CN112907684A - Humidity detection method, device, equipment and medium - Google Patents
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Abstract
The invention discloses a humidity detection method, a device, equipment and a medium, wherein in the embodiment of the invention, a fog image is obtained, a foreground fog image in the fog image and a corresponding fog area in the foreground fog image are determined, a gray probability density curve is determined according to a first number of pixel points of a gray value and a second number of pixel points contained in the foreground fog image aiming at each gray value of the foreground fog image, and whether the humidity is normal or not is determined according to the gray value corresponding to a peak value of the gray probability density curve, a preset gray value threshold, the fog area and a preset fog area threshold. In the embodiment of the invention, the foreground fog image in the fog image is determined based on the obtained fog image, and whether the humidity is normal is determined based on the foreground fog image, so that the influence of an immittable factor can be effectively avoided, and the accuracy of humidity detection is improved.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a humidity detection method, a humidity detection device, humidity detection equipment and a humidity detection medium.
Background
Along with the stricter and stricter requirements for humidity in various industries, such as electronic parking lots, textile factories, printing workshops and the like, the requirement for accurate humidity detection is also increased. In the prior art, a common humidity monitoring method mainly includes: wet and dry bulb wet measurement and electronic humidity sensor wet measurement.
If the wet and dry ball method is used for measuring the humidity, the wet ball is required to be regularly added with water and the wet ball gauze is required to be replaced, the labor cost is increased, in addition, the humidity lookup table is mainly used for calculating the environment humidity, and if the water jacket, the water quality and the wind speed of the gauze do not meet the requirements, the humidity measurement result can be adversely affected. When the electronic humidity sensor is used for measuring humidity, the electronic humidity sensor is affected by dust, oil stains and harmful gases, and the electronic humidity sensor is aged along with the increase of time, so that the measuring result is affected. Therefore, in the prior art, both the wet dry bulb measurement method and the wet electronic humidity sensor measurement method are adopted, and the humidity measurement result is inaccurate due to irresistible factors.
Disclosure of Invention
The invention provides a humidity detection method, a humidity detection device, humidity detection equipment and a humidity detection medium, which are used for solving the problem of inaccurate humidity detection in the prior art.
The invention provides a humidity detection method, which comprises the following steps:
obtaining a fog image, and determining a foreground fog image in the fog image;
determining a corresponding fog area in the foreground fog image, and determining a gray probability density curve according to a first number of pixel points of the gray value and a second number of pixel points contained in the foreground fog image aiming at each gray value of the foreground fog image;
and determining whether the humidity is normal or not according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold value, the fog area and a preset fog area threshold value.
Further, the determining a foreground fog image of the fog images comprises:
and obtaining a foreground fog image in the fog image according to the fog image and a pre-trained Gaussian mixture model.
Further, determining a gray probability density curve according to the first number of the pixel points of the gray value and the second number of the pixel points included in the foreground fog image includes:
determining the probability density of the gray value according to the quotient of the first number of the pixel points of the gray value and the second number of the pixel points contained in the foreground fog image;
and determining a gray level probability density curve according to the probability density of each gray level value.
Further, determining whether the humidity is normal according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold, the fog area and a preset fog area threshold includes:
determining the relative offset of the gray value according to the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold;
determining a relative difference value of the fog areas according to the fog areas and a preset fog area threshold value;
and determining whether the humidity is normal or not according to the relative gray value offset and the relative fog area difference.
Further, the determining the relative shift amount of the gray value according to the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold includes:
determining the absolute value of the difference value between the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold value as the actual gray value offset;
and determining the ratio of the actual gray value offset to the preset gray value threshold as the gray value relative offset.
Further, the determining whether the humidity is normal according to the relative gray value offset and the relative fog area difference comprises:
if the gray value relative offset is smaller than a preset gray value relative offset threshold, and the fog-out area relative difference is smaller than a preset fog-out area relative difference threshold, determining that the humidity is normal;
and if the relative deviation amount of the gray value is greater than a preset relative deviation amount threshold of the gray value, or the relative difference value of the fog-out area is greater than a preset relative difference value threshold of the fog-out area, determining that the humidity is abnormal.
The invention provides a humidity detection device, comprising:
the acquisition module is used for acquiring a fog image and determining a foreground fog image in the fog image;
the determining module is used for determining a corresponding fog area in the foreground fog image and determining a gray probability density curve according to a first number of pixel points of the gray value and a second number of pixel points contained in the foreground fog image aiming at each gray value of the foreground fog image; and determining whether the humidity is normal or not according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold value, the fog area and a preset fog area threshold value.
Further, the obtaining module is specifically configured to obtain a foreground fog image in the fog image according to the fog image and a pre-trained gaussian mixture model.
Further, the determining module is specifically configured to determine the probability density of the gray value according to a quotient of the first number of the pixels of the gray value and the second number of the pixels included in the foreground fog image; and determining a gray level probability density curve according to the probability density of each gray level value.
Further, the determining module is specifically configured to determine a relative shift amount of a gray value according to a gray value corresponding to a peak value of the gray probability density curve and a preset gray value threshold; determining a relative difference value of the fog areas according to the fog areas and a preset fog area threshold value; and determining whether the humidity is normal or not according to the relative gray value offset and the relative fog area difference.
Further, the determining module is specifically configured to determine an absolute value of a difference between a gray value corresponding to a peak of the gray probability density curve and a preset gray value threshold as an actual gray value offset; and determining the ratio of the actual gray value offset to the preset gray value threshold as the gray value relative offset.
Further, the determining module is specifically configured to determine that the humidity is normal if the relative shift amount of the gray scale value is smaller than a preset threshold value of the relative shift amount of the gray scale value, and the relative difference value of the fog-out area is smaller than a preset threshold value of the relative difference value of the fog-out area; and if the relative deviation amount of the gray value is greater than a preset relative deviation amount threshold of the gray value, or the relative difference value of the fog-out area is greater than a preset relative difference value threshold of the fog-out area, determining that the humidity is abnormal.
The invention provides an electronic device comprising a processor for implementing the steps of the humidity detection method as described in any one of the above when executing a computer program stored in a memory.
The present invention provides a computer-readable storage medium storing a computer program executable by a terminal, the program, when run on the terminal, causing the terminal to perform the steps of any of the humidity detection methods described above.
In the embodiment of the invention, a fog image is obtained, a foreground fog image in the fog image and a corresponding fog area in the foreground fog image are determined, a gray level probability density curve is determined according to a first number of pixel points corresponding to each gray level value of the foreground fog image and a second number of pixel points contained in the foreground fog image, and whether the humidity is normal or not is determined according to a gray level value corresponding to a peak value of the gray level probability density curve, a preset gray level threshold value, the fog area and a preset fog area threshold value. In the embodiment of the invention, the foreground fog image in the fog image is determined based on the obtained fog image, and whether the humidity is normal is determined based on the foreground fog image, so that the influence of an immittable factor can be effectively avoided, and the accuracy of humidity detection is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic process diagram of a humidity detection method according to an embodiment of the present invention;
FIG. 2 is a graph of gray scale probability density provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a process for determining whether humidity is abnormal according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a humidity detection apparatus according to an embodiment of the present invention;
fig. 5 is an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to improve the accuracy of humidity detection, embodiments of the present invention provide a humidity detection method, apparatus, device, and medium.
Example 1:
fig. 1 is a schematic process diagram of a humidity detection method according to an embodiment of the present invention, where the process includes the following steps:
s101: and obtaining a fog image, and determining a foreground fog image in the fog image.
The humidity detection method provided by the embodiment of the invention is applied to electronic equipment, and the electronic equipment can be image acquisition equipment, a terminal, intelligent household equipment or other electronic equipment such as a server.
In the embodiment of the present invention, the obtained fog image may be a fog image on the outlet side of the humidifier or a fog image obtained from other electronic devices capable of generating fog, and assuming that the obtained fog image is obtained from the outlet side of the humidifier, since the humidity is related to the fog generation amount of the humidifier, if the fog generation amount of the humidifier is normal, the humidity is normal, and if the fog generation amount of the humidifier is abnormal, the humidity is abnormal, therefore, in order to determine whether the humidity is normal, the fog generation amount on the outlet side of the humidifier may be measured. In order to measure the fog output of the outlet side of the humidifier, in the embodiment of the invention, the electronic device acquires a fog image of the outlet side of the humidifier, and determines the fog output of the humidifier based on the fog image. In order to collect a fog image on the outlet side of the humidifier, the electronic equipment is installed at a preset position on the outlet side of the humidifier in advance, the preset position can ensure that the electronic equipment can acquire the fog image on the outlet side of the humidifier at the preset position, and the fog image can reflect the fog amount of the fog outlet of the humidifier.
In addition, when the electronic device collects the fog image, the fog image can be collected according to a preset time interval. In order to improve the accuracy of determining humidity detection, after a fog image is obtained, a foreground fog image in the fog image, namely an image capable of reflecting the fog condition in the fog image, is determined.
S102: and determining a corresponding fog area in the foreground fog image, and determining a gray probability density curve according to the first number of pixel points of the gray value and the second number of pixel points contained in the foreground fog image aiming at each gray value of the foreground fog image.
The fog condition can be reflected by the size of the fog outlet area, and whether the humidity is abnormal or not is further determined. Therefore, in the embodiment of the present invention, in order to determine the fog area, the foreground fog image including the fog situation is determined first, and then the corresponding fog area in the foreground fog image is determined. Specifically, at the in-process of determining the fog area, because the size of the image that obtains is fixed, that is to say the total number of the pixel that contains in the foreseeing image, consequently can be according to the quantity of the fog pixel that contains in the prospect fog image, determine the fog area.
Because the fog is generally white, the fog amount condition can be determined according to the number of pixel points with higher gray values in the foreground fog image. Specifically, in the embodiment of the present invention, in order to determine whether the humidity is abnormal, a gray probability density curve may be determined according to each gray value in the foreground fog image, according to the first number of pixel points of the gray value, and the second number of pixel points included in the foreground fog image, where the gray probability density curve records probability density values corresponding to the gray values.
S103: and determining whether the humidity is normal or not according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold value, the fog area and a preset fog area threshold value.
In order to accurately determine whether the humidity is normal, a gray value threshold and a fog area threshold are preset, and in the embodiment of the invention, whether the humidity is normal is determined according to a gray value corresponding to a peak value of the gray probability density curve, a preset gray value threshold, the fog area and the preset fog area threshold.
Whether the humidity is abnormal can be determined according to the comparison between the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold value and the comparison between the fog-out area and a preset fog-out area threshold value, specifically, if the gray value corresponding to the peak value of the gray probability density curve is larger than the preset gray value threshold value and the fog-out area is larger than the preset fog-out area threshold value, the fog-out amount is normal and the humidity is normal; if the gray value corresponding to the peak value of the gray probability density curve is smaller than or equal to a preset gray value threshold value, or the fog area is larger than a preset fog area threshold value, the fog amount is abnormal, and the humidity is abnormal.
Whether the humidity is abnormal can be determined according to the difference value between the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold value and the difference value between the fog outlet area and a preset fog outlet area threshold value, specifically, if the difference value between the gray value corresponding to the peak value of the gray probability density curve and the preset gray value threshold value is greater than a preset first difference threshold value and the difference value between the fog outlet area and the preset fog outlet area threshold value is greater than a preset second difference threshold value, the fog amount is normal and the humidity is normal; if the difference value between the gray value corresponding to the peak value of the gray level probability density curve and the preset gray value threshold is smaller than or equal to a preset first difference threshold, or the difference value between the fog-forming area and the preset fog-forming area threshold is smaller than or equal to a preset second difference threshold, the fog quantity is abnormal, and the humidity is abnormal.
In the embodiment of the invention, the foreground fog image in the fog image is determined based on the obtained fog image, and whether the humidity is normal is determined based on the foreground fog image, so that the accuracy of humidity detection can be effectively improved.
Example 2:
in order to accurately determine a foreground fog image, on the basis of the foregoing embodiment, in an embodiment of the present invention, the determining a foreground fog image in the fog image includes:
and obtaining a foreground fog image in the fog image according to the fog image and a pre-trained Gaussian mixture model.
In order to obtain a foreground fog image in a fog image, in the embodiment of the invention, a Gaussian mixture model is trained in advance, and the fog image is input into the Gaussian mixture model trained in advance to obtain the foreground fog image in the fog image.
Before the fog image is input into a pre-trained Gaussian mixture model, training the Gaussian mixture model, wherein the Gaussian mixture model comprises at least two Gaussian models, and the number of the Gaussian models in the Gaussian mixture model is set according to actual requirements. And initializing matrix parameters of each Gaussian model, wherein the matrix parameters comprise a mean value, a variance and a weight value.
And training the Gaussian mixture model according to the obtained sample fog image until the trained Gaussian mixture model is obtained, wherein the training process of the Gaussian mixture model is the prior art and is not repeated herein.
In the process of obtaining the foreground fog image based on the pre-trained Gaussian mixture model, aiming at each pixel point in the obtained fog image and aiming at each Gaussian model of the pre-trained Gaussian mixture model, the following operations are carried out: if there are three gaussian models in the gaussian model mixture, for the sake of distinction, a first gaussian model of the gaussian model mixture is referred to as a first gaussian model, a second gaussian model of the gaussian model mixture is referred to as a second gaussian model, and a third gaussian model of the gaussian model mixture is referred to as a third gaussian model. Firstly, comparing the pixel value of the pixel point with the mean value of a first Gaussian model, determining the difference value between the pixel value corresponding to the pixel point and the mean value, if the difference value is less than twice of the variance of the first Gaussian model of a pre-trained Gaussian mixture model, determining that the pixel point meets the condition of the first Gaussian model, and directly determining that the pixel point is the pixel point in a background image without sequentially carrying out the operations with a second Gaussian model and a third Gaussian model. That is to say, in order to determine a pixel point in the background image, when the pixel point, the first gaussian model, the second gaussian model, and the third gaussian model are sequentially subjected to the above operations for each pixel point, as long as a condition of any one of the gaussian models is satisfied, the pixel point is determined to be a pixel point in the background image.
And if the pixel point and each Gaussian model in the Gaussian mixture model perform the operation, determining the pixel point as a pixel point in the foreground image if the pixel point does not meet the condition of each Gaussian model.
In the embodiment of the present invention, because at least two gaussian models exist in the gaussian mixture model, for each pixel point in the foreground fog image, for each gaussian model in the gaussian mixture model, a determination is made as to whether the pixel point meets the requirement of the corresponding gaussian model, if the pixel point meets the requirement of at least one gaussian model, the pixel point is considered as a pixel point in the background image, otherwise, the pixel point is considered as a pixel point in the foreground image.
When the foreground image is determined, whether the pixel point is a pixel point in the foreground image or a pixel point in the background image is determined for each pixel point, if the pixel point is a pixel point in the foreground image, the pixel value of the pixel point is assigned to be 255 in the mask image, if the pixel point is a pixel point in the background image, the pixel value of the pixel point is assigned to be 0 in the mask image, and according to the assignment condition of the pixel point, a mask image (mask) can be determined. In the mask image, a pixel point with a pixel value of 255 is marked as 1, a pixel point with a pixel value of 0 is marked as 0, and the mask image and a fog image obtained by electronic equipment are subjected to bitwise AND operation to obtain a foreground fog image.
The training process of the gaussian mixture model and the process of determining the foreground fog image according to the gaussian mixture model and the fog image are the prior art, and are not described herein again.
Example 3:
in order to determine a gray level probability density curve, on the basis of the foregoing embodiments, in an embodiment of the present invention, the determining a gray level probability density curve according to the first number of pixel points of the gray level value and the second number of pixel points included in the foreground fog image includes:
determining the probability density of the gray value according to the quotient of the first number of the pixel points of the gray value and the second number of the pixel points contained in the foreground fog image;
and determining a gray level probability density curve according to the probability density of each gray level value.
In the embodiment of the present invention, since there may be a plurality of gray values in the foreground fog image, the gray value may have a value range of [0,255%]Therefore, the probability density of the gray value can be determined according to the quotient of the first number of the pixels with the gray value in the foreground fog image and the second number of the pixels contained in the foreground fog image, for example, if the gray value is a, the number of the pixels with the gray value a in the foreground fog image is nrIf the total number of pixel points included in the foreground fog image is n, the probability density of the gray value A is n
By adopting the method, the probability density of each gray value can be determined, so that a gray probability density curve can be determined according to the probability density of each gray value, wherein the abscissa of the gray probability density curve is the gray value, and the ordinate is the probability density corresponding to each gray value.
In addition, in order to ensure the accuracy of the gray scale probability density curve, before determining the gray scale probability density curve of the foreground fog image, denoising and graying are performed on the foreground fog image, wherein the denoising and graying are the prior art and are not described herein again.
Example 4:
in order to accurately determine whether humidity is abnormal, on the basis of the foregoing embodiments, in an embodiment of the present invention, the determining whether humidity is normal according to a gray value corresponding to a peak of the gray probability density curve, a preset gray value threshold, the fog area, and a preset fog area threshold includes:
determining the relative offset of the gray value according to the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold;
determining a relative difference value of the fog areas according to the fog areas and a preset fog area threshold value;
and determining whether the humidity is normal or not according to the relative gray value offset and the relative fog area difference.
In the embodiment of the invention, a standard fog image is stored in advance, the standard fog image can be a fog image which accords with the standard fog output in the collected fog image, and in addition, the fog image which accords with the standard fog output is stored in front of the electronic equipment, and the fog image which accords with the standard fog output is subjected to denoising processing and graying processing.
Because the probability density corresponding to each gray value is recorded in the probability density curve, the current fog output amount can be determined according to the comparison between the gray probability density curve of the standard fog image and the gray probability density curve of the foreground fog image, and then whether the humidity is normal or not can be determined.
Specifically, for convenience of description, a gray value corresponding to a peak of a gray probability density curve of the foreground fog image is referred to as a first gray value, a gray value corresponding to a peak of a gray probability density curve of the standard fog image is referred to as a second gray value, a gray value larger than the second gray value among gray values of the gray probability density curve of the foreground fog image is referred to as a third gray value, and a gray value smaller than the second gray value among gray values of the gray probability density curve of the foreground fog image is referred to as a fourth gray value. After the gray probability density curve of the foreground fog image is obtained, if the peak value of the gray probability density curve of the foreground fog image is shifted to the right compared with the gray probability density curve of the standard fog image, that is, if the first gray value is greater than the second gray value, if the area of the curve corresponding to the third gray value is greater than a preset area threshold, it is indicated that the fog amount is large, wherein the area of the curve corresponding to the third gray value is the area enclosed by the curve corresponding to the third gray value and the horizontal axis; if the peak value of the gray probability density curve of the foreground fog image is shifted to the left compared with the gray probability density curve of the standard fog image, that is, if the first gray value is smaller than the second gray value, if the area of the curve corresponding to the fourth gray value is larger than a preset area threshold, it is determined that the fog amount is smaller, wherein the area of the curve corresponding to the fourth gray value is the area surrounded by the curve corresponding to the fourth gray value and the horizontal axis, and the area threshold may be one half of the area corresponding to the gray probability density curve of the standard fog image.
Therefore, in the embodiment of the present invention, in order to measure the fog amount conveniently, the relative shift amount of the gray value may be determined according to the gray value corresponding to the peak of the gray probability density curve and the preset threshold of the gray value, and the fog condition may be determined according to the shift amount. The gray value relative offset is a relative deviation value of the gray value corresponding to the peak value of the foreground fog image and the gray value corresponding to the peak value of the standard fog image.
In order to determine the relative offset of the gray value, the relative offset of the gray value is determined according to the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold, wherein the preset gray value threshold is a gray value corresponding to the peak value of the gray probability density curve of the standard fog image stored in advance.
In the embodiment of the invention, a relative difference of the fog areas is determined according to the fog area and a preset fog area threshold, wherein the number of fog pixel points contained in the foreground fog image can be determined as the fog area corresponding to the foreground fog image. Specifically, in the process of determining the relative difference value of the fog areas, the difference value of the fog area of the foreground fog image and a preset fog area threshold value is determined, wherein the preset fog area threshold value is the fog area of a standard fog image stored in advance. Since the fog area may be larger than the preset fog area threshold and the fog area may also be smaller than the preset fog area threshold, in order to determine whether the humidity is abnormal according to the relative difference of the fog areas, in the embodiment of the present invention, after determining the difference between the fog area of the foreground fog image and the preset fog area threshold, and determining the absolute value of the difference, then determining the quotient of the absolute value of the difference and the preset fog area threshold, and determining the quotient as the relative difference of the fog areas. If the fog area of the pre-stored standard fog image is s1That is to say thatThe preset fog-out area threshold value is s1The fog area of the foreground fog image is s0If the difference between the fog area of the foreground fog image and the preset fog area threshold is | s0-s1The relative difference of the fog area is
In order to accurately determine the relative shift amount of the gray scale value, on the basis of the foregoing embodiments, in an embodiment of the present invention, the determining the relative shift amount of the gray scale value according to the gray scale value corresponding to the peak of the gray scale probability density curve and a preset gray scale value threshold includes:
determining the difference value between the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold value as the actual gray value offset;
and determining the ratio of the actual gray value offset to the preset gray value threshold as the gray value relative offset.
In the embodiment of the present invention, an absolute value of a difference between a gray value corresponding to a peak of a gray probability density curve and a preset gray value threshold is determined as an actual gray value offset, where a gray value corresponding to a peak of a gray probability density curve of the foreground fog image may be greater than a preset gray value threshold or may be smaller than the preset gray value threshold. Therefore, in the embodiment of the present invention, in order to determine whether the humidity is abnormal after determining the relative shift amount of the gray scale value subsequently, after determining the difference between the gray scale value corresponding to the peak of the gray scale probability density curve and the preset threshold of the gray scale value, the absolute value of the difference is determined, and the absolute value of the difference is determined as the actual shift amount of the gray scale value. After the actual gray value offset is determined, the ratio of the actual gray value offset to a preset gray value threshold is determined, and the ratio is determined as the gray value relative offset.
If the gray value corresponding to the peak of the gray probability density curve is 100 and the preset threshold value of the gray value is 127, the actual shift amount of the gray value is 27, and the relative shift amount of the gray value is 27
Fig. 2 is a graph of a gray scale probability density according to an embodiment of the present invention, and is described with reference to fig. 2.
The solid line is a gray probability density curve corresponding to the reference image, that is, a gray probability density curve of the standard fog image, and the dotted line is a gray probability density curve corresponding to the collected image, that is, a gray probability density curve of the foreground fog image, wherein an abscissa of the gray probability density curve is a gray value r, the gray value r represents the brightness of a single pixel point, wherein the gray value range is [0,255], the larger the gray value is, the brighter the representative brightness is, the smaller the gray value is, the smaller the representative brightness is, and the ordinate is the probability density corresponding to each gray value.
When the actual gray value offset is determined based on the gray probability density curve of the foreground fog image and the gray probability density curve of the standard fog image, determining the difference value of the gray value corresponding to the peak value of the gray probability density curve of the standard fog image and the gray value corresponding to the peak value of the gray probability density curve of the foreground fog image, namely delta r, and determining the absolute value of the difference value as the actual gray value offset.
In order to accurately determine whether the humidity is abnormal, on the basis of the foregoing embodiments, in an embodiment of the present invention, the determining whether the humidity is normal according to the relative shift amount of the gray scale value and the relative difference value of the fogging area includes:
if the gray value relative offset is smaller than a preset gray value relative offset threshold, and the fog-out area relative difference is smaller than a preset fog-out area relative difference threshold, determining that the humidity is normal;
and if the relative deviation amount of the gray value is greater than a preset relative deviation amount threshold of the gray value, or the relative difference value of the fog-out area is greater than a preset relative difference value threshold of the fog-out area, determining that the humidity is abnormal.
In order to determine whether the humidity is abnormal or not, after the gray value relative offset and the fog area relative difference are determined, whether the humidity is abnormal or not is determined according to the comparison result of the gray value relative offset and a preset gray value relative offset threshold and the comparison result of the fog area relative difference and a preset fog area relative difference threshold.
If the gray value relative offset is smaller than a preset gray value relative offset threshold, the fog amount of the fog image is closer to that of the standard fog image, and if the gray value relative offset is larger than the preset gray value relative offset threshold, the fog amount difference of the fog image and the standard fog image is larger; if the relative difference value of the fog output areas is smaller than a preset relative difference value threshold value of the fog output areas, the fog output of the fog image is closer to that of the standard fog image, and if the relative difference value of the fog output areas is larger than the preset relative difference value threshold value of the fog output areas, the fog output difference of the fog image and the standard fog image is larger.
Specifically, when determining whether the humidity is abnormal, if the relative shift of the gray value is smaller than a preset threshold of the relative shift of the gray value and the relative difference of the fog-out area is smaller than a preset threshold of the relative difference of the fog-out area, it is indicated that the fog-out amount of the fog image is closer to that of the standard fog image, and therefore the humidity is normal; if the relative shift amount of the gray scale value is greater than the preset threshold value of the relative shift amount of the gray scale value, or the relative difference value of the fog-out area is greater than the preset threshold value of the relative difference value of the fog-out area, it is indicated that the fog-out amount difference between the fog image and the standard fog image is large, and therefore the humidity is abnormal.
Fig. 3 is a schematic diagram of a process for determining whether humidity is abnormal according to an embodiment of the present invention, and the description will be given with reference to fig. 3.
Firstly, selecting a monitoring point, acquiring a video, namely installing the electronic equipment at a preset position in advance, acquiring each frame of fog outlet image acquired by the electronic equipment, then carrying out fog outlet background modeling according to the fog outlet image, namely pre-training the finished Gaussian mixture model, inputting the acquired fog outlet image to the pre-trained Gaussian mixture model, and acquiring a standard fog image.
The method comprises the steps that an electronic device collects an image to be detected, namely a fog-out image to be detected, a foreground fog image is determined according to the fog-out image and a pre-trained Gaussian mixture model, a gray probability density curve and a fog-out area of the foreground fog image are determined, whether the gray probability density curve of the foreground fog image meets a standard or not is determined according to the gray probability density curve, the fog-out area of the foreground fog image and the gray probability density curve and the fog-out area of a standard fog image, whether the gray probability density curve of the foreground fog image meets the standard or not is determined, whether the fog area meets the standard or not is determined, if both the gray probability density curve and the fog-out area meet the standard, the fog amount is determined to meet the standard, the humidity is normal, and if any one of the gray probability density curve and the fog-out area does not meet the standard.
Example 5:
fig. 4 is a schematic structural diagram of a humidity detection apparatus according to an embodiment of the present invention, where the apparatus includes:
the acquiring module 401 is configured to acquire a fog image and determine a foreground fog image in the fog image;
a determining module 402, configured to determine a corresponding fog area in the foreground fog image, and determine, for each gray value of the foreground fog image, a gray probability density curve according to a first number of pixel points of the gray value and a second number of pixel points included in the foreground fog image; and determining whether the humidity is normal or not according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold value, the fog area and a preset fog area threshold value.
In a possible implementation manner, the obtaining module 401 is specifically configured to obtain a foreground fog image in the fog image according to the fog image and a pre-trained gaussian mixture model.
In a possible implementation manner, the determining module 402 is specifically configured to determine the probability density of the gray value according to a quotient of a first number of the pixels of the gray value and a second number of the pixels included in the foreground fog image; and determining a gray level probability density curve according to the probability density of each gray level value.
In a possible implementation manner, the determining module 402 is specifically configured to determine a relative offset of a gray value according to a gray value corresponding to a peak of the gray probability density curve and a preset gray value threshold; determining a relative difference value of the fog areas according to the fog areas and a preset fog area threshold value; and determining whether the humidity is normal or not according to the relative gray value offset and the relative fog area difference.
In a possible implementation manner, the determining module 402 is specifically configured to determine an absolute value of a difference between a gray value corresponding to a peak of the gray probability density curve and a preset gray value threshold as an actual gray value offset; and determining the ratio of the actual gray value offset to the preset gray value threshold as the gray value relative offset.
In a possible implementation manner, the determining module 402 is specifically configured to determine that the humidity is normal if the gray-value relative offset is smaller than a preset gray-value relative offset threshold, and the fog-out area relative difference is smaller than a preset fog-out area relative difference threshold; and if the relative deviation amount of the gray value is greater than a preset relative deviation amount threshold of the gray value, or the relative difference value of the fog-out area is greater than a preset relative difference value threshold of the fog-out area, determining that the humidity is abnormal.
Example 6:
on the basis of the foregoing embodiments, some embodiments of the present invention further provide an electronic device, as shown in fig. 5, including: the system comprises a processor 501, a communication interface 502, a memory 503 and a communication bus 504, wherein the processor 501, the communication interface 502 and the memory 503 are communicated with each other through the communication bus 504.
The memory 503 has stored therein a computer program which, when executed by the processor 501, causes the processor 501 to perform the steps of:
obtaining a fog image, and determining a foreground fog image in the fog image;
determining a corresponding fog area in the foreground fog image, and determining a gray probability density curve according to a first number of pixel points of the gray value and a second number of pixel points contained in the foreground fog image aiming at each gray value of the foreground fog image;
and determining whether the humidity is normal or not according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold value, the fog area and a preset fog area threshold value.
Further, the processor 501 is further configured to obtain a foreground fog image in the fog image according to the fog image and a pre-trained gaussian mixture model.
Further, the processor 501 is further configured to determine the probability density of the gray value according to a quotient of the first number of the pixel points of the gray value and the second number of the pixel points included in the foreground fog image; and determining a gray level probability density curve according to the probability density of each gray level value.
Further, the processor 501 is further configured to determine a relative shift amount of a gray value according to a gray value corresponding to a peak value of the gray probability density curve and a preset gray value threshold; determining a relative difference value of the fog areas according to the fog areas and a preset fog area threshold value; and determining whether the humidity is normal or not according to the relative gray value offset and the relative fog area difference.
Further, the processor 501 is further configured to determine an absolute value of a difference between a gray value corresponding to a peak of the gray probability density curve and a preset gray value threshold as an actual gray value offset; and determining the ratio of the actual gray value offset to the preset gray value threshold as the gray value relative offset.
Further, the processor 501 is further configured to determine that the humidity is normal if the relative shift amount of the gray scale value is smaller than a preset threshold value of the relative shift amount of the gray scale value, and the relative difference value of the fog-out area is smaller than a preset threshold value of the relative difference value of the fog-out area; and if the relative deviation amount of the gray value is greater than a preset relative deviation amount threshold of the gray value, or the relative difference value of the fog-out area is greater than a preset relative difference value threshold of the fog-out area, determining that the humidity is abnormal.
The communication bus mentioned in the above server may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.
The communication interface 502 is used for communication between the above-described electronic apparatus and other apparatuses.
The Memory may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a central processing unit, a Network Processor (NP), and the like; but may also be a Digital instruction processor (DSP), an application specific integrated circuit, a field programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or the like.
Example 7:
on the basis of the foregoing embodiments, an embodiment of the present invention further provides a computer-readable storage medium, in which a computer program executable by an electronic device is stored, and when the program is run on the electronic device, the electronic device is caused to execute the following steps:
the memory having stored therein a computer program that, when executed by the processor, causes the processor to perform the steps of:
obtaining a fog image, and determining a foreground fog image in the fog image;
determining a corresponding fog area in the foreground fog image, and determining a gray probability density curve according to a first number of pixel points of the gray value and a second number of pixel points contained in the foreground fog image aiming at each gray value of the foreground fog image;
and determining whether the humidity is normal or not according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold value, the fog area and a preset fog area threshold value.
Further, the determining a foreground fog image of the fog images comprises:
and obtaining a foreground fog image in the fog image according to the fog image and a pre-trained Gaussian mixture model.
Further, determining a gray probability density curve according to the first number of the pixel points of the gray value and the second number of the pixel points included in the foreground fog image includes:
determining the probability density of the gray value according to the quotient of the first number of the pixel points of the gray value and the second number of the pixel points contained in the foreground fog image;
and determining a gray level probability density curve according to the probability density of each gray level value.
Further, determining whether the humidity is normal according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold, the fog area and a preset fog area threshold includes:
determining the relative offset of the gray value according to the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold;
determining a relative difference value of the fog areas according to the fog areas and a preset fog area threshold value;
and determining whether the humidity is normal or not according to the relative gray value offset and the relative fog area difference.
Further, the determining the relative shift amount of the gray value according to the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold includes:
determining the absolute value of the difference value between the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold value as the actual gray value offset;
and determining the ratio of the actual gray value offset to the preset gray value threshold as the gray value relative offset.
Further, the determining whether the humidity is normal according to the relative gray value offset and the relative fog area difference comprises:
if the gray value relative offset is smaller than a preset gray value relative offset threshold, and the fog-out area relative difference is smaller than a preset fog-out area relative difference threshold, determining that the humidity is normal;
and if the relative deviation amount of the gray value is greater than a preset relative deviation amount threshold of the gray value, or the relative difference value of the fog-out area is greater than a preset relative difference value threshold of the fog-out area, determining that the humidity is abnormal.
In the embodiment of the invention, the foreground fog image in the fog image is determined based on the obtained fog image, and whether the humidity is normal is determined based on the foreground fog image, so that the influence of an immittable factor can be effectively avoided, and the accuracy of humidity detection is improved.
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 present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (14)
1. A method of humidity detection, the method comprising:
obtaining a fog image, and determining a foreground fog image in the fog image;
determining a corresponding fog area in the foreground fog image, and determining a gray probability density curve according to a first number of pixel points of the gray value and a second number of pixel points contained in the foreground fog image aiming at each gray value of the foreground fog image;
and determining whether the humidity is normal or not according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold value, the fog area and a preset fog area threshold value.
2. The method of claim 1, wherein the determining a foreground fog image of the fog images comprises:
and obtaining a foreground fog image in the fog image according to the fog image and a pre-trained Gaussian mixture model.
3. The method of claim 1, wherein determining a gray probability density curve based on the first number of pixel points for the gray value and the second number of pixel points included in the foreground fog image comprises:
determining the probability density of the gray value according to the quotient of the first number of the pixel points of the gray value and the second number of the pixel points contained in the foreground fog image;
and determining a gray level probability density curve according to the probability density of each gray level value.
4. The method of claim 1, wherein the determining whether the humidity is normal according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold, the fog area and a preset fog area threshold comprises:
determining the relative offset of the gray value according to the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold;
determining a relative difference value of the fog areas according to the fog areas and a preset fog area threshold value;
and determining whether the humidity is normal or not according to the relative gray value offset and the relative fog area difference.
5. The method of claim 4, wherein determining the relative shift amount of the gray value according to the gray value corresponding to the peak of the gray probability density curve and a preset gray value threshold comprises:
determining the absolute value of the difference value between the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold value as the actual gray value offset;
and determining the ratio of the actual gray value offset to the preset gray value threshold as the gray value relative offset.
6. The method of claim 4, wherein the determining whether humidity is normal according to the relative gray value offset and the relative fogging area difference comprises:
if the gray value relative offset is smaller than a preset gray value relative offset threshold, and the fog-out area relative difference is smaller than a preset fog-out area relative difference threshold, determining that the humidity is normal;
and if the relative deviation amount of the gray value is greater than a preset relative deviation amount threshold of the gray value, or the relative difference value of the fog-out area is greater than a preset relative difference value threshold of the fog-out area, determining that the humidity is abnormal.
7. A humidity detection device, characterized in that the device comprises:
the acquisition module is used for acquiring a fog image and determining a foreground fog image in the fog image;
the determining module is used for determining a corresponding fog area in the foreground fog image and determining a gray probability density curve according to a first number of pixel points of the gray value and a second number of pixel points contained in the foreground fog image aiming at each gray value of the foreground fog image; and determining whether the humidity is normal or not according to the gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold value, the fog area and a preset fog area threshold value.
8. The apparatus according to claim 7, wherein the obtaining module is specifically configured to obtain a foreground fog image in the fog image according to the fog image and a pre-trained gaussian mixture model.
9. The apparatus according to claim 7, wherein the determining module is specifically configured to determine the probability density of the gray value according to a quotient of a first number of pixels of the gray value and a second number of pixels included in the foreground fog image; and determining a gray level probability density curve according to the probability density of each gray level value.
10. The apparatus according to claim 7, wherein the determining module is specifically configured to determine a relative shift amount of a gray value according to a gray value corresponding to a peak value of the gray probability density curve and a preset gray value threshold; determining a relative difference value of the fog areas according to the fog areas and a preset fog area threshold value; and determining whether the humidity is normal or not according to the relative gray value offset and the relative fog area difference.
11. The apparatus according to claim 10, wherein the determining module is specifically configured to determine an absolute value of a difference between a gray value corresponding to a peak of the gray probability density curve and a preset gray value threshold as an actual gray value offset; and determining the ratio of the actual gray value offset to the preset gray value threshold as the gray value relative offset.
12. The device according to claim 10, wherein the determining module is specifically configured to determine that the humidity is normal if the relative shift amount of the grayscale value is smaller than a preset threshold value of the relative shift amount of the grayscale value, and the relative difference value of the fogging area is smaller than a preset threshold value of the relative difference value of the fogging area; and if the gray value deviation relative displacement is larger than a preset relative gray value relative displacement threshold, or the fog area relative difference is larger than a preset fog area relative difference threshold, determining that the humidity is abnormal.
13. An electronic device, characterized in that the electronic device comprises a processor for implementing the steps of the method according to any of claims 1-6 when executing a computer program stored in a memory.
14. A computer-readable storage medium, characterized in that it stores a computer program executable by a terminal, which program, when run on the terminal, causes the terminal to carry out the steps of the method according to any one of claims 1-6.
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