CN111859019A - Method for acquiring page switching response time and related equipment - Google Patents
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Abstract
The embodiment of the invention discloses a method and related equipment for acquiring page switching response time, wherein after a target video is acquired, the target video is a video comprising a software page switching process of a terminal; converting a target video into a multi-frame target image according to a preset frequency; then, performing state classification on the multi-frame target images, and dividing the multi-frame target images into stable state images before or after page switching and switching state images during page switching; the method comprises the steps of carrying out stage division on a target video according to a stable state image and a switching state image to respectively obtain time periods of the target video in a switching stage and the stable stage, and determining the time period length of the switching stage as software page switching response time; therefore, the method and the device can automatically process and acquire the page switching response time, effectively reduce the time required for acquiring the page switching response time and improve the processing efficiency and accuracy for acquiring the page switching response time compared with manual acquisition.
Description
Technical Field
The present invention relates to the field of software technologies, and in particular, to a method and a related device for obtaining page switch response time.
Background
In the field of communications, monitoring the length of the page switch response time of software is important for analyzing the performance of the software. In the prior art, for software which is independently developed, various switching time consumptions can only be counted by piling in codes, the counted time consumptions have a great relationship with positions where the piles are piled in the codes, and the counted results often do not accord with subjective feelings of people. For software which is not independently developed, various switching time-consuming statistics can only depend on manual cloaking stopwatch or manual framing statistics, and the problems of inaccurate statistical result and time consumption exist. Therefore, there is a need to solve the above problems.
Disclosure of Invention
The embodiment of the invention provides a method and related equipment for acquiring page switching response time, which can reduce the time required for acquiring the page switching response time and improve the processing efficiency and accuracy for acquiring the page switching response time.
In a first aspect, an embodiment of the present invention provides a method for obtaining page switch response time, including:
acquiring a target video, wherein the target video comprises a video of a software page switching process of a terminal;
converting the target video into a multi-frame target image according to a preset frequency;
Classifying the state of the multi-frame target image into a stable state image before or after page switching and a switching state image during page switching;
and performing stage division on the target video according to the stable state image and the switching state image to respectively obtain the time periods of the target video in the switching stage and the stable stage, and determining the time period length of the switching stage as the software page switching response time.
Optionally, the software is live video software, and performing state classification on the multiple frames of target images includes:
detecting whether a live broadcaster exists in a first target image and a second target image in the multiple frames of target images according to a pre-trained target detection model, wherein the second target image is a frame image behind the first target image, and when the live broadcaster does not exist in the first target image or the second target image, determining that the first target image or the second target image is a switching state image;
when both the first target image and the second target image have live senders, acquiring image similarity between the first target image and the second target image, wherein when the image similarity is smaller than or equal to a similarity threshold value, the first target image is a stable state image, and the second target image is a switching state image; and when the image similarity is greater than the similarity threshold value, the first target image and the second target image are stable state images.
Optionally, the performing state classification on the multiple frames of target images includes:
detecting whether the multi-frame target image comprises a preset specific icon, wherein the specific icon is an icon on a software page in a stable state;
determining a target image in which the specific icon is detected as a steady-state image;
determining a target image in which the specific icon is not detected as a switching state image.
Optionally, the performing state classification on the multiple frames of target images includes:
detecting whether the multi-frame target image comprises a preset specific icon by using a template matching algorithm, wherein the specific icon is an icon on a software page in a stable state;
determining a target image of the specific icon detected by using a template matching algorithm as a steady-state image;
if the specific icon is not detected from the target image by using the template matching algorithm, detecting whether the target image comprises a preset specific icon or not by using the feature matching algorithm, determining the target image of the specific icon detected by using the feature matching algorithm as a stable state image, and determining the target image of the specific icon not detected by using the feature matching algorithm as a switching state image.
Optionally, the performing state classification on the multiple frames of target images includes:
classifying the target image according to a pre-trained classification neural network model, and determining that the target image is a stable state image or a switching state image, wherein training data of the classification neural network model comprises software page images in a stable stage and a switching stage.
In a second aspect, an embodiment of the present invention provides an apparatus for obtaining page switch response time, including:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring a target video, and the target video comprises a video of a software page switching process of a terminal;
the conversion module is used for converting the target video into a plurality of frames of target images according to a preset frequency;
the classification module is used for carrying out state classification on the multi-frame target images, and dividing the multi-frame target images into stable state images before or after page switching and switching state images during page switching;
and the determining module is used for carrying out stage division on the target video according to the stable state image and the switching state image to respectively obtain the time periods of the target video in the switching stage and the stable stage, and determining the time period length of the switching stage as the software page switching response time.
Optionally, the software is live video software, and the classification module includes:
the first sub-module is used for detecting whether a live broadcast exists in a first target image and a second target image in the multiple frames of target images according to a pre-trained target detection model, wherein the second target image is a frame image behind the first target image, and when the live broadcast does not exist in the first target image or the second target image, the first target image or the second target image is determined to be a switching state image;
the second sub-module is used for acquiring the image similarity between the first target image and the second target image when both the first target image and the second target image have live senders, wherein when the image similarity is smaller than or equal to a similarity threshold value, the first target image is a stable state image, and the second target image is a switching state image; and when the image similarity is greater than the similarity threshold value, the first target image and the second target image are stable state images.
Optionally, the classification module comprises:
the third sub-module is used for detecting whether the multi-frame target image comprises a preset specific icon, wherein the specific icon is an icon on a software page in a stable state; determining a target image in which the specific icon is detected as a steady-state image; determining a target image in which the specific icon is not detected as a switching state image.
In a third aspect, an embodiment of the present invention provides an apparatus for obtaining page switch response time, where the apparatus includes: a processor and a memory;
the processor is connected to the memory, wherein the memory is used for storing a program code, and the processor is used for calling the program code to execute the method for acquiring the page switch response time according to the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer storage medium storing a computer program, where the computer program includes program instructions, and the program instructions, when executed by a processor, perform the method for acquiring page switch response time according to the first aspect.
In the embodiment of the invention, after the target video is obtained, the target video is a video comprising a software page switching process of the terminal; converting a target video into a multi-frame target image according to a preset frequency; then, performing state classification on the multi-frame target images, and dividing the multi-frame target images into stable state images before or after page switching and switching state images during page switching; the method comprises the steps of carrying out stage division on a target video according to a stable state image and a switching state image to respectively obtain time periods of the target video in a switching stage and the stable stage, and determining the time period length of the switching stage as software page switching response time; therefore, the method and the device can automatically process and acquire the page switching response time, effectively reduce the time required for acquiring the page switching response time and improve the processing efficiency and accuracy for acquiring the page switching response time compared with manual acquisition.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1a, fig. 1b, and fig. 1c are schematic scene diagrams of a method for obtaining page switch response time according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method for obtaining page switch response time according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an apparatus for acquiring page switch response time according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an apparatus for acquiring page switch response time according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
It should be understood that the terms "first," "second," and the like in the description and claims of this application and in the drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by the person skilled in the art that the described embodiments of the invention can be combined with other embodiments.
In the application, the software includes computer-side software or mobile-side software, and the scene of software page switching occurring on the terminal includes page switching during software startup (page conversion from un-started software to software startup completion), page jump occurring during software use based on user operation, for example, jump occurring after a user clicks a website on a software page, or jump occurring in a live broadcast application (audio live broadcast application or video live broadcast application), and the user performs sliding switching of a live broadcast room, and the like. The terminal comprises intelligent equipment such as a computer, a notebook computer, a mobile phone and an intelligent watch. The software performance can be evaluated by utilizing the page switching response time obtained by counting the response time of the software page switching, so that the software performance improvement of a user is assisted.
Based on the problems of too long time required for counting page switching response time and low accuracy in the prior art, the method for acquiring the page switching response time can effectively reduce the time required for acquiring the page switching response time and improve the acquisition efficiency and accuracy.
Referring to fig. 1a, fig. 1b and fig. 1c, fig. 1a, fig. 1b and fig. 1c are schematic scene diagrams of a method for obtaining page switch response time according to an embodiment of the present invention; as shown in fig. 1a, the terminal is a mobile phone as an example, the processing terminal 101 is used for executing the method for acquiring the page switch response time, and the processing terminal 101 may be a computer or a notebook. Taking the example of obtaining the software page switching response time of the software installed in the mobile phone, the software takes the example of live video software.
Firstly, after a user 102 starts a recording screen on a mobile phone, the user enters video live broadcast software, a live broadcast room of a user A is switched to a live broadcast room of a user B, then the live broadcast room of a user C is switched, and the mobile phone obtains a recorded video of the switching process. The mobile phone then transmits the recorded video to the processing terminal 101 for processing, so as to obtain a processing result report in HTML format as shown in fig. 1b and fig. 1 c. After obtaining the recorded video, the processing terminal 101 converts the recorded video into a multi-frame image according to a preset frequency, for example, if the preset frequency is 50 ms/piece, the video per second is converted into a 20-frame image; classifying the obtained multi-frame images into stable state images before or after page switching and switching state images in the page switching period; and then, performing stage division on the recorded video according to the stable state image and the switching state image to respectively obtain time periods of the recorded video in the switching stage and the stable stage, and determining the time period length of each switching stage as software page switching response time. In fig. 1b and 1c, for example, the recorded video is converted into 183 frames, the processing result report includes image frames corresponding to each stable stage and switching stage (for easy manual visual inspection), and duration of each stage, and a linear waveform diagram (as in the waveform in fig. 1 c) showing phase transition, and software page switching response time (as in time costs 1.033334 and 0.966667 shown in fig. 1c, and time unit in fig. 1b and 1c is s) corresponding to each page switching. Optionally, as shown in fig. 1c, the result processing report may further show a storage path of the recorded video in the processing terminal 101, a total number of image frames (e.g. 183 frames) corresponding to the recorded video, and an offset between the images of each frame, i.e. a size of the preset frequency.
Therefore, the method of the embodiment of the invention can automatically process the recorded video to obtain the software page switching response time corresponding to each software page switching, and compared with a manual acquisition mode, the acquisition accuracy and efficiency of the switching response time are higher.
Fig. 2 is a schematic flow chart illustrating a method for obtaining page switch response time according to an embodiment of the present invention; the method for acquiring the page switching response time comprises the following steps:
201. acquiring a target video, wherein the target video comprises a video of a software page switching process of a terminal;
specifically, the method for acquiring the target video may be to start a screen recording function of the terminal, record the video when the page switching occurs, where the occurrence frequency of the page switching in the target video is at least one time.
202. Converting a target video into a multi-frame target image according to a preset frequency;
specifically, the preset frequency may be set according to actual needs, for example, the target video is 5 seconds, and the preset frequency is 50 ms/piece, the target video may be converted into a target image of 100 frames.
203. Performing state classification on the multi-frame target images, and dividing the multi-frame target images into stable state images before or after page switching and switching state images during page switching;
Specifically, the state classification is performed on all the target images, the state of each frame of target image is determined, and the target images are classified into stable state images or switching state images. When the target image is before or after page switching, the target image is a stable state image; and when the target image is in the page switching period, the target image is a switching state image.
204. And performing stage division on the target video according to the stable state image and the switching state image to respectively obtain time periods of the target video in the switching stage and the stable stage, and determining the time period length of the switching stage as the software page switching response time.
Specifically, according to the state-classified target image, the target video can be divided into video segments in a switching stage and a stable stage, wherein the time length of the video segment in the switching stage is the software page switching response time. Taking a target video of 5 seconds as an example, assuming that 0-2.2 seconds, 2.8-3.1 seconds and 3.5-5 seconds are stable phases, and 2.2-2.8 seconds and 3.1-3.5 seconds are switching phases, 0.6 seconds and 0.4 seconds are software page switching response times, and two page switching occurs in the target video.
Therefore, by using the method of fig. 2, the page switching response time can be automatically obtained, and compared with manual acquisition, the time required for obtaining the page switching response time is effectively reduced, the processing efficiency and accuracy for obtaining the page switching response time are improved, and the labor cost is saved.
In a possible embodiment, in step 202, a target video needs to be framed to obtain target images, each frame of target image records a frame number and a timestamp, for a plurality of target images, a frame number is assigned to each frame of target image according to an order from the start of recording to the end of recording of the video, and the timestamp is a corresponding time point of the target image in the target video.
In one possible embodiment, step 203 comprises:
detecting whether the multi-frame target image comprises a preset specific icon, wherein the specific icon is an icon on a software page in a stable state;
determining a target image in which the specific icon is detected as a steady-state image;
the target image in which the specific icon is not detected is determined as the switching state image.
In particular, this state classification method is applicable to all software page-switching scenarios. Before the software page switching response time of certain software is acquired, when the page of the software needs to be acquired in advance and is in a stable state, icons existing on the page are used as specific icons of the software, the number of the specific icons of each software is more than one, the specific icons are fixed and non-replaceable elements on the page, such as functional icons in the page, for example, live broadcast software, such as a focus icon, a chat icon, a gift giving icon, a flower sending icon and the like.
When the target images are subjected to state classification, target detection can be performed on each frame of target images according to the specific icons acquired in advance, when the specific icons are detected in the target images, the target images are stable state images, and otherwise, the target images are switching state images. In particular, in order to further improve the state classification accuracy of the target image, the specific icon of a piece of software includes a functional icon positioned at the top of the page and two or more functional icons positioned at the bottom of the page (i.e., the specific icon includes at least one functional icon positioned at the top of the page and at least one functional icon positioned at the bottom of the page), and when a certain target image detects two or more functional icons positioned at the top and the bottom at the same time, the target image is a stable state image, otherwise, the target image is a switching state image. Taking live broadcast software as an example, when a focus icon at the top of a page and a chat icon at the bottom of the page can be detected simultaneously in a certain target image, the target image is a stable state image, but the focus icon and the chat icon cannot be detected, or only the focus icon or the chat icon can be detected, the target image is a switched state image. Similarly, the specific icon of a piece of software may also be a function icon including a function icon located on the left side of the page and two or more function icons located on the right side of the page (i.e., the specific icon includes at least one function icon on the left side of the page and at least one function icon on the right side of the page).
Further, in order to improve the accuracy of target detection, detecting whether a plurality of frames of target images include preset specific icons includes:
detecting whether the multi-frame target image comprises a preset specific icon or not by utilizing a template matching algorithm;
determining a target image of the specific icon detected by using a template matching algorithm as a stable state image;
if the specific icon is not detected from the target image by using the template matching algorithm, detecting whether the target image includes a preset specific icon by using the feature matching algorithm, determining the target image in which the specific icon is detected by using the feature matching algorithm as a stable state image, and determining the target image in which the specific icon is not detected by using the feature matching algorithm as a switching state image.
Specifically, according to the specific icon, the target image is subjected to target detection by utilizing a template matching algorithm and a feature matching algorithm in sequence so as to determine whether the specific icon exists in the target image, and the accuracy of target detection can be effectively improved. When any algorithm detects that a specific icon exists in a target image, the target image is a stable state image; and when the specific icon is not detected in the target image by the two algorithms, the target image is the switching state image.
In one possible embodiment, step 203 comprises:
classifying the target images according to the pre-trained classification neural network model, and determining that the target images are stable state images or switching state images, wherein the training data of the classification neural network model comprises software page images in a stable stage and a switching stage.
In particular, this state classification method is also applicable to all software page-switching scenarios. Before the software page switching response time of a certain software is obtained, page images in a stable state and a switching state are required to be obtained in advance to serve as training data, and a neural network model is trained to obtain a classification neural network model. More specifically, in order to improve training efficiency, when training data are acquired, after a plurality of page images are obtained, the page images are pre-classified according to a pre-trained SVM linear classifier, and then classification results are manually reviewed to classify the page images into two categories, namely a stable state and a switching state, so as to obtain final training data. The training data obtained by the method has higher efficiency than the training data obtained by completely depending on manual classification, and the time for preparing the training data is saved. And then training the model by using the training data to obtain a classified neural network model. And predicting each frame of target image by using the classification neural network model, and determining the target image as a stable state image or a switching state image.
Particularly, in order to improve the state classification accuracy of the target image, the target image can be finally determined to be a stable state image or a switching state image through manual review by combining the prediction results of the specific icon detection and classification neural network model, so that the accuracy of the subsequently obtained page switching response time is improved.
In one possible embodiment, when the software is live video software, step 203 includes:
2031. detecting whether a first target image and a second target image in a plurality of frames of target images have live senders or not according to a pre-trained target detection model, wherein the second target image is a frame image behind the first target image, and when the first target image or the second target image does not have a live sender, determining that the first target image or the second target image is a switching state image;
specifically, for a page switching scene of video live broadcast software, a live broadcast person can be a person or an animal; every two frames of target images are compared in one group, for example, the (1 st frame and the 2 nd frame) is one group, and the (2 nd frame and the 3 rd frame) is one group. The target image without live broadcast person in the live broadcast picture belongs to the switching state image. And when one or two of the first target image and the second target image cannot detect the live broadcast, the target image is a switching state image.
2032. When both the first target image and the second target image have the live broadcast, acquiring the image similarity between the first target image and the second target image, wherein when the image similarity is smaller than or equal to a similarity threshold, the first target image is a stable state image, and the second target image is a switching state image; and when the image similarity is greater than the similarity threshold value, the first target image and the second target image are stable state images.
Specifically, the specific value of the similarity threshold may be set as needed, for example, 99.8%. And the image similarity of a group of images is greater than the similarity threshold value, and the group of images is considered to be in a stable stage. When the first target image and the second target image both have a live player, the image similarity of the group of images needs to be calculated. When the image similarity is greater than the similarity threshold value, the first target image and the second target image are stable state images; and when the image similarity is smaller than or equal to the similarity threshold, the first target image is a stable state image, and the second target image is a switching state image.
It should be noted that, referring to the switching stage from anchor a to anchor B in fig. 1B, in the conventional video live broadcasting software, the manner of switching the live broadcasting room is to slide upward from the bottom of the software page, and following the change of the sliding process, a "mask layer" (as shown by the hatching in fig. 1B) gradually appears at the bottom of the software page, and below the mask layer, the identification image of anchor B (as shown by the live broadcasting avatar of anchor B) is generally loaded. Due to the existence of the "cover layer", even if the face of the anchor B appears in the last frames of images in the switching stage, the frames of images cannot detect the face of the live player, and they are directly determined as the switching state images. In brief, in step 2032, due to the technical characteristics of the existing live video software, the situation that both the first target image and the second target image exist in the live video, the image similarity between the first target image and the second target image is less than or equal to the similarity threshold, the first target image is a switched-state image, and the second target image is a stable-state image does not occur.
All the switched-state images can be determined by using the above steps 2031 and 2032, the remaining target images are stable-state images, and then the software page switching response time can be determined according to the switched-state images and the stable-state images. In this embodiment, a new method for acquiring the software page switching response time of the live video software is provided.
For the image similarity calculation between the first target image and the second target image, an embodiment of the present invention further provides a calculation method, including:
ssim (structural similarity) structural similarity is a fully-referenced image quality evaluation index, and measures image similarity from three aspects of brightness, contrast and structure. The SSIM value range [0,1] indicates that the image distortion is smaller when the value is larger. An SSIM value between the first target image and the second target image may be calculated, and correspondingly, the similarity threshold is an SSIM threshold.
In practical application, the image can be blocked by using a sliding window, the total number of blocks is N, the influence of the window shape on the blocks is considered, the mean value, the variance and the covariance of each window are calculated by adopting Gaussian weighting, then the structural similarity SSIM of the corresponding block is calculated, finally the mean value is taken as the structural similarity measurement of the two images, namely the mean structural similarity MSSIM, and the structural similarity of the two images, namely the image similarity is evaluated by comparing according to the MSSIM and the SSIM threshold value.
In addition, a method for more accurately evaluating the similarity of images is also provided, and comprises the following steps:
PSNR and mutual information values between the first target image and the second target image are calculated;
and when the PSNR is larger than the PSNR threshold value and the mutual information value is larger than the mutual information threshold value, the image similarity of the first target image and the second target image is larger than the similarity threshold value.
Among them, Peak Signal to Noise Ratio (PSNR) is an objective standard for measuring image distortion. The larger the PSNR value between two images, the more similar the two images. Mutual Information (MI) is an important concept in Information theory, describing the correlation between two systems, or how much Information is contained in each other. Mutual information of two images reflects the mutual inclusion degree of information between the two images through the entropy and joint entropy of the two images. When the joint entropy of the two images is smaller, namely the mutual information value is larger, the similarity of the two images is higher.
Further, after the target images are subjected to state classification by using the method, different state labels are added to the stable state images and the switching state images according to the state classification result so as to distinguish the target images in different states, for example, the state label of the stable state image is True. The status flag of the switch status image is flag Flase. An information item may be added to the attribute information of the image for recording the status flag. In step 204, the target video may be divided into stages according to the status markers, and the status markers are classified into one stage, for example, in the target images of 1-15 frames, the 1 st-6 th frames are True, and the 7 th-15 th frames are flag, the time consumption of each stage may be calculated according to the timestamp of the target image, that is, the switching response time of each software page may be obtained, for example, the software page switching response time may be obtained by subtracting the timestamp of the target image of the 7 th frame from the timestamp of the target image of the 15 th frame. According to the software page switching response time, a processing result report can be generated, and for convenience of transmission, the processing result report can be a report in an HTML format, and the effect of the HTML report is shown in FIGS. 1b and 1c, wherein the report shows thumbnails of all image frames of each stage so as to facilitate manual visual inspection, and counts each stage and time consumption.
Based on the description of the above method embodiment for obtaining page switch response time, the embodiment of the present invention further discloses a device for obtaining page switch response time, referring to fig. 3, where fig. 3 is a schematic structural diagram of the device for obtaining page switch response time provided in the embodiment of the present invention, and the device for obtaining page switch response time includes;
an obtaining module 301, configured to obtain a target video, where the target video is a video including a software page switching process of a terminal;
a conversion module 302, configured to convert a target video into multiple frames of target images according to a preset frequency;
a classification module 303, configured to perform state classification on the multiple frames of target images, where the multiple frames of target images are classified into a stable state image before or after page switching and a switching state image during page switching;
the determining module 304 is configured to perform stage division on the target video according to the stable state image and the switching state image, obtain time periods of the target video in the switching stage and the stable stage respectively, and determine a time period length of the switching stage as a software page switching response time.
In one possible embodiment, the software is live video software, and the classification module 303 includes:
The first sub-module is used for detecting whether a first target image and a second target image in a plurality of frames of target images have live senders or not according to a pre-trained target detection model, the second target image is a frame image behind the first target image, and when the first target image or the second target image does not have a live sender, the first target image or the second target image is determined to be a switching state image;
the second sub-module is used for acquiring the image similarity between the first target image and the second target image when both the first target image and the second target image have the live broadcast, wherein the first target image is a stable state image and the second target image is a switching state image when the image similarity is smaller than or equal to a similarity threshold; and when the image similarity is greater than the similarity threshold value, the first target image and the second target image are stable state images.
In one possible embodiment, the classification module 303 includes:
the third sub-module is used for detecting whether the multi-frame target images comprise preset specific icons or not, and the specific icons are icons on the software page in a stable state; the target image in which the specific icon is detected is determined as a steady-state image, and the target image in which the specific icon is not detected is determined as a switching-state image.
The method for detecting whether the multi-frame target image comprises the preset specific icon specifically comprises the following steps:
detecting whether the multi-frame target image comprises a preset specific icon or not by utilizing a template matching algorithm;
determining a target image of the specific icon detected by using a template matching algorithm as a stable state image;
if the specific icon is not detected from the target image by using the template matching algorithm, detecting whether the target image includes a preset specific icon by using the feature matching algorithm, determining the target image in which the specific icon is detected by using the feature matching algorithm as a stable state image, and determining the target image in which the specific icon is not detected by using the feature matching algorithm as a switching state image.
In one possible embodiment, the classification module 303 includes:
and the fourth sub-module is used for classifying the target images according to the pre-trained classification neural network model and determining that the target images are stable state images or switching state images, and the training data of the classification neural network model comprises software page images in a stable stage and a switching stage.
It is worth pointing out that, for a specific function implementation manner of the apparatus for acquiring page switch response time, reference may be made to the description of the method for acquiring page switch response time, and details are not described here again. Each unit or module in the device for acquiring page switch response time may be respectively or completely merged into one or several other units or modules to form, or some unit(s) or module(s) therein may be further split into multiple units or modules with smaller functions to form, which may implement the same operation without affecting implementation of technical effects of embodiments of the present invention. The above units or modules are divided based on logic functions, and in practical applications, the functions of one unit (or module) may also be implemented by a plurality of units (or modules), or the functions of a plurality of units (or modules) may be implemented by one unit (or module).
Based on the description of the method embodiment and the device embodiment, the embodiment of the present invention further provides a device for obtaining the page switch response time.
Fig. 4 is a schematic structural diagram of an apparatus for acquiring page switch response time according to an embodiment of the present invention. As shown in fig. 4, the apparatus for obtaining page switch response time may be applied to the device 400 for obtaining page switch response time, and the device 400 for obtaining page switch response time may include: the processor 401, the network interface 404 and the memory 405, and the apparatus 400 for acquiring page switch response time may further include: a user interface 403, and at least one communication bus 402. Wherein a communication bus 402 is used to enable connective communication between these components. The user interface 403 may include a Display (Display) and a Keyboard (Keyboard), and the selectable user interface 403 may also include a standard wired interface and a standard wireless interface. The network interface 404 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 405 may be a high-speed RAM memory or a non-volatile memory (e.g., at least one disk memory). The memory 405 may alternatively be at least one storage device located remotely from the aforementioned processor 401. As shown in fig. 4, the memory 405, which is a type of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a device control application program.
In the apparatus 400 for acquiring page switch response time shown in fig. 4, the network interface 404 may provide a network communication function; and the user interface 403 is primarily an interface for providing input to a user; and processor 401 may be used to invoke a device control application stored in memory 405 to implement:
acquiring a target video, wherein the target video comprises a video of a software page switching process of a terminal;
converting a target video into a multi-frame target image according to a preset frequency;
performing state classification on the multi-frame target images, and dividing the multi-frame target images into stable state images before or after page switching and switching state images during page switching;
and performing stage division on the target video according to the stable state image and the switching state image to respectively obtain time periods of the target video in the switching stage and the stable stage, and determining the time period length of the switching stage as the software page switching response time.
In an embodiment, when the software is live video software, the processor 401 specifically executes the following steps when performing state classification on multiple frames of target images:
detecting whether a first target image and a second target image in a plurality of frames of target images have live senders or not according to a pre-trained target detection model, wherein the second target image is a frame image behind the first target image, and when the first target image or the second target image does not have a live sender, determining that the first target image or the second target image is a switching state image;
When both the first target image and the second target image have the live broadcast, acquiring the image similarity between the first target image and the second target image, wherein when the image similarity is smaller than or equal to a similarity threshold, the first target image is a stable state image, and the second target image is a switching state image; and when the image similarity is greater than the similarity threshold value, the first target image and the second target image are stable state images.
In one embodiment, when performing state classification on multiple frames of target images, the processor 401 specifically performs the following steps:
detecting whether the multi-frame target image comprises a preset specific icon, wherein the specific icon is an icon on a software page in a stable state;
determining a target image in which the specific icon is detected as a steady-state image;
the target image in which the specific icon is not detected is determined as the switching state image.
In one embodiment, the processor 401 specifically performs the following steps when performing detection on whether multiple frames of target images include a preset specific icon:
detecting whether the multi-frame target image comprises a preset specific icon or not by utilizing a template matching algorithm;
determining a target image of the specific icon detected by using a template matching algorithm as a stable state image;
If the specific icon is not detected from the target image by using the template matching algorithm, detecting whether the target image includes a preset specific icon by using the feature matching algorithm, determining the target image in which the specific icon is detected by using the feature matching algorithm as a stable state image, and determining the target image in which the specific icon is not detected by using the feature matching algorithm as a switching state image.
In one embodiment, when performing state classification on multiple frames of target images, the processor 401 specifically performs the following steps:
classifying the target images according to the pre-trained classification neural network model, and determining that the target images are stable state images or switching state images, wherein the training data of the classification neural network model comprises software page images in a stable stage and a switching stage.
It should be understood that the apparatus 400 for acquiring page switch response time described in the embodiment of the present invention may perform the method for acquiring page switch response time described above, and may also perform the description of the device for acquiring page switch response time described above, which is not described herein again. In addition, the beneficial effects of the same method are not described in detail.
Further, here, it is to be noted that: an embodiment of the present invention further provides a computer storage medium, where a computer program executed by the aforementioned apparatus for acquiring a page switch response time is stored in the computer storage medium, and the computer program includes program instructions, and when the processor executes the program instructions, the description of the method for acquiring a page switch response time can be executed, which is not described herein again. In addition, the beneficial effects of the same method are not described in detail. For technical details not disclosed in the embodiments of the computer storage medium to which the present invention relates, reference is made to the description of the method embodiments of the present invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (10)
1. A method for obtaining page switching response time is characterized by comprising the following steps:
acquiring a target video, wherein the target video comprises a video of a software page switching process of a terminal;
converting the target video into a multi-frame target image according to a preset frequency;
classifying the state of the multi-frame target image into a stable state image before or after page switching and a switching state image during page switching;
and performing stage division on the target video according to the stable state image and the switching state image to respectively obtain the time periods of the target video in the switching stage and the stable stage, and determining the time period length of the switching stage as the software page switching response time.
2. The method of claim 1, wherein the software is live video software, and the status classification of the multiple frames of target images comprises:
Detecting whether a live broadcaster exists in a first target image and a second target image in the multiple frames of target images according to a pre-trained target detection model, wherein the second target image is a frame image behind the first target image, and when the live broadcaster does not exist in the first target image or the second target image, determining that the first target image or the second target image is a switching state image;
when both the first target image and the second target image have live senders, acquiring image similarity between the first target image and the second target image, wherein when the image similarity is smaller than or equal to a similarity threshold value, the first target image is a stable state image, and the second target image is a switching state image; and when the image similarity is greater than the similarity threshold value, the first target image and the second target image are stable state images.
3. The method of claim 1, wherein the state classification of the plurality of frames of target images comprises:
detecting whether the multi-frame target image comprises a preset specific icon, wherein the specific icon is an icon on a software page in a stable state;
Determining a target image in which the specific icon is detected as a steady-state image;
determining a target image in which the specific icon is not detected as a switching state image.
4. The method of claim 1, wherein the state classification of the plurality of frames of target images comprises:
detecting whether the multi-frame target image comprises a preset specific icon by using a template matching algorithm, wherein the specific icon is an icon on a software page in a stable state;
determining a target image of the specific icon detected by using a template matching algorithm as a steady-state image;
if the specific icon is not detected from the target image by using the template matching algorithm, detecting whether the target image comprises a preset specific icon or not by using the feature matching algorithm, determining the target image of the specific icon detected by using the feature matching algorithm as a stable state image, and determining the target image of the specific icon not detected by using the feature matching algorithm as a switching state image.
5. The method of claim 1, wherein the state classification of the plurality of frames of target images comprises:
classifying the target image according to a pre-trained classification neural network model, and determining that the target image is a stable state image or a switching state image, wherein training data of the classification neural network model comprises software page images in a stable stage and a switching stage.
6. An apparatus for obtaining page switch response time, comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring a target video, and the target video comprises a video of a software page switching process of a terminal;
the conversion module is used for converting the target video into a plurality of frames of target images according to a preset frequency;
the classification module is used for carrying out state classification on the multi-frame target images, and dividing the multi-frame target images into stable state images before or after page switching and switching state images during page switching;
and the determining module is used for carrying out stage division on the target video according to the stable state image and the switching state image to respectively obtain the time periods of the target video in the switching stage and the stable stage, and determining the time period length of the switching stage as the software page switching response time.
7. The apparatus of claim 6, wherein the software is live video software, and wherein the classification module comprises:
the first sub-module is used for detecting whether a live broadcast exists in a first target image and a second target image in the multiple frames of target images according to a pre-trained target detection model, wherein the second target image is a frame image behind the first target image, and when the live broadcast does not exist in the first target image or the second target image, the first target image or the second target image is determined to be a switching state image;
The second sub-module is used for acquiring the image similarity between the first target image and the second target image when both the first target image and the second target image have live senders, wherein when the image similarity is smaller than or equal to a similarity threshold value, the first target image is a stable state image, and the second target image is a switching state image; and when the image similarity is greater than the similarity threshold value, the first target image and the second target image are stable state images.
8. The apparatus of claim 6 or 7, wherein the classification module comprises:
the third sub-module is used for detecting whether the multi-frame target image comprises a preset specific icon, wherein the specific icon is an icon on a software page in a stable state; determining a target image in which the specific icon is detected as a steady-state image; determining a target image in which the specific icon is not detected as a switching state image.
9. An apparatus for obtaining page switch response time, comprising: a processor and a memory;
the processor is connected to a memory, wherein the memory is used for storing program codes, and the processor is used for calling the program codes to execute the method for acquiring the page switch response time according to any one of claims 1 to 5.
10. A computer storage medium, characterized in that the computer storage medium stores a computer program comprising program instructions that, when executed by a processor, perform the method of obtaining page switch response time according to any of claims 1-5.
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