CN113160320A - Chessboard angular point detection method and device for camera parameter calibration - Google Patents
Chessboard angular point detection method and device for camera parameter calibration Download PDFInfo
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- 238000004590 computer program Methods 0.000 description 4
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
- G06T2207/20164—Salient point detection; Corner detection
Abstract
The application relates to the technical field of computer vision, and discloses a chessboard angular point detection method for camera parameter calibration. The method comprises the following steps: detecting the checkerboard image by using haar characteristics to obtain haar characteristic values; processing the haar characteristic value into a characteristic value distribution image, wherein the haar characteristic value distribution image comprises a point-shaped area; and calculating the central position of the point region to obtain an angular point. The method improves the accuracy of chessboard detection.
Description
Technical Field
The present application relates to the field of computer vision technologies, and for example, to a method and an apparatus for detecting corner points on a chessboard for calibrating camera parameters.
Background
At present, parameter calibration of a camera is a key step of computer vision on image processing, and a checkerboard calibration method is generally adopted, and the method needs to extract internal corner points of a checkerboard to realize accurate mapping from a world coordinate system of points in an image to a camera coordinate system.
In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art:
the existing detection method cannot accurately detect the angular points of the checkerboard under the influence of multiple factors such as non-uniform illumination, angular point deformation, image noise and the like.
Disclosure of Invention
The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.
The embodiment of the disclosure provides a chessboard angular point detection method and device for camera parameter calibration, so as to solve the technical problem that chessboard angular points cannot be accurately detected in the prior art.
In some embodiments, a chessboard corner point detection method for camera parameter calibration includes:
detecting the checkerboard image by using haar characteristics to obtain haar characteristic values;
processing the haar characteristic value into a characteristic value distribution image, wherein the characteristic value distribution image comprises a point region;
and calculating the central position of the point region to obtain an angular point.
In some embodiments, processing the haar feature values into a feature value distribution image comprises:
and computing the haar characteristic values and then putting the haar characteristic values into an image channel to form the characteristic value distribution image.
In some embodiments, the detecting the checkerboard image by using haar features includes:
and selecting diagonal features in the haar features to carry out convolution calculation on the region with gray gradient in the checkerboard image to obtain the haar feature value.
In some embodiments, the detecting the checkerboard image by using haar features further includes:
and selecting boundary features in haar features to carry out convolution calculation on the region with gray gradient in the checkerboard image to obtain the haar feature value.
In some embodiments, the placing into the image channel after the operation on the haar feature value includes:
and under the condition that the haar characteristic values are multiple, respectively taking absolute values of the haar characteristic values and then respectively putting the haar characteristic values into different image channels.
In some embodiments, the calculating the center position of the dotted region to obtain a corner point includes:
converting the feature value distribution image from a color space BGR to a color space HSV;
searching a pixel set of the dot-shaped region;
and acquiring the central position of the dot region as the corner point based on the pixel set.
In some embodiments, searching for a set of pixels of the punctual region comprises:
searching the set of pixels based on a color threshold of the image channel.
In some embodiments, a chessboard corner detection device for camera parameter calibration comprises a processor and a memory storing program instructions, wherein the processor is configured to perform the method as described above when executing the program instructions.
In some embodiments, a chessboard corner point detection device for camera parameter calibration comprises:
the detection module is configured to detect the checkerboard image by using haar features to obtain haar feature values;
an image processing module configured to process the haar feature values into a feature value distribution image, wherein the haar feature value distribution image includes dotted areas;
and the calculation module is configured to calculate the central position of the point region to obtain the corner point.
In some embodiments, the detection module is further configured to:
and selecting diagonal features in the haar features to carry out convolution calculation on the region with gray gradient in the checkerboard image to obtain the haar feature value.
The chessboard angular point detection method and device for calibrating camera parameters provided by the embodiment of the disclosure can achieve the following technical effects:
the accuracy of checkerboard corner detection is improved when camera parameters are calibrated.
The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.
Drawings
One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:
fig. 1 is a schematic flow chart of a chessboard corner point detection method provided by the embodiment of the present disclosure;
fig. 2 is another schematic flow chart of a chessboard corner point detection method provided by the embodiment of the present disclosure;
fig. 3 is a schematic flow chart of a chessboard corner point detection method provided by the embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a chessboard corner point detection device provided by the embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of a chessboard corner point detection device provided by the embodiment of the present disclosure;
FIG. 6 is an image of a row and column boundary feature in a haar feature;
FIG. 7 is an image of a diagonal feature in a haar feature;
fig. 8 is a photograph of a feature value distribution image obtained by using the chessboard corner detection method provided by the embodiment of the present disclosure.
Reference numerals:
10-a detection module; 20-an image processing module; 30-a calculation module.
Detailed Description
So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.
Referring to fig. 1, an embodiment of the present disclosure provides a chessboard corner point detection method for camera parameter calibration, including:
step 10: detecting the checkerboard image by using haar characteristics to obtain haar characteristic values;
step 20: processing the haar characteristic value into a characteristic value distribution image, wherein the characteristic value distribution image comprises a point region;
step S30: and calculating the central position of the point region to obtain an angular point.
The image is processed by adopting the haar characteristic, the symmetry of the local image area is firstly integrated and then subtracted, the noise is equalized, the obvious effect of the noise is equivalently inhibited, and the noise image is well detected. The chessboard corner detection method provided by the embodiment of the disclosure can detect the corners under the condition of nonuniform image light intensity caused by factors such as detection distance or incident angle and the like, and the corners with insufficient sharpness far away from the center of the image.
In some embodiments, step 20: processing the haar characteristic value into a characteristic value distribution image, comprising:
and computing the haar characteristic values and then putting the haar characteristic values into an image channel to form a characteristic value distribution image.
Referring to fig. 2, step 10: adopting haar characteristic to detect the checkerboard image, including:
and selecting diagonal features in the haar features to carry out convolution calculation on the region with the gray gradient in the checkerboard image to obtain haar feature values.
The corner points in the checkerboard image are detected using only diagonal features (see fig. 7), and accordingly the corner points in the feature value distribution image are located in a circular place-like region.
Referring to fig. 2, step 10: adopt haar characteristic to detect the checkerboard image, still include:
step 110: and selecting boundary features in the haar features to carry out convolution calculation on the region with the gray gradient in the checkerboard image to obtain haar feature values.
And (3) selecting diagonal features and boundary features in the haar features respectively (see fig. 8) and performing convolution calculation on the regions with gray gradients in the checkerboard image respectively to obtain haar feature values.
In fig. 6, the left image is a row edge feature, the right image is a column edge feature, and the two boundary features in the boundary features are used to detect an area with a gray gradient in the checkerboard image, so that horizontal lines and vertical lines of the checkerboard are displayed in the feature value distribution image conveniently.
In some embodiments, the placing into the image channel after the operation on the haar feature value includes:
and under the condition that the haar characteristic values are multiple, respectively taking absolute values of the haar characteristic values and then respectively putting the haar characteristic values into different image channels.
Referring to fig. 3, in some embodiments, step 30: calculating the center position of the point region to obtain an angular point, comprising:
step 300: converting the feature value distribution image from a color space BGR into a color space HSV;
step 310: searching a pixel set of the dot-shaped region;
step 320: and acquiring the central position of the dot region as an angular point based on the pixel set.
In some embodiments, step 310: searching for a set of pixels of a dotted area, comprising:
based on the color threshold of the image channel, a set of pixels is searched.
The specific steps of detecting the inner corner points of the checkerboard by adopting haar characteristics are as follows:
firstly, performing morphological gradient calculation on a checkerboard image, wherein a template for corner detection can be set to be 3 × 3 pixels of a square;
secondly, performing convolution calculation on the region with gray gradient in the checkerboard image by using diagonal features and boundary features (row boundary features and column boundary features) in the haar features to obtain haar feature values;
then, absolute values of haar characteristic values are respectively taken and then are respectively placed into different image channels, and each image channel is normalized to a gray scale interval [0, 255 ];
the distribution of color channels may be: taking an absolute value of haar characteristic values obtained by line boundary characteristic calculation, and putting the haar characteristic values into a B blue channel; taking an absolute value of haar characteristic values obtained by adopting column boundary characteristic calculation, and putting the haar characteristic values into a G green channel; and taking an absolute value of a haar characteristic value obtained by diagonal characteristic calculation, and putting the haar characteristic value into an R [ red ] channel to obtain a characteristic value distribution image, wherein the characteristic value distribution image comprises a dot region.
Then, the feature value distribution image is processed to calculate the center of the dot region, and the inner corners are distributed in the red dot region when viewed from the obtained ground feature value distribution image. Specifically, the method comprises the following steps:
converting the feature value distribution image from a color space BGR into a color space HSV;
searching a pixel set of the dot-shaped area based on the color threshold of the image channel to obtain a red dot-shaped area, wherein the color threshold of the red channel can refer to the specification in table 1;
and processing the dot regions one by one based on the pixel set, and calculating the central position of the dot region to obtain the position of the corner point.
The embodiment of the disclosure detects the angular points in the checkerboard image by using haar features, the detected dot-shaped area is a pixel set, and the central point of the pixel set is calculated to obtain the angular points of the checkerboard. Fig. 8 shows a feature value distribution image photo obtained by the method for detecting a checkerboard corner provided by the embodiment of the present disclosure, where the intersection points where the longitudinal and transverse lines are staggered are checkerboard corners, and the positions of the corners are accurate.
TABLE 1
Referring to fig. 4, an embodiment of the present disclosure provides a chessboard corner point detection device for camera parameter calibration, which includes a processor and a memory storing program instructions, where the processor is configured to execute the chessboard corner point detection method as described above when executing the program instructions. In some embodiments, the chessboard corner point detection device structure as shown in the figure may include: a processor (processor)310 and a memory (memory)320, and may further include a Communication Interface 330 and a bus 340. The processor 310, the communication interface 330 and the memory 320 may communicate with each other through a bus 340. Communication interface 330 may be used for information transfer. The processor 310 may call logic instructions in the memory 320 to perform the method of implementing 3D display of the above-described embodiment.
In addition, the logic instructions in the memory 320 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. The memory 320 is a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 310 executes functional applications and data processing, i.e., implements the method of implementing 3D display in the above-described method embodiments, by executing program instructions/modules stored in the memory 320. The memory 320 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like.
Further, memory 320 may include high speed random access memory and may also include non-volatile memory.
Referring to fig. 5, an embodiment of the present disclosure provides a chessboard corner point detection device for calibrating camera parameters, including:
the detection module 10 is configured to detect the checkerboard image by using haar features to obtain haar feature values;
an image processing module 20 configured to process the haar feature values into a feature value distribution image, wherein the haar feature value distribution image includes dotted areas;
a calculating module 30 configured to calculate a center position of the dotted area, resulting in a corner point.
In some embodiments, the image processing module is further configured to:
and computing the haar characteristic values and then putting the haar characteristic values into an image channel to form a characteristic value distribution image.
In some embodiments, the detection module is further configured to:
and selecting diagonal features in the haar features to carry out convolution calculation on the region with the gray gradient in the checkerboard image to obtain haar feature values.
In some embodiments, the detection module is further configured to:
and selecting boundary features in the haar features to carry out convolution calculation on the region with the gray gradient in the checkerboard image to obtain haar feature values.
In some embodiments, the image processing module is further configured to:
and under the condition that the haar characteristic values are multiple, respectively taking absolute values of the haar characteristic values and then respectively putting the haar characteristic values into different image channels.
In some embodiments, the calculation module 30 is further configured to:
converting the feature value distribution image from a color space BGR into a color space HSV;
searching a pixel set of the dot-shaped region;
and acquiring the central position of the dot region as an angular point based on the pixel set.
In some embodiments, the calculation module 30 is further configured to:
based on the color threshold of the image channel, a set of pixels is searched.
The embodiment of the disclosure provides a computer-readable storage medium, which stores computer-executable instructions configured to execute the above chessboard angular point detection method for camera parameter calibration.
The embodiment of the present disclosure provides a computer program product, including a computer program stored on a computer-readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer executes the above chessboard angular point detection method for calibrating camera parameters.
The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.
The method and the device for detecting the checkerboard corner points for calibrating the camera parameters, provided by the embodiment of the disclosure, can improve the accuracy of the checkerboard corner point detection for calibrating the camera parameters.
The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. The scope of the disclosed embodiments includes the full ambit of the claims, as well as all available equivalents of the claims. As used in this application, although the terms "first," "second," etc. may be used in this application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, unless the meaning of the description changes, so long as all occurrences of the "first element" are renamed consistently and all occurrences of the "second element" are renamed consistently. The first and second elements are both elements, but may not be the same element. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising one" does not exclude the presence of other like elements in a process, method or device that comprises the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.
Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the disclosed embodiments. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit may be merely a division of a logical function, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Claims (10)
1. A chessboard angular point detection method for calibrating camera parameters is characterized by comprising the following steps:
detecting the checkerboard image by using haar characteristics to obtain haar characteristic values;
processing the haar characteristic value into a characteristic value distribution image, wherein the haar characteristic value distribution image comprises a point-shaped area;
and calculating the central position of the point region to obtain an angular point.
2. The method of claim 1, wherein processing the haar eigenvalues into an eigenvalue distribution image comprises:
and computing the haar characteristic values and then putting the haar characteristic values into an image channel to form the characteristic value distribution image.
3. The method according to claim 2, wherein the detecting the checkerboard image by using haar features comprises:
and selecting diagonal features in the haar features to carry out convolution calculation on the region with gray gradient in the checkerboard image to obtain the haar feature value.
4. The method according to claim 3, wherein said detecting a checkerboard image using haar features further comprises:
and selecting boundary features in haar features to carry out convolution calculation on the region with gray gradient in the checkerboard image to obtain the haar feature value.
5. The method of claim 4, wherein the haar eigenvalue is placed into an image channel after being operated on, comprising:
and under the condition that the haar characteristic values are multiple, respectively taking absolute values of the haar characteristic values and then respectively putting the haar characteristic values into different image channels.
6. The method according to claim 5, wherein said calculating the center position of the dotted area to obtain a corner point comprises:
converting the feature value distribution image from a color space BGR to a color space HSV;
searching a pixel set of the dot-shaped region;
and acquiring the central position of the dot region as the corner point based on the pixel set.
7. The method of claim 6, wherein searching for the set of pixels of the dotted area comprises:
searching the set of pixels based on a color threshold of the image channel.
8. A chessboard corner detection device for camera parameter calibration, comprising a processor and a memory having stored program instructions, wherein the processor is configured to perform the method of any of claims 1 to 7 when executing the program instructions.
9. A chessboard angular point detection device for calibrating camera parameters is characterized by comprising:
the detection module is configured to detect the checkerboard image by using haar features to obtain haar feature values;
an image processing module configured to process the haar feature values into a feature value distribution image, wherein the feature value distribution image includes a dotted region;
and the calculation module is configured to calculate the central position of the point region to obtain the corner point.
10. The apparatus of claim 9, wherein the detection module is further configured to:
and selecting diagonal features in the haar features to carry out convolution calculation on the region with gray gradient in the checkerboard image to obtain the haar feature value.
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| PCT/CN2021/071703 WO2021147755A1 (en) | 2020-01-20 | 2021-01-14 | Checkerboard corner detection method and device |
| TW110101666A TW202131282A (en) | 2020-01-20 | 2021-01-15 | Checkerboard corner detection method and device |
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