CN107742274A - Image processing method, device, computer-readable storage medium, and electronic device - Google Patents

Abstract

The present application relates to an image processing method, device, computer-readable storage medium and electronic device. The method comprises: obtaining a set of images to be processed; obtaining a target area in each image to be processed in the set of images to be processed, and classifying the obtained target areas; obtaining beauty parameters corresponding to each type of target area, and performing beauty processing on each type of target area according to the beauty parameters. The above-mentioned image processing method, device, computer-readable storage medium and electronic device improve the efficiency of image processing.

Description

Image processing method, device, computer-readable storage medium, and electronic device

technical field

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

Background technique

Whether at work or in life, taking pictures is an essential skill. In order to take a satisfactory photo, it is not only necessary to improve the shooting parameters during the shooting process, but also to improve the photo itself after the shooting is completed. Beauty treatment refers to a method of beautifying photos. After beauty treatment, the characters in the photos will look more in line with human aesthetics.

Contents of the invention

Embodiments of the present application provide an image processing method, device, computer-readable storage medium, and electronic device, which can improve image processing efficiency.

An image processing method, the method comprising:

Obtain a collection of images to be processed;

Obtain a target area in each image to be processed in the set of images to be processed, and classify the acquired target area;

Acquiring beautification parameters corresponding to each type of target area, and performing beautification processing on each type of target area according to the beautification parameters.

An image processing device, the device comprising:

An image acquisition module, configured to acquire a collection of images to be processed;

An area classification module, configured to acquire a target area in each image to be processed in the set of images to be processed, and classify the acquired target area;

The beautification processing module is configured to obtain the beautification parameters corresponding to each type of target area, and perform beautification processing on each type of target area according to the beautification parameters.

A computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

Obtain a collection of images to be processed;

Obtain a target area in each image to be processed in the set of images to be processed, and classify the acquired target area;

Acquiring beautification parameters corresponding to each type of target area, and performing beautification processing on each type of target area according to the beautification parameters.

An electronic device, comprising a memory and a processor, wherein computer-readable instructions are stored in the memory, and when the instructions are executed by the processor, the processor is made to perform the following steps:

Obtain a collection of images to be processed;

Obtain a target area in each image to be processed in the set of images to be processed, and classify the acquired target area;

Acquiring beautification parameters corresponding to each type of target area, and performing beautification processing on each type of target area according to the beautification parameters.

The above image processing method, device, computer-readable storage medium, and electronic device obtain target areas according to the acquired image collection to be processed, then classify the target areas, and perform beauty treatment in batches according to the classified target areas, thereby improving the Image processing efficiency.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings that need to be used in the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only These are some embodiments of the present application. Those skilled in the art can also obtain other drawings based on these drawings without creative work.

Fig. 1 is an application environment diagram of an image processing method in an embodiment;

Fig. 2 is a flowchart of an image processing method in an embodiment;

Fig. 3 is the flowchart of image processing method in another embodiment;

Fig. 4 is the flowchart of image processing method in another embodiment;

Fig. 5 is a schematic diagram of obtaining depth information in an embodiment;

Fig. 6 is the flow chart of image processing method in another embodiment;

Fig. 7 is a schematic structural diagram of an image processing device in an embodiment;

Fig. 8 is a schematic diagram of an image processing circuit in one embodiment.

Detailed ways

In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.

Fig. 1 is an application environment diagram of an image processing method in an embodiment. As shown in FIG. 1 , the application environment includes a user terminal 102 and a server 104 . The user terminal 102 may be used to collect images to be processed, generate a set of images to be processed, and then send the set of images to be processed to the server 104 . After the server 104 receives the set of images to be processed, it acquires the target area in each image to be processed in the set of images to be processed, classifies the target areas, and then acquires the beauty parameters corresponding to each type of target area, and according to the beauty parameters to perform beautification on the target area. Finally, the server 104 returns the image set to be processed after the beautification process to the user terminal 102 . Among them, the user terminal 102 is an electronic device located at the outermost edge of the computer network, mainly used for inputting user information and outputting processing results, such as a personal computer, a mobile terminal, a personal digital assistant, a wearable electronic device, and the like. The server 104 is a device for responding to service requests and providing computing services, such as one or more computers. It can be understood that, in other embodiments provided by this application, the application environment of the image processing method may only include the user terminal 102, that is, the user terminal 102 is used to collect images to be processed, and to Process images for beautification.

Fig. 2 is a flowchart of an image processing method in an embodiment. As shown in FIG. 2 , the image processing method includes steps 202 to 206 . in:

Step 202, acquiring a set of images to be processed.

In one embodiment, the image to be processed refers to an image in the image set to be processed that needs to be beautified. The image to be processed may be collected by the mobile terminal. A camera that can be used for shooting is installed on the mobile terminal, and the user can initiate a photographing instruction through the mobile terminal. After detecting the photographing instruction, the mobile terminal collects and shoots an image through the camera. The mobile terminal will store the collected images to form an image collection. It can be understood that the image to be processed may also be acquired through other means, which is not limited here. For example, the image to be processed may also be downloaded from a webpage, or imported from an external storage device, and so on. Acquiring a set of images to be processed may specifically include: receiving a beautification command input by a user, and obtaining a set of images to be processed according to the beautification command, wherein the beautification command includes an image identifier. The image identification refers to a unique identification for distinguishing different images to be processed, and the image to be processed is obtained according to the image identification. For example, the image identifier may be one or more of image name, image code, image storage address, and the like. Specifically, after the mobile terminal acquires the set of images to be processed, it may perform beautification processing locally on the mobile terminal, or may send the set of images to be processed to the server for beautification processing.

Step 204, acquire the target area in each image to be processed in the image set to be processed, and classify the acquired target area.

Beautification refers to a method of beautifying photos. For example, performing whitening and skin smoothing treatments on the portraits in the image may also refer to performing cosmetic, face-lifting, and body-slimming treatments on the portraits. The beauty treatment generally only needs to process a certain area in the image, for example, the whitening process can only be processed on the skin area, and the face thinning process can only be processed on the human face area. The target area refers to the area in the image to be processed that needs to be beautified. For example, the target area may refer to a face area, a portrait area, a skin area, a lip area, etc., which is not limited here.

Each image to be processed in the image set to be processed is traversed, and a target area in the image to be processed is extracted. There may be one or more target areas in the image to be processed. For example, there may be one human face or multiple human faces in the image to be processed, and the area where the human face is located is used as the target area. It can be understood that the target area may not exist in the image to be processed, and the image to be processed without the target area does not need to be beautified. The target area in the image to be processed can be obtained by region marking, or directly detected in the image to be processed. The area mark refers to a mark used to indicate the range of the target area in the image to be processed. For example, the target area is marked with a red rectangular frame in the image to be processed, and the area in the red rectangular frame is considered as the target area. Separately extract the target area in the image to be processed, and establish a corresponding relationship with the image identification and position coordinates of the image to be processed. If there are multiple target areas in an image to be processed, each target area is individually associated with the image identification Establish a corresponding relationship with the position coordinates. Wherein, the position coordinate refers to the coordinate representing the position of the target area in the image to be processed, for example, it may be the coordinate of the position of the center of the target area in the image to be processed, or the position of the upper left corner position in the image to be processed coordinate. After the target area is processed, the corresponding image to be processed is found through the image identification, and then the specific position of the target area in the image to be processed is found through the position coordinates, and the target area is restored.

In one embodiment, after all the target areas in the image set to be processed are extracted, a target area set can be formed, and then the target areas in the target area set can be clustered to classify the target areas. Specifically, firstly, the clustering features of the target area are extracted, and then clustering is performed according to the clustering features, so as to classify the target area. For example, if the extracted target area is a human face area, it can be grouped according to the faces of different people. Assuming that the extracted face area includes Zhang San's face and Li Si's face, then after classification, Zhang San's face will be classified into the same class, and Li Si's face will be classified into the same class. It can be understood that, in this embodiment, there is no limitation on classification criteria, for example, human faces may also be classified according to gender, or according to age.

Step 206, acquiring the beautification parameters corresponding to each type of target area, and performing beautification processing on each type of target area according to the beautification parameters.

In the embodiment provided in this application, a one-to-one correspondence is established between the type of the target area and the beautification parameters, and after the target area is classified, the corresponding beautification parameters are obtained according to the type of the target area, and then parameter to process the target region. For example, the face area is classified according to gender, the faces corresponding to men are subjected to skin smoothing, and the faces corresponding to women are subjected to skin grinding, whitening and cosmetic treatment. Generally, an image captured by an image capture device is composed of several pixels, and each pixel may be composed of multiple color channels, and each color channel represents a color component. For example, an image can be composed of three channels of RGB (Red Green Blue, red, green, blue), or it can be composed of three channels of HSV (Hue Saturation Value, hue, saturation, lightness), or it can be composed of CMY (CyanMagenta Yellow , magenta, cyan, yellow) composed of three channels. Then, when the image is beautified, each color channel of the image may be beautified separately, and the processing of each color channel may be different. Specifically, the beautification parameters of each color channel corresponding to each type of target area may be obtained, and beautification processing is performed on each color channel of the target area according to the beautification parameters.

It can be understood that, in the process of processing the target area, the target area can be directly processed in the image to be processed, or the target area can be extracted separately, and the extracted target area can be beautified. After completion, the target area is synthesized with the image to be processed to obtain the beautification image in the image.

In the image processing method provided by the above embodiment, the target area is obtained according to the acquired image set to be processed, and then the target area is classified, and then the beautification parameter is obtained according to the classified target area, and the target area is beautified according to the beautification parameter deal with. In this way, the target areas in the image set to be processed can be beautified in batches, which improves the efficiency of image processing.

Fig. 3 is a flowchart of an image processing method in another embodiment. As shown in FIG. 3 , the image processing method includes step 302 to step 308 . in:

Step 302, acquiring a set of images to be processed.

The set of images to be processed is a set of multiple images to be processed, and the set of images to be processed may contain one or more images to be processed. If the beautification process is performed on the server, each mobile terminal can send the image set to be processed to the server, and the server performs beautification processing on the images to be processed in the image set to be processed after receiving the image set to be processed. When the mobile terminal sends the image set to be processed, it also sends the corresponding terminal ID. After the server completes the processing, it searches for the corresponding mobile terminal according to the terminal ID, and sends the processed image set to the mobile terminal. Wherein, the terminal identifier refers to a unique identifier of the user terminal. For example, the terminal identifier may be at least one of an IP (Internet Protocol, protocol for interconnection between networks) address, a MAC (Media Access Control, media access control) address, and the like.

Step 304, acquiring the face area in each image to be processed in the image set to be processed.

In one embodiment, the face area refers to the area where the face of the person in the image to be processed is located. It is not difficult to understand that the image to be processed is composed of several pixels, and the face area is an area composed of pixels corresponding to the face in the image to be processed. Each image to be processed in the image set to be processed is traversed, and then the face area in the image to be processed is detected. Generally speaking, the image to be processed may contain one or more face regions, and each face region is an independent connected region, and these independent face regions are extracted for beauty treatment respectively. There may also be no face area in the image to be processed, and if there is no face area, the image to be processed will not be processed. Specifically, the face area of the image to be processed can be obtained through the face detection algorithm. The face detection algorithm can include detection methods based on geometric features, eigenface detection methods, linear discriminant analysis methods, and detection methods based on hidden Markov models. This is not limited.

Step 306, acquire the character attribute features corresponding to the face area, and classify the face area according to the character attribute features.

In the embodiments provided in this application, the character attribute feature refers to the feature representing the character attribute corresponding to the face area, for example, the character attribute feature may refer to one or more of gender characteristics, age characteristics, ethnic characteristics, and the like. After the face area is obtained, the character attribute features corresponding to the face area can be identified according to the face recognition algorithm, and then the face areas are classified according to the character attribute features. The face recognition algorithm may be a PCA (principal component analysis, based on principal component analysis) face recognition algorithm and a face recognition algorithm based on KL (Karhuner-Loeve, Karonen-Loeve) transformation, which is not limited here. It is understandable that the size of the face area in the image to be processed is different. Generally, the area of the main face that needs to be highlighted is relatively large, and the area of the face of passers-by is relatively small. Therefore, in this embodiment, the area area corresponding to the face area can be obtained. If the area area is smaller than the area threshold, the beautification process will not be performed, and only the face area whose area area is larger than the area threshold value will be beautified. Then, before step 306, the method may further include: acquiring a face area whose area area is greater than the area threshold.

Step 308, acquiring the beautification parameters corresponding to each type of face area, and performing beautification processing on each type of face area according to the beautification parameters.

The beautification parameter refers to a parameter for performing beautification processing on the image, and the beautification processing is performed on the image according to the beautification parameter. After the face area is classified, the beautification parameters corresponding to each type of face area are obtained, and then the beautification process is performed on the face area according to the beautification parameters. For example, the face area can be of three types: juvenile, young, middle-aged and old, and different beauty treatments are performed on different types of face areas. The eye-enlargement treatment is performed on the young face area, the whitening treatment is performed on the young face area, and the skin smoothing treatment is performed on the middle-aged and elderly face area.

In the image processing method provided by the above-mentioned embodiment, the face area is obtained according to the acquired image set to be processed, and then the face area is classified, and the beautification parameters are obtained according to the classified face area, and the face is processed according to the beautification parameter. The area is beautified. In this way, face areas in the image set to be processed can be beautified in batches, which improves the efficiency of image processing.

Fig. 4 is a flowchart of an image processing method in another embodiment. As shown in FIG. 4 , the image processing method includes step 402 to step 406 . in:

Step 402, acquiring a set of images to be processed.

Step 404, acquire the face area and the corresponding portrait area in each image to be processed in the image set to be processed, and classify the portrait area according to the acquired face area.

In one embodiment, the face area refers to the area where the face of the portrait in the image to be processed is located, and the portrait area refers to the area where the entire portrait in the image to be processed is located. It can be understood that when an image is captured by the image capture device, a depth map corresponding to the image can be acquired at the same time, and pixels in the depth map correspond to pixels in the image. The pixels in the depth map represent the depth information of the corresponding pixels in the image, and the depth information is the depth information from the object corresponding to the pixels to the image acquisition device. For example, the depth information can be acquired through dual cameras, and the obtained depth information corresponding to the pixels can be 1 meter, 2 meters or 3 meters. Then obtaining the face area and the corresponding portrait area may specifically include: obtaining the image to be processed and corresponding depth information; detecting the face area in the image to be processed, and obtaining the portrait in the image to be processed according to the face area and depth information area. It is generally believed that the portrait and the face are on the same vertical plane, and the depth information from the portrait to the image acquisition device is within the same range as the depth information from the face to the image acquisition device. Therefore, after obtaining the face area, the depth information corresponding to the face area can be obtained from the depth map, and then the depth information corresponding to the portrait area can be obtained according to the depth information corresponding to the face area, and then according to the depth information corresponding to the portrait area, that is The portrait area in the image to be processed can be obtained.

Fig. 5 is a schematic diagram of obtaining depth information in an embodiment. As shown in FIG. 5 , the distance _Tc between the first camera 502 and the second camera 504 is known, and the first camera 502 and the second camera 504 respectively take images corresponding to the object 506, and the first clip can be obtained according to the images. _The angle A1 and the second included angle A2, the vertical intersection point between the horizontal line where the first camera 502 to the second camera 504 are located and the object 502 is the intersection point 508 . Suppose the distance from the first camera 502 to the intersection point 508 is T _x , then the distance from the intersection point 508 to the second camera 504 is T _c −T _x , and the depth information of the object 506 ie the vertical distance from the object 506 to the intersection point 508 is T _s . According to the triangle formed by the first camera 502, the object 506 and the intersection point 508, the following formula can be obtained:

Similarly, according to the triangle formed by the second camera 504, the object 506 and the intersection point 508, the following formula can be obtained:

From the above formula, the depth information of the object 506 can be obtained as:

In one embodiment, according to the depth information, the image region where the object in the same depth information range as the human face is located in the image to be processed is extracted. Assuming that there are other objects beside the person, the extracted image region may have Other objects outside the portrait area. At this time, the portrait area can be further extracted according to the color information of the image area. Then obtaining the face area and the corresponding portrait area may specifically include: obtaining the image to be processed and corresponding depth information; detecting the face area in the image to be processed, and obtaining the image area in the image to be processed according to the face area and depth information ; Extract the portrait area according to the color information of the image area. The color information refers to related parameters used to represent the color of an image, for example, the color information may include information such as hue, saturation, lightness and the like of the color in the image. Among them, the hue of the color refers to the angular measure of the color, and its value ranges from 0° to 360°, calculated counterclockwise from red, 0° for red, 120° for green, and 240° for blue. Saturation refers to the degree to which the color is close to the spectrum. Generally, the higher the saturation, the brighter the color; the lower the saturation, the darker the color. Lightness indicates how bright a color is. Different objects often have different color characteristics, that is, the color information presented in the image is also different. For example, the color of trees is green, the sky is blue, the earth is yellow, and so on. The portrait area and areas outside the portrait area can be extracted according to the color information in the image area.

Specifically, the color components of the image area are acquired, and an area of the image area whose color components are within a preset range is extracted as a portrait area. The color component refers to the image component generated by converting the image to be processed into an image from the color dimension. For example, the color component can refer to the RGB color component, CMY color component, HSV color component, etc. of the image. It can be understood that the RGB color Components, CMY color components, and HSV color components can be converted to each other. For example, the HSV color components of the image area are acquired, and the area in the image area where the HSV color components are within a preset range is extracted as the portrait area. Among them, the HSV color components refer to the hue (H), saturation (S), and lightness (V) components of the image respectively. A preset range is set for these three components, and these three components in the image area are set in The area within the preset range is extracted as the portrait area. Specifically, the HSV color components of the image area can be obtained, and the area in the image area that satisfies the condition "H value is between 20-25, S value is between 10-50, and V value is between 50-85" can be obtained as the portrait area.

In one embodiment, classifying the portrait area according to the face area may specifically include: acquiring the character attribute features corresponding to the face area, and classifying the portrait area according to the character attribute features. For example, according to the classification of people, the portrait area can be divided into a portrait area corresponding to Zhang San and a portrait area corresponding to Li Si; according to gender classification, the portrait area can be divided into a portrait area corresponding to men and a portrait area corresponding to women.

Step 406, acquiring the beautification parameters corresponding to each type of portrait area, and performing beautification processing on each type of portrait area according to the beautification parameters.

In the image processing method provided by the above embodiment, the portrait area is obtained according to the acquired image set to be processed, and then the portrait area is classified, and the beautification parameters are obtained according to the classified portrait area, and the beautification is performed on the portrait area according to the beautification parameters deal with. In this way, the portrait areas in the image set to be processed can be beautified in batches, which improves the efficiency of image processing.

Fig. 6 is a flowchart of an image processing method in yet another embodiment. As shown in FIG. 6 , the image processing method includes step 602 to step 610 . in:

Step 602, acquiring a set of images to be processed.

Step 604, acquire the target area in each image to be processed in the image set to be processed, and classify the acquired target area.

In one embodiment, after the mobile terminal acquires the set of images to be processed, it can perform beauty treatment locally on the terminal, or send the set of images to be processed to the server for beauty treatment, or identify the target area on the mobile terminal, Then only the set of extracted target regions is sent to the server for beauty treatment. Specifically, the mobile terminal acquires a set of images to be processed, and acquires a target area in each image to be processed in the set of images to be processed to form a set of target areas. Then the mobile terminal sends the target area set to the server, and the server classifies the target areas in the target area set.

Step 606 , acquiring the beautification category parameter corresponding to each type of target area, where the beautification category parameter is a parameter indicating the beautification processing category.

In an embodiment, the beautification parameter may include a beautification category parameter and a beautification degree parameter. Wherein, the beautification category parameter is a parameter indicating the beautification treatment category, and the beautification degree parameter is a parameter indicating the beautification treatment degree. For example, the beauty category parameters can be whitening treatment, skin smoothing treatment, makeup treatment, eye enlargement treatment, etc., and the beauty level parameters can be divided into five levels: 1st, 2nd, 3rd, 4th, and 5th. . From level 1 to level 5 beauty treatment, the degree of beauty treatment increases. The type of the target area corresponds to the beautification category parameter, and the corresponding beautification category parameter is obtained according to the classified target area. For example, if the acquired target area is lips, add a lip gloss effect to the target area; if the acquired target area is eyes, then enlarge the eyes of the target area.

In step 608, the corresponding physical attribute feature of each target area is obtained, and the corresponding beautification degree parameter is obtained according to the physical attribute feature. The beautification degree parameter is a parameter indicating the degree of beautification processing.

Specifically, the physical property feature refers to a feature value representing the physical property of the target area. The physical attribute feature of the target area may refer to the area size of the target area, or may refer to the depth information corresponding to the target area. During the beautification process, if the same degree of beautification is performed on people at different distances, the image may be severely distorted and the beauty of the image will be reduced. For example, performing skin smoothing on an image will make the skin of the person in the image appear smoother, while at the same time, some detail information may be lost. If the area of the face in the image is relatively small, the facial features of the face will not look clear enough. If a large degree of dermabrasion is performed, the facial features of the face will be completely unclear and the image will lose its beauty. Generally speaking, the larger the area of the target area, the smaller the depth information, that is, the closer the physical distance between the object in the target area and the image acquisition device. The area and/or depth information of the target area may be associated with the beautification degree parameter, and then the corresponding beautification degree parameter may be obtained according to the area area and/or depth information. The acquisition of the corresponding beautification degree parameter of the physical attribute feature may specifically include: obtaining the area area corresponding to each target area, and obtaining the corresponding beautification degree parameter according to the area area; and/or obtaining the depth information corresponding to each target area, And obtain the corresponding beautification degree parameter according to the depth information. For example, if the depth information is within 1 meter, the corresponding beauty level parameter is level 5; if the depth information is between 1 meter and 2 meters, the corresponding beauty level parameter is level 4; if the depth information is between 2 meters and 3 meters, the corresponding beauty level parameter is The degree parameter is 3 levels, the depth information is between 3 meters and 4 meters, corresponding to the beauty level parameter is 2 levels, and the depth information is above 4 meters, corresponding to the beauty level parameter is 1 level.

In one embodiment, the target area is composed of several pixels, then the area of the target area can be expressed as the total number of pixels contained in the target area, or can be expressed as the distance between the target area and the corresponding image to be processed area ratio. Obtain the corresponding beautification degree parameter according to the area area, and the beautification degree parameter indicates the beautification degree parameter corresponding to each pixel in the target area, that is to say, the beautification degree of all pixels in the target area is the same. When obtaining the corresponding beautification degree parameter according to the depth information, the beautification degree parameter corresponding to the depth information of each pixel in the target area can be used, and the obtained beautification degree parameter indicates the degree of beautification processing corresponding to a pixel point, that is, Different pixels in the target area correspond to different degrees of beauty treatment. It is also possible to obtain the corresponding beautification degree parameter according to the depth information of a certain pixel in the target area or the average value of the depth information of all pixels, and the obtained beautification degree parameter represents the corresponding beautification process of the target area. degree, that is, all pixels in the target area are treated with the same degree of beautification. For example, the depth information of a certain point in the target area can be randomly selected to obtain the beautification degree parameter, and the depth information of the central pixel point of the target area can also be selected to obtain the corresponding beautification degree parameter.

In step 610, perform beautification processing on each type of target area according to the beautification category parameter and the beautification degree parameter.

The beautification category parameter determines the type of beautification processing performed on the face area, and the beautification degree parameter determines the degree of beautification processing performed on the face area. For example, if the beautification category parameter is dermabrasion treatment, and the beautification degree parameter is 5 levels, it means that 5 levels of dermabrasion treatment will be performed on the face area. After the beautification process is performed on the target area according to the beautification category parameter and the beautification degree parameter, the target area after the beautification process is synthesized with the image to be processed. It is understandable that since the target area has been beautified, and the remaining areas in the image to be processed except the target area have not been beautified, there may be obvious differences between the two parts. When synthesizing, it is necessary to Transition processing is performed on the boundary of the target area to make the synthesized image look more natural.

In the image processing method provided by the above embodiment, the target area is obtained according to the acquired image set to be processed, and then the target area is classified, and then the beautification parameter is obtained according to the classified target area, and the target area is beautified according to the beautification parameter deal with. In this way, the target areas in the image set to be processed can be beautified in batches, which improves the efficiency of image processing.

In the embodiment provided by this application, the image processing method may also include the following steps:

(1) Obtain a collection of images to be processed.

(2) Obtain the face area and the corresponding portrait area in each image to be processed in the image set to be processed.

(3) Obtain the character attribute features corresponding to the face area, and classify the portrait area according to the character attribute features.

(4) Acquiring the beautification category parameters of each color channel corresponding to each type of portrait area, where the beautification category parameter is a parameter representing a beautification processing category.

(5) Acquiring the area area corresponding to each portrait area, and obtaining the corresponding beautification degree parameter according to the area area; and/or obtaining the depth information corresponding to each portrait area, and obtaining the corresponding beautification degree parameter according to the depth information, The degree of beautification parameter is a parameter indicating the degree of beautification processing.

(6) Perform beautification processing on each color channel of the portrait area according to the beautification category parameter and the beautification degree parameter.

In other embodiments provided by the present application, the image processing method may also include the following steps:

(1) Obtain a collection of images to be processed.

(2) Obtain the face area in each image to be processed in the image set to be processed.

(3) Obtain the character attribute features corresponding to the face area, and classify the face area according to the character attribute features.

(4) Acquiring the beautification category parameters of each color channel corresponding to each type of face area, where the beautification category parameter is a parameter representing a beautification processing category.

(5) Obtain the area area corresponding to each face area, and obtain the corresponding beautification degree parameter according to the area area; and/or obtain the depth information corresponding to each face area, and obtain the corresponding beautification degree parameter according to the depth information, The degree of beautification parameter is a parameter indicating the degree of beautification processing.

(6) Perform beautification processing on each color channel of the face area according to the beautification category parameter and the beautification degree parameter.

Fig. 7 is a schematic structural diagram of an image processing device in an embodiment. As shown in FIG. 7 , the image processing device 700 includes an image acquisition module 702 , an area classification module 704 and a beauty treatment module 706 . in:

An image acquisition module 702, configured to acquire a set of images to be processed.

The area classification module 704 is configured to acquire a target area in each image to be processed in the image set to be processed, and classify the acquired target area.

The beautification processing module 706 is configured to acquire the beautification parameters corresponding to each type of target area, and perform beautification processing on each type of target area according to the beautification parameters.

The image processing device provided in the above embodiment acquires the target area according to the acquired image set to be processed, and then classifies the target area, and then acquires the beautification parameters according to the classified target area, and performs beautification on the target area according to the beautification parameters. deal with. In this way, the target areas in the image set to be processed can be beautified in batches, which improves the efficiency of image processing.

In one embodiment, the area classification module 704 is also used to obtain the face area and the corresponding portrait area in each image to be processed in the image set to be processed; Classification.

In one embodiment, the area classification module 704 is further configured to acquire the character attribute features corresponding to the face area, and classify the portrait area according to the character attribute features.

In one embodiment, the area classification module 704 is also used to obtain the face area in each image to be processed in the image set to be processed; obtain the character attribute features corresponding to the face area, and according to the character attribute features Classify the face regions.

In one embodiment, the beautification processing module 706 is further configured to obtain the beautification parameters of each color channel corresponding to each type of target area, and perform beautification on each color channel of the target area according to the beautification parameters deal with.

In one embodiment, the beautification processing module 706 is also used to obtain the beautification category parameters corresponding to each type of target area, the beautification category parameters are parameters representing the beautification processing category; attribute characteristics, and obtain corresponding beautification degree parameters according to the physical attribute characteristics, and the beautification degree parameters are parameters indicating the degree of beautification treatment; The area is beautified.

In one embodiment, the beautification processing module 706 is further configured to acquire the area area corresponding to each target area, and acquire the corresponding beautification degree parameter according to the area area; and/or acquire the depth information corresponding to each target area , and obtain the corresponding beautification degree parameter according to the depth information.

The division of each module in the above image processing device is for illustration only. In other embodiments, the image processing device may be divided into different modules as required to complete all or part of the functions of the above image processing device.

The embodiment of the present application also provides a computer-readable storage medium. One or more non-transitory computer-readable storage media containing a computer program that, when executed by one or more processors, causes the processors to:

Obtain a collection of images to be processed;

Obtain a target area in each image to be processed in the set of images to be processed, and classify the acquired target area;

Acquiring beautification parameters corresponding to each type of target area, and performing beautification processing on each type of target area according to the beautification parameters.

In one embodiment, the acquiring the target area in each image to be processed in the set of images to be processed performed by the processor, and classifying the target area includes:

Obtain the face area and the corresponding portrait area in each image to be processed in the image set to be processed;

The portrait area is classified according to the face area.

In one embodiment, the acquiring the target area in each image to be processed in the set of images to be processed performed by the processor, and classifying the acquired target area includes:

Obtain the face area and the corresponding portrait area in each image to be processed in the image set to be processed;

The portrait area is classified according to the acquired face area.

In one embodiment, the classifying the portrait area according to the acquired face area performed by the processor includes:

Obtain the person attribute features corresponding to the face area, and classify the portrait area according to the person attribute features.

In one embodiment, the acquiring the target area in each image to be processed in the set of images to be processed performed by the processor, and classifying the acquired target area includes:

Acquiring the face area in each image to be processed in the set of images to be processed;

Obtain the human attribute features corresponding to the human face area, and classify the human face area according to the human attribute feature.

In one embodiment, the acquiring the beautification parameters corresponding to each type of target area performed by the processor, and performing beautification processing on each type of target area according to the beautification parameters includes:

Acquiring beautification parameters of each color channel corresponding to each type of target area, and performing beautification processing on each color channel of the target area according to the beautification parameters.

In one embodiment, the acquiring the beautification parameters corresponding to each type of target area performed by the processor, and performing beautification processing on each type of target area according to the beautification parameters includes:

Obtaining a beauty category parameter corresponding to each type of target area, where the beauty category parameter is a parameter representing a beauty treatment category;

Obtaining the physical attribute feature corresponding to each target area, and obtaining the corresponding beautification degree parameter according to the physical attribute feature, the beautification degree parameter is a parameter indicating the degree of beautification treatment;

Perform beautification processing on each type of target area according to the beautification category parameter and the beautification degree parameter.

In one embodiment, the acquiring the physical attribute characteristics corresponding to each target area performed by the processor, and obtaining the corresponding beauty-beauty degree parameters according to the physical attribute characteristics includes at least one of the following steps:

Acquiring the area area corresponding to each target area, and obtaining the corresponding beautification degree parameter according to the area area;

Depth information corresponding to each target area is obtained, and a corresponding beautification degree parameter is obtained according to the depth information.

The embodiment of the present application also provides an electronic device. The above-mentioned electronic device includes an image processing circuit, and the image processing circuit may be implemented by hardware and/or software components, and may include various processing units defining an ISP (Image Signal Processing, image signal processing) pipeline. Fig. 8 is a schematic diagram of an image processing circuit in one embodiment. As shown in FIG. 8 , for ease of description, only various aspects of the image processing technology related to the embodiment of the present application are shown.

As shown in FIG. 8 , the image processing circuit includes an ISP processor 840 and a control logic 850 . Image data captured by imaging device 810 is first processed by ISP processor 840 , which analyzes the image data to capture image statistics that may be used to determine and/or control one or more parameters of imaging device 810 . Imaging device 810 may include a camera having one or more lenses 812 and an image sensor 814 . Image sensor 814 may include a color filter array (such as a Bayer filter), and image sensor 814 may obtain light intensity and wavelength information captured with each imaging pixel of image sensor 814 and provide a set of raw images that may be processed by ISP processor 840. image data. The sensor 820 (such as a gyroscope) may provide the collected image processing parameters (such as anti-shake parameters) to the ISP processor 840 based on the interface type of the sensor 820 . The interface of the sensor 820 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, image sensor 814 may also send raw image data to sensor 820 , which may provide raw image data to ISP processor 840 based on the sensor 820 interface type, or sensor 820 may store raw image data in image memory 830 .

The ISP processor 840 processes raw image data on a pixel-by-pixel basis in various formats. For example, each image pixel may have a bit depth of 8, 10, 12, or 14 bits, and ISP processor 840 may perform one or more image processing operations on raw image data, gather statistical information about the image data. Among other things, image processing operations can be performed with the same or different bit depth precision.

ISP processor 840 may also receive image data from image memory 830 . For example, the sensor 820 interface sends raw image data to the image memory 830, and the raw image data in the image memory 830 is provided to the ISP processor 840 for processing. The image memory 830 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.

Upon receiving raw image data from the image sensor 814 interface or from the sensor 820 interface or from the image memory 830, the ISP processor 840 may perform one or more image processing operations, such as temporal filtering. The processed image data may be sent to image memory 830 for additional processing before being displayed. The ISP processor 840 may also receive processed data from the image memory 830 for image data processing in the raw domain and in the RGB and YCbCr color spaces. The processed image data may be output to the display 880 for viewing by the user and/or further processed by a graphics engine or a GPU (Graphics Processing Unit, graphics processor). In addition, the output of the ISP processor 840 can also be sent to the image memory 830 , and the display 880 can read image data from the image memory 830 . In one embodiment, image memory 830 may be configured to implement one or more frame buffers. Also, the output of the ISP processor 840 may be sent to an encoder/decoder 870 for encoding/decoding image data. The encoded image data may be saved and decompressed prior to display on the display 880 device.

The steps for the ISP processor 840 to process the image data include: performing VFE (Video Front End, video front end) processing and CPP (Camera Post Processing, camera post processing) processing on the image data. VFE processing of image data may include correction of contrast or brightness of image data, modification of digitally recorded light state data, compensation processing of image data (such as white balance, automatic gain control, gamma correction, etc.), filter processing, etc. CPP processing of image data may include scaling the image, providing preview frames and recording frames for each path. Among them, CPP can use different codecs to process preview frames and record frames. The image data processed by the ISP processor 840 can be sent to the beautifying module 860, so that the image can be beautified before being displayed. The beautification module 860 can perform beautification processing on image data, including: whitening, freckle removal, skin smoothing, face thinning, acne removal, eye enlargement, etc. Wherein, the beautifying module 860 may be a CPU (Central Processing Unit, central processing unit), a GPU, or a co-processor in the mobile terminal. The data processed by the beauty module 860 can be sent to the encoder/decoder 870 for encoding/decoding image data. The encoded image data may be saved and decompressed prior to display on the display 880 device. Wherein, the beautifying module 860 may also be located between the encoder/decoder 870 and the display 880, that is, the beautifying module performs beautifying processing on the imaged image. The aforementioned encoder/decoder 870 may be a CPU, a GPU, or a coprocessor in a mobile terminal.

The statistics determined by the ISP processor 840 may be sent to the control logic 850 unit. For example, statistics may include image sensor 814 statistics such as auto exposure, auto white balance, auto focus, flicker detection, black level compensation, lens 812 shading correction, etc. Control logic 850 may include a processor and/or a microcontroller that executes one or more routines (e.g., firmware) that determine control parameters of imaging device 810 and ISP processing based on received statistical data. The control parameters of the device 840. For example, control parameters of imaging device 810 may include sensor 820 control parameters (eg, gain, integration time for exposure control), camera flash control parameters, lens 812 control parameters (eg, focal length for focus or zoom), or combinations of these parameters. ISP control parameters may include gain levels and color correction matrices for automatic white balance and color adjustment (eg, during RGB processing), as well as lens 812 shading correction parameters.

The image processing method provided by the above embodiment can be realized by using the image processing technology in FIG. 8 .

A computer program product containing instructions, when running on a computer, causes the computer to execute the image processing method provided by the above embodiments.

Any reference to memory, storage, database, or other medium as used herein may include non-volatile and/or volatile memory. Suitable nonvolatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Synchronous Synchlink DRAM (SLDRAM), Memory Bus (Rambus) Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM).

The above-mentioned embodiments only express several implementation modes of the present application, and the description thereof is relatively specific and detailed, but should not be construed as limiting the patent scope of the present application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the scope of protection of the patent application should be based on the appended claims.

Claims (10)

1. An image processing method, characterized in that the method comprises: Obtain a collection of images to be processed; Obtain a target area in each image to be processed in the set of images to be processed, and classify the acquired target area; Acquiring beautification parameters corresponding to each type of target area, and performing beautification processing on each type of target area according to the beautification parameters. 2. The image processing method according to claim 1, wherein the acquiring the target area in each image to be processed in the set of images to be processed, and classifying the acquired target area comprises: Obtain the face area and the corresponding portrait area in each image to be processed in the image set to be processed; The portrait area is classified according to the acquired face area. 3. The image processing method according to claim 2, wherein the classification of the portrait area according to the acquired face area comprises: Obtain the person attribute features corresponding to the face area, and classify the portrait area according to the person attribute features. 4. The image processing method according to claim 1, wherein the acquiring the target area in each image to be processed in the set of images to be processed, and classifying the acquired target area comprises: Acquiring the face area in each image to be processed in the set of images to be processed; Obtain the human attribute features corresponding to the human face area, and classify the human face area according to the human attribute feature. 5. The image processing method according to any one of claims 1 to 4, characterized in that, the acquisition of the beautification parameters corresponding to each type of target area, and each type of beautification parameters respectively according to the beautification parameters Targeted areas for beautification include: Acquiring beautification parameters of each color channel corresponding to each type of target area, and performing beautification processing on each color channel of the target area according to the beautification parameters. 6. The image processing method according to any one of claims 1 to 4, characterized in that, the acquisition of the beautification parameters corresponding to each type of target area, and each type of beautification parameters are performed according to the beautification parameters Targeted areas for beautification include: Obtaining a beauty category parameter corresponding to each type of target area, where the beauty category parameter is a parameter representing a beauty treatment category; Obtaining the physical attribute feature corresponding to each target area, and obtaining the corresponding beautification degree parameter according to the physical attribute feature, the beautification degree parameter is a parameter indicating the degree of beautification treatment; Perform beautification processing on each type of target area according to the beautification category parameter and the beautification degree parameter. 7. The image processing method according to claim 6, wherein said obtaining the corresponding physical attribute feature of each target area, and obtaining the corresponding beautification degree parameter according to the physical attribute feature includes the following steps at least one of: Acquiring the area area corresponding to each target area, and obtaining the corresponding beautification degree parameter according to the area area; Depth information corresponding to each target area is obtained, and a corresponding beautification degree parameter is obtained according to the depth information. 8. An image processing device, characterized in that the device comprises: An image acquisition module, configured to acquire a collection of images to be processed; An area classification module, configured to acquire a target area in each image to be processed in the set of images to be processed, and classify the acquired target area; The beautification processing module is configured to obtain the beautification parameters corresponding to each type of target area, and perform beautification processing on each type of target area according to the beautification parameters. 9. A computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the image processing method according to any one of claims 1 to 7 is implemented. 10. An electronic device, comprising a memory and a processor, wherein computer-readable instructions are stored in the memory, and when the instructions are executed by the processor, the processor executes any one of claims 1 to 7. The image processing method described in the item.