according to claim 8, wherein the posture information includes a deflection angle, and the image acquisition module is configured to:

Obtaining a sample deflection angle corresponding to each sample face image in the preset face image set, to obtain a plurality of sample deflection angles;

Selecting a target sample deflection angle that is the smallest difference from the deflection angle from a plurality of sample deflection angles;

The sample face image corresponding to the target sample deflection angle is used as the target sample face image.
The image processing device according to claim 8, wherein said adjustment module comprises:

Extracting a sub-module, configured to extract an expression feature of the face image;

a first processing submodule, configured to process the expression feature based on a preset algorithm model to obtain an expression feature parameter;

The adjustment submodule is configured to adjust the target sample face image according to the expression feature parameter.
The image processing device of claim 8, wherein the processing module comprises:

Obtaining a sub-module, configured to perform edge feature point detection on the face image, and acquire location information of the edge feature point;

a second processing submodule configured to process the face image in the target picture based on the location information and the target face image.
A storage medium, wherein the storage medium stores a plurality of instructions adapted to be loaded by a processor to perform the following steps:

Obtaining posture information of a face image in the target screen;

Obtaining a target sample face image from the preset face image set according to the posture information;

Extracting an expression feature of the face image, and adjusting the target sample face image according to the expression feature to obtain a target face image;

The face image in the target picture is processed based on the target face image.
An electronic device, comprising a processor and a memory, the processor being electrically connected to the memory, the memory for storing instructions and data; the processor for performing the following steps:

Obtaining posture information of a face image in the target screen;

Obtaining a target sample face image from the preset face image set according to the posture information;

Extracting an expression feature of the face image, and adjusting the target sample face image according to the expression feature to obtain a target face image;

The face image in the target picture is processed based on the target face image.
The electronic device of claim 14, wherein the processor is configured to perform the following steps when determining posture information of the face image in the target picture:

Determining facial feature points of the face image in the target picture;

Generating a facial feature vector according to the facial feature point;

Obtaining a difference value between the facial feature vector and the preset facial feature vector;

Obtaining the posture information of the face image according to the difference value.
The electronic device of claim 14, wherein the attitude information comprises a deflection angle; and when the corresponding sample face image is acquired from the preset face image set according to the posture information, the processor is configured to perform the following step:

Obtaining a sample deflection angle corresponding to each sample face image in the preset face image set, to obtain a plurality of sample deflection angles;

Selecting a target sample deflection angle that is the smallest difference from the deflection angle from a plurality of sample deflection angles;

The sample face image corresponding to the target sample deflection angle is used as the target sample face image.
The electronic device according to claim 14, wherein the processor is configured to perform the following steps when extracting an expression feature of the face image and adjusting the target sample face image according to the expression feature:

Extracting an expression feature of the face image;

The expression feature is processed based on a preset algorithm model to obtain an expression feature parameter;

The target sample face image is adjusted according to the expression feature parameter.
The electronic device of claim 14, wherein the processor is configured to perform the following steps when processing the face image in the target picture based on the target face image:

Performing edge feature point detection on the face image, and acquiring location information of the edge feature point;

The face image in the target picture is processed based on the position information and the target face image.
The electronic device according to claim 18, wherein, when the face image in the target picture is processed based on the position information and the target face image, the processor is configured to perform the following steps:

Generating a face mask according to the location information;

Processing the target face image based on the face mask to obtain the processed target face image;

The face image in the target screen is replaced with the processed target face image.
The electronic device of claim 19, wherein after replacing the face image in the target picture with the processed target face image, the processor is configured to perform the following steps:

Obtaining color information of the original face image before the face image is replaced;

Generating a color adjustment parameter according to the color information;

The color of the current face image is adjusted based on the color adjustment parameter.