CN116017178A - Image processing method and device and electronic equipment - Google Patents

Abstract

The application discloses an image processing method, device and electronic equipment, belonging to the technical field of image processing. The method includes: acquiring a first image, the first image is an image obtained by processing a second image using an image processing algorithm, and the second image is an image collected by a camera; determining a foreground image in the first image The first local high-frequency energy of the edge pixel in the area, and the second local high-frequency energy of the adjacent pixel of the edge pixel; according to the first local high-frequency energy and the second local high-frequency energy , determining a first value of an image blurring parameter of the first image, where the image blurring parameter is used to indicate the intensity of image blurring processing; when the first value is less than or equal to a first threshold, adjust the The image blur processing strength of the above image processing algorithm.

Description

Image processing method, device and electronic equipment

technical field

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

Background technique

The research and development of image processing algorithms is an iterative process of continuous optimization and reconstruction. In this process, it is necessary to frequently evaluate the image processed by the image processing algorithm before and after iteration to ensure that the image will not be degraded due to the iteration of the image processing algorithm. For example, the blurring effect is evaluated on the image processed by the image processing algorithm.

In related technologies, the evaluation method is usually human eye evaluation, which relies on the experience of observers, the evaluation results are not objective enough, and the reliability is low, which leads to low reliability of image processing algorithm adjustment.

Contents of the invention

The purpose of the embodiments of the present application is to provide an image processing method, device and electronic equipment, which can solve the problem in the prior art that the adjustment reliability of the image processing algorithm is low.

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

Acquiring a first image, the first image is an image obtained by using an image processing algorithm to process a second image, and the second image is an image collected by a camera;

determining a first local high-frequency energy of an edge pixel in a foreground image region in the first image, and a second local high-frequency energy of an adjacent pixel of the edge pixel;

Determine a first value of an image blurring parameter of the first image according to the first local high-frequency energy and the second local high-frequency energy, where the image blurring parameter is used to indicate the blurring processing intensity of the image ;

In a case where the first value is less than or equal to a first threshold, the image blur processing intensity of the image processing algorithm is adjusted.

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

The first acquiring module is configured to acquire a first image, the first image is an image obtained by processing a second image using an image processing algorithm, and the second image is an image acquired by a camera;

A first determination module, configured to determine a first local high-frequency energy of an edge pixel in the foreground image region in the first image, and a second local high-frequency energy of an adjacent pixel of the edge pixel;

A second determination module, configured to determine a first value of an image blurring parameter of the first image according to the first local high-frequency energy and the second local high-frequency energy, and the image blurring parameter is used for Indicates the intensity of image blurring;

A first adjustment module, configured to adjust the image blur processing strength of the image processing algorithm when the first value is less than or equal to a first threshold.

In the third aspect, the embodiment of the present application provides an electronic device, the electronic device includes a processor and a memory, the memory stores programs or instructions that can run on the processor, and the programs or instructions are processed by the The steps of the method described in the first aspect are realized when the controller is executed.

In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or an instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented .

In the fifth aspect, the embodiment of the present application provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions, so as to implement the first aspect the method described.

In a sixth aspect, an embodiment of the present application provides a program product, the program product is stored in a storage medium, and the program product is executed by at least one processor to implement the method described in the first aspect.

In the embodiment of the present application, after the first image processed by the image processing algorithm is obtained, the first local high-frequency energy of the edge pixel in the foreground image area in the first image and the first local high-frequency energy of the edge pixel can be determined. The second local high-frequency energy of adjacent pixels is used to determine the first value of the image blurring parameter of the first image, and the image blurring parameter is used to indicate the intensity of image blurring processing. When the first value is less than or equal to the first threshold, that is, the blur effect of the image processing algorithm is poor, the image blur processing intensity of the image processing algorithm may be adjusted to improve the blur effect of the image processing algorithm. Compared with human eye evaluation, by determining the local high-frequency energy of the edge pixel and its adjacent pixels in the foreground image area of the image processed by the image processing algorithm to evaluate the image blur processing strength of the image processing algorithm, it can improve The evaluation reliability of the image processed by the image processing algorithm can further improve the adjustment reliability of the image processing algorithm.

Description of drawings

Fig. 1 is one of the flowcharts of the image processing method provided by the embodiment of the present application;

Fig. 2 is one of the schematic diagrams of the first image provided by the embodiment of the present application;

Fig. 3 is the second schematic diagram of the first image provided by the embodiment of the present application;

Fig. 4 is the second flowchart of the image processing method provided by the embodiment of the present application;

FIG. 5 is a structural diagram of an image processing device provided in an embodiment of the present application;

FIG. 6 is one of the structural diagrams of the electronic device provided by the embodiment of the present application;

FIG. 7 is the second structural diagram of the electronic device provided by the embodiment of the present application.

Detailed ways

The following will clearly describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, but not all of them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments in this application belong to the protection scope of this application.

The terms "first", "second" and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific sequence or sequence. It should be understood that the terms so used are interchangeable under appropriate circumstances such that the embodiments of the application can be practiced in sequences other than those illustrated or described herein, and that references to "first," "second," etc. distinguish Objects are generally of one type, and the number of objects is not limited. For example, there may be one or more first objects. In addition, "and/or" in the specification and claims means at least one of the connected objects, and the character "/" generally means that the related objects are an "or" relationship.

The image processing method provided by the embodiment of the present application will be described in detail below through specific embodiments and application scenarios with reference to the accompanying drawings.

FIG. 1 is one of the flowcharts of the image processing method provided by the embodiment of the present application. The image processing method can be applied to electronic devices. Electronic devices can collect images through cameras, and can process images through image processing algorithms, such as: blurring, toning, and so on. As shown in Figure 1, image processing methods can include:

Step 101, acquire a first image, the first image is an image obtained by processing a second image using an image processing algorithm, and the second image is an image collected by a camera.

The second image can be an image collected by the camera of the electronic device, or an image collected by the camera of other electronic devices. An image collected by a camera of an electronic device or received by a camera of another electronic device. Further, the camera may be a front camera; or, the camera may be a rear camera.

After acquiring the second image, the electronic device may use its own image processing algorithm to process the second image to obtain the first image.

Step 102, determining a first local high-frequency energy of an edge pixel in a foreground image region in the first image, and a second local high-frequency energy of an adjacent pixel of the edge pixel.

In the embodiment of the present application, the blurring effect of the image processing algorithm can be evaluated by determining the local high-frequency energy of the edge pixel point and its adjacent pixel points in the foreground image area of the image processed by the image processing algorithm.

It can be understood that the number of edge pixels included in the foreground image area in the first image is greater than 1. During specific implementation, the blurring effect of the image processing algorithm may be evaluated through local high-frequency energy of at least two edge pixels of the foreground image area in the first image and the local high-frequency energy of its adjacent pixels.

For different edge pixel points, the determination method of its adjacent pixel points may be the same. Adjacent pixels of the edge pixel may include: the nearest pixel above, below, left and/or right of the edge pixel, and further, above may include at least one of the following: upper left, right above and upper right square; bottom may include at least one of the following: bottom left, bottom right, and bottom right, and left and right are similar. As an example, assuming that the size of a pixel is 1×1, record the coordinates of the edge pixel as (x, y), and its adjacent pixels can include adjacent pixels in the X-axis and Y-axis directions, such as: the coordinates are The pixel point of (x-1, y), the pixel point of coordinates (x+1, y), the pixel point of coordinates of (x, y-1), the pixel point of coordinates of (x, y+1).

In step 102, the electronic device may first determine at least two edge pixels and adjacent pixel points of the foreground image area in the first image. Afterwards, for each determined pixel point, its local high-frequency energy can be determined. The embodiment of the present application does not limit the method of determining the local high-frequency energy of the pixel point, and any method that can be used to determine the local high-frequency energy of the pixel point can fall within the protection scope of the embodiment of the present application. For the convenience of distinction, in the embodiment of the present application, the local high-frequency energy of the edge pixel in the foreground image area is called the first local high-frequency energy, and the local high-frequency energy of the adjacent pixels of the edge pixel is called the second local high-frequency energy. Two local high-frequency energy, but it is worth noting that the first local high-frequency energy and the second local high-frequency energy can be determined in the same manner.

Step 103, according to the first local high-frequency energy and the second local high-frequency energy, determine a first value of an image blurring parameter of the first image, where the image blurring parameter is used to indicate image blurring Processing intensity.

In step 103, for each edge pixel, it can be determined whether the transition between the edge pixel and its adjacent pixels is natural according to its own local high-frequency energy and the local high-frequency energy of its adjacent pixels.

In the embodiment of the present application, if an edge pixel point and its adjacent pixel points are too natural, the edge pixel point may be called a blurred natural point or a first pixel point. If an edge pixel point and its adjacent pixel points are too natural, the edge pixel point can be called an unnatural blurred point.

In this way, according to the first local high-frequency energy and the second local high-frequency energy, the number of blurred and unnatural points in the first image can be obtained. Afterwards, the first value of the image blurring parameter of the first image may be determined based on the number of blurred unnatural points in the first image. The first value of the image blurring parameter of the first image may be negatively and positively correlated with the number of blurred unnatural points in the first image, that is, the more the number of blurred unnatural points in the first image, the more blurred the image of the first image is. The smaller the first value of the optimization parameter is, the larger it is vice versa.

The first value of the image blurring parameter of the first image is used to characterize the image blurring processing strength of the first image. Since the first image is obtained by using an image processing algorithm, the value of the image blurring parameter of the first image can be obtained. The first value is used to evaluate the image blur processing strength of the image processing algorithm.

The larger the first value of the image blurring parameter of the first image, the higher the image blurring intensity of the first image, and the higher the image blurring intensity of the image processing algorithm. Conversely, the smaller the first value of the image blurring parameter of the first image, the lower the image blurring intensity of the first image, and the lower the image blurring intensity of the image processing algorithm. The relationship between the image blurring intensity and the image blurring effect may be positively correlated, that is, the higher the image blurring intensity is, the better the image blurring effect is, and vice versa.

Therefore, after the value of the image blurring parameter of the first image, that is, the first value is obtained through step 103, the first value may be compared with the first threshold. The first threshold is the minimum value that indicates that the image blurring effect of the image processing algorithm meets the requirements, and can be set according to the actual demand for the image blurring effect, which is not limited in this embodiment of the present application.

If the first value is less than or equal to the first threshold, it means that the image blurring effect of the image processing algorithm is poor, and step 104 may be performed. If the first value is greater than the first threshold, it indicates that the image blurring effect of the image processing algorithm is better, and the image blurring processing strength of the image processing algorithm may not be adjusted.

Step 104, if the first value is less than or equal to a first threshold, adjust the image blur processing strength of the image processing algorithm.

In the case that the first value of the image blurring parameter of the first image is less than or equal to the first threshold, it indicates that the blurring effect of the image processing algorithm is poor, and the naturalness of blurring needs to be improved, and the image processing algorithm can be optimized , adjust the image processing intensity of the image processing algorithm to improve the blur effect of the image processing algorithm. It can be understood that the blur effect of the image processed by the adjusted image processing algorithm is due to the blur effect of the image obtained by the image processing algorithm before adjustment.

In the image processing method of the embodiment of the present application, after the first image processed by the image processing algorithm is obtained, the first local high-frequency energy of the edge pixel in the foreground image area in the first image can be determined, and the edge pixel The second local high-frequency energy of adjacent pixels of the point is used to determine the first value of the image blurring parameter of the first image, and the image blurring parameter is used to indicate the intensity of image blurring processing. When the first value is less than or equal to the first threshold, that is, the blur effect of the image processing algorithm is poor, the image blur processing intensity of the image processing algorithm may be adjusted to improve the blur effect of the image processing algorithm. In this way, compared with manual evaluation, by determining the local high-frequency energy of the edge pixel and its adjacent pixels in the foreground image area of the image processed by the image processing algorithm, the image blurring processing strength of the image processing algorithm can be evaluated. Improve the evaluation reliability of the image processed by the image processing algorithm, and then improve the adjustment reliability of the image processing algorithm.

In some embodiments, the determining the first local high-frequency energy of an edge pixel in the foreground image region in the first image, and the second local high-frequency energy of an adjacent pixel of the edge pixel, Can include:

For each of the at least two pixels, determine a local area corresponding to the pixel, the local area includes the pixel and at least one adjacent pixel of the pixel;

determining a first local gray value corresponding to the pixel according to the gray value of each pixel in the local area;

performing Gaussian filtering on the first local gray value to obtain a second local gray value corresponding to the pixel;

determining the local high-frequency energy of the pixel according to the first local gray value and the second local gray value;

Wherein, the at least two pixels include edge pixels in the foreground image area in the first image, and adjacent pixels of the edge pixels; In this case, the local high-frequency energy of the pixel is the first local high-frequency energy; when the pixel is an adjacent pixel of the edge pixel, the local high-frequency energy of the pixel is Second local high frequency energy.

In specific implementation, it may first be based on the local area of the pixel. In an optional implementation manner, the center point of the local area of a certain pixel point may be the pixel point, and the size may be predetermined. As an example, assuming that the local area of each pixel includes 3×3 pixels, then the local area of the pixel includes itself, and its upper left, right upper, upper right, right left, right right, lower left The first pixel on the side, right below and bottom left.

Afterwards, the first local gray value corresponding to the pixel can be determined by using the gray value of each pixel in the local region of the pixel.

In some optional implementation manners, the first local gray value s(x, y) of the pixel point (x, y) can be determined by formula (1):

Among them, U(x, y) is the local area of the pixel point (x, y); j is any pixel point in the local area of the pixel point (x, y); f(j) is the gray value of the pixel point j . The grayscale value of pixel j may be the value of any color channel of pixel j, or the average value of all color channels of pixel j.

In some other optional implementation manners, the average value of the grayscale values of each pixel in the local area of the pixel may be determined as the first local grayscale value corresponding to the pixel.

Afterwards, Gaussian filtering may be performed on the first local grayscale value to obtain a second local grayscale value corresponding to the pixel. During specific implementation, the first local gray value may be input into the Gaussian filter, and the first local gray value may be Gaussian filtered using the standard deviation σ of the Gaussian filter to obtain the second local gray value.

Afterwards, the local high-frequency energy of the pixel point may be determined according to the first local gray value and the second local gray value.

In some optional implementation manners, the local high-frequency energy h(x, y) of a pixel point (x, y) can be determined by formula (2):

h(x,y)=(s(x,y)-s _σ (x,y)) ² (2)

Wherein, s _σ (x, y) is the second local gray value of the pixel point (x, y).

In other optional implementation manners, the difference between the first local grayscale value and the second local grayscale value may be used to directly determine the local high-frequency energy of the pixel.

In the above way, the local high-frequency energy of the pixel is obtained through the local gray value of the local area of the pixel and the local gray value after Gaussian filtering. In this way, the local high-frequency energy of the pixel can be obtained by extracting the pixel The high-frequency information of the local area of the pixel is obtained, thereby improving the reliability of determining the local high-frequency energy of the pixel.

In some embodiments, the determining the first value of the image blurring parameter of the first image according to the first local high-frequency energy and the second local high-frequency energy includes:

For each edge pixel in the foreground image area, when the first local high-frequency energy of the edge pixel is greater than the mean value of the second local high-frequency energy of adjacent pixels of the edge pixel , determining the edge pixel as the first pixel;

A first value of an image blurring parameter of the first image is determined according to the number of the first pixels in the at least two edge pixels.

In this embodiment, it is determined whether the edge pixel is the first pixel by comparing the local high-frequency energy of the edge pixel with the mean value of the local high-frequency energy of adjacent pixels.

In some optional implementations, h(x, y) can be compared with 1/2(h(x-1, y)+h(x+1, y)) to determine the pixel point (x, y ) is the first pixel. In other optional implementation manners, h(x, y) can be compared with 1/2(h(x, y-1), h(x, y+1)), and the pixel point (x, y+1) can be determined y) Whether it is the first pixel.

When the local high-frequency energy of an edge pixel is greater than the average value of local high-frequency energy of its adjacent pixels, it indicates that the energy gradient of the edge pixel is relatively large, and the transition between the edge pixel and the background is unnatural, and the It is determined as the unnatural point of blurring.

In the case that the local high-frequency energy of an edge pixel is less than or equal to the mean value of the local high-frequency energy of its adjacent pixels, it means that the energy gradient of the edge pixel is small, and the transition between the edge pixel and the background is natural, which can be Determine it as the first pixel point.

Afterwards, the number of first pixels in the first image may be counted and compared with the first preset value, so as to determine the first value of the image blur parameter of the first image.

In some optional implementation manners, the first preset value may be preset. In other optional implementations, the first preset value can be determined based on the number N _he of edge pixels in the foreground image area of the selected first image, where N _he is an integer greater than 1, such as the first preset value It can be 1/4(N _he ).

When the number N _hn of the first pixels is greater than the first preset value, the first value of the image blurring parameter of the first image may be determined as unqualified, that is, less than or equal to the first threshold.

In a case where N _hn is less than or equal to the first preset value, the first value of the image blur parameter of the first image may be determined as qualified, that is, greater than the first threshold.

In the above manner, by comparing the local high-frequency energy of each edge pixel point with the mean value of the local high-frequency energy of adjacent pixel points, the number of blurred unnatural points in the first image is determined, and then, based on the The first value of the image blurring parameter of the first image is determined by comparing the number of blurred unnatural points with the first threshold, so that the first value of the image blurring parameter of the first image can truly reflect the first The blurring effect of the image improves the evaluation reliability of the blurring effect of the first image, thereby improving the adjustment reliability of the image processing algorithm.

In the embodiment of this application, it is considered that the quality of the image may be affected by other factors of the image besides the blurred naturalness of the image, such as: chromatic aberration, facial shadow, composition inclination, imaging distance and imaging edge offset etc. Therefore, in the embodiment of the present application, an image processing algorithm may be adjusted, or image shooting may be guided, by evaluating other factors of the image.

In some embodiments, after the acquisition of the first image, the method further includes:

determining the mean value of each color gamut parameter of a first area in the first image, the first area being an area including a first element;

Determine the color difference value of the first area according to the mean value of each color gamut parameter of the first area and the reference value of each color gamut parameter of the first area;

Determine a second value of an image beautification parameter of the first image according to the color difference value, where the image beautification parameter is used to indicate the intensity of image beautification processing;

In a case where the second value is less than or equal to a second threshold, the intensity of image beautification processing of the image processing algorithm is adjusted.

In this embodiment, the image beautification strength of the image processing algorithm can be evaluated by calculating the value of the image beautification parameter of the image processed by the image processing algorithm.

The first area may be an area including the first element, and the first element may be an element that the user requires a high degree of color naturalness, such as a human face, a green plant, etc., and may be preset.

Each color gamut parameter of the first area may be obtained by averaging values of each color gamut parameter of each pixel in the first area.

The reference values of the color gamut parameters in the first area can be preset, and can be used as a reference color for determining whether the color of the first area is natural, and can also be called a memory color. Therefore, the average value of each color gamut parameter in the first area may be compared with the reference value of each color gamut parameter in the first area to obtain a color difference value in the first area.

The mean value of each color gamut parameter in the first area and the reference value of each color gamut parameter in the first area belong to the same color gamut, such as RGB color gamut, Lab color gamut, and the like.

The Lab color gamut is used as an example for illustration. In this example, the values of the color gamut parameters of the pixels in the first area can be averaged first to obtain the mean value C(r, g, b) of each color gamut parameter of the RGB color gamut, where C(r) represents the first The color mean value of the red channel of the region, C(g) represents the color mean value of the color channel of the first region, and C(b) represents the color mean value of the blue channel of the first region. Afterwards, convert C(r, g, b) of the RGB color gamut to C(L, a, b) of the Lab color gamut, where C(L) represents the average illuminance of the first area, and C(a) represents the C(b) represents the color mean value of the first region ranging from blue to yellow. Record each color gamut parameter of the first area as V(L, a, b). The color difference value D of the first area can be calculated by formula (3):

The second value of the image beautification parameter of the first image is used to characterize the intensity of the image beautification processing of the first image. Since the first image is processed by an image processing algorithm, the second value of the image beautification parameter of the first image can be used , to evaluate the image beautification processing strength of the image processing algorithm.

The larger the second value of the image beautification parameter of the first image, the higher the image beautification processing intensity of the first image, and the higher the image beautification processing intensity of the image processing algorithm. Conversely, the lower the second value of the image beautification parameter of the first image, the lower the image beautification processing intensity of the first image, and the lower the image beautification processing intensity of the image processing algorithm. The intensity of image beautification processing can be positively correlated with the effect of image toning, that is, the higher the intensity of image beautification processing, the better the image effect, and vice versa.

The second value of the image beautification parameter of the first image is negatively correlated with the color difference value of the first region in the first image, that is, the larger the color difference value of the first region in the first image, the greater the value of the image beautification parameter of the first image. The smaller the binary value is, the larger it is.

After the color difference value of the first area is acquired, the color difference value may be compared with a second preset value. Wherein, the second preset value is a preset maximum value used to characterize the undistorted color of the region, which can be set according to requirements, which is not limited in this embodiment of the present application.

If the color difference value is greater than the second preset value, it indicates that the color of the first region is distorted, and the second value of the image beautification parameter of the first image may be determined as unqualified, that is, less than or equal to the second threshold. The second value is the minimum value representing that the image toning effect of the image processing algorithm meets the requirements, which can be set according to the actual requirement on the toning effect, which is not limited in this embodiment of the present application.

If the color difference value is less than or equal to the second threshold, it means that the color of the first region is not distorted, and the second value of the image beautification parameter of the first image can be determined as qualified, that is, greater than the second threshold.

In the case that the second value of the image beautification parameter of the first image is less than or equal to the second threshold value, it indicates that the color toning effect of the image processing algorithm is poor and needs to be improved, and the image beautification processing intensity of the image processing algorithm can be optimized. , to improve the toning effect of the image processing algorithm. It can be understood that the toning effect of the image processed by the adjusted image processing algorithm is due to the toning effect of the image obtained by using the unadjusted image processing algorithm.

In the manner described above, the toning effect of the image processing algorithm can be evaluated by evaluating the beautification processing intensity of the first region in the image obtained through image processing algorithm processing. When the value of the image beautification parameter of the image is low, the image processing algorithm can be adjusted to optimize the toning effect of the image processing algorithm. In this way, the evaluation reliability of the image processing algorithm can be improved, and further the adjustment reliability of the image processing algorithm can be improved.

In some embodiments, after the acquisition of the first image, the method further includes:

determining a gray mean value of a second area in the first image, the second area being an area including a second element;

For each pixel in the second area, if the grayscale value of the pixel is smaller than the average grayscale value, determine the pixel as a second pixel;

determining a third value of an image shading parameter of the first image according to the number of the second pixel points in the second area, where the image shading parameter is used to indicate the intensity of image shading processing;

In a case where the third value is less than or equal to a third threshold, a first operation is performed, and the first operation includes at least one of the following:

Outputting first prompt information, the first prompt information is used to prompt the user to adjust the shooting position;

Adjust the image shading intensity of the image processing algorithm.

In this embodiment, the second image may be an image collected by a camera of the electronic device itself, and further, the electronic device may be in a shooting state.

The second area may be an area including a second element, and the second element may be an element for which the user requires high naturalness of shadows, such as a human face, and may be preset.

The shadow in the second area may be caused by improper processing of the image processing algorithm, or it may be caused by the inappropriate shooting position of the user. Therefore, the electronic device can calculate the image shadow of the second area of the image obtained by using the image processing algorithm. The value of the parameter is used to perform at least one of the following: evaluate the image shadow processing strength of the image processing algorithm, and determine whether to guide the user to adjust the shooting position, so as to avoid affecting the image quality due to the excessive shadow of the second area.

During specific implementation, the average gray value of the second region in the first image may be determined first. The average gray value of the second area can be obtained by averaging the gray values of the pixels in the second area.

Afterwards, the grayscale value of each pixel in the second area may be compared with the grayscale mean value I _m respectively. A pixel point whose grayscale value is smaller than the average grayscale value is determined as a second pixel point, which may also be called a shadow point. Further, pixel points whose gray values are greater than (1/2)I _m and less than _Im can be determined as shadow points.

The value of the image shadow parameter of the first image is used to characterize the intensity of the image shadow processing of the first image. On the one hand, since the first image is processed by an image processing algorithm, the value of the image shadow parameter of the first image can be used, to evaluate the image shadow processing strength of the image processing algorithm; on the other hand, it can be determined whether to guide the user to adjust the shooting position through the value of the image shadow parameter of the first image. The higher the value of the image shading parameter of the first image, the higher the image shading processing intensity representing the first image, and the higher the image shading processing intensity of the image processing algorithm. Conversely, the lower the value of the image shading parameter of the first image, the lower the image shading processing intensity representing the first image, and the higher the image shading processing intensity of the image processing algorithm. The image shading intensity is positively correlated with the image shading effect, the higher the image shading intensity is, the better the image shading effect is, and vice versa.

The value of the image shadow parameter of the first image is negatively correlated with the shadow points of the second area in the first image, that is, the more shadow points of the second area in the first image, the lower the second value of the image beautification parameter of the first image , and vice versa. During specific implementation, the number of shadow points in the second area may be counted and compared with the third preset value to determine the value of the image shadow parameter of the first image.

The third preset value is a preset maximum value within an allowable range for representing the shadow of the second area, which can be set according to requirements, and is not limited in this embodiment of the present application. In an optional implementation manner, the third preset value may be preset. In other optional implementation manners, the third preset value may be determined based on the number N _h of pixels in the second area, for example, the third preset value may be 1/3(N _h ).

If the number of shadow points is greater than the third preset threshold, it means that the shadow area of the second area is too large, and the value of the image shadow parameter of the first image can be determined to be unqualified, that is, less than or equal to the third threshold. The third threshold may be set according to actual requirements for shadow effects, which is not limited in this embodiment of the present application.

If the number of shadow points is less than or equal to the third threshold, it means that the shadow area of the second area is within the allowable range, and the value of the image shadow parameter of the first image can be determined to be qualified, that is, greater than the third threshold.

In the case where the value of the image shadow parameter of the first image is less than or equal to the third threshold, it indicates that the shadow effect of the image processing algorithm is poor and needs to be improved, and the image processing algorithm can be optimized to improve the shadow effect of the image processing algorithm . Alternatively, it indicates that the user's shooting position is inappropriate and the shooting position needs to be adjusted, and the first prompt message may be output to remind the user to adjust the shooting position, so as to avoid affecting the image quality due to the large shadow area of the second area.

Through the above method, the shadow effect of the image processing algorithm can be evaluated by evaluating the value of the shadow processing parameter in the image processed by the image processing algorithm, and/or, it can be determined whether to guide the user to adjust the shooting position. When the value of the image shadow parameter is low, the image processing algorithm can be adjusted to perform at least one of the following: optimize the shadow effect of the image processing algorithm, thereby improving the quality of the image processed by the image processing algorithm; the output is used to prompt the user Tips for adjusting the shooting position. In this way, it is possible to avoid improving the shadow effect of the image processing algorithm, or to improve the shooting effect.

In some embodiments, after the acquisition of the first image, the method further includes:

Determining the first key point and the second key point of the face area, the first key point and the second key point satisfying: when the face is not tilted, the first key point and the The angle between the connecting line of the second key point and the vertical direction is smaller than the fourth threshold;

determining the target angle between the connecting line between the first key point and the second key point and the vertical direction;

If the included target angle is greater than the fourth threshold, second prompt information is output, where the second prompt information is used to prompt the user to adjust the shooting angle.

In this embodiment, the second image may be an image collected by a camera of the electronic device itself, and further, the electronic device may be in a shooting state.

The user may rotate the electronic device to find a good shooting angle when taking a photo, but if the rotation angle is too large, the visual effect of the image will be poor. Therefore, in this embodiment, it is possible to determine whether to prompt the user to adjust the shooting angle by detecting the inclination of the face area in the image, so as to avoid affecting the image quality due to the excessive inclination of the face area.

During specific implementation, two key points in the face area may be pre-selected to determine the inclination of the face area. It is worth noting that when the face of the two key points is not tilted, the connecting line is parallel to the vertical direction. For example: the first key point can be the midpoint of the nose, and the second key point can be the midpoint of the two eyes.

In order to determine the inclination of the face area in the first image, as shown in FIG. 2 , the first key point 21 and the second key point 22 of the face area in the first image can be found first. Afterwards, the angle θ25 between the connecting line 23 of the first key point 21 and the second key point 22 of the face area in the first image and the vertical direction 24 is determined.

After that, the included angle is compared with the fourth threshold, and then it is determined whether to output the second prompt information for prompting the user to adjust the shooting angle.

The fourth threshold is the preset maximum value of the shooting angle used to characterize the face area within the allowable range, which can be set according to requirements, and is not limited in this embodiment of the present application.

If the included angle is greater than the fourth threshold, it indicates that the inclination of the face area is too large, and the second prompt information may be output.

If the included angle is less than or equal to the fourth threshold, it means that the inclination of the face area is within the allowable range, and the second prompt information may not be output.

Through the above method, it can be determined whether to guide the user to adjust the shooting angle by determining the inclination of the face area in the first image. When the inclination is too large, prompt information for prompting the user to adjust the shooting angle may be output. In this way, excessive inclination of the face area can be avoided, thereby improving image quality.

In some embodiments, after the acquisition of the first image, the method further includes:

Determining the number of pixels included in the face area in the first image;

In the case that the number of pixels included in the face area is greater than the fifth threshold, third prompt information is output, where the third prompt information is used to prompt the user to stay away from the camera.

In this embodiment, the second image may be an image collected by a camera of the electronic device itself, and further, the electronic device may be in a shooting state.

If you get too close to the camera when shooting, the scene will be stretched, resulting in unnatural magnification. In order to prevent the user from being unnaturally stretched because the user is too close to the camera, in this embodiment, it is possible to determine whether to prompt the user to adjust the distance between himself and the camera by detecting the proportion of the face area in the image to avoid the user being too close to the camera. Being close to the camera causes the image quality to be stretched unnaturally.

During specific implementation, the number of pixels included in the face area in the first image can be counted, and then compared with the fifth threshold to determine whether to output the third prompt information for prompting the user to stay away from the camera.

The fifth threshold is a preset minimum value used to indicate that the proportion of the human face in the image is too large, which can be set according to requirements, and is not limited in this embodiment of the present application. In some optional implementation manners, the fifth threshold may be preset. In other optional implementation manners, the fifth threshold may be determined based on the total number of pixels N included in the first image, for example, the fifth threshold may be (1/3)N.

If the number of pixels included in the face area is greater than the fifth threshold, it means that the face area accounts for too much of the image, and there is a high possibility that the image is stretched unnaturally, and the third prompt information can be output.

In the case that the number of pixels included in the face area is less than or equal to the fifth threshold, it means that the proportion of the face area in the image is within the allowable range, and the possibility of the image being stretched unnaturally is low, and the third prompt may not be output information.

Through the above method, it can be determined whether to guide the user to adjust the distance from the camera by determining the proportion of the face area in the image. When the proportion of the face area in the image is too large, a prompt message for prompting the user to stay away from the camera may be output. In this way, the possibility of being unnaturally stretched due to being too close to the camera can be avoided, thereby improving image quality.

In some embodiments, after the acquisition of the first image, the method further includes:

Determining target pixels located in the edge area of the first image among the pixels of the face area in the first image;

If the number of the target pixels is greater than the sixth threshold, output fourth prompt information, where the fourth prompt information is used to prompt the user to adjust the camera position.

In this embodiment, the second image may be an image collected by a camera of the electronic device itself, and further, the electronic device may be in a shooting state.

Due to the limitations of the convex lens imaging and the size of the camera sensor (sensor), the part imaged on the edge of the sensor will be squeezed, affecting the imaging visual effect. In this embodiment, it is possible to determine whether to prompt the user to adjust the camera position by detecting the imaging position of the face, so as to prevent the image quality from being squeezed because the face is too close to the edge.

During specific implementation, the edge area of the first image can be delineated first, taking the 1:1 imaging ratio as an example, as shown in Figure 3, the first image is divided into uniform 7×7, a total of 49 blocks, of which 24 blocks are on the edge are marked as marginal regions. For a 16:9 or 4:3 imaging ratio, it is divided into 8×6, a total of 48 blocks, and 24 blocks at the edge are marked as edge areas.

Afterwards, it is possible to determine the number of target pixels whose pixels in the face area in the first image are located in the edge area of the first image, and compare it with the sixth threshold, and then decide whether to output the first threshold for prompting the user to adjust the camera. Four prompt information.

The sixth threshold is a preset minimum value used to indicate that a face is close to an edge, which can be set according to requirements, and is not limited in this embodiment of the present application. In some optional implementation manners, the sixth threshold may be preset. In other optional implementation manners, the sixth threshold may be determined based on the total number of pixels N _h included in the face area in the first image, for example, the sixth threshold may be (1/2)N _h .

If the number of target pixels is greater than the sixth threshold, it means that the face is close to the edge, and fourth prompt information can be output to prompt the user to adjust the camera position so that the person's imaging position is far away from the edge.

If the number of target pixels is less than or equal to the sixth threshold, it means that the human face is not close to the edge, and the fourth prompt information may not be output.

Through the above method, it can be determined whether to guide the user to adjust the camera position by determining the proportion of the target pixel points located in the edge area of the image in the face area. When the proportion is too large, the output can be used to prompt the user to adjust the camera position so that the image position of the person is far away from the edge, thereby improving the image quality.

It should be noted that the various optional embodiments introduced in the embodiments of the present application can be implemented in combination with each other without conflicting with each other, or can be implemented independently, which is not limited in the embodiments of the present application.

In order to facilitate understanding of the image processing method provided by the above embodiment, the above image processing method will be described below using a specific scenario embodiment.

In this scenario embodiment, in order to provide users with a better selfie experience, the aesthetic quality of selfie photos can be evaluated from multiple angles.

There are many factors that affect the selfie effect. In this scene example, six of the most important factors are selected for detection, as shown in Figure 4, which include chromatic aberration, blurred naturalness, face shadows, composition gradient, Image distance and image edge offset.

In this scenario embodiment, the severity of these six influencing factors is first accurately quantified, and then it is determined whether feedback to the user or the image processing algorithm is required based on the quantified results.

For chromatic aberration and blur naturalness, the main reason comes from the processing of the algorithm. When the degree of influence is high, it should be fed back to the algorithm for parameter adjustment.

For compositional tilt, imaging distance, and imaging edge offset, the main factor is the shooting environment or shooting angle selected by the user. If the impact is serious, it should be timely fed back to the user for adjustment.

As for the impact of facial shadows, there are reasons for the user's choice, and it should also be within the scope of algorithm processing. At this time, the results should be fed back to both the processing algorithm and the user in real time.

In this scene embodiment, the self-portrait aesthetic perception evaluation of the user can be realized by memory color shift calculation, blurred naturalness detection, face shadow detection, composition slope detection, imaging distance detection, and portrait edge offset detection. Can include the following:

1. Color difference detection.

With the development of photography technology, users began to pay attention to the authenticity of the photos taken, instead of just pursuing high-saturation images. For example, in the past, they pursued the whitening effect of human skin and the bright colors of plants. Now users pay more attention to cameras. Whether the shooting effect can restore the real color appearance in nature. To this end, in this scenario embodiment, the memory color system will be used to compare the color of the target area in the selfie photo to measure whether the image processing algorithm is overly beautifying the captured image. The comparison method can be divided into the following steps:

(1) Detect the memory color area: Use the target recognition algorithm to identify the face area and green plant area in the image.

(2) Calculate the color of the memory color area: calculate the average value of the pixels in the face area and the green plant area respectively, and obtain the color mean C _h (r, g, b) and C _g (r, g, b) corresponding to the RGB color gamut .

Then convert C _h (r, g, b) and C _g (r, g, b) of the RGB color gamut to C _h (L, a, b) and C _g (L, a, b) of the Lab color gamut .

(3) Memory color area comparison: use formula (3) to calculate C _h (L, a, b) and C _g (L, a, b) and face memory color V _h (L, a, b), green plants The deviation of the memory color V _h (L, a, b) is obtained to obtain D _h and D _g .

(4) Feedback evaluation: If one or both of D _h and D _g is greater than 10, it will be fed back to the image processing algorithm, and if it is less than 10, no feedback will be given.

2. Detection of blurred naturalness.

In order to provide users with a better selfie effect, the selfie mode of the mobile phone camera usually uses the dual-camera depth map technology to add a blur effect to the background of the characters, so as to achieve the purpose of highlighting the main character. However, excessive blurring can easily lead to an unnatural transition between the foreground and the background, separating the characters from the background and affecting the visual aesthetics. For this reason, this scenario embodiment proposes to evaluate the blur effect of the image processing algorithm by calculating the gradient of high-frequency energy between the foreground edge of the person and the background. Specific steps are as follows:

(1) Detection of edge points: use the target recognition algorithm to identify the face area in the selfie photo, list the pixel points on the edge of the face area as candidate points (subsequent calculation of high-frequency gradient naturalness point by point), and count the candidates at the same time The number of points is denoted as N _he .

(2) Calculate high-frequency energy: calculate the local high-frequency energy of the candidate pixel point (x, y) and the local high-frequency energy of its adjacent pixels in the X and Y axis directions, where the calculation formula of the local high-frequency energy h can be: Formula (2).

The local high-frequency energy h(x, y), h(x-1, y), h(x+1, y) of the pixel point (x, y) and its adjacent pixels in the X and Y axis directions can be calculated respectively ), h(x, y-1), h(x, y+1).

(3) Calculation of energy gradient naturalness: compare the local high-frequency energy of the pixel (x, y) and the adjacent pixels. If h(x, y)>1/2(h(x-1, y)+h(x+1, y)) or h(x, y)>1/2(h(x, y-1) , h(x, y+1)), then the candidate point is marked as a blurred unnatural point, otherwise it is marked as a blurred natural point.

(4) Feedback evaluation: Through (2) and (3), all candidate points can be divided into blurred natural points and blurred unnatural points. Count the number of blurred and unnatural pixels, denoted as N _hn , if N _hn >1/4(N _he ), it is considered that the blurred naturalness of the selfie photo needs to be improved, and the result is fed back to the image processing algorithm for optimization , otherwise no feedback is given.

3. Face shadow detection.

Due to the limitation of the scene when the user takes a selfie, for example, the light source is above the top of the head, the light is blocked and the face area cannot be directly illuminated, resulting in the face area being darker than the surrounding environment, resulting in a shadow area, and the final imaging aesthetic effect is not good. For this reason, this scenario embodiment proposes to distinguish the proportion of the shadow area and the non-shadow area of the face by dividing the brightness threshold, so as to determine whether the shadow area of the photo is too large to affect the aesthetic perception quality. Specific steps are as follows:

(1) Count the size of the face area: Use the face recognition algorithm to identify the face area, calculate the average gray value of this area and record it as I _m , and count the total number of pixels in the face area as N _h .

(2) Statistics on the size of the shadow area: compare the pixel gray value I of the face area with Im _m pixel by pixel, if (1/2) I _m < I < I _m , it is marked as a shadow area, recorded as N _s .

(3) Feedback evaluation: Quantify the proportion of N _s , if N _s >(1/3)N _h , it means that the shadow of the selfie photo is too heavy, which is caused by improper processing of the image algorithm and poor light source position of the user shooting scene , at this time, the evaluation result should be fed back to the user and the image processing algorithm at the same time.

4. Composition tilt detection.

Users tend to rotate their mobile phones to find a good shooting angle when taking selfies, but if the rotation angle is too large, the imaging visual effect will be poor. In addition, if the rotation exceeds 90 degrees, the image processing algorithm will automatically rotate and crop the photo to adjust to the centered angle. In order to assist the user to choose a suitable shooting angle, the embodiment of this scenario proposes the following method to detect the inclination of the composition of the shooting, and the specific steps are as follows:

(1) Construct slope line: use face recognition algorithm to realize face key point detection, as shown in Figure 2. Select the two key points marked in Figure 2 and connect them into a straight line as the slope line, and the vertical line as the reference line.

(2) Calculate the inclination angle: calculate the inclination angle formed by two straight lines and record it as θ.

(3) Feedback evaluation: If θ is greater than 40°, it is considered that the inclination of the composition is too large, the result is fed back to the user and the user is suggested to adjust the shooting angle.

5. Imaging distance detection.

Due to the limitations of the camera sensor and the physical properties of the lens, if the scene is too close to the camera when shooting, the scene will be stretched, resulting in unnatural magnification. In order to prevent the user from getting too close to the lens and being stretched unnaturally when taking a selfie, the embodiment of this scenario proposes a method of estimating the imaging distance by calculating the proportion of the face area.

Specific steps are as follows:

(1) Count the face area: Use the existing face detection algorithm to identify the face area, and count the number of pixels in this area, denoted as N _h , and count the total number of photo pixels as N.

(2) Feedback evaluation: compare N _h and N, if N _h > (1/3) N, it is considered that the imaging distance of the person is too close, and the user should be reminded to keep away from the lens properly.

6. Imaging edge offset detection

Due to the limitation of the convex lens imaging and the size of the camera sensor, the part imaged on the edge of the sensor will be squeezed, which will affect the imaging visual effect. In order to help users take more aesthetically pleasing images, this scenario embodiment proposes the following method to detect the imaging position of a person to assist the user in taking pictures.

Specific steps are as follows:

(1) Imaging area division: Taking the 1:1 imaging ratio as an example, as shown in Figure 3, the imaging screen is divided into uniform 7×7, a total of 49 blocks, of which 24 blocks on the edge are marked as edge areas (for 16:9 , the 4:3 imaging ratio is divided into 8×6, a total of 48 blocks, and the same edge 24 blocks are marked as edge areas).

(2) The proportion of the edge of the face area: use the existing face detection algorithm to identify the face area, and count the number of pixels in this area as N _h , and count the number of pixels in the edge area of the face area as N _e .

(3) Feedback evaluation: Calculate the edge ratio of _Ne , if _Ne >(1/2)N _h , it is considered that the image of the person is too close to the edge, and the user is advised to adjust the camera position.

The embodiment of this scenario can perform aesthetic evaluation on the user's self-portrait photos, and detect whether there is room for improvement in six aspects, including color difference, blurred naturalness, face shadow, and composition slope, and then feedback the detection results to the algorithm or the user in a timely manner Make adjustments for better shooting results.

The image processing method provided in the embodiment of the present application may be executed by an image processing device. In the embodiment of the present application, the image processing device executed by the image processing device is taken as an example to describe the image processing device provided in the embodiment of the present application.

As shown in Figure 5, the image processing device 500 may include:

The first acquisition module 501 is configured to acquire a first image, the first image is an image obtained by processing a second image using an image processing algorithm, and the second image is an image collected by a camera;

The first determination module 502 is configured to determine the first local high-frequency energy of edge pixels in the foreground image area in the first image, and the second local high-frequency energy of adjacent pixels of the edge pixels;

The second determination module 503 is configured to determine a first value of an image blurring parameter of the first image according to the first local high-frequency energy and the second local high-frequency energy, and the image blurring parameter is used Indicates the intensity of image blurring;

The first adjustment module 504 is configured to adjust the image blur processing strength of the image processing algorithm when the first value is less than or equal to a first threshold.

In some embodiments, the first determination module includes:

The first determination unit is configured to determine, for each of the at least two pixel points, a local area corresponding to the pixel point, the local area including the pixel point and at least one adjacent pixel of the pixel point point;

The second determination unit is configured to determine the first local gray value corresponding to the pixel according to the gray value of each pixel in the local area;

a first acquisition unit, configured to perform Gaussian filtering on the first local gray value to obtain a second local gray value corresponding to the pixel;

The third determination unit is used for

determining the local high-frequency energy of the pixel according to the first local gray value and the second local gray value;

Wherein, the at least two pixels include edge pixels in the foreground image area in the first image, and adjacent pixels of the edge pixels; In this case, the local high-frequency energy of the pixel is the first local high-frequency energy; when the pixel is an adjacent pixel of the edge pixel, the local high-frequency energy of the pixel is Second local high frequency energy.

In some embodiments, the second determination module includes:

The fourth determining unit is configured to, for each edge pixel in the foreground image area, the first local high-frequency energy at the edge pixel is greater than the second local high-frequency energy of adjacent pixels of the edge pixel In the case of the mean value of the frequency energy, the edge pixel is determined as the first pixel;

The fifth determination unit is configured to determine a first value of an image blur parameter of the first image according to the number of the first pixels in the at least two edge pixels.

In some embodiments, the device also includes:

A third determining module, configured to determine the mean value of each color gamut parameter of a first area in the first image, where the first area is an area including a first element;

Determine the color difference value of the first area according to the mean value of each color gamut parameter of the first area and the reference value of each color gamut parameter of the first area;

A fourth determining module, configured to determine a second value of an image beautification parameter of the first image according to the color difference value, where the image beautification parameter is used to indicate the intensity of image beautification processing;

The second adjustment module is configured to adjust the image beautification processing strength of the image processing algorithm when the second value is less than or equal to a second threshold.

In some embodiments, the device also includes:

A fifth determination module, configured to determine the gray mean value of a second area in the first image, where the second area is an area including a second element;

A sixth determination module, for each pixel in the second area, if the gray value of the pixel is smaller than the average gray value, determine the pixel as the second pixel ;

A seventh determination module, configured to determine a third value of an image shading parameter of the first image according to the number of the second pixels in the second area, the image shading parameter being used to indicate image shading processing strength;

An executing module, configured to execute a first operation when the third value is less than or equal to a third threshold, and the first operation includes at least one of the following:

Outputting first prompt information, the first prompt information is used to prompt the user to adjust the shooting position;

Adjust the image shading intensity of the image processing algorithm.

In some embodiments, the device also includes:

The eighth determination module is used to determine the first key point and the second key point of the human face area, the first key point and the second key point satisfy: when the human face is not tilted, the The angle between the connecting line between the first key point and the second key point and the vertical direction is smaller than a fourth threshold;

A ninth determination module, configured to determine a target angle between the connecting line between the first key point and the second key point and the vertical direction;

The first output module is configured to output second prompt information when the included target angle is greater than the fourth threshold, and the second prompt information is used to prompt the user to adjust the shooting angle.

In some embodiments, the device also includes:

A tenth determination module, configured to determine the number of pixels included in the face area in the first image;

The second output module is configured to output third prompt information when the number of pixels included in the face area is greater than a fifth threshold, and the third prompt information is used to prompt the user to stay away from the camera.

In some embodiments, the device also includes:

An eleventh determination module, configured to determine a target pixel located in an edge area of the first image among the pixels of the face area in the first image;

The third output module is configured to output fourth prompt information when the number of target pixels is greater than a sixth threshold, and the fourth prompt information is used to prompt the user to adjust the camera position.

The image processing apparatus 500 provided in the embodiment of the present application can implement various processes implemented in the method embodiment, and to avoid repetition, details are not repeated here.

The image processing apparatus in the embodiment of the present application may be an electronic device, or may be a component in the electronic device, such as an integrated circuit or a chip. The electronic device may be a terminal, or other devices other than the terminal. Exemplarily, the electronic device may be a mobile phone, a tablet computer, a notebook computer, a handheld computer, a vehicle electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR)/virtual reality (virtual reality, VR) Devices, robots, wearable devices, ultra-mobile personalcomputers (ultra-mobile personalcomputer, UMPC), netbooks or personal digital assistants (personal digital assistant, PDA), etc., can also serve as servers, network attached storage (Network Attached Storage, NAS) , a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., which are not specifically limited in this embodiment of the present application.

The image processing device in the embodiment of the present application may be a device with an operating system. The operating system may be an Android (Android) operating system, an ios operating system, or other possible operating systems, which are not specifically limited in this embodiment of the present application.

Optionally, as shown in FIG. 6 , the embodiment of the present application also provides an electronic device 600, including a processor 601 and a memory 602, and the memory 602 stores programs or instructions that can run on the processor 601. The When the programs or instructions are executed by the processor 601, the steps of the above-mentioned image processing method embodiments can be realized, and the same technical effect can be achieved, so in order to avoid repetition, details are not repeated here.

It should be noted that the electronic devices in the embodiments of the present application include the above-mentioned mobile electronic devices and non-mobile electronic devices.

FIG. 7 is a hardware structural diagram of an electronic device implementing an embodiment of the present application.

The electronic device 700 includes, but is not limited to: a radio frequency unit 701, a network module 702, an audio output unit 703, an input unit 704, a sensor 705, a display unit 706, a user input unit 707, an interface unit 708, a memory 709, and a processor 710, etc. part.

Those skilled in the art can understand that the electronic device 700 can also include a power supply (such as a battery) for supplying power to various components, and the power supply can be logically connected to the processor 710 through the power management system, so that the management of charging, discharging, and function can be realized through the power management system. Consumption management and other functions. The structure of the electronic device shown in FIG. 7 does not constitute a limitation to the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine some components, or arrange different components, and details will not be repeated here. .

Wherein, the processor 710 is used for:

Acquiring a first image, the first image is an image obtained by using an image processing algorithm to process a second image, and the second image is an image collected by a camera;

determining a first local high-frequency energy of an edge pixel in a foreground image region in the first image, and a second local high-frequency energy of an adjacent pixel of the edge pixel;

determining a first value of an image blurring parameter of the first image according to the first local high-frequency energy and the second local high-frequency energy, where the image blurring parameter is used to indicate an image blurring processing intensity;

In a case where the first value is less than or equal to a first threshold, the image blur processing intensity of the image processing algorithm is adjusted.

In some embodiments, the processor 710 is configured to:

For each of the at least two pixels, determine a local area corresponding to the pixel, the local area includes the pixel and at least one adjacent pixel of the pixel;

determining a first local gray value corresponding to the pixel according to the gray value of each pixel in the local area;

performing Gaussian filtering on the first local gray value to obtain a second local gray value corresponding to the pixel;

determining the local high-frequency energy of the pixel according to the first local gray value and the second local gray value;

Wherein, the at least two pixels include edge pixels in the foreground image area in the first image, and adjacent pixels of the edge pixels; In this case, the local high-frequency energy of the pixel is the first local high-frequency energy; when the pixel is an adjacent pixel of the edge pixel, the local high-frequency energy of the pixel is Second local high frequency energy.

In some embodiments, the processor 710 is configured to:

For each edge pixel in the foreground image area, when the first local high-frequency energy of the edge pixel is greater than the mean value of the second local high-frequency energy of adjacent pixels of the edge pixel , determining the edge pixel as the first pixel;

A first value of an image blurring parameter of the first image is determined according to the number of the first pixels in the at least two edge pixels.

In some embodiments, the processor 710 is configured to:

determining an average value of each color gamut parameter of a first area in the first image, the first area being an area including a first element;

Determine the color difference value of the first area according to the mean value of each color gamut parameter of the first area and the reference value of each color gamut parameter of the first area;

Determine a second value of an image beautification parameter of the first image according to the color difference value, where the image beautification parameter is used to indicate the intensity of image beautification processing;

In a case where the second value is less than or equal to a second threshold, the intensity of image beautification processing of the image processing algorithm is adjusted.

In some embodiments, the processor 710 is configured to:

determining a gray mean value of a second area in the first image, the second area being an area including a second element;

For each pixel in the second area, if the grayscale value of the pixel is smaller than the average grayscale value, determine the pixel as a second pixel;

determining a third value of an image shading parameter of the first image according to the number of the second pixel points in the second area, where the image shading parameter is used to indicate the intensity of image shading processing;

In a case where the third value is less than or equal to a third threshold, a first operation is performed, and the first operation includes at least one of the following:

Outputting first prompt information, the first prompt information is used to prompt the user to adjust the shooting position;

Adjust the image shading intensity of the image processing algorithm.

In some embodiments, the processor 710 is configured to:

Determining the first key point and the second key point of the face area, the first key point and the second key point satisfying: when the face is not tilted, the first key point and the The angle between the connecting line of the second key point and the vertical direction is smaller than the fourth threshold;

determining the target angle between the connecting line between the first key point and the second key point and the vertical direction;

If the included target angle is greater than the fourth threshold, second prompt information is output, where the second prompt information is used to prompt the user to adjust the shooting angle.

In some embodiments, the processor 710 is configured to:

Determining the number of pixels included in the face area in the first image;

In the case that the number of pixels included in the face area is greater than the fifth threshold, third prompt information is output, where the third prompt information is used to prompt the user to stay away from the camera.

In some embodiments, the processor 710 is configured to:

Determining target pixels located in the edge area of the first image among the pixels of the face area in the first image;

If the number of the target pixels is greater than the sixth threshold, output fourth prompt information, where the fourth prompt information is used to prompt the user to adjust the camera position.

The electronic device provided in the embodiments of the present application can implement various processes implemented in the method embodiments, and details are not described here to avoid repetition.

It should be understood that, in the embodiment of the present application, the input unit 704 may include a graphics processor (Graphics Processing Unit, GPU) 7041 and a microphone 7042. Camera) to process the image data of still pictures or videos. The display unit 706 may include a display panel 7061, and the display panel 7061 may be configured in the form of a liquid crystal display, an organic light emitting diode, or the like. The user input unit 707 includes at least one of a touch panel 7071 and other input devices 7072 . The touch panel 7071 is also called a touch screen. The touch panel 7071 may include two parts, a touch detection device and a touch controller. Other input devices 7072 may include, but are not limited to, physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, and joysticks, which will not be repeated here.

The memory 709 can be used to store software programs as well as various data. The memory 709 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required by at least one function (such as a sound playing function, image playback function, etc.), etc. Furthermore, memory 709 may include volatile memory or nonvolatile memory, or, memory 709 may include both volatile and nonvolatile memory. Among them, the non-volatile memory can be read-only memory (Read-Only Memory, ROM), programmable read-only memory (Programmable ROM, PROM), erasable programmable read-only memory (Erasable PROM, EPROM), electrically programmable Erase Programmable Read-Only Memory (Electrically EPROM, EEPROM) or Flash. Volatile memory can be random access memory (Random Access Memory, RAM), static random access memory (Static RAM, SRAM), dynamic random access memory (Dynamic RAM, DRAM), synchronous dynamic random access memory (Synchronous DRAM, SDRAM), double data rate synchronous dynamic random access memory (Double Data Rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (Enhanced SDRAM, ESDRAM), synchronous connection dynamic random access memory (Synch link DRAM , SLDRAM) and Direct Memory Bus Random Access Memory (Direct Rambus RAM, DRRAM). The memory 709 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

The processor 710 may include one or more processing units; optionally, the processor 710 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to the operating system, user interface, and application programs, etc., Modem processors mainly process wireless communication signals, such as baseband processors. It can be understood that the foregoing modem processor may not be integrated into the processor 710 .

The embodiment of the present application also provides a readable storage medium, the readable storage medium stores a program or an instruction, and when the program or instruction is executed by a processor, each process of the above-mentioned image processing method embodiment is realized, and can achieve the same To avoid repetition, the technical effects will not be repeated here.

Wherein, the processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes a computer-readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk, and the like.

The embodiment of the present application further provides a chip, the chip includes a processor and a communication interface, the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the above image processing method embodiment Each process can achieve the same technical effect, so in order to avoid repetition, it will not be repeated here.

It should be understood that the chips mentioned in the embodiments of the present application may also be called system-on-chip, system-on-chip, system-on-a-chip, or system-on-a-chip.

The embodiment of the present application provides a computer program product, the program product is stored in a storage medium, and the program product is executed by at least one processor to implement the various processes in the above image processing method embodiment, and can achieve the same technical effect , to avoid repetition, it will not be repeated here.

It should be noted that, in this document, the term "comprising", "comprising" or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements, It also includes other elements not expressly listed, or elements inherent in the process, method, article, or device. Without further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus comprising that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved. Functions are performed, for example, the described methods may be performed in an order different from that described, and various steps may also be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

Through the description of the above embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus a necessary general-purpose hardware platform, and of course also by hardware, but in many cases the former is better implementation. Based on such an understanding, the technical solution of the present application can be embodied in the form of computer software products, which are stored in a storage medium (such as ROM/RAM, magnetic disk, etc.) , optical disc), including several instructions to enable a terminal (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present application.

The embodiments of the present application have been described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementations. The above-mentioned specific implementations are only illustrative and not restrictive. Those of ordinary skill in the art will Under the inspiration of this application, without departing from the purpose of this application and the scope of protection of the claims, many forms can also be made, all of which belong to the protection of this application.

Claims (10)

1. An image processing method, characterized in that, comprising: Acquiring a first image, the first image is an image obtained by using an image processing algorithm to process a second image, and the second image is an image collected by a camera; determining a first local high-frequency energy of an edge pixel in a foreground image region in the first image, and a second local high-frequency energy of an adjacent pixel of the edge pixel; determining a first value of an image blurring parameter of the first image according to the first local high-frequency energy and the second local high-frequency energy, where the image blurring parameter is used to indicate an image blurring processing intensity; In a case where the first value is less than or equal to a first threshold, the image blur processing intensity of the image processing algorithm is adjusted. 2. The method according to claim 1, wherein said determining the first local high-frequency energy of the edge pixel in the foreground image area in the first image, and the adjacent pixels of the edge pixel The second local high-frequency energy of the point, including: For each of the at least two pixels, determine a local area corresponding to the pixel, the local area includes the pixel and at least one adjacent pixel of the pixel; determining a first local gray value corresponding to the pixel according to the gray value of each pixel in the local area; performing Gaussian filtering on the first local gray value to obtain a second local gray value corresponding to the pixel; determining the local high-frequency energy of the pixel according to the first local gray value and the second local gray value; Wherein, the at least two pixels include edge pixels in the foreground image area in the first image, and adjacent pixels of the edge pixels; In this case, the local high-frequency energy of the pixel is the first local high-frequency energy; when the pixel is an adjacent pixel of the edge pixel, the local high-frequency energy of the pixel is Second local high frequency energy. 3. The method according to claim 1, characterized in that, according to the first local high-frequency energy and the second local high-frequency energy, the first step of determining the image blur parameter of the first image is values, including: For each edge pixel in the foreground image area, when the first local high-frequency energy of the edge pixel is greater than the mean value of the second local high-frequency energy of adjacent pixels of the edge pixel , determining the edge pixel as the first pixel; A first value of an image blurring parameter of the first image is determined according to the number of the first pixels in the at least two edge pixels. 4. The method according to claim 1, characterized in that, after the acquisition of the first image, the method further comprises: determining an average value of each color gamut parameter of a first area in the first image, the first area being an area including a first element; Determine the color difference value of the first area according to the mean value of each color gamut parameter of the first area and the reference value of each color gamut parameter of the first area; Determine a second value of an image beautification parameter of the first image according to the color difference value, where the image beautification parameter is used to indicate the intensity of image beautification processing; In a case where the second value is less than or equal to a second threshold, the intensity of image beautification processing of the image processing algorithm is adjusted. 5. The method according to claim 1, characterized in that, after the acquisition of the first image, the method further comprises: determining a gray mean value of a second area in the first image, the second area being an area including a second element; For each pixel in the second area, if the grayscale value of the pixel is smaller than the average grayscale value, determine the pixel as a second pixel; determining a third value of an image shading parameter of the first image according to the number of the second pixel points in the second area, where the image shading parameter is used to indicate the intensity of image shading processing; In a case where the third value is less than or equal to a third threshold, a first operation is performed, and the first operation includes at least one of the following: Outputting first prompt information, the first prompt information is used to prompt the user to adjust the shooting position; Adjust the image shading intensity of the image processing algorithm. 6. The method according to claim 1, wherein the first image includes a face image area, and after the acquisition of the first image, the method further includes: Determining the first key point and the second key point of the face area, the first key point and the second key point satisfying: when the face is not tilted, the first key point and the The angle between the connecting line of the second key point and the vertical direction is smaller than the fourth threshold; determining the target angle between the connecting line between the first key point and the second key point and the vertical direction; If the included target angle is greater than the fourth threshold, second prompt information is output, where the second prompt information is used to prompt the user to adjust the shooting angle. 7. The method according to claim 1, wherein the first image includes a face image area, and after the acquisition of the first image, the method further includes: Determining the number of pixels included in the face area in the first image; In the case that the number of pixels included in the face area is greater than the fifth threshold, third prompt information is output, where the third prompt information is used to prompt the user to stay away from the camera. 8. The method according to claim 1, wherein the first image comprises a face image area, and after the acquisition of the first image, the method further comprises: Determining target pixels located in the edge area of the first image among the pixels of the face area in the first image; If the number of the target pixels is greater than the sixth threshold, output fourth prompt information, where the fourth prompt information is used to prompt the user to adjust the camera position. 9. An image processing device, comprising: The first acquiring module is configured to acquire a first image, the first image is an image obtained by processing a second image using an image processing algorithm, and the second image is an image acquired by a camera; A first determination module, configured to determine a first local high-frequency energy of an edge pixel in the foreground image region in the first image, and a second local high-frequency energy of an adjacent pixel of the edge pixel; A second determination module, configured to determine a first value of an image blurring parameter of the first image according to the first local high-frequency energy and the second local high-frequency energy, and the image blurring parameter is used for Indicates the intensity of image blurring; A first adjustment module, configured to adjust the image blur processing strength of the image processing algorithm when the first value is less than or equal to a first threshold. 10. An electronic device, characterized in that it includes a processor and a memory, the memory stores programs or instructions that can run on the processor, and when the programs or instructions are executed by the processor, the implementation of the claims Steps of the image processing method described in any one of 1 to 8.

Images