CN114425657A - A method of laser marking color map - Google Patents

Abstract

The invention relates to the technical field of laser marking, in particular to a laser marking color image method, which comprises the following steps: randomly selecting a color graph, and presetting a positive integer value n as a layering number according to the color graph characteristics; extracting n groups of different RGB values of the color map directly by using a color extractor or selecting n groups of different RGB values with strongest capability of describing the color map from a color library by using a description capability statistical method; splitting the color map into n layers corresponding to single RGB values according to the n groups of different RGB values with the strongest ability for describing the color map; and successively marking each layered layer on the target working surface by using a laser marking machine to finally form a colorful image. The invention ensures that the selection of the colors and the laser parameters of the picture has higher rationality, and the colors in the original picture can be reproduced as much as possible on the premise of ensuring the processing efficiency. The invention simplifies the working process of the laser marking color picture and improves the processing efficiency.

Description

A method of laser marking color map

technical field

The invention relates to a laser marking color image method, which belongs to the technical field of laser marking.

Background technique

As another major invention of mankind after atomic energy, computer and semiconductor, laser is called "the fastest knife", "the most accurate ruler" and "the brightest light". Due to the characteristics of high brightness, high directionality, high monochromaticity and high coherence of laser, compared with traditional processing methods, laser comprehensive advantages are obvious. With the continuous progress of laser technology and the reduction of cost, the penetration rate of laser has been continuously improved, and the application field has also rapidly expanded from material processing, communication and optical storage to scientific research, military, instrument sensing and other fields. With the recovery of the domestic manufacturing industry and the growing demand for emerging businesses, the domestic laser industry has ushered in a new round of rapid growth.

Laser marking is a technology that uses the thermal effect of laser to ablate the surface material of an object to leave a permanent mark. Compared with traditional electrochemical, mechanical and other labeling methods, it has the advantages of no pollution, high speed, high quality, great flexibility, and non-contact work. In recent years, laser marking has replaced traditional marking methods in many fields and has become a conventional processing method, and even formed a new industrial standard. Laser marking machine is a mechatronics equipment that integrates laser, optics, precision machinery, computer and other technologies. It is mainly composed of laser, optical system and controller, of which the controller is the core component. The controller has gone through two development stages of hardware numerical control (nC) and computer numerical control (CnC). Since the 1990s, with the rapid development of computer technology and the continuous improvement of cost performance, computer-based numerical control system has become the mainstream of control system development.

The use of lasers for marking applications has been widely accepted by various sectors of the industry, thus placing higher requirements on laser color marking technology. Since the laser parameters are in one-to-one correspondence with the colors produced on the metal surface, the pictures cannot be directly processed, and the marking of complex color images cannot be realized. The thermal effect generated by the laser acting on the surface to be marked forms an oxide film on the surface, and the thickness of the oxide film is controlled by the overlapping of laser beam energy and pulse action, thereby realizing marking of different colors. Regarding the color image marking method, a mixed color marking method is currently known. Color mixing marking, using the color mixing principle, decomposes complex multi-color marking tasks into three primary color markings to achieve color effects. This method requires that the unmarked color points are small enough, and the pigment resolution is lower than that of the human eye. This can only be achieved. The resolution of the human eye is 0.02mm, and the distance between the color point and the color point is less than 0.02mm. When the laser beam acts within this distance, the thermal effect will affect each other, resulting in a change in the thickness of the formed oxide film, which in turn causes color. Point color change, this change is not a simple superposition of three primary colors, the interference effect based on the oxide film is unpredictable, so the expected effect cannot be achieved, and the final marking image will appear color confusion.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a laser marking method for color images, which can restore the original image in all aspects of shape, color, light and shade, improve the degree of restoration of the original image, and realize the marking of complex color images.

A method of laser marking color image, comprising the following steps:

1) Select a color map, preset a positive integer value n as the number of layers according to the characteristics of the color map, and n takes 5 to 20;

2) directly use the color picker to extract the n groups of different RGB values of the color map or use the descriptive power statistics method to select the n groups of different RGB values with the strongest ability to describe the color map in the color library;

3) the described color map is split into layers corresponding to n single RGB values according to the screened out description color map with the strongest n groups of different RGB values; Concrete steps are as follows:

3-1) Establish an empty list, denoted as Flist, calculate its corresponding description coefficient DC for all data in the color library, and filter out n groups of data that meet the requirements after the results obtained are incrementally sorted and put into Flist;

3-2) After the data screening is completed, the images are layered according to the n groups of data in the list Flist, and the RGB data of the original image is converted into a three-dimensional array with a size of m×p×3;

3-3) According to the n groups of data of Flist, sequentially generate n arrays consisting of single pixel values and having a size of m×p×3;

3-4) Calculate the Euclidean distance D _ij of each pixel point by operating the original image array and the generated n arrays. The expression of D _ij is as follows:

R _oij , _{Go oij} , and Bo _oij in the formula represent the RGB parameter values of the original image at x=i, y=j point, R _n , G _n , B _n represent a set of color data in the list Flist,

3-5) Combine the generated n Euclidean arrays of m×p×1 size into m×p×n three-dimensional arrays, and take the position corresponding to the minimum value in the third dimension to obtain m×p×1 Array M, the value of each element of the array represents the most consistent value at this pixel is the Value group data in Flist;

3-6) Expand the array M into n arrays with serial numbers from 1 to n and size m×p×1. The value of this point is the same as the serial number of the expanded array. The value of this array is 1, and the value of this point is 1. The value is different from the serial number of the expanded array, the array value is 0;

3-7) Multiply the obtained array by the data value corresponding to the serial number in Flist, and modify all [0, 0, 0] values to [255, 255, 255], and finally get n m×p×3 size Array, that is, the image after layering;

4) Use the laser marking machine to mark each sub-layer after layering on the target working surface one by one, and finally form a color map.

Preferably, in the step 2, the descriptive ability statistics method is used to select n groups of color RGB values with the strongest descriptive ability in the color library, as follows:

1) After the original picture is divided into n layers, each group of data in the color library is differentiated from the RGB of each pixel of the picture to obtain the Euclidean distance d _ij between each pixel,

In the formula: R _oij , _{Go oij} , _{Bo oij} represent the RGB parameter values of the original image at x=i, y=j point; R _l , G _l , B _l represent a group of RGB parameter values of the color library data;

2) Calculate the description ability of each group of color data to the picture According to the coefficient DC, select n groups of different RGB values with the strongest ability to describe the color picture according to the DC value, and the specific calculation formula is as follows:

Preferably, the specific steps of screening out n groups of data that meet the requirements and putting them into Flist in the step 3 are as follows:

1) According to the incrementally sorted DC, take out the data in turn;

2) Determine whether there is similar data in the Flist list, if so, return to step 1, if not, add the data to another new Flist;

3) Determine whether the number of Flist data is equal to n, if not, return to step 1, if it is equal to n, output a new Flist list.

The advantages of the present invention are: the present invention matches the picture with the laser parameters, can restore the color and shape of the color picture, and has higher reducibility for the color picture, the present invention simplifies the working process of the laser marking machine, and improves the working efficiency .

Description of drawings

The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention.

FIG. 1 is a schematic diagram of the flow structure of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

A method for laser marking color image, comprising the following steps:

1) select a color map, preset a positive integer value n as the number of layers according to the color map characteristics, and n is 20;

2) directly use the color picker to extract the n groups of different RGB values of the color map or use the descriptive ability statistics method to select the n groups of different RGB values with the strongest ability to describe the color map in the color library; the specific steps are as follows:

2-1) After the original picture is divided into 20 layers, each group of data in the color library is differentiated from the RGB of each pixel of the picture to obtain the Euclidean distance d _ij between each pixel,

In the formula: R _oij , _{Go oij} , _{Bo oij} represent the RGB parameter values of the original image at x=i, y=j point; R _l , G _l , B _l represent a group of RGB parameter values of the color library data;

2-2) Calculate the description ability of each group of color data to the picture According to the coefficient DC, select n groups of different RGB values with the strongest ability to describe the color picture according to the DC value, and the specific calculation formula is as follows:

Get 20 sets of RGB values: [201, 143, 129], [161, 114, 86], [155, 120, 98], [36, 35, 33], [2, 5, 10], [123, 121, 145], [158, 127, 36], [188, 193, 212 ], [197, 197, 197], [127, 83, 46], [131, 115, 89], [164, 102, 41], [104, 107, 122], [107, 146, 40], [135, 197, 50], [143, 195, 58], [108, 168 , 44], [115, 151, 51], [79, 61, 47], [79, 80, 60].

2-3) the described color map is divided into the layers corresponding to 20 single RGB values according to the 20 groups of different RGB values with the strongest description color map ability screened out; Concrete steps are as follows:

3-1) Create an empty list, denoted as Flist, calculate its corresponding description coefficient DC for all the data in the color library, and filter out 20 groups of data that meet the requirements after sorting the obtained results into Flist; the details are as follows:

3-1-1) According to the incrementally sorted DC, take out the data in turn;

3-1-2) Determine whether there is similar data in the Flist list, if there is, return to step 1, if not, add the data to another new Flist;

3-1-3) Determine whether the number of Flist data is equal to 20, if it is not equal to 20, return to step 1, if it is equal to 20, output a new Flist list.

3-2) After the data screening is completed, the images are layered according to the 20 groups of data in the list Flist, and the RGB data of the original image is converted into a three-dimensional array with a size of m×p×3;

3-3) According to the 20 groups of data of Flist, 20 arrays consisting of a single pixel value and having a size of m×p×3 are sequentially generated;

3-4) Calculate the Euclidean distance D _ij of each pixel point by operating the original image array with the generated 20 arrays. The expression of D _ij is as follows:

R _oij , _{Go oij} , and Bo _oij in the formula represent the RGB parameter values of the original image at x=i, y=j point, R _n , G _n , B _n represent a set of color data in the list Flist,

3-5) Combine the generated 20 m×p×1 Euclidean arrays into m×p×n three-dimensional arrays, and take the position corresponding to the minimum value in the third dimension to obtain m×p×1 Array M, the value of each element of the array represents the most consistent value at this pixel is the Value group data in Flist;

3-6) Expand the array M into 20 arrays with serial numbers ranging from 1 to 20 and size m×p×1. The value of this point is the same as the serial number of the expanded array. The value of this array is 1, and the value of this point is 1. The value is different from the serial number of the expanded array, the array value is 0;

3-7) Multiply the obtained array by the data value corresponding to the serial number in Flist, and modify all [0, 0, 0] values to [255, 255, 255], and finally get 20 m×p×3 size Array, that is, the image after layering;

4) Turn on the laser marking machine, set the parameters, and find the working parameters of the marking machine corresponding to the RGB value of each layer; input the 20 monochrome layers into the computer; set the marking parameters corresponding to each layer. Start the marking machine and start marking.

Claims (3)

1. a laser marking color image method, is characterized in that, comprises the following steps: 1) Select a color map, preset a positive integer value n as the number of layers according to the characteristics of the color map, and n takes 5 to 20; 2) directly use the color picker to extract the n groups of different RGB values of the color map or use the descriptive power statistics method to select the n groups of different RGB values with the strongest ability to describe the color map in the color library; 3) the described color map is split into layers corresponding to n single RGB values according to the screened out description color map with the strongest n groups of different RGB values; Concrete steps are as follows: 3-1) Establish an empty list, denoted as Flist, calculate its corresponding description coefficient DC for all data in the color library, and filter out n groups of data that meet the requirements after the results obtained are incrementally sorted and put into Flist; 3-2) After the data screening is completed, the images are layered according to the n groups of data in the list Flist, and the RGB data of the original image is converted into a three-dimensional array with a size of m×p×3; 3-3) According to the n groups of data of Flist, sequentially generate n arrays consisting of single pixel values and having a size of m×p×3; 3-4) Calculate the Euclidean distance D _ij of each pixel point by operating the original image array and the generated n arrays. The expression of D _ij is as follows:

R _oij , _{Go oij} , and Bo _oij in the formula represent the RGB parameter values of the original image at x=i, y=j point, R _n , G _n , B _n represent a set of color data in the list Flist, 3-5) Combine the generated n Euclidean arrays of m×p×1 size into m×p×n three-dimensional arrays, and take the position corresponding to the minimum value in the third dimension to obtain m×p×1 Array M, the value of each element of the array represents the most consistent value at this pixel is the Value group data in Flist; 3-6) Expand the array M into n arrays with serial numbers from 1 to n and size m×p×1. The value of this point is the same as the serial number of the expanded array. The value of this array is 1, and the value of this point is 1. The value is different from the serial number of the expanded array, the array value is 0; 3-7) Multiply the obtained array by the data value corresponding to the serial number in Flist, and modify all [0, 0, 0] values to [255, 255, 255], and finally get n m×p×3 size Array, that is, the image after layering; 4) Use the laser marking machine to mark each sub-layer after layering on the target working surface one by one, and finally form a color map. 2. laser marking color image method according to claim 1, is characterized in that, adopts descriptive ability statistical method in the color library to select the n group color RGB value of strongest descriptive ability in described step 2, is specifically as follows: 1) After the original picture is divided into n layers, each group of data in the color library is differentiated from the RGB of each pixel of the picture to obtain the Euclidean distance d _ij between each pixel,

In the formula: R _oij , _{Go oij} , _{Bo oij} represent the RGB parameter values of the original image at x=i, y=j point; R _l , G _l , B _l represent a group of RGB parameter values of the color library data; 2) Calculate the description ability of each group of color data to the picture According to the coefficient DC, select n groups of different RGB values with the strongest ability to describe the color picture according to the DC value, and the specific calculation formula is as follows:

3. laser marking color image method according to claim 1, is characterized in that, in described step 3, the specific steps that screen out n groups of data that meet the requirements and put into Flist are as follows: 1) According to the incrementally sorted DC, take out the data in turn; 2) Determine whether there is similar data in the Flist list, if so, return to step 1, if not, add the data to another new Flist; 3) Determine whether the number of Flist data is equal to n, if not, return to step 1, if it is equal to n, output a new Flist list.

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