CN112734916B - Color confocal parallel measurement three-dimensional morphology reduction method based on image processing - Google Patents
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Abstract
The invention provides a color confocal parallel measurement three-dimensional morphology reduction method based on image processing, which comprises the steps of firstly carrying out target extraction and image splicing on a plurality of images obtained by a line scanning parallel color confocal system to obtain spliced image information required to be processed; secondly, obtaining a centroid communication area of each measured point through morphological processing and a centroid extraction algorithm; and finally, carrying out 'H value-height' conversion on the centroid communication area of each measured point in the spliced image, obtaining the height of the corresponding measured point according to the H value, and realizing the reconstruction of the three-dimensional surface topography of the object by combining an interpolation fitting algorithm. The algorithm of the invention avoids the influence of image noise caused by stray light and defocusing light by using a mode of matting with a mask, can accurately extract a target light spot region to be processed, improves the processing precision, and simultaneously greatly shortens the processing time and improves the processing efficiency by using the image splicing.
Description
Technical Field
The invention relates to the field of image processing in optical detection, in particular to a color confocal parallel measurement three-dimensional morphology reduction method based on image processing.
Background
When the color confocal parallel measuring device is combined with a color camera to be used as a sensor to acquire images, the influence of stray light and defocused light is often encountered in the image processing process, and the acquired image quality is different, so that the centroids of all light spots are difficult to recognize at one time to position, extract and process the target measured point. Especially, in the process of parallel measurement, the number of the collected images is large, the processing time is long, and the processing efficiency is low.
In patent application CN109373927A, application No. 2018.09.28, a color confocal three-dimensional topography measuring method and system, a color confocal technology and a color camera are combined to obtain the three-dimensional topography of the surface of an object, but the invention only aims at a single-point color confocal measuring system, and does not apply a color conversion algorithm to parallel measurement, and cannot solve the problems of multiple images and multiple measured points.
In patent application CN111288928A, application No. 2020.03.12, entitled method, device, equipment and storage medium for measuring three-dimensional topography of object surface, an algorithm applied to color confocal parallel measurement is disclosed, but the algorithm is long in time consumption and low in measurement efficiency, influences of stray light and defocused light are easily introduced in the processing process, and the measurement precision is low.
Disclosure of Invention
The invention aims to solve the problems that the prior art can not process images aiming at a plurality of images and a plurality of measured objects, the influence of stray light and interference light is easily introduced in the image processing process, and the measurement precision is low.
Aiming at the problems, the invention provides a color confocal parallel measurement three-dimensional morphology reduction method based on image processing, which comprises the following steps:
s1, inputting K images of K different measured points obtained in the online scanning process of the sample object, wherein K is an integer greater than or equal to 2;
s2, sequentially carrying out mask matting and image splicing on the K images to generate spliced images;
s3, performing morphological opening operation processing on the spliced image, and extracting a centroid connection area of each measured point;
s4, performing color conversion on each centroid communication area to obtain the height value of each measured point;
and S5, combining the three-dimensional drawing command with the two-dimensional data interpolation fitting to finally obtain the three-dimensional shape of the whole measured object surface after the interpolation fitting.
As a further improvement of the image processing-based color confocal parallel measurement three-dimensional morphology reduction method, step S1 specifically includes: when the sample object performs line scanning movement, the color camera takes pictures to obtain K pictures corresponding to K different positions; l light spots generated by line scanning are uniformly distributed on each picture in a straight line, and the total light spots to be processed is K x L; k images are input into the computer.
As a further improvement of the image processing-based color confocal parallel measurement three-dimensional morphology reduction method of the present invention, step S2 includes the following steps:
s21, carrying out image preprocessing on the input K images, removing redundant background areas and reserving light spot areas to be processed;
s22, performing mask matting on the preprocessed image to obtain a target image required by splicing;
s23, generating a white negative film;
s24, setting an ROI area on the white film;
and S25, splicing the target images obtained in the step S22 on the white negative film obtained in the step S24 to generate a spliced image containing all the information of the measured points.
As a further improvement of the image processing-based color confocal parallel measurement three-dimensional morphology restoration method, in step S21, the image preprocessing includes rotating and cropping the image, so as to remove redundant background areas and only reserve the light spot area to be processed, thereby saving memory and improving processing efficiency.
As a further improvement of the image processing-based color confocal parallel measurement three-dimensional morphology reduction method, in step S22, the mask matting specifically includes: setting mask parameters, setting the mask value of the target light spot position to be non-null, and setting other positions to be 0; and executing mask parameters, extracting required light spot target pixels, shielding redundant background pixels and generating a shape image in linear arrangement.
As a further improvement of the image processing-based color confocal parallel measurement three-dimensional morphology reduction method of the present invention, in step S23: the size of the white film is calculated from the number K of input images and the total length of L spots in each image, so that the white film just covers all K × L spots measured.
As a further improvement of the image processing-based color confocal parallel measurement three-dimensional morphology reduction method, step S25 specifically includes: and (4) superposing each image extracted by the mask in the step (S22) to the ROI area corresponding to the white negative film one by one, and splicing to generate a complete spliced image containing information of all measured points.
As a further improvement of the image processing-based color confocal parallel measurement three-dimensional morphology reduction method of the present invention, in step S3: the morphological open operation processing is to corrode the image, and then expand the corroded result to remove the noise point of the image, so that the denoised image tends to be smooth and better to identify the mass center; the process of extracting the centroid communication area of each measured point comprises the following steps: and (3) extracting the centroid coordinates of each measured point by using a centroid extraction algorithm, and intercepting a corresponding circle by combining the light spot radius of the image to obtain a centroid communication area of each measured point.
As a further improvement of the image processing-based color confocal parallel measurement three-dimensional morphology reduction method, step S4 specifically includes: converting the RGB space into the HSI space by using an RGB-HSI color space conversion algorithm, and extracting a hue parameter H value of each centroid connected region, wherein the H value conversion formula is as follows:
and combining the corresponding curve of the H value-height to obtain the height information of each measured point.
As a further improvement of the image processing-based color confocal parallel measurement three-dimensional morphology reduction method, in the step S5, a bicubic interpolation method is adopted for fitting, and compared with a common bilinear interpolation method and a nearest neighbor interpolation method, the bicubic interpolation method is large in calculation amount, most complex in algorithm, most accurate in calculation, better in interpolation fitting result, capable of generating smoother edges, least in quality loss of processed images and best in effect.
The invention has the following beneficial effects:
compared with the traditional binarization centroid extraction algorithm, the algorithm uses a mask to extract a plurality of target images, and avoids the influence of image noise caused by stray light and defocused light in a background area; the centroid connected region is obtained by morphology, the target light spot region required to be processed can be accurately extracted, and the measurement precision is greatly improved; the color conversion algorithm is applied to parallel measurement, and data processing can be performed on a plurality of images and a plurality of measured points; meanwhile, the processing speed is improved to a certain extent by the application of image splicing, and the algorithm can effectively process a plurality of color images obtained by color confocal parallel measurement in real time to generate a measured object surface three-dimensional topography map containing all measured points.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a general flowchart of an algorithm of a color confocal parallel measurement algorithm based on image processing according to an embodiment of the present invention.
Fig. 2 is a flowchart of an image stitching algorithm provided in the embodiment of the present invention.
Fig. 3 is an exemplary diagram of patterns after image stitching according to an embodiment of the present invention.
FIG. 4 is a graph showing the result of the reduction of the three-dimensional topography of the surface of the object according to the embodiment of the present invention.
Detailed Description
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
The embodiment of the invention discloses a color confocal parallel measurement three-dimensional morphology reduction method based on image processing on the basis of monitoring the focused light reflection of each position on the surface of a sample object by using a color confocal parallel measurement device and collecting images by using a color camera as a sensor, and a computer carrier is used for carrying out object surface three-dimensional morphology reduction on the collected images.
Referring to fig. 1, a general flowchart of a color confocal parallel measurement three-dimensional morphology reduction method based on image processing and a flowchart of an image stitching algorithm provided in fig. 2 are shown, in which an embodiment of the color confocal parallel measurement algorithm based on image processing includes the following steps:
s1, inputting K images of K different measured points obtained in the online scanning process of the sample object, wherein K is an integer greater than or equal to 2 and can be 100, 500, 1000 and the like;
s2, sequentially carrying out mask matting and image splicing on the K images to generate spliced images;
s3, performing morphological opening operation processing on the spliced image, and extracting a centroid connection area of each measured point;
s4, performing color conversion on each centroid communication area to obtain the height value of each measured point;
and S5, utilizing the three-dimensional drawing instruction to combine with the two-dimensional data interpolation fitting to finally obtain the three-dimensional shape of the whole measured object surface after the interpolation fitting.
The above steps are further explained below.
Wherein, step S1 specifically includes: when the sample object performs line scanning motion along the direction vertical to the optical fiber bundle, the color camera takes pictures to obtain K pictures corresponding to K different positions; assuming that L light spots generated by line scanning are uniformly distributed on each picture in a straight line, and K x L light spots to be processed are total; k images are input into the computer.
Wherein, step S2 includes the following four substeps:
and S21, performing image preprocessing on the input K images, wherein the image preprocessing comprises rotating and cutting the images to remove redundant background areas and reserve light spot areas to be processed so as to save memory and improve processing efficiency.
S22, performing mask matting on the preprocessed image to obtain a target image required by splicing; the mask cutout specifically is: setting mask parameters, setting the mask value of the target light spot position to be non-null, and setting other positions to be 0; and executing mask parameters, extracting required light spot target pixels, shielding redundant background pixels, and generating a shape image similar to a linear arrangement.
S23, generating a white negative film; the size of the white film is calculated from the number K of input images and the total length of L spots in each image, so that the white film just covers all K × L spots measured.
S24, the ROI area of the white film generated in the step S23 is set.
And S25, overlapping each image stripped by the mask in the step S22 to the ROI area corresponding to the white film in the step S24 one by one, and splicing to generate a complete spliced image containing information of all measured points.
Wherein, the ROI area is called Region of Interest, and Chinese means the ROI area. In machine vision and image processing, a region to be processed is outlined from a processed image in the form of a box, a circle, an ellipse, an irregular polygon, or the like, and is called a region of interest. In the invention, the ROI area of the image linearly changes in the white negative film according to a certain rule.
In the above step S3: the morphological open operation processing is to corrode the image, and then expand the corroded result to remove the noise point of the image, so that the denoised image tends to be smooth and better to identify the mass center; the process of extracting the centroid communication area of each measured point comprises the following steps: and (3) extracting the centroid coordinates of each measured point by using a centroid extraction algorithm, and intercepting a corresponding circle by combining the light spot radius of the image to obtain a centroid communication area of each measured point.
The step S4 is specifically: converting the RGB space into the HSI space by using an RGB-HSI color space conversion algorithm, and extracting a hue parameter H value of each centroid connected region, wherein the H value conversion formula is as follows:
and combining the corresponding curve of the H value-height to obtain the height information of each measured point.
In the step S5, a bicubic interpolation method is used for fitting, and compared with a conventional bilinear interpolation method and a nearest neighbor interpolation method, the bicubic interpolation method has the advantages of large calculation amount, most complex algorithm, most accurate calculation, better interpolation fitting result, capability of generating smoother edges, least loss of quality of the processed image, and best effect.
In summary, the present invention firstly uses the image stitching technology to perform the matting processing on the result acquired by the color camera by using the mask, and then cuts out the required target areas, and then stitches the target areas together. And further extracting the mass center by using a mass center extraction algorithm, realizing H value-height conversion by combining a color conversion algorithm, and carrying out reduction and interpolation fitting on the three-dimensional shape of the surface of the object. Compared with the traditional binarization centroid extraction algorithm, the algorithm uses a mask to extract a plurality of target images, and avoids the influence of stray light and defocused light in a background area; the centroid communication area is obtained by morphology, so that the measurement precision is greatly improved; the color conversion algorithm is applied to parallel measurement, and data processing can be performed on a plurality of images and a plurality of measured points; meanwhile, the processing speed is improved to a certain extent by the application of image splicing, and the algorithm can effectively process a plurality of color images obtained by color confocal parallel measurement in real time to generate a measured object surface three-dimensional topography map containing all measured points.
The above steps are explained below by way of example.
Specifically, the color confocal parallel measurement three-dimensional shape reduction method based on image processing is to use a character E on the surface of a 1-element coin as a standard reflection surface to obtain a three-dimensional shape reduction image, wherein the three-dimensional shape reduction image is a curved surface image formed by respectively using the abscissa and the ordinate of a measured point as the x and the y coordinates of a three-dimensional shape reduction curve of an object on the basis of an H value-height calibration curve, using a height value corresponding to the measured point as the z coordinate of the three-dimensional shape reduction curve of the object, connecting the heights of all the measured points and performing two-dimensional interpolation fitting. In this embodiment, in the online scanning process of the sample object, the test area of the sample object is divided into 500 test points, 500 images are obtained, and each image has 42 linearly arranged light spots generated by the linear scanning, and all the tested points are 500 × 42 in total.
Referring to fig. 1, the calibration process of the present invention specifically includes the following steps:
s1, input image: 500 images of the sample object at 500 different positions obtained during the line scan are input.
In this embodiment, when the sample performs a one-dimensional motion in a direction perpendicular to the line scanning direction during the line scanning process, 500 color images of the sample at 500 different positions can be obtained by simultaneously photographing with the color camera. The subsequent image processing is performed on the basis of these 500 images. Each image has 42 linear spots generated by linear scanning, and the total number of the spots to be processed is 500 x 42.
S2, image splicing: the image stitching operation is performed on the input batch images, and specifically comprises the following steps: s21, S22, S23, S24 and S25, please refer to fig. 2.
S21, performing image preprocessing on the input batch images, specifically:
the image preprocessing comprises operations such as rotation, cutting and the like, redundant background areas are removed, and only light spot areas to be processed are reserved, so that the memory is saved, and the processing efficiency is improved;
s22, performing mask matting on the preprocessed image to obtain a target image required by splicing, specifically:
when the mask parameters are used, the operation is only executed on the pixel points with the mask value being non-empty, and the values of other pixel points are set to be 0, so that the required light spot target pixel can be extracted, the redundant background pixel is shielded, and a shape image similar to a fiber bundle structure is directly generated. The inventor uses a self-made mask to scratch a target image, and aims to avoid the problem that the accuracy of the target image is influenced due to the fact that the mask generated according to an original image is not accurate enough due to the fact that the quality conditions of light spots at different positions in the image are not consistent.
S23, generating a negative film with proper size and white color, specifically:
the color of the negative is white and the size of the negative is calculated from the number of input images and the total length of 42 light spots contained in each image after preprocessing, so that the negative just covers all 500 x 42 light spots obtained by measurement.
S24, setting the ROI of the white negative film.
S25, carrying out image splicing on the image extracted by the mask to generate a spliced image containing information of all measured points, specifically:
the images extracted by the mask are superposed into the corresponding ROI areas of the white negative film one by one, the ROI areas of the images are linearly changed in the white negative film according to a certain rule, so that a complete spliced image containing information of all measured points is generated by splicing, and an 'E' character formed by splicing a plurality of images obtained by experiments can be seen from the image in a hidden way, wherein the image is a spliced image which is shown in a figure 3. The following processing is all performed on the basis of the spliced "E" word.
S3, extracting a centroid connected region: the spliced image is subjected to morphological processing, and the centroid connected region is extracted, specifically:
through morphological open operation processing, namely, the image is corroded firstly, and then the corroded result is expanded, so that the noise point of the image is removed, the denoised image tends to be smooth, and the centroid recognition is better carried out. Then, extracting the centroid coordinate of each measured point by using a centroid extraction algorithm, and intercepting a corresponding circle by combining the light spot radius of the image to obtain a centroid communication area of each measured point;
s4, color space conversion: and carrying out color conversion on each centroid communication area to obtain a corresponding height value, specifically:
converting the RGB space into the HSI space by using an RGB-HSI color space conversion algorithm, extracting the H value of each centroid connected region, constructing an H function based on an RGB-HSI conversion geometric formula, and converting the RGB value of the color information acquired by the color camera into a hue parameter H value related to the wavelength. Wherein, the H value conversion formula is as follows:
and then combining an 'H value-height' corresponding curve obtained by an earlier calibration experiment to obtain an 'H value-height' conversion relation, and obtaining height information of each measured point by knowing the H value.
S5, two-dimensional interpolation fitting and three-dimensional shape generation: the three-dimensional topography of the whole measured object surface after interpolation fitting is finally obtained by combining the three-dimensional drawing instruction with two-dimensional data interpolation fitting, as shown in fig. 4, the steps are specifically as follows:
and according to the actual position coordinate information of each measured point, utilizing a three-dimensional drawing instruction to combine with two-dimensional data interpolation fitting to finally obtain the three-dimensional appearance of the whole measured object surface after interpolation fitting. The inventor adopts a bicubic interpolation method for fitting, and compared with a common bilinear interpolation method and a nearest neighbor interpolation method, the bicubic interpolation method has the advantages of large calculated amount, most complex algorithm, most accurate calculation, better interpolation fitting result, capability of generating smoother edges, least loss of the quality of the processed image and optimal effect.
Claims (5)
1. A color confocal parallel measurement three-dimensional morphology reduction method based on image processing is characterized by comprising the following steps:
s1, inputting K images of K different measured points obtained in the online scanning process of the sample object, wherein K is an integer greater than or equal to 2;
s2, sequentially carrying out mask matting and image splicing on the K images to generate spliced images;
s3, performing morphological opening operation processing on the spliced image, and extracting a centroid connection area of each measured point;
s4, performing color conversion on each centroid communication area to obtain the height value of each measured point;
s5, utilizing a three-dimensional drawing command to combine with two-dimensional data interpolation fitting to finally obtain the three-dimensional appearance of the whole measured object surface after interpolation fitting;
wherein the step S2 includes the steps of:
s21, carrying out image preprocessing on the input K images, removing redundant background areas and reserving light spot areas to be processed;
s22, performing mask matting on the preprocessed image to obtain a target image required by splicing;
s23, generating a white negative film;
s24, setting an ROI area on the white film;
s25, splicing each target image obtained in the step S22 on the white negative film obtained in the step S24 to generate a spliced image containing information of all measured points;
in step S21, the image preprocessing includes rotating and cropping the image;
in step S22, the mask matting specifically includes: setting mask parameters, setting the mask value of the target light spot position to be non-null, and setting other positions to be 0; executing mask parameters, extracting required light spot target pixels, shielding redundant background pixels, and generating a shape image in linear arrangement;
in step S23: the size of the white film is calculated according to the number K of input images and the total length of L light spots in each image, so that the white film just covers all measured K x L light spots;
step S25 specifically includes: and (4) superposing each image extracted by the mask in the step (S22) to the ROI area corresponding to the white negative film one by one, and splicing to generate a complete spliced image containing information of all measured points.
2. The image-processing-based color confocal parallel measurement three-dimensional morphology restoration method according to claim 1, wherein the step S1 specifically comprises:
when the sample object performs line scanning movement, the color camera takes pictures to obtain K pictures corresponding to K different positions; l light spots generated by line scanning are uniformly distributed on each picture in a straight line, and the total light spots to be processed is K x L; k images are input into the computer.
3. The image-processing-based color confocal parallel measurement three-dimensional morphology restoration method according to claim 1, wherein in step S3:
the morphological opening operation processing comprises the following steps: corroding the image, and then expanding the corroded result; the process of extracting the centroid communication area of each measured point comprises the following steps: and (3) extracting the centroid coordinates of each measured point by using a centroid extraction algorithm, and intercepting a corresponding circle by combining the light spot radius of the image to obtain a centroid communication area of each measured point.
4. The image-processing-based color confocal parallel measurement three-dimensional morphology restoration method according to claim 1, wherein the step S4 specifically comprises:
converting the RGB space into the HSI space by using an RGB-HSI color space conversion algorithm, and extracting a hue parameter H value of each centroid connected region, wherein the H value conversion formula is as follows:
and combining the corresponding curve of the H value-height to obtain the height information of each measured point.
5. The image-processing-based color confocal parallel measurement three-dimensional morphology restoration method according to claim 1, wherein in step S5, a bicubic interpolation method is used for fitting.
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