CN111294516A - Alum image processing method and system, electronic device and medium - Google Patents
Alum image processing method and system, electronic device and medium Download PDFInfo
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Abstract
The invention provides an alum blossom image processing method, which comprises the following steps: the method comprises the following steps of collecting a real-time image of underwater alum blossom, and carrying out enhancement processing on the real-time image to obtain enhanced image information, wherein the enhancement processing comprises one of the following steps: contrast limitation and brightness adjustment; carrying out binarization processing on the enhanced image information to obtain binarized image information; determining outline information of the alum blossom according to the binarized image information, wherein the outline information comprises one of the following items: the method comprises the following steps of alumen ustum outline area, area, alumen ustum image gray level variance, alumen ustum image gradient, alumen ustum image kurtosis, alumen ustum image entropy and alumen ustum image fractal dimension. The method has the advantages that the real-time monitoring and analysis of alum blossom characteristics in the coagulation process are replaced by naked eyes, the reliability and the precision are improved, and the system error is reduced.
Description
Technical Field
The invention relates to the technical field of industrial detection, in particular to an alum blossom image processing method, an alum blossom image processing system, electronic equipment and a medium.
Background
Coagulation is an important link of water treatment, and not only influences the subsequent treatment process, but also influences the effluent quality and the treatment cost. How to determine the better coagulant adding amount according to the change of the water quality of the incoming water by a dosing system is a problem which is generally concerned and needs to be solved in the water supply and drainage industry for a long time.
Generally, the alum blossom characteristics in the coagulation process are identified by naked eyes, and whether the dosage needs to be adjusted or not is judged manually. The dosage can not be controlled on line in real time according to the change of factors such as raw water quality and the like, and the metering and feeding in the real sense can not be achieved, so that the water quality condition is difficult to master, and the excessive consumption of a coagulant and the labor waste are caused.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, the present invention provides a method, a system, an electronic device and a medium for processing alum blossom image, which are used to solve the problem that underwater alum blossom is inconvenient to detect.
In order to achieve the above and other related objects, the present invention provides a method for processing alum blossom image, comprising: the method comprises the following steps of collecting a real-time image of underwater alum blossom, and carrying out enhancement processing on the real-time image to obtain enhanced image information, wherein the enhancement processing comprises one of the following steps: contrast limitation and brightness adjustment; carrying out binarization processing on the enhanced image information to obtain binarized image information; determining outline information of the alum blossom according to the binarized image information, wherein the outline information comprises one of the following items: the method comprises the following steps of alumen ustum outline area, area, alumen ustum image gray level variance, alumen ustum image gradient, alumen ustum image kurtosis, alumen ustum image entropy and alumen ustum image fractal dimension.
Optionally, the step of limiting the contrast includes: and limiting the contrast of the real-time image, and acquiring a threshold histogram.
Optionally, the step of adjusting the brightness includes: and adjusting the brightness of the real-time image, and carrying out binarization on the real-time image according to the gray level of the alum blossom outline.
Optionally, the step of performing binarization processing on the enhanced image information includes: extracting a alum blossom outline, and determining an alum blossom outline region and a region area; and determining a water background area according to the alumen ustum outline area.
Optionally, the mathematical expression for determining the area of the region is:
wherein Equisalrentdiameter is the equivalent diameter of the alum blossom outline area, and ContourARea is the total area of the alum blossom outline area.
Optionally, the mathematical expression for determining the gray variance of the alum blossom image is as follows:
wherein s is alum blossom image gray variance, xiIs the image of alumen ustumAnd (3) the gray level of the pixel points, i is more than or equal to 1, M is the average gray level of the alum blossom image, and n is the total number of pixels of the alum blossom image.
Optionally, the mathematical expression for determining the gradient of the alum blossom image is as follows:
wherein, skew is the gradient of alum blossom image, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
Optionally, the mathematical expression for determining the kurtosis of the alum blossom image is as follows:
wherein kurt is alumen ustum image kurtosis, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
Optionally, the mathematical expression for determining the entropy of the alum blossom image is as follows:
wherein H is the entropy of the alumen ustum image, and P (i) is the ratio of the number of pixels with the pixel value of i to the total number of pixels of the alumen ustum image
Optionally, the mathematical expression for determining the fractal dimension of the alum blossom image is as follows:
A＝＝αLDf
wherein A is the area of the alumen ustum outline region, L is the outline perimeter of the alumen ustum outline region, α is the proportionality constant of the alumen ustum outline region, and Df is the fractal dimension of the alumen ustum image.
An alum blossom image processing system, comprising: the image acquisition unit is used for acquiring real-time images of the underwater alum blossom; the image processing unit is used for enhancing the real-time image to obtain enhanced image information, and performing binarization processing on the enhanced image information to obtain binarized image information; an image analysis unit, configured to determine contour information of alum blossom according to the binarized image information, where the contour information includes one of: the method comprises the following steps of alumen ustum outline area, area, alumen ustum image gray level variance, alumen ustum image gradient, alumen ustum image kurtosis, alumen ustum image entropy and alumen ustum image fractal dimension.
An apparatus, comprising: one or more processors; and one or more machine readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform one or more of the methods described herein.
One or more machine readable media having instructions stored thereon that, when executed by one or more processors, cause an apparatus to perform one or more of the methods described.
As described above, the alumen ustum image processing method, system, electronic device, and medium according to the present invention have the following advantages:
acquiring outline information of the alum blossom through image acquisition, enhancement processing and binarization processing, wherein the outline information comprises one of the following: the method comprises the following steps of alumen ustum outline area, area, alumen ustum image gray level variance, alumen ustum image gradient, alumen ustum image kurtosis, alumen ustum image entropy and alumen ustum image fractal dimension. The method has the advantages that the real-time monitoring and analysis of alum blossom characteristics in the coagulation process are replaced by naked eyes, the reliability and the precision are improved, and the system error is reduced.
Drawings
Fig. 1 is a schematic flow chart of the alum blossom image processing method provided in embodiment 1.
FIG. 2 is a schematic diagram of collecting real-time images of underwater alum floc.
Fig. 3 is a schematic diagram of a real-time image enhancement process.
FIG. 4 is a schematic view of a silhouette region of alum blossom.
Fig. 5 is a schematic diagram of a hardware structure of a terminal device according to an embodiment.
Fig. 6 is a schematic diagram of a hardware structure of a terminal device according to another embodiment.
Fig. 7 is a schematic structural diagram of an alum blossom image processing system according to embodiment 2.
Description of the element reference numerals
1 image acquisition unit
2 image processing unit
3 image analysis unit
1100 input device
1101 first processor
1102 output device
1103 first memory
1104 communication bus
1200 processing assembly
1201 second processor
1202 second memory
1203 communication assembly
1204 Power supply Assembly
1205 multimedia assembly
1206 voice assembly
1207 input/output interface
1208 sensor assembly
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the components related to the present invention are only shown in the drawings rather than drawn according to the number, shape and size of the components in actual implementation, and the type, quantity and proportion of the components in actual implementation may be changed freely, and the layout of the components may be more complicated.
Referring to fig. 1, embodiment 1 provides an alum blossom image processing method, including:
s1: the method for acquiring the real-time image of the underwater alumen ustum includes capturing the real-time image, please refer to fig. 2, and may further include acquiring a pixel frame through real-time shooting to determine the real-time image, performing enhancement processing on the real-time image to acquire enhanced image information, where the enhancement processing includes one of the following: contrast limitation and brightness adjustment are carried out, and an image synergy effect is achieved through the contrast limitation and the brightness adjustment, so that the area and the position of alum blossom in a real-time image can be conveniently determined;
s2: the method comprises the steps of carrying out binarization processing on enhanced image information to obtain binarized image information, converting the enhanced image information into a binarized discrete gray information image, and conveniently establishing a corresponding relation between a specific gray value and an alum blossom outline area in the alum blossom image;
s3: based on the binarized image information, for example, the outline information of the alum blossom can be determined according to the corresponding relation between a specific gray value and an alum blossom outline area in the alum blossom image, and the outline information comprises one of the following: the underwater alumen ustum detection method has the advantages that the states of the underwater alumen ustum are judged by detecting the outline information, so that the underwater alumen ustum is replaced by observing the underwater alumen ustum with naked eyes, the detection accuracy of the alumen ustum is improved, the system error caused by the naked eye observation is avoided, and the standard management, the energy conservation and the consumption reduction are facilitated, and the personnel reduction and the efficiency improvement are facilitated. Can ensure the quality of the discharged water, realize the reduction of the number of workers and the unmanned coagulation unit, reduce the total dosage and eliminate the daily potential safety hazard
In some implementations, the step of contrast limiting includes: the contrast of the real-time image is limited, a threshold histogram is obtained, the contrast capable of increasing the significance of the alum blossom relative to the water background is determined by adjusting the contrast, the contrast capable of enhancing the significance is limited and the real-time image is adjusted, the significance and the identification degree of the alum blossom outline are improved, in some implementation processes, the contrast of the local part of the real-time image can be limited, so as to further improve the significance of the alum blossom outline and further enhance the identification degree, for example, the brightness value of the image is redistributed by calculating the histogram of each significant area of the real-time image, so that the real-time image is more suitable for improving the local contrast of the image and enhancing the edge information of the image, which is beneficial to image segmentation, please refer to fig. 3.
In some implementations, the step of adjusting the brightness includes: the brightness of the real-time image is adjusted, and the real-time image is binarized according to the gray level of the alumen ustum outline, for example, the binarization threshold value at the pixel position is determined according to the pixel value distribution of the neighborhood block of the pixel, so that the binarization threshold value at each pixel position is not fixed and is determined by the distribution of the neighborhood pixels around the pixel position. The binarization threshold value of the image area with higher brightness is generally higher, while the binarization threshold value of the image area with lower brightness is correspondingly smaller. Local image regions of different brightness, contrast, texture will have corresponding local binarization thresholds.
Referring to fig. 4, in some implementations, the step of performing binarization processing on the enhanced image information includes: extracting a alum blossom outline, and determining an alum blossom outline region and a region area; the water background area is determined according to the alumen ustum outline area, the target underwater alumen ustum and the water background are divided, the picture is concise and clear, the field operator can know the real-time situation of the underwater alumen ustum more intuitively, and the basis and reference are provided for subsequent detection and regulation. In some factual processes, the mathematical expression to determine the area of a region is:
the method comprises the steps of obtaining equivalent diameters of all alum blossom outline areas, obtaining an average value, determining the equivalent diameters of all alum blossom outline areas, and obtaining a total surface of all alum blossom outline areas, wherein the equivalent diameters of all alum blossom outline areas are equal to the equivalent diameters of all alum blossom outline areas, and ContourARea is the total surface of all alum blossom outline areas.
After obtaining the binary image information, calculating the area, the perimeter and the equivalent diameter of the region by analyzing and processing the related parameters, wherein the data can be used as the visual representation of the underwater alum blossom; the total area, the black area ratio, the average area of the alum flowers, the average circumference of the alum flowers and the average equivalent diameter of the alum flowers can be obtained through a series of calculation processing, and the total distribution and the form condition of the current underwater alum flowers can be directly, clearly and accurately known through the calculation results; through a series of calculation processing, the gray level variance of the alum blossom image, the gradient of the alum blossom image, the kurtosis of the alum blossom image, the entropy of the alum blossom image and the fractal dimension of the alum blossom image can be obtained.
The alumen ustum image gray variance can be obtained and represents the dispersion degree of the random variable around the central value, and in some implementation processes, the mathematical expression for determining the alumen ustum image gray variance is as follows:
wherein s is alum blossom image gray variance, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
The gradient of the alum blossom image represents the asymmetric degree of the random variable and the central distribution, and the gradient is inclined to the right, the value is positive, and the value is negative towards the left. In some implementations, the mathematical expression for determining the slope of the alum blossom image is:
wherein, skew is the gradient of alum blossom image, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
The kurtosis of the alum blossom image represents the characteristic number of the peak value height of a probability density distribution curve at the average value, and in some implementation processes, the mathematical expression of the kurtosis of the alum blossom image is determined as follows:
wherein kurt is alumen ustum image kurtosis, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
The entropy value is the entropy of an image, i.e. the entropy of information of an image, which is simply to quantize the information. The degree of disorder of information is expressed by the magnitude of entropy. Generally, the amount of information included in an image is large, the larger the entropy value is, and in some implementation processes, the mathematical expression for determining the entropy of the alum blossom image is as follows:
wherein H is the entropy of the alum blossom image, and P (i) is the ratio of the number of pixels with the pixel value of i to the total number of pixels of the alum blossom image.
The fractal dimension of the alum blossom image contains information such as density of alum blossom and the like, can be used for expressing flocculation effect, the change of the fractal dimension can reflect the forming process and the rule of alum blossom, and in some implementation processes, the mathematical expression for determining the fractal dimension of the alum blossom image is as follows:
A＝＝αLDf
wherein, A is the area of the alum blossom outline area, L is the outline perimeter of the alum blossom outline area, α is the proportionality constant of the alum blossom outline area, Df is the fractal dimension of the alum blossom image, and the formula is used for logarithm removal:
lnA＝DflnL+lnα
and acquiring the areas A and the perimeters P corresponding to all alumen ustum, respectively carrying out logarithm removal, and fitting the data by using a least square method to obtain a corresponding straight line, wherein the slope of the straight line is the fractal dimension in the processing time period.
Referring to fig. 7, embodiment 2 provides an alum blossom image processing system, including:
the image acquisition unit 1 is used for acquiring real-time images of underwater alumen ustum;
the image processing unit 2 is used for performing enhancement processing on the real-time image to obtain enhanced image information, and performing binarization processing on the enhanced image information to obtain binarized image information;
an image analysis unit 3, configured to determine contour information of the alum blossom according to the binarized image information, where the contour information includes one of: the method comprises the following steps of alumen ustum outline area, area, alumen ustum image gray level variance, alumen ustum image gradient, alumen ustum image kurtosis, alumen ustum image entropy and alumen ustum image fractal dimension.
In some implementations, the step of contrast limiting includes: the contrast of the real-time image is limited, a threshold histogram is obtained, the contrast capable of increasing the significance of the alum blossom relative to the water background is determined by adjusting the contrast, the contrast capable of enhancing the significance is limited and the real-time image is adjusted, the significance and the identification degree of the alum blossom outline are improved, in some implementation processes, the contrast of the local part of the real-time image can be limited, so as to further improve the significance of the alum blossom outline and further enhance the identification degree, for example, the brightness value of the image is redistributed by calculating the histogram of each significant area of the real-time image, so that the real-time image is more suitable for improving the local contrast of the image and enhancing the edge information of the image, which is beneficial to image segmentation, please refer to fig. 3.
In some implementations, the step of adjusting the brightness includes: the brightness of the real-time image is adjusted, and the real-time image is binarized according to the gray level of the alumen ustum outline, for example, the binarization threshold value at the pixel position is determined according to the pixel value distribution of the neighborhood block of the pixel, so that the binarization threshold value at each pixel position is not fixed and is determined by the distribution of the neighborhood pixels around the pixel position. The binarization threshold value of the image area with higher brightness is generally higher, while the binarization threshold value of the image area with lower brightness is correspondingly smaller. Local image regions of different brightness, contrast, texture will have corresponding local binarization thresholds.
Referring to fig. 4, in some implementations, the step of performing binarization processing on the enhanced image information includes: extracting a alum blossom outline, and determining an alum blossom outline region and a region area; the water background area is determined according to the alumen ustum outline area, the target underwater alumen ustum and the water background are divided, the picture is concise and clear, the field operator can know the real-time situation of the underwater alumen ustum more intuitively, and the basis and reference are provided for subsequent detection and regulation. In some factual processes, the mathematical expression to determine the area of a region is:
the method comprises the steps of obtaining equivalent diameters of all alum blossom outline areas, obtaining an average value, determining the equivalent diameters of all alum blossom outline areas, and obtaining a total surface of all alum blossom outline areas, wherein the equivalent diameters of all alum blossom outline areas are equal to the equivalent diameters of all alum blossom outline areas, and ContourARea is the total surface of all alum blossom outline areas.
After obtaining the binary image information, calculating the area, the perimeter and the equivalent diameter of the region by analyzing and processing the related parameters, wherein the data can be used as the visual representation of the underwater alum blossom; the total area, the black area ratio, the average area of the alum flowers, the average circumference of the alum flowers and the average equivalent diameter of the alum flowers can be obtained through a series of calculation processing, and the total distribution and the form condition of the current underwater alum flowers can be directly, clearly and accurately known through the calculation results; through a series of calculation processing, the gray level variance of the alum blossom image, the gradient of the alum blossom image, the kurtosis of the alum blossom image, the entropy of the alum blossom image and the fractal dimension of the alum blossom image can be obtained.
The alumen ustum image gray variance can be obtained and represents the dispersion degree of the random variable around the central value, and in some implementation processes, the mathematical expression for determining the alumen ustum image gray variance is as follows:
wherein s is alum blossom chartVariance of image gray scale, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
The gradient of the alum blossom image represents the asymmetric degree of the random variable and the central distribution, and the gradient is inclined to the right, the value is positive, and the value is negative towards the left. In some implementations, the mathematical expression for determining the slope of the alum blossom image is:
wherein, skew is the gradient of alum blossom image, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
The kurtosis of the alum blossom image represents the characteristic number of the peak value height of a probability density distribution curve at the average value, and in some implementation processes, the mathematical expression of the kurtosis of the alum blossom image is determined as follows:
wherein kurt is alumen ustum image kurtosis, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
The entropy value is the entropy of an image, i.e. the entropy of information of an image, which is simply to quantize the information. The degree of disorder of information is expressed by the magnitude of entropy. Generally, the amount of information included in an image is large, the larger the entropy value is, and in some implementation processes, the mathematical expression for determining the entropy of the alum blossom image is as follows:
wherein H is the entropy of the alum blossom image, and P (i) is the ratio of the number of pixels with the pixel value of i to the total number of pixels of the alum blossom image.
The fractal dimension of the alum blossom image contains information such as density of alum blossom and the like, can be used for expressing flocculation effect, the change of the fractal dimension can reflect the forming process and the rule of alum blossom, and in some implementation processes, the mathematical expression for determining the fractal dimension of the alum blossom image is as follows:
A＝＝αLDf
wherein, A is the area of the alum blossom outline area, L is the outline perimeter of the alum blossom outline area, α is the proportionality constant of the alum blossom outline area, Df is the fractal dimension of the alum blossom image, and the formula is used for logarithm removal:
lnA＝DflnL+lnα
and acquiring the areas A and the perimeters P corresponding to all alumen ustum, respectively carrying out logarithm removal, and fitting the data by using a least square method to obtain a corresponding straight line, wherein the slope of the straight line is the fractal dimension in the processing time period.
An embodiment of the present application further provides an apparatus, which may include: one or more processors; and one or more machine readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the method of fig. 1. In practical applications, the device may be used as a terminal device, and may also be used as a server, where examples of the terminal device may include: the mobile terminal includes a smart phone, a tablet computer, an electronic book reader, an MP3 (Moving Picture Experts Group Audio Layer III) player, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop, a vehicle-mounted computer, a desktop computer, a set-top box, an intelligent television, a wearable device, and the like.
The present embodiment also provides a non-volatile readable storage medium, where one or more modules (programs) are stored in the storage medium, and when the one or more modules are applied to a device, the device may execute instructions (instructions) included in the data processing method in fig. 4 according to the present embodiment.
Fig. 5 is a schematic diagram of a hardware structure of a terminal device according to an embodiment of the present application. As shown, the terminal device may include: an input device 1100, a first processor 1101, an output device 1102, a first memory 1103, and at least one communication bus 1104. The communication bus 1104 is used to implement communication connections between the elements. The first memory 1103 may include a high-speed RAM memory, and may also include a non-volatile storage NVM, such as at least one disk memory, and the first memory 1103 may store various programs for performing various processing functions and implementing the method steps of the present embodiment.
Alternatively, the first processor 1101 may be, for example, a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor, or other electronic components, and the first processor 1101 is coupled to the input device 1100 and the output device 1102 through a wired or wireless connection.
Optionally, the input device 1100 may include a variety of input devices, such as at least one of a user-oriented user interface, a device-oriented device interface, a software programmable interface, a camera, and a sensor. Optionally, the device interface facing the device may be a wired interface for data transmission between devices, or may be a hardware plug-in interface (e.g., a USB interface, a serial port, etc.) for data transmission between devices; optionally, the user-facing user interface may be, for example, a user-facing control key, a voice input device for receiving voice input, and a touch sensing device (e.g., a touch screen with a touch sensing function, a touch pad, etc.) for receiving user touch input; optionally, the programmable interface of the software may be, for example, an entry for a user to edit or modify a program, such as an input pin interface or an input interface of a chip; the output devices 1102 may include output devices such as a display, audio, and the like.
In this embodiment, the processor of the terminal device includes a function for executing each module of the speech recognition apparatus in each device, and specific functions and technical effects may refer to the above embodiments, which are not described herein again.
Fig. 6 is a schematic hardware structure diagram of a terminal device according to an embodiment of the present application. FIG. 6 is a specific embodiment of the implementation of FIG. 5. As shown, the terminal device of the present embodiment may include a second processor 1201 and a second memory 1202.
The second processor 1201 executes the computer program code stored in the second memory 1202 to implement the method described in fig. 4 in the above embodiment.
The second memory 1202 is configured to store various types of data to support operations at the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, such as messages, pictures, videos, and so forth. The second memory 1202 may include a Random Access Memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.
Optionally, a second processor 1201 is provided in the processing assembly 1200. The terminal device may further include: communication component 1203, power component 1204, multimedia component 1205, speech component 1206, input/output interfaces 1207, and/or sensor component 1208. The specific components included in the terminal device are set according to actual requirements, which is not limited in this embodiment.
The processing component 1200 generally controls the overall operation of the terminal device. The processing assembly 1200 may include one or more second processors 1201 to execute instructions to perform all or part of the steps of the data processing method described above. Further, the processing component 1200 can include one or more modules that facilitate interaction between the processing component 1200 and other components. For example, the processing component 1200 can include a multimedia module to facilitate interaction between the multimedia component 1205 and the processing component 1200.
The power supply component 1204 provides power to the various components of the terminal device. The power components 1204 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the terminal device.
The multimedia components 1205 include a display screen that provides an output interface between the terminal device and the user. In some embodiments, the display screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the display screen includes a touch panel, the display screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation.
The voice component 1206 is configured to output and/or input voice signals. For example, the voice component 1206 includes a Microphone (MIC) configured to receive external voice signals when the terminal device is in an operational mode, such as a voice recognition mode. The received speech signal may further be stored in the second memory 1202 or transmitted via the communication component 1203. In some embodiments, the speech component 1206 further comprises a speaker for outputting speech signals.
The input/output interface 1207 provides an interface between the processing component 1200 and peripheral interface modules, which may be click wheels, buttons, etc. These buttons may include, but are not limited to: a volume button, a start button, and a lock button.
The sensor component 1208 includes one or more sensors for providing various aspects of status assessment for the terminal device. For example, the sensor component 1208 may detect an open/closed state of the terminal device, relative positioning of the components, presence or absence of user contact with the terminal device. The sensor assembly 1208 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact, including detecting the distance between the user and the terminal device. In some embodiments, the sensor assembly 1208 may also include a camera or the like.
The communication component 1203 is configured to facilitate communications between the terminal device and other devices in a wired or wireless manner. The terminal device may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In one embodiment, the terminal device may include a SIM card slot therein for inserting a SIM card therein, so that the terminal device may log onto a GPRS network to establish communication with the server via the internet.
As can be seen from the above, the communication component 1203, the voice component 1206, the input/output interface 1207 and the sensor component 1208 referred to in the embodiment of fig. 6 can be implemented as the input device in the embodiment of fig. 5.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which can be made by those skilled in the art without departing from the spirit and technical spirit of the present invention be covered by the claims of the present invention.
Claims (13)
1. An alum blossom image processing method is characterized by comprising the following steps:
the method comprises the following steps of collecting a real-time image of underwater alum blossom, and carrying out enhancement processing on the real-time image to obtain enhanced image information, wherein the enhancement processing comprises one of the following steps: contrast limitation and brightness adjustment;
carrying out binarization processing on the enhanced image information to obtain binarized image information;
determining outline information of the alum blossom according to the binarized image information, wherein the outline information comprises one of the following items: the method comprises the following steps of alumen ustum outline area, area, alumen ustum image gray level variance, alumen ustum image gradient, alumen ustum image kurtosis, alumen ustum image entropy and alumen ustum image fractal dimension.
2. The method for processing alumen ustum image according to claim 1, wherein the step of limiting the contrast comprises: and limiting the contrast of the real-time image, and acquiring a threshold histogram.
3. The method for processing alumen ustum image according to claim 1, wherein the step of adjusting brightness comprises: and adjusting the brightness of the real-time image, and carrying out binarization on the real-time image according to the gray level of the alum blossom outline.
4. The alumen ustum image processing method according to claim 1, wherein the step of binarizing the enhanced image information includes: extracting a alum blossom outline, and determining an alum blossom outline region and a region area; and determining a water background area according to the alumen ustum outline area.
5. The alumen ustum image processing method according to claim 4, wherein the mathematical expression for determining the area of the region is:
wherein Equisalrentdiameter is the equivalent diameter of the alum blossom outline area, and ContourARea is the total area of the alum blossom outline area.
6. The method for processing an alumen ustum image according to claim 1, wherein the mathematical expression for determining the variance of the gray level of the alumen ustum image is as follows:
wherein s is alum blossom image gray variance, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
7. The method for processing an alumen ustum image according to claim 1, wherein the mathematical expression for determining the inclination of an alumen ustum image is:
wherein, skew is the gradient of alum blossom image, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
8. The method for processing an alumen ustum image according to claim 1, wherein the mathematical expression for determining the kurtosis of an alumen ustum image is:
wherein kurt is alumen ustum image kurtosis, xiThe gray scale of each pixel point of the alum blossom image is represented, n is more than or equal to i and more than or equal to 1, M is the average gray scale value of the alum blossom image, and n is the total number of pixels of the alum blossom image.
9. The method for processing alum blossom image according to claim 1, wherein the mathematical expression for determining the entropy of alum blossom image is:
wherein H is the entropy of the alum blossom image, and P (i) is the ratio of the number of pixels with the pixel value of i to the total number of pixels of the alum blossom image.
10. The method for processing the alumen ustum image according to claim 1, wherein the mathematical expression for determining the fractal dimension of the alumen ustum image is as follows:
A＝＝αLDf
wherein A is the area of the alumen ustum outline region, L is the outline perimeter of the alumen ustum outline region, α is the proportionality constant of the alumen ustum outline region, and Df is the fractal dimension of the alumen ustum image.
11. An alum blossom image processing system, comprising:
the image acquisition unit is used for acquiring real-time images of the underwater alum blossom;
the image processing unit is used for enhancing the real-time image to obtain enhanced image information, and performing binarization processing on the enhanced image information to obtain binarized image information;
an image analysis unit, configured to determine contour information of alum blossom according to the binarized image information, where the contour information includes one of: the method comprises the following steps of alumen ustum outline area, area, alumen ustum image gray level variance, alumen ustum image gradient, alumen ustum image kurtosis, alumen ustum image entropy and alumen ustum image fractal dimension.
12. An apparatus, comprising:
one or more processors; and
one or more machine-readable media having instructions stored thereon that, when executed by the one or more processors, cause the apparatus to perform the method recited by one or more of claims 1-10.
13. One or more machine-readable media having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform the method recited by one or more of claims 1-10.
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