CN111754385A - Data point model processing method and system, detection method and system and readable medium - Google Patents
Data point model processing method and system, detection method and system and readable medium Download PDFInfo
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Abstract
The invention provides a data point model processing method and a system thereof, a detection method and a system thereof, and a readable medium, wherein the data point model processing method comprises the following steps: dividing a model to be processed into a plurality of grids; marking each grid, and marking the model to be processed as a binary image to form a grid image; judging a connected domain of the grid image, and acquiring an additional grid image when the grid image comprises a main grid image and an additional grid image which are separated; removing initial data points corresponding to the data points in the additional grid image from the initial model. The data point model processing method can accelerate the processing speed, thereby increasing the detection speed.
Description
Technical Field
The invention relates to the field of data point model processing, in particular to a data point model processing method and a data point model processing system, a data point model detection method and a data point model detection system, and a readable medium.
Background
With the development of modern industry, precision machining is used in more and more fields; meanwhile, the machining precision is also required to be higher and higher. In order to meet the requirement of machining precision and improve the qualification rate of machined samples, the machining process and the machined products need to be frequently tested for shape distortion so as to ensure that the distortion is within a tolerable range.
Existing distortion detection methods can be classified into contact detection methods and non-contact detection methods. In a contact detection method, such as three-coordinate detection, a probe needs to be in contact with an object to be detected during detection, so that the object to be detected is easily damaged. Non-contact detection methods, including optical detection methods such as binocular vision, chromatic dispersion confocal and structured light detection, do not contact the object to be detected, can reduce damage and distortion of the object to be detected, and are increasingly widely used.
In the process of detecting the distortion of the object to be detected by an optical detection method, the surface of the object to be detected needs to be scanned to produce three-dimensional point cloud. In the scanning process, objects around the object to be detected are easy to detect, so that the obtained three-dimensional point cloud comprises the detection information of the object to be detected and the additional detection information of the objects around the object to be detected. The additional detection information is easily treated as part of the object to be detected, resulting in false detection.
In the prior art, the method for removing the additional detection information is complex, so that the detection efficiency is low.
Disclosure of Invention
The invention aims to provide a data point model processing method, which can improve the processing speed of a data point model, thereby improving the detection speed and reducing the false detection rate.
In order to solve the above problems, the present invention provides a data point model processing method, including: providing an initial model comprising a main model and an additional model separated from each other, the main model and the additional model each comprising an initial data point; forming a model to be processed according to an initial model, wherein the initial data point forms a data point in the model to be processed; dividing a model to be processed into a plurality of grids; marking each grid, and marking the model to be processed as a binary image to form a grid image; judging a connected domain of the grid image, and acquiring an additional grid image when the grid image comprises a main grid image and an additional grid image which are separated; and removing initial data points corresponding to the data points in the additional grid image from the initial model to form a target model.
Optionally, the initial model is a three-dimensional point cloud; the model to be processed is a two-dimensional image; the step of forming a model to be processed from the initial model comprises: providing a projection direction; and carrying out projection processing on the initial model along the projection direction to form the model to be processed.
Optionally, the number of the projection directions is multiple, and the data point model processing method further includes: and repeating the cycle operation processing until additional grid images of all the additional models are obtained, wherein the cycle operation processing comprises the steps from the projection processing to the connected domain judgment.
Optionally, the number of the additional models is multiple, and the repeated loop operation process further includes: removing initial data points corresponding to the data points in the additional grid image from the initial model; or, after repeating the loop operation processing until additional grid images of all additional models are acquired, removing initial data points corresponding to the data points in all the additional grid images from the initial model.
Optionally, the step of forming the model to be processed according to the initial model includes: and enabling the model to be processed to be the same as the initial model, wherein the data point is the same as the initial data point.
Optionally, the step of marking processing includes: setting a first quantity threshold; comparing the number of the data points in the grid with the first number threshold, and marking the grid as image points when the number of the data points in the grid is greater than or equal to the first number threshold; otherwise, mark the grid as blank.
Optionally, the initial model is an image; the initial data points comprise gray values of pixels in the initial model; the step of the marking process comprises: setting a second quantity threshold; obtaining a reference number according to the number of data points meeting the gray condition in the grid; when the reference number is greater than or equal to a second number threshold, marking the grid as an image point; otherwise, marking the grids as blank points; alternatively, the step of labeling processing comprises: setting a first gray threshold; acquiring a grid gray value according to the gray value of each data point in the grid; and comparing the grid gray value with a first gray threshold value, and respectively marking the grids meeting different comparison results as image points and blank points.
Correspondingly, the technical scheme of the invention also provides a detection method, which comprises the following steps: detecting an object to be detected to obtain an initial model; and processing the initial model according to a data point model processing method to form a target model.
Optionally, the method further includes: providing a design model; and comparing the target model with the design model to obtain the distortion of the object to be measured.
Optionally, the object to be detected is detected by a detection device, where the detection device includes an objective lens; the projection direction includes: a first projection direction perpendicular to the optical axis of the objective lens; and/or a second projection direction parallel to the optical axis of the objective lens.
The technical solution of the present invention also provides a data point model processing system, including: an input system for providing an initial model, the initial model comprising a plurality of initial data points, the initial model comprising a main model and an additional model that are separate from each other; the data processing system is used for forming a model to be processed according to an initial model, and the initial data points form data points in the model to be processed; the mesh division system is used for dividing the model to be processed into a plurality of meshes; the marking system is used for marking each grid and marking the model to be processed as a binary image to form a grid image; a connected domain determination system for determining a connected domain for the grid image, and acquiring an additional grid image when the grid image includes a main grid image and an additional grid image which are separated; and the removal system is used for removing the initial data points corresponding to the data points in the additional grid image from the initial model to form a target model.
Optionally, the initial model is a three-dimensional point cloud; the model to be processed is a two-dimensional image; the data processing system includes: and the projection system is used for carrying out projection processing on the initial model along the projection direction to form the model to be processed.
Optionally, the data processing system includes: and the equivalent system is used for enabling the model to be processed to be the same as the initial model, and the data point is the same as the initial data point.
Optionally, the marking system includes: a setting system for setting a first quantity threshold; the comparison system is used for comparing the number of the data points in the grid with the first number threshold value, and when the number of the data points in the grid is greater than or equal to the first number threshold value, marking the grid as an image point; otherwise, mark the grid as blank.
The technical scheme of the invention provides a detection system, which comprises: the model acquisition system is used for detecting the object to be detected to acquire an initial model; and the data point model processing system is used for carrying out data point model processing on the initial model to form a target model.
Optionally, the input system is further configured to provide a design model; the detection system further comprises: and the distortion detection system is used for comparing the target model and the design model to obtain the distortion of the object to be detected.
The technical solution of the present invention also provides a computer readable medium, which includes executable instructions, and when executed, the executable instructions cause a processor to execute the data point model processing method.
Compared with the prior art, the technical scheme of the invention has the following advantages:
in the data point model processing method provided by the technical scheme of the invention, the model to be processed is divided into a plurality of grids, each grid is marked, and the initial model is marked as a binary image to form a grid image. After the initial model is marked as a binary image, an additional grid image can be obtained by a connected domain judgment method, and an algorithm for processing a data point model can be improved; in addition, as the grid can comprise a plurality of data points, the number of the data points of each grid image can be reduced, so that the complexity of judging the connected domain is simplified, the speed of judging the connected domain is increased, and the speed of processing the data point model is increased.
Further, the method comprises the steps of providing a projection direction, and carrying out projection processing on the initial model along the projection direction to form the model to be processed. The projection processing can convert the three-dimensional point cloud into a two-dimensional model to be processed, and the operation can be simplified by processing the two-dimensional model to be processed subsequently, so that the processing speed of the data point model is increased.
In the detection method provided by the technical scheme of the invention, the initial data points corresponding to the data points in the additional grid image are removed from the initial model by the data point model processing method, so that the calculation speed can be increased, and the false detection can be reduced.
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FIG. 1 is a flow chart of steps of an embodiment of a data point model processing method of the present invention;
fig. 2 to 5 are schematic structural diagrams of steps in an embodiment of a data point model processing method according to the present invention.
Detailed Description
There are many problems with detection methods, such as: the data point model is slow and inefficient to process.
In order to solve the technical problem, the invention provides a data point model processing method, which comprises the following steps: dividing a model to be processed into a plurality of grids; marking each grid, and marking the model to be processed as a binary image to form a grid image; judging a connected domain of the grid image, and acquiring an additional grid image when the grid image comprises a main grid image and an additional grid image which are separated; removing initial data points corresponding to the data points in the additional grid image from the initial model. The data point model processing method can accelerate the processing speed, thereby increasing the detection speed. The method can accelerate the processing speed, thereby increasing the detection speed.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
FIG. 1 is a flow chart of steps in an embodiment of a data point model processing method of the present invention.
Step S01, providing an initial model, wherein the initial model comprises a main model and an additional model which are separated from each other, and the main model and the additional model both comprise initial data points;
step S02, forming a model to be processed according to an initial model, wherein the initial data point forms a data point in the model to be processed;
step S03, dividing the model to be processed into a plurality of grids;
step S04, marking each grid, and marking the model to be processed as a binary image to form a grid image;
step S05, judging the connected domain of the grid image, and acquiring an additional grid image when the grid image comprises a main grid image and an additional grid image which are separated;
step S06, removing the initial data points corresponding to the additional mesh image from the initial model to form a target model.
The data point model processing method of the present invention will be described in detail below with reference to the accompanying drawings.
Fig. 2 to 5 are schematic structural diagrams of steps in an embodiment of a data point model processing method according to the present invention.
The data point model processing method according to the present invention will be described with reference to fig. 2 to 5.
Referring to fig. 2, step S01 is executed to provide an initial model, which includes a main model 100 and an additional model 110 that are separated from each other; the main model 100 and the additional model 110 each include initial data points.
In this embodiment, the initial model is a three-dimensional point cloud. Specifically, the initial model is a three-dimensional point cloud obtained by three-dimensional detection of the object to be detected through a three-dimensional detection device, and the three-dimensional detection device comprises a chromatic dispersion confocal device, a laser triangulation detection device, a laser confocal device or a white light interference device. In other embodiments, the initial model may be a two-dimensional image, in particular a two-dimensional image taken by a camera or a microscope.
In this embodiment, the object to be detected is a transparent object, and the additional model 110 is a tool image of the bottom of the object to be detected by the detection light of the detection device penetrating through the object to be detected.
The initial model is formed from a large number of initial data points.
In this embodiment, the initial data point is a three-dimensional coordinate point. In other embodiments, when the detection device is an imaging device, the initial data points are pixel points of a two-dimensional image, and represent a relationship between a two-dimensional position coordinate of a surface point of the object to be measured and the light intensity.
In this embodiment, each initial data point has a serial number.
Referring to fig. 2, step S02 is executed to form the to-be-processed model 130 according to the initial model, and the initial data points form data points in the to-be-processed model 130.
It should be noted that, in this embodiment, the initial model is a three-dimensional point cloud; the model to be processed 130 is a two-dimensional image. The step of forming the model to be processed 130 from the initial model includes: providing a projection direction; and performing projection processing on the initial model along the projection direction to form the model to be processed 130.
The initial model is subjected to projection processing, so that three-dimensional point cloud can be converted into a two-dimensional image, the calculation complexity of subsequent connected domain judgment can be simplified, and the data point model processing speed can be increased.
The detection device comprises an objective lens and a light source, wherein the light source is used for generating detection light, and the objective lens is used for collecting the detection light and enabling the detection light to be incident to the surface of an object to be detected. In this embodiment, the detection device is a chromatic dispersion confocal device, and the objective lens is further configured to collect signal light returned from the surface of the object to be measured.
The number of the projection directions may be one or more.
The projection direction includes: a first projection direction perpendicular to the optical axis of the objective lens; and/or a second projection direction parallel to the optical axis of the objective lens.
When the number of the projection directions is plural, the data point model processing method further includes: and repeating the steps from the projection processing to the connected domain judgment.
In this embodiment, the number of the additional models 110 is one. The projection direction comprises a first projection direction a, and the first projection direction a is perpendicular to the optical axis of the objective lens of the detection device.
In this embodiment, the initial model is projected along the first projection direction a, and the projection of the main model 100 along the first projection direction a and the projection of the additional model 110 along the first projection direction a can be separated from each other.
In other embodiments, when the alignment direction of the additional model and the main model is perpendicular to the optical axis of the objective lens of the detection device, the projection direction includes a second projection direction b, and the second projection direction b is parallel to the optical axis of the objective lens of the detection device.
In the process of obtaining the initial model by detecting the object to be detected through the optical detection device, the additional model 110 is located at one side of the main model 100, and the additional model 110 and the main model 100 are arranged along the first projection direction a or the second projection direction b. Therefore, projecting the initial model in the first projection direction a and the second projection direction b can separate the projections of the main model 100 and the additional model from each other. Thus, in some embodiments, the plurality of projection directions may be made to include a first projection direction and a second projection direction, and the step of projection processing to connected domain determination may be repeated until additional mesh images of all additional models are acquired.
In other embodiments, when the initial model is a two-dimensional image, the step of forming the model to be processed according to the initial model includes: and enabling the model to be processed to be the same as the initial model, and enabling the data point to be the same as the initial data point. When the initial model is a three-dimensional point cloud, the step of forming a model to be processed according to the initial model comprises the following steps: and enabling the model to be processed to be the same as the initial model, and enabling the data point to be the same as the initial data point.
In this embodiment, after the model to be processed is formed, the data point is a projection of the initial data point.
The step of projection processing includes: and removing the components of the initial point cloud along the projection direction to form a data point, so that the initial point cloud forms a two-dimensional image to be processed.
Referring to fig. 4, step S03 is executed to divide the model to be processed into a plurality of grids.
The model to be processed is divided into a plurality of grids, and the initial model can be converted into the grid image 120 through subsequent marking processing, so that the number of pixels of the grid image 120 can be reduced, the calculation amount of subsequent connected domain judgment is reduced, and the processing speed of the data point model can be increased.
In this embodiment, the grid is square. In other embodiments, the grid may be rectangular, equilateral hexagonal, or triangular.
In this embodiment, the model to be processed is a two-dimensional image, and the grid is a two-dimensional grid. In other embodiments, the step of forming the model to be processed from the initial model comprises: and enabling the model to be processed to be the same as the initial model, and enabling the data point to be the same as the initial data point. And if the initial model is a three-dimensional point cloud, the model to be processed is a three-dimensional point cloud, and the grid is a three-dimensional grid.
With continued reference to fig. 4, step S04 is executed to perform a labeling process on each mesh, and label the model to be processed as a binary image to form the mesh image 120.
The initial model is converted into the grid image 120, so that the number of data points of the grid image 120 can be reduced, the calculation amount of subsequent connected domain judgment is reduced, and the processing speed of the data point model can be increased.
In this embodiment, each grid is marked according to the number of data points in each grid.
Specifically, the marking process includes: setting a first quantity threshold; comparing the number of the data points in the grid with the first number threshold, and marking the grid as image points when the number of the data points in the grid is greater than or equal to the first number threshold; otherwise, mark the grid as blank.
In this embodiment, the three-dimensional morphology of the surface of the object to be measured is obtained by scanning and detecting the surface of the object to be measured, so as to form an initial model. The initial data points are three-dimensional position coordinate data of the surface of the object to be measured. The additional model 110 is a tool surface where the detection light penetrates through the object to be measured and reaches the lower part of the object to be measured, and the detection light is reflected by the tool surface to return signal light, so that initial data of the additional model 110 is formed. Other positions are out of the field of view of the detection device or the detection light is blocked and cannot be reached, so that signal light cannot be formed, and no initial data point or no data point exists. Therefore, the mesh image 120 can be formed by labeling each mesh according to the number of data points.
Specifically, in this embodiment, the image point is represented by data 1, and the blank point is represented by data "0".
If the first number threshold is too small, the influence of noise on the grid image 120 is not easily eliminated, so that the separated main grid image 101 and the additional grid image 111 are not easily acquired subsequently; if the first number threshold is too large, it tends to increase the error of the grid image 120 from the initial model, thereby reducing the accuracy of the data point model processing. In this embodiment, the first number threshold is 1-15.
In other embodiments, when the initial data point is a gray scale value for each pixel of the two-dimensional image. The initial data points comprise gray values of pixels in the initial model; the step of the marking process comprises: setting a second quantity threshold; obtaining a reference number according to the number of data points in the grid which meet the first gray scale condition; when the reference number is greater than or equal to the second number threshold, marking the grid as an image point; otherwise, marking the grids as blank points; alternatively, the step of labeling processing comprises: setting a first gray threshold; acquiring a grid gray value according to the gray value of each data point in the grid; and comparing the grid gray value with a first gray threshold value, and respectively marking the grids meeting different comparison results as image points and blank points.
Specifically, the marking process further includes: a second gray level threshold is set. When the gray values of the main model and the additional model are both larger than the gray value of the background of the initial model, the gray value in the grid is larger than or equal to a second gray threshold; when the gray values of the main model and the additional model are both smaller than the gray value of the background of the initial model, the gray value in the grid is smaller than or equal to a second gray threshold;
when the gray values of the main model and the additional model are both larger than the gray value of the background of the initial model, the step of respectively marking the grids meeting different comparison results as image points and blank points comprises the following steps: when the grid gray is greater than or equal to a first gray threshold value, marking as an image point; otherwise, mark as blank spot. When the gray values of the main model and the additional model are both smaller than the gray value of the background of the initial model, the step of respectively marking the grids meeting different comparison results as image points and blank points comprises the following steps: when the grid gray level is less than or equal to a first gray level threshold value, marking as an image point; otherwise, mark as blank spot.
The grid gray scale is the average value of the gray scale values of the data points in each grid or the sum of the gray scale values of the data points in each grid.
Continuing to refer to 4, step S05, a connected domain determination is made on the mesh image 120, and when the mesh image 120 includes the separated main mesh image 101 and additional mesh image 111, the additional mesh image 111 is acquired.
By the connected component determination, it can be determined whether the projection direction can separate the main mesh image 101 and the additional mesh image 111, and data points of the additional mesh image 111 can be acquired, so that the additional model 110 in the initial model can be removed by the subsequent processing.
In this embodiment, the method for judging the connected domain includes an eight-connected algorithm and a four-connected algorithm.
In this embodiment, the model to be processed is obtained by projecting the initial model along the first projection direction. The additional model 110 is separated from the projection of the main model 100 along the first projection direction, the main grid image 101 and the additional grid image 111 are separated from each other.
In other embodiments, a plurality of different projection directions are set, and when the obtained mesh images are a connected whole, the projection directions are replaced to repeat a loop operation process including the steps of S02 to S05 until additional mesh images of all additional models are obtained. Specifically, when there is one additional model, the steps of S02 to S05 are repeated until an additional mesh image is acquired.
Referring to fig. 5, step S06 is executed to remove the initial data points corresponding to the data points in the additional mesh image 111 (shown in fig. 4) from the initial model.
In this embodiment, the initial data points all have serial numbers; the data points have corresponding sequence numbers.
In this embodiment, removing the initial data points corresponding to the data points in the additional mesh image 111 from the initial model includes: acquiring serial numbers of data points in the additional grid image 111 as serial numbers to be removed; acquiring an initial data point with the serial number to be removed as an initial data point to be removed; and removing the initial data points to be removed from the initial model to obtain a target model.
In this embodiment, the number of the additional models 110 is one, and after the additional grid is obtained, the initial data points corresponding to the data points in the additional grid image 111 are removed from the initial model to form the target model.
In other implementations, the number of additional models may be multiple. And repeating the circulating operation processing until all the additional images of the additional point clouds are obtained, and removing the initial data points corresponding to the data points in all the additional images from the initial point clouds. Or, the circulating step further comprises: removing initial data points corresponding to the data points in the additional grid image from the initial model.
The technical scheme of the invention also provides a detection method, which comprises the following steps:
and detecting the object to be detected to obtain an initial model.
In this embodiment, the detection system is a three-dimensional detection device, and the initial model is a three-dimensional point cloud. Specifically, the three-dimensional detection device comprises a chromatic dispersion confocal device, a laser triangulation detection device, a laser confocal device or a white light interference device.
In other embodiments, the detection system may be a two-dimensional detection system, such as a two-dimensional image taken by a camera or microscope. The initial model may be a two-dimensional image.
In this embodiment, the material of the detection object is a transparent material. In other embodiments, the material of the detection object may also be a non-transparent material.
The main model 100 is a point cloud of an object to be measured; the additional model 110 is a point cloud of other objects around the test object.
The model 130 to be processed is processed according to the above-mentioned data point model processing method to form the target model 140.
In this embodiment, the data point model processing method is the same as the data point model processing method shown in fig. 1 to 5, and is not repeated here.
In this embodiment, the detection method further includes: providing a design model; and comparing the target model 140 with the design model to obtain the distortion of the object to be measured.
The appearance processing method can increase the processing speed, so that the detection speed of the object to be detected can be increased. In addition, the additional model 110 in the initial model is removed by the data point model processing method, so that the influence of the additional model 110 on the distortion detection result can be avoided, the false detection rate can be reduced, and the precision of the detection result can be improved.
An embodiment of the present invention further provides a data point model processing system, including: an input system for providing an initial model comprising a plurality of initial data points, the initial model comprising a main model 100 and an additional model 110 separated from each other;
a data processing system for forming a model to be processed 130 from an initial model, the initial data points forming data points in the model to be processed;
a mesh dividing system for dividing the model 130 to be processed into a plurality of meshes;
the marking system is used for marking each grid, and marking the model 130 to be processed as a binary image to form a grid image 120;
a connected domain determination system for performing connected domain determination on the mesh image 120, and acquiring the additional mesh image 111 when the mesh image 120 includes the separated main mesh image 101 and additional mesh image 111;
a removal system for removing initial data points corresponding to the data points in the additional mesh image 111 from the initial model to form a target model 140.
The data processing system includes: and the projection system is configured to perform projection processing on the initial model along a projection direction to form the model to be processed 130.
In other embodiments, the data processing system comprises: and the equivalent system is used for enabling the model to be processed to be the same as the initial model, and the data point is the same as the initial data point.
The marking system includes: a setting system for setting a first quantity threshold; and the comparison system is used for comparing the number of the data points in the grid with the first number threshold, marking the grid as an image point when the number of the data points in the grid is greater than or equal to the first number threshold, and otherwise, marking the grid as a blank point.
An embodiment of the present invention further provides a detection system, including: the model acquisition system is used for detecting the object to be detected to acquire an initial model; a data point model processing system, which is the same as the data point model processing system described in the previous embodiment.
In this embodiment, the input system is further configured to provide a design model;
in this embodiment, the detection system further includes: and the distortion detection system is used for comparing the target model 140 with the design model to obtain the distortion of the object to be detected.
The present invention also provides a non-transitory computer readable medium comprising executable instructions that, when executed, cause a processor to perform the data point model processing method as shown in fig. 1 to 5.
Although the present invention is disclosed above, the present invention is not limited thereto. Various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (17)
1. A method for processing a data point model, comprising:
providing an initial model comprising a main model and an additional model separated from each other, the main model and the additional model each comprising an initial data point;
forming a model to be processed according to an initial model, wherein the initial data point forms a data point in the model to be processed;
dividing a model to be processed into a plurality of grids;
marking each grid, and marking the model to be processed as a binary image to form a grid image;
judging a connected domain of the grid image, and acquiring an additional grid image when the grid image comprises a main grid image and an additional grid image which are separated;
and removing initial data points corresponding to the data points in the additional grid image from the initial model to form a target model.
2. The data point model processing method of claim 1, wherein the initial model is a three-dimensional point cloud; the model to be processed is a two-dimensional image;
the step of forming a model to be processed from the initial model comprises: providing a projection direction; and carrying out projection processing on the initial model along the projection direction to form the model to be processed.
3. The data point model processing method of claim 2, wherein the number of projection directions is plural, the data point model processing method further comprising: and repeating the cycle operation processing until additional grid images of all the additional models are obtained, wherein the cycle operation processing comprises the steps from the projection processing to the connected domain judgment.
4. The data point model processing method of claim 3, wherein the number of the additional models is plural, and the iterative loop operation process further comprises: removing initial data points corresponding to the data points in the additional grid image from the initial model;
or, after repeating the loop operation processing until additional grid images of all additional models are acquired, removing initial data points corresponding to the data points in all the additional grid images from the initial model.
5. The data point model processing method of claim 1, wherein the step of forming the model to be processed based on the initial model comprises: and enabling the model to be processed to be the same as the initial model, wherein the data point is the same as the initial data point.
6. The data point model processing method of claim 1, wherein the step of labeling process comprises: setting a first quantity threshold; comparing the number of the data points in the grid with the first number threshold, and marking the grid as image points when the number of the data points in the grid is greater than or equal to the first number threshold; otherwise, mark the grid as blank.
7. The data point model processing method of claim 1, wherein the initial model is an image; the initial data points comprise gray values of pixels in the initial model;
the step of the marking process comprises: setting a second quantity threshold; obtaining a reference number according to the number of data points meeting the gray condition in the grid; when the reference number is greater than or equal to a second number threshold, marking the grid as an image point; otherwise, marking the grids as blank points;
alternatively, the step of labeling processing comprises: setting a first gray threshold; acquiring a grid gray value according to the gray value of each data point in the grid; and comparing the grid gray value with a first gray threshold value, and respectively marking the grids meeting different comparison results as image points and blank points.
8. A method of detection, comprising:
detecting an object to be detected to obtain an initial model;
the data point model processing method according to any one of claims 1 to 7, wherein the initial model is processed to form a target model.
9. The detection method of claim 8, further comprising: providing a design model; and comparing the target model with the design model to obtain the distortion of the object to be measured.
10. The inspection method of claim 8, wherein the object to be inspected is inspected by an inspection apparatus comprising an objective lens;
the projection direction includes: a first projection direction perpendicular to the optical axis of the objective lens; and/or a second projection direction parallel to the optical axis of the objective lens.
11. A data point model processing system, comprising:
an input system for providing an initial model, the initial model comprising a plurality of initial data points, the initial model comprising a main model and an additional model that are separate from each other;
the data processing system is used for forming a model to be processed according to an initial model, and the initial data points form data points in the model to be processed;
the mesh division system is used for dividing the model to be processed into a plurality of meshes;
the marking system is used for marking each grid and marking the model to be processed as a binary image to form a grid image;
a connected domain determination system for determining a connected domain for the grid image, and acquiring an additional grid image when the grid image includes a main grid image and an additional grid image which are separated;
and the removal system is used for removing the initial data points corresponding to the data points in the additional grid image from the initial model to form a target model.
12. The data point model processing system of claim 11, wherein the initial model is a three-dimensional point cloud; the model to be processed is a two-dimensional image;
the data processing system includes: and the projection system is used for carrying out projection processing on the initial model along the projection direction to form the model to be processed.
13. The data point model processing system of claim 11, wherein the data processing system comprises: and the equivalent system is used for enabling the model to be processed to be the same as the initial model, and the data point is the same as the initial data point.
14. The data point model processing system of claim 11, wherein the labeling system comprises:
a setting system for setting a first quantity threshold;
the comparison system is used for comparing the number of the data points in the grid with the first number threshold value, and when the number of the data points in the grid is greater than or equal to the first number threshold value, marking the grid as an image point; otherwise, mark the grid as blank.
15. A detection system, comprising:
the model acquisition system is used for detecting the object to be detected to acquire an initial model;
the data point model processing system of any one of claims 11 to 14, configured to perform data point model processing on the initial model to form the target model.
16. The inspection system of claim 15, wherein the input system is further configured to provide a design model;
the detection system further comprises: and the distortion detection system is used for comparing the target model and the design model to obtain the distortion of the object to be detected.
17. A computer readable medium comprising executable instructions that when executed cause a processor to perform the data point model processing method of any of claims 1-7.
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