CN113034382B - Brightness uniformity adjustment method, brightness uniformity adjustment device, computer device, and readable storage medium - Google Patents
Brightness uniformity adjustment method, brightness uniformity adjustment device, computer device, and readable storage medium Download PDFInfo
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Abstract
The application relates to a brightness uniformity adjusting method, a device, computer equipment and a readable storage medium, wherein the brightness uniformity adjusting method obtains an original gray level map by obtaining a brightness distribution map of a light source to be measured and carrying out graying treatment on the brightness distribution map; acquiring a gray value interval in which a gray value of an original gray image is positioned, and calculating the gray uniformity of the original gray image based on the gray value interval; if the gray level uniformity is inconsistent with the preset target uniformity, adjusting the gray level value of the original gray level image based on the target uniformity to obtain a target gray level image; and generating a target mask image according to the original gray scale image and the target gray scale image. The brightness uniformity adjusting method provided by the application is not influenced by the characteristics of inverted cone shape and the like of the light emitted by the light source, so that the brightness uniformity adjusting method provided by the application has a better effect.
Description
Technical Field
The present application relates to the field of 3D printing technology, and in particular, to a brightness uniformity adjustment method, a brightness uniformity adjustment device, a computer device, and a readable storage medium.
Background
Photo-curing printing techniques are commonly used in 3D printers. The working principle of a photo-curing 3D printer is to harden the liquid resin in the container with a light source to produce the desired 3D shape. For the light source of the photo-curing 3D printer, a single-bead light source or a multi-bead parallel light source is generally selected. Because the light emitted by the single-bulb light source or the parallel light sources with multiple bulbs is uneven, the accuracy of the printing model can be affected.
In the conventional art, the distribution of the intensity of a light source is generally identified by using an exposure map of the light source, and the uniformity of the light source is improved by adjusting the brightness of the light source. However, since the light emitted from the light source is in the shape of an inverted cone, the closer to the light source, the higher the light intensity, and at the boundary of the light source, the light intensity has a certain decreasing effect, so that the effect of improving the uniformity of the light source by adjusting the brightness of the light source is poor.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a brightness uniformity adjustment method, apparatus, computer device, and readable storage medium.
In a first aspect, an embodiment of the present application provides a brightness uniformity adjustment method, including:
acquiring a brightness distribution diagram of a light source to be detected, and carrying out graying treatment on the brightness distribution diagram to obtain an original gray diagram;
Acquiring a gray value interval in which a gray value of an original gray image is positioned, and calculating the gray uniformity of the original gray image based on the gray value interval;
if the gray level uniformity is inconsistent with the preset target uniformity, adjusting the gray level value of the original gray level image based on the target uniformity to obtain a target gray level image;
And generating a target mask image according to the original gray level image and the target gray level image, wherein the target mask image is used for being arranged on one side of the light emitting surface of the light source to be detected in the 3D printing process.
In one embodiment, calculating the gray uniformity of the original gray map based on the gray value interval includes:
obtaining a maximum gray value and a minimum gray value of an original gray image according to the gray value interval;
and determining the gray uniformity according to the ratio of the minimum gray value to the maximum gray value.
In one embodiment, the adjusting the gray value of the original gray map based on the target uniformity to obtain the target gray map includes:
Obtaining a target gray value interval according to the ratio between the maximum gray value and the target uniformity and the ratio between the minimum gray value and the target uniformity;
And adjusting the gray value of the original gray image to be within a target gray value interval to obtain a target gray image.
In one embodiment, acquiring a gray value interval in which a gray value in an original gray map is located includes:
Partitioning the original gray level image to obtain an original partitioned gray level image;
and acquiring the average value of the gray value of each region in the original partitioned gray map, and determining a gray value interval based on the average value of the gray value of each region.
In one embodiment, generating a target mask image from an original gray scale image and a target gray scale image includes:
And calculating the difference between the original gray scale image and the target gray scale image to obtain a target mask image.
In one embodiment, if the light source to be measured is a non-single light source, the gray processing is performed on the luminance distribution map to obtain an original gray map, including:
carrying out graying treatment on the brightness distribution diagram to obtain an initial gray diagram;
performing perspective transformation on the initial gray level image to obtain a corrected gray level image, wherein the corrected gray level image comprises a brightness stripe boundary;
and carrying out interpolation processing on the brightness stripe boundary in the corrected gray level image to obtain an original gray level image.
In one embodiment, interpolation processing is performed on a brightness stripe boundary in the corrected gray scale map to obtain an original gray scale map, including:
according to the corrected gray level map, determining gray level distribution of row coordinates and gray level distribution of column coordinates of the corrected gray level map;
Determining a mean curve of the brightness stripe boundary according to the gray value distribution of the row coordinates and the gray value distribution of the column coordinates;
Determining a gradient change curve of the mean curve based on the mean curve;
and carrying out interpolation processing on the brightness fringe boundary according to the gradient change curve to obtain a target gray level diagram.
In one embodiment, if the light source to be detected is a non-single light source, generating the target mask image according to the original gray scale image and the target gray scale image includes:
determining compensation values of the initial mask image and the brightness stripe boundary according to the target gray level image and the original gray level image;
and determining a target mask image according to the initial mask image and the compensation value.
In a second aspect, an embodiment of the present application provides a brightness uniformity adjustment device, including:
the acquisition module is used for acquiring a brightness distribution diagram of the light source to be detected, and carrying out graying treatment on the brightness distribution diagram to obtain an original gray level diagram;
the calculation module is used for acquiring a gray value interval in which each gray value in the original gray image is positioned and calculating the gray uniformity of the original gray image based on the gray value interval;
the determining module is used for adjusting the gray value of the original gray map based on the target uniformity if the gray uniformity is inconsistent with the preset target uniformity, so as to obtain a target gray map;
The generating module is also used for generating a target mask image according to the original gray level image and the target gray level image, wherein the target mask image is arranged on one side of the light emitting surface of the light source to be detected in the 3D printing process.
In a third aspect, an embodiment of the present application provides a computer device, including a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the brightness uniformity adjustment method provided in the above embodiment when executing the computer program.
In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the brightness uniformity adjustment method as provided in the above embodiment.
The embodiment of the application provides a brightness uniformity adjusting method, a brightness uniformity adjusting device, computer equipment and a readable storage medium. The method comprises the steps of carrying out graying treatment on an obtained brightness distribution diagram of a light source to be detected to obtain an original gray diagram; calculating the gray uniformity of the original gray map based on a gray value interval in which the gray value of the obtained original gray map is located; if the gray level uniformity is inconsistent with the target uniformity, adjusting the gray level value of the original gray level image based on the target uniformity to obtain a target gray level image; and generating a target mask image according to the original gray scale image and the target gray scale image. The brightness uniformity adjusting method provided by the embodiment of the application is not influenced by the characteristics of inverted cone shape and the like of the brightness of the light beam emitted by the light source to be measured, and the gray value of the original gray map is adjusted based on the target uniformity, so that the uniformity of the obtained target gray map is the target uniformity. Therefore, the effect of adjusting the brightness uniformity of the light source to be tested is better by using the target mask image generated according to the target gray level image and the original gray level image, so that the uniformity of the brightness distribution diagram of the adjusted light source to be tested can be improved, and the accuracy of the printing model can be further improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments or the conventional techniques of the present application, the drawings required for the descriptions of the embodiments or the conventional techniques will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to the drawings without inventive effort for different persons skilled in the art.
Fig. 1 is a flowchart illustrating a brightness uniformity adjustment method according to an embodiment of the present application;
fig. 2 is a flowchart illustrating a brightness uniformity adjustment method according to an embodiment of the application;
fig. 3 is a flowchart illustrating a brightness uniformity adjustment method according to an embodiment of the application;
fig. 4 is a flowchart illustrating a step of a brightness uniformity adjustment method according to an embodiment of the application;
fig. 5 is a flowchart illustrating a brightness uniformity adjustment method according to an embodiment of the application;
FIG. 6 is an initial gray scale provided by one embodiment of the present application;
FIG. 7 is an original gray scale image provided by one embodiment of the present application;
Fig. 8 is a flowchart illustrating a brightness uniformity adjustment method according to an embodiment of the application;
FIG. 9 is a schematic diagram of a gray value distribution of a corrected gray map according to an embodiment of the present application;
FIG. 10 is a schematic diagram of gray scale map distribution of column coordinates of a modified gray scale map according to an embodiment of the present application;
FIG. 11 is a schematic diagram of a slope change curve according to an embodiment of the present application;
FIG. 12 is a schematic diagram of a slope change curve according to an embodiment of the present application;
Fig. 13 is a flowchart illustrating a step of a brightness uniformity adjustment method according to an embodiment of the application;
fig. 14 is a schematic structural diagram of a brightness uniformity adjusting device according to an embodiment of the application;
Fig. 15 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order that the above objects, features and advantages of the application will be readily understood, a more particular description of the application will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. The present application may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit of the application, whereby the application is not limited to the specific embodiments disclosed below.
The following describes the technical scheme of the present application and how the technical scheme of the present application solves the technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
The numbering of the components itself, e.g. "first", "second", etc., is used herein merely to distinguish between the described objects and does not have any sequential or technical meaning.
The brightness uniformity adjusting method provided by the application can be used in a 3D printer. The 3D printer works by hardening the liquid resin in the container with a light source to create the desired 3D model. The brightness uniformity adjusting method provided by the application can adjust the brightness uniformity of the light beam emitted by the light source in the 3D printer, so that the precision of the 3D model generated by the 3D printer is higher.
The brightness uniformity adjusting method provided by the application can be realized through computer equipment. Computer devices include, but are not limited to, control chips, personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The method provided by the application can be realized by JAVA software and can also be applied to other software.
Referring to fig. 1, an embodiment of the present application provides a brightness uniformity adjustment method, which includes the following specific steps:
step 101, obtaining a brightness distribution diagram of a light source to be detected, and carrying out graying treatment on the brightness distribution diagram to obtain an original gray scale diagram.
The computer equipment acquires a brightness distribution diagram of a light source to be detected, wherein the light source to be detected refers to a light source used by a 3D printer, and the light source can be a single-bulb light source or a parallel light source. The luminance profile may be transmitted to the computer device after acquisition by the camera. The present embodiment does not limit the obtaining of the luminance distribution diagram of the light source to be measured, the kind and structure of the light source to be measured, and the like, as long as the functions thereof can be realized. After the computer equipment acquires the brightness distribution diagram, the computer equipment carries out graying treatment on the brightness distribution diagram, and the brightness distribution diagram is converted into an original gray diagram. The original gray scale map, like the luminance profile, can still reflect the distribution and characteristics of the chrominance and luminance levels throughout and locally of the image. The method of graying the luminance distribution map according to the present embodiment is not limited in any way as long as the function thereof can be realized.
Step 102, acquiring a gray value interval in which the gray value of the original gray image is located, and calculating the gray uniformity of the original gray image based on the gray value interval.
The computer equipment can obtain the gray value of the original gray map according to the obtained original gray map, and can obtain the gray value interval where the gray value is located according to the obtained gray value. The gray value interval may refer to an interval formed by a minimum gray value and a maximum gray value of gray values of an original gray map. The computer equipment can calculate the gray uniformity of the original gray map according to the obtained gray value interval. The gray uniformity is used to represent the uniformity of the gray distribution of the original gray map. The method of acquiring the gray value interval in which the gray value of the original gray map is located is not limited in this embodiment, as long as the gray value interval can be obtained.
Step 103, if the gray level uniformity is inconsistent with the preset target uniformity, adjusting the gray level value of the original gray level image based on the target uniformity to obtain a target gray level image;
The computer equipment compares the obtained gray level uniformity with a preset target uniformity, and if the gray level uniformity is consistent with the target uniformity, the gray level distribution uniformity of the original gray level image is indicated to reach the gray level distribution uniformity of the gray level image required by a user, so that the brightness distribution uniformity of the light source to be detected can be indicated. If the gray level uniformity is inconsistent with the target uniformity, the gray level distribution uniformity of the original gray level map is not consistent with the gray level distribution uniformity of the gray level map required by the user. The preset target uniformity may be set by the user according to actual needs and stored in the memory of the computer device. After the computer equipment judges that the gray level uniformity is inconsistent with the target uniformity, the gray level value in the original gray level image is adjusted based on the preset target uniformity, so that the uniformity of the gray level distribution of the adjusted original gray level image reaches the gray level distribution uniformity of the gray level image of a user. That is, the uniformity of the gradation distribution of the target gradation map can be a preset target uniformity. The specific method of adjusting the gray value of the original gray map based on the target uniformity is not limited in this embodiment, as long as the function thereof can be realized.
And 104, generating a target mask image according to the original gray level image and the target gray level image, wherein the target mask image is used for being arranged on one side of the light emitting surface of the light source to be tested in the 3D printing process.
The computer device may generate a target mask image from the obtained raw gray-scale image and the target gray-scale image. The target mask image may represent a region where the original gray-scale image and the target gray-scale image are different. When the 3D printer is used to print the model, the target mask image is set on one side of the light emitting surface of the light source to be measured. The target mask image can be used for shielding part of light beams emitted by the light source to be detected, so that the brightness distribution of the light beams passing through the target mask image is more uniform.
According to the brightness uniformity adjusting method provided by the embodiment of the application, the obtained brightness distribution diagram of the light source to be detected is subjected to gray processing to obtain an original gray diagram; calculating the gray uniformity of the original gray map based on a gray value interval in which the gray value of the obtained original gray map is located; if the gray level uniformity is inconsistent with the target uniformity, adjusting the gray level value of the original gray level image based on the target uniformity to obtain a target gray level image; and generating a target mask image according to the original gray scale image and the target gray scale image. The brightness uniformity adjusting method provided by the embodiment of the application is not influenced by the characteristics of inverted cone shape and the like of the brightness of the light beam emitted by the light source to be measured, and the gray value of the original gray map is adjusted based on the target uniformity, so that the uniformity of the obtained target gray map is the target uniformity. Therefore, the effect of adjusting the brightness uniformity of the light source to be tested is better by using the target mask image generated according to the target gray level image and the original gray level image, so that the uniformity of the brightness distribution diagram of the adjusted light source to be tested can be improved, and the accuracy of the printing model can be further improved.
Referring to fig. 2, in one embodiment, calculating the gray uniformity of the original gray map based on the gray value interval includes:
step 201, obtaining a maximum gray value and a minimum gray value of an original gray image according to a gray value interval;
the gray value interval is a gray value interval formed by the minimum gray value and the minimum gray value of the gray values in the original gray map, and the computer equipment can acquire the maximum gray value and the minimum gray value of the original gray map according to the obtained gray value interval.
Step 202, determining the gray uniformity according to the ratio of the minimum gray value to the maximum gray value.
The computer device may obtain the gray uniformity of the original gray map by calculating the ratio of the minimum gray value to the maximum gray value of the original gray map. In a specific embodiment, assuming that the gray value interval of the original gray map is [ minData, maxData ] and the gray uniformity is a, the gray uniformity can be expressed as: a= minData/maxData.
In this embodiment, the method for calculating the gray uniformity is simple and easy to understand, so that the efficiency for calculating the target mask image can be improved, and the practicability of the brightness uniformity adjustment method can be improved.
Referring to fig. 3, in one embodiment, the adjusting the gray value of the original gray map based on the target uniformity to obtain the target gray map includes:
Step 301, obtaining a target gray value interval according to the ratio between the maximum gray value and the target uniformity and the ratio between the minimum gray value and the target uniformity;
The computer equipment can obtain the maximum gray value and the minimum gray value of the original gray image according to the gray value interval, and can obtain the target minimum gray value in the target gray value interval by calculating the ratio of the minimum gray value to the target uniformity; the target maximum gray value in the target gray value interval can be obtained by calculating the ratio of the maximum gray value to the target uniformity; the interval formed by the target minimum gray value and the target maximum gray value is the target gray value interval. In a specific embodiment, assuming that the target uniformity is B and the target gray value interval is [ minDataB, maxDataB ], the calculation method of the target gray value interval may be expressed as: minDataB = minData/B, minDataB = minData/B.
Step 302, adjusting the gray value of the original gray image to be within the target gray value interval, so as to obtain the target gray image.
And the computer equipment adjusts the gray values in the original gray-scale image to be within the target gray-scale value interval according to the calculated target gray-scale value interval to form a required target gray-scale image. Specifically, when the gray value in the original gray map is smaller than the target minimum gray value of the target gray value interval, setting the gray value as the target minimum gray value; when the gray value in the original gray map is greater than the target maximum gray value of the target gray value interval, the gray value is set as the target maximum gray value.
In this embodiment, the gray values of the original gray map are adjusted to be within the target gray value interval, so that the gray values in the obtained target gray values are distributed within the target gray value interval, and the uniformity of the target gray map can reach the target uniformity preset by the user. In addition, the method for calculating the target gray value interval and the target gray map in the embodiment is simple, and the efficiency of obtaining the target gray map can be improved.
Referring to fig. 4, in one embodiment, acquiring a gray value interval in which a gray value in an original gray map is located includes:
And 401, partitioning the original gray level map to obtain the original partitioned gray level map.
The computer device may divide the original gray-scale image into a plurality of areas according to a preset division rule, and record the divided original gray-scale image as an original division gray-scale image. In a specific embodiment, the preset partitioning rule may be a preset pattern, that is, the computer device divides the original gray-scale image into a plurality of areas according to the preset image, so as to obtain the original partitioned gray-scale image. The preset image may be a rectangle with a preset length and width. The length and width of the rectangle may be set by the user according to the size of the acquired luminance profile. When the computer equipment carries out partition processing on the original gray level image according to the preset rectangle, if the pixel points of the area where the last row or the last column is positioned are insufficient to enable the pixel points to form the preset rectangle, the pixel points of the area where the last row or the last column is positioned are filled according to the pixel points of the adjacent area of the last row or the last column so as to enable the pixel points to form the preset rectangle.
Step 402, obtaining the average value of the gray value of each region in the original partitioned gray level graph, and determining a gray level interval based on the average value of the gray value of each region.
After the original subarea gray-scale image is obtained, the computer equipment obtains the gray-scale value of each area of the original subarea gray-scale image, calculates the average value of the gray-scale values of each area, and takes the calculated average value as the gray-scale value of the area, so that the gray-scale values of each area of the original subarea gray-scale image can be obtained. The computer equipment obtains the minimum gray value and the maximum gray value of the original subarea gray level graph according to the gray value of each area, and can determine a gray value interval, wherein the minimum gray value and the maximum gray value forming interval are the gray value interval.
In this embodiment, after the computer device performs the partition processing on the original gray-scale image according to the predetermined partition rule, the average value of the gray-scale values of each region in the partitioned original partitioned gray-scale image is calculated, so that the gray-scale value interval is determined, and the calculation efficiency of determining the gray-scale value interval can be improved.
In one embodiment, generating a target mask image from an original gray scale map and a target gray scale map includes:
And calculating the difference between the original gray scale image and the target gray scale image to obtain a target mask image.
The computer device may obtain a region different between the original gray-scale image and the target gray-scale image by calculating a difference between the original gray-scale image and the obtained target gray-scale image, thereby obtaining the target mask image. The present embodiment does not impose any limitation on the method of specifically calculating the difference between the original gray-scale image and the target gray-scale image, as long as the function thereof can be realized. In this embodiment, the calculation method for obtaining the target mask image is simple, and the calculation efficiency can be improved.
Referring to fig. 5, in one embodiment, if the light source to be measured is a non-single light source, the gray scale processing is performed on the luminance distribution map to obtain an original gray scale map, which includes:
Step 501, performing graying processing on the brightness distribution diagram to obtain an initial gray diagram.
When the light source to be measured is a non-single light source, specifically, when the light source to be measured is a parallel light source, the computer device performs graying processing on the obtained luminance distribution map when obtaining the original gray map, so as to obtain the initial gray map, as shown in fig. 6. The description of the method for obtaining the luminance distribution map and the method for performing the graying processing on the luminance map may refer to the description in the above embodiment, and will not be repeated here.
Step 502, performing perspective transformation on the initial gray level image to obtain a corrected gray level image, wherein the corrected gray level image comprises a brightness stripe boundary.
When the light source to be measured is a non-single light source, the computer equipment performs perspective transformation processing on the initial gray level image after acquiring the initial gray level image, and a corrected gray level image is obtained. The perspective transformation is to rotate the initial gray-scale image around the perspective axis by a certain angle according to the perspective rotation law by utilizing the condition that the perspective center, the phase point and the target point are collinear, and destroy the original projection light beam bundle, and still keep the original gray-scale image unchanged. When the light source to be measured is a non-single light source, the camera cannot be completely parallel to and directly opposite to all the non-single light sources when acquiring the brightness distribution map, and an inclined image exists in the acquired brightness distribution map, and the inclined image also exists in the initial gray level map. According to the embodiment, through perspective transformation of the initial gray level image, the inclined image in the initial gray level image can be corrected, so that the corrected initial gray level image can be obtained, and the gray level image is corrected. Because the light source to be measured is a non-single light source, the light beams emitted by each light source can be influenced by the light beams emitted by other adjacent light sources, so that the superimposed light beams among the light sources exist in the finally formed brightness distribution diagram, and the obtained corrected gray level diagram comprises brightness fringe boundaries. The brightness stripe boundaries are formed by the superimposed light beams between the individual light sources.
And 503, performing interpolation processing on the brightness stripe boundary in the corrected gray level image to obtain an original gray level image.
After the corrected gray level map is obtained, the computer device performs interpolation processing on the brightness stripe boundary existing in the corrected gray level map, so that the gray level value at the brightness stripe boundary is the same as the gray level value of the adjacent area, and therefore the brightness stripe boundary in the corrected gray level map can be eliminated, and the original gray level map is obtained, as shown in fig. 7.
In this embodiment, when the light source to be measured is probably a non-single light source, the situation that an inclined image exists in the initial gray scale image and a brightness stripe boundary exists is considered, perspective transformation and interpolation processing are performed on the initial gray scale image, so that the obtained original gray scale image is more accurate, a target mask image obtained after the original gray scale image is processed later is more accurate, and the brightness uniformity adjusting effect can be improved.
Referring to fig. 8, in one embodiment, interpolation is performed on a luminance fringe boundary in a corrected gray-scale image to obtain an original gray-scale image, which includes:
Step 801, according to the corrected gray scale map, determining gray scale value distribution of row coordinates and gray scale value distribution of column coordinates of the corrected gray scale map.
After obtaining the corrected gray scale, the computer equipment obtains the distribution of gray scale values of the direction of the row coordinates of the corrected gray scale, and obtains the gray scale value distribution of the row coordinates of the corrected gray scale, as shown in fig. 9; the distribution of the gray values in the direction of the column coordinates of the corrected gray map is obtained, and the gray map distribution of the column coordinates of the corrected gray map is obtained, as shown in fig. 10.
Step 802, determining a mean curve of the brightness stripe boundary according to the gray value distribution of the row coordinates and the gray value distribution of the column coordinates.
The computer equipment can determine the corresponding brightness stripe boundary of the corrected gray level image in the row coordinate direction according to the obtained gray level value distribution of the row coordinate, and can determine the gray level value distribution at the brightness stripe boundary by acquiring the gray level value distribution of the corrected gray level image in the row coordinate direction, so as to determine the mean curve of the brightness stripe boundary in the row coordinate direction. Similarly, the computer device can determine the brightness stripe boundary of the corrected gray-scale image in the column coordinate direction according to the obtained gray-scale value distribution of the column coordinate, and can determine the gray-scale value distribution at the brightness stripe boundary by acquiring the gray-scale value distribution of the corrected gray-scale image in the column coordinate direction, so that the mean curve of the brightness stripe boundary in the row coordinate direction can be determined.
Step 803, determining a gradient change curve of the mean curve based on the mean curve.
The computer device may obtain a slope change curve corresponding to the mean curve by calculating the slope of the mean curve of the luminance fringe boundary in the row coordinate direction, as shown in fig. 11. The black square points in fig. 11 represent the slopes of the mean curves in the row coordinate direction, and X and Y marked in fig. 11 are coordinate values of the corresponding black square points. Similarly, the computer device may calculate the slope of the mean curve of the brightness stripe boundary in the column coordinate direction, and the slope change curve corresponding to the mean curve, as shown in fig. 12. The black square points in fig. 12 represent the slopes of the mean curves in the column coordinate directions, and X and Y indicated in fig. 12 are coordinate values of the corresponding black square points.
And 804, carrying out interpolation processing on the brightness stripe boundary according to the gradient change curve to obtain a target gray level diagram.
The computer equipment carries out interpolation processing on the brightness stripe boundary in the row coordinate direction of the corrected gray level graph according to the gradient change curve corresponding to the average value curve of the brightness stripe boundary in the row coordinate direction of the corrected gray level graph, so that the brightness stripe boundary in the row coordinate direction of the corrected gray level graph can be eliminated. Similarly, the computer device performs interpolation processing on the brightness stripe boundary in the column coordinate direction of the corrected gray scale according to the gradient change curve corresponding to the obtained average value curve of the brightness stripe boundary in the column coordinate direction of the corrected gray scale, so that the brightness stripe boundary in the row coordinate direction of the corrected gray scale can be eliminated. The computer device may obtain the target gray-scale image by eliminating the luminance stripe boundary in the row coordinate direction and the luminance stripe boundary in the column coordinate direction of the corrected gray-scale image.
The method for eliminating and correcting the brightness stripe boundary in the gray scale map is simple and easy to understand, and the efficiency of determining the target gray scale map can be improved.
Referring to fig. 13, in one embodiment, if the light source to be measured is a non-single light source, generating a target mask image according to the original gray scale image and the target gray scale image includes:
and 131, determining compensation values of the initial mask image and the brightness stripe boundary according to the target gray level image and the original gray level image.
Step 132, determining a target mask image according to the initial mask image and the compensation value.
According to the obtained original gray level image and target gray level image when the light source to be detected is a non-single light source, the computer equipment calculates the difference value between the original gray level image and the target gray level image when the light source to be detected is a non-single light source, so that an initial mask image and a compensation value of a brightness fringe boundary can be obtained. And adding the initial mask image and the compensation value to obtain the target mask image when the light source to be detected is a non-single light source. In this embodiment, when determining the target mask map, the situation that the light source to be measured is a non-single light source is considered, so that the accuracy of obtaining the target mask image can be improved, and therefore the effect of adjusting the brightness distribution uniformity of the light source to be measured by using the target mask image can be improved, and the brightness uniformity adjustment method provided by the embodiment has stronger practicability.
It should be understood that, although the steps in the flowcharts in the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the figures may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of other steps or other steps.
Referring to fig. 14, an embodiment of the present application provides a brightness uniformity adjustment device 10 including an obtaining module 11, a calculating module 12, a determining module 13, and a generating module 14. Wherein,
The acquisition module 11 is used for acquiring a brightness distribution diagram of the light source to be detected, and carrying out graying treatment on the brightness distribution diagram to obtain an original gray level diagram;
The calculating module 12 is configured to obtain a gray value interval in which a gray value in the original gray map is located, and calculate gray uniformity of the original gray map based on the gray value interval;
The determining module 13 is configured to adjust a gray value of the original gray map based on the target uniformity if the gray uniformity is inconsistent with the preset target uniformity, so as to obtain a target gray map;
The generating module 14 is configured to generate a mask image according to the original gray scale image and the target gray scale image, where the target mask image is configured to be disposed on one side of the light emitting surface of the light source to be tested in the 3D printing process.
In one embodiment, the computing module 12 includes an acquisition unit 121 and a determination unit 122, where
The obtaining unit 121 is configured to obtain a maximum gray value and a minimum gray value of the original gray map according to the gray value interval;
The determining unit 122 is configured to determine the gray uniformity according to a ratio of the minimum gray value to the minimum gray value.
In one embodiment, the determining module 13 is further configured to obtain a target gray value interval according to a ratio between the maximum gray value and the target uniformity and a ratio between the minimum gray value and the target uniformity; and adjusting the gray value of the original gray image to be within a target gray value interval to obtain a target gray image.
In one embodiment, the computing module 12 is specifically further configured to perform partition processing on the original gray-scale image, so as to obtain an original fecal sewage gray-scale image; and acquiring the average value of the gray value of each region in the original partitioned gray map, and determining a gray value interval based on the average value of the gray value of each region.
In one embodiment, the generating module 14 is specifically configured to calculate a difference between the original gray-scale image and the target gray-scale image, so as to obtain a mask image.
In one embodiment, the acquisition module 11 comprises a first processing unit, a transformation unit and a second processing unit. Wherein,
The first processing unit is used for carrying out graying processing on the brightness distribution diagram to obtain an initial gray diagram;
The transformation unit is used for performing perspective transformation on the initial gray level image to obtain a corrected gray level image, wherein the corrected gray level image comprises a brightness stripe boundary;
The second processing unit is used for carrying out interpolation processing on the brightness stripe boundary in the corrected gray level image to obtain an original gray level image.
In one embodiment, the second processing unit is specifically configured to determine a gray value distribution of row coordinates and a gray value distribution of column coordinates of the corrected gray map according to the corrected gray map; determining a mean curve of the brightness stripe boundary according to the gray value distribution of the row coordinates and the gray value distribution of the column coordinates; determining a gradient change curve of the mean curve based on the mean curve; and carrying out interpolation processing on the brightness fringe boundary according to the gradient change curve to obtain a target gray level diagram.
In one embodiment, the generating module 14 is further configured to determine a compensation value for the boundary of the initial mask image and the brightness stripe according to the target gray scale map and the original gray scale map; determining a target mask image according to the initial mask image and the compensation value
The specific limitation of the brightness uniformity adjusting apparatus 10 described above may be referred to as limitation of the brightness uniformity adjusting method, and is not described herein. The various modules in the brightness uniformity adjustment device 10 may be implemented in whole or in part by software, hardware, and combinations thereof. The above devices, modules or units may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above devices or modules.
Referring to fig. 15, in one embodiment, a computer device is provided, which may be a server, and an internal structure thereof may be as shown in fig. 15. The computer device includes a processor, memory, network interface, and database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes non-volatile storage media, internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing the brightness distribution diagram of the light source to be tested, the preset target uniformity and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer device, when executed by the processor, implements a brightness uniformity adjustment method.
It will be appreciated by those skilled in the art that the structure shown in fig. 15 is merely a block diagram of a portion of the structure associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements are applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory having a computer program stored therein and a processor, the processor when executing the computer program performing the steps of:
acquiring a brightness distribution diagram of a light source to be detected, and carrying out graying treatment on the brightness distribution diagram to obtain an original gray diagram;
Acquiring a gray value interval in which a gray value of an original gray image is positioned, and calculating the gray uniformity of the original gray image based on the gray value interval;
if the gray level uniformity is inconsistent with the preset target uniformity, adjusting the gray level value of the original gray level image based on the target uniformity to obtain a target gray level image;
And generating a target mask image according to the original gray level image and the target gray level image, wherein the target mask image is used for being arranged on one side of the light emitting surface of the light source to be detected in the 3D printing process.
In one embodiment, the processor when executing the computer program further performs the steps of: obtaining a maximum gray value and a minimum gray value of an original gray image according to the gray value interval; and determining the gray uniformity according to the ratio of the minimum gray value to the maximum gray value.
In one embodiment, the processor when executing the computer program further performs the steps of: obtaining a target gray value interval according to the ratio between the maximum gray value and the target uniformity and the ratio between the minimum gray value and the target uniformity; and adjusting the gray value of the original gray image to be within a target gray value interval to obtain a target gray image.
In one embodiment, the processor when executing the computer program further performs the steps of: partitioning the original gray level image to obtain an original partitioned gray level image; and acquiring the average value of the gray value of each region in the original partitioned gray map, and determining a gray value interval based on the average value of the gray value of each region.
In one embodiment, the processor when executing the computer program further performs the steps of: and calculating the difference value between the original gray scale image and the target gray scale image to obtain the target mask image.
In one embodiment, the processor when executing the computer program further performs the steps of: carrying out graying treatment on the brightness distribution diagram to obtain an initial gray diagram; performing perspective transformation on the initial gray level image to obtain a corrected gray level image, wherein the corrected gray level image comprises a brightness stripe boundary; and carrying out interpolation processing on the brightness stripe boundary in the corrected gray level image to obtain an original gray level image.
In one embodiment, the processor when executing the computer program further performs the steps of: according to the corrected gray level map, determining gray level distribution of row coordinates and gray level distribution of column coordinates of the corrected gray level map; determining a mean curve of the brightness stripe boundary according to the gray value distribution of the row coordinates and the gray value distribution of the column coordinates; determining a gradient change curve of the mean curve based on the mean curve; and carrying out interpolation processing on the brightness fringe boundary according to the gradient change curve to obtain a target gray level diagram.
In one embodiment, the processor when executing the computer program further performs the steps of: determining compensation values of the initial mask image and the brightness stripe boundary according to the target gray level image and the original gray level image; and determining a target mask image according to the initial mask image and the compensation value.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring a brightness distribution diagram of a light source to be detected, and carrying out graying treatment on the brightness distribution diagram to obtain an original gray diagram;
Acquiring a gray value interval in which a gray value of an original gray image is positioned, and calculating the gray uniformity of the original gray image based on the gray value interval;
if the gray level uniformity is inconsistent with the preset target uniformity, adjusting the gray level value of the original gray level image based on the target uniformity to obtain a target gray level image;
And generating a target mask image according to the original gray level image and the target gray level image, wherein the target mask image is used for being arranged on one side of the light emitting surface of the light source to be detected in the 3D printing process.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining a maximum gray value and a minimum gray value of an original gray image according to the gray value interval; and determining the gray uniformity according to the ratio of the minimum gray value to the maximum gray value.
In one embodiment, the computer program when executed by the processor further performs the steps of: obtaining a target gray value interval according to the ratio between the maximum gray value and the target uniformity and the ratio between the minimum gray value and the target uniformity; and adjusting the gray value of the original gray image to be within a target gray value interval to obtain a target gray image.
In one embodiment, the computer program when executed by the processor further performs the steps of: partitioning the original gray level image to obtain an original partitioned gray level image; and acquiring the average value of the gray value of each region in the original partitioned gray map, and determining a gray value interval based on the average value of the gray value of each region.
In one embodiment, the computer program when executed by the processor further performs the steps of: and calculating the difference value between the original gray scale image and the target gray scale image to obtain the target mask image.
In one embodiment, the computer program when executed by the processor further performs the steps of: carrying out graying treatment on the brightness distribution diagram to obtain an initial gray diagram; performing perspective transformation on the initial gray level image to obtain a corrected gray level image, wherein the corrected gray level image comprises a brightness stripe boundary; and carrying out interpolation processing on the brightness stripe boundary in the corrected gray level image to obtain an original gray level image.
In one embodiment, the computer program when executed by the processor further performs the steps of: according to the corrected gray level map, determining gray level distribution of row coordinates and gray level distribution of column coordinates of the corrected gray level map; determining a mean curve of the brightness stripe boundary according to the gray value distribution of the row coordinates and the gray value distribution of the column coordinates; determining a gradient change curve of the mean curve based on the mean curve; and carrying out interpolation processing on the brightness fringe boundary according to the gradient change curve to obtain a target gray level diagram.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining compensation values of the initial mask image and the brightness stripe boundary according to the target gray level image and the original gray level image; and determining a target mask map according to the initial mask image and the compensation value.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application and are described in detail herein without thereby limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A brightness uniformity adjustment method, comprising:
acquiring a brightness distribution diagram of a light source to be detected, and carrying out graying treatment on the brightness distribution diagram to obtain an initial gray diagram; the light source to be measured is a non-single light source;
Performing perspective transformation on the initial gray level image to obtain a corrected gray level image, wherein the corrected gray level image comprises a brightness stripe boundary;
Interpolation processing is carried out on the brightness stripe boundary in the corrected gray level image to obtain an original gray level image;
partitioning the original gray level image to obtain an original partitioned gray level image;
acquiring the average value of the gray values of each region in the original partitioned gray map, and determining a gray value interval based on the average value of the gray values of each region;
obtaining a maximum gray value and a minimum gray value of the original gray map according to the gray value interval;
determining gray uniformity according to the ratio of the minimum gray value to the maximum gray value;
if the gray level uniformity is inconsistent with a preset target uniformity, adjusting the gray level value of the original gray level map based on the target uniformity to obtain a target gray level map;
Generating a target mask image according to the original gray level image and the target gray level image, wherein the target mask image is used for being arranged on one side of a light emitting surface of the light source to be detected in the 3D printing process;
the interpolation processing is performed on the brightness stripe boundary in the corrected gray level image to obtain an original gray level image, which comprises the following steps:
according to the corrected gray scale map, determining gray scale value distribution of row coordinates and gray scale value distribution of column coordinates of the corrected gray scale map;
Determining a mean curve of the brightness stripe boundary according to the gray value distribution of the row coordinates and the gray value distribution of the column coordinates;
determining a gradient change curve of the mean curve based on the mean curve;
And carrying out interpolation processing on the brightness stripe boundary according to the gradient change curve to obtain an original gray level diagram.
2. The brightness uniformity adjustment method according to claim 1, wherein the adjusting the gray value of the original gray map based on the target uniformity to obtain a target gray map comprises:
Obtaining a target gray value interval according to the ratio between the maximum gray value and the target uniformity and the ratio between the minimum gray value and the target uniformity;
And adjusting the gray value of the original gray image to be within the target gray value interval to obtain the target gray image.
3. The brightness uniformity adjustment method according to claim 1, wherein generating a target mask image from the original gray-scale image and the target gray-scale image comprises:
And calculating the difference value between the original gray scale image and the target gray scale image to obtain the target mask image.
4. The method of claim 1, wherein the raw gray scale map characterizes the distribution and characteristics of the chrominance and luminance levels of the entire and portions of the image, and the luminance map characterizes the distribution and characteristics of the chrominance and luminance levels of the entire and portions of the image.
5. The method according to claim 1, wherein the gray value interval is an interval formed by a minimum gray value and a maximum gray value of gray values of the original gray map.
6. The brightness uniformity adjustment method according to claim 1, wherein said gray level uniformity is used to characterize the uniformity of the gray level distribution of said original gray level map.
7. The method of claim 1, wherein generating a target mask image from the original gray scale map and the target gray scale map if the light source to be measured is a non-single light source, comprises:
Determining compensation values of an initial mask image and the brightness stripe boundary according to the target gray level image and the original gray level image;
and determining a target mask image according to the initial mask image and the compensation value.
8. A brightness uniformity adjustment device, comprising:
The acquisition module is used for acquiring a brightness distribution diagram of the light source to be detected, and carrying out graying treatment on the brightness distribution diagram to obtain an initial gray diagram; the light source to be measured is a non-single light source; performing perspective transformation on the initial gray level image to obtain a corrected gray level image, wherein the corrected gray level image comprises a brightness stripe boundary; interpolation processing is carried out on the brightness stripe boundary in the corrected gray level image to obtain an original gray level image; partitioning the original gray level image to obtain an original partitioned gray level image; the interpolation processing is performed on the brightness stripe boundary in the corrected gray level image to obtain an original gray level image, which comprises the following steps: according to the corrected gray scale map, determining gray scale value distribution of row coordinates and gray scale value distribution of column coordinates of the corrected gray scale map; determining a mean curve of the brightness stripe boundary according to the gray value distribution of the row coordinates and the gray value distribution of the column coordinates; determining a gradient change curve of the mean curve based on the mean curve; performing interpolation processing on the brightness stripe boundary according to the gradient change curve to obtain an original gray level diagram;
the calculation module is used for obtaining the average value of the gray value of each region in the original partitioned gray level graph and determining a gray level interval based on the average value of the gray value of each region; obtaining a maximum gray value and a minimum gray value of the original gray map according to the gray value interval; determining gray uniformity according to the ratio of the minimum gray value to the maximum gray value;
The determining module is used for adjusting the gray value of the original gray map based on the target uniformity if the gray uniformity is inconsistent with the preset target uniformity, so as to obtain a target gray map;
The generating module is further used for generating a target mask image according to the original gray level image and the target gray level image, and the target mask image is used for being arranged on one side of the light emitting surface of the light source to be detected in the 3D printing process.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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| CN114281274A (en) * | 2021-11-30 | 2022-04-05 | 深圳市纵维立方科技有限公司 | Method for adjusting brightness uniformity, printing method, printing system and equipment |
| CN114559653B (en) * | 2022-01-07 | 2024-01-19 | 宁波智造数字科技有限公司 | Photo-curing 3D printing uniformity adjustment method using cube matrix |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107941808A (en) * | 2017-11-10 | 2018-04-20 | 中国计量大学 | 3D printing Forming Quality detecting system and method based on machine vision |
| CN108564633A (en) * | 2018-01-05 | 2018-09-21 | 珠海市杰理科技股份有限公司 | Gray-scale Image Compression method, apparatus and computer equipment |
| CN108961175A (en) * | 2018-06-06 | 2018-12-07 | 平安科技（深圳）有限公司 | Face luminance regulating method, device, computer equipment and storage medium |
| KR20190011419A (en) * | 2017-07-25 | 2019-02-07 | 주식회사 레이 | Calibration Method of 3D Printer |
| CN109801240A (en) * | 2019-01-15 | 2019-05-24 | 武汉鸿瑞达信息技术有限公司 | A kind of image enchancing method and image intensifier device |
| CN109856164A (en) * | 2019-02-02 | 2019-06-07 | 上海福赛特机器人有限公司 | A kind of machine vision acquires the optimization device and its detection method of a wide range of image |
| CN112172149A (en) * | 2020-09-30 | 2021-01-05 | 深圳市创想三维科技有限公司 | Method, device and equipment for automatically improving printing effect and storage medium |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2002022602A (en) * | 2000-07-07 | 2002-01-23 | Sony Corp | Equipment and method for inspecting unevenness of quantity of light |
| WO2011005316A1 (en) * | 2009-07-07 | 2011-01-13 | Thomson Licensing | Method and system for brightness correction for three-dimensional (3d) projection |
-
2021
- 2021-02-23 CN CN202110199525.2A patent/CN113034382B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20190011419A (en) * | 2017-07-25 | 2019-02-07 | 주식회사 레이 | Calibration Method of 3D Printer |
| CN107941808A (en) * | 2017-11-10 | 2018-04-20 | 中国计量大学 | 3D printing Forming Quality detecting system and method based on machine vision |
| CN108564633A (en) * | 2018-01-05 | 2018-09-21 | 珠海市杰理科技股份有限公司 | Gray-scale Image Compression method, apparatus and computer equipment |
| CN108961175A (en) * | 2018-06-06 | 2018-12-07 | 平安科技（深圳）有限公司 | Face luminance regulating method, device, computer equipment and storage medium |
| CN109801240A (en) * | 2019-01-15 | 2019-05-24 | 武汉鸿瑞达信息技术有限公司 | A kind of image enchancing method and image intensifier device |
| CN109856164A (en) * | 2019-02-02 | 2019-06-07 | 上海福赛特机器人有限公司 | A kind of machine vision acquires the optimization device and its detection method of a wide range of image |
| CN112172149A (en) * | 2020-09-30 | 2021-01-05 | 深圳市创想三维科技有限公司 | Method, device and equipment for automatically improving printing effect and storage medium |
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