CN116546175A - Intelligent control method and device for realizing projector based on automatic induction - Google Patents
Intelligent control method and device for realizing projector based on automatic induction Download PDFInfo
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- H04N9/00—Details of colour television systems
- H04N9/12—Picture reproducers
- H04N9/31—Projection devices for colour picture display, e.g. using electronic spatial light modulators [ESLM]
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Abstract
The invention relates to the technical field of intelligent control, and discloses an intelligent control method for realizing a projector based on automatic induction, which comprises the following steps: receiving a starting instruction of a projector, starting the projector according to the starting instruction, correcting a display projection picture according to a pre-built perspective transformation method, obtaining display brightness of the corrected projection picture, adjusting the display brightness of the corrected projection picture according to a preset brightness threshold value, obtaining a characteristic image of the adjusted projection picture according to a pre-built convolutional neural network algorithm, and reconstructing a low-resolution image into a high-resolution image according to a pre-built high-resolution image generation model to obtain the high-resolution projection picture. The invention also provides an intelligent control device, electronic equipment and a computer readable storage medium for realizing the projector based on automatic induction. The invention can solve the problem of poor projection effect caused by the lack of consideration of the intensity of ambient light and the resolution of pictures.
Description
Technical Field
The present invention relates to the field of intelligent control technologies, and in particular, to an intelligent control method and apparatus for implementing a projector based on auto-induction, an electronic device, and a computer readable storage medium.
Background
A projector, also known as a projector, is a device that can project images or video onto a curtain. Compared with a large display screen, the projector has high portability and large projection screen, but the display effect of a projection picture is greatly influenced by the projection direction, the light intensity and the picture resolution, so that the use effect of the projector is improved by relying on an intelligent control method.
At present, intelligent control on a projector is mostly focused on correcting picture deformation caused by various factors such as inclination of a projection direction, assembly tolerance and the like, the method can effectively realize angle and direction control on a projection picture, and when ambient light conversion and picture resolution are different, the display effect of a projection picture can be good and bad, and the projector can only be manually adjusted, so that the use experience of the projector is greatly influenced.
Disclosure of Invention
The invention provides an intelligent control method, an intelligent control device and a computer readable storage medium for realizing a projector based on automatic induction, which mainly aim to solve the problem of poor projection effect caused by not considering the problems of ambient light intensity and picture resolution.
In order to achieve the above object, the present invention provides an intelligent control method for implementing a projector based on auto-induction, including:
Receiving a starting instruction of a projector, starting the projector according to the starting instruction, and obtaining a loaded projection picture and a curtain starting signal;
controlling the curtain to descend according to the curtain starting signal to obtain a display projection picture;
based on the loaded projection picture, correcting the displayed projection picture according to a pre-constructed perspective transformation method to obtain a corrected projection picture;
acquiring the display brightness of the corrected projection picture, and adjusting the display brightness of the corrected projection picture according to a preset brightness threshold value to obtain an adjusted projection picture;
acquiring a characteristic image of the adjusted projection picture according to a pre-constructed convolutional neural network algorithm, wherein the characteristic image comprises a low-resolution image and a high-resolution image;
obtaining standard features with maximum similarity with the low-resolution image in the high-resolution image based on a pre-constructed Pair-wise model and a similarity calculation formula, wherein a loss function adopted by the Pair-wise model is shown in the following formula:
d n ＝||a n -b n || 2
wherein Loss represents a Loss value adopted in the training process of the Pair-wise model, N represents a number of training samples, and N represents the number of training samples, wherein each training sample consists of a high-resolution image and a low-resolution image, c n C) if the label value indicating whether the nth input training sample corresponds to the input high-resolution image and the input low-resolution image correspond to each other n Equal to 1, otherwise c n Equal to 0, d n Representing the Euclidean distance between features of high and low resolution images in an nth training sample, a n Representing the eigenvalues of the high resolution image in the nth training sample, b n Representing the characteristic value of the low-resolution image in the nth training sample, and m represents the interval parameter of the loss function;
and reconstructing the low-resolution image into a high-resolution image according to a pre-constructed high-resolution image generation model based on the standard features to obtain a high-resolution projection picture, so as to realize intelligent control of the projector.
Optionally, the controlling the curtain to descend according to the curtain start signal includes:
emitting an infrared signal based on the curtain start signal;
converting the infrared signal into an electrical pulse control signal;
and controlling the curtain to descend according to the electric pulse control signal.
Optionally, the correcting the display projection picture according to the pre-constructed perspective transformation method to obtain a corrected projection picture includes:
respectively establishing a plane rectangular coordinate system on the loaded projection picture and the curtain;
Designing a reference square projection picture according to the plane rectangular coordinate system, and using a cross division mark to represent the vertex of the reference square projection picture to obtain a loading mark point and a display mark point;
acquiring a coordinate transformation matrix based on the loading mark points and the display mark points;
obtaining an inverse transformation matrix of the coordinate transformation matrix according to a pre-constructed matrix transformation formula;
and performing perspective transformation on the loaded projection picture by using the inverse transformation matrix to obtain a corrected projection picture.
Optionally, the coordinate transformation matrix is as follows:
U＝(a’ 1 ,a’ 2 ，a’ 3 ,a’ 4 )(a 1 ，a 2 ,a 3 ,a 4 ) -1
wherein U represents a coordinate transformation matrix, (a '' 1 ,a’ 2 ,a’ 3 ,a’ 4 ) Coordinates of a display mark point representing a display projected picture, (a) 1 ,a 2 ,a 3 ,a 4 ) And the coordinates of the loading mark point for loading the projection picture are represented.
Optionally, the acquiring the characteristic image of the adjusted projection picture according to the pre-constructed convolutional neural network algorithm includes:
performing a first convolution operation of the pre-construction on the adjusted projection picture to obtain a projection primary feature map;
and performing a second convolution operation on the projection primary feature map to obtain a feature image:
wherein o is i Representing the position of the ith pixel of the characteristic image in the characteristic image, B is the set of all pixel positions of the characteristic image, P (o) i ) Pixel value representing the ith pixel of the feature image, L (o i ) The i-th pixel of the projection primary characteristic diagram is calculated through BP layer to obtain a pixel value, K (o i +Δo i ) Representing the pixel value of the i-th pixel in the pixel-corrected projected primary feature map.
Optionally, the first convolution operation is as follows:
W 2 ＝(W 1 -C)/J+1
H 2 ＝(H 1 -(C)/J+1
wherein W is 2 And H 2 Representing the width and height, W, of the projected primary feature map, respectively 1 And H 1 And C represents the receptive field size of the convolution layer in the first convolution operation, and J is the step length of the first convolution operation.
Optionally, the extracting, based on the pre-constructed Pair-wise model and the similarity calculation formula, the feature with the maximum similarity with the low-resolution image in the high-resolution image to obtain the standard feature further includes:
based on the low-resolution image, a pre-constructed Pair-wise model is utilized to obtain low-resolution features;
and clustering the high-resolution image by adopting a pre-constructed K-means clustering algorithm to obtain a high-resolution image dictionary, wherein the high-resolution image dictionary consists of high-resolution features.
Optionally, the clustering processing is performed on the high-resolution image by adopting a pre-constructed K-means clustering algorithm to obtain a high-resolution image dictionary, which includes:
Clustering the high-resolution image based on a preset cluster number to obtain a cluster set, wherein the number of clusters in the cluster set is equal to the cluster number;
acquiring a cluster center of each cluster in the cluster, and acquiring a high-resolution feature set based on the cluster center;
and acquiring a high-resolution dictionary based on the high-resolution feature set.
Optionally, the high resolution image generation model is as follows:
wherein Y is j Target representing jth high resolution imageThe sign j represents the number of the high resolution image and the low resolution image, C represents the normalization constant, f j Representing low-resolution characteristic value d obtained by the jth low-resolution image through a Pair-wise model i The i-th standard feature value is represented, i represents the number of standard features, and M represents the number of standard features.
In order to solve the above problems, the present invention further provides an intelligent control device for implementing a projector based on auto-induction, the device comprising:
the projector starting module is used for receiving a starting instruction of the projector, starting the projector according to the starting instruction, and obtaining a loaded projection picture and a curtain starting signal;
controlling the curtain to descend according to the curtain starting signal to obtain a display projection picture;
The perspective transformation module is used for correcting the display projection picture according to a pre-constructed perspective transformation method based on the loaded projection picture to obtain a corrected projection picture;
the brightness adjusting module is used for obtaining the display brightness of the corrected projection picture, and adjusting the display brightness of the corrected projection picture according to a preset brightness threshold value to obtain an adjusted projection picture;
the resolution adjustment module is used for acquiring a characteristic image of the adjusted projection picture according to a pre-constructed convolutional neural network algorithm, wherein the characteristic image comprises a low-resolution image and a high-resolution image;
obtaining standard features with maximum similarity with the low-resolution image in the high-resolution image based on a pre-constructed Pair-wise model and a similarity calculation formula, wherein a loss function adopted by the Pair-wise model is shown in the following formula:
d n ＝||a n -b n || 2
wherein Loss represents Loss adopted in the training process of the Pair-wise modelLosing value, N represents the number of training samples, wherein each training sample consists of a high resolution image and a low resolution image, c n C) if the label value indicating whether the nth input training sample corresponds to the input high-resolution image and the input low-resolution image correspond to each other n Equal to 1, otherwise c n Equal to 0, d n Representing the Euclidean distance between features of high and low resolution images in an nth training sample, a n Representing the eigenvalues of the high resolution image in the nth training sample, b n Representing the characteristic value of the low-resolution image in the nth training sample, and m represents the interval parameter of the loss function;
and reconstructing the low-resolution image into a high-resolution image according to a pre-constructed high-resolution image generation model based on the standard features to obtain a high-resolution projection picture, so as to realize intelligent control of the projector.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores instructions executable by the at least one processor to implement the intelligent projector control method based on auto-induction.
In order to solve the above problems, the present invention further provides a computer readable storage medium having at least one instruction stored therein, the at least one instruction being executed by a processor in an electronic device to implement the above-described intelligent control method for implementing a projector based on auto-induction.
In order to solve the problems in the background art, the method comprises the steps of firstly receiving a starting instruction of a projector, starting the projector according to the starting instruction, obtaining a loaded projection picture and a curtain starting signal, controlling the curtain to descend according to the curtain starting signal, obtaining a displayed projection picture, correcting the displayed projection picture according to a pre-constructed perspective transformation method based on the loaded projection picture, and obtaining a corrected projection picture; acquiring the display brightness of the corrected projection picture, and adjusting the display brightness of the corrected projection picture according to a preset brightness threshold value to obtain an adjusted projection picture; acquiring a characteristic image of the adjusted projection picture according to a pre-constructed convolutional neural network algorithm, wherein the characteristic image comprises a low-resolution image and a high-resolution image; and obtaining the standard feature with the maximum similarity with the low-resolution image in the high-resolution image based on the pre-constructed Pair-wise model and a similarity calculation formula. Based on the standard features, the low-resolution image is reconstructed into a high-resolution image according to a pre-constructed high-resolution image generation model, a high-resolution projection picture is obtained, intelligent control of a projector is achieved, and therefore the problems of display brightness and resolution of the projection picture are considered. Therefore, the intelligent control method, the intelligent control device, the electronic equipment and the computer readable storage medium for realizing the projector based on the automatic induction can solve the problem of poor projection effect caused by not considering the problems of ambient light intensity and picture resolution.
Drawings
Fig. 1 is a schematic flow chart of an intelligent control method for implementing a projector based on auto-induction according to an embodiment of the invention;
FIG. 2 is a functional block diagram of an intelligent control device for implementing a projector based on auto-sensing according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device for implementing the intelligent control method for implementing a projector based on auto-induction according to an embodiment of the invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an intelligent control method for realizing a projector based on automatic induction. The execution main body of the intelligent control method for realizing the projector based on automatic induction comprises at least one of a server, a terminal and the like which can be configured to execute the method provided by the embodiment of the application. In other words, the intelligent control method for implementing the projector based on auto-sensing may be performed by software or hardware installed in a terminal device or a server device. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1:
referring to fig. 1, a flow chart of an intelligent control method for implementing a projector based on auto-induction according to an embodiment of the invention is shown. In this embodiment, the method for implementing intelligent control of a projector based on auto-induction includes:
s1, receiving a starting instruction of the projector, starting the projector according to the starting instruction, and obtaining a loading projection picture and a curtain starting signal.
In the embodiment of the invention, the starting instruction of the projector can be integrated into the mobile equipment through the APP or conveniently sent by a user through a remote controller and the like. For example, a small sheet is taken as a teacher, and some courseware pictures need to be projected onto a curtain for students to watch, so that a starting instruction of the projector is sent. It should be explained that the courseware picture is the loading projection picture. When the projector is started, a curtain starting signal is sent out to control the curtain to descend.
And S2, controlling the curtain to descend according to the curtain starting signal to obtain a display projection picture.
In detail, the controlling the curtain to descend according to the curtain start signal includes:
emitting an infrared signal based on the curtain start signal;
converting the infrared signal into an electrical pulse control signal;
And controlling the curtain to descend according to the electric pulse control signal.
It is to be explained that the curtain carries the infrared sensing device, and when the projector was opened, the infrared sensing device sensed the start signal, converts infrared signal into electric pulse signal control motor and makes the curtain descend. When the curtain descends to the bottom, the travel switch cuts off the circuit of the motor. When the infrared sensing device can not sense infrared signals, the motor is controlled to reversely rotate to realize the shrinkage of the curtain.
It should be emphasized that the motor in the present invention is a stepper motor. The stepper motor can easily achieve accurate positioning and precisely control the rotational angle and speed of various devices by using pulse signals, and furthermore, due to its mechanical design, the stepper motor will maintain its position when stopped. An electrical pulse signal refers to a voltage or current signal that makes abrupt or transitions within a short time interval. The operation of the pulse control servo motor is to control the angular displacement through the pulse quantity, realize positioning control and control the operation speed and acceleration of the motor through the frequency of receiving the pulse.
And S3, correcting the display projection picture according to a pre-constructed perspective transformation method based on the loaded projection picture to obtain a corrected projection picture.
It should be understood that, due to various factors such as inclination of the projection direction, unavoidable errors in manufacturing and assembling the projector, the loaded projection picture is directly loaded onto the projector to be projected, and the picture obtained on the vertical screen is not an ideal rectangular picture, so that picture deformation phenomena such as translation, rotation, stretching and scaling can exist to affect the normal viewing effect, and therefore, correction is required for displaying the projection picture.
In detail, the correcting the display projection picture according to the pre-constructed perspective transformation method to obtain a corrected projection picture includes:
respectively establishing a plane rectangular coordinate system on the loaded projection picture and the curtain;
designing a reference square projection picture according to the plane rectangular coordinate system, and using a cross division mark to represent the vertex of the reference square projection picture to obtain a loading mark point and a display mark point;
acquiring a coordinate transformation matrix based on the loading mark points and the display mark points;
obtaining an inverse transformation matrix of the coordinate transformation matrix according to a pre-constructed matrix transformation formula;
and performing perspective transformation on the loaded projection picture by using the inverse transformation matrix to obtain a corrected projection picture.
It should be understood that the reference square projection picture is designed according to a plane rectangular coordinate system, is parallel to the plane in which the plane rectangular coordinate system is located, and has a square shape. The cross partitions are marked as "+" for marking the vertices of the reference square projection image. The loading mark points are the cross division marks of the reference square projection picture, and the number of the loading mark points is 4. After the reference square projection picture is projected by the projector, projection points marked by cross division can be obtained on the curtain, namely display marking points. For example, the loading mark point A can be projected to obtain a display mark point B on the curtain. According to the rectangular plane coordinate system, the coordinates of the loading mark point and the display mark point can be obtained, and the coordinates of the loading mark point can be obtained by multiplying the coordinates of the loading mark point by the coordinate transformation matrix. And solving an inverse matrix of the coordinate transformation matrix according to a matrix transformation formula, namely an inverse transformation matrix. For example, if the coordinate transformation matrix is C, the matrix transformation formula is C -1 .
It should be explained that, the coordinates of the loaded projection picture can be transformed by using the inverse transformation matrix, the loaded projection picture with compensation deformation can be obtained, the loaded projection picture is projected to the curtain through the projector, the loaded projection picture can generate picture deformation when projected, which is equivalent to multiplying the coordinates of the loaded projection picture by the coordinate transformation matrix, the picture deformation is offset with the compensation deformation, at the moment, the coordinates of the picture projected to the curtain are the same as the coordinates of the loaded projection picture, and the picture deformation caused by various factors such as inclination of the projection direction, assembly tolerance and the like is corrected.
In detail, the coordinate transformation matrix is as follows:
U＝(a’ 1 ,a’ 2 ,a’ 3 ,a’ 4 )(a 1 ,a 2 ,a 3 ,a 4 ) -1
wherein U represents a coordinate transformation matrix, (a '' 1 ,a’ 2 ,a’ 3 ,a’ 4 ) Coordinates of a display mark point representing a display projected picture, (a) 1 ,a 2 ,a 3 ,a 4 ) And the coordinates of the loading mark point for loading the projection picture are represented.
And S4, acquiring the display brightness of the corrected projection picture, and adjusting the display brightness of the corrected projection picture according to a preset brightness threshold value to obtain an adjusted projection picture.
It should be understood that when the ambient light is strong, the display brightness of the corrected projection picture becomes low, and the picture display becomes blurred, so that it should be emphasized that the projector provided by the invention has a display brightness monitoring system, which can monitor the display brightness of the corrected projection picture in real time, and when the display brightness of the corrected projection picture is lower than the brightness threshold, the brightness of the light source of the projector is increased, so that the display brightness of the corrected projection picture reaches the brightness threshold, thereby realizing intelligent control of the projector brightness.
S5, acquiring a characteristic image of the adjusted projection picture according to a pre-constructed convolutional neural network algorithm, wherein the characteristic image comprises a low-resolution image and a high-resolution image.
It should be explained that the convolutional neural network algorithm is a type of feedforward neural network that includes convolutional calculation and has a depth structure, and can be used to extract features of an image. The invention extracts the characteristic images by performing a first convolution operation and a second convolution operation on the adjusted projection picture, wherein the characteristic images can be divided into two types, one type is a low-resolution image, and the other type is a high-resolution characteristic image.
In detail, the acquiring the characteristic image of the adjusted projection picture according to the pre-constructed convolutional neural network algorithm includes:
performing a first convolution operation of the pre-construction on the adjusted projection picture to obtain a projection primary feature map;
and performing a second convolution operation on the projection primary feature map to obtain a feature image:
wherein o is i Representing the position of the ith pixel of the characteristic image in the characteristic image, B is the set of all pixel positions of the characteristic image, P (o) i ) Pixel value representing the ith pixel of the feature image, L (o i ) The i-th pixel of the projection primary characteristic diagram is calculated through BP layer to obtain a pixel value, K (o i +Δo i ) Representing the pixel value of the i-th pixel in the pixel-corrected projected primary feature map.
In detail, the first convolution operation is as follows:
W 2 ＝(W 1 -C)/J+1
H 2 ＝(H 1 -C)/J+1
wherein W is 2 And H 2 Representing the width and height, W, of the projected primary feature map, respectively 1 And H 1 And C represents the receptive field size of the convolution layer in the first convolution operation, and J is the step length of the first convolution operation.
S6, obtaining standard features with maximum similarity with the low-resolution image in the high-resolution image based on a pre-constructed Pair-wise model and a similarity calculation formula.
In detail, the loss function adopted by the Pair-wise model is shown in the following formula:
d n ＝||a n -b n || 2
wherein Loss represents a Loss value adopted in the training process of the Pair-wise model, N represents a number of training samples, and N represents the number of training samples, wherein each training sample consists of a high-resolution image and a low-resolution image, c n Representing the nth input training sampleIf the corresponding label value is not, if the high resolution image and the low resolution image are input to be corresponding to each other, c n Equal to 1, otherwise c n Equal to 0, d n Representing the Euclidean distance between features of high and low resolution images in an nth training sample, a n Representing the eigenvalues of the high resolution image in the nth training sample, b n Representing the eigenvalues of the low resolution image in the nth training sample, and m represents the interval parameter of the loss function.
It should be emphasized that the similarity calculation formula may be expressed asAs can be seen from the formula, the magnitude of the similarity value is related to the euclidean distance, and when the euclidean distance is larger, the similarity is smaller, and conversely, the similarity is larger.
It is to be understood that the Pair-wise model solves the ordering problem by approximating the classification problem. Any two low resolution images and high resolution images are combined as training samples. A classifier is learned, and the classifier gives a classification label of 1 or 0 according to whether the high resolution image corresponds to the low resolution image for the input training sample. According to the invention, a similarity calculation formula is adopted to calculate the similarity value of the high-resolution image and the low-resolution image in the training sample, then the high-resolution images with the maximum similarity value are sorted according to the similarity value, and the standard features are obtained.
In detail, the extracting, based on the pre-constructed Pair-wise model and the similarity calculation formula, the feature with the maximum similarity with the low-resolution image in the high-resolution image to obtain the standard feature, and before the extracting, further includes:
based on the low-resolution image, a pre-constructed Pair-wise model is utilized to obtain low-resolution features;
and clustering the high-resolution image by adopting a pre-constructed K-means clustering algorithm to obtain a high-resolution image dictionary, wherein the high-resolution image dictionary consists of high-resolution features.
It is emphasized that the Pair-wise model may also be used to extract features. And inputting the input low-resolution image into a corresponding branch in the Pair-wise model to extract the corresponding feature of the input low-resolution image, thereby obtaining the low-resolution feature.
It is to be understood that the K-means clustering is a clustering algorithm based on sample set partitioning, and can realize data feature extraction, analysis and prediction. K-means clustering constructs K classes by dividing a sample set into K subsets, and dividing n samples into K classes, each sample having a minimum center distance to its belonging class. The method comprises the steps of carrying out clustering treatment on a high-resolution image, dividing the high-resolution image into k subsets to obtain a cluster set, extracting the cluster center of the cluster to obtain a feature set of the high-resolution image, and obtaining a high-resolution dictionary based on the feature set.
In detail, the clustering processing is performed on the high-resolution image by adopting a pre-constructed K-means clustering algorithm to obtain a high-resolution image dictionary, which comprises the following steps:
clustering the high-resolution image based on a preset cluster number to obtain a cluster set, wherein the number of clusters in the cluster set is equal to the cluster number;
acquiring a cluster center of each cluster in the cluster, and acquiring a high-resolution feature set based on the cluster center;
and acquiring a high-resolution dictionary based on the high-resolution feature set.
S7, reconstructing the low-resolution image into a high-resolution image according to a pre-constructed high-resolution image generation model based on the standard features to obtain a high-resolution projection picture, and realizing intelligent control of the projector.
It is to be understood that the method obtains the standard feature with the maximum similarity with the low-resolution image through the Pair-wise model of the sorting algorithm, and utilizes the standard feature to generate the model according to the high-resolution image so as to reconstruct the low-resolution image into the high-resolution image. It is emphasized that the invention can convert the low resolution image into the high resolution image through the high resolution image generation model, thereby improving the resolution of the projection image, enhancing the viewing experience of the projector and realizing the intelligent control of the projector.
In detail, the high resolution image generation model is as follows:
wherein Y is j A label indicating the j-th high-resolution image, j indicating the indication numbers of the high-resolution image and the low-resolution image, C indicating the normalization constant, f j Representing low-resolution characteristic value d obtained by the jth low-resolution image through a Pair-wise model i The i-th standard feature value is represented, i represents the number of standard features, and M represents the number of standard features.
It can be understood that although the process of controlling the projector to project the picture is influenced by the intensity of ambient light and the resolution of the picture, the viewing experience is changed, and the picture display brightness and the resolution of the projector can be intelligently controlled based on automatic sensing by the method provided by the invention, so that the intelligent control of the projector during the picture projection is realized.
In order to solve the problems in the background art, the method comprises the steps of firstly receiving a starting instruction of a projector, starting the projector according to the starting instruction, obtaining a loaded projection picture and a curtain starting signal, controlling the curtain to descend according to the curtain starting signal, obtaining a displayed projection picture, correcting the displayed projection picture according to a pre-constructed perspective transformation method based on the loaded projection picture, and obtaining a corrected projection picture; acquiring the display brightness of the corrected projection picture, and adjusting the display brightness of the corrected projection picture according to a preset brightness threshold value to obtain an adjusted projection picture; acquiring a characteristic image of the adjusted projection picture according to a pre-constructed convolutional neural network algorithm, wherein the characteristic image comprises a low-resolution image and a high-resolution image; and obtaining the standard feature with the maximum similarity with the low-resolution image in the high-resolution image based on the pre-constructed Pair-wise model and a similarity calculation formula. Based on the standard features, the low-resolution image is reconstructed into a high-resolution image according to a pre-constructed high-resolution image generation model, a high-resolution projection picture is obtained, intelligent control of a projector is achieved, and therefore the problems of display brightness and resolution of the projection picture are considered. Therefore, the intelligent control method, the intelligent control device, the electronic equipment and the computer readable storage medium for realizing the projector based on the automatic induction can solve the problem of poor projection effect caused by not considering the problems of ambient light intensity and picture resolution.
Example 2:
fig. 2 is a functional block diagram of an intelligent control device for implementing a projector based on auto-induction according to an embodiment of the present invention.
The intelligent control device 100 for realizing the projector based on automatic sensing can be installed in electronic equipment. Depending on the functions implemented, the intelligent control device 100 for implementing a projector based on auto-sensing may include a projector starting module 101, a perspective transformation module 102, a brightness adjustment module 103, and a resolution adjustment module 104. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
The projector starting module 101 is configured to receive a starting instruction of a projector, and start the projector according to the starting instruction to obtain a loaded projection picture and a curtain starting signal;
controlling the curtain to descend according to the curtain starting signal to obtain a display projection picture;
the perspective transformation module 102 is configured to correct the displayed projection picture according to a pre-constructed perspective transformation method based on the loaded projection picture, so as to obtain a corrected projection picture;
The brightness adjustment module 103 is configured to obtain a display brightness of the corrected projection picture, and adjust the display brightness of the corrected projection picture according to a preset brightness threshold value to obtain an adjusted projection picture;
the resolution adjustment module 104 is configured to obtain a feature image of the adjusted projection picture according to a pre-constructed convolutional neural network algorithm, where the feature image includes a low resolution image and a high resolution image;
obtaining standard features with maximum similarity with the low-resolution image in the high-resolution image based on a pre-constructed Pair-wise model and a similarity calculation formula, wherein a loss function adopted by the Pair-wise model is shown in the following formula:
d n ＝||a n -b n || 2
wherein Loss represents a Loss value adopted in the training process of the Pair-wise model, N represents a number of training samples, and N represents the number of training samples, wherein each training sample consists of a high-resolution image and a low-resolution image, c n C) if the label value indicating whether the nth input training sample corresponds to the input high-resolution image and the input low-resolution image correspond to each other n Equal to 1, otherwise c n Equal to 0, d n Representing the Euclidean distance between features of high and low resolution images in an nth training sample, a n Representing the eigenvalues of the high resolution image in the nth training sample, b n Representing the characteristic value of the low-resolution image in the nth training sample, and m represents the interval parameter of the loss function;
and reconstructing the low-resolution image into a high-resolution image according to a pre-constructed high-resolution image generation model based on the standard features to obtain a high-resolution projection picture, so as to realize intelligent control of the projector.
In detail, the modules in the intelligent control device 100 for implementing a projector based on auto-induction in the embodiment of the present invention use the same technical means as the intelligent control method for implementing a projector based on auto-induction described in fig. 1, and can produce the same technical effects, which are not described herein.
Example 3:
fig. 3 is a schematic structural diagram of an electronic device for implementing an intelligent control method for implementing a projector based on auto-induction according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a bus 12 and a communication interface 13, and may further comprise a computer program stored in the memory 11 and executable on the processor 10, such as an intelligent control program for implementing a projector based on auto-induction.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various data, such as a code for implementing an intelligent control program of a projector by auto-sensing, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective parts of the entire electronic device using various interfaces and lines, executes or executes programs or modules (e.g., an intelligent Control program for implementing a projector by auto-sensing, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process the data.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
Further, the electronic device 1 may also comprise a network interface, optionally the network interface may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used for establishing a communication connection between the electronic device 1 and other electronic devices.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The smart control program for implementing the projector by auto-sensing stored in the memory 11 of the electronic device 1 is a combination of instructions that, when executed in the processor 10, implement:
Receiving a starting instruction of a projector, starting the projector according to the starting instruction, and obtaining a loaded projection picture and a curtain starting signal;
controlling the curtain to descend according to the curtain starting signal to obtain a display projection picture;
based on the loaded projection picture, correcting the displayed projection picture according to a pre-constructed perspective transformation method to obtain a corrected projection picture;
acquiring the display brightness of the corrected projection picture, and adjusting the display brightness of the corrected projection picture according to a preset brightness threshold value to obtain an adjusted projection picture;
acquiring a characteristic image of the adjusted projection picture according to a pre-constructed convolutional neural network algorithm, wherein the characteristic image comprises a low-resolution image and a high-resolution image;
obtaining standard features with maximum similarity with the low-resolution image in the high-resolution image based on a pre-constructed Pair-wise model and a similarity calculation formula, wherein a loss function adopted by the Pair-wise model is shown in the following formula:
d n ＝||a n -b n || 2
wherein Loss represents a Loss value adopted in the training process of the Pair-wise model, N represents a number of training samples, and N represents the number of training samples, wherein each training sample consists of a high-resolution image and a low-resolution image, c n C) if the label value indicating whether the nth input training sample corresponds to the input high-resolution image and the input low-resolution image correspond to each other n Equal to 1, otherwise c n Equal to 0, d n Representing the Euclidean distance between features of high and low resolution images in an nth training sample, a n Representing the eigenvalues of the high resolution image in the nth training sample, b n Representing the characteristic value of the low-resolution image in the nth training sample, and m represents the interval parameter of the loss function;
and reconstructing the low-resolution image into a high-resolution image according to a pre-constructed high-resolution image generation model based on the standard features to obtain a high-resolution projection picture, so as to realize intelligent control of the projector.
Specifically, the specific implementation method of the above instruction by the processor 10 may refer to descriptions of related steps in the corresponding embodiments of fig. 1 to 2, which are not repeated herein.
Further, the modules/units integrated in the electronic device 1 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer memory, a Read-only memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
receiving a starting instruction of a projector, starting the projector according to the starting instruction, and obtaining a loaded projection picture and a curtain starting signal;
controlling the curtain to descend according to the curtain starting signal to obtain a display projection picture;
based on the loaded projection picture, correcting the displayed projection picture according to a pre-constructed perspective transformation method to obtain a corrected projection picture;
acquiring the display brightness of the corrected projection picture, and adjusting the display brightness of the corrected projection picture according to a preset brightness threshold value to obtain an adjusted projection picture;
acquiring a characteristic image of the adjusted projection picture according to a pre-constructed convolutional neural network algorithm, wherein the characteristic image comprises a low-resolution image and a high-resolution image;
obtaining standard features with maximum similarity with the low-resolution image in the high-resolution image based on a pre-constructed Pair-wise model and a similarity calculation formula, wherein a loss function adopted by the Pair-wise model is shown in the following formula:
d n ＝||a n -b n || 2
Wherein Loss represents a Loss value adopted in the training process of the Pair-wise model, N represents a number of training samples, and N represents the number of training samples, wherein each training sample consists of a high-resolution image and a low-resolution image, c n C) if the label value indicating whether the nth input training sample corresponds to the input high-resolution image and the input low-resolution image correspond to each other n Equal to 1, otherwise c n Equal to 0, d n Representing the Euclidean distance between features of high and low resolution images in an nth training sample, a n Representing the eigenvalues of the high resolution image in the nth training sample, b n Representing the characteristic value of the low-resolution image in the nth training sample, and m represents the interval parameter of the loss function;
and reconstructing the low-resolution image into a high-resolution image according to a pre-constructed high-resolution image generation model based on the standard features to obtain a high-resolution projection picture, so as to realize intelligent control of the projector.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. An intelligent control method for realizing a projector based on automatic induction is characterized by comprising the following steps:
receiving a starting instruction of a projector, starting the projector according to the starting instruction, and obtaining a loaded projection picture and a curtain starting signal;
controlling the curtain to descend according to the curtain starting signal to obtain a display projection picture;
based on the loaded projection picture, correcting the displayed projection picture according to a pre-constructed perspective transformation method to obtain a corrected projection picture;
acquiring the display brightness of the corrected projection picture, and adjusting the display brightness of the corrected projection picture according to a preset brightness threshold value to obtain an adjusted projection picture;
acquiring a characteristic image of the adjusted projection picture according to a pre-constructed convolutional neural network algorithm, wherein the characteristic image comprises a low-resolution image and a high-resolution image;
obtaining standard features with maximum similarity with the low-resolution image in the high-resolution image based on a pre-constructed Pair-wise model and a similarity calculation formula, wherein a loss function adopted by the Pair-wise model is shown in the following formula:
d n ＝||a n -b n || 2
wherein Loss represents a Loss value adopted in the training process of the Pair-wise model, N represents a number of training samples, and N represents the number of training samples, wherein each training sample consists of a high-resolution image and a low-resolution image, c n C) if the label value indicating whether the nth input training sample corresponds to the input high-resolution image and the input low-resolution image correspond to each other n Equal to 1, otherwise c n Equal to 0, d n Representing the Euclidean distance between features of high and low resolution images in an nth training sample, a n Representing the high score in the nth training sampleCharacteristic value of resolution image, b n Representing the characteristic value of the low-resolution image in the nth training sample, and m represents the interval parameter of the loss function;
and reconstructing the low-resolution image into a high-resolution image according to a pre-constructed high-resolution image generation model based on the standard features to obtain a high-resolution projection picture, so as to realize intelligent control of the projector.
2. The intelligent control method for implementing a projector based on auto-induction according to claim 1, wherein the controlling the curtain to descend according to the curtain start signal comprises:
emitting an infrared signal based on the curtain start signal;
converting the infrared signal into an electrical pulse control signal;
and controlling the curtain to descend according to the electric pulse control signal.
3. The intelligent control method for implementing a projector based on auto-induction according to claim 1, wherein the correcting the displayed projection picture according to the pre-constructed perspective transformation method to obtain a corrected projection picture comprises:
Respectively establishing a plane rectangular coordinate system on the loaded projection picture and the curtain;
designing a reference square projection picture according to the plane rectangular coordinate system, and using a cross division mark to represent the vertex of the reference square projection picture to obtain a loading mark point and a display mark point;
acquiring a coordinate transformation matrix based on the loading mark points and the display mark points;
obtaining an inverse transformation matrix of the coordinate transformation matrix according to a pre-constructed matrix transformation formula;
and performing perspective transformation on the loaded projection picture by using the inverse transformation matrix to obtain a corrected projection picture.
4. The intelligent projector control method based on auto-induction according to claim 3, wherein the coordinate transformation matrix is as follows:
U＝(a’ 1 ,a’ 2 ,a’ 3 ,a’ 4 )(a 1 ,a 2 ,a 3 ,a 4 ) -1
wherein U represents a coordinate transformation matrix, (a '' 1 ,a’ 2 ,a’ 3 ,a’ 4 ) Coordinates of a display mark point representing a display projected picture, (a) 1 ,a 2 ,a 3 ,a 4 ) And the coordinates of the loading mark point for loading the projection picture are represented.
5. The intelligent control method for implementing a projector based on auto-induction according to claim 1, wherein the obtaining the feature image of the adjusted projection picture according to the pre-constructed convolutional neural network algorithm comprises:
Performing a first convolution operation of the pre-construction on the adjusted projection picture to obtain a projection primary feature map;
and performing a second convolution operation on the projection primary feature map to obtain a feature image:
wherein o is i Representing the position of the ith pixel of the characteristic image in the characteristic image, B is the set of all pixel positions of the characteristic image, P (o) i ) Pixel value representing the ith pixel of the feature image, L (o i ) Represents the pixel value, K (o i +Δo i ) Representing the pixel value of the i-th pixel in the pixel-corrected projected primary feature map.
6. The intelligent projector control method based on auto-induction according to claim 5, wherein the first convolution operation is as follows:
W 2 ＝(W 1 -C)/J+1
H 2 ＝(H 1 -C)/J+1
wherein W is 2 And H 2 Representing the width and height, W, of the projected primary feature map, respectively 1 And H 1 And C represents the receptive field size of the convolution layer in the first convolution operation, and J is the step length of the first convolution operation.
7. The intelligent control method for implementing a projector based on auto-induction according to claim 1, wherein before the standard feature with the highest similarity to the low-resolution image in the high-resolution image is obtained based on the pre-constructed Pair-wise model and a similarity calculation formula, the method further comprises:
Based on the low-resolution image, a pre-constructed Pair-wise model is utilized to obtain low-resolution features;
and clustering the high-resolution image by adopting a pre-constructed K-means clustering algorithm to obtain a high-resolution image dictionary, wherein the high-resolution image dictionary consists of high-resolution features.
8. The intelligent control method for implementing a projector based on auto-induction according to claim 7, wherein the clustering of the high-resolution images using a pre-constructed K-means clustering algorithm to obtain a high-resolution image dictionary comprises:
clustering the high-resolution image based on a preset cluster number to obtain a cluster set, wherein the number of clusters in the cluster set is equal to the cluster number;
acquiring a cluster center of each cluster in the cluster, and acquiring a high-resolution feature set based on the cluster center;
and acquiring a high-resolution dictionary based on the high-resolution feature set.
9. The intelligent projector control method based on auto-induction according to claim 1, wherein the high resolution image generation model is as follows:
wherein Y is j A label indicating the j-th high-resolution image, j indicating the indication numbers of the high-resolution image and the low-resolution image, C indicating the normalization constant, f j Representing low-resolution characteristic value d obtained by the jth low-resolution image through a Pair-wise model i The i-th standard feature value is represented, i represents the number of standard features, and M represents the number of standard features.
10. An apparatus for realizing intelligent projector control based on auto-induction, the apparatus comprising:
the projector starting module is used for receiving a starting instruction of the projector, starting the projector according to the starting instruction, and obtaining a loaded projection picture and a curtain starting signal;
controlling the curtain to descend according to the curtain starting signal to obtain a display projection picture;
the perspective transformation module is used for correcting the display projection picture according to a pre-constructed perspective transformation method based on the loaded projection picture to obtain a corrected projection picture;
the brightness adjusting module is used for obtaining the display brightness of the corrected projection picture, and adjusting the display brightness of the corrected projection picture according to a preset brightness threshold value to obtain an adjusted projection picture;
the resolution adjustment module is used for acquiring a characteristic image of the adjusted projection picture according to a pre-constructed convolutional neural network algorithm, wherein the characteristic image comprises a low-resolution image and a high-resolution image;
Obtaining standard features with maximum similarity with the low-resolution image in the high-resolution image based on a pre-constructed Pair-wise model and a similarity calculation formula, wherein a loss function adopted by the Pair-wise model is shown in the following formula:
d n ＝||a n -b n || 2
wherein Loss represents a Loss value adopted in the training process of the Pair-wise model, N represents a number of training samples, and N represents the number of training samples, wherein each training sample consists of a high-resolution image and a low-resolution image, c n C) if the label value indicating whether the nth input training sample corresponds to the input high-resolution image and the input low-resolution image correspond to each other n Equal to 1, otherwise c n Equal to 0, d n Representing the Euclidean distance between features of high and low resolution images in an nth training sample, a n Representing the eigenvalues of the high resolution image in the nth training sample, b n Representing the characteristic value of the low-resolution image in the nth training sample, and m represents the interval parameter of the loss function;
and reconstructing the low-resolution image into a high-resolution image according to a pre-constructed high-resolution image generation model based on the standard features to obtain a high-resolution projection picture.
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Effective date of registration: 20231117 Address after: Room 416, Building 1, No. 939 Yonghe Road, Qinzao Street, Chongchuan District, Nantong City, Jiangsu Province, 226000 Patentee after: Nantong Jiemi Technology Co.,Ltd. Address before: 518000 Comprehensive Building 103, Changcheng Road, Tongfukang Shuitian Industrial Zone, Shuitian Community, Shiyan Street, Bao'an District, Shenzhen, Guangdong Province Patentee before: SHENZHEN CHUANGJIANG NETWORK TECHNOLOGY Co.,Ltd. |