CN114549864A - Intelligent lamp control method and system based on environment image - Google Patents
Intelligent lamp control method and system based on environment image Download PDFInfo
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Abstract
The invention provides an intelligent lamp control method based on an environment image, which comprises the steps of firstly, acquiring HSV (hue, saturation and value) data and IR (infrared) gray scale data of the environment image; constructing a characteristic sequence according to HSV data and IR gray data of the environment image; inputting the characteristic sequence into a trained network model to obtain the color and brightness corresponding to the intelligent lamp; sending a color and brightness instruction corresponding to the intelligent lamp to control the intelligent lamp; based on image recognition and machine learning, the color and brightness of the intelligent lamp can be quickly adjusted according to the environment information.
Description
Technical Field
The invention relates to the field of intelligent home furnishing, in particular to an intelligent lamp control method and an intelligent lamp control system based on an environment image.
Background
With the development of smart homes, the personalized requirements of users can be met more and more, and the smart lamp is used as important smart furniture and is responsible for illuminating the whole indoor environment to provide a comfortable life and working environment for people; however, in different weather, different spaces and environments, the light and brightness are different, and in this case, the brightness and color of the required light are also different.
In the prior art, CN105135263N discloses a brightness adaptive adjustment desk lamp based on image analysis, in the scheme, a photosensitive sensor is arranged to sense ambient brightness and transmit the ambient brightness to a control module, and the control module adjusts the brightness of the desk lamp according to a control mechanism, so that the hardware structure and the control mechanism are complex and are not suitable for the configuration of the existing smart home.
Disclosure of Invention
The invention mainly aims to overcome the defects in the prior art, and provides an intelligent lamp control method based on an environment image.
The invention adopts the following technical scheme:
an intelligent lamp control method based on environment images comprises the following steps:
acquiring HSV data and IR gray data of an environment image;
constructing a characteristic sequence according to HSV data and IR gray data of the environment image;
inputting the characteristic sequence into a trained network model to obtain the color and brightness corresponding to the intelligent lamp;
and sending a color and brightness instruction corresponding to the intelligent lamp to control the intelligent lamp.
The method comprises the following steps of training a network model, specifically:
acquiring HSV data and IR gray data of an environment image;
constructing a characteristic sequence according to HSV data and IR gray data of the environment image; determining the color corresponding to the intelligent lamp as a first label according to HSV data of the environment image, and determining the brightness corresponding to the intelligent lamp as a second label according to IR gray data of the environment image;
and inputting the characteristic sequence, the first label and the second label into a pre-trained network model for training to obtain a trained network model.
Specifically, determining a color corresponding to the intelligent lamp as a first label according to HSV data of the environment image, and determining a brightness corresponding to the intelligent lamp as a second label according to IR grayscale data of the environment image, specifically:
obtaining the color with the highest proportion in the environment according to HSV data of the environment image, and determining the color corresponding to the intelligent lamp by combining the color type of the intelligent lamp;
according to the IR gray scale data of the environment image, determining the average IR gray scale value of the environment image, obtaining corresponding environment brightness data, and adding the corresponding environment brightness data to a set threshold value to determine the brightness of the intelligent lamp.
Specifically, a feature sequence is constructed according to HSV data and IR gray data of the environment image, and specifically comprises the following steps:
taking the average value of the H values of the pixels in the image as a first characteristic value;
taking the average value of the S values of the pixels in the image as a second characteristic value;
taking the average value of the V values of the pixels in the image as a third characteristic value;
taking the average IR value of the pixels in the image as a fourth characteristic value;
and constructing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value as a characteristic sequence.
Another aspect of an embodiment of the present invention provides an intelligent lamp control system based on an environmental image, including:
acquiring an environment data unit: acquiring HSV data and IR gray data of an environment image;
constructing a characteristic sequence unit: constructing a characteristic sequence according to HSV data and IR gray data of the environment image;
acquiring an intelligent lamp data unit: inputting the characteristic sequence into a trained network model to obtain the color and brightness corresponding to the intelligent lamp;
a transmission instruction unit: and sending a color and brightness instruction corresponding to the intelligent lamp to control the intelligent lamp.
Specifically, the system further comprises a model training unit for training the network model, specifically:
acquiring HSV data and IR gray data of an environment image;
constructing a characteristic sequence according to HSV data and IR gray data of the environment image; determining the color corresponding to the intelligent lamp as a first label according to HSV data of the environment image, and determining the brightness corresponding to the intelligent lamp as a second label according to IR gray data of the environment image;
and inputting the characteristic sequence, the first label and the second label into a pre-trained network model for training to obtain a trained network model.
Specifically, determining a color corresponding to the intelligent lamp as a first label according to HSV data of the environment image, and determining a brightness corresponding to the intelligent lamp as a second label according to IR grayscale data of the environment image, specifically:
obtaining the color with the highest proportion in the environment according to HSV data of the environment image, and determining the color corresponding to the intelligent lamp by combining the color type of the intelligent lamp;
according to the IR gray scale data of the environment image, determining the average IR gray scale value of the environment image, obtaining corresponding environment brightness data, and adding the corresponding environment brightness data to a set threshold value to determine the brightness of the intelligent lamp.
Specifically, a feature sequence is constructed according to HSV data and IR gray data of the environment image, and specifically comprises the following steps:
taking the average value of the H values of the pixels in the image as a first characteristic value;
taking the average value of the S values of the pixels in the image as a second characteristic value;
taking the average value of the V values of the pixels in the image as a third characteristic value;
taking the average IR value of the pixels in the image as a fourth characteristic value;
and constructing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value as a characteristic sequence.
An embodiment of the present invention provides an electronic device, including: the intelligent lamp control method based on the environment image comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the computer program to realize the steps of the intelligent lamp control method based on the environment image.
Yet another aspect of the embodiments of the present invention provides a computer-readable storage medium, in which a computer program is stored, and the computer program, when executed by a processor, implements the above-mentioned steps of an intelligent lamp control method based on environmental images.
As can be seen from the above description of the present invention, compared with the prior art, the present invention has the following advantages:
(1) the invention provides an intelligent lamp control method based on an environment image, which comprises the steps of firstly, obtaining HSV data and IR gray data of the environment image; constructing a characteristic sequence according to HSV data and IR gray data of the environment image; inputting the characteristic sequence into a trained network model to obtain the color and brightness corresponding to the intelligent lamp; sending a color and brightness instruction corresponding to the intelligent lamp to control the intelligent lamp; based on image recognition and machine learning, the color and brightness of the intelligent lamp can be quickly adjusted according to the environmental information.
(2) According to the intelligent lamp control method based on the environment image, four input characteristic values are constructed based on HSV data and IR gray data of the environment image, the attribute of the environment image is comprehensively represented, and a training model is accurate.
Drawings
Fig. 1 is a flowchart of an intelligent lamp control method based on an environmental image according to an embodiment of the present invention;
fig. 2 is a system structure diagram of an intelligent lamp control based on an environment image according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an embodiment of an electronic device according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an embodiment of a computer-readable storage medium according to an embodiment of the present invention.
The invention is described in further detail below with reference to the figures and specific examples.
Detailed Description
The invention provides an intelligent lamp control method based on an environment image, which is based on image recognition and machine learning, can quickly adjust the color and brightness of an intelligent lamp according to environment information, is simple to operate, has accurate recognition, does not need hardware equipment, and accords with the configuration of the existing household intelligent lamp.
Fig. 1 is a flowchart of an intelligent lamp control method based on an environmental image, which specifically includes:
s101: acquiring HSV data and IR gray data of an environment image;
it is worth to be noted that, a 3D camera product currently has a binocular structured light (RGB + IR) scheme and a TOF (single IR camera) scheme, and the embodiment of the present invention may adopt a structure form in which the TOF scheme is added to the RGB camera.
Specifically, frame synchronization signals are added to color RGB and infrared IR, RGB data and IR gray data are synchronously acquired, then the acquired RGB color gamut picture is converted into an HSV color gamut, and the proportion of each color interval is counted according to a preset color threshold value to determine the color of the indicator light;
through experimental verification, compared with RGB and CMY color spaces, the HSV color gamut space is adopted to train the model and identify, and the obtained light color and brightness are more matched with environment information and are more easily received by a user.
Conversion relationship from RGB color space to HSV color gamut space:
R′＝R/255
G′＝G/255
B′＝B/255
Cmax＝max(R′,G′B′)
Cmin＝min(R′,G′B′)
Δ＝Cmax-Cmin
V＝Cmax
wherein R ', G ' and B ' are intermediate variables.
S102: constructing a characteristic sequence according to HSV data and IR gray data of the environment image;
specifically, a feature sequence is constructed according to HSV data and IR gray data of the environment image, and specifically comprises the following steps:
taking the average value of the H values of the pixels in the image as a first characteristic value;
taking the average value of the S values of the pixels in the image as a second characteristic value;
taking the average value of the V values of the pixels in the image as a third characteristic value;
taking the average IR value of the pixels in the image as a fourth characteristic value;
and constructing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value as a characteristic sequence.
S103: inputting the characteristic sequence into a trained network model to obtain the color and brightness corresponding to the intelligent lamp;
the network model of the embodiment of the invention includes but is not limited to: many-to-many cyclic neural network model, LSTM model.
The embodiment of the invention adopts a many-to-many cyclic neural network model:
the Recurrent Neural Network (RNN) increases the transverse connection among the units of the hidden layer on the basis of the common Back Propagation (BP) Neural Network, and can realize the transmission of the value of the Neural unit of the previous time sequence to the current Neural unit through a weight matrix, thereby leading the Neural Network to have the memory function and having good applicability to the processing of the natural language processing with context connection or the machine learning problem of the time sequence. For a standard RNN structure, the RNN's body structure inputs at time t have a circular edge in addition to the input layer Xt to provide the hidden state passed from time t-1.
The applicability of the RNN model varies according to the number of output and input sequences, the RNN may have a variety of different structures, the 5 structures are in turn: one-to-one, one-to-many, many-to-one, spaced many-to-many, synchronous many-to-many. Different structures naturally have different application occasions, and the 5 RNN model structures can respectively correspond to Vanilla neural networks, picture title generation, emotion analysis, machine translation and context prediction application scenes.
The data input of the invention is a characteristic sequence, the required output is the luminous flux and the color temperature of the lamp, and the natural sequence conforms to 2 structures of the RNN model with more intervals and more synchronization. The biggest difference between the two is that the model cannot utilize the association relationship between the features in the input feature sequence at intervals as many as the multiple modes, and the model can synchronize as many as the multiple modes. Therefore, the embodiment of the invention selects the RNN model with a synchronous multi-to-multi structure, namely the synchronous many-to-many cyclic neural network model, as the basic network structure.
S104: and sending the corresponding color and brightness instruction of the intelligent lamp to control the intelligent lamp.
The method comprises the following steps of training a network model, specifically:
acquiring HSV data and IR gray data of an environment image;
constructing a characteristic sequence according to HSV data and IR gray data of the environment image; determining the color corresponding to the intelligent lamp as a first label according to HSV data of the environment image, and determining the brightness corresponding to the intelligent lamp as a second label according to IR gray data of the environment image;
and inputting the characteristic sequence, the first label and the second label into a pre-trained network model for training to obtain a trained network model.
Specifically, determining a color corresponding to the intelligent lamp as a first label according to HSV data of the environment image, and determining a brightness corresponding to the intelligent lamp as a second label according to IR grayscale data of the environment image, specifically:
obtaining the color with the highest proportion in the environment according to HSV data of the environment image, and determining the color corresponding to the intelligent lamp by combining the color type of the intelligent lamp;
according to the IR gray scale data of the environment image, determining the average IR gray scale value of the environment image, obtaining corresponding environment brightness data, and adding the corresponding environment brightness data to a set threshold value to determine the brightness of the intelligent lamp.
Referring to fig. 2, another aspect of the present invention provides an intelligent lamp control system based on an environment image, including:
acquisition environment data unit 201: acquiring HSV data and IR gray data of an environment image;
it is worth to be noted that, a 3D camera product currently has a binocular structured light (RGB + IR) scheme and a TOF (single IR camera) scheme, and the embodiment of the present invention may adopt a structure form in which the TOF scheme is added to the RGB camera.
Specifically, frame synchronization signals are added to color RGB and infrared IR, RGB data and IR gray data are synchronously acquired, then the acquired RGB color gamut picture is converted into an HSV color gamut, and the proportion of each color interval is counted according to a preset color threshold value to determine the color of the indicator light;
through experimental verification, compared with RGB and CMY color spaces, the HSV color gamut space is adopted to train the model and identify, and the obtained light color and brightness are more matched with environment information and are more easily received by a user.
Conversion relationship from RGB color space to HSV color gamut space:
R′＝R/255
G′＝G/255
B′＝B/255
Cmax＝max(R′,G′B′)
Cmin＝min(R′,G′B′)
Δ＝Cmax-Cmin
V＝Cmax
wherein R ', G ' and B ' are intermediate variables.
Build feature sequence unit 202: constructing a characteristic sequence according to HSV data and IR gray data of the environment image;
specifically, a feature sequence is constructed according to HSV data and IR gray data of the environment image, and specifically comprises the following steps:
taking the average value of the H values of the pixels in the image as a first characteristic value;
taking the average value of the S values of the pixels in the image as a second characteristic value;
taking the average value of the V values of the pixels in the image as a third characteristic value;
taking the average IR value of the pixels in the image as a fourth characteristic value;
constructing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value as a characteristic sequence
Get smart lamp data unit 203: inputting the characteristic sequence into a trained network model to obtain the color and brightness corresponding to the intelligent lamp;
the network model of the embodiment of the invention includes but is not limited to: many-to-many cyclic neural network model, LSTM model.
The embodiment of the invention adopts a many-to-many circulation neural network model:
the Recurrent Neural Network (RNN) increases the transverse connection among the units of the hidden layer on the basis of the common Back Propagation (BP) Neural Network, and can realize the transmission of the value of the Neural unit of the previous time sequence to the current Neural unit through a weight matrix, thereby leading the Neural Network to have the memory function and having good applicability to the processing of the natural language processing with context connection or the machine learning problem of the time sequence. For a standard RNN structure, the RNN's body structure inputs at time t have a circular edge in addition to the input layer Xt to provide the hidden state passed from time t-1.
The applicability of the RNN model varies according to the number of output and input sequences, the RNN may have a variety of different structures, the 5 structures are in turn: one-to-one, one-to-many, many-to-one, spaced many-to-many, synchronous many-to-many. Different structures naturally have different application occasions, and the 5 RNN model structures can respectively correspond to Vanilla neural networks, picture title generation, emotion analysis, machine translation and context prediction application scenes.
The data input of the invention is a characteristic sequence, the required output is the luminous flux and the color temperature of the lamp, and the natural sequence conforms to 2 structures of the RNN model with more intervals and more synchronization. The biggest difference between the two is that the model cannot utilize the association relationship between the features in the input feature sequence at intervals as many as the multiple modes, and the model can synchronize as many as the multiple modes. Therefore, the embodiment of the invention selects the RNN model with a synchronous multi-to-multi structure, namely the synchronous many-to-many cyclic neural network model, as the basic network structure in the modeling
The transmission instruction unit 204: and sending a color and brightness instruction corresponding to the intelligent lamp to control the intelligent lamp.
Specifically, the system further comprises a model training unit for training the network model, specifically:
acquiring HSV data and IR gray data of an environment image;
constructing a characteristic sequence according to HSV data and IR gray data of the environment image; determining the color corresponding to the intelligent lamp as a first label according to HSV data of the environment image, and determining the brightness corresponding to the intelligent lamp as a second label according to IR gray data of the environment image;
and inputting the characteristic sequence, the first label and the second label into a pre-trained network model for training to obtain a trained network model.
Specifically, determining a color corresponding to the intelligent lamp as a first label according to HSV data of the environment image, and determining a brightness corresponding to the intelligent lamp as a second label according to IR grayscale data of the environment image, specifically:
obtaining the color with the highest proportion in the environment according to HSV data of the environment image, and determining the color corresponding to the intelligent lamp by combining the color type of the intelligent lamp;
according to the IR gray scale data of the environment image, determining the average IR gray scale value of the environment image, obtaining corresponding environment brightness data, and adding the corresponding environment brightness data to a set threshold value to determine the brightness of the intelligent lamp.
As shown in fig. 3, an embodiment of the present invention provides an electronic device 300, which includes a memory 310, a processor 320, and a computer program 511 stored in the memory 320 and executable on the processor 320, wherein the processor 320 executes the computer program 311 to implement a method for controlling an intelligent lamp based on an environmental image according to an embodiment of the present invention.
In a specific implementation, when the processor 320 executes the computer program 311, any of the embodiments corresponding to fig. 1 may be implemented.
Since the electronic device described in this embodiment is a device used for implementing a data processing apparatus in the embodiment of the present invention, based on the method described in this embodiment of the present invention, a person skilled in the art can understand the specific implementation manner of the electronic device in this embodiment and various variations thereof, so that how to implement the method in this embodiment of the present invention by the electronic device is not described in detail herein, and as long as the person skilled in the art implements the device used for implementing the method in this embodiment of the present invention, the device used for implementing the method in this embodiment of the present invention belongs to the protection scope of the present invention.
Referring to fig. 4, fig. 4 is a schematic diagram illustrating an embodiment of a computer-readable storage medium according to the present invention.
As shown in fig. 4, the present embodiment provides a computer-readable storage medium 400, on which a computer program 411 is stored, and when the computer program 411 is executed by a processor, the computer program 411 implements an intelligent lamp control method based on an environment image according to an embodiment of the present invention.
In a specific implementation, the computer program 411 may implement any of the embodiments corresponding to fig. 1 when executed by a processor.
It should be noted that, in the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to relevant descriptions of other embodiments for parts that are not described in detail in a certain embodiment.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The invention provides an intelligent lamp control method based on an environment image, which comprises the steps of firstly, obtaining HSV data and IR gray data of the environment image; constructing a characteristic sequence according to HSV data and IR gray data of the environment image; inputting the characteristic sequence into a trained network model to obtain the color and brightness corresponding to the intelligent lamp; sending a color and brightness instruction corresponding to the intelligent lamp to control the intelligent lamp; based on image recognition and machine learning, the color and brightness of the intelligent lamp can be quickly adjusted according to the environmental information.
According to the intelligent lamp control method based on the environment image, four input characteristic values are constructed based on HSV data and IR gray data of the environment image, the attribute of the environment image is comprehensively represented, and a training model is accurate.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. The above description is merely exemplary of the present application and is presented to enable those skilled in the art to understand and practice the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The above description is only an embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any insubstantial modifications made by using the design concept should fall within the scope of infringing the present invention.
Claims (10)
1. An intelligent lamp control method based on environment images is characterized by comprising the following steps:
acquiring HSV data and IR gray data of an environment image;
constructing a characteristic sequence according to HSV data and IR gray data of the environment image;
inputting the characteristic sequence into a trained network model to obtain the color and brightness corresponding to the intelligent lamp;
and sending a color and brightness instruction corresponding to the intelligent lamp to control the intelligent lamp.
2. An intelligent lamp control method based on environmental images as claimed in claim 1, further comprising training a network model before acquiring HSV data and IR gray data of the environmental images, specifically:
acquiring HSV data and IR gray data of an environment image;
constructing a characteristic sequence according to HSV data and IR gray data of the environment image; determining the color corresponding to the intelligent lamp as a first label according to HSV data of the environment image, and determining the brightness corresponding to the intelligent lamp as a second label according to IR gray data of the environment image;
and inputting the characteristic sequence, the first label and the second label into a pre-trained network model for training to obtain a trained network model.
3. The method according to claim 2, wherein determining a color corresponding to the smart lamp as a first label according to HSV data of the environment image, and determining a brightness corresponding to the smart lamp as a second label according to IR gray data of the environment image specifically comprises:
obtaining the color with the highest proportion in the environment according to HSV data of the environment image, and determining the color corresponding to the intelligent lamp by combining the color type of the intelligent lamp;
according to the IR gray scale data of the environment image, determining the average IR gray scale value of the environment image, obtaining corresponding environment brightness data, and adding the corresponding environment brightness data to a set threshold value to determine the brightness of the intelligent lamp.
4. An intelligent lamp control method based on environment images as claimed in claim 1, wherein a characteristic sequence is constructed according to HSV data and IR gray data of the environment images, specifically:
taking the average value of the H values of the pixels in the image as a first characteristic value;
taking the average value of the S values of the pixels in the image as a second characteristic value;
taking the average value of the V values of the pixels in the image as a third characteristic value;
taking the average IR value of the pixels in the image as a fourth characteristic value;
and constructing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value as a characteristic sequence.
5. An intelligent lamp control system based on environmental images, comprising:
acquiring an environment data unit: acquiring HSV data and IR gray data of an environment image;
constructing a characteristic sequence unit: constructing a characteristic sequence according to HSV data and IR gray data of the environment image;
acquiring an intelligent lamp data unit: inputting the characteristic sequence into a trained network model to obtain the color and brightness corresponding to the intelligent lamp;
a transmission instruction unit: and sending a color and brightness instruction corresponding to the intelligent lamp to control the intelligent lamp.
6. An intelligent lamp control system based on environmental images according to claim 5, further comprising a model training unit for training a network model, specifically:
acquiring HSV data and IR gray data of an environment image;
constructing a characteristic sequence according to HSV data and IR gray data of the environment image; determining the color corresponding to the intelligent lamp as a first label according to HSV data of the environment image, and determining the brightness corresponding to the intelligent lamp as a second label according to IR gray data of the environment image;
and inputting the characteristic sequence, the first label and the second label into a pre-trained network model for training to obtain a trained network model.
7. The system according to claim 6, wherein the color corresponding to the smart lamp is determined as a first label according to HSV data of the environment image, and the brightness corresponding to the smart lamp is determined as a second label according to IR gray data of the environment image, specifically:
obtaining the color with the highest proportion in the environment according to HSV data of the environment image, and determining the color corresponding to the intelligent lamp by combining the color type of the intelligent lamp;
according to the IR gray scale data of the environment image, determining the average IR gray scale value of the environment image, obtaining corresponding environment brightness data, and adding the corresponding environment brightness data to a set threshold value to determine the brightness of the intelligent lamp.
8. An intelligent lamp control system based on environmental images as claimed in claim 5, wherein a characteristic sequence is constructed according to HSV data and IR gray data of the environmental images, specifically:
taking the average value of the H values of the pixels in the image as a first characteristic value;
taking the average value of the S values of the pixels in the image as a second characteristic value;
taking the average value of the V values of the pixels in the image as a third characteristic value;
taking the average IR value of the pixels in the image as a fourth characteristic value;
and constructing the first characteristic value, the second characteristic value, the third characteristic value and the fourth characteristic value as a characteristic sequence.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, wherein the processor implements the method steps of any of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 4.
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