CN114723602A - Image processing method, image processing device, terminal and readable storage medium - Google Patents

Abstract

The present application discloses an image processing method, an image processing device, a terminal and a non-volatile computer-readable storage medium. The image processing method includes: converting the image to be processed into the LAB color space to obtain a first image; performing clustering processing on the first image to obtain multiple clustering results; and corresponding to the multiple clustering results and each clustering result The sum of the chromatic aberrations gets the theme color. The image processing method, image processing device, terminal, and non-volatile computer-readable storage medium of the embodiments of the present application perform clustering processing by converting the image to be processed into the LAB color space, and obtain the theme according to the color difference sum corresponding to the clustering result color to generate a theme color that matches the perception of the human eye.

Description

Image processing method, image processing device, terminal and readable storage medium

technical field

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

Background technique

Image theme color is a variety of colors extracted from an image that best represent the main color of the image. That is to say, in a colorful picture, the number of different colors corresponds to the proportion of the color in the picture, and the theme color is calculated by calculating the number of pixels of different colors in the picture. The main color of the color watermark (color palette) is often not simply the RGB value that appears the most, it should conform to the habits of the human eye and be the visual focus.

SUMMARY OF THE INVENTION

Embodiments of the present application provide an image processing method, an image processing apparatus, a terminal, and a non-volatile computer-readable storage medium, which are used to generate a theme color that conforms to human eye perception.

The image processing method of the embodiment of the present application includes: converting the image to be processed into the LAB color space to obtain a first image; performing clustering processing on the first image to obtain multiple clustering results; The class result and the sum of the color differences corresponding to each of the clustering results obtain the theme color.

The image processing apparatus according to the embodiment of the present application includes: a first acquisition module, configured to convert the to-be-processed image to the LAB color space to acquire the first image; and a clustering module, configured to perform clustering processing on the first image to acquire a plurality of clustering results; and a color obtaining module, configured to obtain the theme color according to the plurality of the clustering results and the sum of the color differences corresponding to each of the clustering results.

A terminal according to an embodiment of the present application includes: one or more processors, a memory; and one or more programs, wherein the one or more programs are stored in the memory and are processed by the one or more processors Executed, the program includes instructions for the image processing method of the embodiment of the present application. The image processing method of the embodiment of the present application includes: converting the image to be processed into the LAB color space to obtain a first image; performing clustering processing on the first image to obtain multiple clustering results; The class result and the sum of the color differences corresponding to each of the clustering results obtain the theme color. .

The non-volatile computer-readable storage medium containing the computer program of the embodiment of the present application, when the computer program is executed by one or more processors, causes the processor to implement the instructions of the image processing method of the embodiment of the present application. The image processing method includes: converting an image to be processed into a LAB color space to obtain a first image; performing clustering processing on the first image to obtain a plurality of clustering results; and according to a plurality of the clustering results and each The color difference sum corresponding to the clustering result obtains the theme color.

The image processing method, image processing device, terminal, and non-volatile computer-readable storage medium of the embodiments of the present application perform clustering processing by converting the image to be processed into the LAB color space, and obtain the theme according to the color difference sum corresponding to the clustering result color. The LAB color space is the most suitable color space for the visual perception of the human eye. Clustering in the LAB color space can make the color corresponding to the clustering result conform to the perception of the human eye, so as to generate the theme color that conforms to the perception of the human eye.

Additional aspects and advantages of embodiments of the present application will be set forth, in part, in the following description, and in part will be apparent from the following description, or learned by practice of the present application.

Description of drawings

The above and/or additional aspects and advantages of the present application will become apparent and readily understood from the following description of embodiments in conjunction with the accompanying drawings, wherein:

1 is a schematic flowchart of an image processing method according to some embodiments of the present application;

2 is a schematic structural diagram of an image processing apparatus according to some embodiments of the present application;

3 is a schematic structural diagram of a terminal according to some embodiments of the present application;

4 is a schematic flowchart of an image processing method according to some embodiments of the present application;

5 is a schematic flowchart of an image processing method according to some embodiments of the present application;

6 is a schematic flowchart of an image processing method according to some embodiments of the present application;

7 is a schematic diagram of the position of the center point of the image processing method according to some embodiments of the present application;

8 is a schematic diagram of a clustering scene of an image processing method according to some embodiments of the present application;

9 is a schematic flowchart of an image processing method according to some embodiments of the present application;

10 is a schematic diagram of a clustering scene of an image processing method according to some embodiments of the present application;

11 is a schematic diagram of a clustering scene of an image processing method according to some embodiments of the present application;

12 is a schematic diagram of a clustering scene of an image processing method according to some embodiments of the present application;

13 is a schematic flowchart of an image processing method according to some embodiments of the present application;

14 is a schematic diagram of a clustering scene of an image processing method according to some embodiments of the present application;

15 is a schematic flowchart of an image processing method according to some embodiments of the present application;

16 is a schematic flowchart of an image processing method according to some embodiments of the present application;

17 is a schematic diagram of a YUV image and a corresponding color watermark of some embodiments of the present application;

FIG. 18 is a schematic diagram of a connection relationship between a computer-readable storage medium and a processor according to some embodiments of the present application.

Detailed ways

Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary, only used to explain the embodiments of the present application, and should not be construed as limitations on the embodiments of the present application.

Referring to FIG. 1 , an embodiment of the present application provides an image processing method. Image processing methods include:

02: Convert the image to be processed to the LAB color space to obtain the first image;

03: perform clustering processing on the first image to obtain a plurality of clustering results; and

04: Obtain the theme color according to multiple clustering results and the sum of the color differences corresponding to each clustering result.

Referring to FIG. 2 , an embodiment of the present application further provides an image processing apparatus 10 . The image processing device 10 includes a first acquisition module 11 , a clustering module 13 and a color acquisition module 14 . The first acquisition module 11 is used to implement the method in 02 . The clustering module 13 is used to implement the method in 03. The color acquisition module 14 is used to implement the method in 04 . That is, the first acquisition module 10 is used to convert the image to be processed into the LAB color space to acquire the first image. The clustering module 13 is configured to perform clustering processing on the first image to obtain multiple clustering results. The color obtaining module 14 is configured to obtain the theme color according to the multiple clustering results and the sum of the color differences corresponding to each clustering result.

Referring to FIG. 3 , an embodiment of the present application further provides a terminal 100 . The terminal 100 may be a mobile phone, a desktop computer, a notebook computer, a server, a smart home appliance, a smart wearable device, a game console, a digital camera, etc., which are not listed here. Terminal 100 includes one or more processors 80, memory 90, and one or more programs. One or more of these programs are stored in memory 90 and executed by one or more processors 80 . Please refer to FIG. 1 , the program includes instructions for executing the image processing method of the embodiment of the present application. That is, the program includes instructions for performing the methods in 02, 03, and 04.

Among them, the image theme color is a variety of colors extracted from an image that can best represent the main color of the image. In this embodiment of the present application, the image to be processed and the first image are representations of the same frame of image in different color spaces. The image processing method, the image processing device 10 and the terminal 100 according to the embodiments of the present application perform clustering processing by converting the to-be-processed image to the LAB color space, and obtain the theme color according to the sum of the color differences corresponding to the clustering result.

The LAB color space is a color-opposite space, including three components L, a, and b. , where dimension L represents luminance, a and b represent color-opposite dimensions, a represents the component from green to red, and b represents the component from blue to yellow. LAB is designed based on human perception of color. The human eye will produce three color perceptions when stimulated by light: white to black, red to green, yellow to blue, and L of LAB color space. The three components of , a and b are the color channels corresponding to these three visual sense designs. If the three parameters of L, a, and b have a similar range of change, then it will bring about a similar range of visual changes. Therefore, the LAB color space is the most suitable color space for human visual perception. Clustering in the LAB color space can make the color corresponding to the clustering result conform to the perception of the human eye. The theme color is obtained according to the sum of the color differences corresponding to the clustering results, which can reduce the gradient color in the theme color, and obtain a theme color with distinct hue, distinctive features, and visual focus.

Further description will be given below in conjunction with the accompanying drawings.

The images to be processed may include images in various formats. In one embodiment, the image to be processed is an image in the LAB color space, and subsequent processing is performed between the images to be processed as the first image.

Referring to FIG. 4 , in some embodiments, the image to be processed is an image in the XYZ color space, and the pixels of the image to be processed include a first XYZ component, a second XYZ component and a third XYZ component. 02: Convert the image to be processed to the LAB color space to obtain the first image, including:

021: Obtain the LAB three-channel value of the pixel of the image to be processed according to the first XYZ component, the second XYZ component, the third XYZ component, the preset mapping relationship and the preset hyperparameter of the image to be processed; and

022: Acquire a first image according to the LAB three-channel value of each pixel of the image to be processed.

Please refer to FIG. 2 , in some embodiments, the first acquisition module 11 can also be used to implement the methods in 021 and 022, that is, the first acquisition module 11 can also be used to: according to the first XYZ component of the pixel to be processed, the first The second XYZ component, the third XYZ component, the preset mapping relationship and the preset hyperparameters, obtain the LAB three-channel value of the pixel of the image to be processed; and 022: according to the LAB three-channel value of each pixel of the image to be processed, Get the first image.

Referring to FIG. 3 , in some embodiments, the program further includes instructions for executing the methods in 021 and 022 .

The XYZ color space is based on the RGB color system, replacing the actual RGB three primary colors with three ideal primary colors. The XYZ color space includes X-axis, Y-axis, and Z-axis. The coordinates of a certain color on the X-axis represent the proportion of its red color, the coordinates on the Y-axis represent its green ratio, and the coordinates on the Z-axis represent its blue color. proportion. An image pixel in the XYZ color space includes a first XYZ component, a second XYZ component, and a third XYZ component, which represent the ratio of red, green, and blue to the pixel, respectively. Hereinafter, the first XYZ component is designated as X, the second XYZ component is designated as Y, and the third XYZ component is designated as Z for description.

Let the channel values of the L, A, and B channels of the LAB color space be L*, a*, and b* respectively, and the following formulas 1, 2 and 3 can be used to convert the image to be processed in the XYZ color space into LAB colors. First image of space.

Formula one:

Formula two:

Formula three:

X_n、Y_n、Z_n为超参数。在一个实施例中，X_n的取值为95.047，Y_n的取值为100.000，Z_n的取值为108.883，以准确地得到XYZ色彩空间到LAB色彩空间的映射。in,

X _n , Y _n , and Z _n are hyperparameters. In one embodiment, the value of X _n is 95.047, the value of Y _n is 100.000, and the value of Z _n is 108.883, so as to accurately obtain the mapping from the XYZ color space to the LAB color space.

As in formula 1, L* in the LAB color space represents the brightness, and the brightness in the XYZ color space is represented by the value of the Y component. Formula 1 associates the luminance relationship between the LAB color space and the XYZ color space to obtain the L* channel value in the LAB color space.

As in formula 2, a* in the LAB color space represents the component from green to red, while in the XYZ color space the X component represents the proportion of red, and the Y component represents the proportion of green. Formula 2 associates the red-green relationship between the LAB color space and the XYZ color space to obtain the a* channel value in the LAB color space.

As in formula 3, b* in the LAB color space represents the component from blue to yellow, while in the XYZ color space the Z component represents the proportion of blue, the Y component represents the proportion of green, and green and yellow are adjacent. Equation 3 associates the blue-green relationship between the LAB color space and the XYZ color space to obtain the b* channel value in the LAB color space.

Referring to FIG. 1 , in some embodiments, the image to be processed is an image in an RGB color space, and the RGB color space is a color space based on three primary colors of red, green, and blue. Converting an image in the RGB color space to the LAB color space requires the use of the XYZ color space as a medium. Assuming that the pixel values of the image to be processed in the three color channels of R, G, and B are in the range of [0, 255], and the value range is set to range, the following formula 4 can be used. Process the image to obtain the first image in XYZ color space.

Formula four:

in,

Arrange to get:

In this way, the X, Y, and Z component values in the corresponding XYZ color space can be determined according to the pixel values of the image to be processed in the three color channels of R, G, and B, so as to obtain the corresponding first image.

In some embodiments, since the conversion relationship between the XYZ color space and the LAB color space is relatively clear, any image to be processed in any color space can be converted into an image in the XYZ color space, and then converted to the LAB color space to obtain first image.

Referring to FIG. 5, in some embodiments, the image processing method further includes:

05: Get YUV image;

06: Downsampling the YUV image to obtain the second image;

07: Convert the second image to the RGB color space to obtain the third image; and

08: Convert the third image to XYZ color space to obtain the image to be processed.

Referring to FIG. 2 , in some embodiments, the image processing apparatus 10 further includes a second acquisition module 12 , a third acquisition module 15 , a downsampling module 16 and a fourth acquisition module 17 . The second processing module 12 is used to implement the method in 05 . The downsampling module 16 is used to implement the method in 06 . The third acquisition module 15 is used to implement the method in 07. The fourth acquisition module 17 is used to implement the method in 08 . That is, the second processing module 12 is used to acquire YUV images. The down-sampling module 16 is used for down-sampling the YUV image to obtain the second image. The third acquisition module 15 is used for converting the second image into the RGB color space to acquire the third image. The fourth acquisition module 17 is used for converting the third image into the XYZ color space to acquire the image to be processed. Referring to FIG. 3 , in some embodiments, the program further includes instructions for executing the methods in 05 , 06 , 07 and 08 .

Usually, images captured by mobile phones or cameras are YUV images. According to the methods in 05, 06, 07, and 08, the YUV image can be converted into an image in the RGB color space for subsequent image processing. Downsampling YUV images can improve the speed of subsequent image processing and reduce the time users spend waiting to generate theme colors. In one embodiment, the YUV image is reduced by 16 times to obtain the third image, and in other embodiments, it can also be reduced by 8 times, 4 times, 2 times, 32 times, etc., which are not listed here.

In some embodiments, the second image can be converted to the RGB color space according to the following formula 5 to obtain the third image.

Formula five:

Wherein, Y, C _b , and C _γ are the components of the three channels of the third image in the YUV color space, respectively, and R, G, and B are the components of the three channels corresponding to the third image converted to the RGB color space.

The third image is an image in the RGB color space. Please combine the formula 4 to convert the third image in the RGB color space into the image to be processed in the XYZ color space. Combine the formula 1, formula 2, and formula 3 to convert the XYZ color space. The image to be processed in the space is converted into the first image in the LAB color space for processing.

Please refer to FIG. 6, in some embodiments, 03: perform clustering processing on the first image to obtain a plurality of clustering results, including:

031: Determine a plurality of center points in the first image according to the number of color classifications, and each center point corresponds to a color classification;

032: Obtain the first color value of each color classification and the second color value of each pixel in the first image;

033: Divide the pixel into corresponding color classifications according to the difference between the second color value of each pixel and the first color value of each color classification;

034: obtain the average value of the second color value of each pixel in each color classification; and

035: Update the center point and the first color value corresponding to each color classification according to the average value corresponding to each color classification.

Please refer to FIG. 2, in some embodiments, the clustering module 13 can also be used to implement the methods in 031, 032, 033, 034 and 035, that is, the clustering module 13 can also be used to: obtain the first color classification The color value and the second color value of each pixel in the first image; according to the difference between the second color value of each pixel and the first color value of each color classification, the pixels are divided into corresponding color classifications obtaining the average value of the second color value of each pixel in each color classification; and updating the center point and the first color value corresponding to each color classification according to the corresponding average value of each color classification. Referring to FIG. 3 , in some embodiments, the program further includes instructions for executing the methods in 031 , 032 , 033 , 034 and 035 .

In some embodiments, the number of color classifications is twice the target number of theme colors. The target number of theme colors is a preset value, indicating how many colors are used to reflect the main color of an image. For example, the number of theme colors is 5, which means that 5 different colors are used to reflect the main color of an image. The number of clustering results is the same as the number of color classifications, that is, how many kinds of color classifications there are, how many kinds of clustering results can be obtained in the end. Please refer to Figure 1. The number of color classifications is twice the number of theme colors, that is, the number of clustering results is twice the number of theme colors. In this way, according to multiple clustering results and the sum of the color differences corresponding to each clustering result When obtaining the theme color, you can select the appropriate color from the colors corresponding to the multiple clustering results as the theme color, for example, filter out the colors with similar color difference in the multiple clustering results, so that the finally obtained theme colors have obvious differences. difference to get a bright theme color. In other embodiments, the number of color classifications is not limited to twice the target number of the theme color, and can be any number greater than the target number of the theme color, for example, the number of color classifications is 1 more than the theme color target number, 2, 3, etc., for another example, the number of color classifications is 3 times, 4 times, 5 times, etc., the target number of the theme colors, etc., which are not listed here.

Please refer to FIG. 7 , the center point is a position point selected in the first image, and the image may be a to-be-processed image in an RGB color space, or an initial image in other color spaces, which is not limited here. In the embodiment illustrated in FIG. 5 , the number of targets for theme colors is 5, the number of color classifications is 10, and the number of center points is 10. When determining the center point, the image is divided into 4 parts in the horizontal direction by 3 dividing lines, and the image is divided into 4 parts in the vertical direction by the other 3 dividing lines. At the intersections D1 and D2 formed by the above 6 dividing lines , D3, D4, D5, D6, D7, D8, D9, set the intersection D1, D2, D3, D4, D5, D7, D8, D9 as the center point, and set the intersection D4 and D5 at the midpoint D10, The midpoint D11 of the intersection D6 and the intersection D5 is set as the center point, and 10 center points are obtained. In other embodiments, the distribution of the center points may not be limited to the distribution manner illustrated in FIG. 5 , which is not limited here. For example, in yet another embodiment, the center points are uniformly distributed in the image.

The process of clustering is to classify each pixel in the first image by color into the color classification with the closest color. The first color value is the color corresponding to the position where the center point is located, and the first color value is represented by the L*, A*, and B* channel values. For example, assume that the first color value corresponding to the center point Ai is Yai=(Li*, Ai*, Bi*). The second color value is the color corresponding to the position of the pixel point, and the second color value is represented by the L*, A*, and B* channel values. For example, it is assumed that the second color value corresponding to the pixel point Pj is Ypj=(Lj*, Aj*, Bj*). The difference value reflects the color difference between the first color value Yai and the second color value Ypj. The smaller the difference value is, the closer the corresponding color of the first color value Yai is to the color corresponding to the second color value Ypj. In the clustering process, each pixel point Pj is divided into the color classification where the center point Ai with the closest color is located.

Taking the pixel point P1 as an example, the difference values between the pixel point P1 and each center point Ai are obtained respectively to determine the cluster with the closest color to the pixel point P1. Assuming that the difference value between the colors corresponding to the pixel point P1 and the center point A2 is the smallest, and the center point A2 corresponds to the color classification S2, the pixel point P1 is divided into the color classification S2. By analogy, the difference values between each pixel point Pj and each center point Ai are obtained respectively to determine the color classification Sk,k∈[1,i] corresponding to each pixel point Pj, respectively.

Referring to FIG. 8 , as an example, there are determined center points A1 and A2 in the first image, which correspond to color classification S1 and color classification S2 respectively. For convenience of description, other pixel points in the first image are omitted in the example of FIG. 6 , and the clustering of pixel points P1 , P2 , P3 , P4 , P5 , and P6 will be described. Let the pixels with the smallest difference values between the colors corresponding to the center point A1 be P1, P2, and P3, and the pixels with the smallest difference values between the colors corresponding to the center point A2 be P4, P5, and P6, then P1 , P2, P3 are divided into color classification S1, and P4, P5, P6 are divided into color classification S2. In FIG. 6, the upper side of the dividing line O1 is the color classification S1, and the lower side of the dividing line O1 is the color classification S2.

After a division is performed, the average value of the second color value Ypj of each pixel point Pj in each color classification Sk is obtained, and the center point Ai and the first color corresponding to each color classification Sk are updated according to the average value corresponding to each color classification Sk. A color value Yai. In this way, taking the average value of the second color values Ypj of the pixels Pj included in the divided color classification Sk as the first color value Yai corresponding to the color classification Sk can more accurately represent the color characteristics of the divided color classification Sk as a whole. The updated center point Ai is the position point corresponding to the updated first color value Yai in the LAB color space.

Referring to FIG. 8 , as an example, let the average value of the second color values Ypj of each pixel point Pj in each color classification Sk be Ydk. After one division, the average value of the second color values Yp1, Yp2, and Yp3 of the pixels P1, P2, and P3 in the color classification S1 is Yd1, and Yd1 is taken as the value of the first color value Ya1 of the updated color classification S1. , the center point corresponding to the updated color classification S1 is A3; the average value of the second color values Yp4, Yp5, and Yp6 of the pixel points P4, P5, and P6 in the color classification S2 is Yd2, and Yd2 is used as the first color of the updated color classification S2. A color value The value of the first color value Ya2, the center point corresponding to the updated color classification S1 is A4.

Referring to FIG. 9, in some embodiments, 03: perform clustering processing on the first image to obtain multiple clustering results, further comprising:

036: according to the difference value between the second color value of each pixel and the updated first color value of each color classification, the pixel is divided into the corresponding color classification;

037: End the clustering process when the number of updates of the center point corresponding to the color classification reaches a preset number of times threshold, and output the first color value corresponding to the center point as the clustering result; or

038: End the clustering process when the center point corresponding to the color classification before and after the update is unchanged, and output the first color value corresponding to the center point as the clustering result.

Please refer to FIG. 2 , in some embodiments, the clustering module 13 can also be used to implement the method in 036, 037 or 038, that is, the clustering module 13 can also be used to: according to the second color value of each pixel and each The difference value between the updated first color values of the color classifications divides the pixel points into corresponding color classifications; when the update times of the center points corresponding to the color classifications reaches the preset times threshold, the clustering process ends, and the The first color value corresponding to the center point is output as the clustering result; or the clustering process is ended when the center point corresponding to the color classification before and after the update is unchanged, and the first color value corresponding to the center point is output as the clustering result. Referring to FIG. 3 , in some embodiments, the program further includes instructions for executing the method in 026 , 027 or 028 .

Please refer to FIG. 8, FIG. 10, and FIG. 11, after completing the update of the center point Ai and the first color value Yai, the second color value Yai of each pixel point Pi is updated according to the updated first color value Yai of each color classification Sk. The color value Ypj performs the next clustering, and the clustering ends when the number of updates of the center point Ai reaches the preset number threshold, or when the updated first color value Yai is the same as the first color value before the update Yai end clustering. Ending the clustering means accepting the first color value Yai corresponding to the color classification Sk as the clustering result, that is, receiving the first color value Yai as the representative color representing the color classification Sk.

Please refer to FIG. 8 , FIG. 10 and FIG. 11 . FIG. 10 is the next clustering based on the clustering of FIG. 8 . In this clustering, the pixels with the smallest difference value between the colors corresponding to the center point A3 are P1, P2, and P4, and the pixels with the smallest difference value between the colors corresponding to the center point A2 are P3 and P5. , P6, P1, P2, P4 are divided into color classification S1, P3, P5, P6 are divided into color classification S2. In FIG. 10, the left side of the dividing line O1 is the color classification S1, and the right side of the dividing line O1 is the color classification S2. As shown in Figure 11, after dividing and updating the center point, the center point of the color classification S1 is A5, and the value Yd1 of the corresponding first color value Ya1 is the second color value Yp1, Yp2 of the pixel points P1, P2, and P4. The center point of the color classification S2 is A6, and the value Yd2 of the corresponding first color value Ya2 is the average value of the second color values Yp3, Yp5, and Yp6 of the pixel points P3, P5, and P6.

Please refer to FIG. 12 , which is the next clustering based on the clustering in FIG. 11 . Suppose that after this clustering, the center point of color classification S1 is updated to A7, and the center point of color classification S2 is updated to A8. In one embodiment, the position of A7 in the LAB color space is the same as the position of A5 in the color space, and the position of A8 in the LAB color space is the same as the position of A6 in the color space, then the clustering is ended, and this After the sub-clustering, the first color value Ya1 corresponding to the color classification S1 and the first color value Ya2 corresponding to the color classification S2 are output as the clustering result. In yet another embodiment, the position of A7 in the LAB color space is different from the position of A5 in the color space, and the position of A8 in the LAB color space is different from the position of A6 in the color space, but the number of updates of the center point has been When the preset number of times threshold is reached, the clustering is ended, and the first color value Ya1 corresponding to the color classification S1 and the first color value Ya2 corresponding to the color classification S2 after this clustering are output as the clustering result.

Referring to FIG. 13, in some embodiments, 033: Divide the pixels into corresponding color categories according to the difference between the second color value of each pixel and the first color value of each color category, including :

0331: Obtain the first LAB component of the first color value and the second LAB component of the second color value;

0332: Obtain the hue rotation item, the luminance compensation value, the chrominance compensation value and the hue compensation value according to the first LAB component and the second LAB component;

0333: Obtain a difference value according to the first LAB component, the second LAB component, the hue rotation term, the luminance compensation value, the chrominance compensation value, and the hue compensation value; and

0334: Select the smallest difference value among the plurality of difference values between the second color value of the pixel point and the plurality of first color values, and divide the pixel point into a color classification corresponding to the smallest difference value.

Please refer to FIG. 2 , in some embodiments, the clustering module 13 can also be used to implement the methods in 0331, 0332, 0333 and 0334, that is, the clustering module 13 can also be used to: obtain the first LAB component of the first color value and the second LAB component of the second color value; obtain the hue rotation item, the luminance compensation value, the chromaticity compensation value and the hue compensation value according to the first LAB component and the second LAB component; according to the first LAB component, the second LAB component, The hue rotation item, the luminance compensation value, the chromaticity compensation value, and the hue compensation value obtain the difference value; and select the minimum difference value among the plurality of difference values between the second color value of the pixel point and the plurality of first color values, and use Pixel points are divided into the color classification corresponding to the minimum difference value. Referring to FIG. 3 , in some embodiments, the program further includes instructions for executing the methods in 0231 , 0232 , 0233 and 0234 .

Please refer to Fig. 14, set the first color value Ya1 of the center point A1 = (L ₁ , a ₁ , b ₁ ), the first color value of the center point A2 Ya2 = (L ₂ , a ₂ , b ₂ ), the pixel point The second color value Yp1 of P1=(L ₁ ^* , a ₁ ^* , b ₁ ^* ), the difference between the center point A1 and the pixel point P1 is E1, and the difference between the center point A2 and the pixel point P1 is the value of E1 E2. The difference value E1 can be calculated by the following formula 6:

Formula six:

Wherein, K _L , K _C , and K _H are all preset values, and in one embodiment, the values of K _L , K _C , and K _H are all 1.

△L=L ₁ -L ₁ ^* , △C=C1-C2,

h1=atan2(b ₁ , a ₁ ) mod 360°, h2=atan2(b ₁ ^* , a ₁ ^* ) mod 360°;

Among them, _RT is the hue rotation term, and the introduction of the hue rotation term _RT allows the difference value E1 to better adapt to the blue area near the hue angle of 275°, avoiding inaccurate differences in the blue area. S _L is a brightness compensation value, so that the difference value E1 can accurately reflect the brightness difference. S _C is the chromaticity compensation value, so that the difference value E1 can accurately reflect the chromaticity difference. S _H is the hue compensation value, so that the difference value E1 can accurately reflect the hue difference. Similar to the method for calculating the difference value E1, the calculated difference value E2 can be calculated by replacing the components L ₁ , a ₁ , and b ₁ in formula 5 with L ₂ , a ₂ , and b ₂ . After the difference value E1 and the difference value E2 are calculated respectively, the pixel point P1 is divided into the color classification corresponding to the smallest difference value among the difference value E1 and the difference value E2. For example, if E1<E2, the pixel point P1 is divided into the color classification S1. Similarly, when the number of center points is i, the difference value Ek between the second color value Yp1 of the pixel point P1 and each first color value Yai is calculated respectively, and the pixel point P1 is divided into the minimum difference value Ekmin corresponding to The color classification of Skmin. where k∈[1,i]. By analogy, each pixel point Pj can be divided into the corresponding color classification Sk.

After obtaining the clustering results, select n clustering results with the largest sum of color differences with other clustering results from the clustering results to generate n theme colors to ensure that the multiple theme colors are distinct and representative to avoid choosing similar-colored gradients as the theme color.

Please refer to FIG. 15, in some embodiments, 04: obtain the theme color according to a plurality of clustering results and the sum of the color differences corresponding to each clustering result, including:

041: Obtain the color difference sum corresponding to each clustering result respectively; and

042: Obtain the first n clustering results in descending order of the sum of color differences to generate n theme colors, where n is the target number of theme colors.

Please refer to FIG. 2, in some embodiments, the color acquisition module 14 can also be used to implement the methods in 041 and 042, that is, the clustering module 13 can also be used to: respectively acquire the color difference sum corresponding to each clustering result; and The sum of the color differences is obtained in descending order of the first n clustering results to generate n theme colors respectively, and the value of n is the target number of theme colors. Referring to FIG. 3 , in some embodiments, the program further includes instructions for executing the methods in 041 and 042 .

In some embodiments, let the clustering result be Jm, the sum of the color differences corresponding to the clustering result Jm is Zm, and m=6, that is, 6 clustering results are obtained after clustering, and n=3, that is, 3 clustering results are generated. Theme color. First, the clustering result Jm is mapped back to the RGB color space to obtain the RGB color value Um corresponding to the clustering result Jm, and the color difference sum Zm of the color value Um is the sum of the color differences between the color value Um and other color values. For example, see the table below:

Among them, the number where the horizontal and vertical coordinates intersect represents the color difference between the color value of the horizontal axis and the color value of the vertical axis. For example, the value of the color difference between the color value U1 and the color value U1 is 0, and the color value U1 and the color value U2 The value of the color difference between is 1. The final statistics of Z1, Z2, Z3, Z4, Z5, and Z6 are the sum of the color differences corresponding to the color values U1, U2, U3, U4, U5, and U6, respectively. Among them, the color difference sum Z6 is the largest, indicating that the color value U6 corresponding to the clustering result J6 has the largest color difference between the color value corresponding to each other clustering result; the color difference sum Z1 is the smallest, indicating that the color value U1 corresponding to the clustering result J1 is different from other The color difference of the color value corresponding to each clustering result is the smallest. In the case of n=3, the first 3 clustering results with the largest sum of color differences are selected to generate the theme color. In this example, the first 3 clustering results with the largest sum of color differences are clustering results J6, clustering results J5, For the clustering result J4, the color value U6, the color value U5, and the color value U4 corresponding to the clustering result J6, the clustering result J5, and the clustering result J4, respectively, are used as the theme color. In other embodiments, after obtaining the top n clustering results with the largest sum of color differences, the color value in the RGB color space corresponding to the clustering result is not limited to be used as the theme color, but can also be the color value of other color spaces, For example, the color value of the HSV color space, the color value of the LAB color space, the color value of the YUV space, etc., are not limited here.

Referring to FIG. 16, in some embodiments, the image processing method further includes:

09: Convert the third color value corresponding to the theme color to the YUV color space to generate a color watermark; and

010: Sort the color watermarks according to the brightness value of the third color value and display them together with the YUV image.

Referring to FIG. 2 , in some embodiments, the image processing apparatus 10 further includes a watermark generating module 18 and a sorting module 19 . The watermark generation module 18 is used to implement the method in 09. The sorting module 19 is used to implement the method in 010. That is, the watermark generation module 18 is configured to convert the third color value corresponding to the theme color into the YUV color space to generate a color watermark. The sorting module 19 is used for sorting the color watermarks according to the luminance value of the third color value and displaying them together with the YUV image. Referring to FIG. 3 , in some embodiments, the program further includes instructions for executing the methods in 09 and 010 .

The third color value is the color value corresponding to the theme color in the RGB color space. The color watermark is usually displayed on the display screen of the terminal 100 such as a mobile phone, a camera, a computer, etc. The image obtained by such a terminal 100 shooting an object is usually a YUV image. Therefore, generating a color watermark in the YUV format can easily use the color watermark to edit the data stored in the terminal 100. YUV images.

In an embodiment, according to the above formulas 1 to 5, the YUV image of the terminal device is sequentially converted into an RGB color space image, an XYZ color space image, and a LAB color space image for corresponding processing. The theme color can be generated according to the LAB color space image corresponding to the YUV image, and then the third color value corresponding to the theme color is converted back to the YUV color space to generate and sort the color watermark corresponding to the YUV color space.

Please refer to Figure 17, the sorted color watermarks are displayed together with the YUV image, so that the user can intuitively see the color watermark that conforms to the color theme of the YUV image. In some embodiments, the user can use the color watermark to realize functions such as making a personal homepage and performing color screening of pictures, so as to enhance the user's immersive interactive experience when browsing pictures.

In one embodiment, in order to conform to the visual effect of the human eye, after the color watermarks are generated, the color watermarks are sorted according to the brightness value of the third color value, so that the color watermarks seen by the user are sequentially from light to dark or from dark to light Arranged for beautiful visual effects.

Referring to FIG. 17 , an embodiment of the present application further provides a non-volatile computer-readable storage medium 800 including a computer program 801 . One or more non-volatile computer-readable storage media 800 containing a computer program 801 in the embodiments of the present application, when the computer program 801 is executed by one or more processors 80, enables the processor 80 to execute any of the above-mentioned implementations The adjustment method of the mode, for example, implements one or more of steps 01, 02, and 03.

For example, computer program 801, when executed by one or more processors 80, causes the processors 80 to perform the following steps:

02: Convert the image to be processed to the LAB color space to obtain the first image;

03: perform clustering processing on the first image to obtain a plurality of clustering results; and

04: Obtain the theme color according to multiple clustering results and the sum of the color differences corresponding to each clustering result.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples", etc. The particular feature, structure, material or characteristic described by example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, those skilled in the art may combine and combine the different embodiments or examples described in this specification, as well as the features of the different embodiments or examples, without conflicting each other.

Any description of a process or method in the flowcharts or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing a specified logical function or step of the process , and the scope of the preferred embodiments of the present application includes alternative implementations in which the functions may be performed out of the order shown or discussed, including performing the functions substantially concurrently or in the reverse order depending upon the functions involved, which should It is understood by those skilled in the art to which the embodiments of the present application belong.

Although the embodiments of the present application have been shown and described above, it should be understood that the above-mentioned embodiments are exemplary and should not be construed as limitations on the present application. Variations, modifications, substitutions and alterations are made to the above-described embodiments.

Claims (12)

1. an image processing method, is characterized in that, comprises: Convert the image to be processed to the LAB color space to obtain the first image; performing a clustering process on the first image to obtain a plurality of clustering results; and The theme color is obtained according to a plurality of the clustering results and the sum of the color differences corresponding to each of the clustering results. 2 . The image processing method according to claim 1 , wherein the pixels of the to-be-processed image comprise a first XYZ component, a second XYZ component and a third XYZ component, and the to-be-processed image is converted to LAB colors. 3 . space to acquire the first image, including: According to the first XYZ component, the second XYZ component, the third XYZ component, the preset mapping relationship and the preset hyperparameters of the pixels of the image to be processed, obtain the pixel of the image to be processed. LAB three-channel value; and The first image is acquired according to the LAB three-channel value of each pixel of the to-be-processed image. 3. The image processing method according to claim 1, wherein the performing clustering processing on the first image to obtain a plurality of clustering results, comprising: Determine a plurality of center points in the first image according to the number of color classifications, and each of the center points corresponds to one of the color classifications; Obtain the first color value of each of the color classifications and the second color value of each pixel in the first image; According to the difference value between the second color value of each of the pixel points and the first color value of each of the color categories, the pixel points are divided into the corresponding color categories; obtaining the average value of the second color value of each of the pixel points in each color classification; and The center point and the first color value corresponding to each of the color categories are updated according to the average value corresponding to each of the color categories. 4. The image processing method according to claim 3, wherein the performing clustering processing on the first image to obtain a plurality of clustering results further comprises: According to the difference between the second color value of each pixel and the updated first color value of each color classification, the pixel is divided into the corresponding color classification; End the clustering process when the update times of the center points corresponding to the color classifications reaches a preset times threshold, and output the first color value corresponding to the center points as the clustering result; or When the center point corresponding to the color classification is unchanged before and after the update, the clustering process is ended, and the first color value corresponding to the center point is output as the clustering result. 5. The image processing method according to claim 3, wherein the number of color classifications is twice the target number of the theme color. 6. The image processing method according to claim 3, characterized in that, according to the difference value between the second color value of each of the pixel points and the first color value of each of the color classifications The pixel points are divided into the corresponding color classification, including: Obtain the first LAB component of the first color value and the second LAB component of the second color value; Acquire a hue rotation item, a luminance compensation value, a chrominance compensation value and a hue compensation value according to the first LAB component and the second LAB component; obtaining the difference value from the first LAB component, the second LAB component, the hue rotation term, the luminance compensation value, the chrominance compensation value, and the hue compensation value; and selecting the minimum difference value among the plurality of difference values between the second color value of the pixel point and the plurality of the first color values, and dividing the pixel point into the minimum difference value corresponding to the Color Classification. 7. The image processing method according to claim 1, wherein the obtaining the theme color according to a plurality of the clustering results and the color difference sum corresponding to each of the clustering results, comprising: respectively obtaining the sum of the color differences corresponding to each of the clustering results; and The first n clustering results are obtained in descending order of the sum of the color differences to generate n the theme colors respectively, and the value of n is the target number of the theme colors. 8. The image processing method according to claim 1, wherein the image processing method further comprises: Get YUV image; performing down-sampling processing on the YUV image to obtain a second image; converting the second image to the RGB color space to obtain a third image; and Convert the third image to XYZ color space to obtain the image to be processed. 9. The image processing method according to claim 8, wherein the image processing method further comprises: converting the third color value corresponding to the theme color to the YUV color space to generate a color watermark; and The color watermarks are sorted according to the luminance value of the third color value and displayed together with the YUV image. 10. An image processing device, comprising: The first acquisition module is used to convert the to-be-processed image to the LAB color space to acquire the first image; a clustering module for performing clustering processing on the first image to obtain a plurality of clustering results; and The color obtaining module is configured to obtain the theme color according to a plurality of the clustering results and the sum of the color differences corresponding to each of the clustering results. 11. A terminal, wherein the terminal comprises: one or more processors, memory; and one or more programs, wherein the one or more programs are stored in the memory and executed by the one or more processors, the programs include a instructions for the image processing method described above. 12. A non-volatile computer-readable storage medium containing a computer program that, when executed by one or more processors, causes the processors to implement the method of any one of claims 1 to 8 Instructions for image processing methods.

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