CN116781823A - Control method for mobile phone screen brightness and display terminal thereof - Google Patents
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Abstract
The application discloses a control method of mobile phone screen brightness and a display terminal thereof, in particular to the field of electric power data processing, comprising a retina perception module, a perception data processing module, a cloud data constant value judging module, a brightness adjusting module and a visual fatigue prompting module, wherein the retina perception is adopted to acquire the eyeball motion track of a user and the blink frequency is adopted to judge whether eyes are tired or not, the data is rated, the cloud data center constant value is compared according to the data grade, the mobile phone screen brightness is adaptively adjusted according to the eye consumption degree of the user, and the user is encouraged to carry out eye rest and posture adjustment through the grade prompt, so that the mobile phone screen brightness is beneficial to reducing the time of exposure to blue light of the mobile phone, reducing the risk of causing visual fatigue and sleep problem, the screen brightness can be increased to provide better visibility under the condition of eye relaxation, and the screen brightness can be reduced to reduce the eye fatigue under the condition of excessive eye consumption.
Description
Technical Field
The application relates to the technical field of communication, in particular to a control method for mobile phone screen brightness and a display terminal thereof.
Background
The popularization and wide use of mobile phones enable people to touch the mobile phone screen for a long time, the current mobile phone screen brightness control is to sense the ambient light through a light sensor to adjust the brightness, when the backlight brightness is inconsistent with the ambient light intensity, the human eyes can generate visual fatigue, the fatigue can cause irreversible damage to the human eyes, the eyes are in a highly concentrated and stressed state for a long time, and the risk of using the visual fatigue is increased.
The application adopts retina perception to obtain the eyeball movement track of the user and the blink times to judge whether the eyes are tired, the data is rated, the constant value of the cloud data center is compared according to the data grade, the screen brightness of the mobile phone is adaptively adjusted according to the eye use degree of the user, and the user is encouraged to rest the eyes and adjust the posture through grade prompt, so that the mobile phone is reasonably used, and the eye health is protected.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, an embodiment of the present application provides a method for controlling brightness of a mobile phone screen and a display terminal thereof, which solve the problems set forth in the above-mentioned background art through a brightness adjusting module.
In order to achieve the above purpose, the present application provides the following technical solutions: the device comprises a retina sensing module, a sensing data processing module, a cloud data constant value judging module, a brightness adjusting module and a visual fatigue prompting module;
retina perception module: acquiring an eyeball motion track and blink times by utilizing an infrared imaging technology and a convolutional neural network model, wherein the method comprises two subunits of tracking the eyeball motion track and recording the blink times;
a perception data processing module: processing eyeball movement track and blink frequency data by Gaussian filtering and edge detection;
cloud data constant value judging module: judging the eyeball movement track by utilizing a Bezier curve, calculating the average value of the blink frequency score, and comparing the average value with the constant value of cloud data to obtain data, wherein the method comprises two subunits of judging the eyeball movement track and judging the average value of the blink frequency score;
and a brightness adjusting module: classifying transmission data, establishing a Stokes function, and adjusting brightness by subjective perception of human eyes;
visual fatigue prompting module: the user is periodically reminded of resting and relaxing eyes through the fatigue grade corresponding to the brightness of the mobile phone screen brightness control system, and the display terminal can be a display terminal such as a mobile phone, a tablet, a computer and the like, and is not particularly limited.
In a preferred embodiment, the retina sensing module includes two subunits, namely tracking eye movement track and recording blink times, and specifically includes the following:
first, tracking an eyeball motion track: the infrared light source of the mobile phone camera is used for irradiating the eyeball, the infrared light reflected by the eyeball is received through the mobile phone camera, the radiation in the infrared spectrum range is used for acquiring an image, the position and the movement track of the eyeball are recorded in real time, the image and the data are generated to represent the movement condition of the eyeball, and the position change, the movement speed and the movement direction of the eyeball in time are provided by the image and the data.
Secondly, recording blink times: the method comprises the steps of detecting the position and the area of an eye by using a convolutional neural network, collecting an image data set of the eye by the convolutional neural network, adjusting the image size, establishing a convolutional network model, dividing the data set into a training set, a verification set and a test set, training the convolutional neural model by using the training set, updating model parameters, enabling the model to gradually learn the characteristics of the eye, analyzing the characteristics of the area of the eye, judging the state of the eye, including eye opening, eye closing and blinking, judging that blinking is performed once when the state of the eye changes from eye opening to eye closing and then from eye closing to eye opening, and recording the blinking times by using a counter.
In a preferred embodiment, the sensing data processing module includes two subunits of eyeball motion data processing and blink frequency data processing, and specifically includes the following:
s201, eyeball movement data processing: correcting an eyeball moving image, removing noise by using an image processing algorithm, and highlighting eyeball characteristics, wherein the image processing algorithm is Gaussian filtering, and the specific formula is as follows:
；
wherein the method comprises the steps ofRepresenting blurred pixel values, +.>Represents the standard deviation of the gaussian filter, +.>Representing pixel values within the filter range, < >>、/>And (3) representing the index of the filter, positioning the pupil position in each frame of image by utilizing the pupil color, taking the pupil position as an initial target position, extracting pupil key points from an image area around the initial target position, matching the characteristics of the subsequent frame number with the initial characteristics by utilizing a characteristic matching algorithm, and continuously estimating and updating to track according to the matching result.
S202, blink frequency data processing: shooting an eye blink image by a mobile phone camera, analyzing eyelid states by utilizing computer vision and an image processing technology to extract eye blink characteristics, recording eye blink times, dividing eyelid areas by using edge detection, calculating the shape characteristics of the eyelid, and obtaining the area, perimeter and length-width ratio of the eyelid, wherein the specific formula of calculation is as follows:
eyelid area:；
eyelid circumference:；
aspect ratio:；
wherein S represents the eyelid area,the number of pixels representing the eyelid contour, +.>Representing the actual length of the unit pixel, C representing the eyelid circumference, < ->Boundary length representing eyelid contour, ++>Represents eyelid aspect ratio, < >>Representing the length of the long side>The table shows the length of the short side, the color distribution analysis method is used for extracting the color characteristics of the eyelid, the color distribution analysis method selects a color space to describe the color characteristics of the eyelid, the eyelid is converted into the color space for the eyelid area, the pixel value distribution of each color channel in the area is counted, the difference of the eye colors under the eye-opening and eye-closing state is reduced, the optical flow analysis is used for extracting the motion characteristics of the eyelid, the optical flow analysis is used for calculating the motion vector of each pixel in the eyelid, and the specific formula is as follows:
；
wherein the method comprises the steps ofRepresenting the minimum error of the estimated motion vector, x, y representing the gradient of the image in the x, y directions, t representing the gray level difference at two points in time, u, v representing the components of the motion vector.
In a preferred embodiment, the cloud data constant value judging module includes two subunits for judging the eye movement track and judging the blink frequency average constant value, and specifically includes the following contents:
s301, judging an eyeball motion track: the method comprises the steps of determining the fixation point of an eyeball in an observation process by analyzing processed eyeball motion track data, wherein the fixation point judging condition is that whether the time of the eyeball staying at a certain position reaches the fixation condition or not is determined, the fixation point judging condition is realized by setting a time window, if the eyeball position is kept stable and does not move rapidly in the time window, namely, the fixation point is considered as a fixation point, the saccade path of the eyeball is estimated and calculated by utilizing a curve fitting method, and the curve fitting is a Bezier curve, wherein the specific formula is as follows:
；
wherein the method comprises the steps ofCoordinates representing points on the curve, +.>Representing the position on the curve, the value range is 0,1]Parameter of->、/>、/>、/>And representing coordinates of the control points, connecting the discrete eyeball position data to form a smooth movement track, displaying the gaze point distribution through a cloud data center, comparing the normal eyeball movement track data, and explaining the result of the eyeball movement track data.
S302, judging the average constant value of the blink times: setting a threshold value of a normal blink frequency score average constant value as 15 times/min, transmitting processed blink frequency data to a cloud data center, and calculating a blink frequency score average value, wherein a specific calculation formula is as follows:
；
wherein A represents the average value of the blink times, C represents the processed blink times, min represents one minute, the obtained average value of the blink times is compared with the constant value of the cloud data center, and fatigue grade data are classified and transmitted to the brightness adjusting module.
In a preferred embodiment, the brightness adjustment module classifies the transmitted data, assigns different brightness values, classifies the data into serious fatigue grades if the average value of the blink times is smaller than 10 times/min and the eyeballs frequently move and the pupils frequently change, classifies the data into moderate fatigue grades if the average value of the blink times is 10 times/min to 15 times/min and the eyeballs movement track accords with the fitting track, classifies the data into mild fatigue grades if the average value of the blink times is larger than 15 times/min, stores the fatigue grade data in a cloud data center, adjusts the brightness by combining a mobile phone screen brightness control system, the mobile phone screen brightness control system defines a corresponding relation between the fatigue grade and the light sensation, the brightness control model is established through an empirical law of a stokep function, the subjective brightness of the human eyes and the physical brightness of a light source are in a non-linear relation, but a relation of a power function, when the brightness of the light source is increased, the subjective brightness of the human eyes is correspondingly increased, the fatigue grade is correspondingly increased, the sensitivity of the human eyes to the brightness change is higher in a lower brightness range, the sensitivity of the human eyes to the brightness change is lower than that of the brightness is a specific stokep function, and the specific formula is lower in the brightness range:
；
wherein the method comprises the steps ofIndicating visual perception intensity, < >>、/>Represents the measured constant, +.>Representing the light source brightness.
In a preferred embodiment, the visual fatigue prompting module periodically prompts the user to rest and relax eyes through the corresponding fatigue level of the brightness of the mobile phone screen brightness control system, sends three prompting messages of green, yellow and red to the user according to the set time interval of the mobile phone screen brightness, wherein the three prompting messages comprise prompting of changing the screen color through a blue light filter to relieve eye fatigue, blinking and bad gesture, encouraging the user to rest and adjust gesture, forcibly weakening the mobile phone screen brightness, and informing the user that the fatigue level exceeds the highest level.
In a preferred embodiment, the method specifically comprises the following steps:
101. acquiring an eyeball motion track and blink times by utilizing an infrared imaging technology and a convolutional neural network model, wherein the method comprises two subunits of tracking the eyeball motion track and recording the blink times;
102. processing eyeball movement track and blink frequency data by Gaussian filtering and edge detection;
103. judging the eyeball movement track by utilizing a Bezier curve, calculating the average value of the blink frequency score, and comparing the average value with the constant value of cloud data to obtain data, wherein the method comprises two subunits of judging the eyeball movement track and judging the average value of the blink frequency score;
104. classifying transmission data, establishing a Stokes function, and adjusting brightness by subjective perception of human eyes;
105. the user is periodically reminded of resting and relaxing eyes through the fatigue grade corresponding to the brightness of the mobile phone screen brightness control system, and the display terminal can be a display terminal such as a mobile phone, a tablet, a computer and the like, and is not particularly limited;
the application has the technical effects and advantages that:
according to the application, the eye movement track of the user and the blink frequency are obtained through retina perception, whether the eyes are tired or not is judged, the data are rated, the constant value of the cloud data center is compared according to the data grade, the screen brightness of the mobile phone is adaptively adjusted according to the eye use degree of the user, the user is encouraged to rest and adjust the posture of the eyes through grade prompt, the time of exposure to blue light of the mobile phone is reduced, the risks of causing visual fatigue and sleep problems are reduced, the screen of the mobile phone is more comfortable and easy to read due to proper brightness, the screen brightness can be increased to provide better visibility under the eye relaxation condition, and the screen brightness can be reduced to reduce the eye fatigue under the eye use condition.
Drawings
FIG. 1 is a flow chart of the system of the present application.
Fig. 2 is a block diagram of the system architecture of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The embodiment provides a control method for mobile phone screen brightness and a display terminal thereof as shown in fig. 1, and specifically comprises the following steps:
101. acquiring an eyeball motion track and blink times by utilizing an infrared imaging technology and a convolutional neural network model, wherein the method comprises two subunits of tracking the eyeball motion track and recording the blink times;
102. processing eyeball movement track and blink frequency data by Gaussian filtering and edge detection;
103. judging the eyeball movement track by utilizing a Bezier curve, calculating the average value of the blink frequency score, and comparing the average value with the constant value of cloud data to obtain data, wherein the method comprises two subunits of judging the eyeball movement track and judging the average value of the blink frequency score;
104. classifying transmission data, establishing a Stokes function, and adjusting brightness by subjective perception of human eyes;
105. the user is periodically reminded of resting and relaxing eyes through the fatigue grade corresponding to the brightness of the mobile phone screen brightness control system, and the display terminal can be a display terminal such as a mobile phone, a tablet, a computer and the like, and is not particularly limited;
the embodiment provides a display terminal application of mobile phone screen brightness as shown in fig. 2, which specifically includes: the device comprises a retina sensing module, a sensing data processing module, a cloud data constant value judging module, a brightness adjusting module and a visual fatigue prompting module;
retina perception module: acquiring an eyeball motion track and blink times by utilizing an infrared imaging technology and a convolutional neural network model, wherein the method comprises two subunits of tracking the eyeball motion track and recording the blink times;
a perception data processing module: processing eyeball movement track and blink frequency data by Gaussian filtering and edge detection;
cloud data constant value judging module: judging the eyeball movement track by utilizing a Bezier curve, calculating the average value of the blink frequency score, and comparing the average value with the constant value of cloud data to obtain data, wherein the method comprises two subunits of judging the eyeball movement track and judging the average value of the blink frequency score;
and a brightness adjusting module: classifying transmission data, establishing a Stokes function, and adjusting brightness by subjective perception of human eyes;
visual fatigue prompting module: the user is periodically reminded of resting and relaxing eyes through the fatigue grade corresponding to the brightness of the mobile phone screen brightness control system, and the display terminal can be a display terminal such as a mobile phone, a tablet, a computer and the like, and is not particularly limited.
101. Acquiring an eyeball motion track and blink times by utilizing an infrared imaging technology and a convolutional neural network model, wherein the method comprises two subunits of tracking the eyeball motion track and recording the blink times;
in this embodiment, a specific description is provided of a retina sensing module, where the retina sensing module includes two subunits for tracking an eye movement track and recording blink times, and the retina sensing module specifically includes the following:
first, tracking an eyeball motion track: the infrared light source of the mobile phone camera is used for irradiating the eyeball, the infrared light reflected by the eyeball is received through the mobile phone camera, the radiation in the infrared spectrum range is used for acquiring an image, the position and the movement track of the eyeball are recorded in real time, the image and the data are generated to represent the movement condition of the eyeball, and the position change, the movement speed and the movement direction of the eyeball in time are provided by the image and the data.
Secondly, recording blink times: the method comprises the steps of detecting the position and the area of an eye by using a convolutional neural network, collecting an image data set of the eye by the convolutional neural network, adjusting the image size, establishing a convolutional network model, dividing the data set into a training set, a verification set and a test set, training the convolutional neural model by using the training set, updating model parameters, enabling the model to gradually learn the characteristics of the eye, analyzing the characteristics of the area of the eye, judging the state of the eye, including eye opening, eye closing and blinking, judging that blinking is performed once when the state of the eye changes from eye opening to eye closing and then from eye closing to eye opening, and recording the blinking times by using a counter.
The neural network model in this embodiment may be a convolutional neural network model or other type of neural network model, which is not specifically limited herein.
102. Processing eyeball movement track and blink frequency data by Gaussian filtering and edge detection;
in this embodiment, a specific description is provided of a sensing data processing module, where the sensing data processing module includes two subunits of eye movement data processing and blink frequency data processing, and specifically includes the following:
s201, eyeball movement data processing: correcting an eyeball moving image, removing noise by using an image processing algorithm, and highlighting eyeball characteristics, wherein the image processing algorithm is Gaussian filtering, and the specific formula is as follows:
；
wherein the method comprises the steps ofRepresenting blurred pixel values, +.>Represents the standard deviation of the gaussian filter, +.>Representing pixel values within the filter range, < >>、/>And (3) representing the index of the filter, positioning the pupil position in each frame of image by utilizing the pupil color, taking the pupil position as an initial target position, extracting pupil key points from an image area around the initial target position, matching the characteristics of the subsequent frame number with the initial characteristics by utilizing a characteristic matching algorithm, and continuously estimating and updating to track according to the matching result.
S202, blink frequency data processing: shooting an eye blink image by a mobile phone camera, analyzing eyelid states by utilizing computer vision and an image processing technology to extract eye blink characteristics, recording eye blink times, dividing eyelid areas by using edge detection, calculating the shape characteristics of the eyelid, and obtaining the area, perimeter and length-width ratio of the eyelid, wherein the specific formula of calculation is as follows:
eyelid area:；
eyelid circumference:；
aspect ratio:；
wherein S represents the eyelid area,the number of pixels representing the outline of the eyelid,/>representing the actual length of the unit pixel, C representing the eyelid circumference, < ->Boundary length representing eyelid contour, ++>Represents eyelid aspect ratio, < >>Representing the length of the long side>Representing short side length, extracting eyelid color characteristics by using a color distribution analysis method, wherein the color distribution analysis method selects a color space to describe eyelid color characteristics, converting the eyelid color characteristics into the color space for an eyelid area, counting pixel value distribution of each color channel in the area, reducing eye color difference under the eye-opening and eye-closing state, extracting eyelid motion characteristics by using optical flow analysis, and calculating motion vectors of all pixels in the eyelid by using the optical flow analysis method, wherein the specific formula is as follows:
；
wherein the method comprises the steps ofRepresenting the minimum error of the estimated motion vector, x, y representing the gradient of the image in the x, y directions, t representing the gray level difference at two points in time, u, v representing the components of the motion vector.
103. Judging the eyeball movement track by utilizing a Bezier curve, calculating the average value of the blink frequency score, and comparing the average value with the constant value of cloud data to obtain data, wherein the method comprises two subunits of judging the eyeball movement track and judging the average value of the blink frequency score;
in this embodiment, a specific description is provided of a cloud data constant value judging module, where the cloud data constant value judging module includes two subunits for judging an eyeball motion track and judging a blink frequency average constant value, and specifically includes the following contents:
s301, judging an eyeball motion track: the method comprises the steps of determining the fixation point of an eyeball in an observation process by analyzing processed eyeball motion track data, wherein the fixation point judging condition is that whether the time of the eyeball staying at a certain position reaches the fixation condition or not is determined, the fixation point judging condition is realized by setting a time window, if the eyeball position is kept stable and does not move rapidly in the time window, namely, the fixation point is considered as a fixation point, the saccade path of the eyeball is estimated and calculated by utilizing a curve fitting method, and the curve fitting is a Bezier curve, wherein the specific formula is as follows:
；
wherein the method comprises the steps ofCoordinates representing points on the curve, +.>Representing the position on the curve, the value range is 0,1]Parameter of->、/>、/>、/>And representing coordinates of the control points, connecting the discrete eyeball position data to form a smooth movement track, displaying the gaze point distribution through a cloud data center, comparing the normal eyeball movement track data, and explaining the result of the eyeball movement track data.
S302, judging the average constant value of the blink times: setting a threshold value of a normal blink frequency score average constant value as 15 times/min, transmitting processed blink frequency data to a cloud data center, and calculating a blink frequency score average value, wherein a specific calculation formula is as follows:
；
wherein A represents the average value of the blink times, C represents the processed blink times, min represents one minute, the obtained average value of the blink times is compared with the constant value of the cloud data center, and fatigue grade data are classified and transmitted to the brightness adjusting module.
104. Classifying transmission data, establishing a Stokes function, and adjusting brightness by subjective perception of human eyes;
in this embodiment, it is specifically required to specify that the brightness adjustment module classifies the transmitted data, assigns different brightness values, classifies the transmitted data as a serious fatigue level if the average value of the blink times is less than 10 times/min and the eyeball frequently moves and the pupil frequently changes, classifies the transmitted data as a moderate fatigue level if the average value of the blink times is 10 times/min to 15 times/min and the eyeball motion track accords with the fitting track, classifies the transmitted data as a mild fatigue level if the average value of the blink times is greater than 15 times/min, stores the fatigue level data in a cloud data center, and adjusts the brightness by combining a mobile phone screen brightness control system, wherein the mobile phone screen brightness control system defines a corresponding relation between the fatigue level and the light sensation, the brightness control model is established by an empirical law of a stokep function, the subjective brightness of the human eye and the physical brightness of the light source are not in a linear relation, but a power function relation, the subjective brightness of the human eye correspondingly increases when the brightness of the light source increases, the fatigue level correspondingly increases in a lower brightness range, the human eye has a higher sensitivity to the brightness change, and has a lower sensitivity to the specific stokep function in a higher sensitivity range to the human eye change, and the specific formula is:
；
wherein the method comprises the steps ofIndicating visual perception intensity, < >>、/>Represents the measured constant, +.>Representing the light source brightness.
105. The user is periodically reminded of resting and relaxing eyes through the fatigue grade corresponding to the brightness of the mobile phone screen brightness control system, and the display terminal can be a display terminal such as a mobile phone, a tablet, a computer and the like, and is not particularly limited;
in this embodiment, a specific illustration is to be provided by the visual fatigue prompting module, where the visual fatigue prompting module periodically prompts the user to rest and relax eyes through the fatigue level corresponding to the brightness of the mobile phone screen brightness control system, and sends three kinds of prompting information including green, yellow and red to the user according to the time interval set by the mobile phone screen brightness intensity, where the three kinds of prompting information include changing the screen color through a blue light filter to relieve eye fatigue, blinking and bad gesture prompting, encourages the user to perform eye rest and adjust gesture, forcibly weakens the mobile phone screen brightness, informs the user that the fatigue level has exceeded the highest level, and the display terminal may be a display terminal such as a mobile phone, a tablet, a computer, etc., which is not limited specifically herein.
The formula in the application is a formula which is obtained by removing dimension and taking the numerical calculation, and is closest to the actual situation by acquiring a large amount of data and performing software simulation, and the preset proportionality coefficient in the formula is set by a person skilled in the art according to the actual situation or is obtained by simulating the large amount of data.
In the embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (7)
1. A control method for the brightness of a mobile phone screen is characterized by comprising the following steps: the method specifically comprises the following steps:
step 101, acquiring an eyeball motion track and blink times by utilizing an infrared imaging technology and a convolutional neural network model, wherein the method comprises two subunits of tracking the eyeball motion track and recording the blink times;
step 102, processing eyeball movement track and blink frequency data by Gaussian filtering and edge detection;
step 103, judging an eyeball motion track by utilizing a Bezier curve, calculating a blink frequency average value, and comparing the calculated blink frequency average value with a cloud data constant value to obtain data, wherein the step comprises two subunits of judging the eyeball motion track and judging the blink frequency average value;
step 104, grading transmission data, establishing a Stokes function, and adjusting brightness by subjective perception of human eyes;
step 105, periodically reminding the user to rest and relax eyes through the fatigue level corresponding to the brightness of the mobile phone screen brightness control system.
2. The method for controlling the brightness of a mobile phone screen according to claim 1, wherein: in step 101, the eye movement track and the blink frequency are obtained by using an infrared imaging technology and a convolutional neural network model, including two subunits of tracking the eye movement track and recording the blink frequency.
3. The method for controlling the brightness of a mobile phone screen according to claim 1, wherein: in step 102, the eye movement track and blink frequency data are processed by gaussian filtering and edge detection, and the color characteristics of the eyelid are extracted by using a color distribution analysis method, wherein the specific formula is as follows:
；
；
；
wherein the method comprises the steps ofRepresenting blurred pixel values, +.>Represents the standard deviation of the gaussian filter, +.>Representing pixel values within the filter range, < >>、/>Representing the index of the filter, S representing the eyelid area, < >>The number of pixels representing the eyelid contour, +.>Representing the actual length of the unit pixel, C representing the eyelid circumference, < ->Boundary length representing eyelid contour, ++>Represents eyelid aspect ratio, < >>Representing the length of the long side>Representing the length of the short side->Representing the minimum error of the estimated motion vector, x, y representing the gradient of the image in the x, y directions, t representing the gray level difference at two points in time, u, v representing the components of the motion vector.
4. The method for controlling the brightness of a mobile phone screen according to claim 1, wherein: in step 103, the bezier curve is used to determine the eye movement track, calculate the average value of the blink frequency, and compare the average value with the constant value of the cloud data to obtain data, including two subunits, namely, determine the eye movement track and determine the average value of the blink frequency, the specific formulas are as follows:
；
；
wherein the method comprises the steps ofCoordinates representing points on the curve, +.>Representing the position on the curve, the value range is 0,1]Parameter of->、/>、/>、/>The coordinates of the control point are represented, A represents the mean value of the blink times, C represents the processed blink times, and Min represents one minute.
5. The method for controlling the brightness of a mobile phone screen according to claim 1, wherein: in the step 104, the transmission data is classified, a stokep function is established, and the subjective perception of human eyes is utilized to adjust the brightness;
；
wherein the method comprises the steps ofIndicating visual perception intensity, < >>、/>Represents the measured constant, +.>Representing the light source brightness.
6. The method for controlling the brightness of a mobile phone screen according to claim 1, wherein: in step 105, the user is periodically reminded to rest and relax eyes by the fatigue level corresponding to the brightness of the mobile phone screen brightness control system.
7. A display terminal of mobile phone screen brightness is applied to a control method of mobile phone screen brightness according to any one of claims 1-6, which is characterized in that: the device comprises a retina sensing module, a sensing data processing module, a cloud data constant value judging module, a brightness adjusting module and a visual fatigue prompting module;
retina perception module: acquiring an eyeball motion track and blink times by utilizing an infrared imaging technology and a convolutional neural network model, wherein the method comprises two subunits of tracking the eyeball motion track and recording the blink times;
a perception data processing module: processing eyeball movement track and blink frequency data by Gaussian filtering and edge detection;
cloud data constant value judging module: judging the eyeball movement track by utilizing a Bezier curve, calculating the average value of the blink frequency score, and comparing the average value with the constant value of cloud data to obtain data, wherein the method comprises two subunits of judging the eyeball movement track and judging the average value of the blink frequency score;
and a brightness adjusting module: classifying transmission data, establishing a Stokes function, and adjusting brightness by subjective perception of human eyes;
visual fatigue prompting module: the user is periodically reminded of resting and relaxing eyes through the fatigue level corresponding to the brightness of the mobile phone screen brightness control system.
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