CN116883769A - Icon test threshold generation method, device, equipment and storage medium - Google Patents
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Abstract
The invention belongs to the technical field of vehicle testing, and discloses an icon testing threshold generating method, device, equipment and storage medium. The method comprises the steps of obtaining video image data corresponding to a vehicle-mounted icon to be detected; dividing video image data into initial image data, first image data and second image data; threshold prediction is carried out based on the first image data and the initial image data, and a first test threshold is obtained; threshold prediction is carried out based on the second image data and the initial image data, and a second test threshold is obtained; and generating an icon test threshold according to the first test threshold and the second test threshold. The icon test threshold can be automatically generated by acquiring the video image data for threshold prediction, manual intervention is not needed, the setting mode is simple, multiple threshold predictions can be performed, the icon test threshold is generated according to the test threshold obtained by the multiple threshold predictions, prediction errors caused by single prediction are avoided, and the rationality of the generated threshold is ensured.
Description
Technical Field
The present invention relates to the field of vehicle testing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for generating an icon testing threshold.
Background
At present, icons in an automobile instrument panel are tested, namely, an automatic test on the automobile instrument is generally realized by utilizing a single camera, whether the icons are displayed correctly is judged mainly by capturing pictures with a certain frame number, the ratio of the number of images containing the icons to be tested to the total number of the pictures is carried out, and then the ratio is compared with a preset threshold value;
the preset threshold value is usually obtained by manually performing estimation through a test repeated in the earlier stage, however, for meters with more complex and diversified patterns, different intermittent icon frequencies are often different, meanwhile, the threshold value setting is also different, the mode of manually estimating is often inaccurate, and when hardware (camera and meter) is transformed, the frame rate of the camera (how many pictures are arranged in one second) and the flicker frequency of the intermittent icon (the icon to be detected on the meter) are also changed, so that the threshold value is often required to be measured again.
The foregoing is provided merely for the purpose of facilitating understanding of the technical solutions of the present invention and is not intended to represent an admission that the foregoing is prior art.
Disclosure of Invention
The invention mainly aims to provide an icon test threshold generation method, device, equipment and storage medium, and aims to solve the technical problems that in the prior art, when a picture in an automobile instrument panel is tested, the threshold needs to be estimated manually and the actual use effect is poor.
In order to achieve the above object, the present invention provides an icon test threshold generating method, which includes the following steps:
acquiring video image data corresponding to a vehicle-mounted icon to be tested;
dividing the video image data into initial image data, first image data, and second image data;
threshold prediction is carried out based on the first image data and the initial image data, and a first test threshold is obtained;
threshold prediction is carried out based on the second image data and the initial image data, and a second test threshold is obtained;
and generating an icon test threshold according to the first test threshold and the second test threshold.
Optionally, the step of performing threshold prediction based on the first image data and the initial image data to obtain a first test threshold includes:
determining an image frame rate and an icon flickering frequency according to the initial image data;
constructing icon statistical data according to the first image data;
and carrying out threshold prediction according to the image frame rate, the icon flickering frequency and the icon statistical data through a preset threshold prediction model to obtain a first test threshold.
Optionally, the step of determining the image frame rate and the icon flickering frequency according to the initial image data includes:
performing image disassembly on the initial image data to obtain an initial picture set;
counting the initial picture set to obtain the total number of pictures and the number of the icon-lighting pictures;
and determining an image frame rate according to the total number of pictures and the image duration of the initial image data, and determining the flicker frequency of the icons according to the number of the pictures which are lightened by the icons and the total number of the pictures.
Optionally, the step of constructing icon statistics according to the first image data includes:
second-level splitting is carried out on the first image data to obtain a plurality of groups of image data;
carrying out data statistics on pictures contained in each group of image data to obtain the number of the icon-lighting pictures and the total number of the images corresponding to each group of image data;
and aggregating the number of the icon lightening pictures and the total number of the images corresponding to each group of image data to obtain icon statistical data.
Optionally, the step of generating the icon test threshold according to the first test threshold and the second test threshold includes:
acquiring an absolute value of a difference value between the first test threshold value and the second test threshold value;
acquiring an icon test strict level corresponding to the vehicle-mounted icon to be tested;
searching a difference limiting threshold corresponding to the icon testing strictness level;
and if the absolute value of the difference is larger than the difference limiting threshold, calculating the average value of the first test threshold and the second test threshold to obtain an icon test threshold.
Optionally, after the step of searching for the difference defining threshold corresponding to the icon testing stringency level, the method further includes:
if the absolute value of the difference is smaller than or equal to the difference limiting threshold, searching a threshold selection rule corresponding to the icon testing strict grade;
and selecting an icon test threshold from the first test threshold and the second test threshold based on the threshold selection rule.
Optionally, the step of dividing the video image data into initial image data, first image data and second image data includes:
intercepting an image frame with a preset time length from the video image data to obtain initial image data;
equally dividing the intercepted video image data into first image data and second image data according to the image duration.
In addition, in order to achieve the above object, the present invention also provides an icon test threshold generating device, which includes the following modules:
the acquisition module is used for acquiring video image data corresponding to the vehicle-mounted icon to be detected;
the dividing module is used for dividing the video image data into initial image data, first image data and second image data;
the prediction module is used for carrying out threshold prediction based on the first image data and the initial image data to obtain a first test threshold;
the prediction module is further used for performing threshold prediction based on the second image data and the initial image data to obtain a second test threshold;
and the generating module is used for generating an icon test threshold according to the first test threshold and the second test threshold.
In addition, in order to achieve the above object, the present invention also proposes an icon test threshold generating device, including: the icon test threshold generation device comprises a processor, a memory and an icon test threshold generation program which is stored in the memory and can run on the processor, wherein the icon test threshold generation program realizes the steps of the icon test threshold generation method when being executed by the processor.
In addition, in order to achieve the above object, the present invention also proposes a computer-readable storage medium having stored thereon an icon test threshold generation program that, when executed, implements the steps of the icon test threshold generation method described above.
The method comprises the steps of obtaining video image data corresponding to a vehicle-mounted icon to be detected; dividing video image data into initial image data, first image data and second image data; threshold prediction is carried out based on the first image data and the initial image data, and a first test threshold is obtained; threshold prediction is carried out based on the second image data and the initial image data, and a second test threshold is obtained; and generating an icon test threshold according to the first test threshold and the second test threshold. The icon test threshold can be automatically generated by acquiring the video image data for threshold prediction, manual intervention is not needed, the setting mode is simple, multiple threshold predictions can be performed, the icon test threshold is generated according to the test threshold obtained by the multiple threshold predictions, prediction errors caused by single prediction are avoided, and the rationality of the generated threshold is ensured.
Drawings
FIG. 1 is a schematic diagram of an electronic device of a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a first embodiment of an icon test threshold generation method according to the present invention;
FIG. 3 is a flowchart illustrating a second embodiment of a method for generating an icon test threshold according to the present invention;
FIG. 4 is a flowchart illustrating a third embodiment of a method for generating an icon test threshold according to the present invention;
FIG. 5 is a flowchart illustrating a threshold generation execution process according to an embodiment of the present invention;
fig. 6 is a block diagram showing the construction of a first embodiment of the icon test threshold generating apparatus of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
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 invention.
Referring to fig. 1, fig. 1 is a schematic diagram of an icon test threshold generating device of a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the electronic device may include: a processor 1001, such as a central processing unit (Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (WI-FI) interface). The Memory 1005 may be a high-speed random access Memory (Random Access Memory, RAM) or a stable nonvolatile Memory (NVM), such as a disk Memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Those skilled in the art will appreciate that the structure shown in fig. 1 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or may be arranged in different components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and an icon test threshold generation program may be included in the memory 1005 as one storage medium.
In the electronic device shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 in the electronic device of the present invention may be provided in the icon test threshold generating device, where the electronic device invokes the icon test threshold generating program stored in the memory 1005 through the processor 1001, and executes the icon test threshold generating method provided by the embodiment of the present invention.
An embodiment of the present invention provides a method for generating an icon test threshold, and referring to fig. 2, fig. 2 is a flowchart of a first embodiment of the method for generating an icon test threshold according to the present invention.
In this embodiment, the icon test threshold generating method includes the following steps:
step S10: and acquiring video image data corresponding to the vehicle-mounted icon to be tested.
It should be noted that, the execution body of the present embodiment may be the icon test threshold generating device (abbreviated as threshold generating device) or the vehicle itself, and the threshold generating device may be a controller in the vehicle, such as an ECU controller, or other devices that may implement the same or similar functions, which is not limited in this embodiment, and in the present embodiment and the embodiments described below, the icon test threshold generating method of the present invention is described by taking the threshold generating device as an example.
It should be noted that, the vehicle-mounted icon to be tested may be a vehicle dashboard icon that needs to be tested whether to display correctly, the vehicle-mounted icon to be tested may be preset by a manager of the threshold generating device according to actual needs, the manager may pre-designate a plurality of vehicle dashboard icons as the vehicle-mounted icons to be tested, and when the vehicle-mounted icons to be tested are a plurality of vehicle-mounted icons, the icon test threshold is generated independently, and the icon test threshold is not affected by each other, that is, the icon test threshold generating method provided by the invention is executed once for each vehicle-mounted icon to generate the icon test threshold corresponding to the vehicle-mounted icon to be tested.
In practical use, the obtaining of the video image data corresponding to the to-be-tested vehicle-mounted icon may be reading the video image data corresponding to the to-be-tested vehicle-mounted icon from an image acquisition device used in the test, where the image acquisition device may be a preset device capable of performing image acquisition, such as a camera, and the video image data corresponding to the to-be-tested vehicle-mounted icon may be video image data containing the to-be-tested vehicle-mounted icon in a video picture.
In a specific implementation, since the prediction of the threshold value can be completed by acquiring video image data with a sufficient duration, when acquiring video image data corresponding to the to-be-tested vehicle-mounted icon, a manager of the threshold value generating device may set a duration required for the test in advance (for example, set the duration required for the test to 35S), and then read video image data with a duration consistent with the duration required for the test from an image acquisition device used during the test, where the duration corresponds to the to-be-tested vehicle-mounted icon.
Step S20: the video image data is divided into initial image data, first image data, and second image data.
It should be noted that, the dividing the video image data into the initial image data, the first image data and the second image data may be dividing the video image data into the initial image data, the first image data and the second image data according to a video dividing rule preset by a manager of the threshold generating device.
Further, in order to ensure reasonable division of the video image data, step S20 in this embodiment may include:
intercepting an image frame with a preset time length from the video image data to obtain initial image data;
equally dividing the intercepted video image data into first image data and second image data according to the image duration.
It should be noted that, the preset duration may be set by a manager of the threshold generating device according to actual needs, the initial image data is generally used for counting the image frame rate and the icon flicker frequency, and at this time, in order to ensure accuracy of icon flicker frequency statistics, the preset duration may be set to N times of a normal flicker frequency of the to-be-tested vehicle icon, where N may be preset by a manager of the threshold generating device, for example: assuming that the normal blinking frequency of the on-board icon to be tested is once a second and N is set to 5, the preset duration is 5 seconds at this time.
In a specific implementation, since the threshold prediction is performed once according to the first image data and the second image data, in order to reduce the error in the prediction as much as possible and ensure the reasonability of the finally generated icon test threshold, the lengths of the image data used in the threshold prediction need to be ensured to be consistent, so that the intercepted video image data can be equally divided into the first image data and the second image data according to the image duration.
Step S30: and carrying out threshold prediction based on the first image data and the initial image data to obtain a first test threshold.
It should be noted that, the threshold prediction is performed based on the first image data and the initial image data, and the first test threshold may be obtained by performing threshold prediction according to the first image data and the initial image data through a preset threshold prediction model, where the threshold prediction model may be a pre-trained deep learning model, such as an RNN model or other similar model.
Step S40: and carrying out threshold prediction based on the second image data and the initial image data to obtain a second test threshold.
It should be noted that, based on the second image data and the initial image data, the mode of obtaining the second test threshold is consistent with the mode of obtaining the first test threshold, which is not described herein. The threshold prediction model used for performing the threshold prediction twice may be the same deep learning model, or may be two different models which are constructed based on the same algorithm, but are respectively trained, which is not limited in this embodiment.
Of course, in a specific implementation process, video image data with a longer length may be acquired, and threshold prediction may be performed more times, which is not limited in this embodiment.
Step S50: and generating an icon test threshold according to the first test threshold and the second test threshold.
It should be noted that, generating the icon test threshold according to the first test threshold and the second test threshold may be calculating an average value between the first test threshold and the second test threshold, and using the average value as the icon test threshold corresponding to the vehicle-mounted icon to be tested (i.e. a threshold for determining whether the vehicle-mounted icon to be tested can be normally displayed when the vehicle-mounted icon to be tested is tested).
The embodiment obtains video image data corresponding to the vehicle-mounted icon to be tested; dividing video image data into initial image data, first image data and second image data; threshold prediction is carried out based on the first image data and the initial image data, and a first test threshold is obtained; threshold prediction is carried out based on the second image data and the initial image data, and a second test threshold is obtained; and generating an icon test threshold according to the first test threshold and the second test threshold. The icon test threshold can be automatically generated by acquiring the video image data for threshold prediction, manual intervention is not needed, the setting mode is simple, multiple threshold predictions can be performed, the icon test threshold is generated according to the test threshold obtained by the multiple threshold predictions, prediction errors caused by single prediction are avoided, and the rationality of the generated threshold is ensured.
Referring to fig. 3, fig. 3 is a flowchart illustrating a second embodiment of an icon test threshold generating method according to the present invention.
Based on the above-mentioned first embodiment, the step S30 of the method for generating an icon test threshold of the present embodiment includes:
step S301: and determining an image frame rate and an icon flickering frequency according to the initial image data.
It should be noted that the image frame rate may be a frame rate of video image data, that is, the number of pictures contained in each second of image in the video image data. The icon blinking frequency may be a frequency at which the on-vehicle icon to be measured is lighted when image capturing is performed.
Further, in order to accurately acquire the image frame rate and the icon flicker frequency, step S301 in this embodiment may include:
performing image disassembly on the initial image data to obtain an initial picture set;
counting the initial picture set to obtain the total number of pictures and the number of the icon-lighting pictures;
and determining an image frame rate according to the total number of pictures and the image duration of the initial image data, and determining the flicker frequency of the icons according to the number of the pictures which are lightened by the icons and the total number of the pictures.
It should be noted that, the image disassembling is performed on the initial image data, and the initial image set may be obtained by disassembling the initial image data, decomposing the initial image data into a single image, and aggregating the images obtained after the disassembly into a set, thereby obtaining the initial image set. Because the existing video data generally adopts a compression technology to directly disassemble the image, the obtained image may lack certain characteristics, and therefore, when the original image data is disassembled, a special video disassembling tool can be adopted to disassemble the image.
In actual use, the statistics is performed on the initial picture set, the obtained total number of pictures and the number of the icon-lighting pictures can be the number of the pictures contained in the initial picture set, the total number of the pictures is obtained, the number of the pictures, contained in the initial picture set, of the on-vehicle icons to be tested, which are lighted, is counted, and the number of the icon-lighting pictures is obtained.
In a specific implementation, determining the image frame rate according to the total number of pictures and the image duration of the initial image data may be to divide the total number of pictures and the image duration of the initial image data, thereby obtaining the image frame rate. Determining the icon flickering frequency according to the number of the icon-lighting pictures and the total number of the pictures may be to divide the number of the icon-lighting pictures and the total number of the pictures, thereby obtaining the icon flickering frequency.
Step S302: and constructing icon statistical data according to the first image data.
It should be noted that, the icon statistics data may include the number of pictures and the number of icon lighting pictures included in the image data corresponding to each second in the first image data.
In a specific implementation, in order to accurately obtain the icon statistics, step S302 in this embodiment may include:
second-level splitting is carried out on the first image data to obtain a plurality of groups of image data;
carrying out data statistics on pictures contained in each group of image data to obtain the number of the icon-lighting pictures and the total number of the images corresponding to each group of image data;
and aggregating the number of the icon lightening pictures and the total number of the images corresponding to each group of image data to obtain icon statistical data.
It should be noted that, second-level splitting is performed on the first image data, so as to obtain multiple sets of image data, where the first image data is split into multiple sets of image data with one set of first image data being every second, for example: assuming that the first image data is video image data of 15 seconds in length, it can be split into 15 sets of image data at this time.
It should be noted that, performing data statistics on the pictures included in the image data to obtain the number of the icon-lighting pictures and the total number of the images corresponding to the image data may be that image disassembly is performed on the image data to obtain a picture set corresponding to the image data, then the total number of the picture sets is obtained, the total number of the images is obtained, and the number of the pictures including the lighted vehicle-mounted icons to be tested in the picture set is counted, so as to obtain the number of the icon-lighting pictures.
In practical use, the icon lighting picture number and the total image number corresponding to each group of image data are aggregated, and the icon statistical data may be data obtained by aggregating the icon lighting picture number and the total image number corresponding to each group of image data into a preset format, where the preset format is related to an input format of a preset threshold prediction model.
Step S303: and carrying out threshold prediction according to the image frame rate, the icon flickering frequency and the icon statistical data through a preset threshold prediction model to obtain a first test threshold.
In actual use, the threshold prediction is performed according to the image frame rate, the icon flicker frequency and the icon statistical data through the preset threshold prediction model, the first test threshold is obtained by generating model input parameters according to the image frame rate, the icon flicker frequency and the icon statistical data, inputting the model input parameters into the preset threshold prediction model, and obtaining a prediction result output by the preset threshold prediction model, so as to obtain the first test threshold.
The embodiment determines the image frame rate and the icon flickering frequency according to the initial image data; constructing icon statistical data according to the first image data; and carrying out threshold prediction according to the image frame rate, the icon flickering frequency and the icon statistical data through a preset threshold prediction model to obtain a first test threshold. Because the data is subjected to statistical processing before the model is input, the model is prevented from further carrying out the statistics, and the structural complexity of the model is reduced.
Referring to fig. 4, fig. 4 is a flowchart illustrating a third embodiment of an icon test threshold generating method according to the present invention.
Based on the above-mentioned first embodiment, the step S50 of the method for generating an icon test threshold of the present embodiment includes:
step S501: and obtaining an absolute value of a difference value between the first test threshold value and the second test threshold value.
It should be noted that, obtaining the absolute value of the difference between the first test threshold and the second test threshold may be obtaining the difference by subtracting the second test threshold from the first test threshold, and then obtaining the absolute value of the difference by calculating the absolute value of the difference.
Step S502: and obtaining the icon testing strict grade corresponding to the to-be-tested vehicle-mounted icon.
It should be noted that the icon test severity level may be quantized data for representing the severity of the icon to be tested, and the higher the icon test severity level, the stronger the severity of the icon to be tested when the icon to be tested is tested.
In a specific implementation, the obtaining of the icon test severity level corresponding to the to-be-tested vehicle-mounted icon may be searching for the icon test severity level corresponding to the to-be-tested vehicle-mounted icon in a preset icon level mapping table, where the preset icon level mapping table may include a mapping relationship between the to-be-tested vehicle-mounted icon and the icon test severity level, and the mapping relationship may be preset by a manager of the threshold generating device.
Step S503: and searching a difference limiting threshold corresponding to the icon testing strictness level.
It should be noted that, searching the difference limit threshold corresponding to the icon test strict level may be searching the difference limit threshold corresponding to the icon test strict level in a preset level difference mapping table, where the preset level difference mapping table may include a mapping relationship between the icon test strict level and the difference limit threshold, and the mapping relationship may be preset by a manager of the threshold generating device.
In practical use, the higher the icon test strictness level is, the stronger the strictness is when the to-be-tested vehicle-mounted icon is tested, and the smaller the corresponding fault tolerance is, so that the icon test strictness level and the difference limiting threshold are in inverse proportion.
Step S504: and if the absolute value of the difference is larger than the difference limiting threshold, calculating the average value of the first test threshold and the second test threshold to obtain an icon test threshold.
If the absolute value of the difference is greater than the difference limiting threshold, it means that the difference between the predicted values obtained by performing the threshold prediction twice is greater, and at this time, in order to avoid the error of the generated icon test threshold as much as possible, to ensure the reasonability of the icon test threshold that is finally obtained, the average value of the first test threshold and the second test threshold may be used as the icon test threshold.
In actual use, in order to ensure that the generated threshold meets the actual use requirement when the difference between the two predictions is smaller, step S503 in this embodiment may further include:
if the absolute value of the difference is smaller than or equal to the difference limiting threshold, searching a threshold selection rule corresponding to the icon testing strict grade;
and selecting an icon test threshold from the first test threshold and the second test threshold based on the threshold selection rule.
It should be noted that if the absolute value of the difference is smaller than or equal to the difference limiting threshold, it means that the difference between the predicted values obtained by two threshold predictions is smaller, at this time, rationality can be ensured by adopting any one of the two threshold predictions, at this time, an icon test threshold can be generated according to the threshold selection requirement in the actual test, so that a threshold selection rule corresponding to the strict level of the icon test can be searched. The threshold selection rule corresponding to each icon testing strict level can be preset by a manager of the threshold generation device.
In a specific implementation, the threshold selection rule at least includes two types of taking a maximum value and taking a minimum value. If the threshold selection rule is the maximum value, selecting the icon test threshold from the first test threshold and the second test threshold based on the threshold selection rule at this time may be selecting the maximum value of the first test threshold and the second test threshold as the icon test threshold;
if the threshold selection rule is the minimum value, selecting the icon test threshold from the first test threshold and the second test threshold based on the threshold selection rule may be selecting the minimum value of the first test threshold and the second test threshold as the icon test threshold.
For ease of understanding, the description will now be given with reference to fig. 5, but the present solution is not limited thereto. Fig. 5 is a schematic diagram of a threshold generation execution flow in this embodiment, as shown in fig. 5, at the beginning, the threshold generation device will perform new icon detection, that is, detect whether there is an on-vehicle icon to be tested for which an icon test threshold is not generated yet, if so, will read in a 35S video stream, that is, read video image data with a duration of 35 seconds corresponding to the on-vehicle icon to be tested, then take an image included in the first 5 seconds of the 35 seconds video stream as initial image data, perform scanning statistics on the initial image data, obtain a current frame rate FPS (that is, the image frame rate) and an icon frequency FV (that is, the icon flicker frequency), then extract an image frame 15 seconds before capturing the 35 seconds of the video stream of the first 5 seconds (that is, for the 30 seconds of the video stream remaining after capturing the video stream of the first 5 seconds), counting the total number Bi of images contained in each group and the number Ai of images with the lighted vehicle-mounted icons to be tested by taking one 1 second as a group (time is T), thereby obtaining 15 groups (A1, B1) … (A15, B15) of data (namely the icon statistical data), inputting the obtained image frame rate, the icon flicker frequency and the icon statistical data into a deep learning module M1 for threshold prediction to obtain a current optimal threshold S1 (namely the first test threshold), then similarly carrying out threshold prediction according to the 15 second image frame after the 35 second video stream of the previous 5 seconds is intercepted to obtain a current optimal threshold S2 (namely the second test threshold), then generating a threshold optimal threshold S according to S1 and S2 through a reliability confirming module, and carrying out association record on the S and the current vehicle-mounted icons to be tested, then detecting whether there are icons to be tested for which the icon test threshold is not set (i.e. the judgment of detecting all frequency icons in figure 5 is finished), if yes, the process is ended, and if not, the process returns to the step of starting new icon detection in fig. 5.
The absolute value of the difference value between the first test threshold value and the second test threshold value is obtained; acquiring an icon test strict level corresponding to the vehicle-mounted icon to be tested; searching a difference limiting threshold corresponding to the icon testing strictness level; and if the absolute value of the difference is larger than the difference limiting threshold, calculating the average value of the first test threshold and the second test threshold to obtain an icon test threshold. Because whether the difference between the first test threshold and the second test threshold is too large or not is detected according to the difference limiting threshold corresponding to the icon test strict level of the to-be-tested vehicle-mounted icon, different modes are selected to generate the icon test threshold according to the first test threshold and the second test threshold, and the rationality of the finally generated icon test threshold is ensured.
In addition, the embodiment of the invention also provides a storage medium, wherein an icon test threshold generating program is stored on the storage medium, and the icon test threshold generating program realizes the steps of the icon test threshold generating method when being executed by a processor.
Referring to fig. 6, fig. 6 is a block diagram showing the structure of a first embodiment of the present invention.
As shown in fig. 6, the icon test threshold generating device provided by the embodiment of the invention includes:
the acquisition module 10 is used for acquiring video image data corresponding to the vehicle-mounted icon to be detected;
a dividing module 20 for dividing the video image data into initial image data, first image data and second image data;
a prediction module 30, configured to perform threshold prediction based on the first image data and the initial image data, so as to obtain a first test threshold;
the prediction module 30 is further configured to perform threshold prediction based on the second image data and the initial image data, so as to obtain a second test threshold;
the generating module 40 is configured to generate an icon test threshold according to the first test threshold and the second test threshold.
The embodiment obtains video image data corresponding to the vehicle-mounted icon to be tested; dividing video image data into initial image data, first image data and second image data; threshold prediction is carried out based on the first image data and the initial image data, and a first test threshold is obtained; threshold prediction is carried out based on the second image data and the initial image data, and a second test threshold is obtained; and generating an icon test threshold according to the first test threshold and the second test threshold. The icon test threshold can be automatically generated by acquiring the video image data for threshold prediction, manual intervention is not needed, the setting mode is simple, multiple threshold predictions can be performed, the icon test threshold is generated according to the test threshold obtained by the multiple threshold predictions, prediction errors caused by single prediction are avoided, and the rationality of the generated threshold is ensured.
Further, the prediction module 30 is further configured to determine an image frame rate and an icon flicker frequency according to the initial image data; constructing icon statistical data according to the first image data; and carrying out threshold prediction according to the image frame rate, the icon flickering frequency and the icon statistical data through a preset threshold prediction model to obtain a first test threshold.
Further, the prediction module 30 is further configured to perform image disassembly on the initial image data to obtain an initial picture set; counting the initial picture set to obtain the total number of pictures and the number of the icon-lighting pictures; and determining an image frame rate according to the total number of pictures and the image duration of the initial image data, and determining the flicker frequency of the icons according to the number of the pictures which are lightened by the icons and the total number of the pictures.
Further, the prediction module 30 is further configured to split the first image data in a second level to obtain multiple sets of image data; carrying out data statistics on pictures contained in each group of image data to obtain the number of the icon-lighting pictures and the total number of the images corresponding to each group of image data; and aggregating the number of the icon lightening pictures and the total number of the images corresponding to each group of image data to obtain icon statistical data.
Further, the generating module 40 is further configured to obtain an absolute value of a difference between the first test threshold and the second test threshold; acquiring an icon test strict level corresponding to the vehicle-mounted icon to be tested; searching a difference limiting threshold corresponding to the icon testing strictness level; and if the absolute value of the difference is larger than the difference limiting threshold, calculating the average value of the first test threshold and the second test threshold to obtain an icon test threshold.
Further, the generating module 40 is further configured to search a threshold selection rule corresponding to the icon testing strict level if the absolute value of the difference is less than or equal to the difference defining threshold; and selecting an icon test threshold from the first test threshold and the second test threshold based on the threshold selection rule.
Further, the dividing module 20 is further configured to intercept an image frame of a pre-set duration from the video image data, to obtain initial image data; equally dividing the intercepted video image data into first image data and second image data according to the image duration.
It should be understood that the foregoing is illustrative only and is not limiting, and that in specific applications, those skilled in the art may set the invention as desired, and the invention is not limited thereto.
It should be noted that the above-described working procedure is merely illustrative, and does not limit the scope of the present invention, and in practical application, a person skilled in the art may select part or all of them according to actual needs to achieve the purpose of the embodiment, which is not limited herein.
In addition, technical details that are not described in detail in this embodiment may refer to the icon test threshold generating method provided in any embodiment of the present invention, and are not described herein again.
Furthermore, it should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. Read Only Memory)/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. An icon test threshold generation method is characterized by comprising the following steps:
acquiring video image data corresponding to a vehicle-mounted icon to be tested;
dividing the video image data into initial image data, first image data, and second image data;
threshold prediction is carried out based on the first image data and the initial image data, and a first test threshold is obtained;
threshold prediction is carried out based on the second image data and the initial image data, and a second test threshold is obtained;
and generating an icon test threshold according to the first test threshold and the second test threshold.
2. The icon test threshold generation method according to claim 1, wherein the step of performing threshold prediction based on the first image data and the initial image data to obtain a first test threshold includes:
determining an image frame rate and an icon flickering frequency according to the initial image data;
constructing icon statistical data according to the first image data;
and carrying out threshold prediction according to the image frame rate, the icon flickering frequency and the icon statistical data through a preset threshold prediction model to obtain a first test threshold.
3. The icon test threshold generation method of claim 2, wherein the step of determining an image frame rate and an icon flicker frequency from the initial image data includes:
performing image disassembly on the initial image data to obtain an initial picture set;
counting the initial picture set to obtain the total number of pictures and the number of the icon-lighting pictures;
and determining an image frame rate according to the total number of pictures and the image duration of the initial image data, and determining the flicker frequency of the icons according to the number of the pictures which are lightened by the icons and the total number of the pictures.
4. The icon test threshold generation method of claim 2, wherein the step of constructing icon statistics from the first image data includes:
second-level splitting is carried out on the first image data to obtain a plurality of groups of image data;
carrying out data statistics on pictures contained in each group of image data to obtain the number of the icon-lighting pictures and the total number of the images corresponding to each group of image data;
and aggregating the number of the icon lightening pictures and the total number of the images corresponding to each group of image data to obtain icon statistical data.
5. The icon test threshold generation method of claim 1, wherein the step of generating an icon test threshold from the first test threshold and the second test threshold comprises:
acquiring an absolute value of a difference value between the first test threshold value and the second test threshold value;
acquiring an icon test strict level corresponding to the vehicle-mounted icon to be tested;
searching a difference limiting threshold corresponding to the icon testing strictness level;
and if the absolute value of the difference is larger than the difference limiting threshold, calculating the average value of the first test threshold and the second test threshold to obtain an icon test threshold.
6. The icon testing threshold generation method of claim 5, further comprising, after the step of searching for the icon testing severity level corresponding difference defining threshold:
if the absolute value of the difference is smaller than or equal to the difference limiting threshold, searching a threshold selection rule corresponding to the icon testing strict grade;
and selecting an icon test threshold from the first test threshold and the second test threshold based on the threshold selection rule.
7. The icon testing threshold generation method of any one of claims 1-6, wherein the step of dividing the video image data into initial image data, first image data, and second image data includes:
intercepting an image frame with a preset time length from the video image data to obtain initial image data;
equally dividing the intercepted video image data into first image data and second image data according to the image duration.
8. An icon test threshold generating device is characterized by comprising the following modules:
the acquisition module is used for acquiring video image data corresponding to the vehicle-mounted icon to be detected;
the dividing module is used for dividing the video image data into initial image data, first image data and second image data;
the prediction module is used for carrying out threshold prediction based on the first image data and the initial image data to obtain a first test threshold;
the prediction module is further used for performing threshold prediction based on the second image data and the initial image data to obtain a second test threshold;
and the generating module is used for generating an icon test threshold according to the first test threshold and the second test threshold.
9. An icon test threshold generation apparatus, characterized in that the icon test threshold generation apparatus comprises: a processor, a memory and an icon test threshold generation program stored on the memory and executable on the processor, which when executed by the processor, implements the steps of the icon test threshold generation method of any of claims 1-7.
10. A computer-readable storage medium, wherein an icon test threshold generation program is stored on the computer-readable storage medium, which when executed implements the steps of the icon test threshold generation method of any one of claims 1-7.
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