CN114461829A - Method for vectorization of traditional culture memory symbol subgraph - Google Patents
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Abstract
The invention discloses a traditional culture memory symbol subgraph vectorization-oriented method, which comprises the following steps: s1, calculating pixel points in the image, determining element outlines and extracting complete sub-elements; s2, carrying out image processing on the sub-elements, and dividing the sub-elements into different areas; s3, approximating the edges of each area and converting the edges into smooth vector area outlines; and S4, integrating the data information in each region, acquiring and storing the regional vectorization result, and outputting the vector diagram of the sub-elements. Obtaining accurate and complete sub-elements through less interaction, and realizing local element vectorization through a vectorization technology to obtain corresponding vector materials, thereby completing the whole process of the sub-element vectorization in the traditional culture image and obtaining a vector diagram of the local elements in the image; therefore, the vector diagram of the local sub-elements in the image is quickly obtained, the quality of the material is improved, and the difficulty of secondary creation of the cultural material is greatly reduced.
Description
Technical Field
The invention relates to the technical field of computer image processing, in particular to a traditional culture memory symbol subgraph vectorization-oriented method.
Background
Under the current era background, the method for digitalizing and recycling cultural heritage is an effective method for protecting and inheriting culture, a Chinese cultural material library is a cultural gold mine for the digitalized promotion of cultural resources, the cultural gold mine is explored, and the method has epoch-making significance for inheriting and spreading Chinese culture, changing cultural production modes and developing cultural productivity, and an image vectorization technology is a core tool for constructing the Chinese cultural material library.
Vector graphics have many advantages over raster images, such as image quality independent of resolution, arbitrary scaling without distortion; the storage is compact, the file is small, and efficient storage and transmission can be realized; and editing and modifying the geometric primitives are supported. Image vectorization is the conversion of a grid-type image into a vector-formatted image.
At present, materials in a Chinese culture material library still mainly comprise a whole vector diagram, one image with culture connotation usually comprises one or more sub-elements with rich connotation, the vector diagram of the elements is particularly important when culture creation is carried out, under the common condition, a user needs certain post-processing operation to obtain a local vector diagram from the complete vector diagram, and the use efficiency of the materials in the material library is greatly reduced. In addition, under the influence of an image acquisition environment, the image quality is uneven, the vectorization of the whole image is directly carried out on the image with an unsatisfactory effect, the effect of the obtained vector diagram is unsatisfactory, the literacy value of the vector diagram cannot be fully exerted, and the use value of materials in a material library is reduced.
An effective solution to the problems in the related art has not been proposed yet.
Disclosure of Invention
Aiming at the problems in the related art, the invention provides a method for quantizing a traditional culture memory symbol subgraph vector, so as to overcome the technical problems in the prior related art.
Therefore, the invention adopts the following specific technical scheme:
a vectorization method for a traditional culture memory symbol subgraph comprises the following steps:
s1, calculating pixel points in the image, determining element outlines and extracting complete sub-elements;
s2, performing image processing on the sub-elements, and dividing the sub-elements into different areas;
s3, approximating the edges of each area and converting the edges into smooth vector area outlines;
and S4, integrating the data information in each region, acquiring and storing the regional vectorization result, and outputting the vector diagram of the sub-elements.
Further, the calculating of pixel points in the image, determining element contours and extracting complete sub-elements includes the following steps:
s11, selecting characteristic components representing the edges of the target elements, and carrying out weighted summation on the characteristic component costs to calculate the local costs from each pixel point to the adjacent pixel points;
s12, calculating channel difference between adjacent pixel points by fusing color gradient characteristics, and improving the fit degree of the element extraction contour;
s13, searching the minimum cost path by using Dijkstra algorithm to obtain the complete closed element contour.
Further, the characteristic components include a laplacian zero point, a gradient magnitude, and a gradient direction.
Further, the formula for calculating the local cost from each pixel point to the adjacent pixel point by performing weighted summation on the feature component cost is as follows:
C(p,q)＝wG·fG(q)+wZ·fZ(q)+wD·fD(p,q)；
where C (p, q) denotes the local cost of the directed link from pixel p to neighboring pixel q, fZDenotes Laplace zero, fGDenotes the magnitude of the gradient, fDDenotes the direction of the gradient, wG＝0.43， wZ＝0.43，wD＝0.14。
Further, by fusing the color gradient features, the channel difference between adjacent pixel points is calculated, and the fitting degree of the element extraction contour is improved, which comprises the following steps:
s121, fusing color gradient characteristics, converting the image into a space of a color system (CIE-Lab), and separating Lab channels;
s122, calculating the difference between the L channel and the ab fusion channel between the adjacent pixel points, wherein the calculation formula is as follows:
fC(q)＝(Cr⊙Cl,Ct⊙Cb)；
wherein f isC(q) represents a color gradient and (q),
△Lij＝△Li-△Lj，△aij＝△ai-△aj，△bij＝△bi-△bj，Cr、Cl、Ctand CbAnd respectively the weighted average values of the pixel points in the right, left, upper and lower neighborhoods in the space of the color system.
And S123, substituting the difference value into the calculation process of the local cost, improving the fitting degree of the element extraction outline, and reducing the interaction times.
Further, the formula for substituting the difference value into the calculation process of the local cost is as follows:
C(p,q)＝wG·[βfG(q)+(1-β)fZ(q)]+wZ·fZ(q)+wD·fD(p,q)；
where C (p, q) denotes the local cost of the directed link from pixel p to neighboring pixel q, fZDenotes Laplace zero, fGDenotes the magnitude of the gradient, fDDenotes the direction of the gradient, wG＝0.43， wZ＝0.43，wD＝0.14。
Further, the finding a minimum cost path by using a Dijkstra algorithm to obtain a completely closed element contour includes the following steps:
s131, selecting any point on the element boundary for expansion, and adding the local cost of the point to the adjacent node;
s132, selecting the adjacent node with the minimum accumulated cost to continue expansion;
and S133, repeating the node expansion to form a sequence until the sequence is superposed with the initial point, and acquiring a completely closed element outline.
Further, the image processing the sub-elements and dividing the sub-elements into different regions includes the following steps:
s21, clustering the regions in the image by using a clustering algorithm, and setting a certain spatial distance and color distance;
s22, carrying out multiple iterations, replacing the original pixel values with the converged pixel values, merging locally similar pixel points, and obtaining different color regions;
and S23, extracting the edge structure of each region, carrying out edge detection on the regions by using a multi-stage edge detection (canny) operator, calculating the gradient size and direction of the image, carrying out non-maximum value inhibition and double-threshold value screening, and obtaining the region edges.
Further, the approximating the edges of each region and converting the edges into a smooth vector region profile includes the following steps:
s31, simplifying the obtained edge curve through Douglas-Puck (Douglas-Peucker) algorithm;
s32, iteratively selecting points on the curve, which are farthest from the corresponding straight-line segment and larger than a set threshold value, as polygon vertexes, and connecting the points to obtain a fitting polygon;
and S33, fitting and smoothing the polygon by using a Bezier curve, and converting the polygon into an accurate and smooth vector contour.
Further, the data information includes vector contour information and color information.
The invention has the beneficial effects that: accurate and complete sub-elements are obtained through less interaction, local element vectorization is realized through a vectorization technology, and corresponding vector materials are obtained, so that the whole process of the sub-element vectorization in the traditional culture image is completed, and a vector diagram of the local elements in the image is obtained. Compared with a general image extraction method, the method ensures the attaching degree of the extracted outline during element extraction, greatly reduces the interaction times, and simultaneously enables a user to quickly obtain a local sub-element vector diagram in the image through less interaction by the two-stage process provided by the invention, thereby improving the quality of the material in the material library, greatly reducing the difficulty of secondary creation of the cultural material and enabling the cultural material to exert the maximum creation value.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a method for vectorization of a traditional culture memory symbol oriented subgraph according to an embodiment of the invention.
Detailed Description
According to the embodiment of the invention, a method for vectorization of a traditional culture memory symbol subgraph is provided.
Referring to the drawings and the detailed description, the invention will be further explained, as shown in fig. 1, in an embodiment of the invention, a method for vectorization of a traditional culture memory symbol oriented subgraph includes the following steps:
s1, calculating pixel points in the image, determining element outlines and extracting complete sub-elements;
in this step, the problem of searching for the edge profile of the element is regarded as the problem of searching for the optimal path in the process of searching for the graph, and the optimal path must be a section of path which is attached to the boundary of the element, so that the link between the pixel with the strong edge characteristic and other pixel points has low local cost.
Wherein, S1 includes the following steps:
s11, selecting characteristic components (the characteristic components include a Laplace zero point, gradient size and gradient direction) representing the edges of the target elements, and performing weighted summation on the characteristic component costs to calculate the local costs from each pixel point to the adjacent pixel points, wherein the calculation formula is as follows:
C(p,q)＝wG·fG(q)+wZ·fZ(q)+wD·fD(p,q)；
where C (p, q) denotes the local cost of the directed link from pixel p to neighboring pixel q, fZDenotes Laplace zero, fGDenotes the magnitude of the gradient, fDDenotes the direction of the gradient, wG＝0.43， wZ＝0.43，wD＝0.14。
The gradient magnitude cost term is primarily a description of the edge strength, and locations with strong edge strength should pay less. And (5) convolving the image with a sobel operator to obtain the gradient size.
The Laplace zero point cost item is mainly used for describing boundary positioning, and the convolution operation is carried Out on the image and a Laplace operator to obtain OutLIf OutL(q) 0 or when there is a neighbor of a different symbol, let fZ(q) is 0, otherwise fZ(q)＝1。
The gradient direction cost term is mainly used for adding a smooth constraint to the position of the change of the boundary direction and endowing a larger cost to the position of the abrupt change of the direction. Let D (p) be the unit vector perpendicular to the gradient direction at point p, the gradient direction cost calculation formula is as follows:
dp(p,q)＝D(p)·L(p,q)；
dq(p,q)＝L(p,q)·D(q)；
where L (p, q) represents the link direction between two points such that p differs from the final determined link direction by less than 90 degrees. When the gradient directions of two pixels are similar to each other and the link direction between them is also similar, the cost of the gradient direction feature is low.
And considering the problem of the area with inconsistent gray level and color change in the image, further optimizing the gray level gradient penalty term. Given an input image, it is first converted to the perceptually uniform CIE-Lab color space, followed by color gradient fusion of the color images described below.
S12, calculating channel difference between adjacent pixel points by fusing color gradient characteristics, and improving the fit degree of the element extraction contour;
wherein, S12 includes the following steps:
s121, fusing color gradient characteristics, converting the image into a space of a color system (CIE-Lab), and separating Lab channels;
s122, calculating the difference between the L channel and the ab fusion channel between the adjacent pixel points, wherein the calculation formula is as follows:
fC(q)＝(Cr⊙Cl,Ct⊙Cb)；
wherein f isC(q) represents a color gradient and (q),
△Lij＝△Li-△Lj，△aij＝△ai-△aj，△bij＝△bi-△bj，Cr、Cl、Ctand CbThe weighted average values of the pixel points in the right, left, upper and lower neighborhoods in a color system (CIE-Lab) space are respectively as follows:
Cr＝(2c(x+1,y)+c(x+1,y-1)+c(x+1,y+1))/4；
Cl＝(2c(x-1,y)+c(x-1,y-1)+c(x-1,y+1))/4；
Ct＝(2c(x,y+1)+c(x-1,y+1)+c(x+1,y+1))/4；
Cd＝(2c(x,y-1)+c(x-1,y-1)+c(x+1,y-1))/4；
c (x, y) is the CIE-Lab value at the (x, y) position in the image.
When searching for an edge, a position where the color gradient changes more is more likely to be a target boundary, which is consistent with the gray gradient change.
And S123, substituting the difference value into the calculation process of the local cost, improving the fitting degree of the element extraction outline, and reducing the interaction times.
The formula for substituting the difference value into the calculation process of the local cost is as follows:
C(p,q)＝wG·[βfG(q)+(1-β)fZ(q)]+wZ·fZ(q)+wD·fD(p,q)；
where C (p, q) represents the local cost of directionally linking from pixel p to neighboring pixel q, fZDenotes Laplace zero, fGDenotes the magnitude of the gradient, fDDenotes the direction of the gradient, wG＝0.43， wZ＝0.43，wD＝0.14。
S13, searching the minimum cost path by using Dijkstra algorithm to obtain the complete closed element contour.
Wherein, S13 includes the following steps:
s131, selecting any point on the element boundary for expansion, and adding the local cost of the point to the adjacent node;
s132, selecting the adjacent node with the minimum accumulated cost to continue expansion;
and S133, repeating the node expansion to form a sequence until the sequence is superposed with the initial point, and acquiring a completely closed element outline.
S2, performing image processing on the sub-elements, and dividing the sub-elements into different areas;
wherein, S2 includes the following steps:
s21, clustering the regions in the image by using a clustering algorithm, and setting a certain spatial distance and color distance;
s22, carrying out multiple iterations, replacing the original pixel values with the converged pixel values, merging locally similar pixel points, and obtaining different color regions;
the specific steps of performing color clustering and edge detection on the elements to obtain different regions in the elements are as follows: firstly, arbitrarily selecting a feature point in a feature space, dividing a circular region with a radius of R around the feature point, calculating offset vectors from the center point to other feature points in the region, and specifying that the estimation weight of the offset vector of the feature point closer to the center point is larger, the calculation formula is as follows:
and moving the central point according to the obtained vector, then carrying out iterative solution, stopping moving when the mean shift quantity meets the requirement of being smaller than a set error, obtaining a clustered image, dividing the image into different color regions, replacing the color of each region by the color of the characteristic point, storing the color information of each region, and facilitating subsequent color filling.
And S23, extracting the edge structure of each region, carrying out edge detection on the regions by using a multi-stage edge detection (canny) operator, calculating the gradient size and direction of the image, carrying out non-maximum value inhibition and double-threshold value screening, and obtaining the region edges.
For each successive edge, a straight line AB is connected between the points A, B at the beginning and end of the curve, the straight line being the chord of the curve, the point C on the curve having the greatest distance from the straight line segment is calculated, and the distance d from AB is calculated. The distance is compared with a predetermined threshold value threshold, and if the distance is smaller than the threshold value threshold, the straight line segment is used as an approximation of a curve, and the curve segment is processed. If the distance is greater than the threshold, the curve is divided into two segments, AC and BC, with C, and the above calculations are performed separately for the two segments. When all the curves are processed, the broken lines formed by all the dividing points are connected in sequence, and the broken lines can be used as the approximation of the curves.
S3, approximating the edges of each area and converting the edges into smooth vector area outlines;
wherein, S3 includes the following steps:
s31, simplifying the obtained edge curve through Douglas-Puck (Douglas-Peucker) algorithm;
s32, iteratively selecting points on the curve, which are farthest from the corresponding straight-line segment and larger than a set threshold value, as polygon vertexes, and connecting the points to obtain a fitting polygon;
and S33, fitting and smoothing the polygon by using a Bezier curve, and converting the polygon into an accurate and smooth vector contour.
Wherein, the cubic Bezier curve is defined as follows:
Bn(t)＝P0(1-t)3+3P1t(1-t)2+3P2t2(1-t)+P3t3,t∈[0,1]；
the first two points are the starting point and the middle point of the curve segment, and the middle two points need to be obtained through calculation. Is provided with PiTangential direction of point and Pi-1Pi+1The directions are the same, and it is sufficient to ensure that the control point in front of the point and the control point behind the point are both on the tangent line, so the coordinates of the two control points are as follows:
Ai(xi+a(xi+1-xi-1),yi+a(yi+1-yi-1))；
Bi(xi+1+b(xi+2-xi),yi+1+b(yi+2-yi))；
and finishing the conversion of the polygon into the vector outline according to the starting point and the end point of each edge of the polygon and the coordinates of the control points.
And S4, integrating the data information in each region, acquiring and storing the regional vectorization result, and outputting the vector diagram of the sub-elements.
And the data information comprises vector outline information and color information, and the result is written into an svg format for storage.
In summary, by means of the above technical solution of the present invention, accurate and complete sub-elements are obtained through less interaction, and local element vectorization is realized through a vectorization technology to obtain corresponding vector materials, so that all processes of sub-element vectorization in a traditional culture image are completed, and a vector diagram of local elements in the image is obtained. Compared with a general image extraction method, the method ensures the attaching degree of the extracted outline during element extraction, greatly reduces the interaction times, and simultaneously ensures that a user can quickly obtain a local sub-element vector diagram in the image through less interaction by the two-stage flow provided by the invention, thereby improving the quality of the material in the material library, greatly reducing the difficulty of secondary creation of the cultural material and enabling the cultural material to exert the maximum creation value thereof.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A vectorization method for a traditional culture memory symbol subgraph is characterized by comprising the following steps:
s1, calculating pixel points in the image, determining element outlines and extracting complete sub-elements;
s2, performing image processing on the sub-elements, and dividing the sub-elements into different areas;
s3, approximating the edges of each area and converting the edges into smooth vector area outlines;
and S4, integrating the data information in each region, acquiring and storing the regional vectorization result, and outputting the vector diagram of the sub-elements.
2. The method for performing vectorization on a traditionally cultural memory symbol subgraph according to claim 1, wherein the steps of calculating pixel points in an image, determining element outlines and extracting complete sub-elements comprise:
s11, selecting characteristic components representing the edges of the target elements, and carrying out weighted summation on the characteristic component costs to calculate the local costs from each pixel point to the adjacent pixel points;
s12, calculating the channel difference between adjacent pixel points by fusing the color gradient characteristics, and improving the fitting degree of the element extraction contour;
and S13, searching the minimum cost path by using the Dixtera algorithm to obtain the complete closed element outline.
3. The method for vectorization of a traditional culture memory symbol subgraph according to claim 2, wherein the feature components include laplacian zeros, gradient magnitude and gradient direction.
4. The method for performing vectorization on a traditional culture memory symbol subgraph according to claim 3, wherein the calculation formula for performing weighted summation on the feature component cost to calculate the local cost from each pixel point to the adjacent pixel point is as follows:
C(p,q)＝wG·fG(q)+wZ·fZ(q)+wD·fD(p,q)；
where C (p, q) denotes the local cost of the directed link from pixel p to neighboring pixel q, fZDenotes the Laplace zero, fGDenotes the magnitude of the gradient, fDDenotes the direction of the gradient, wG＝0.43，wZ＝0.43，wD＝0.14。
5. The method for performing vectorization on a traditional culture memory symbol subgraph according to claim 4, wherein the channel difference between adjacent pixel points is calculated by fusing color gradient features, so as to improve the fit degree of element extraction outlines, and the method comprises the following steps:
s121, fusing color gradient characteristics, converting the image into a color system space, and separating Lab channels;
s122, calculating the difference between the L channel and the ab fusion channel between the adjacent pixel points, wherein the calculation formula is as follows:
fC(q)＝(Cr⊙Cl,Ct⊙Cb)；
wherein f isC(q) represents a color gradient and (q),
△Lij＝△Li-△Lj，△aij＝△ai-△aj，△bij＝△bi-△bj，Cr、Cl、Ctand CbAnd respectively the weighted average values of the pixel points in the right, left, upper and lower neighborhoods in the space of the color system.
And S123, substituting the difference value into the calculation process of the local cost, improving the fitting degree of the element extraction outline, and reducing the interaction times.
6. The method for vectorization of a traditional culture memory symbol oriented subgraph according to claim 5, wherein the formula for substituting the difference value into the calculation process of the local cost is as follows:
C(p,q)＝wG·[βfG(q)+(1-β)fZ(q)]+wZ·fZ(q)+wD·fD(p,q)；
where C (p, q) denotes the local cost of the directed link from pixel p to neighboring pixel q, fZDenotes the Laplace zero point, fGDenotes the magnitude of the gradient, fDIndicating the direction of the gradient，wG＝0.43，wZ＝0.43，wD＝0.14。
7. The method for vectorization of a traditional culture memory symbol subgraph according to claim 6, wherein the step of searching the minimum cost path by using the dix-tesla algorithm to obtain the complete closed element contour comprises the following steps:
s131, selecting any point on the element boundary for expansion, and adding the local cost of the point to the adjacent node;
s132, selecting the adjacent node with the minimum accumulated cost to continue expansion;
and S133, repeating the node expansion to form a sequence until the sequence is superposed with the initial point, and acquiring a complete closed element outline.
8. The method for performing traditional culture memory symbol oriented subgraph vectorization according to claim 1, wherein the sub-elements are subjected to image processing and divided into different regions, and the method comprises the following steps:
s21, clustering the areas in the image by using a clustering algorithm, and setting a certain spatial distance and a certain color distance;
s22, carrying out multiple iterations, replacing the original pixel values with the converged pixel values, merging locally similar pixel points, and obtaining different color regions;
s23, extracting the edge structure of each region, performing edge detection on the regions by using a multi-stage edge detection operator, calculating the size and the direction of the image gradient, performing non-maximum suppression and double-threshold screening, and acquiring the region edges.
9. The method for performing vectorization on a traditional culture memory symbol subgraph according to claim 1, wherein the approximating each region edge to a smooth vector region contour comprises the following steps:
s31, simplifying the obtained edge curve through a Douglas-Pock algorithm;
s32, iteratively selecting points on the curve, which are farthest from the corresponding straight-line segment and larger than a set threshold value, as polygon vertexes, and connecting the points to obtain a fitting polygon;
and S33, fitting and smoothing the polygon by using a Bezier curve, and converting the polygon into an accurate and smooth vector contour.
10. The method for performing subpicture vectorization on a traditional culture memory symbol according to claim 1, wherein the data information comprises vector contour information and color information.
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