CN112907684B - 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. According to the embodiment of the invention, the foreground fog image in the fog image is determined based on the acquired fog image, and whether the humidity is normal or not is determined based on the foreground fog image, so that the influence of an irresistible factor can be effectively avoided, and the accuracy of humidity detection is improved.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to a method, an apparatus, a device, and a medium for detecting humidity.
Background
Along with the increasingly stringent requirements of various industries on humidity, such as electronic yards, textile factories, printing workshops and the like, the requirements of people on humidity for accurate detection are also increasing. In the prior art, a common humidity monitoring method mainly comprises the following steps: wet and dry bulb wet detection and electronic humidity sensor wet detection.
If the wet method is adopted for measuring the humidity by adopting a dry wet ball, water is required to be added to the wet ball at regular intervals, and wet ball gauze is replaced, so that the labor cost is increased. However, when the electronic humidity sensor is used for measuring humidity, the electronic humidity sensor is affected by dust, oil dirt and harmful gas, and aging phenomenon is generated by the electronic humidity sensor along with the increase of time, and the measurement result is also affected. Therefore, in the prior art, whether the dry-wet ball wet measurement method and the electronic humidity sensor wet measurement method are adopted, the humidity measurement result is inaccurate due to the irresistible factors.
Disclosure of Invention
The invention provides a humidity detection method, a device, equipment and a 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:
acquiring a fog image and determining a foreground fog image in the fog image;
determining a corresponding fog outlet area in the front Jing Wuqi image, and determining a gray probability density curve according to a first number of pixels of the gray value and a second number of pixels contained in the front Jing Wuqi image for each gray value of the front Jing Wuqi 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, the fog outlet area and a preset fog outlet area threshold.
Further, 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 the pre-trained Gaussian mixture model.
Further, the determining the gray probability density curve according to the first number of pixels of the gray value and the second number of pixels contained in the front Jing Wuqi 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 front Jing Wuqi image;
A gray scale probability density curve is determined based on the probability density of each gray scale value.
Further, 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 includes:
Determining a gray value relative offset according to a gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold;
Determining a fog area relative difference value according to the fog area and a preset fog area threshold;
and determining whether the humidity is normal or not according to the gray value relative offset and the fog outlet area relative difference.
Further, determining the gray value relative offset according to the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold value includes:
determining an absolute value of a difference value between a gray value corresponding to a peak value 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 whether the humidity is normal according to the gray value relative offset and the fog area relative difference value includes:
if the gray value relative offset is smaller than a preset gray value relative offset threshold and the fog outlet area relative difference is smaller than a preset fog outlet area relative difference threshold, determining that the humidity is normal;
and if the gray value relative offset is greater than a preset relative gray value relative offset threshold, or the fog outlet area relative difference is greater than a preset fog outlet area relative difference threshold, determining that the humidity is abnormal.
The invention provides a humidity detection device, which comprises:
The acquisition module is used for acquiring a fog image and determining a foreground fog image in the fog image;
A determining module, configured to determine a corresponding fog area in the front Jing Wuqi image, and determine, for each gray value of the front Jing Wuqi image, a gray probability density curve according to a first number of pixels of the gray value and a second number of pixels included in the front Jing Wuqi 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, the fog outlet area and a preset fog outlet area threshold.
Further, the acquiring module is specifically configured to acquire a foreground fog image in the fog image according to the fog image and a pre-trained mixed gaussian model.
Further, the determining module is specifically configured to determine a probability density of the gray value according to a quotient of the first number of pixels of the gray value and the second number of pixels included in the front Jing Wuqi image; a gray scale probability density curve is determined based on the probability density of each gray scale value.
Further, the determining module is specifically configured to determine a gray value relative offset according to a gray value corresponding to a peak value of the gray probability density curve and a preset gray value threshold; determining a fog area relative difference value according to the fog area and a preset fog area threshold; and determining whether the humidity is normal or not according to the gray value relative offset and the fog outlet area relative difference.
Further, the determining module is specifically configured to determine an absolute value of a difference value between a gray value corresponding to a peak value 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 gray value relative offset is smaller than a preset gray value relative offset threshold, and the fog area relative difference is smaller than a preset fog area relative difference threshold; and if the gray value relative offset is greater than a preset relative gray value relative offset threshold, or the fog outlet area relative difference is greater than a preset fog outlet area relative difference threshold, determining that the humidity is abnormal.
The present invention provides an electronic device comprising a processor for implementing the steps of any of the humidity detection methods described 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, which when run on the terminal causes the terminal to perform the steps of any one of the above-mentioned humidity detection methods.
In the embodiment of the invention, a fog image is obtained, a foreground fog image in the fog image and a corresponding fog outlet area in the foreground fog image are determined, a gray probability density curve is determined according to a first number of pixel points corresponding to each gray 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 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 outlet area and a preset fog outlet area threshold. According to the embodiment of the invention, the foreground fog image in the fog image is determined based on the acquired fog image, and whether the humidity is normal or not is determined based on the foreground fog image, so that the influence of an irresistible factor can be effectively avoided, and the accuracy of humidity detection is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the following description are only some embodiments of the present invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
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 probability density according to 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 detecting device according to an embodiment of the present invention;
fig. 5 is an electronic device provided in an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to improve accuracy of humidity detection, the embodiment of the invention provides a humidity detection method, a device, equipment and a 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, terminals, intelligent home equipment or other electronic equipment such as a server.
In the embodiment of the invention, the obtained mist image may be a mist image at the outlet side of the humidifier, or may be a mist image obtained from other electronic devices capable of emitting mist, and the obtained mist image is assumed to be obtained from the outlet side of the humidifier, and since the humidity is related to the amount of mist emitted from the humidifier, if the amount of mist emitted from the humidifier is normal, the humidity is normal, and if the amount of mist emitted from the humidifier is abnormal, the humidity is abnormal, so in order to determine whether the humidity is normal, the amount of mist emitted from the outlet side of the humidifier may be measured. In order to measure the mist output of the humidifier, in the embodiment of the invention, the electronic device collects a mist image of the humidifier on the outlet side, and determines the mist output of the humidifier based on the mist image. In order to collect the fog image of the outlet side of the humidifier, the electronic equipment is pre-installed at a preset position of the outlet side of the humidifier, the preset position can ensure that the electronic equipment can acquire the fog image of the outlet side of the humidifier at the preset position, and the fog image can reflect the fog output of the fog outlet of the humidifier.
In addition, when the electronic equipment collects fog images, the fog images can be collected according to preset time intervals. 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 fog conditions in the fog image, is determined.
S102: and determining a corresponding fog-emitting area in the front Jing Wuqi image, and determining a gray probability density curve according to a first number of pixels of the gray value and a second number of pixels contained in the front Jing Wuqi image for each gray value of the front Jing Wuqi image.
The mist condition can be reflected due to the size of the mist-generating area, so that whether the humidity is abnormal or not can be determined. Therefore, in the embodiment of the invention, in order to determine the fog area, a foreground fog image containing the fog emergence condition is first determined, and then the corresponding fog emergence area in the foreground fog image is determined. Specifically, in the process of determining the fog area, since the size of the acquired image is fixed, that is, the total number of pixels included in the image is predicted, the fog area can be determined according to the number of fog pixels included in the foreground fog image.
Because fog is generally white, the fog quantity condition can be determined according to the number of pixel points with higher gray values in a 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 pixels of the gray value, and according to the second number of pixels included in the foreground fog image, where probability density values corresponding to each gray value are recorded.
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, the fog outlet area and a preset fog outlet area threshold.
In order to accurately determine whether the humidity is normal, a gray value threshold and a fog outlet 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 outlet area and a preset fog outlet area threshold.
Whether the humidity is abnormal or not can be determined according to the comparison of the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold value and the comparison of the fog outlet area and a preset fog outlet 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 outlet area is larger than the preset fog outlet area threshold value, the fog outlet 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 abnormal fog amount and abnormal humidity are indicated.
Whether the humidity is abnormal or not can be determined according to the difference value between the gray value corresponding to the peak value of the gray probability density curve and the preset gray value threshold, 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 is larger than the preset first difference value threshold, and the difference value between the fog output area and the preset fog output area threshold is larger than the preset second difference value threshold, the fog output is normal, and the humidity is normal; 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 is smaller than or equal to a preset first difference value threshold, or the difference value between the fog outlet area and the preset fog outlet area threshold is smaller than or equal to a preset second difference value threshold, the abnormal fog outlet amount and the abnormal humidity are indicated.
According to the embodiment of the invention, the foreground fog image in the fog image is determined based on the acquired fog image, and whether the humidity is normal or not 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 the foreground fog image, in the embodiment of the present invention, the determining the foreground fog image in the fog image includes:
And obtaining a foreground fog image in the fog image according to the fog image and the 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 which is trained in advance, so that the foreground fog image in the fog image is obtained.
Before the mist image is input into a pre-trained Gaussian mixture model, training the Gaussian mixture model, wherein the Gaussian mixture model is provided with at least two Gaussian mixture models, and the number of the Gaussian mixture models in the Gaussian mixture model is set according to actual requirements. For each Gaussian model, initializing matrix parameters of the Gaussian model, wherein the matrix parameters comprise a mean value, a variance and a weight.
Training the Gaussian mixture model according to the obtained sample fog image until the Gaussian mixture model after training is obtained, wherein the training process of the Gaussian mixture model is the prior art and is not described herein.
In the process of acquiring a foreground fog image based on a pre-trained Gaussian mixture model, aiming at each pixel point in the acquired fog image, and aiming at each Gaussian mixture model of the pre-trained Gaussian mixture model, the following operation is carried out: if there are three gaussian models in the mixture gaussian model, for convenience of distinction, a first gaussian model of the mixture gaussian model is referred to as a first gaussian model, a second gaussian model of the mixture gaussian model is referred to as a second gaussian model, and a third gaussian model of the mixture gaussian model is referred to as a third gaussian model. Firstly, comparing a pixel value of the pixel point with a mean value of a first Gaussian model, determining a difference value between the pixel value corresponding to the pixel point and the mean value, and if the difference value is smaller than twice of a variance of the first Gaussian model of the pre-trained Gaussian mixture model, determining that the pixel point meets the condition of the first Gaussian model, and directly confirming that the pixel point is a pixel point in a background image without carrying out the operation with a second Gaussian model and a third Gaussian model in sequence. That is, in order to determine a pixel in the background image, when the pixel is sequentially subjected to the above operations with the first gaussian model, the second gaussian model, and the third gaussian model for each pixel, the pixel is determined to be the pixel in the background image as long as the condition of any one gaussian model is satisfied.
And if the pixel point does not meet the condition of each Gaussian model when the pixel point and each Gaussian model in the Gaussian mixture model perform the operation, determining the pixel point as the pixel point in the foreground image.
In the embodiment of the invention, since at least two gaussian models exist in the mixed gaussian model, for each pixel point in the foreground fog image, for each gaussian model in the mixed gaussian model, a judgment is made as to whether the pixel point meets the requirements of the corresponding gaussian model, if the pixel point meets the requirements of at least one gaussian model, the pixel point is considered to be the pixel point in the background image, otherwise, the pixel point is considered to be the pixel point in the foreground image.
When determining a foreground image, determining whether the pixel point is a pixel point in the foreground image or a pixel point in the background image for each pixel point, if the pixel point is a pixel point in the foreground image, assigning a pixel value of the pixel point to 255 in a mask image, if the pixel point is a pixel point in the background image, assigning a pixel value of the pixel point to 0in the mask image, and determining a mask image (MASKING IMAGE, mask) according to the assignment condition of the pixel point. 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 the electronic equipment are subjected to bit-wise AND operation to obtain a foreground fog image.
The training process of the mixture gaussian model and the process of determining the foreground fog image according to the mixture gaussian model and the fog image are the prior art, and are not described herein.
Example 3:
In order to determine a gray probability density curve, in the embodiments of the present invention, determining a gray probability density curve according to a first number of pixels of the gray value and a second number of pixels included in the front Jing Wuqi 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 front Jing Wuqi image;
A gray scale probability density curve is determined based on the probability density of each gray scale 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], so that the probability density of the gray value may be determined according to the quotient of the first number of pixels of the gray value in the foreground fog image and the second number of pixels included in the foreground fog image, for example, if the gray value is a, the number of pixels of the gray value of a in the foreground fog image is n r, and the total number of pixels included in the foreground fog image is n, the probability density of the gray value a is
The probability density of each gray value can be determined by adopting the method, 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 probability density corresponding to each 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 probability density curve, before determining the gray 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.
Example 4:
In order to accurately determine whether the humidity is abnormal, based on the foregoing embodiments, in the embodiment of the present invention, 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 fogging area, and a preset fogging area threshold includes:
Determining a gray value relative offset according to a gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold;
Determining a fog area relative difference value according to the fog area and a preset fog area threshold;
and determining whether the humidity is normal or not according to the gray value relative offset and the fog outlet area relative difference.
In the embodiment of the invention, the standard fog image is pre-stored, the standard fog image can be a fog image which accords with the standard fog output in the acquired fog image, and the fog image which accords with the standard fog output is subjected to denoising and graying before being stored in the electronic equipment.
Because the probability density corresponding to each gray value is recorded in the probability density curve, the current fog output can be determined according to the comparison of the gray probability density curve of the standard fog image and the gray probability density curve of the foreground fog image, so as to determine whether the humidity is normal.
Specifically, for convenience of description, a gray value corresponding to a peak value of the gray probability density curve of the foreground fog image is referred to as a first gray value, a gray value corresponding to a peak value of the gray probability density curve of the standard fog image is referred to as a second gray value, a gray value greater than the second gray value among the 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 the 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 larger than the second gray value, if the area of the curve corresponding to the third gray value is larger than the preset area threshold value, the fog output is larger, 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, 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 transverse axis, and the area threshold can be one half of the area corresponding to the gray probability density curve of the standard fog image.
Therefore, in the embodiment of the invention, in order to conveniently measure the fog amount, the relative offset of the gray value can be determined according to the gray value corresponding to the peak value of the gray probability density curve and the preset gray value threshold, and the fog condition can be determined according to the offset condition. The gray value relative offset is a relative offset value of a gray value corresponding to a peak value of the foreground fog image and a gray value corresponding to a 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 the 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, the relative difference value of the fog areas is determined according to the fog outlet areas and the preset fog outlet area threshold, wherein the number of fog pixel points contained in the foreground fog image can be determined as the fog outlet area corresponding to the foreground fog image. Specifically, in the process of determining the relative difference value of the fog areas, firstly determining the difference value of the fog outlet area of the foreground fog image and a preset fog outlet area threshold value, wherein the preset fog outlet area threshold value is the fog outlet area of a pre-stored standard fog image. Since the fogging area may be larger than the preset fogging area threshold and the fogging area may be smaller than the preset fogging area threshold, in order to facilitate determining whether the humidity is abnormal according to the fogging area relative difference value, in the embodiment of the invention, after determining the difference value of the fogging area of the foreground fog image and the preset fogging area threshold, determining the absolute value of the difference value, then determining the quotient of the absolute value of the difference value and the preset fogging area threshold, and determining the quotient as the fogging area relative difference value. If the pre-stored standard fog image has a fog area s 1, that is, the preset fog area threshold is s 1, and the foreground fog image has a fog area s 0, the difference between the fog area of the foreground fog image and the preset fog area threshold is |s 0-s1 |, and the relative difference between the fog areas is
In order to accurately determine the gray value relative offset, in the embodiments of the present invention, determining the gray value relative offset according to the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold value includes:
Determining a difference value between a gray value corresponding to a peak value of the gray probability density curve and a preset gray value threshold value 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 the embodiment of the invention, the absolute value of the difference between the gray value corresponding to the peak value of the gray probability density curve and the preset gray value threshold is determined as the actual gray value offset, wherein the gray value corresponding to the peak value of the gray probability density curve of the foreground fog image may be greater than the preset gray value threshold or may be less than the preset gray value threshold. Therefore, in the embodiment of the invention, in order to facilitate the subsequent determination of the relative offset of the gray value and then determine whether the humidity is abnormal, after determining the difference between the gray value corresponding to the peak value of the gray probability density curve and the preset gray value threshold, the absolute value of the difference is determined, and the absolute value of the difference is determined as the actual gray value offset. After determining the actual gray value offset, determining the ratio of the actual gray value offset to a preset gray value threshold, and determining the ratio as the gray value relative offset.
If the gray value corresponding to the peak value of the gray probability density curve is 100, the preset gray value threshold is 127, the actual gray value offset is 27, and the gray value relative offset is
Fig. 2 is a graph of gray probability density according to an embodiment of the present invention, and will now be described with reference to fig. 2.
The solid line is a gray probability density curve corresponding to a reference image, namely a gray probability density curve of a standard fog image, the dotted line is a gray probability density curve corresponding to an acquired image, namely a gray probability density curve of a foreground fog image, wherein the abscissa of the gray probability density curve is a gray value r, the gray value r represents the brightness of a single pixel point, the range of the gray value is [0,255], the larger the gray value represents the brightness, the smaller the gray value represents the brightness, and the ordinate is the probability density corresponding to each gray value.
When determining the actual gray value offset 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 between 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 deltar, and determining the absolute value of the difference as the actual gray value offset.
In order to accurately determine whether the humidity is abnormal, in the embodiments of the present invention, determining whether the humidity is normal according to the gray value relative offset and the fog area relative difference value includes:
if the gray value relative offset is smaller than a preset gray value relative offset threshold and the fog outlet area relative difference is smaller than a preset fog outlet area relative difference threshold, determining that the humidity is normal;
and if the gray value relative offset is greater than a preset relative gray value relative offset threshold, or the fog outlet area relative difference is greater than a preset fog outlet area relative difference threshold, determining that the humidity is abnormal.
In order to determine whether the humidity is abnormal, after the gray value relative offset and the fog outlet area relative difference value are determined, whether the humidity is abnormal is determined according to the comparison result of the gray value relative offset and a preset gray value relative offset threshold value and according to the comparison result of the fog outlet area relative difference value and a preset fog outlet area relative difference value threshold value.
If the gray value relative offset is smaller than the preset gray value relative offset threshold, the fog output 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 output of the fog image is larger than that of the standard fog image; if the relative difference of the fog outlet areas is smaller than the preset relative difference threshold of the fog outlet areas, the fog outlet amounts of the fog image and the standard fog image are closer, and if the relative difference of the fog outlet areas is larger than the preset relative difference threshold of the fog outlet areas, the fog outlet differences of the fog image and the standard fog image are larger.
Specifically, when determining whether the humidity is abnormal, if the gray value relative offset is smaller than the preset gray value relative offset threshold and the fog outlet area relative difference is smaller than the preset fog outlet area relative difference threshold, the fog outlet amount of the fog image is closer to that of the standard fog image, so that the humidity is normal; if the gray value relative offset is greater than the preset gray value relative offset threshold, or the fog outlet area relative difference is greater than the preset fog outlet area relative difference threshold, the fog outlet difference between the fog image and the standard fog image is larger, so that 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 of fig. 3 will be given.
Firstly, selecting monitoring points, collecting videos, namely, installing electronic equipment in a preset position in advance, acquiring each frame of fog outlet image collected by the electronic equipment, then carrying out fog outlet background modeling according to the fog outlet images, namely, pre-training to finish a Gaussian mixture model, and inputting the collected fog outlet images into the pre-trained Gaussian mixture model to obtain a standard fog image.
The method comprises the steps that an electronic device collects an image to be detected, namely a fog outlet image to be detected, a foreground fog image is determined according to the fog outlet image and a pre-trained Gaussian mixture model, a gray probability density curve and a fog outlet area of the foreground fog image are determined, whether the gray probability density curve of the foreground fog image meets the standard or not is determined according to the gray probability density curve and the fog outlet area of the foreground fog image and the gray probability density curve and the fog outlet area of a standard fog image, the fog area is determined to enable the gray probability density curve of the foreground fog image to meet the standard or not, if the gray probability density curve and the fog outlet 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 outlet area does not meet the standard, the fog amount is determined to not meet the standard, and the humidity is abnormal.
Example 5:
Fig. 4 is a schematic structural diagram of a humidity detection device according to an embodiment of the present invention, where the device includes:
An acquisition module 401, 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 front Jing Wuqi image, and determine, for each gray value of the front Jing Wuqi image, a gray probability density curve according to a first number of pixels of the gray value and a second number of pixels included in the front Jing Wuqi 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, the fog outlet area and a preset fog outlet area threshold.
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 mixed gaussian model.
In a possible implementation manner, the determining module 402 is specifically configured to determine a probability density of the gray value according to a quotient value of a first number of pixels of the gray value and a second number of pixels included in the front Jing Wuqi images; a gray scale probability density curve is determined based on the probability density of each gray scale value.
In a possible implementation manner, the determining module 402 is specifically configured to determine a gray value relative offset according to a gray value corresponding to a peak value of the gray probability density curve and a preset gray value threshold; determining a fog area relative difference value according to the fog area and a preset fog area threshold; and determining whether the humidity is normal or not according to the gray value relative offset and the fog outlet area relative difference.
In a possible implementation manner, the determining module 402 is specifically configured to determine, as an actual gray value offset, 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; 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 area relative difference is smaller than a preset fog area relative difference threshold; and if the gray value relative offset is greater than a preset relative gray value relative offset threshold, or the fog outlet area relative difference is greater than a preset fog outlet area relative difference threshold, 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 device 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 in communication 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:
acquiring a fog image and determining a foreground fog image in the fog image;
determining a corresponding fog outlet area in the front Jing Wuqi image, and determining a gray probability density curve according to a first number of pixels of the gray value and a second number of pixels contained in the front Jing Wuqi image for each gray value of the front Jing Wuqi 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, the fog outlet area and a preset fog outlet area threshold.
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 mixed gaussian model.
Further, the processor 501 is further configured to determine a probability density of the gray value according to a quotient of the first number of pixels of the gray value and the second number of pixels included in the front Jing Wuqi images; a gray scale probability density curve is determined based on the probability density of each gray scale value.
Further, the processor 501 is further configured to determine a gray value relative offset according to a gray value corresponding to a peak value of the gray probability density curve and a preset gray value threshold; determining a fog area relative difference value according to the fog area and a preset fog area threshold; and determining whether the humidity is normal or not according to the gray value relative offset and the fog outlet area relative 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 gray value relative offset is less than a preset gray value relative offset threshold and the fog area relative difference is less than a preset fog area relative difference threshold; and if the gray value relative offset is greater than a preset relative gray value relative offset threshold, or the fog outlet area relative difference is greater than a preset fog outlet area relative difference threshold, determining that the humidity is abnormal.
The communication bus mentioned by the server may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, etc. The communication bus may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, the figures are shown with only one bold line, but not with only one bus or one type of bus.
The communication interface 502 is used for communication between the electronic device and other devices described above.
The Memory may include random access Memory (Random Access Memory, RAM) or may include Non-Volatile Memory (NVM), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the aforementioned processor.
The processor may be a general-purpose processor, including a central processing unit, a network processor (Network Processor, NP), etc.; but also digital instruction processors (DIGITAL SIGNAL Processing units, DSPs), application specific integrated circuits, field programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
Example 7:
On the basis of the above embodiments, the embodiments of the present invention further provide a computer readable storage medium having stored therein a computer program executable by an electronic device, which when run on the electronic device, causes the electronic device to perform the steps of:
The memory has stored therein a computer program which, when executed by the processor, causes the processor to perform the steps of:
acquiring a fog image and determining a foreground fog image in the fog image;
determining a corresponding fog outlet area in the front Jing Wuqi image, and determining a gray probability density curve according to a first number of pixels of the gray value and a second number of pixels contained in the front Jing Wuqi image for each gray value of the front Jing Wuqi 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, the fog outlet area and a preset fog outlet area threshold.
Further, 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 the pre-trained Gaussian mixture model.
Further, the determining the gray probability density curve according to the first number of pixels of the gray value and the second number of pixels contained in the front Jing Wuqi 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 front Jing Wuqi image;
A gray scale probability density curve is determined based on the probability density of each gray scale value.
Further, 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 includes:
Determining a gray value relative offset according to a gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold;
Determining a fog area relative difference value according to the fog area and a preset fog area threshold;
and determining whether the humidity is normal or not according to the gray value relative offset and the fog outlet area relative difference.
Further, determining the gray value relative offset according to the gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold value includes:
determining an absolute value of a difference value between a gray value corresponding to a peak value 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 whether the humidity is normal according to the gray value relative offset and the fog area relative difference value includes:
if the gray value relative offset is smaller than a preset gray value relative offset threshold and the fog outlet area relative difference is smaller than a preset fog outlet area relative difference threshold, determining that the humidity is normal;
and if the gray value relative offset is greater than a preset relative gray value relative offset threshold, or the fog outlet area relative difference is greater than a preset fog outlet area relative difference threshold, determining that the humidity is abnormal.
According to the embodiment of the invention, the foreground fog image in the fog image is determined based on the acquired fog image, and whether the humidity is normal or not is determined based on the foreground fog image, so that the influence of an irresistible factor can be effectively avoided, and the accuracy of humidity detection is improved.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A method of humidity detection, the method comprising:
acquiring a fog image and determining a foreground fog image in the fog image;
determining a corresponding fog outlet area in the front Jing Wuqi image, and determining a gray probability density curve according to a first number of pixels of the gray value and a second number of pixels contained in the front Jing Wuqi image for each gray value of the front Jing Wuqi image;
determining whether the humidity is normal or not according to a gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold, the fog outlet area and a preset fog outlet area threshold;
wherein determining the gray probability density curve according to the first number of pixels of the gray value and the second number of pixels contained in the front Jing Wuqi 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 in the foreground fog image and the second number of the pixel points contained in the foreground fog image; determining a gray probability density curve according to the probability density of each gray value;
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 outlet area and a preset fog outlet area threshold comprises:
Determining a gray value relative offset according to a gray value corresponding to the peak value of the gray probability density curve and a preset gray value threshold;
Determining a fog area relative difference value according to the fog area and a preset fog area threshold;
and determining whether the humidity is normal or not according to the gray value relative offset and the fog outlet area relative difference.
2. The method of claim 1, wherein the determining a foreground fog image in the fog image comprises:
And obtaining a foreground fog image in the fog image according to the fog image and the pre-trained Gaussian mixture model.
3. The method of claim 1, wherein determining the gray value relative offset based on the gray value corresponding to the peak of the gray probability density curve and a preset gray value threshold comprises:
determining an absolute value of a difference value between a gray value corresponding to a peak value 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.
4. The method of claim 1, wherein determining whether the humidity is normal based on the gray value relative offset and the fog area relative difference comprises:
if the gray value relative offset is smaller than a preset gray value relative offset threshold and the fog outlet area relative difference is smaller than a preset fog outlet area relative difference threshold, determining that the humidity is normal;
and if the gray value relative offset is greater than a preset relative gray value relative offset threshold, or the fog outlet area relative difference is greater than a preset fog outlet area relative difference threshold, determining that the humidity is abnormal.
5. A humidity detection apparatus, the apparatus comprising:
the acquisition module is used for acquiring a fog image and determining a foreground fog image in the fog image;
A determining module, configured to determine a corresponding fog area in the front Jing Wuqi image, and determine, for each gray value of the front Jing Wuqi image, a gray probability density curve according to a first number of pixels of the gray value and a second number of pixels included in the front Jing Wuqi image; determining whether the humidity is normal or not according to a gray value corresponding to the peak value of the gray probability density curve, a preset gray value threshold, the fog outlet area and a preset fog outlet area threshold;
The determining module is specifically configured to determine a probability density of the gray value according to a quotient of a first number of pixels of the gray value in the foreground fog image and a second number of pixels included in the foreground fog image; determining a gray probability density curve according to the probability density of each gray value;
The determining module is specifically configured to determine a relative offset 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 fog area relative difference value according to the fog area and a preset fog area threshold; and determining whether the humidity is normal or not according to the gray value relative offset and the fog outlet area relative difference.
6. The apparatus of claim 5, 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 mixed gaussian model.
7. The apparatus according to claim 5, 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.
8. The apparatus of claim 5, wherein the determining module is specifically configured to determine that the humidity is normal if the gray value relative offset is less than a preset gray value relative offset threshold and the fog area relative difference is less than a preset fog area relative difference threshold; and if the gray value deviation relative displacement is larger than a preset relative gray value relative displacement threshold value or the fog outlet area relative difference is larger than a preset fog outlet area relative difference threshold value, determining that the humidity is abnormal.
9. An electronic device comprising a processor for implementing the steps of the method according to any of claims 1-4 when executing a computer program stored in a memory.
10. A computer readable storage medium, characterized in that it stores a computer program executable by a terminal, which, when run on the terminal, causes the terminal to perform the steps of the method according to any of claims 1-4.
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