CN111814740A - Pointer instrument reading identification method and device, computer equipment and storage medium - Google Patents
Pointer instrument reading identification method and device, computer equipment and storage medium Download PDFInfo
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Abstract
The application relates to a method and a device for identifying reading of a pointer instrument, computer equipment and a storage medium. The method comprises the steps of obtaining an image including a pointer instrument to be detected, and determining the image of the pointer instrument to be detected according to a target detection algorithm. And judging whether the image of the pointer instrument to be detected is matched with the preset template image or not based on an accelerated robust feature algorithm, and if so, determining the key point of the image of the pointer instrument to be detected according to the preset key point of the preset template image. And constructing a scale curve of the pointer instrument to be measured according to the key points. And determining a suspected pointer point according to the gray value of the coordinate point on the scale curve. And determining the pointer position of the pointer instrument to be measured according to the gray value between the circle center point of the scale and the suspected pointer point. According to the pointer position, the reading of the pointer instrument to be detected is determined, and the recognition method of the reading of the pointer instrument is high in inspection efficiency and accuracy of various pointer instruments.
Description
Technical Field
The application relates to the technical field of intelligent power equipment, in particular to a pointer instrument reading identification method and device, computer equipment and a storage medium.
Background
With the development of power systems, the requirements on the service quality and the safe operation level of a power grid are continuously improved. The important components of the power grid are all kinds of substations, and the power equipment needs to be regularly patrolled and examined to strictly ensure the stable and reliable operation of the power equipment. The inspection of the transformer substation refers to the routine work that the transformer substation regularly inspects and inspects the transformer equipment in the jurisdiction range of the transformer substation and ensures the normal and reliable operation of the power equipment. A plurality of devices of the transformer substation are provided with various pointer instruments, and the device is very important for polling various pointer instruments.
In the traditional technology, the inspection of various pointer instruments is mainly performed manually, workers carry a paper standard inspection homework book, observe the reading of a pointer at a short distance and fill in inspection results. However, such inspection methods are inefficient and prone to errors.
Disclosure of Invention
In view of the above, it is necessary to provide a method, an apparatus, a computer device and a storage medium for identifying a reading of a pointer meter.
In one aspect, an embodiment of the present application provides a method for identifying a reading of a pointer instrument, including:
acquiring an image to be detected, wherein the image to be detected comprises an image of a pointer instrument to be detected;
determining the type of the pointer instrument to be detected and the position of the pointer instrument to be detected in the image to be detected according to a target detection algorithm to obtain an image of the pointer instrument to be detected;
judging whether the pointer instrument image to be detected is matched with a preset template image or not based on an accelerated robust feature algorithm;
if the pointer instrument image to be detected is matched with the preset template image, determining key points of the pointer instrument image to be detected according to preset key points of the preset template image, wherein the key points comprise a scale starting point, a scale middle point, a scale end point and a scale center point of the pointer instrument to be detected;
according to the key points, a scale curve of the pointer instrument to be detected on the pointer instrument image to be detected is constructed;
determining a suspected pointer point according to the gray value of the coordinate point on the scale curve;
determining the pointer position of the pointer instrument to be measured according to the gray value between the circle center point of the scale and the suspected pointer point;
and determining the reading of the pointer instrument to be measured according to the pointer position.
In one embodiment, the determining whether the image of the pointer instrument to be measured matches the preset template image based on the accelerated robust feature algorithm includes:
acquiring the characteristic points of the pointer instrument image to be detected based on the accelerated robust characteristic algorithm to obtain the characteristic points to be detected, and acquiring the characteristic points of the preset template image to obtain preset characteristic points;
and judging whether the pointer instrument image to be detected is matched with the preset template image or not according to the feature point to be detected and the preset feature point.
In one embodiment, the determining whether the pointer instrument image to be measured matches the preset template image according to the feature point to be measured and the preset feature point includes:
calculating the Euclidean distance between the feature point to be measured and the preset feature point;
if the Euclidean distance is within a preset distance range, determining that the pointer instrument image to be detected is matched with the preset template image;
and if the Euclidean distance is not within the preset distance range, returning to execute the acceleration-based robust feature algorithm, and judging whether the image of the pointer instrument to be detected is matched with the preset template image.
In one embodiment, the determining the key point of the pointer instrument to be measured according to a preset key point of a preset template image includes:
determining a homography matrix according to the feature points to be detected and the preset feature points;
and determining the key point of the pointer instrument to be detected according to the homography matrix and the preset key point in the preset template image.
In one embodiment, the determining a suspected pointer point according to a gray scale value of a coordinate point on the scale curve includes:
determining a gray value to be measured according to the gray value of the coordinate point on the scale curve based on a gradient projection method;
and if the gray value to be detected is within a preset gray value range, determining the coordinate point corresponding to the gray value to be detected as the suspected pointer point.
In one embodiment, the determining the pointer position of the pointer instrument to be measured according to the gray value between the circle center point of the scale and the suspected pointer point includes:
calculating the variance of the gray value between the circle center point of the scale and the suspected pointer point;
and determining the pointer position of the pointer instrument to be detected according to the variance.
In one embodiment, the target detection algorithm is a fast RCNN algorithm.
In another aspect, an embodiment of the present application further provides an apparatus for identifying a reading of a pointer instrument, including:
the acquisition module is used for acquiring an image to be detected, wherein the image to be detected comprises an image of a pointer instrument to be detected;
the determining module is used for determining the type of the pointer instrument to be detected and the position of the pointer instrument to be detected in the image to be detected according to a target detection algorithm to obtain an image of the pointer instrument to be detected;
the judging module is used for judging whether the pointer instrument image to be detected is matched with a preset template image or not based on an accelerated robust feature algorithm;
the determining module is further configured to determine a key point of the pointer instrument image to be detected according to a preset key point of the preset template image if the pointer instrument image to be detected is matched with the preset template image, wherein the key point includes a scale starting point, a scale middle point, a scale end point and a scale center point of the pointer instrument to be detected;
the construction module is used for constructing a scale curve of the pointer instrument to be detected on the pointer instrument image to be detected according to the key points;
the determining module is further used for determining a suspected pointer point according to the gray value of the coordinate point on the scale curve;
the determining module is further used for determining the pointer position of the pointer instrument to be measured according to the gray value between the scale circle center point and the suspected pointer point;
and the determining module is also used for determining the reading of the pointer instrument to be measured according to the pointer position.
The present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method when executing the computer program.
The present application also provides a computer-readable storage medium having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method as set forth above.
The embodiment of the application provides a pointer instrument reading identification method and device, computer equipment and a storage medium. The method comprises the steps of obtaining an image to be detected containing an image of a pointer instrument to be detected, determining the image of the pointer instrument to be detected according to a target detection algorithm, processing the image, and matching the image with a preset template image. And determining the key points of the pointer instrument image to be detected according to the preset key points of the preset template image. And determining the pointer position of the pointer instrument to be measured according to the key point through a related algorithm, so that the reading of the pointer instrument to be measured can be determined. The reading of discernment pointer instrument of automizing like this does not need staff's on-the-spot tour, can improve efficiency and the accuracy of patrolling and examining various pointer instruments, simultaneously, can reduce the waste of manpower resources.
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In order to more clearly illustrate the technical solutions in the embodiments or the conventional technologies of the present application, the drawings used in the description of the embodiments or the conventional technologies will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart illustrating steps of a method for identifying a reading of a pointer meter according to an embodiment of the present application;
FIG. 2 is a flow chart illustrating steps of a method for identifying a reading of a pointer meter according to an embodiment of the present application;
FIG. 3 is a flow chart illustrating steps of a method for identifying a reading of a pointer meter according to an embodiment of the present application;
FIG. 4 is a flowchart illustrating steps of a method for identifying a reading of a pointer meter according to an embodiment of the present application;
FIG. 5 is a flowchart illustrating steps of a method for identifying a reading of a pointer meter according to an embodiment of the present application;
FIG. 6 is a flowchart illustrating steps of a method for identifying a reading of a pointer meter according to an embodiment of the present application;
FIG. 7 is a schematic structural diagram of an apparatus for identifying reading of a pointer meter according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of embodiments in many different forms than those described herein and that modifications may be made by one skilled in the art without departing from the spirit and scope of the application and it is therefore not intended to be limited to the specific embodiments disclosed below.
The following describes the technical solutions of the present application and how to solve the technical problems with the technical solutions of the present application in detail with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
The method for identifying the reading of the pointer instrument can be applied to any occasions where the reading of the pointer instrument is needed, and can identify the reading of various pointer instruments, such as: a barometer, a thermometer, an oil pressure gauge and the like. The method of pointer meter reading may be implemented by computer devices including, but not limited to, personal computers, laptops, smartphones, tablets, and portable wearable devices. The image to be measured can be acquired in real time or can be stored in a memory of the computer equipment in advance. The identification method of the pointer instrument reading provided by the application can be realized through Python, can also be applied to other software, and realizes voltage regulation through other programming languages.
Referring to fig. 1, an embodiment of the present application provides a method for identifying a pointer instrument reading, where the embodiment takes a computer device as an execution subject to describe a method for identifying a pointer instrument reading of a substation, and the method includes:
s100, acquiring an image to be detected, wherein the image to be detected comprises an image of a pointer instrument to be detected.
The image to be measured can be a shot image of the pointer instrument needing to be read in the substation, and the image comprises the image of the pointer instrument to be measured and a background image. The camera that can pass through on patrolling and examining the robot shoots the image that awaits measuring in the transformer substation, also can shoot the image that awaits measuring through the camera on the unmanned aerial vehicle. After the image to be detected is shot by the inspection robot or the unmanned aerial vehicle, the image can be directly sent to the computer equipment and stored in a memory of the computer equipment. When the computer equipment needs to identify the reading of the pointer instrument, the image to be detected can be directly obtained from the memory. The embodiment does not set any limit to the method for acquiring the image to be measured.
S200, determining the type of the pointer instrument to be detected and the position of the pointer instrument to be detected in the image to be detected according to a target detection algorithm to obtain the image of the pointer instrument to be detected.
The target detection algorithm may be a deep neural network based detection algorithm, such as: convolutional neural network (R-CNN), OverTeat, FastCNN, SSD, and YOLO, among others. The present embodiment does not set any limit to the target detection algorithm to be used, as long as the function thereof can be achieved. The type of the pointer instrument to be detected in the image to be detected and the position of the pointer instrument to be detected in the image to be detected can be determined through a target detection algorithm. For example: the pointer instrument to be detected in the image to be detected can be determined to be a thermometer through a target detection algorithm, and a coordinate point of the thermometer in the image to be detected can be obtained. The image of the pointer instrument to be detected can be an image obtained by intercepting the pointer instrument to be detected, or an image obtained by marking the position of the pointer instrument to be detected in the image to be detected.
And S300, judging whether the image of the pointer instrument to be detected is matched with the preset template image or not based on the accelerated robust feature algorithm.
The Speeded Up Robust Features algorithms (SURFs) is a Robust image recognition and description algorithm. The SURF algorithm uses the determinant value of the Hessian matrix as the characteristic point for detection and uses the integral graph for accelerating operation, so that the image of the pointer instrument to be detected and the image of the preset template can be better identified, and whether the image of the pointer instrument to be detected and the image of the preset template are matched or not can be better judged. The preset template image is an image of various pointer meters which a worker previously photographed in various directions and is stored in a memory of a computer device. After the type of the pointer instrument to be detected is determined, when the type of the pointer instrument to be detected needs to be matched with the image of the pointer instrument to be detected, the computer equipment directly searches for a preset template image which is the same as the type of the pointer instrument to be detected in a memory for matching.
S400, if the image of the pointer instrument to be measured is matched with the preset template image, determining key points of the image of the pointer instrument to be measured according to preset key points of the preset template image, wherein the key points comprise a scale starting point, a scale middle point, a scale end point and a scale center point of the pointer instrument to be measured.
When a worker makes a preset template image, preset key points are directly marked on the preset template image, and the preset key points comprise a preset scale starting point, a scale middle point, a scale end point and a scale center point of a pointer instrument on the preset template image. If the pointer instrument is circular, the scale starting point and the scale end point are coincided. If the image of the pointer instrument to be detected is matched with the preset template image, the preset key points can correspond to the key points of the pointer instrument to be detected on the image of the pointer instrument to be detected one by one, and the key points of the image of the pointer instrument to be detected can be determined according to the preset key points. The embodiment does not limit the method for determining the key points of the image of the pointer instrument to be measured according to the preset key points of the preset template image.
And S500, constructing a scale curve of the pointer instrument to be detected on the pointer instrument image to be detected according to the key points.
And constructing a scale curve of the pointer instrument to be detected on the pointer instrument image to be detected according to the scale starting point, the scale middle point and the scale end point in the key points. If the pointer instrument to be measured is circular, the obtained scale curve is a circular curve; if the pointer instrument to be measured is fan-shaped, the obtained scale curve is an arc curve.
And S600, determining a suspected pointer point according to the gray value of the coordinate point on the scale curve.
And the coordinate points on the scale curve comprise the scale points of the pointer instrument to be measured and the points of intersection of the pointer and the scale curve. The gray value refers to the color depth of the scale points of the pointer instrument to be measured on the scale curve and the points where the pointer intersects with the scale curve, and the range of the gray value is generally from 0 to 255, white is 255, and black is 0. The suspected pointer points are points which are possibly intersected by the pointer and the scale curve in coordinate points on the scale curve, and the number of the suspected pointer points can be multiple. The gray values corresponding to different coordinate points on the scale curve may be different or the same. According to the gray value of the coordinate points on the scale curve, suspected pointer points in the coordinate points on the scale curve can be determined.
S700, determining the pointer position of the pointer instrument to be measured according to the gray value between the circle center point of the scale and the suspected pointer point.
The gray value between the circle center point of the scale and the suspected pointer point, namely the gray value on the straight line formed between the circle center point of the scale and the suspected pointer point. If the number of the suspected pointer points is one, and the gray values between the circle center point of the scale and the suspected pointer points are the same, the straight line formed by the circle center point of the scale and the suspected pointer points can be determined to be the pointer of the pointer instrument to be detected, and then the position of the pointer can be determined. If the number of the suspected pointer points is multiple, the pointer of the pointer instrument to be measured can be determined according to the gray value on each straight line obtained through statistics by counting the gray value on the straight line formed by the circle center point of the scale and each suspected pointer point, and then the position of the pointer is determined.
And S800, determining the reading of the pointer instrument to be measured according to the pointer position.
After the position of the pointer is determined, the reading of the pointer instrument to be measured can be determined according to the intersection point of the pointer and the scale curve and the scale of the pointer instrument to be measured of the type. In a specific embodiment, the reading of the pointer instrument to be measured can be determined according to the scale of the pointer instrument to be measured by calculating the included angle between the pointer and the straight line formed between the central point of the scale and the starting point of the scale and the percentage of the included angle to the angle of the whole pointer instrument. For example: the pointer instrument to be measured is a thermometer with a circular dial, the scale of the thermometer is 0-10 ℃, the included angle of a straight line formed between the pointer and the central point of the scale and the initial point of the scale, which is obtained through calculation, accounts for 50% of the angle of the whole pointer instrument, and then the reading of the thermometer is 5 ℃.
In the method for identifying the reading of the pointer instrument provided by this embodiment, the image of the pointer instrument to be detected is determined by obtaining the image to be detected including the image of the pointer instrument to be detected according to the target detection algorithm, and the image is processed and matched with the preset template image. And determining the key points of the pointer instrument image to be detected according to the preset key points of the preset template image. And determining the pointer position of the pointer instrument to be measured according to the key point through a related algorithm, so that the reading of the pointer instrument to be measured can be determined. The reading of the pointer instrument of discernment of automizing like this does not need staff's on-the-spot tour, can improve efficiency and the accuracy of patrolling and examining to the various pointer instruments of transformer substation, simultaneously, can reduce the waste of manpower resources. In addition, the method provided by the embodiment can directly store the reading of the pointer instrument without recording by workers, and can avoid omission or errors.
Referring to fig. 2, in an embodiment, the step S300 of determining whether the image of the pointer instrument to be measured matches the preset template image based on the accelerated robust feature algorithm includes:
s310, acquiring the characteristic points of the pointer instrument image to be detected based on the accelerated robust characteristic algorithm to obtain the characteristic points to be detected, and acquiring the characteristic points of the preset template image to obtain the preset characteristic points.
The acceleration steady characteristic algorithm processes the image of the pointer instrument to be detected by adopting a square filter, and can detect the characteristic points of the image of the pointer instrument to be detected by using a Hessian matrix to obtain the characteristic points to be detected. Similarly, the preset template image is processed based on the accelerated robust feature algorithm, so that the preset feature points can be obtained. The number of the feature points to be detected and the number of the preset feature points are multiple, and the feature points to be detected and the preset feature points are in one-to-one correspondence.
And S320, judging whether the image of the pointer instrument to be detected is matched with the preset template image or not according to the characteristic point to be detected and the preset characteristic point.
The method for judging whether the image of the pointer instrument to be detected is matched with the preset template image according to the feature point to be detected and the preset feature point is not limited in this embodiment as long as the function of the method can be realized. In this embodiment, the speed of obtaining the feature point to be measured and the preset feature point by using the accelerated robust feature algorithm is faster, and the recognition rate of the reading of the pointer instrument to be measured can be improved.
Referring to fig. 3, in an embodiment, the step S320 of determining whether the image of the pointer instrument to be measured matches the preset template image according to the feature point to be measured and the preset feature point includes:
s321, calculating the Euclidean distance between the feature point to be measured and the preset feature point.
The euclidean distance, also known as the euclidean metric, is a commonly used definition of distance. The Euclidean distance between the feature point to be measured and the preset feature point refers to the real distance between the feature point to be measured and the preset feature point in the Euclidean space, wherein the Euclidean space refers to the two-dimensional space where the feature point to be measured and the preset feature point are located, the Euclidean distance between the feature point to be measured and the preset feature point is calculated, and the Euclidean distance between each feature point to be measured and the corresponding preset feature point is calculated.
S322, judging whether the Euclidean distance is within a preset range.
And S323, if the Euclidean distance is within a preset distance range, determining that the image of the pointer instrument to be detected is matched with the preset template image.
Whether the Euclidean distance between each characteristic point to be detected and the corresponding preset characteristic point is within the preset distance range or not is judged through computer equipment, if the Euclidean distance between each characteristic point to be detected and the corresponding preset characteristic point is within the preset distance range, the characteristic point to be detected is matched with the preset characteristic point, namely, the characteristic point to be detected can be projected onto the preset characteristic point, the matching of the image of the pointer instrument to be detected and the preset template image can be determined, in other words, the pointer instrument to be detected in the image of the pointer instrument to be detected can be projected onto the preset pointer instrument in the preset template image.
And if the Euclidean distance is not within the preset distance range, returning to execute the acceleration-based robust feature algorithm, and judging whether the image of the pointer instrument to be detected is matched with the preset template image.
If the Euclidean distance between each feature point to be detected and the corresponding preset feature point is not within the preset distance range, it indicates that the feature point to be detected is not matched with the preset feature point, that is, the feature point to be detected cannot be projected onto the preset feature point, the pointer instrument image to be detected is not matched with the preset template image, the step S300 is returned to, and the template image which can be matched with the pointer instrument image to be detected is searched in the preset template image again.
In this embodiment, by calculating the euclidean distance between the feature point to be measured and the preset feature point, whether the feature point to be measured matches the preset feature point can be determined more clearly, and thus whether the image of the pointer instrument to be measured matches the image of the preset template can be determined more clearly. When the data is not matched, the data can be returned in time for re-matching, and the subsequent identification accuracy of the reading of the pointer instrument to be detected can be improved.
Referring to fig. 4, in an embodiment, the step S400 of determining the key point of the pointer instrument to be measured according to the preset key point of the preset template image includes:
and S410, determining a homography matrix according to the characteristic points to be detected and the preset characteristic points.
And S420, determining the key points of the pointer instrument to be detected according to the homography matrix and the key points in the preset template image.
After determining that the pointer instrument image to be detected is matched with the preset template image, the feature point to be detected is also matched with the preset feature point, that is, the feature point to be detected can be projected onto the preset feature point corresponding to the feature point to be detected. According to the homography matrix determined by the characteristic points to be detected and the preset characteristic points, the mapping relation between the characteristic points to be detected and the preset characteristic points can be represented, and the mapping relation between the pointer instrument image to be detected and the preset template image can also be represented. According to the homography matrix, a preset scale starting point, a scale middle point, a scale end point and a scale center point in key points in a preset template image can be mapped to a pointer instrument to be detected in a pointer instrument image to be detected, and then the scale starting point, the scale middle point, the scale end point and the scale center point in the key points of the pointer instrument to be detected can be obtained.
In this embodiment, the homography matrix is obtained through the mapping relationship between the feature points to be measured and the preset feature points. The preset key points in the preset template image can be accurately mapped to the pointer instrument to be detected in the pointer instrument image to be detected according to the homography matrix, and the key points of the pointer instrument to be detected can be determined. According to the characteristic points to be detected and the preset characteristic points, whether the image of the pointer instrument to be detected is matched with the preset template image or not can be judged, the key points of the pointer instrument to be detected can also be determined, and the practicability of the pointer instrument reading identification method can be improved.
Referring to fig. 5, in an embodiment, the step S600 of determining the suspected pointer point according to the gray-level value of the coordinate point on the calibration curve includes:
s610, determining the gray value to be measured according to the gray value of the coordinate point on the scale curve based on the gradient projection method.
S620, if the gray value to be detected is within the preset gray value range, determining the coordinate point corresponding to the gray value to be detected as the suspected pointer point.
The basic idea of the gradient projection method is: when the iteration point is in the feasible domain, taking the negative gradient direction at the point as a feasible descending method; when the iteration point is on the boundary of the feasible region, taking the projection of the direction of the negative gradient at the point on the boundary of the feasible region generates a feasible descending direction. And (3) adopting a gradient projection method for the gray values of all coordinate points on the scale curve to obtain an optimal solution in the gray values of all coordinate points, and taking the optimal solution as the gray value to be measured. Judging whether the gray value to be detected is within a preset gray value range or not through computer equipment, and if the gray value to be detected is within the preset gray value range, determining the coordinate point corresponding to the gray value to be detected as a suspected pointer point; and if the gray value to be detected is not in the preset gray value range, the coordinate point corresponding to the gray value to be detected is not the suspected pointer point.
In this embodiment, the suspected pointer points existing on the scale curve are preliminarily determined by using a gradient projection method, so that the calculation amount can be reduced for subsequently and accurately determining the pointer position of the pointer instrument to be measured, and the calculation efficiency is improved.
Referring to fig. 6, in an embodiment, the step S700 of determining the pointer position of the pointer instrument to be measured according to the gray scale value between the circle center point of the scale and the suspected pointer point includes:
and S710, calculating the variance of the gray values between the circle center point of the scale and the suspected pointer point.
And S720, determining the pointer position of the pointer instrument to be measured according to the variance.
One or more suspected pointer points can be obtained through the computer equipment. If one suspected pointer point exists, the gray value of each point on a straight line formed by the circle center point of the scale and the suspected pointer point is counted, and the variance of the gray values of all the points is calculated. The variance of the gray-scale values of all the dots can describe the degree of dispersion of the gray-scale values of all the dots. If the variance of the gray values of all the points is smaller than the preset variance, the fact that the discrete degree of the gray values of all the points is small is shown, namely, a straight line formed by the circle center point of the scale and the suspected pointer point exists actually, and the straight line between the suspected pointer point and the circle center point of the scale is the pointer of the pointer instrument to be detected, so that the pointer position of the pointer instrument to be detected can be determined. If the variance of the gray-scale values of all the points is greater than or equal to the preset variance, it indicates that the degree of dispersion of the gray-scale values of all the points is large, that is, a straight line directly formed by the circle center of the degree and the suspected pointer point does not exist, and the suspected pointer point is not an actual pointer point, and the process returns to step S300. If the suspected pointer points are multiple, the gray value of each point on a straight line formed by the circle center point of the scale and each suspected pointer point is counted, and the straight line between one suspected pointer point and the circle center point of the scale can be determined to be the pointer of the pointer instrument to be measured according to the same method.
In this embodiment, by calculating the gray value between the circle center point of the scale and the suspected pointer point, whether the suspected pointer point is an actual pointer point can be further determined, and the accuracy of subsequent reading of the pointer instrument to be measured can be improved.
In one embodiment, the target detection algorithm is the fast RCNN algorithm. The four basic steps of the target detection algorithm include: candidate region generation, feature point extraction, classification and position refinement. The fast RCNN algorithm can unify the four basic steps of the target detection algorithm into a deep network framework, all the calculation is not repeated, the running speed can be increased, and the speed of identifying the reading of the pointer instrument to be detected can be increased.
In a specific embodiment, after the type of the pointer instrument to be detected and the position of the pointer instrument to be detected in the image to be detected are determined according to the target detection algorithm in step S200 to obtain the image of the pointer instrument to be detected, step S300 further includes preprocessing the image of the pointer instrument to be detected before determining whether the image of the pointer instrument to be detected matches the preset template image based on the accelerated robust feature algorithm. Specifically, histogram equalization processing can be performed on the pointer instrument image to be measured. During histogram equalization, the gray scale distribution of the pointer instrument image to be measured is adjusted, so that the distribution of the pointer instrument image to be measured on the 0-255 gray scale is more balanced, the contrast of the pointer instrument image to be measured can be improved, and the pointer instrument image to be measured can be conveniently matched with the preset template image. Moreover, the problem of light reflection of the image to be detected due to illumination when the image to be detected is shot can be solved. The image of the pointer instrument to be detected can be denoised, and noise pollution in the transmission process of the image of the pointer instrument to be detected can be avoided. The image of the pointer instrument to be detected can be cut, so that the separation of the foreground and the background is realized, in other words, the separation of the image of the pointer instrument to be detected in the image to be detected and other images is realized, and the matching of the image of the pointer instrument to be detected and the preset template image is facilitated. The image of the pointer instrument to be detected can be corrected, and the influence of the inclination of the image of the pointer instrument to be detected on subsequent matching is avoided.
It should be understood that, although the steps in the flowcharts in the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in the figures may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.
Referring to fig. 7, an embodiment of the present application provides an apparatus 10 for identifying a reading of a pointer meter, which includes an obtaining module 100, a determining module 200, a determining module 300, and a constructing module 400. Wherein the content of the first and second substances,
the obtaining module 100 is configured to obtain an image to be detected, where the image to be detected includes an image of a pointer instrument to be detected.
The determining module 200 is configured to determine the type of the pointer instrument to be detected and the position of the pointer instrument to be detected in the image to be detected according to a target detection algorithm, so as to obtain an image of the pointer instrument to be detected.
The judging module 300 is configured to judge whether the image of the pointer instrument to be detected matches the preset template image based on an accelerated robust feature algorithm.
The determining module 200 is further configured to determine a key point of the pointer instrument image to be detected according to a preset key point of the preset template image if the pointer instrument image to be detected is matched with the preset template image, where the key point includes a scale start point, a scale middle point, a scale end point, and a scale center point of the pointer instrument to be detected.
The construction module 400 is configured to construct a scale curve of the pointer instrument to be measured on the image of the pointer instrument to be measured according to the key point.
The determining module 200 is further configured to determine a suspected pointer point according to a gray value of a coordinate point on the scale curve.
The determining module 200 is further configured to determine a pointer position of the pointer instrument to be measured according to the gray value between the scale circle center point and the suspected pointer point.
The determining module 200 is further configured to determine, according to the pointer position, a reading of the pointer instrument to be measured.
In an embodiment, the determining module 300 is further configured to obtain a feature point of the pointer instrument image to be detected based on the accelerated robust feature algorithm, to obtain a feature point to be detected, and to obtain a feature point of the preset template image, to obtain a preset feature point; and judging whether the pointer instrument image to be detected is matched with the preset template image or not according to the feature point to be detected and the preset feature point.
In one embodiment, the determining module 300 is further configured to calculate a euclidean distance between the feature point to be measured and the preset feature point; if the Euclidean distance is within a preset distance range, determining that the pointer instrument image to be detected is matched with the preset template image; and if the Euclidean distance is not within the preset distance range, returning to execute the acceleration-based robust feature algorithm, and judging whether the image of the pointer instrument to be detected is matched with the preset template image.
In one embodiment, the determining module 200 is further configured to determine a homography matrix according to the feature point to be detected and the preset feature point; and determining the key point of the pointer instrument to be detected according to the homography matrix and the preset key point in the preset template image.
In one embodiment, the determining module 200 is further configured to determine a gray value to be measured according to a gray value of a coordinate point on the calibration curve based on a gradient projection method; and if the gray value to be detected is within a preset gray value range, determining the coordinate point corresponding to the gray value to be detected as the suspected pointer point.
In one embodiment, the determining module 200 is further configured to calculate a variance of the gray value between the circle center point of the scale and the suspected pointer point; and determining the pointer position of the pointer instrument to be detected according to the variance.
For the specific definition of the identification device 10 for the reading of the pointer instrument, reference may be made to the above definition of the identification method for the reading of the pointer instrument, which is not described herein again. The various modules in the pointer meter reading identification apparatus 10 may be implemented in whole or in part by software, hardware, and combinations thereof. The above devices, modules or units may be embedded in hardware or independent from a processor in a computer device, or may be stored in a memory in the computer device in software, so that the processor can call and execute operations corresponding to the above devices or modules.
Referring to fig. 8, in one embodiment, a computer device is provided, and the computer device may be a server, and the internal structure thereof may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. And the database of the computer equipment is used for storing preset template images, images to be detected and the like. The network interface of the computer device is used for connecting and communicating with an external terminal through a network. The computer device, when executed by a processor, implements a method of identifying pointer meter readings.
Those skilled in the art will appreciate that the architecture shown in fig. 8 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the following steps when executing the computer program:
acquiring an image to be detected, wherein the image to be detected comprises an image of a pointer instrument to be detected;
determining the type of the pointer instrument to be detected and the position of the pointer instrument to be detected in the image to be detected according to a target detection algorithm to obtain an image of the pointer instrument to be detected;
judging whether the pointer instrument image to be detected is matched with a preset template image or not based on an accelerated robust feature algorithm;
if the pointer instrument image to be detected is matched with the preset template image, determining key points of the pointer instrument image to be detected according to preset key points of the preset template image, wherein the key points comprise a scale starting point, a scale middle point, a scale end point and a scale center point of the pointer instrument to be detected;
according to the key points, a scale curve of the pointer instrument to be detected on the pointer instrument image to be detected is constructed;
determining a suspected pointer point according to the gray value of the coordinate point on the scale curve;
determining the pointer position of the pointer instrument to be measured according to the gray value between the circle center point of the scale and the suspected pointer point;
and determining the reading of the pointer instrument to be measured according to the pointer position.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring the characteristic points of the pointer instrument image to be detected based on the accelerated robust characteristic algorithm to obtain the characteristic points to be detected, and acquiring the characteristic points of the preset template image to obtain preset characteristic points; and judging whether the pointer instrument image to be detected is matched with the preset template image or not according to the feature point to be detected and the preset feature point.
In one embodiment, the processor when executing the computer program further performs the steps of: calculating the Euclidean distance between the feature point to be measured and the preset feature point; if the Euclidean distance is within a preset distance range, determining that the pointer instrument image to be detected is matched with the preset template image; and if the Euclidean distance is not within the preset distance range, returning to execute the acceleration-based robust feature algorithm, and judging whether the image of the pointer instrument to be detected is matched with the preset template image.
In one embodiment, the processor when executing the computer program further performs the steps of: determining a homography matrix according to the feature points to be detected and the preset feature points; and determining the key point of the pointer instrument to be detected according to the homography matrix and the preset key point in the preset template image.
In one embodiment, the processor when executing the computer program further performs the steps of: determining a gray value to be measured according to the gray value of the coordinate point on the scale curve based on a gradient projection method; and if the gray value to be detected is within a preset gray value range, determining the coordinate point corresponding to the gray value to be detected as the suspected pointer point.
In one embodiment, the processor when executing the computer program further performs the steps of: calculating the variance of the gray value between the circle center point of the scale and the suspected pointer point; and determining the pointer position of the pointer instrument to be detected according to the variance.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring an image to be detected, wherein the image to be detected comprises an image of a pointer instrument to be detected;
determining the type of the pointer instrument to be detected and the position of the pointer instrument to be detected in the image to be detected according to a target detection algorithm to obtain an image of the pointer instrument to be detected;
judging whether the pointer instrument image to be detected is matched with a preset template image or not based on an accelerated robust feature algorithm;
if the pointer instrument image to be detected is matched with the preset template image, determining key points of the pointer instrument image to be detected according to preset key points of the preset template image, wherein the key points comprise a scale starting point, a scale middle point, a scale end point and a scale center point of the pointer instrument to be detected;
according to the key points, a scale curve of the pointer instrument to be detected on the pointer instrument image to be detected is constructed;
determining a suspected pointer point according to the gray value of the coordinate point on the scale curve;
determining the pointer position of the pointer instrument to be measured according to the gray value between the circle center point of the scale and the suspected pointer point;
and determining the reading of the pointer instrument to be measured according to the pointer position.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring the characteristic points of the pointer instrument image to be detected based on the accelerated robust characteristic algorithm to obtain the characteristic points to be detected, and acquiring the characteristic points of the preset template image to obtain preset characteristic points; and judging whether the pointer instrument image to be detected is matched with the preset template image or not according to the feature point to be detected and the preset feature point.
In one embodiment, the computer program when executed by the processor further performs the steps of: calculating the Euclidean distance between the feature point to be measured and the preset feature point; if the Euclidean distance is within a preset distance range, determining that the pointer instrument image to be detected is matched with the preset template image; and if the Euclidean distance is not within the preset distance range, returning to execute the acceleration-based robust feature algorithm, and judging whether the image of the pointer instrument to be detected is matched with the preset template image.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a homography matrix according to the feature points to be detected and the preset feature points; and determining the key point of the pointer instrument to be detected according to the homography matrix and the preset key point in the preset template image.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a gray value to be measured according to the gray value of the coordinate point on the scale curve based on a gradient projection method; and if the gray value to be detected is within a preset gray value range, determining the coordinate point corresponding to the gray value to be detected as the suspected pointer point.
In one embodiment, the computer program when executed by the processor further performs the steps of: calculating the variance of the gray value between the circle center point of the scale and the suspected pointer point; and determining the pointer position of the pointer instrument to be detected according to the variance.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method of identifying a reading of a pointer meter, comprising:
acquiring an image to be detected, wherein the image to be detected comprises an image of a pointer instrument to be detected;
determining the type of the pointer instrument to be detected and the position of the pointer instrument to be detected in the image to be detected according to a target detection algorithm to obtain an image of the pointer instrument to be detected;
judging whether the pointer instrument image to be detected is matched with a preset template image or not based on an accelerated robust feature algorithm;
if the pointer instrument image to be detected is matched with the preset template image, determining key points of the pointer instrument image to be detected according to preset key points of the preset template image, wherein the key points comprise a scale starting point, a scale middle point, a scale end point and a scale center point of the pointer instrument to be detected;
according to the key points, a scale curve of the pointer instrument to be detected on the pointer instrument image to be detected is constructed;
determining a suspected pointer point according to the gray value of the coordinate point on the scale curve;
determining the pointer position of the pointer instrument to be measured according to the gray value between the circle center point of the scale and the suspected pointer point;
and determining the reading of the pointer instrument to be measured according to the pointer position.
2. The method of claim 1, wherein the determining whether the pointer instrument image to be measured matches a preset template image based on an accelerated robust feature algorithm comprises:
acquiring the characteristic points of the pointer instrument image to be detected based on the accelerated robust characteristic algorithm to obtain the characteristic points to be detected, and acquiring the characteristic points of the preset template image to obtain preset characteristic points;
and judging whether the pointer instrument image to be detected is matched with the preset template image or not according to the feature point to be detected and the preset feature point.
3. The method according to claim 2, wherein the determining whether the pointer instrument image to be measured and the preset template image match according to the feature point to be measured and the preset feature point comprises:
calculating the Euclidean distance between the feature point to be measured and the preset feature point;
if the Euclidean distance is within a preset distance range, determining that the pointer instrument image to be detected is matched with the preset template image;
and if the Euclidean distance is not within the preset distance range, returning to execute the acceleration-based robust feature algorithm, and judging whether the image of the pointer instrument to be detected is matched with the preset template image.
4. The method according to claim 2, wherein the determining the key point of the pointer instrument to be measured according to the preset key point of the preset template image comprises:
determining a homography matrix according to the feature points to be detected and the preset feature points;
and determining the key point of the pointer instrument to be detected according to the homography matrix and the preset key point in the preset template image.
5. The method of claim 1, wherein determining suspected pointer points according to gray-scale values of coordinate points on the scale curve comprises:
determining a gray value to be measured according to the gray value of the coordinate point on the scale curve based on a gradient projection method;
and if the gray value to be detected is within a preset gray value range, determining the coordinate point corresponding to the gray value to be detected as the suspected pointer point.
6. The method as claimed in claim 1, wherein the determining the pointer position of the pointer instrument to be measured according to the gray scale value between the circle center point of the scale and the suspected pointer point comprises:
calculating the variance of the gray value between the circle center point of the scale and the suspected pointer point;
and determining the pointer position of the pointer instrument to be detected according to the variance.
7. The method of claim 1, wherein the target detection algorithm is a master RCNN algorithm.
8. An apparatus for identifying a reading of a pointer meter, comprising:
the acquisition module is used for acquiring an image to be detected, wherein the image to be detected comprises an image of a pointer instrument to be detected;
the determining module is used for determining the type of the pointer instrument to be detected and the position of the pointer instrument to be detected in the image to be detected according to a target detection algorithm to obtain an image of the pointer instrument to be detected;
the judging module is used for judging whether the pointer instrument image to be detected is matched with a preset template image or not based on an accelerated robust feature algorithm;
the determining module is further configured to determine a key point of the pointer instrument image to be detected according to a preset key point of the preset template image if the pointer instrument image to be detected is matched with the preset template image, wherein the key point includes a scale starting point, a scale middle point, a scale end point and a scale center point of the pointer instrument to be detected;
the construction module is used for constructing a scale curve of the pointer instrument to be detected on the pointer instrument image to be detected according to the key points;
the determining module is further used for determining a suspected pointer point according to the gray value of the coordinate point on the scale curve;
the determining module is further used for determining the pointer position of the pointer instrument to be measured according to the gray value between the scale circle center point and the suspected pointer point;
and the determining module is also used for determining the reading of the pointer instrument to be measured according to the pointer position.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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