CN116090163B - Mosaic tile color selection method and related equipment - Google Patents
Mosaic tile color selection method and related equipment Download PDFInfo
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Abstract
The invention discloses a mosaic tile color selection method and related equipment, wherein the method comprises the following steps: establishing a set coverage model, forming a set of colors of an existing tile and colors in a first color space image and a second color space image, and ensuring that the colors in the first color space image and the second color space image are covered by at least any subset of the set; calculating the average distance between the colors in the first and second color space images and the color of the existing tile to obtain a first partial added color; establishing a cluster analysis model, dividing colors in the second color space image into classes, and calculating a membership matrix and a cluster center of the second color space image; and classifying colors closest to Euclidean distance between the cluster centers in the second color space image into one class, wherein the center point of each class is a new cluster center, and obtaining the added colors of the second part. According to the invention, the tile color closest to the manufacturer in the color can be obtained according to the original image provided by the user.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a mosaic tile color selection method, a mosaic tile color selection system, a mosaic tile color selection terminal and a mosaic tile color selection computer readable storage medium.
Background
Mosaic is one of the longest known decoration arts, and with the development of modern technology, mosaic tiles fully utilizing computer technology and image processing technology become one of the fire and heat choices for laying household wall and floor tiles. The mosaic tiles are small and exquisite in appearance, rich in color and various in variety, and can be matched as required to form countless beautiful patterns; the waterproof performance is strong, the chemical property is stable, the waterproof performance is high-temperature resistant, the heat is not absorbed, the radiation is prevented, the decorative effect is good, and the waterproof performance is widely applied to places such as science and technology museums, cinema, clubs and the like.
However, the color of the mosaic tile is limited by the technology and the cost, the color of the mosaic tile is almost identical to the color of the picture given by the user, the user can only select the tile with the closest color to the original picture within a certain range for splicing according to the original picture color, so that the workload of the user is greatly increased, and the user often falls into the difficulty of color selection.
In order to reduce the workload of manual color selection of customers, the prior mosaic tile manufacturer can produce 22 kinds of mosaic tiles, and needs to research a color selection method of the mosaic tiles, and can automatically match the tile color closest to the original picture color in the colors which can be produced by the manufacturer. The color is an important visual property of the image, for the color of the picture, the color mode is a color standard which is commonly used in the prior art and respectively represents red, green and blue, 16777216 different colors can be generated by matching the three colors together in the format, and the method required by the manufacturer is that the color number of the ceramic tile closest to any one of 22 colors produced by the manufacturer can be output.
Accordingly, the prior art is still in need of improvement and development.
Disclosure of Invention
The invention mainly aims to provide a mosaic tile color selection method, a mosaic tile color selection system, a mosaic tile color selection terminal and a mosaic tile color selection computer readable storage medium, and aims to solve the problem that the color of a tile closest to the original picture cannot be automatically matched in colors which can be produced by factories according to the original picture colors in the prior art.
In order to achieve the above purpose, the invention provides a mosaic tile color selection method, which comprises the following steps:
establishing a set coverage model, forming a set by the colors of the existing ceramic tiles and the colors in the first color space distribution image and the second color space distribution image, and ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set;
in the set coverage model, calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color, and sorting according to the average distance to obtain a first part of tile color to be added;
establishing a cluster analysis model, dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center;
And classifying the colors closest to the Euclidean distance between the second color space distribution image and the clustering centers into one type, calculating the center point of each type, and obtaining a new clustering center, wherein the colors corresponding to the new clustering center are the colors of the second part of ceramic tiles needing to be added.
Optionally, the mosaic tile color selecting method includes building a set coverage model, forming a set of colors of an existing tile and colors in a first color space distribution image and a second color space distribution image, and ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set;
the method for building the set coverage model comprises the steps of building a set coverage model by combining colors of an existing ceramic tile with colors in a first color space distribution image and a second color space distribution image, and ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set, and specifically comprises the following steps:
the colors in the first color space distribution image and the second color space distribution image form a set T, the total colors in the set T are 416 colors, and the colors of the existing tile and the colors in the set T are common To form a set C, introducing decision variables:wherein x is j T is a decision variable j For any one color in set T, C n For the coverage subset in set C, the other wise representation is not within the coverage of set C;
establishing a set coverage model:x j =0, j=1, 2,..416, wherein constraints are used to ensure that each element T in the set T j Are all collected by at least any subset C of the collection C n Covering.
The method comprises the steps of establishing a set coverage model, forming a set of colors of the existing ceramic tile and colors in a first color space distribution image and a second color space distribution image, ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set, and further comprising the following steps:
and converting RGB values of colors in the first color space distribution image and the second color space distribution image and colors of the existing tiles into HSV values.
Optionally, in the mosaic tile color selecting method, in the set coverage model, calculating an average distance between each color in the first color space distribution image and the second color space distribution image and an existing tile color, and sorting according to the average distance to obtain a first part of tile color to be added;
In the set coverage model, calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color, and sorting according to the average distance to obtain a first part of tile color to be added, wherein the method specifically comprises the following steps:
calculating each color of the first and second color space distribution images and the existing tile colorAverage distance of color:wherein d jn Representing the Euclidean distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color;
and sequencing the average distances from large to small, acquiring corresponding colors according to the positions of the average distances in the color space, and selecting the colors to fill the blank of the existing tile colors to obtain the first part of tile colors which need to be increased.
The calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color specifically comprises the following steps:
acquiring Euclidean distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color: Wherein h is j ，s j ，v j The coordinate value of each color in the first color space distribution image and the second color space distribution image in HSV color space is respectively, h n ，s n ，v n Coordinate values of the existing tile colors in the HSV color space are respectively obtained;
and acquiring the average distance between each color in the first color space distribution image and the second color space distribution image and the color of the existing tile.
Optionally, establishing a cluster analysis model, dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center;
the establishing a cluster analysis model, dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center, wherein the method specifically comprises the following steps:
establishing a cluster analysis model, and dividing the colors in the second color space distribution image into classes to obtain an objective function of fuzzy clustering:wherein an objective function J (U, C) represents a weighted square sum of distances from coordinates corresponding to colors in the second color space distribution image to each cluster center, and a membership degree matrix U= [ U ] jn ]Representing the degree to which the colors in the second color space distribution image belong to each class, M being the number of colors in the second color space distribution image, N being the number of cluster centers, f being a weighting index, dist (c n ，k j ) Refers to the distance between the color in the second color space distribution image and each cluster center, c n Is the coordinate corresponding to the cluster center, k j Is the coordinates corresponding to the colors in the second color space distribution image;
after obtaining the objective function of fuzzy clustering, a Lagrange method is used to obtain a membership matrix u jn ，Wherein m is the number of color correspondences in the second color space distribution image, dist 2 nj The square of the distance between the coordinates corresponding to the colors in the second color space distribution image and each clustering center is referred to;
obtaining the cluster center E n ：Wherein u is nj ＝1/u jn 。
Optionally, classifying the colors closest to the Euclidean distance between the second color space distribution image and the clustering centers into one type, and calculating the center point of each type to obtain a new clustering center, wherein the colors corresponding to the new clustering center are the colors of the second part of ceramic tiles needing to be added;
the method comprises the steps of classifying colors closest to Euclidean distance between the second color space distribution image and the clustering centers into one class, calculating center points of each class to obtain new clustering centers, wherein the colors corresponding to the new clustering centers are the colors of the second part of ceramic tiles needing to be added, and specifically comprises the following steps:
Calculating Euclidean distance between colors in the second color space distribution image and the clustering center, wherein each color in the second color space distribution image obtains N distances, and the colors closest to the Euclidean distance between the second color space distribution image and the clustering center are classified;
and calculating the center point of each class to obtain a new cluster center, wherein the color corresponding to the new cluster center is the color of the tile of the second part which needs to be added.
In addition, in order to achieve the above purpose, the present invention further provides a system for selecting colors of mosaic tiles, wherein the system for selecting colors of mosaic tiles comprises:
the color collection module is used for establishing a collection coverage model, forming a collection of the colors of the existing tiles and the colors in the first color space distribution image and the second color space distribution image, and ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the collection;
the first color selection module is used for calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color in the set coverage model, and sequencing according to the average distance to obtain a first part of tile color to be added;
The cluster analysis module is used for dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center;
and the second color selection module is used for classifying the colors closest to the Euclidean distance between the clustering centers in the second color space distribution image into one type, calculating the center point of each type, and obtaining a new clustering center, wherein the colors corresponding to the new clustering center are the colors of the second part of tiles which need to be added.
In addition, to achieve the above object, the present invention also provides a terminal, wherein the terminal includes: the method comprises the steps of a mosaic tile color selection method, wherein the mosaic tile color selection method comprises a memory, a processor and a mosaic tile color selection program which is stored in the memory and can run on the processor, and the mosaic tile color selection program is executed by the processor.
In addition, to achieve the above object, the present invention also provides a computer readable storage medium storing a program for selecting a color of a mosaic tile, which when executed by a processor, implements the steps of a method for selecting a color of a mosaic tile as described above.
In the invention, a set coverage model is established, the colors of the existing ceramic tiles, the colors in the first color space distribution image and the second color space distribution image form a set, and each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set; in the set coverage model, calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color, and sorting according to the average distance to obtain a first part of tile color to be added; establishing a cluster analysis model, dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center; and classifying the colors closest to the Euclidean distance between the second color space distribution image and the clustering centers into one type, calculating the center point of each type, and obtaining a new clustering center, wherein the colors corresponding to the new clustering center are the colors of the second part of ceramic tiles needing to be added. According to the invention, the RGB values of the colors in the first color space image and the second color space image and the existing tile are converted into HSV values, on one hand, the colors similar to the colors of the existing tile are obtained by calculating the distances between the positions corresponding to the colors of the first color space image and the second color space image in the color space and the positions corresponding to the colors of the existing tile in the color space, and on the other hand, the colors corresponding to the cluster center positions in the second color space image are searched, so that the diversity of the colors of the tile and the expressive force of the spliced image are increased, and meanwhile, the color requirements of customers are met.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of the mosaic tile color selection method of the present invention;
FIG. 2 is a first color space distribution diagram of the mosaic tile color selection method of the present invention;
FIG. 3 is a second color space distribution diagram of the mosaic tile color selection method of the present invention;
FIG. 4 is a schematic RGB color mixing diagram of the mosaic tile color selection method of the present invention;
FIG. 5 is a schematic diagram of a preferred embodiment of the system for color selection of mosaic tiles of the present invention;
FIG. 6 is a schematic diagram of the operating environment of a preferred embodiment of the terminal of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear and clear, the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The color selecting method for the mosaic tile according to the preferred embodiment of the present invention, as shown in fig. 1, comprises the following steps:
and S10, establishing a set coverage model, forming a set of colors of the existing ceramic tile and colors in the first color space distribution image and the second color space distribution image, and ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set.
In fig. 2 and fig. 3, 216 colors, 200 colors and RGB values corresponding to the first color space distribution image and the second color space distribution image are respectively given, it can be seen that the color distribution in fig. 2 is relatively uniform, and the color distribution is formed by combining 6 fixed values respectively taken by R, G, B, and is regular equidistant data, wherein R respectively takes 6 values of 0, 43, 86, 129, 172 and 215; g takes 6 values of 20, 63, 106, 149, 192, 235, respectively; b takes 6 values of 39, 82, 125, 168, 211, 254, respectively.
The color distribution in fig. 3 is relatively more random, and the corresponding color change is generated along with the increase of the coordinate values in the spatial color system, and the R, G, B value is not fixed at this time, so that the color change is also more complex than that in fig. 2, and it can be seen that the color corresponding to the image 3 has a higher frequency in the color of a part of the segments and a lower frequency in the color of a part of the segments.
And converting RGB values of colors in the first color space distribution image and the second color space distribution image and colors of the existing tiles into HSV values.
The color of the tile closest to the 22 colors produced by the existing manufacturer is found out, and the similarity between the two colors is basically calculated.
In the RGB color space, the value range of R, G, B is [0, 255], in the HSV color space, the value range of H is [0 DEG, 360 DEG ], the value range of S is [0,1], the value range of V is [0, 255], wherein H represents hue, S represents saturation, and V represents brightness.
The reason why the RGB values of the colors are converted into HSV values is that the boundaries between the RGB values and the R, G, B values are not clear, when the RGB format is used for calculating the color difference, the color difference is easy to be influenced by factors such as illumination, the color difference is difficult to accurately divide, the HSV cannot be influenced by the illumination, the problem is avoided by describing the colors by adjusting the hue H, the saturation S and the brightness V, the HSV can be regarded as a three-dimensional space coordinate system, and the euclidean distance is calculated to judge the color types by measuring the distances of coordinate points corresponding to different colors in space.
Specifically, the first color space distribution image and the second color space distribution image are divided intoThe colors of the existing tiles and the colors in the set T form a set C, and decision variables are introduced:wherein x is j T is a decision variable j For any one color in set T, C n For the coverage subset in set C, otherwise represents not within the coverage of set C, 1 represents "yes", and 0 represents "not".
Establishing a set coverage model:x j =0, j=1, 2,..416, wherein the constraint +.>For ensuring each element T in the set T j Are all collected by at least any subset C of the collection C n Covering.
The aggregate coverage model can completely cover the color types of the first color space and the second color space through the newly added tile color and the existing tile color, and the modeling method is simple and easy to understand and clear and definite.
And step S20, in the set coverage model, calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color, and sequencing according to the average distance to obtain the tile color of the first part which needs to be increased.
Specifically, the euclidean distance between each color of the first color space distribution image and the second color space distribution image and the existing tile color is acquired:wherein h is j ，s j ，v j The coordinate value of each color in the first color space distribution image and the second color space distribution image in HSV color space is respectively, h n ，s n ，v n Coordinate values of the existing tile colors in the HSV color space are respectively obtained.
Where euclidean distance is a commonly used distance definition, which is the true distance between two points in an n-dimensional space.
Calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the color of the existing tile:wherein d jn Representing the euclidean distance between each color of the first color space distribution image and the second color space distribution image and the existing tile color.
And sequencing the average distances from large to small, acquiring corresponding colors according to the positions of the average distances in the color space, and selecting the colors to fill the blank of the existing tile colors to obtain the first part of tile colors which need to be increased.
And step S30, establishing a cluster analysis model, dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a clustering center.
Before the cluster analysis model is built, a fuzzy set is built, and according to the RGB color mixing principle, besides the three primary colors of red R, green G and blue B, there are yellow Y formed by mixing red R and green G, magenta M formed by mixing red R and blue B, cyan C formed by mixing green G and blue B, white W formed by mixing red R, green G and blue B, and black which is completely absent in the three primary colors of light, as shown in fig. 4.
The fuzzy sets may be built on the basis of several color classes in FIG. 4, if there is a number U (k) ε [0,1 for any element k in the color gamut Y]Correspondingly, U is called fuzzy set on Y, and U k Known as the membership of k to Y. When K varies in Y, U (K) is a function called the membership function of U. The closer the membership U (K) is to 1, the higher the degree that K belongs to Y; the closer U (k) is to 0, the more k isThe lower the degree of Y.
Specifically, a cluster analysis model is established, colors in the second color space distribution image are divided into classes, and an objective function of fuzzy clustering is obtained:wherein an objective function J (U, C) represents a weighted square sum of distances from coordinates corresponding to colors in the second color space distribution image to each cluster center, and a membership degree matrix U= [ U ] jn ]Representing the degree to which the colors in the second color space distribution image belong to each class, M being the number of colors in the second color space distribution image, N being the number of cluster centers, f being a weighting index, dist (c n ，k j ) Refers to the distance between the color in the second color space distribution image and each cluster center, c n Is the coordinate corresponding to the cluster center, k j Is the coordinates of the color correspondence in the second color space distribution image.
The clustering analysis model is an FCM model, the FCM is an excellent clustering analysis algorithm, and the clustering analysis model is a clustering algorithm based on division, wherein the idea is that the similarity between objects divided into the same cluster is maximized, and the similarity between different clusters is minimized.
Fuzzy clustering does not directly assign data points to a class, except for computing the cluster center, but rather computes a membership matrix,the above problems can be translated intoThe established FCM model can calculate the membership degree of each point to all classes, and the reliability of the result is higher.
Then, a Lagrangian method is used to obtain a membership matrix u jn ，
Wherein m is the number of color correspondences in the second color space distribution image, dist 2 nj Refers to the square of the distance between the coordinates corresponding to the colors in the second color space distribution image and each cluster center.
Obtaining the cluster center E n ：Wherein u is nj ＝1/u jn 。
The membership matrix U and the cluster center C are found to be associated with each other, and each of the membership matrix U and the cluster center C includes continuous iteration and updating of U and C, and the membership matrix U and the cluster center C move toward a direction in which the objective function J (U, C) continuously decreases, so that a stable state is finally reached, and the value of U and C in the state is the final membership matrix and the cluster center.
And S40, classifying the colors closest to the Euclidean distance between the clustering centers in the second color space distribution image into one type, and calculating the center point of each type to obtain a new clustering center, wherein the colors corresponding to the new clustering center are the colors of the second part of ceramic tiles needing to be added.
Specifically, the euclidean distance between the colors in the second color space distribution image and the clustering center is calculated, each color in the second color space distribution image obtains N distances, and the colors closest to the euclidean distance between the colors in the second color space distribution image and the clustering center are classified.
And calculating the center point of each class to obtain a new cluster center, wherein the color corresponding to the new cluster center is the color of the tile of the second part which needs to be added.
The color obtained for the first portion tile color together with the second portion tile color is the color that the present invention requires to add.
In addition, on the basis of the invention, each color has a certain coverage range, so long as the color of the original image is within the coverage range, the tile with the color can be selected, and the coverage rate of the color is higher when one color is added, therefore, the utilization rate of the colors can be evaluated to be higher by establishing an evaluation index system, and the expression effect of the image can be enhanced by improving the utilization rate.
Furthermore, the RGB color space in the present invention can also be converted into LAB color space to calculate the Euclidean distance between two color points.
In addition, the aggregate coverage model used in the invention can be used for solving the invention, and has many applications in the actual production problems of logistics distribution, facility site selection, road orientation and the like; the fuzzy clustering algorithm provided by the invention is also widely applied to the fields of data classification processing, image segmentation, pattern recognition, data mining and the like.
Further, as shown in fig. 5, based on the above-mentioned color selecting method for mosaic tiles, the present invention further provides a color selecting system for mosaic tiles, where the color selecting system for mosaic tiles includes:
a color aggregation module 51, configured to build an aggregate coverage model, aggregate the colors of the existing tile with the colors in the first color space distribution image and the second color space distribution image, and ensure that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the aggregate.
The first color selection module 52 is configured to calculate, in the set coverage model, an average distance between each color of the first color space distribution image and the second color space distribution image and an existing tile color, and order the colors according to the average distance, so as to obtain a first portion of tile colors that need to be added.
The cluster analysis module 53 is configured to divide the colors in the second color space distribution image into classes, calculate a membership matrix of the second color space distribution image by using a fuzzy clustering method, and obtain a cluster center.
And the second color selection module 54 is configured to assign colors closest to euclidean distances between the second color space distribution images and the cluster centers to one type, calculate center points of each type, and obtain new cluster centers, where colors corresponding to the new cluster centers are colors of a second part of tiles to be added.
Further, as shown in fig. 6, based on the above-mentioned method and system for selecting colors for mosaic tiles, the invention further provides a terminal correspondingly, which comprises a processor 10, a memory 20 and a display 30. Fig. 6 shows only some of the components of the terminal, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may alternatively be implemented.
The memory 20 may in some embodiments be an internal storage unit of the terminal, such as a hard disk or a memory of the terminal. The memory 20 may in other embodiments also be an external storage device of the terminal, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal. Further, the memory 20 may also include both an internal storage unit and an external storage device of the terminal. The memory 20 is used for storing application software installed in the terminal and various data, such as program codes of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores a mosaic tile color selection program 40, and the mosaic tile color selection program 40 is executable by the processor 10 to implement a mosaic tile color selection method according to the present invention.
The processor 10 may in some embodiments be a central processing unit (Central Processing Unit, CPU), microprocessor or other data processing chip for executing program code or processing data stored in the memory 20, for example for performing the one mosaic tile color selection method, etc.
The display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like in some embodiments. The display 30 is used for displaying information at the terminal and for displaying a visual user interface. The components 10-30 of the terminal communicate with each other via a system bus.
In one embodiment, the following steps are implemented when the processor 10 executes the program 40 for selecting the color of the mosaic tile in the memory 20:
establishing a set coverage model, forming a set by the colors of the existing ceramic tiles and the colors in the first color space distribution image and the second color space distribution image, and ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set;
in the set coverage model, calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color, and sorting according to the average distance to obtain a first part of tile color to be added;
Establishing a cluster analysis model, dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center;
and classifying the colors closest to the Euclidean distance between the second color space distribution image and the clustering centers into one type, calculating the center point of each type, and obtaining a new clustering center, wherein the colors corresponding to the new clustering center are the colors of the second part of ceramic tiles needing to be added.
The method for building the set coverage model comprises the steps of building a set coverage model, forming a set of colors of an existing ceramic tile and colors in a first color space distribution image and a second color space distribution image, and ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set, and specifically comprises the following steps:
the colors in the first color space distribution image and the second color space distribution image form a set T, 416 colors in the set T are combined, the colors of the existing tiles and the colors in the set T form a set C, and decision variables are introduced:wherein x is j T is a decision variable j For any one color in set T, C n For the coverage subset in set C, the other wise representation is not within the coverage of set C;
establishing a set coverage model:x j =0, j=1, 2,..416, wherein the constraint +.>For ensuring each element T in the set T j Are all collected by at least any subset C of the collection C n Covering.
In the set coverage model, calculating an average distance between each color in the first color space distribution image and the second color space distribution image and an existing tile color, and sorting according to the average distance to obtain a first part of tile color to be added, wherein the method specifically comprises the following steps:
calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the color of the existing tile:wherein d jn Representing the Euclidean distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color;
and sequencing the average distances from large to small, acquiring corresponding colors according to the positions of the average distances in the color space, and selecting the colors to fill the blank of the existing tile colors to obtain the first part of tile colors which need to be increased.
Wherein, the calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color specifically comprises:
acquiring the first color space distribution image and the second color spaceEuclidean distance between each color in the inter-distributed image and the existing tile color:wherein h is j ，s j ，v j The coordinate value of each color in the first color space distribution image and the second color space distribution image in HSV color space is respectively, h n ，s n ，v n Coordinate values of the existing tile colors in the HSV color space are respectively obtained;
and acquiring the average distance between each color in the first color space distribution image and the second color space distribution image and the color of the existing tile.
The establishing a cluster analysis model divides colors in the second color space distribution image into classes, calculates a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtains a cluster center, and specifically comprises the following steps:
establishing a cluster analysis model, and dividing the colors in the second color space distribution image into classes to obtain an objective function of fuzzy clustering:wherein an objective function J (U, C) represents a weighted square sum of distances from coordinates corresponding to colors in the second color space distribution image to each cluster center, and a membership degree matrix U= [ U ] jn ]Representing the degree to which the colors in the second color space distribution image belong to each class, M being the number of colors in the second color space distribution image, N being the number of cluster centers, f being a weighting index, dist (c n ，k j ) Refers to the distance between the color in the second color space distribution image and each cluster center, c n Is the coordinate corresponding to the cluster center, k j Is the coordinates corresponding to the colors in the second color space distribution image;
after obtaining the objective function of fuzzy clustering, a Lagrange method is used to obtain a membership matrix u jn ，Wherein m is the number of color correspondences in the second color space distribution image, dist 2 nj The square of the distance between the coordinates corresponding to the colors in the second color space distribution image and each clustering center is referred to;
obtaining the cluster center E n ：Wherein u is nj ＝1/u jn 。
The method comprises the steps of classifying colors closest to Euclidean distance between the second color space distribution image and the clustering centers into one class, calculating center points of each class to obtain new clustering centers, wherein the colors corresponding to the new clustering centers are the colors of the second part of ceramic tiles needing to be added, and specifically comprises the following steps:
calculating Euclidean distance between colors in the second color space distribution image and the clustering center, wherein each color in the second color space distribution image obtains N distances, and the colors closest to the Euclidean distance between the second color space distribution image and the clustering center are classified;
And calculating the center point of each class to obtain a new cluster center, wherein the color corresponding to the new cluster center is the color of the tile of the second part which needs to be added.
Wherein, the building of the set coverage model, the color of the existing tile and the color in the first color space distribution image and the second color space distribution image form a set, and each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set, and the method further comprises the following steps:
and converting RGB values of colors in the first color space distribution image and the second color space distribution image and colors of the existing tiles into HSV values.
The invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a mosaic tile color selection program, and the mosaic tile color selection program realizes the steps of the mosaic tile color selection method when being executed by a processor.
In summary, the invention provides a mosaic tile color selection method and related equipment, wherein the method comprises the following steps: establishing a set coverage model, forming a set by the colors of the existing ceramic tiles and the colors in the first color space distribution image and the second color space distribution image, and ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set; in the set coverage model, calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color, and sorting according to the average distance to obtain a first part of tile color to be added; establishing a cluster analysis model, dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center; and classifying the colors closest to the Euclidean distance between the second color space distribution image and the clustering centers into one type, calculating the center point of each type, and obtaining a new clustering center, wherein the colors corresponding to the new clustering center are the colors of the second part of ceramic tiles needing to be added. According to the invention, the RGB values of the colors in the first color space image and the second color space image and the existing tile are converted into HSV values, on one hand, the colors similar to the colors of the existing tile are obtained by calculating the distances between the positions corresponding to the colors of the first color space image and the second color space image in the color space and the positions corresponding to the colors of the existing tile in the color space, and on the other hand, the colors corresponding to the cluster center positions in the second color space image are searched, so that the diversity of the colors of the tile and the expressive force of the spliced image are increased, and meanwhile, the color requirements of customers are met.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or terminal comprising the element.
Of course, those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by a computer program for instructing relevant hardware (e.g., processor, controller, etc.), the program may be stored on a computer readable storage medium, and the program may include the above described methods when executed. The computer readable storage medium may be a memory, a magnetic disk, an optical disk, etc.
It is to be understood that the invention is not limited in its application to the examples described above, but is capable of modification and variation in light of the above teachings by those skilled in the art, and that all such modifications and variations are intended to be included within the scope of the appended claims.
Claims (6)
1. The mosaic tile color selection method is characterized by comprising the following steps of:
establishing a set coverage model, forming a set by the colors of the existing ceramic tiles and the colors in the first color space distribution image and the second color space distribution image, and ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set;
the method comprises the steps of establishing a set coverage model, forming a set of colors of the existing ceramic tile and colors in a first color space distribution image and a second color space distribution image, ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set, and further comprising the following steps:
converting RGB values of colors in the first color space distribution image and the second color space distribution image and colors of the existing tiles into HSV values;
in the set coverage model, calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color, and sorting according to the average distance to obtain a first part of tile color to be added;
In the set coverage model, calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color, and sorting according to the average distance to obtain a first part of tile color to be added, wherein the method specifically comprises the following steps:
calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the color of the existing tile:wherein d jn Representing the Euclidean distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color;
sequencing the average distances from large to small, acquiring corresponding colors according to the positions of the average distances in the color space, and selecting the colors to fill the blank of the existing tile colors to obtain the color of the first part of the tile to be added;
establishing a cluster analysis model, dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center;
the establishing a cluster analysis model, dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center, wherein the method specifically comprises the following steps:
Build-up of a gatherThe class analysis model divides the colors in the second color space distribution image into classes to obtain an objective function of fuzzy clustering:wherein an objective function J (U, C) represents a weighted square sum of distances from coordinates corresponding to colors in the second color space distribution image to each cluster center, and a membership degree matrix U= [ U ] jn ]Representing the degree to which the colors in the second color space distribution image belong to each class, M being the number of colors in the second color space distribution image, N being the number of cluster centers, f being a weighting index, dist (c n ，k j ) Refers to the distance between the color in the second color space distribution image and each cluster center, c n Is the coordinate corresponding to the cluster center, k j Is the coordinates corresponding to the colors in the second color space distribution image;
after obtaining the objective function of fuzzy clustering, a Lagrange method is used to obtain a membership matrix u jn ，
Wherein m is the number of color correspondences in the second color space distribution image, dist 2 nj The square of the distance between the coordinates corresponding to the colors in the second color space distribution image and each clustering center is referred to;
obtaining the cluster center E n ：Wherein u is nj ＝1/u jn ；
The colors closest to Euclidean distance between the second color space distribution image and the clustering centers are classified into one type, and the center point of each type is calculated to obtain a new clustering center, wherein the colors corresponding to the new clustering center are the colors of the second part of ceramic tiles needing to be added;
the method comprises the steps of classifying colors closest to Euclidean distance between the second color space distribution image and the clustering centers into one class, calculating center points of each class to obtain new clustering centers, wherein the colors corresponding to the new clustering centers are the colors of the second part of ceramic tiles needing to be added, and specifically comprises the following steps:
calculating Euclidean distance between colors in the second color space distribution image and the clustering center, wherein each color in the second color space distribution image obtains N distances, and the colors closest to the Euclidean distance between the second color space distribution image and the clustering center are classified;
and calculating the center point of each class to obtain a new cluster center, wherein the color corresponding to the new cluster center is the color of the tile of the second part which needs to be added.
2. The mosaic tile color selection method according to claim 1, wherein said building a set coverage model forms a set of colors of an existing tile with colors in a first color space distribution image and a second color space distribution image, and ensures that each color in said first color space distribution image and said second color space distribution image is covered by at least any subset of said set, comprising in particular:
The colors in the first color space distribution image and the second color space distribution image form a set T, 416 colors in the set T are combined, the colors of the existing tiles and the colors in the set T form a set C, and decision variables are introduced:wherein x is j T is a decision variable j For any one color in set T, C n For the coverage subset in set C, the other wise representation is not within the coverage of set C;
establishing a set coverage model:x j ＝0，j＝1，2，..,416, wherein the constraint ∈ ->For ensuring each element T in the set T j Are all collected by at least any subset C of the collection C n Covering.
3. The mosaic tile color selection method according to claim 1, wherein said calculating an average distance of each color of said first and second color space distribution images from an existing tile color comprises:
acquiring Euclidean distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color:wherein h is j ，s j ，v j The coordinate value of each color in the first color space distribution image and the second color space distribution image in HSV color space is respectively, h n ，s n ，v n Coordinate values of the existing tile colors in the HSV color space are respectively obtained;
And acquiring the average distance between each color in the first color space distribution image and the second color space distribution image and the color of the existing tile.
4. A mosaic tile color selection system, wherein the mosaic tile color selection system comprises:
the color collection module is used for establishing a collection coverage model, forming a collection of the colors of the existing tiles and the colors in the first color space distribution image and the second color space distribution image, and ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the collection;
the method comprises the steps of establishing a set coverage model, forming a set of colors of the existing ceramic tile and colors in a first color space distribution image and a second color space distribution image, ensuring that each color in the first color space distribution image and the second color space distribution image is covered by at least any subset of the set, and further comprising the following steps:
converting RGB values of colors in the first color space distribution image and the second color space distribution image and colors of the existing tiles into HSV values;
the first color selection module is used for calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color in the set coverage model, and sequencing according to the average distance to obtain a first part of tile color to be added;
In the set coverage model, calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color, and sorting according to the average distance to obtain a first part of tile color to be added, wherein the method specifically comprises the following steps:
calculating the average distance between each color in the first color space distribution image and the second color space distribution image and the color of the existing tile:wherein d jn Representing the Euclidean distance between each color in the first color space distribution image and the second color space distribution image and the existing tile color;
sequencing the average distances from large to small, acquiring corresponding colors according to the positions of the average distances in the color space, and selecting the colors to fill the blank of the existing tile colors to obtain the color of the first part of the tile to be added;
the cluster analysis module is used for dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center;
the establishing a cluster analysis model, dividing the colors in the second color space distribution image into classes, calculating a membership matrix of the second color space distribution image by a fuzzy clustering method, and obtaining a cluster center, wherein the method specifically comprises the following steps:
Establishing a cluster analysis model, and dividing the colors in the second color space distribution image into classes to obtain an objective function of fuzzy clustering:wherein an objective function J (U, C) represents a weighted square sum of distances from coordinates corresponding to colors in the second color space distribution image to each cluster center, and a membership degree matrix U= [ U ] jn ]Representing the degree to which the colors in the second color space distribution image belong to each class, M being the number of colors in the second color space distribution image, N being the number of cluster centers, f being a weighting index, dist (c n ，k j ) Refers to the distance between the color in the second color space distribution image and each cluster center, c n Is the coordinate corresponding to the cluster center, k j Is the coordinates corresponding to the colors in the second color space distribution image;
after obtaining the objective function of fuzzy clustering, a Lagrange method is used to obtain a membership matrix u jn ，
Wherein m is the number of color correspondences in the second color space distribution image, dist 2 nj The square of the distance between the coordinates corresponding to the colors in the second color space distribution image and each clustering center is referred to;
obtaining the cluster center E n ：Wherein u is nj ＝1/u jn ；
The second color selection module is used for classifying colors closest to Euclidean distances between the clustering centers in the second color space distribution image into one type, calculating center points of each type, and obtaining a new clustering center, wherein the colors corresponding to the new clustering center are the colors of the second part of ceramic tiles needing to be added;
the method comprises the steps of classifying colors closest to Euclidean distance between the second color space distribution image and the clustering centers into one class, calculating center points of each class to obtain new clustering centers, wherein the colors corresponding to the new clustering centers are the colors of the second part of ceramic tiles needing to be added, and specifically comprises the following steps:
calculating Euclidean distance between colors in the second color space distribution image and the clustering center, wherein each color in the second color space distribution image obtains N distances, and the colors closest to the Euclidean distance between the second color space distribution image and the clustering center are classified;
and calculating the center point of each class to obtain a new cluster center, wherein the color corresponding to the new cluster center is the color of the tile of the second part which needs to be added.
5. A terminal, the terminal comprising: a memory, a processor and a program stored on the memory and operable on the processor for colour selection of mosaic tiles, which when executed by the processor performs the steps of a method for colour selection of mosaic tiles as claimed in any one of claims 1 to 3.
6. A computer readable storage medium, characterized in that it stores a program for color selection of mosaic tiles, which when executed by a processor, implements the steps of the method for color selection of mosaic tiles according to any one of claims 1-3.
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