CN117036490A - Method, device, computer equipment and medium for detecting preset bit offset of camera - Google Patents
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Abstract
The application relates to a method, a device, computer equipment and a medium for detecting preset bit offset of a camera, which are used for acquiring preset bit data of the camera and extracting characteristics of the preset bit data of the camera; searching a target window for preset bit data of the camera based on preset window parameters; respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a target window corresponding region in the preset position data when the camera moves; matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result; and obtaining a detection result of the preset bit offset of the camera according to the characteristic point matching result. The whole scheme can realize high-efficiency and accurate detection of the preset bit offset of the camera.
Description
Technical Field
The present application relates to the field of camera technologies, and in particular, to a method and apparatus for detecting a preset bit offset of a camera, a computer device, and a storage medium.
Background
Under the condition that the camera is used for a long time, certain preset position offset is generated, so that the camera cannot accurately observe the target position, and therefore the preset position offset of the camera holder needs to be detected and timely reminded.
In the prior art, the preset bit offset detection mainly comprises manual detection, preset bit characteristic parameters and image recognition, but the manual detection result is not accurate enough and cannot be detected in real time, so that the accuracy of image analysis is affected in actual use; the preset bit characteristic parameters are different according to the camera equipment parameters of different manufacturers, so that the differences exist; however, the conventional image recognition has a defect of low recognition efficiency.
It can be seen that the conventional camera preset bit offset detection scheme cannot realize efficient and accurate detection.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an efficient and accurate method, apparatus, computer device, and storage medium for detecting a preset bit offset of a camera.
In a first aspect, the present application provides a method for detecting a preset bit offset of a camera. The method comprises the following steps:
acquiring preset position data of a camera, and extracting features of the preset position data of the camera;
searching a target window for preset position data of the camera based on preset window parameters;
respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a region corresponding to the target window in the preset position data when the camera moves;
matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result;
and obtaining a preset bit offset detection result of the camera according to the characteristic point matching result.
In one embodiment, the obtaining the preset bit data of the camera, and performing feature extraction on the preset bit data of the camera includes:
acquiring preset position data of a camera;
performing contrast-limited self-adaptive histogram equalization enhancement processing on the preset bit data of the camera to obtain the preset bit data of the camera after processing;
and extracting the characteristics of the processed camera preset bit data.
In one embodiment, extracting features from the processed preset bit data of the camera includes:
and extracting the characteristics of the processed camera preset bit data through a sift algorithm.
In one embodiment, the performing the target window search on the preset camera preset bit data based on the preset window parameter includes:
and searching a target window for the preset bit data of the camera based on the preset window size and the step length.
In one embodiment, the searching the target window for the preset bit data of the camera based on the preset window size and the step length includes:
acquiring a preset window dividing starting point and a dividing direction;
dividing the preset position data of the camera into a plurality of data windows based on the preset window dividing starting point and dividing direction and the preset window size and step length;
counting the number of the feature points corresponding to each data window;
generating a data window sequence based on the sequence of the data window from the large to the small in the number of the feature points;
and screening a window which is positioned in front of the data window sequence as a target window.
In one embodiment, the matching the first feature point set and the second feature point set to obtain a feature point matching result includes:
and adopting a knn-cache matching algorithm to match the first characteristic point set with the second characteristic point set to obtain a characteristic point matching result.
In one embodiment, the obtaining the detection result of the preset bit offset of the camera according to the feature point matching result includes:
acquiring a preset similarity threshold;
judging whether the similarity of the feature point matching result characterization is smaller than the preset similarity threshold value or not;
if yes, judging the preset bit offset of the camera;
if not, judging that the preset bit of the camera is not shifted.
In a second aspect, the present application further provides a device for detecting a preset bit offset of a camera. The device comprises:
the preprocessing module is used for acquiring preset position data of the camera and extracting characteristics of the preset position data of the camera;
the window searching module is used for searching a target window for preset bit data of the camera based on preset window parameters;
the feature point extraction module is used for respectively acquiring preset position data of the camera and a first feature point set and a second feature point set of a region corresponding to the target window in the preset position data when the camera moves;
the matching module is used for matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result;
and the detection module is used for obtaining a detection result of the preset bit offset of the camera according to the characteristic point matching result.
In a third aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring preset position data of a camera, and extracting features of the preset position data of the camera;
searching a target window for preset position data of the camera based on preset window parameters;
respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a region corresponding to the target window in the preset position data when the camera moves;
matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result;
and obtaining a preset bit offset detection result of the camera according to the characteristic point matching result.
In a fourth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring preset position data of a camera, and extracting features of the preset position data of the camera;
searching a target window for preset position data of the camera based on preset window parameters;
respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a region corresponding to the target window in the preset position data when the camera moves;
matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result;
and obtaining a preset bit offset detection result of the camera according to the characteristic point matching result.
The method, the device, the computer equipment and the storage medium for detecting the preset bit offset of the camera acquire preset bit data of the camera and extract characteristics of the preset bit data of the camera; searching a target window for preset position data of the camera based on preset window parameters; respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a region corresponding to the target window in the preset position data when the camera moves; matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result; and obtaining a preset bit offset detection result of the camera according to the characteristic point matching result. In the whole process, the target window is screened out based on the number of the feature points, the data processing amount is reduced in a specific detection mode, and the preset bit offset detection of the camera is carried out based on the feature point matching result, so that the accuracy of the detection is ensured, and the whole scheme can realize the efficient and accurate preset bit offset detection of the camera.
Drawings
FIG. 1 is a diagram of an application environment for a camera preset bit offset detection method in one embodiment;
FIG. 2 is a flow chart of a method for detecting a preset bit offset of a camera according to an embodiment;
FIG. 3 is a flowchart of a method for detecting a preset bit offset of a camera according to another embodiment;
FIG. 4 is a block diagram illustrating a configuration of a camera preset bit offset detection apparatus according to an embodiment;
fig. 5 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The method for detecting the preset bit offset of the camera, provided by the embodiment of the application, can be applied to an application environment as shown in fig. 1. The terminal 102 is connected with the camera 104 to detect the preset bit offset of the camera, the terminal 102 obtains preset bit data of the camera, and performs feature extraction on the preset bit data of the camera; searching a target window for preset bit data of the camera based on preset window parameters; respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a target window corresponding region in the preset position data when the camera moves; matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result; and obtaining a detection result of the preset bit offset of the camera according to the characteristic point matching result. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, and the internet of things devices may be smart speakers, smart televisions, smart air conditioners, smart vehicle devices, and the like. The portable wearable device may be a smart watch, smart bracelet, headset, or the like.
In one embodiment, as shown in fig. 2, a method for detecting a preset bit offset of a camera is provided, and the method is applied to the terminal 102 in fig. 1 for illustration, and includes the following steps:
s100: acquiring preset position data of the camera, and extracting features of the preset position data of the camera.
Under the condition that the camera is used for a long time, certain preset position offset is generated, so that the camera cannot accurately observe the target position, and therefore the preset position offset of the camera holder needs to be detected and timely reminded. And setting each preset bit of the camera according to the requirement, collecting images in the state of each preset bit of the camera, obtaining preset bit data of the camera, and storing the preset bit data. When the offset detection is actually carried out, the preset bit data of the camera are directly read from the storage space, and the preset bit data are subjected to feature extraction to obtain image feature points.
Specifically, the preset bit data may be preset bit image data, and the image feature extraction is performed on the preset bit image data after the preset bit image data is obtained. In addition, the preset bit data here may also be acoustic wave detection data, scan data, or the like.
S200: and searching a target window for preset bit data of the camera based on the preset window parameters.
The preset window parameters are preset parameters, which may specifically include parameters of two dimensions of a window size and a step size, and the image may be divided into a plurality of data windows based on the preset window parameters. Here, window searching can be performed on preset bit data of the camera based on the number of feature points corresponding to each data window, and a certain number of data windows with relatively large number of feature points can be specifically searched to obtain a target window. The number of target windows to be finally screened can be preset according to actual conditions. Taking the preset number as 5 as an example, after the preset bit data of the camera are subjected to data window division based on preset window parameters, counting the number of characteristic points contained in each data window, and sorting according to the number of the characteristic points contained in the preset bit data of each camera from more to less to obtain a data window sequence, and selecting the first 5 data windows in the data window sequence to obtain a target window.
S300: and respectively acquiring preset position data of the camera and a first characteristic point set and a second characteristic point set of a target window corresponding region in the preset position data when the camera moves.
After the camera actually moves, preset position data of the camera during movement are obtained, and a first characteristic point set of a target window corresponding area in the preset position data of the camera and a second characteristic point set of the target window corresponding area in the preset position data of the camera during movement are respectively obtained for the same target window. It will be appreciated that when there are a plurality of target windows, the corresponding first feature point set and second feature point set may be acquired based on different target windows. In other words, the first feature point set and the second feature point set are based on the target window, and when there are a plurality of target windows, the first feature point set and the second feature point set corresponding to the different target windows are respectively obtained, and then the matching process of the next step S400 is performed.
S400: and matching the first characteristic point set with the second characteristic point set to obtain a characteristic point matching result.
And matching the first characteristic point set with the second characteristic point set, and determining the characteristic points matched with the first characteristic point set and the second characteristic point set. Specifically, a matching algorithm may be used to match the first feature point set and the second feature point set, and the matching algorithm may specifically be a knn _match algorithm.
S500: and obtaining a detection result of the preset bit offset of the camera according to the characteristic point matching result.
And determining a final detection result of the preset bit offset of the camera according to the characteristic point matching result. Specifically, if a large number of matching feature points exist, the fact that higher similarity exists between preset bit data and preset bit data in motion is indicated, namely the preset bit offset degree of the camera is smaller at the moment, the camera belongs to a reasonable offset range, and the fact that the preset bit of the camera is not offset at the moment is judged; if the matching feature points are fewer, the fact that a larger difference exists between the preset bit data and the preset bit data in the motion process is indicated, namely the preset bit offset degree of the camera is larger than the preset bit offset degree of the camera, and the preset bit offset of the camera possibly occurs. Further, the feature point matching result can be compared with a preset threshold value to finally determine whether the preset bit of the camera is shifted.
The method for detecting the preset bit offset of the camera acquires preset bit data of the camera, and performs feature extraction on the preset bit data of the camera; searching a target window for preset bit data of the camera based on preset window parameters; respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a target window corresponding region in the preset position data when the camera moves; matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result; and obtaining a detection result of the preset bit offset of the camera according to the characteristic point matching result. In the whole process, the target window is screened out based on the number of the feature points, the data processing amount is reduced in a specific detection mode, and the preset bit offset detection of the camera is carried out based on the feature point matching result, so that the accuracy of the detection is ensured, and the whole scheme can realize the efficient and accurate preset bit offset detection of the camera.
As shown in fig. 3, in one embodiment, S100 includes:
s120: acquiring preset position data of a camera;
s140: performing contrast-limited self-adaptive histogram equalization enhancement processing on preset bit data of the camera to obtain processed preset bit data of the camera;
s160: and extracting the characteristics of the processed camera preset bit data.
And setting each preset bit of the camera according to the requirement, collecting preset bit data of the camera, and preprocessing the preset bit data of the camera. Specifically, the preprocessing includes first performing gradation processing on an image; and then, enhancing the image by using self-adaptive histogram equalization limiting the contrast ratio, and effectively improving the characteristic contrast ratio of the image. And extracting the characteristics of the preprocessed image.
In one embodiment, feature extraction of the processed camera preset bit data includes: and extracting the characteristics of the processed preset bit data of the camera through a sift algorithm.
In this embodiment, feature extraction is performed on the processed camera preset bit data by using a sift algorithm. Specifically, let K denote the number of all extracted feature points, W denote the image width, and H denote the image height, then the position of a certain feature point on the image may be expressed as:
。
in one embodiment, performing a target window search on camera preset bit data based on preset window parameters includes: and searching a target window for preset bit data of the camera based on the preset window size and the step length.
The preset window parameters include a preset window size and a step size, and these parameters can be preset based on the actual situation.
In one embodiment, performing the target window search on the camera preset bit data based on the preset window size and the step size includes: acquiring a preset window dividing starting point and a dividing direction; dividing the preset position data of the camera into a plurality of data windows based on a preset window dividing starting point and dividing direction and a preset window size and step length; counting the number of the feature points corresponding to each data window; generating a data window sequence based on the sequence of the data window from the large to the small in the number of the feature points; the window in the sequence of data windows is screened as the target window.
The starting point and the dividing direction of the preset window can be set according to actual conditions. Specifically, a predetermined window Win (size (w, h), p #x, y)), where size is the window size, w is the width of the window, h is the height of the window, p (x, y) is the upper left angular position of the window relative to the entire image, and the step size. And moving the window from the left upper corner p (0, 0) of the basic image according to steps from left to right and from top to bottom, and storing the number of feature points in the window after each movement. Suppose that move N times, useParameters representing one of the window movements, use +.>The number of feature points per window after each movement is indicated. Then according to->Sorting from big to small is performed:and taking out the window with the most front M characteristic points to obtain a target window.
In one embodiment, the matching the first feature point set and the second feature point set to obtain a feature point matching result includes: and adopting a knn-cache matching algorithm to match the first characteristic point set with the second characteristic point set to obtain a characteristic point matching result.
Specifically, a knn _match algorithm is adopted for each first feature point set and each second feature point set to match, and then the matching results of all the first feature point sets and the second feature points are accumulated and averaged to obtain a final feature point matching result. Further, a threshold r is set to obtain a good matching point number of. Setting a certain threshold value d, and storing the Euclidean distance value of the obtained better matching point smaller than the threshold value d, wherein the number of stored better matching points is +.>. Finally, according to the following calculation formula, the feature point matching result P is obtained:
。
in short, the above process is to set a threshold r to extract a better matching pointThen resetting the threshold d, and starting from the better matching point according to the set threshold d>The best matching point is extracted from the Chinese herbal medicine>Finally according to the best matching point +.>Occupy the better matching point->And obtaining a characteristic point matching result by proportion.
In one embodiment, obtaining the detection result of the preset bit offset of the camera according to the feature point matching result includes: acquiring a preset similarity threshold; judging whether the similarity of the feature point matching result characterization is smaller than a preset similarity threshold value or not; if yes, judging the preset bit offset of the camera; if not, judging that the preset bit of the camera is not shifted.
The preset similarity threshold is a similarity threshold for judging whether the preset bit offset setting of the camera occurs or not based on historical experience. If the finally obtained feature point matching result shows that the similarity is smaller than the preset similarity threshold value, the method shows that compared with an initial state, the preset position of the camera in motion is greatly shifted; if the finally obtained feature point matching result shows that the similarity is not smaller than the preset similarity threshold, the method shows that compared with the initial state, the preset position of the camera in motion is not obviously deviated. In the practical application example, the specific calculation formula is as follows:
in order to describe the method for detecting the preset bit offset of the camera in detail, the preset bit data will be taken as an example of a preset bit image, and the method will be described in detail. In this specific application example, the method for detecting the preset bit offset of the camera includes the following steps:
1. acquiring a preset position image of a camera;
2. performing self-adaptive histogram equalization enhancement processing for limiting contrast on a preset bit image of the camera to obtain a preset bit image of the camera after processing;
3. extracting image characteristics of the processed preset bit images of the camera;
4. searching a target window for a preset image of the camera based on preset window parameters, wherein the target window is a preset number of image windows with larger image feature points, for example, 3 image windows with larger image feature points are searched out from 10 image windows to serve as target windows;
5. respectively acquiring a preset image of a camera and a first characteristic point set and a second characteristic point set of a target window corresponding region in the preset image when the camera moves;
6. matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result;
7. acquiring a preset similarity threshold, and judging the preset bit offset of the camera if the similarity represented by the feature point matching result is smaller than the preset similarity threshold; if the similarity represented by the feature point matching result is not smaller than the preset similarity threshold, judging that the preset bit of the camera is not offset.
It should be understood that, although the steps in the flowcharts related to the above embodiments are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.
Based on the same inventive concept, the embodiment of the application also provides a camera preset bit offset detection device for realizing the above mentioned camera preset bit offset detection method. The implementation of the solution provided by the device is similar to the implementation described in the above method, so the specific limitation in the embodiments of the preset bit offset detection device for a camera provided below may be referred to as the limitation of the preset bit offset detection method for a camera hereinabove, and will not be described herein.
In one embodiment, as shown in fig. 4, there is provided a camera preset bit offset detection apparatus, comprising:
the preprocessing module 100 is configured to obtain preset position data of the camera, and perform feature extraction on the preset position data of the camera;
the window searching module 200 is used for searching a target window for preset bit data of the camera based on preset window parameters;
the feature point extraction module 300 is configured to obtain preset position data of the camera, and a first feature point set and a second feature point set of a region corresponding to a target window in the preset position data when the camera moves, respectively;
the matching module 400 is configured to match the first feature point set and the second feature point set to obtain a feature point matching result;
the detection module 500 is configured to obtain a detection result of the preset bit offset of the camera according to the feature point matching result.
The camera preset position deviation detection device acquires preset position data of the camera and performs feature extraction on the preset position data of the camera; searching a target window for preset bit data of the camera based on preset window parameters; respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a target window corresponding region in the preset position data when the camera moves; matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result; and obtaining a detection result of the preset bit offset of the camera according to the characteristic point matching result. In the whole process, the target window is screened out based on the number of the feature points, the data processing amount is reduced in a specific detection mode, and the preset bit offset detection of the camera is carried out based on the feature point matching result, so that the accuracy of the detection is ensured, and the whole scheme can realize the efficient and accurate preset bit offset detection of the camera.
In one embodiment, the preprocessing module 100 is further configured to obtain preset bit data of the camera; performing contrast-limited self-adaptive histogram equalization enhancement processing on preset bit data of the camera to obtain processed preset bit data of the camera; and extracting the characteristics of the processed camera preset bit data.
In one embodiment, the preprocessing module 100 is further configured to perform feature extraction on the processed preset bit data of the camera through a sift algorithm.
In one embodiment, the window searching module 200 is further configured to perform a target window search on the preset bit data of the camera based on the preset window size and the step size.
In one embodiment, the window searching module 200 is further configured to obtain a preset window dividing start point and a dividing direction; dividing the preset position data of the camera into a plurality of data windows based on a preset window dividing starting point and dividing direction and a preset window size and step length; counting the number of the feature points corresponding to each data window; generating a data window sequence based on the sequence of the data window from the large to the small in the number of the feature points; the window in the sequence of data windows is screened as the target window.
In one embodiment, the matching module 400 is further configured to match the first feature point set and the second feature point set by using a knn _match algorithm, so as to obtain a feature point matching result.
In one embodiment, the detection module 500 is further configured to obtain a preset similarity threshold; judging whether the similarity of the feature point matching result characterization is smaller than a preset similarity threshold value or not; if yes, judging the preset bit offset of the camera; if not, judging that the preset bit of the camera is not shifted.
The modules in the camera preset bit offset detection device can be realized in whole or in part by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 5. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device 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 includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program, when executed by a processor, implements a method for detecting a camera preset bit offset. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by those skilled in the art that the structure shown in FIG. 5 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring preset position data of the camera, and extracting features of the preset position data of the camera;
searching a target window for preset bit data of the camera based on preset window parameters;
respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a target window corresponding region in the preset position data when the camera moves;
matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result;
and obtaining a detection result of the preset bit offset of the camera according to the characteristic point matching result.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring preset position data of a camera; performing contrast-limited self-adaptive histogram equalization enhancement processing on preset bit data of the camera to obtain processed preset bit data of the camera; and extracting the characteristics of the processed camera preset bit data.
In one embodiment, the processor when executing the computer program further performs the steps of:
and extracting the characteristics of the processed preset bit data of the camera through a sift algorithm.
In one embodiment, the processor when executing the computer program further performs the steps of:
and searching a target window for preset bit data of the camera based on the preset window size and the step length.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring a preset window dividing starting point and a dividing direction; dividing the preset position data of the camera into a plurality of data windows based on a preset window dividing starting point and dividing direction and a preset window size and step length; counting the number of the feature points corresponding to each data window; generating a data window sequence based on the sequence of the data window from the large to the small in the number of the feature points; the window in the sequence of data windows is screened as the target window.
In one embodiment, the processor when executing the computer program further performs the steps of:
and adopting a knn-cache matching algorithm to match the first characteristic point set with the second characteristic point set to obtain a characteristic point matching result.
In one embodiment, the processor when executing the computer program further performs the steps of:
acquiring a preset similarity threshold; judging whether the similarity of the feature point matching result characterization is smaller than a preset similarity threshold value or not; if yes, judging the preset bit offset of the camera; if not, judging that the preset bit of the camera is not shifted.
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 preset position data of the camera, and extracting features of the preset position data of the camera;
searching a target window for preset bit data of the camera based on preset window parameters;
respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a target window corresponding region in the preset position data when the camera moves;
matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result;
and obtaining a detection result of the preset bit offset of the camera according to the characteristic point matching result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring preset position data of a camera; performing contrast-limited self-adaptive histogram equalization enhancement processing on preset bit data of the camera to obtain processed preset bit data of the camera; and extracting the characteristics of the processed camera preset bit data.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and extracting the characteristics of the processed preset bit data of the camera through a sift algorithm.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and searching a target window for preset bit data of the camera based on the preset window size and the step length.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a preset window dividing starting point and a dividing direction; dividing the preset position data of the camera into a plurality of data windows based on a preset window dividing starting point and dividing direction and a preset window size and step length; counting the number of the feature points corresponding to each data window; generating a data window sequence based on the sequence of the data window from the large to the small in the number of the feature points; the window in the sequence of data windows is screened as the target window.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and adopting a knn-cache matching algorithm to match the first characteristic point set with the second characteristic point set to obtain a characteristic point matching result.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring a preset similarity threshold; judging whether the similarity of the feature point matching result characterization is smaller than a preset similarity threshold value or not; if yes, judging the preset bit offset of the camera; if not, judging that the preset bit of the camera is not shifted.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the embodiments provided herein may include at least one of a relational database and a non-relational database. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processor referred to in the embodiments provided in the present application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, or the like, but is not limited thereto.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of the application should be assessed as that of the appended claims.
Claims (10)
1. A method for detecting a preset bit offset of a camera, the method comprising:
acquiring preset position data of a camera, and extracting features of the preset position data of the camera;
searching a target window for preset position data of the camera based on preset window parameters;
respectively acquiring preset position data of a camera and a first characteristic point set and a second characteristic point set of a region corresponding to the target window in the preset position data when the camera moves;
matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result;
and obtaining a preset bit offset detection result of the camera according to the characteristic point matching result.
2. The method of claim 1, wherein the obtaining camera preset bit data and the feature extracting the camera preset bit data comprises:
acquiring preset position data of a camera;
performing contrast-limited self-adaptive histogram equalization enhancement processing on the preset bit data of the camera to obtain the preset bit data of the camera after processing;
and extracting the characteristics of the processed camera preset bit data.
3. The method of claim 2, wherein feature extraction of the processed camera preset bit data comprises:
and extracting the characteristics of the processed camera preset bit data through a sift algorithm.
4. The method of claim 1, wherein the performing a target window search on the camera preset bit data based on preset window parameters comprises:
and searching a target window for the preset bit data of the camera based on the preset window size and the step length.
5. The method of claim 4, wherein the searching for the target window for the camera preset bit data based on a preset window size and a step size comprises:
acquiring a preset window dividing starting point and a dividing direction;
dividing the preset camera preset data into a plurality of data windows based on the preset window dividing starting point and dividing direction and the preset window size and step length;
counting the number of the feature points corresponding to each data window;
generating a data window sequence based on the sequence of the data window from the large to the small in the number of the feature points;
and screening a window which is positioned in front of the data window sequence as a target window.
6. The method of claim 1, wherein the matching the first set of feature points with the second set of feature points to obtain a feature point matching result comprises:
and adopting a knn-cache matching algorithm to match the first characteristic point set with the second characteristic point set to obtain a characteristic point matching result.
7. The method according to claim 1, wherein obtaining a camera preset bit offset detection result according to the feature point matching result comprises:
acquiring a preset similarity threshold;
judging whether the similarity of the feature point matching result characterization is smaller than the preset similarity threshold value or not;
if yes, judging the preset bit offset of the camera;
if not, judging that the preset bit of the camera is not shifted.
8. A camera preset bit offset detection apparatus, the apparatus comprising:
the preprocessing module is used for acquiring preset position data of the camera and extracting characteristics of the preset position data of the camera;
the window searching module is used for searching a target window for preset bit data of the camera based on preset window parameters;
the feature point extraction module is used for respectively acquiring preset position data of the camera and a first feature point set and a second feature point set of a region corresponding to the target window in the preset position data when the camera moves;
the matching module is used for matching the first characteristic point set and the second characteristic point set to obtain a characteristic point matching result;
and the detection module is used for obtaining a detection result of the preset bit offset of the camera according to the characteristic point matching result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.
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