CN117670657A - Multi-image stitching method, system, equipment and storage medium based on halcon - Google Patents
Multi-image stitching method, system, equipment and storage medium based on halcon Download PDFInfo
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Abstract
The invention provides a multi-image stitching method, a system, equipment and a storage medium based on halcon, and relates to the technical field of image stitching. Using a halcon Gaussian function on the image subjected to distortion elimination, and giving a larger weight to pixels closer to a central point in a window function to reduce noise influence; generating an integral image; converting the image to a scale space; extracting feature points of the image after the scale transformation; and (3) adjusting the pixel weight of the characteristic point to be in direct proportion to the distance between the current processing point and the left boundary of the overlapping area, and splicing the images by adopting affine change. The invention effectively improves the detection precision of large products and the detection of the related visual defects at the splicing position.
Description
Technical Field
The invention relates to the technical field of image stitching, in particular to a multi-image stitching method, system and device based on halcon and a storage medium.
Background
Image stitching is not simple to coincide two images with a common area with the same area, but because the angles and positions of the two images are different, the internal parameters and external parameters of the camera are different when shooting are different, so simple coverage stitching is unreasonable. Therefore, for image stitching, it is necessary to perform corresponding transformation (affine transformation) on one image as a reference and then to perform simple translation on the affine transformed image to coincide with a common region of the reference image.
At present, there are several image stitching methods based on different algorithms, such as Harris corner detection method, FAST algorithm, etc. Each of these techniques has some drawbacks or deficiencies. Such as: the Harris corner detection method has robustness and rotation invariance. However, it is scale-variant and the processing time is long. The FAST algorithm has rotational invariance and dimensional invariance, but when noise exists, the FAST algorithm has poor performance and long processing time.
Disclosure of Invention
Therefore, the embodiment of the invention provides a multi-image stitching method, a system, equipment and a storage medium based on halcon, which are used for solving the problems of low image stitching precision and restoration degree, long processing time and the like under the noise condition in the prior art.
In order to solve the above problems, an embodiment of the present invention provides a multi-image stitching method based on halcon, including:
s1: calibrating a camera to obtain internal and external parameters of the camera, and performing distortion elimination on images in a halcon to eliminate the situation that the edges of different images are spliced abnormally when the images are spliced;
s2: using a halcon Gaussian function on the image subjected to distortion elimination, and giving a larger weight to pixels closer to a central point in a window function to reduce noise influence;
s3: generating an integral image, wherein the area of the integral image of a pixel point is equal to the sum of all points from the pixel point to an origin point through an integral image formula;
s4: converting the image into a scale space, namely performing scale conversion on the original image;
s5: extracting feature points of the image after the scale transformation;
s6: and (3) adjusting the pixel weight of the characteristic point to be in direct proportion to the distance between the current processing point and the left boundary of the overlapping area, and splicing the images by adopting affine change.
Preferably, the mathematical expression of the gaussian function is:
G(x)＝(1/(sqrt(2*π)*σ)*exp(-(x 2 )/(2*σ 2 ))
where x is an argument, σ is a standard deviation, pi is a circumference ratio, sqrt () is used to calculate the square root of a given parameter, exp () is an exponential function with the base of the natural logarithm e.
Preferably, the window function is a window of custom 5×5 pixel points.
Preferably, the method for performing scale transformation on the original image is as follows:
the original image and the scale kernel function are subjected to convolution operation, and the formula is as follows:
L(x,y,σ)＝G(x,y,σ)*I(x,y)
wherein the method comprises the steps of
Where L (x, y, σ) is the spatial dimension of the image, I (x, y) is the original image, G (x, y, σ) is a two-dimensional gaussian function that can be scaled, σ is the degree of blurring of the image, and pi is the circumference ratio.
Preferably, the method for extracting the feature points of the image after the scale transformation comprises the following steps:
and forming an area taking the pixel point as a unit by taking the characteristic point as a center, dividing a small area in the area, dividing required sampling points in the small area again, obtaining descriptors capable of describing the characteristics of the area, and then carrying out corresponding characteristic matching in a halcon, wherein the descriptors are similar, namely identical characteristic points exist in different pictures.
Preferably, the method for splicing the images by affine change by adjusting the pixel weight of the feature point to be in direct proportion to the distance between the current processing point and the left boundary of the overlapping area comprises the following steps:
and optimizing the joint between two pictures, comparing the pixel weights of the characteristic points from the leftmost edge of the overlapping area, enabling the pixel weights to be in direct proportion to the distance between the current processing point and the left boundary of the overlapping area, creating a projection matrix, and splicing the images by adopting affine change.
Preferably, the image stitching method comprises pictures taken by at least two cameras.
The embodiment of the invention also provides a multi-image stitching system based on the halcon, which is used for realizing the multi-image stitching method based on the halcon, and specifically comprises the following steps:
the camera calibration module is used for calibrating a camera to obtain internal and external parameters of the camera, and carrying out distortion elimination on images in a halcon to eliminate the situation that the edges of different images are spliced abnormally when the images are spliced;
a pixel weight giving module, which is used for using a halcon Gaussian function to the image subjected to distortion elimination, and giving a larger weight to the pixels which are closer to the center point in a window function so as to reduce noise influence;
the integral image generation module is used for generating an integral image, and a pixel point is subjected to an integral image formula, wherein the area of the integral image is equal to the sum of all points from the pixel point to an origin point;
the scale transformation module is used for transforming the image into a scale space, namely, performing scale transformation on the original image;
the feature point extraction module is used for extracting feature points of the image after the scale transformation;
and the image splicing module is used for splicing the images by adopting affine change by adjusting the pixel weight of the characteristic points to make the characteristic points proportional to the distance between the current processing point and the left boundary of the overlapping area.
The embodiment of the invention also provides electronic equipment, which comprises a processor, a memory and a bus system, wherein the processor and the memory are connected through the bus system, the memory is used for storing instructions, and the processor is used for executing the instructions stored by the memory so as to realize the multi-image splicing method based on the halcon.
The embodiment of the invention also provides a computer storage medium which stores a computer software product, wherein the computer software product comprises a plurality of instructions for enabling a piece of computer equipment to execute the multi-image splicing method based on the halcon.
From the above technical scheme, the invention has the following advantages:
the embodiment of the invention provides a multi-image stitching method, a system, equipment and a storage medium based on halcon, which are used for obtaining internal and external parameters of a camera by calibrating the camera, and eliminating distortion of images in halcon, so as to eliminate the unnatural situation of stitching at the edge of different images during stitching; using a halcon Gaussian function on the image subjected to distortion elimination, and giving a larger weight to pixels closer to a central point in a window function to reduce noise influence; generating an integral image, wherein the area of the integral image of a pixel point is equal to the sum of all points from the pixel point to an origin point through an integral image formula; converting the image into a scale space, namely performing scale conversion on the original image; extracting feature points of the image after the scale transformation; and (3) adjusting the pixel weight of the characteristic point to be in direct proportion to the distance between the current processing point and the left boundary of the overlapping area, and splicing the images by adopting affine change. The invention improves the defects existing in the prior art, and enables a plurality of mixed and overlapped images to realize seamless splicing under the conditions of parallax, lens distortion, scene movement, exposure difference and the like of a plurality of pictures through an algorithm, thereby effectively improving the detection precision of large products and the detection of related visual defects at the splicing position in industrialization.
Drawings
For a clearer description of embodiments of the invention or of solutions in the prior art, reference will be made to the accompanying drawings, which are intended to be used in the examples, for a clearer understanding of the characteristics and advantages of the invention, by way of illustration and not to be interpreted as limiting the invention in any way, and from which, without any inventive effort, a person skilled in the art can obtain other figures. Wherein:
fig. 1 is a flowchart of a multi-image stitching method based on halcon according to an embodiment;
FIG. 2 is a schematic diagram of two images after distortion removal in an embodiment;
fig. 3 is a schematic diagram of feature point extraction on two images in the embodiment;
fig. 4 is a schematic diagram of two pictures after being spliced in the embodiment;
fig. 5 is a block diagram of a multi-image stitching system based on halcon, according to an exemplary embodiment.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a multi-image stitching method based on halcon, where the method includes:
s1: calibrating a camera to obtain internal and external parameters of the camera, and performing distortion elimination on images in a halcon to eliminate the situation that the edges of different images are spliced abnormally when the images are spliced;
s2: using a halcon Gaussian function on the image subjected to distortion elimination, and giving a larger weight to pixels closer to a central point in a window function to reduce noise influence;
s3: generating an integral image, wherein the area of the integral image of a pixel point is equal to the sum of all points from the pixel point to an origin point through an integral image formula;
s4: converting the image into a scale space, namely performing scale conversion on the original image;
s5: extracting feature points of the image after the scale transformation;
s6: and (3) adjusting the pixel weight of the characteristic point to be in direct proportion to the distance between the current processing point and the left boundary of the overlapping area, and splicing the images by adopting affine change.
According to the technical scheme, the multi-image stitching method based on the halcon is characterized in that the camera is calibrated to obtain the internal and external parameters of the camera, the distortion of the images is eliminated in the halcon, and the phenomenon that the edges of different images are not natural when being stitched is eliminated; using a halcon Gaussian function on the image subjected to distortion elimination, and giving a larger weight to pixels closer to a central point in a window function to reduce noise influence; generating an integral image, wherein the area of the integral image of a pixel point is equal to the sum of all points from the pixel point to an origin point through an integral image formula; converting the image into a scale space, namely performing scale conversion on the original image; extracting feature points of the image after the scale transformation; and (3) adjusting the pixel weight of the characteristic point to be in direct proportion to the distance between the current processing point and the left boundary of the overlapping area, and splicing the images by adopting affine change. The invention improves the defects existing in the prior art, and enables a plurality of mixed and overlapped images to realize seamless splicing under the conditions of parallax, lens distortion, scene movement, exposure difference and the like of a plurality of pictures through an algorithm, thereby effectively improving the detection precision of large products and the detection of related visual defects at the splicing position in industrialization.
In the implementation, in step S1, the camera is calibrated to obtain the internal and external parameters of the camera, and the distortion of the images is eliminated in the halcon, so that the situation that the edges of different images are not natural when being spliced is eliminated. In this embodiment, the camera is calibrated by the calibration board to obtain two images after distortion removal, as shown in fig. 2 below.
In this embodiment, in step S2, a halcon gaussian function is used for the image subjected to distortion cancellation, and noise influence is reduced by giving a larger weight to a pixel closer to the center point in a window function.
Specifically, the mathematical expression of the gaussian function is:
G(x)＝(1/(sqrt(2*π)*σ)*exp(-(x 2 )/(2*σ 2 ))
where x is an argument, σ is a standard deviation, pi is a circumference ratio, sqrt () is used to calculate the square root of a given parameter, exp () is an exponential function with the base of the natural logarithm e.
The window function is a window custom assuming 5 x 5 pixels.
In this embodiment, in step S3, an integral image is generated, and the area of the integral image is equal to the sum of all points from the pixel to the origin by the integral image formula.
In this implementation, in step S4, the image is transformed into a scale space, i.e. the original image is scaled. The scale transformation mode is that convolution operation is carried out on the original image and a scale kernel function, so that interference on feature point extraction is eliminated when the scale of the image is changed;
specifically, the convolution operation is carried out on the original image and the scale kernel function, and the formula is as follows:
L(x,y,σ)＝G(x,y,σ)*I(x,y)
wherein the method comprises the steps of
Where L (x, y, σ) is the spatial dimension of the image, I (x, y) is the original image, G (x, y, σ) is a two-dimensional gaussian function that can be scaled, σ is the degree of blurring of the image, and pi is the circumference ratio.
In the present embodiment, in step S5, feature point extraction is performed on the image after the scale conversion. As shown in fig. 3.
Specifically, firstly, an area taking a pixel point as a unit is formed by taking a characteristic point as a center, a small area is subdivided in the area, required sampling points are subdivided in the area, a descriptor capable of describing the characteristic of the area is obtained, then corresponding characteristic matching is carried out in a halcon, and the similar descriptors are that the same characteristic points exist in different pictures. And obtaining excellent matching points between overlapping areas in the two pictures through convolution.
In this embodiment, in step S6, the feature point pixel weights are adjusted to be proportional to the distance of the current processing point from the left boundary of the overlapping region, and the images are spliced by affine variation. As shown in fig. 4.
The image stitching method in this embodiment includes pictures taken by at least two cameras.
Specifically, a spliced picture is created, an image is spliced, the joint between two pictures is optimized, characteristic point pixel weight comparison is carried out from the leftmost side of an overlapped area, the characteristic point pixel weight comparison is in direct proportion to the distance between a current processing point and the left boundary of the overlapped area, a projection matrix is created, and affine change is adopted for splicing the images. In the embodiment, affine stitching is performed on the images by utilizing a halcon function:
gen_bundle_adjusted_mosaic(Images:MosaicImage:HomMatrices2D,StackingOrder,TransformDomain:TransMat2D)
the TransMat2D is a projection matrix, and comprises translation operation of converting each image into a spliced image.
Example two
As shown in fig. 5, the present invention provides a multi-image stitching system based on halcon, which is configured to implement the multi-image stitching method based on halcon according to the first embodiment, and specifically includes:
the camera calibration module 10 is used for calibrating a camera to obtain internal and external parameters of the camera, and performing distortion elimination on images in a halcon to eliminate the situation that the edges of different images are spliced abnormally when the images are spliced;
a pixel weight giving module 20 for using a halcon gaussian function on the image after distortion cancellation, in which noise influence is reduced by giving a greater weight to pixels nearer to the center point;
an integral image generating module 30, configured to generate an integral image, where an area of the integral image is equal to a sum of all points from a pixel point to an origin point through an integral image formula;
a scale transformation module 40, configured to transform the image into a scale space, i.e. scale transforming the original image;
the feature point extraction module 50 is used for extracting feature points of the image after the scale transformation;
the image stitching module 60 is configured to stitch the images with affine variation by adjusting the weights of the pixels of the feature points to be proportional to the distance between the current processing point and the left boundary of the overlapping region.
The embodiments of the multi-image stitching system based on halcon in the present embodiment are applicable to implementing the multi-image stitching method based on halcon, so that the embodiments of the multi-image stitching system based on halcon can be seen in the embodiments of the multi-image stitching method based on halcon, for example, the camera calibration module 10, the pixel weight giving module 20, the integral image generating module 30, the scale transformation module 40, the feature point extraction module 50, and the image stitching module 60, which are respectively used to implement steps S1, S2, S3, S4, S5, and S6 in the multi-image stitching method based on halcon, so that the detailed description of the embodiments of the respective portions will be referred to for avoiding redundancy.
Example III
The embodiment of the invention also provides electronic equipment, which comprises a processor, a memory and a bus system, wherein the processor and the memory are connected through the bus system, the memory is used for storing instructions, and the processor is used for executing the instructions stored by the memory so as to realize the multi-image splicing method based on the halcon.
Example IV
The embodiment of the invention also provides a computer storage medium which stores a computer software product, wherein the computer software product comprises a plurality of instructions for enabling a piece of computer equipment to execute the multi-image splicing method based on the halcon.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations and modifications of the present invention will be apparent to those of ordinary skill in the art in light of the foregoing description. It is not necessary here nor is it exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (10)
1. The multi-image stitching method based on the halcon is characterized by comprising the following steps of:
s1: calibrating a camera to obtain internal and external parameters of the camera, and performing distortion elimination on images in a halcon to eliminate the situation that the edges of different images are spliced abnormally when the images are spliced;
s2: using a halcon Gaussian function on the image subjected to distortion elimination, and giving a larger weight to pixels closer to a central point in a window function to reduce noise influence;
s3: generating an integral image, wherein the area of the integral image of a pixel point is equal to the sum of all points from the pixel point to an origin point through an integral image formula;
s4: converting the image into a scale space, namely performing scale conversion on the original image;
s5: extracting feature points of the image after the scale transformation;
s6: and (3) adjusting the pixel weight of the characteristic point to be in direct proportion to the distance between the current processing point and the left boundary of the overlapping area, and splicing the images by adopting affine change.
2. The halcon-based multi-image stitching method according to claim 1, wherein the mathematical expression of the gaussian function is:
G(x)＝(1/(sqrt(2*π)*σ)*exp(-(x 2 )/(2*σ 2 ))
where x is an argument, σ is a standard deviation, pi is a circumference ratio, sqrt () is used to calculate the square root of a given parameter, exp () is an exponential function with the base of the natural logarithm e.
3. The halcon-based multi-image stitching method according to claim 1, wherein the window function is a window custom assuming 5 x 5 pixels.
4. The halcon-based multi-image stitching method according to claim 1, wherein the method for scaling the original image is as follows:
the original image and the scale kernel function are subjected to convolution operation, and the formula is as follows:
L(x，y，σ)＝G(x，y，σ)*I(x，y)
wherein the method comprises the steps of
Where L (x, y, σ) is the spatial dimension of the image, I (x, y) is the original image, G (x, y, σ) is a two-dimensional gaussian function that can be scaled, σ is the degree of blurring of the image, and pi is the circumference ratio.
5. The multi-image stitching method based on halcon according to claim 1, wherein the method for extracting feature points from the scale-transformed image is as follows:
and forming an area taking the pixel point as a unit by taking the characteristic point as a center, dividing a small area in the area, dividing required sampling points in the small area again, obtaining descriptors capable of describing the characteristics of the area, and then carrying out corresponding characteristic matching in a halcon, wherein the descriptors are similar, namely identical characteristic points exist in different pictures.
6. The multi-image stitching method based on halcon according to claim 1, wherein the method for stitching images by affine variation is as follows:
and optimizing the joint between two pictures, comparing the pixel weights of the characteristic points from the leftmost edge of the overlapping area, enabling the pixel weights to be in direct proportion to the distance between the current processing point and the left boundary of the overlapping area, creating a projection matrix, and splicing the images by adopting affine change.
7. The halcon-based multi-image stitching method according to claim 6, wherein the image stitching method comprises pictures taken by at least two cameras.
8. A multi-image stitching system based on halcon, wherein the system is used for implementing the multi-image stitching method based on halcon according to any one of claims 1 to 7, and specifically comprises:
the camera calibration module is used for calibrating a camera to obtain internal and external parameters of the camera, and carrying out distortion elimination on images in a halcon to eliminate the situation that the edges of different images are spliced abnormally when the images are spliced;
a pixel weight giving module, which is used for using a halcon Gaussian function to the image subjected to distortion elimination, and giving a larger weight to the pixels which are closer to the center point in a window function so as to reduce noise influence;
the integral image generation module is used for generating an integral image, and a pixel point is subjected to an integral image formula, wherein the area of the integral image is equal to the sum of all points from the pixel point to an origin point;
the scale transformation module is used for transforming the image into a scale space, namely, performing scale transformation on the original image;
the feature point extraction module is used for extracting feature points of the image after the scale transformation;
and the image splicing module is used for splicing the images by adopting affine change by adjusting the pixel weight of the characteristic points to make the characteristic points proportional to the distance between the current processing point and the left boundary of the overlapping area.
9. An electronic device comprising a processor, a memory and a bus system, the processor and the memory being connected by the bus system, the memory being configured to store instructions, the processor being configured to execute the instructions stored by the memory to implement the halcon-based multi-image stitching method according to any one of claims 1 to 7.
10. A computer storage medium storing a computer software product comprising instructions for causing a computer device to perform the halcon-based multi-image stitching method according to any one of claims 1 to 7.
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