CN108513069A - Image processing method, image processing device, storage medium and electronic equipment - Google Patents

Abstract

The embodiment of the application discloses an image processing method, an image processing device, a storage medium and electronic equipment, wherein a tele scene image and a wide-angle scene image which are synchronously acquired and are in the same scene are acquired; then, carrying out face recognition on the tele scene image to obtain the face characteristics of the tele scene image; then binding the face characteristics of the tele scene image with the wide scene image; and finally, presetting the wide-angle scene image according to the face characteristics bound by the wide-angle scene image. When the scheme is applied to long-distance portrait shooting or multi-person group shooting, the face features of people in a scene to be shot can be extracted through a long-focus scene image with a small view finding range but a large portrait account, and then the extracted face features and a wide-angle scene image with a large view finding range but a small portrait account are used for processing the wide-angle scene image, so that the problem of difficulty in extracting the face features can be solved, and the purpose of improving the image processing accuracy is achieved.

Description

Image processing method, device, storage medium and electronic equipment

technical field

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

Background technique

Electronic devices such as mobile phones usually provide users with a camera function. With the continuous advancement of camera modules and other hardware and image processing algorithms, the camera functions of electronic devices are becoming more and more powerful, and users are using electronic devices more and more frequently. Take a picture.

At present, in addition to basic camera functions, electronic devices further provide image processing functions, such as beautifying and modifying human faces in images. The premise of realizing image processing is to recognize the facial features in the image. However, when the electronic device is shooting a person at a distance, or taking a group photo with multiple people, the face in the captured image will be small, which is difficult to realize. Accurate extraction of facial features affects the accuracy of subsequent image processing on the image.

Contents of the invention

Embodiments of the present application provide an image processing method, device, storage medium, and electronic equipment, which can improve the accuracy of image processing.

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

Obtain synchronously acquired telephoto scene images and wide-angle scene images of the same scene;

Perform face recognition on the telephoto scene image to obtain facial features of the telephoto scene image;

Binding the facial features to the wide-angle scene image;

Preset processing is performed on the wide-angle scene image according to the facial features bound to the wide-angle scene image.

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

An image acquisition module, configured to acquire synchronously acquired telephoto scene images and wide-angle scene images of the same scene;

An image recognition module, configured to perform face recognition on the telephoto scene image to obtain facial features of the telephoto scene image;

A feature binding module, configured to bind the facial features to the wide-angle scene image;

The image processing module is configured to perform preset processing on the wide-angle scene image according to the facial features bound to the wide-angle scene image.

In the third aspect, the storage medium provided by the embodiments of the present application has a computer program stored thereon, and when the computer program is run on a computer, the computer is made to execute the image processing method provided in any embodiment of the present application.

In a fourth aspect, the embodiment of the present application provides an electronic device, including a central processing unit and a memory, the memory has a computer program, and the central processor is used to execute any one of the implementations of the present application by invoking the computer program. The image processing method provided by the example.

In the embodiment of the present application, at first, the synchronously acquired telephoto scene image and wide-angle scene image of the same scene are acquired; then face recognition is performed on the telephoto scene image to obtain the face features of the telephoto scene image; The face feature is bound to the wide-angle scene image; finally, the wide-angle scene image is preset to be processed according to the face feature bound to the wide-angle scene image. When the technical solution provided by the embodiment of the present application is applied to long-distance portrait shooting or a group photo of multiple people, the facial features of the people in the scene to be shot can be extracted from the telephoto scene image with a small viewing range but a large proportion of portraits, and then The extracted face features and wide-angle scene images with a large viewing range but a small proportion of portraits are used to process the wide-angle scene images, so as to overcome the difficulty of face feature extraction and achieve the purpose of improving the accuracy of image processing .

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that need to be used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

FIG. 1 is a schematic flowchart of an image processing method provided by an embodiment of the present application.

FIG. 2 is a schematic diagram of installation positions of a telephoto camera and a wide-angle camera in an embodiment of the present application.

FIG. 3 is an example diagram of a telephoto scene image and a wide-angle scene image of a scene to be photographed collected in an embodiment of the present application.

Fig. 4 is a schematic diagram of an operation of binding facial features in the embodiment of the present application.

Fig. 5 is a schematic diagram of another operation of binding facial features in the embodiment of the present application.

Fig. 6 is a schematic diagram of determining an image to be processed from multiple wide-angle scene images in the embodiment of the present application.

Fig. 7 is a schematic diagram of the operation of determining the face image to be processed from the images to be processed in the embodiment of the present application.

FIG. 8 is a schematic diagram of the operation of acquiring a target face image from other wide-angle scene images in the embodiment of the present application.

FIG. 9 is a schematic diagram of an operation for replacing a face image to be processed with a target face image in the embodiment of the present application.

Fig. 10 is a schematic diagram of the operation of acquiring a target facial image from a facial image database in the embodiment of the present application.

Fig. 11 is a schematic diagram of another operation for replacing the face image to be processed with the target face image in the embodiment of the present application.

Fig. 12 is a schematic diagram of the operation of acquiring a target face image from the target electronic device in the embodiment of the present application.

Fig. 13 is another schematic flowchart of the image processing method provided in the embodiment of the present application.

FIG. 14 is a schematic structural diagram of an image processing device provided by an embodiment of the present application.

FIG. 15 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

FIG. 16 is another schematic structural diagram of an electronic device provided by an embodiment of the present application.

FIG. 17 is a schematic diagram of a detailed structure of the image processing circuit in the embodiment of the present application.

FIG. 18 is a schematic diagram of another detailed structure of the image processing circuit in the embodiment of the present application.

Detailed ways

Referring to the drawings, wherein the same reference numerals represent the same components, the principles of the present application are exemplified by being implemented in a suitable computing environment. The following description is based on illustrated specific embodiments of the present application, which should not be construed as limiting other specific embodiments of the present application that are not described in detail here.

In the following description, specific embodiments of the present application will be described with reference to steps and symbols executed by one or more computers, unless otherwise stated. Accordingly, these steps and operations will several times be referred to as being computer-implemented, which herein refers to operations by a computer processing unit of electronic signals representing data in a structured form. This operation transforms the data or maintains it at a location in the computer's memory system that can reconfigure or otherwise alter the operation of the computer in a manner well known to testers in the art. The data structures maintained by the data are physical locations in the memory that have certain characteristics defined by the data format. However, the principle of the present application is described in the above text, which is not meant to be a limitation, and testers in the field will understand that the various steps and operations described below can also be implemented in hardware.

The term "module" as used herein may be considered a software object that executes on the computing system. The various components, modules, engines and services described herein can be considered as implementation objects on the computing system. The devices and methods described herein can be implemented in the form of software, and of course can also be implemented in hardware, all of which are within the protection scope of the present application.

The terms "first", "second" and "third" in this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "include" and "have", as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or modules is not limited to the listed steps or modules, but some embodiments also include steps or modules that are not listed, or some embodiments Other steps or modules inherent to these processes, methods, products or devices are also included.

Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The occurrences of this phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is understood explicitly and implicitly by those skilled in the art that the embodiments described herein can be combined with other embodiments.

An embodiment of the present application provides an image processing method. The execution subject of the image processing method may be the image processing device provided in the embodiment of the present application, or an electronic device integrated with the image processing device, wherein the image processing device may use hardware or realized by software. Wherein, the electronic device may be a device such as a smart phone, a tablet computer, a palmtop computer, a notebook computer, or a desktop computer.

Please refer to FIG. 1 , which is a schematic flowchart of an image processing method provided in an embodiment of the present application. The specific flow of the image processing method provided in the embodiment of the present application may be as follows:

In step 101, a synchronously acquired telephoto scene image and a wide-angle scene image of the same scene are acquired.

Please refer to FIG. 2. In the embodiment of the present application, the electronic device includes a telephoto camera and a wide-angle camera. In one arrangement shown in FIG. 2, the telephoto camera and the wide-angle camera are horizontally arranged on the same plane of the electronic device. And there is a certain distance between the telephoto camera and the wide-angle camera. In addition, the electronic device is configured with a telephoto image buffer queue and a wide-angle image buffer queue, wherein the telephoto image buffer queue is used for buffering images collected by the telephoto camera, and the wide-angle image buffer queue is used for buffering images collected by the wide-angle camera. It should be noted that during specific implementation, the arrangement of the telephoto camera and the wide-angle camera can be set by those skilled in the art according to actual needs.

In the embodiment of the present application, after the electronic device starts the camera application, it will start the telephoto camera and the wide-angle camera synchronously, and control the telephoto camera and the wide-angle camera to synchronously collect the images of the scenes to be shot at the same frame rate, and obtain the images to be shot respectively. A telephoto scene image and a wide-angle scene image of the scene. Wherein, the telephoto camera stores the collected telephoto scene image into the telephoto image buffer queue, and the wide-angle camera stores the collected wide-angle scene image into the wide-angle image buffer queue.

For example, please refer to FIG. 3. In the scene to be shot shown in FIG. 3, there are three different characters including character C, character D, and character E. For the telephoto scene image, the wide-angle scene image of the scene to be shot is collected by the wide-angle camera. Obviously, the portrait in the telephoto scene image is larger and easier to identify than the portrait in the wide-angle scene image.

In the embodiment of the present application, when the electronic device receives the triggered image capture request, it extracts the telephoto scene of the same scene that is synchronously collected by the telephoto camera and the wide-angle camera respectively from the queue of telephoto scene images and the queue of wide-angle scene images. images as well as wide-angle scene images.

Optionally, in an embodiment, obtaining synchronously acquired telephoto scene images and wide-angle scene images of the same scene may include:

Telephoto scene images and wide-angle scene images of the same scene that are synchronously collected at multiple collection moments are acquired to obtain a telephoto scene image set and a wide-angle scene image set.

Wherein, the number of telephoto scene images in the set of telephoto scene images is the same as the number of wide-angle scene images in the set of wide-angle scene images. The number of objects to acquire the telephoto scene image/wide-angle telephoto image may be determined according to the frame rate information of the telephoto camera/wide-angle camera.

Specifically, when the electronic device receives the triggered image capture request, it first obtains the frame rate information of the telephoto camera/wide-angle camera, where the frame rate information is used to describe the scene image captured by the telephoto camera/wide-angle camera within a unit time For example, when the obtained frame rate information is 30fps, it means that the telephoto camera collects 30 telephoto scene images of the scene to be shot per second, and the wide-angle camera collects 30 wide-angle scene images of the scene to be shot per second; For another example, when the obtained frame rate information is 15fps, it means that the telephoto camera collects 15 telephoto scene images of the scene to be shot per second, and the wide-angle camera collects 15 wide-angle scene images of the scene to be shot per second.

Among them, the main reason affecting the frame rate information of the telephoto camera/wide-angle camera is the brightness of the ambient light. When the brightness of the ambient light is high (or the electronic device is in a bright light environment), the telephoto camera/wide-angle camera can display in a short period of time. (such as 30ms) to complete the exposure, so that it can capture scene images at a higher frame rate; and when the ambient light brightness is low (or the electronic device is in a dark environment), the telephoto camera/wide-angle camera takes a long time The time (such as 40ms-60ms, or more) to complete the exposure, so that it can only capture scene images at a lower frame rate.

Afterwards, according to the frame rate information of the telephoto camera/wide-angle camera, determine the number of targets corresponding to the frame rate information, wherein the number of targets and the frame rate information can be proportional to each other, for example, in the frame rate of the telephoto camera/wide-angle camera If the rate information is 30fps, the number of targets can be determined to be 8. When the frame rate information of the telephoto camera/wide-angle camera is 15 fps, the number of targets can be determined to be 6.

After determining the target number, extract the telephoto scene images and wide-angle scene images whose number is the target number from the telephoto image cache queue and the wide-angle image cache queue, wherein the telephoto scene images with the same collection time (or synchronous collection) The scene image and the wide-angle scene image constitute an image group, and a target number of image groups is obtained in total.

For example, when the number of targets is determined to be "4", four telephoto scene images with acquisition times t1, t2, t3 and t4 are extracted from the telephoto image buffer queue, and the acquisition time is extracted from the wide-angle image buffer queue. The four wide-angle scene images at time t1, t2, t3 and t4 respectively will obtain 4 image groups corresponding to the acquisition time t1, t2, t3 and t4 respectively.

In step 102, face recognition is performed on the obtained telephoto scene image to obtain the face features of the telephoto scene image;

In the embodiment of the present application, when there are multiple telephoto scene images obtained, face recognition is performed on the multiple telephoto scene images obtained; when only one telephoto scene image is obtained, the Face recognition is performed on an acquired telephoto scene image.

Among them, the face feature is used to describe the facial image contained in the telephoto scene image, including but not limited to face size, eye opening and closing degree, and expression type (such as anger, disgust, fear, happiness, sadness and surprise, etc.).

It should be noted that the embodiment of the present application does not specifically limit which face recognition technology is used to perform face recognition on the telephoto scene image, and those skilled in the art can select an appropriate face recognition technology according to actual needs.

In step 103, the face feature of the telephoto scene image is bound to the wide-angle scene image;

Wherein, after the face recognition of the acquired telephoto scene image is completed, the face features recognized from the telephoto scene image are bound to the same wide-angle scene image at the time of collection.

For example, please refer to Figure 4, the telephoto scene image and the wide-angle telephoto image at the acquisition time t1 are obtained, and then the facial features are recognized from the telephoto scene image at the acquisition time t1, and the recognized person The face feature is bound to the wide-angle scene image at the same collection time as t1, and used as the face feature of the wide-angle scene image.

For another example, please refer to Fig. 5, four image groups at the acquisition time t1, t2, t3, and t4 are obtained, and then, face feature 1 is recognized from the telephoto scene image at the acquisition time t1, and the recognition The detected face feature 1 is bound to the wide-angle scene image at the same collection time t1, and the face feature 2 is identified from the telephoto scene image at the collection time t2, and the recognized face feature 2 is combined with the collection time Bind the wide-angle scene image at the same time as t2, identify the face feature 3 from the telephoto scene image at the collection time t3, and bind the recognized face feature 3 with the wide-angle scene image at the same collection time as t3 It is determined that the face feature 4 is identified from the telephoto scene image at the acquisition time t4, and the recognized face feature 4 is bound to the wide-angle scene image at the same acquisition time t4.

In step 104, preset processing is performed on the wide-angle scene image according to the face features bound to the wide-angle scene image.

In the embodiment of the present application, after the binding of the facial features is completed, the wide-angle scene image is further subjected to preset processing according to the bound human face feature of the wide-angle scene image. Specifically, in an optional implementation manner, when there are multiple acquired wide-angle scene images, preset processing is performed on the wide-angle scene images according to the facial features bound to the wide-angle scene images, including:

According to the facial features bound to each wide-angle scene image, determine the number of matching face images that meet the preset conditions contained in each wide-angle scene image;

The wide-angle scene image containing the largest number of matching face images is used as the image to be processed;

Preset processing is performed on the image to be processed according to the facial features bound to the image to be processed.

Among them, the preset condition can be set according to the actual shooting needs. For example, when taking a group photo with multiple people, if you need to shoot everyone’s surprised expression, you can configure the preset condition as “surprised expression type”; When taking a group photo with multiple people, if you need to capture the expressions of everyone opening their eyes and smiling, you can configure the preset facial features as "the expression type is smiling and eyes open".

For example, referring to FIG. 6, four wide-angle scene images are obtained in total, namely: wide-angle scene image A, wide-angle scene image B, wide-angle scene image C, and wide-angle scene image D. The scene includes 3 different characters, Bing Ding Wu, and the preset condition is configured as "the expression is laughing". According to the facial features bound to each of the 4 wide-angle scene images, it is determined that the wide-angle scene image A contains a matching face It is determined that there is no matching facial image in the wide-angle scene image B, and it is determined that the wide-angle scene image C includes two matching facial images (that is, the facial image of C and the facial image of D ), it is determined that a matching facial image (i.e., the facial image of E) is included in the wide-angle scene image D, obviously, the number of matching facial images contained in the wide-angle scene image C is the largest, so the wide-angle scene image C is used as the pending processing image.

After the image to be processed is determined, preset processing can be performed on the determined image to be processed according to the facial features bound to the image to be processed. Specifically, according to the facial features bound to the image to be processed, the Images undergo preset processing, including:

According to the facial features bound to the images to be processed, determine the facial images to be processed that do not meet the preset conditions in the images to be processed;

Obtaining a target face image corresponding to the face image to be processed from other wide-angle scene images other than the image to be processed, the target face image meets a preset condition, and belongs to the same person as the face image to be processed;

Replace the pending face image with the target face image.

Wherein, after the image to be processed is determined from the acquired multiple wide-angle scene images, the face image to be processed is further determined from the images to be processed, wherein the face image to be processed does not meet the preset condition.

For example, referring to FIG. 7, the determined image to be processed includes the face image of person C, the face image of person D, and the face image of person E. As shown in FIG. 7, according to the person bound to the image to be processed Face features, determine that the expression type of C’s face image is “big laugh”, the expression type of D’s face image is also “big smile”, and the expression type of E’s face image is “depressed”, if the preset condition is “ If the facial expression type is "laughing", then the facial images of C and D meet the preset conditions, and the facial image of E does not meet the preset conditions. Therefore, the facial image of E is determined as the facial image to be processed.

After determining the facial image to be processed among the images to be processed, further obtain a target facial image corresponding to the facial image to be processed from other wide-angle scene images except the image to be processed, wherein the target facial image It belongs to the same person as the face image to be processed, and the target face image meets the preset condition. For example, referring to Fig. 8 and Fig. 6, among the four wide-angle scene images shown in Fig. 6, the wide-angle scene image C is determined as the image to be processed, and the expression type of the E face image is "depressed", which is the same as the preset The condition "the expression type is laughing" does not match, and it is further determined to be a face image to be processed. Afterwards, in other wide-angle scene images (here, scene image A, scene image B, and scene image D) except the image to be processed (here, scene image C), find whether there is an expression type that is "laughing" E’s face image (that is, the face image of character E), obviously, as shown in Figure 6, there is an E’s face image whose expression type is "big laugh" in the wide-angle scene image D, at this time, the wide-angle scene image The face image of E in D is determined as the target face image, and the face image of E is extracted from the wide-angle scene image D for subsequent processing.

After the target face image corresponding to the face image to be processed is acquired, the face image to be processed can be replaced with the target face image. For example, please refer to FIG. 9 and FIG. 6 in combination. After obtaining four wide-angle scene images (i.e. wide-angle scene image A, wide-angle scene image B, wide-angle scene image C and wide-angle scene image D) shown in FIG. Scene image C is determined as the image to be processed, and further the face image of E in the wide-angle scene image C (i.e. the face image of person E) is determined as the face image to be processed; after that, the face of E in the wide-angle scene image D The internal image is determined as the target facial image; afterward, the E facial image in the wide-angle scene image C is replaced with the E facial image in the wide-angle scene image D, thereby obtaining the replaced wide-angle scene image C, as shown in Figure 9 It is shown that in the replaced wide-angle scene image C, the expression types of the facial images of each person are "laughing", which all meet the preset condition "the expression type is laughing".

In the embodiment of the present application, after the image replacement is completed, the replaced image to be processed can be used as the result image of the image capture request.

Optionally, in an embodiment, after replacing the face image to be processed with the target face image, it also includes:

Noise reduction processing is performed on the replaced image to be processed according to other wide-angle scene images except the image to be processed.

Wherein, the image to be processed may be denoised in a multi-frame denoising manner. For example, a total of 4 wide-angle scene images are obtained, which are respectively wide-angle scene image A, wide-angle scene image B, wide-angle scene image C, and wide-angle scene image D, wherein the wide-angle scene image D is determined to be an image to be processed, then it can be determined according to The wide-angle scene image A, the wide-angle scene image B, and the wide-angle scene image C perform multi-frame noise reduction on the wide-angle scene image D.

Specifically, when performing multi-frame noise reduction, the wide-angle scene image A, wide-angle scene image D, wide-angle scene image B, and wide-angle scene image C may be aligned first, and the pixel values of each group of aligned pixels are acquired. If the pixel values of the same group of aligned pixels are similar, then the mean value of the pixel values of the group of aligned pixels can be calculated, and then the pixel value of the corresponding pixel of the wide-angle scene image D can be replaced by the mean value of the pixel values. If the pixel values of the same group of aligned pixels differ greatly, then the pixel values in the wide-angle scene image D may not be adjusted.

For example, pixel P1 in wide-angle scene image A, pixel P2 in wide-angle scene image D, pixel P3 in wide-angle scene image B, and pixel P4 in wide-angle scene image C are a group of mutually aligned pixels, where the pixel value of P1 is 101, the pixel value of P2 is 102, the pixel value of P3 is 103, and the pixel value of P4 is 104, then the average pixel value of this group of aligned pixels is 102.5, then the terminal can use the wide-angle scene image D in P2 The pixel value of the pixel is adjusted from 102 to 102.5, so as to perform noise reduction processing on the P2 pixel in the wide-angle scene image D. If the pixel value of P1 is 80, the pixel value of P2 is 102, the pixel value of P3 is 83, and the pixel value of P4 is 90, then the terminal does not need to adjust the pixel value of P2 at this time because there is a large difference between their pixel values. That is, the pixel value of P2 remains unchanged at 102.

Optionally, in an embodiment, after obtaining the target face image corresponding to the face image to be processed from other wide-angle scene images except the image to be processed, the method further includes:

When failing to obtain the target facial image from other wide-angle scene images, it is determined whether the target facial image is stored in the locally preset facial image library;

When the target facial image is stored in the facial image library, the target facial image stored in the facial image library is extracted.

Considering the failure to acquire the target facial image from other wide-angle scene images, an alternative solution for acquiring the target facial image is provided here.

Specifically, a facial image library may be pre-created locally on the electronic device, and the facial image library is used to store facial images acquired by the electronic device. For example, after the electronic device captures the external scene each time and obtains the wide-angle scene image corresponding to the external scene, it performs face recognition on the wide-angle scene image, and stores the facial image recognized from the wide-angle scene image in the face in the image library.

Correspondingly, when the target facial image fails to be acquired from other wide-angle scene images, it may be searched in the locally preset facial image library whether there is a target facial image corresponding to the facial image to be processed stored. Wherein, the target face image and the face image to be processed belong to the same person, and the target face image meets a preset condition.

If the target face image corresponding to the face image to be processed is found in the face image library, the target face image corresponding to the face image to be processed is extracted from the face image library. For example, please refer to FIG. 10 and FIG. 6 in combination, for the facial image to be processed (ie, the facial image E) determined in the image to be processed shown in FIG. 6 , extract the corresponding facial image to be processed from the facial image library. The target facial image of the image (the target facial image is identified from a certain image taken in history before, and stored in the facial image library), as shown in Figure 10, the extracted target facial image and the target facial image to be processed The facial images belong to the same person, that is, they belong to the same person E, and the expression type of the target facial image is "laughing", which meets the configured preset condition "the expression type is laughing".

Specifically, the target facial image stored in the facial image library is extracted, including:

When a plurality of target facial images are stored in the facial image database, the storage time of each target facial image is obtained;

Extract the target face image in the face image library whose storage time is closest to the current time.

For example, three target face images corresponding to the image to be processed are found in the face image database, which are target face image 1, target face image 2 and target face image 3 respectively, and target face image 1 is acquired The storage time of the target facial image 2 is t1, the storage time of the target facial image 2 is t2, and the storage time of the target facial image 3 is t3. If the storage time t1 is 1 day away from the current time, the storage time The time between the input time t2 and the current time is 2 days, and the time between the storage time t3 and the current time is 7 days. Obviously, the storage time of the target facial image 1 is the closest to the current time. The target image 1 is extracted for subsequent processing.

Specifically, since the target facial image is extracted from the facial image library at this time, the target facial image may be collected from different shooting scenes, and the lighting of the shooting scenes is different, so in order to improve Image effects after image processing, replacing the face image to be processed with the target face image, including:

Obtain the illumination information of the face image to be processed;

Migrate the obtained lighting information to the target face image;

Replace the pending face image with the migrated target face image.

Wherein, when migrating the illumination information, an appropriate illumination migration algorithm may be selected according to actual needs to migrate the illumination information of the image to be processed to the target facial image. Among them, the optional lighting migration algorithm includes but not limited to: lighting migration algorithm based on quotient map, lighting migration algorithm based on 3D model, lighting migration algorithm based on filter decomposition, lighting migration algorithm based on eigendecomposition, etc.

After the migration of the illumination information is completed, the determined facial image to be processed is replaced with the migrated target facial image, so that all facial images in the replaced image to be processed meet the preset conditions, and the illumination information unanimous.

For example, referring to FIG. 11, after the determined lighting information of a facial image to be processed is migrated to its corresponding target facial image, the target facial image and the facial image to be processed obtain the same lighting effect, and then the The determined face image to be processed is replaced with the migrated target face image, so that all face images in the replaced image to be processed meet the preset conditions and the illumination information is consistent.

It should be noted that the embodiment of the present application does not specifically limit which light migration algorithm is used, and it can be selected by those skilled in the art according to actual needs. It can be the light migration algorithm listed in the embodiment of the present application, or it can be Light migration algorithms not listed in the examples of this application.

Optionally, in an embodiment, after judging whether the target facial image is stored in the locally preset facial image library, it also includes:

When the target facial image corresponding to the facial image to be processed is not stored in the facial image library, the person information corresponding to the facial image to be processed is obtained;

Sending an image acquisition request to the target electronic device corresponding to the acquired person information, wherein the image acquisition request is used to instruct the target electronic device to search for and return the target face image corresponding to the face image to be processed;

The target face image returned by the target electronic device is received.

Considering the failure to obtain the target face image from the local face image library, that is, when the target face image corresponding to the face image to be processed is not stored in the local face image library, another method is provided here A scheme for obtaining target face images.

Specifically, when the search of the face image library is completed, and the target face image corresponding to the face image to be processed is not found in the face image library, that is, no corresponding target face image to be processed is stored in the face image library. When the face image is the target face image, obtain the person information corresponding to the face image to be processed, in other words, determine the face image of "who" the face image to be processed is.

After obtaining the character information corresponding to the face image to be processed, further according to the association between the locally pre-stored character information and the electronic device (the association is used to describe which user an electronic device belongs to), determine the obtained character information The corresponding target electronic device. For example, referring to FIG. 6, for the image to be processed shown in FIG. 6, it is determined that the face image to be processed is the face image of E, and the person information corresponding to the face image of E will be obtained at this time as "person E", And further determine the mobile phone of character E as the target electronic device.

After the target electronic device is determined, an image acquisition request is generated according to a pre-agreed message format, and the generated image acquisition request is sent to the determined target electronic device. Wherein, the image acquisition request is used to instruct the target electronic device to search whether there is a target facial image corresponding to the facial image to be processed locally, and if found, return the found target facial image. Specifically, the target electronic device also pre-creates a local facial image library. When receiving an image acquisition request, it searches in its local facial image library according to the instruction of the image acquisition request. The target face image of the face image, then return the found target face image.

Correspondingly, after the image acquisition request is sent to the target electronic device, the target face image returned by the target electronic device is received.

For example, please refer to FIG. 12. On the one hand, after the electronic device acquires the image to be processed (shown as a group photo image of three persons A, D, E), it determines that the face image of E in the image to be processed is the face image to be processed. The local facial image database is searched, and the target facial image corresponding to the facial image to be processed is not found. At this time, it is determined that E's mobile phone is the target electronic device, and an image acquisition request is sent to the target electronic device; on the other hand, After the target electronic device receives the image acquisition request from the electronic device, it searches in the local face image database, and finds the target face image corresponding to the face image to be processed. The image is returned to the electronic device.

Specifically, in an optional implementation manner, when one wide-angle scene image is acquired, preset processing is performed on the wide-angle scene image according to the facial features bound to the wide-angle scene image, including:

According to the facial features bound to the wide-angle scene image, determine the face images to be processed that do not meet the preset conditions in the wide-angle scene image;

Acquiring a target face image corresponding to the face image to be processed from a locally preset face image library;

The determined facial image to be processed is replaced with the acquired target facial image.

For example, the obtained wide-angle scene image includes the face image of person C, the face image of person D, and the face image of person E, and the expression of person C's face image is determined according to the facial features bound to the wide-angle scene image. The type is "laughing", the expression type of D's facial image is also "laughing", and the expression type of E's facial image is "depressed", if the preset condition is "the expression type is laughing", then the expression type of C's face The face image and the face image D meet the preset condition, and the face image E does not meet the preset condition. Therefore, the face image E is determined as the face image to be processed.

After the facial image to be processed is determined from the wide-angle scene image, whether a target facial image corresponding to the facial image to be processed can be stored in the locally preset facial image library, wherein the target facial image is the same as The face images to be processed belong to the same person, and the target face image meets the preset condition.

After the target face image corresponding to the face image to be processed is acquired, the face image to be processed can be replaced with the target face image. Since the target face image is extracted from the face image library at this time, the target face image may be collected from different shooting scenes, and the lighting of the shooting scenes is different, so the determined face to be processed Replace the internal image with the acquired target face image, first migrate the illumination information of the face image to be processed to the target face image, and then replace the face image to be processed with the migrated target face image, thus making the replacement In the subsequent wide-angle scene image, all face images meet the preset conditions, and the illumination information is consistent.

It can be seen from the above that when the technical solution provided by the embodiment of the present application is applied to long-distance portrait shooting or a group photo of multiple people, the person in the scene to be shot can be extracted from the telephoto scene image with a small viewing range but a large proportion of portraits. Face features, and then the extracted face features and wide-angle scene images with a large viewing area but a small proportion of portraits are used to process the wide-angle scene images, so as to overcome the difficulty of face feature extraction and improve image processing. purpose of accuracy.

The image processing method of the present application will be further introduced below on the basis of the methods described in the above embodiments. Please refer to Figure 13, the image processing method may include:

In step 201, multiple image groups of the same scene are acquired, and each image group includes a telephoto scene image and a wide-angle scene image at the same acquisition time.

Please refer to FIG. 2. In the embodiment of the present application, the electronic device includes a telephoto camera and a wide-angle camera. In one arrangement shown in FIG. 2, the telephoto camera and the wide-angle camera are horizontally arranged on the same plane of the electronic device. And there is a certain distance between the telephoto camera and the wide-angle camera. In addition, the electronic device is configured with a telephoto image buffer queue and a wide-angle image buffer queue, wherein the telephoto image buffer queue is used for buffering images collected by the telephoto camera, and the wide-angle image buffer queue is used for buffering images collected by the wide-angle camera. It should be noted that during specific implementation, the arrangement of the telephoto camera and the wide-angle camera can be set by those skilled in the art according to actual needs.

In the embodiment of the present application, after the electronic device starts the camera application, it will start the telephoto camera and the wide-angle camera synchronously, and control the telephoto camera and the wide-angle camera to synchronously collect the images of the scenes to be shot at the same frame rate, and obtain the images to be shot respectively. A telephoto scene image and a wide-angle scene image of the scene. Wherein, the telephoto camera stores the collected telephoto scene image into the telephoto image buffer queue, and the wide-angle camera stores the collected wide-angle scene image into the wide-angle image buffer queue.

For example, please refer to FIG. 3. In the scene to be shot shown in FIG. 3, there are three different characters including character C, character D, and character E. For the telephoto scene image, the wide-angle scene image of the scene to be shot is collected by the wide-angle camera. Obviously, the portrait in the telephoto scene image is larger and easier to identify than the portrait in the wide-angle scene image.

Specifically, when the electronic device receives the triggered image capture request, it first obtains the frame rate information of the telephoto camera/wide-angle camera, where the frame rate information is used to describe the scene image captured by the telephoto camera/wide-angle camera within a unit time For example, when the obtained frame rate information is 30fps, it means that the telephoto camera collects 30 telephoto scene images of the scene to be shot per second, and the wide-angle camera collects 30 wide-angle scene images of the scene to be shot per second; For another example, when the obtained frame rate information is 15fps, it means that the telephoto camera collects 15 telephoto scene images of the scene to be shot per second, and the wide-angle camera collects 15 wide-angle scene images of the scene to be shot per second.

Among them, the main reason affecting the frame rate information of the telephoto camera/wide-angle camera is the brightness of the ambient light. When the brightness of the ambient light is high (or the electronic device is in a bright light environment), the telephoto camera/wide-angle camera can display in a short period of time. (such as 30ms) to complete the exposure, so that it can capture scene images at a higher frame rate; and when the ambient light brightness is low (or the electronic device is in a dark environment), the telephoto camera/wide-angle camera takes a long time The time (such as 40ms-60ms, or more) to complete the exposure, so that it can only capture scene images at a lower frame rate.

Afterwards, according to the frame rate information of the telephoto camera/wide-angle camera, determine the number of targets corresponding to the frame rate information, wherein the number of targets and the frame rate information can be proportional to each other, for example, in the frame rate of the telephoto camera/wide-angle camera If the rate information is 30fps, the number of targets can be determined to be 8. When the frame rate information of the telephoto camera/wide-angle camera is 15 fps, the number of targets can be determined to be 6.

After determining the target number, extract the telephoto scene images and wide-angle scene images whose number is the target number from the telephoto image cache queue and the wide-angle image cache queue, wherein the telephoto scene images with the same collection time (or synchronous collection) The scene image and the wide-angle scene image constitute an image group, and a target number of image groups is obtained in total.

For example, when the number of targets is determined to be "4", four telephoto scene images with acquisition times t1, t2, t3 and t4 are extracted from the telephoto image buffer queue, and the acquisition time is extracted from the wide-angle image buffer queue. The four wide-angle scene images at time t1, t2, t3 and t4 respectively will obtain 4 image groups corresponding to the acquisition time t1, t2, t3 and t4 respectively.

In step 202, the face features of the telephoto scene images in each image group are acquired, and the acquired face features of each telephoto scene image are bound to the wide-angle scene images in the group.

Among them, the face feature is used to describe the facial image contained in the telephoto scene image, including but not limited to face size, eye opening and closing degree, and expression type (such as anger, disgust, fear, happiness, sadness and surprise, etc.).

It should be noted that the embodiment of the present application does not specifically limit which face recognition technology is used to perform face recognition on the telephoto scene image, and those skilled in the art can select an appropriate face recognition technology according to actual needs.

For example, please refer to Fig. 5, four image groups with acquisition time t1, t2, t3 and t4 are acquired respectively, after that, face feature 1 is recognized from the telephoto scene image at acquisition time t1, and the identified The face feature 1 is bound to the wide-angle scene image at the same collection time t1, and the face feature 2 is recognized from the telephoto scene image at the collection time t2, and the recognized face feature 2 is combined with the collection time Bind the wide-angle scene image at t2, identify the face feature 3 from the telephoto scene image at the collection time t3, and bind the recognized face feature 3 with the wide-angle scene image at the same collection time t3 , recognize the face feature 4 from the telephoto scene image at the collection time t4, and bind the recognized face feature 4 with the wide-angle scene image at the same collection time t4.

In step 203, according to the facial features bound to each wide-angle scene image, the number of matching face images that meet the preset conditions contained in each wide-angle scene image is determined.

In step 204, the wide-angle scene image containing the largest number of matching facial images is taken as the image to be processed.

Among them, the preset condition can be set according to the actual shooting needs. For example, when taking a group photo with multiple people, if you need to shoot everyone’s surprised expression, you can configure the preset condition as “surprised expression type”; When taking a group photo with multiple people, if you need to capture the expressions of everyone opening their eyes and smiling, you can configure the preset facial features as "the expression type is smiling and eyes open".

For example, referring to FIG. 6, four wide-angle scene images are obtained in total, namely: wide-angle scene image A, wide-angle scene image B, wide-angle scene image C, and wide-angle scene image D. The scene includes 3 different characters, Bing Ding Wu, and the preset condition is configured as "the expression is laughing". According to the facial features bound to each of the 4 wide-angle scene images, it is determined that the wide-angle scene image A contains a matching face It is determined that there is no matching facial image in the wide-angle scene image B, and it is determined that the wide-angle scene image C includes two matching facial images (that is, the facial image of C and the facial image of D ), it is determined that a matching facial image (i.e., the facial image of E) is included in the wide-angle scene image D, obviously, the number of matching facial images contained in the wide-angle scene image C is the largest, so the wide-angle scene image C is used as the pending processing image.

In step 205, among the images to be processed, the face images to be processed that do not meet the preset conditions are determined.

Wherein, after the image to be processed is determined from the acquired multiple wide-angle scene images, the face image to be processed is further determined from the images to be processed, wherein the face image to be processed does not meet the preset condition.

For example, referring to FIG. 7, the determined image to be processed includes the face image of person C, the face image of person D, and the face image of person E. As shown in FIG. 7, according to the person bound to the image to be processed Face features, determine that the expression type of C’s face image is “big laugh”, the expression type of D’s face image is also “big smile”, and the expression type of E’s face image is “depressed”, if the preset condition is “ If the facial expression type is "laughing", then the facial images of C and D meet the preset conditions, and the facial image of E does not meet the preset conditions. Therefore, the facial image of E is determined as the facial image to be processed.

In step 206, from other wide-angle scene images except the image to be processed, a target face image corresponding to the face image to be processed is obtained, the target face image meets the preset conditions, and belongs to the face image to be processed. same person.

After determining the facial image to be processed among the images to be processed, further obtain a target facial image corresponding to the facial image to be processed from other wide-angle scene images except the image to be processed, wherein the target facial image It belongs to the same person as the face image to be processed, and the target face image meets the preset condition. For example, referring to Fig. 8 and Fig. 6, among the four wide-angle scene images shown in Fig. 6, the wide-angle scene image C is determined as the image to be processed, and the expression type of the E face image is "depressed", which is the same as the preset The condition "the expression type is laughing" does not match, and it is further determined to be a face image to be processed. Afterwards, in other wide-angle scene images (here, scene image A, scene image B, and scene image D) except the image to be processed (here, scene image C), find whether there is an expression type that is "laughing" E’s face image (that is, the face image of character E), obviously, as shown in Figure 6, there is an E’s face image whose expression type is "big laugh" in the wide-angle scene image D, at this time, the wide-angle scene image The face image of E in D is determined as the target face image, and the face image of E is extracted from the wide-angle scene image D for subsequent processing.

In step 207, the face image to be processed is replaced with the target face image.

After the target face image corresponding to the face image to be processed is acquired, the face image to be processed can be replaced with the target face image. For example, please refer to FIG. 9 and FIG. 6 in combination. After obtaining four wide-angle scene images (i.e. wide-angle scene image A, wide-angle scene image B, wide-angle scene image C and wide-angle scene image D) shown in FIG. Scene image C is determined as the image to be processed, and further the face image of E in the wide-angle scene image C (i.e. the face image of person E) is determined as the face image to be processed; after that, the face of E in the wide-angle scene image D The internal image is determined as the target facial image; afterward, the E facial image in the wide-angle scene image C is replaced with the E facial image in the wide-angle scene image D, thereby obtaining the replaced wide-angle scene image C, as shown in Figure 9 It is shown that in the replaced wide-angle scene image C, the expression types of the facial images of each person are "laughing", which all meet the preset condition "the expression type is laughing".

In the embodiment of the present application, after the image replacement is completed, the replaced image to be processed can be used as the result image of the image capture request.

In an embodiment, an image processing device 400 is also provided, please refer to FIG. 14 , which is a schematic structural diagram of the image processing device 400 provided in an embodiment of the present application. Wherein the image processing device 400 is applied to electronic equipment, the image processing device 400 includes an image acquisition module 401, an image recognition module 402, a feature binding module 403 and an image processing module 404, as follows:

An image acquisition module 401, configured to acquire synchronously acquired telephoto scene images and wide-angle scene images of the same scene;

The image recognition module 402 is configured to perform face recognition on the acquired telephoto scene image to obtain the face features of the telephoto scene image;

The feature binding module 403 is used to bind the face feature of the telephoto scene image with the wide-angle scene image;

The image processing module 404 is configured to perform preset processing on the wide-angle scene image according to the facial features bound to the wide-angle scene image.

In an embodiment, when there are multiple wide-angle scene images acquired by the image acquisition module 401, the image processing module 404 is specifically used to:

According to the facial features bound to each wide-angle scene image, determine the number of matching face images that meet the preset conditions contained in each wide-angle scene image;

The wide-angle scene image containing the largest number of matching face images is used as the image to be processed;

Preset processing is performed on the image to be processed according to the facial features bound to the image to be processed.

In an embodiment, the image processing module 404 is also specifically used for:

According to the facial features bound to the images to be processed, determine the facial images to be processed that do not meet the preset conditions in the images to be processed;

Obtaining a target face image corresponding to the face image to be processed from other wide-angle scene images other than the image to be processed, the target face image meets a preset condition, and belongs to the same person as the face image to be processed;

Replace the pending face image with the target face image.

In one embodiment, the image processing device 400 also includes a noise reduction processing module for:

Noise reduction processing is performed on the replaced image to be processed according to other wide-angle scene images except the image to be processed.

In an embodiment, the image processing module 404 is also used for:

When failing to obtain the target facial image from other wide-angle scene images except the image to be processed, it is determined whether the target facial image is stored in the locally preset facial image library;

When the target facial image is stored in the facial image library, the target facial image stored in the facial image library is extracted.

In one embodiment, the image processing module 404 is specifically used for:

Obtain the illumination information of the face image to be processed;

Migrate the obtained lighting information to the target face image;

Replace the pending face image with the migrated target face image.

In an embodiment, the image processing module 404 is also used for:

When the target face image is not stored in the face image library, obtain the person information corresponding to the face image to be processed;

Send an image acquisition request to the target electronic device corresponding to the person information, the image acquisition request is used to instruct the target electronic device to search for and return the target face image;

The target face image returned by the target electronic device is received.

During specific implementation, each of the above modules can be implemented as an independent entity, or can be combined arbitrarily as the same or several entities. The specific implementation of each of the above units can refer to the previous embodiments, and will not be repeated here.

As can be seen from the above, the image processing device of this embodiment can use the image acquisition module 401 to acquire synchronously acquired telephoto scene images and wide-angle scene images of the same scene; the image recognition module 402 can perform face recognition on the acquired telephoto scene images , obtain the face feature of the telephoto scene image; bind the face feature of the telephoto scene image with the wide-angle scene image by the feature binding module 403; bind the face feature of the wide-angle scene image by the image processing module 404, Preset processing for wide-angle scene images. When the technical solution provided by the embodiment of the present application is applied to long-distance portrait shooting or a group photo of multiple people, the facial features of the people in the scene to be shot can be extracted from the telephoto scene image with a small viewing range but a large proportion of portraits, and then The extracted face features and wide-angle scene images with a large viewing range but a small proportion of portraits are used to process the wide-angle scene images, so as to overcome the difficulty of face feature extraction and achieve the purpose of improving the accuracy of image processing .

The embodiment of the present application also provides an electronic device. Referring to FIG. 15 , an electronic device 500 includes a central processing unit 501 and a memory 502 . Wherein, the CPU 501 is electrically connected to the memory 502 .

The central processing unit 500 is the control center of the electronic device 500, using various interfaces and lines to connect various parts of the entire electronic device, by running or loading computer programs stored in the memory 502, and calling data stored in the memory 502 , executing various functions of the electronic device 500 and processing data, thereby realizing accurate identification of the user's gender.

The memory 502 can be used to store software programs and modules, and the central processing unit 501 executes various functional applications and data processing by running the computer programs and modules stored in the memory 502 . Memory 502 may mainly include a program storage area and a data storage area, wherein the program storage area may store operating systems, computer programs required by at least one function (such as sound playback function, image playback function, etc.); Data created by the use of electronic devices, etc. In addition, the memory 502 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage devices. Correspondingly, the memory 502 may also include a memory controller to provide the central processing unit 501 with access to the memory 502 .

In this embodiment of the present application, the central processing unit 501 in the electronic device 500 executes the image processing method in any of the above-mentioned embodiments by running the computer program stored in the memory 502, for example: acquiring synchronously acquired long-term images of the same scene focus scene image and wide-angle scene image; perform face recognition on the obtained telephoto scene image to obtain the face features of the telephoto scene image; bind the obtained face features of the telephoto scene image with the wide-angle scene image; The face features bound to the corner scene images are preset to the wide-angle scene images.

Please also refer to FIG. 16 , in some embodiments, the electronic device 500 may further include: a display 503 , a radio frequency circuit 504 , an audio circuit 505 , a power supply 506 , an image processing circuit 507 and a graphics processor 508 . Wherein, the display 503 , the radio frequency circuit 504 , the audio circuit 505 and the power supply 506 are respectively electrically connected to the central processing unit 501 .

The display 503 can be used to display information input by or provided to the user and various graphical user interfaces, and these graphical user interfaces can be composed of graphics, text, icons, videos and any combination thereof. The display 503 may include a display panel. In some implementation manners, the display panel may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD) or an organic light-emitting diode (Organic Light-Emitting Diode, OLED).

The radio frequency circuit 504 can be used to send and receive radio frequency signals to establish wireless communication with network equipment or other electronic equipment through wireless communication, and to send and receive signals with network equipment or other electronic equipment.

The audio circuit 505 can be used to provide an audio interface between the user and the electronic device through a speaker or a microphone.

The power supply 506 may be used to power various components of the electronic device 500 . In some embodiments, the power supply 506 can be logically connected to the central processing unit 501 through a power management system, so as to implement functions such as management of charging, discharging, and power consumption through the power management system.

The image processing circuit 507 can be implemented using hardware and/or software components, and can include various processing units defining an ISP (ImageSignal Processing, image signal processing) pipeline. Please refer to FIG. 17 . In one embodiment, the image processing circuit 507 includes an ISP Processor 5071 and control logic 5072. Image data captured by camera 5073 is first processed by ISP processor 5071 which analyzes the image data to capture image statistics that can be used to determine and/or control one or more parameters of camera 5073. The camera 5073 may include a camera having one or more lenses 50731 and an image sensor 50732 . The image sensor 50732 may include a color filter array (such as a Bayer filter), and the image sensor 50732 may acquire light intensity and wavelength information captured by each imaging pixel of the image sensor 50732 and provide a set of raw images that may be processed by the ISP processor 5071. image data. The sensor 5074 (such as a gyroscope) may provide the collected image processing parameters (such as anti-shake parameters) to the ISP processor 5071 based on the interface type of the sensor 5074 . The interface of the sensor 5074 may utilize a SMIA (Standard Mobile Imaging Architecture, Standard Mobile Imaging Architecture) interface, other serial or parallel camera interfaces, or a combination of the above interfaces.

In addition, the image sensor 50732 may also send raw image data to the sensor 5074, the sensor 5074 may provide the raw image data to the ISP processor 5071 based on the sensor 5074 interface type, or the sensor 5074 may store the raw image data in the image memory 5075.

The ISP processor 5071 processes raw image data pixel by pixel in various formats. For example, each image pixel may have a bit depth of 8, 10, 12, or 14 bits, and the ISP processor 5071 may perform one or more image processing operations on the raw image data and collect statistical information about the image data. Among other things, image processing operations can be performed with the same or different bit depth precision.

The ISP processor 5071 can also receive image data from the image memory 5075 . For example, the sensor 5074 interface sends the original image data to the image memory 5075, and the original image data in the image memory 5075 is provided to the ISP processor 5071 for processing. The image memory 5075 may be a part of a memory device, a storage device, or an independent dedicated memory in an electronic device, and may include a DMA (Direct Memory Access, Direct Memory Access) feature.

When receiving raw image data from the image sensor 50732 interface or from the sensor 5074 interface or from the image memory 5075, the ISP processor 5071 may perform one or more image processing operations, such as temporal filtering. Processed image data may be sent to image memory 5075 for additional processing before being displayed. The ISP processor 5071 receives processed data from the image memory 5075, and performs image data processing in the original domain and in RGB and YCbCr color spaces on the processed data. The image data processed by the ISP processor 5071 may be output to the display 503 for viewing by the user and/or further processed by the graphics engine or image processor 507 . In addition, the output of the ISP processor 5071 can also be sent to the image memory 5075 , and the display 503 can read image data from the image memory 5075 . In one embodiment, image memory 5075 may be configured to implement one or more frame buffers. In addition, the output of the ISP processor 5071 may be sent to an encoder/decoder 5076 in order to encode/decode image data. The encoded image data may be saved and decompressed before being displayed on the display 503 device. Encoder/decoder 5076 can be implemented by CPU or GPU or coprocessor.

Statistics determined by the ISP processor 5071 may be sent to the control logic 5072 unit. For example, statistical data may include image sensor 50732 statistical information such as automatic exposure, automatic white balance, automatic focus, flicker detection, black level compensation, lens 50731 shading correction, etc. The control logic 5072 can include a processor and/or a microcontroller that executes one or more routines (e.g., firmware) that can determine control parameters for the camera 5073 and the ISP processor based on statistics received. 5071 control parameters. For example, the control parameters of the camera 5073 may include sensor 5074 control parameters (such as gain, integration time of exposure control, anti-shake parameters, etc.), camera flash control parameters, lens 50731 control parameters (such as focal length for focusing or zooming), or these parameters The combination. ISP control parameters may include gain levels and color correction matrices for automatic white balance and color adjustment (eg, during RGB processing), and lens 50731 shading correction parameters, among others.

The image processor 508 converts and drives the display data that the electronic device needs to display, and provides a row scan signal to the display 503 to control the correct display of the display 503 .

Further, on the basis of the image processing circuit 507 described in the above embodiment, the image processing circuit 507 is further introduced, please refer to FIG. 18 , the difference from the above embodiment is that the camera 5073 includes a telephoto camera 507301 and a wide-angle camera 507302, the telephoto camera 507301 includes a first lens 507311 and a first image sensor 507321, and the wide-angle camera 507302 includes a second lens 507312 and a second image sensor 507322.

Among them, there is no restriction on the performance parameters (such as focal length, aperture size, resolution, etc.) of the telephoto camera 507301 and the wide-angle camera 507302. The telephoto camera 507301 and the wide-angle camera 507302 can be arranged in the same plane of the electronic device, for example, they can be arranged on the back or front of the electronic device at the same time. The installation distance of the dual cameras on the electronic device can be determined according to the size of the electronic device and/or the shooting effect. For example, in order to make the images captured by the telephoto camera 507301 and the wide-angle camera 507302 have a high degree of overlap, the telephoto camera 507301 and the wide-angle camera 507302 can be combined. The wide-angle camera 507302 should be installed as close as possible, for example, within 10mm.

The embodiment of the present application also provides a storage medium, the storage medium stores a computer program, and when the computer program is run on the computer, the computer is made to execute the image processing method in any of the above-mentioned embodiments, for example: to acquire the same scene captured synchronously The telephoto scene image and the wide-angle scene image; perform face recognition on the obtained telephoto scene image to obtain the face features of the telephoto scene image; bind the obtained face features of the telephoto scene image with the wide-angle scene image ; Perform preset processing on the wide-angle scene image according to the facial features bound to the angle scene image.

In the embodiment of the present application, the storage medium may be a magnetic disk, an optical disk, a read only memory (Read Only Memory, ROM), or a random access memory (Random Access Memory, RAM), or the like.

In the foregoing embodiments, the descriptions of each embodiment have their own emphases, and for parts not described in detail in a certain embodiment, reference may be made to relevant descriptions of other embodiments.

It should be noted that, for the image processing method of the embodiment of the present application, ordinary testers in the field can understand that all or part of the flow of the image processing method of the embodiment of the present application can be completed by controlling the relevant hardware through a computer program , the computer program can be stored in a computer-readable storage medium, such as stored in the memory of an electronic device, and executed by at least one central processing unit in the electronic device, and the execution process can include, for example, the implementation of an image processing method example process. Wherein, the storage medium may be a magnetic disk, an optical disk, a read-only memory, a random access memory, and the like.

For the image processing device of the embodiment of the present application, its various functional modules may be integrated into one processing chip, or each module may exist separately physically, or two or more modules may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software function modules. If the integrated modules are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium, such as a read-only memory, magnetic disk or optical disk.

An image processing method, device, storage medium, and electronic equipment provided by the embodiments of the present application have been described in detail above. In this paper, specific examples are used to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only It is used to help understand the method and its core idea of this application; at the same time, for those skilled in the art, according to the idea of this application, there will be changes in the specific implementation and application scope. In summary, the content of this specification does not It should be understood as a limitation on the present application.

Claims (10)

1. a kind of image processing method, which is characterized in that including：

Obtain synchronous acquisition, Same Scene focal length scene image and wide-angle scene image；

Recognition of face is carried out to the focal length scene image, obtains the face characteristic of the focal length scene image；

The face characteristic and the wide-angle scene image are bound；

According to the face characteristic bound in the wide-angle scene image, default processing is carried out to the wide-angle scene image.

2. image processing method as described in claim 1, which is characterized in that the wide-angle scene image got be it is multiple When, according to the face characteristic bound in the wide-angle scene image, carrying out default processing to the wide-angle scene image includes：

According to the face characteristic bound in each wide-angle scene image, determine it is including in each wide-angle scene image, meet default item The number of the matching face image of part；

To include to match the largest number of wide-angle scene images of face image as pending image；

According to the face characteristic bound in the pending image, default processing is carried out to the pending image.

3. image processing method as claimed in claim 2, which is characterized in that according to the face bound in the pending image Feature carries out default processing to the pending image, including：

According to the face characteristic bound in the pending image, determines in the pending image and do not meet waiting for for preset condition Handle face image；

From other wide-angle scene images in addition to the pending image, the mesh of the corresponding pending face image is obtained Face image is marked, the target face image meets the preset condition, and belongs to same person with the pending face image Object；

The pending face image is replaced with into the target face image.

4. image processing method as claimed in claim 3, which is characterized in that replace with the pending face image described After target face image, further include：

Noise reduction process is carried out to the replaced pending image according to other wide-angle scene images.

5. image processing method as described in claim 3 or 4, which is characterized in that from its in addition to the pending image In its wide-angle scene image, after the target face image for obtaining the corresponding pending face image, further include：

When obtaining the target face image failure from other wide-angle scene images, local preset face's figure is judged As whether being stored with the target face image in library；

When being stored with the target face image in the face image library, extract stored in the face image library it is described Target face image.

6. image processing method as claimed in claim 5, which is characterized in that replace with the pending face image described Target face image, including：

Obtain the Lighting information of the pending face image；

The Lighting information got is migrated to the target face image；

The pending face image is replaced with into the target face image after migration.

7. image processing method as claimed in claim 5, which is characterized in that judge in local preset face image library whether It is stored with after the target face image, including：

When not being stored with the target face image in the face image library, it is corresponding to obtain the pending face image People information；

It sends image and obtains request to the corresponding target electronic device of the people information, described image obtains request and is used to indicate The target electronic device searches and returns to the target face image；

Receive the target face image that the target electronic device returns.

8. a kind of image processing apparatus, which is characterized in that including：

Image collection module, for obtaining synchronous acquisition, Same Scene focal length scene image and wide-angle scene image；

Picture recognition module obtains the people of the focal length scene image for carrying out recognition of face to the focal length scene image Face feature；

Feature binding module, for binding the face characteristic and the wide-angle scene image；

Image processing module, for the face characteristic bound in the wide-angle scene image, to the wide-angle scene image Carry out default processing.

9. a kind of storage medium, is stored thereon with computer program, which is characterized in that when the computer program on computers When operation so that the computer executes image processing method as described in any one of claim 1 to 7.

10. a kind of electronic equipment, including central processing unit and memory, the memory are stored with computer program, feature It is, the central processing unit is by calling the computer program, for executing such as claim 1-7 any one of them figures As processing method.