CN117689672A - Facial mask nutrient solution spraying method based on image processing - Google Patents
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Abstract
The invention relates to a facial mask nutrient solution spraying method based on image processing, which comprises the steps of controlling the total mass of nutrient solution sprayed on each facial mask in the spraying process of each facial mask, and recording the total mass value; after spraying is finished, acquiring an image of a mask, recording the time length L from the completion of spraying to the acquisition of the image, and pre-establishing a mapping relation between gray scale values of gray scale images of the mask and the corresponding concentration of nutrient solution of the mask; measuring the liquid diffusion coefficient of the nutrient solution on the target mask substrate in advance; according to the method and the device, the high concentration area and the low concentration area of the nutrient solution on each divided image unit can be calculated according to the mapping relation, and the concentrated area and the missing area of the nutrient solution spraying on each divided image unit can be calculated according to the liquid diffusion coefficient of the target nutrient solution, the time length L from the completion of spraying to the image acquisition, and the mask nutrient solution spraying can be accurately realized and controlled in a feedback mode.
Description
Technical Field
The invention belongs to the field of cosmetic production, and particularly relates to a facial mask nutrient solution spraying method based on image processing.
Background
In the related art, whether the spraying of the mask nutrient solution is uniform or not has a great influence on the quality of the mask, and some related technologies improve the spraying uniformity of the mask nutrient solution through image processing, for example, the technologies disclosed in patent document CN117122803a perform spraying control through mounting monitoring equipment, image acquisition and image processing, but in practice, specific technical details cannot be given, the uniform control of the spraying of the mask can not be determined through the image processing, only a perceptual recognition can be performed, and the spraying of the mask nutrient solution cannot be accurately realized and feedback controlled.
Disclosure of Invention
The invention aims to provide a facial mask nutrient solution spraying method based on image processing, which aims to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme:
the mask nutrient solution spraying method based on image processing comprises the steps that firstly, in the spraying process of each mask, the total mass of nutrient solution sprayed on each mask is controlled, and the total mass value is recorded; then after spraying is finished, acquiring an image of the mask, recording the time length L from the completion of spraying to the acquisition of the image, and pre-establishing a mapping relation between gray scale values of gray scale images of the mask and the corresponding concentration of nutrient solution of the mask; measuring the liquid diffusion coefficient of the nutrient solution on the target mask substrate in advance; processing the acquired mask image into a gray level image according to the acquired mask image, processing the gray level image into a plurality of divided image units, and calculating a nutrient solution high concentration area and a nutrient solution low concentration area on each divided image unit according to a mapping relation;
then calculating a concentrated region and a missing region of nutrient solution spraying on each divided image unit during spraying according to the liquid diffusion coefficient of the target nutrient solution and the time length L from the completion of spraying to the acquisition of an image; and then determining a concentrated area and a missing area of the whole nutrient solution spraying of the acquired mask image, evaluating the concentrated area and the missing area of the spraying, and evaluating the uniformity of the spraying so as to adjust the spraying area.
Further, the mapping relation between the gray level value and the corresponding mask concentration value is that,
gray values y=ln (a+b (x 100-C))ln (a+b (x 100-C))d, where y is a gray value, x is a mask concentration value, A, B, C, D are constants, a is a section adjustment constant fixed to 1, B is a mask concentration value range adjustment constant, C is a number retention adjustment constant, and D is a gray value range adjustment constant.
Further, the mapping relation between the gray level value and the corresponding mask concentration value is that,
gray values y=ln (a+b (x 100-C))ln (a+b (x 100-C))d, where y is a gray value, x is a mask concentration value, A, B, C, D are constants, a is an interval adjustment constant fixed to 1, B is a mask concentration value range adjustment constant, C is a number retention adjustment constant, D is a gray value range adjustment constant, b=3, c=0.1, d=10, and x is a value range (C/100, 10/100).
Further, the gray scale image is processed into a plurality of divided image units, namely, the gray scale image is divided into a plurality of small area images with equal areas in average.
Further, calculating a high concentration area and a low concentration area of the nutrient solution on each divided image unit according to the mapping relation, specifically, calculating a concentration value of a target sampling point mask on each divided image unit according to the mapping relation, then determining an average value of the concentration of the mask of all the target sampling points, wherein an area with the concentration larger than the average value of the concentration of the mask of the target sampling points is the high concentration area of the nutrient solution, otherwise, the area with the concentration larger than the average value of the concentration of the mask of the target sampling points is the low concentration area of the nutrient solution; after the high concentration area and the low concentration area of the nutrient solution on each of the divided image units are calculated, the diffusion direction of the nutrient solution is determined to be the diffusion from the high concentration area to the low concentration area of the nutrient solution, then the original high concentration area and the low concentration area of the nutrient solution on each of the divided image units during spraying can be calculated according to the liquid diffusion coefficient of the target nutrient solution and the time length L from the completion of spraying to the acquisition of the image, the original high concentration area of the nutrient solution on the divided image units is the concentrated area of the nutrient solution spraying on the divided image units, and the original low concentration area of the nutrient solution on the divided image units is the missing area of the nutrient solution spraying on the divided image units.
Further, adjacent concentrated areas sprayed by all nutrient solutions are spliced together, namely concentrated areas sprayed by the whole nutrient solution of the collected facial mask, and adjacent missing areas sprayed by all nutrient solutions are spliced together, namely missing areas sprayed by the whole nutrient solution of the collected facial mask.
Advantageous effects
According to the method and the device, the high concentration area and the low concentration area of the nutrient solution on each divided image unit can be calculated according to the mapping relation, and the concentrated area and the missing area of the nutrient solution spraying on each divided image unit can be calculated according to the liquid diffusion coefficient of the target nutrient solution, the time length L from the completion of spraying to the image acquisition, and the mask nutrient solution spraying can be accurately realized and controlled in a feedback mode.
Drawings
Fig. 1 is a mapping relation function image of a specific gray scale value and a corresponding mask concentration value.
Detailed Description
The technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, 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.
The application discloses a facial mask nutrient solution spraying method based on image processing, which comprises the steps that firstly, in the spraying process of each facial mask, the total mass of nutrient solution sprayed on each facial mask is controlled, and the total mass value is recorded; then after spraying is finished, acquiring an image of the mask, recording the time length L from the completion of spraying to the acquisition of the image, and pre-establishing a mapping relation between gray scale values of gray scale images of the mask and the corresponding concentration of nutrient solution of the mask; the mapping relationship between the gray values (0 to 255) and the corresponding mask concentration values is that the gray value y=ln (a+b (x 100-C))ln (a+b (x 100-C)) ×d, where y is the gray value, x is the mask concentration value, A, B, C, D is a constant, a is an interval adjustment constant fixed to 1, B is a mask concentration value range adjustment constant, C is a number retention adjustment constant, D is a gray value range adjustment constant, and B, C, D values, such as b=3, c=0.1, and d=10, are determined according to the specific type of nutrient solution; in a general x-value range (C/100, 10/100), the mapping relationship between the gray-scale value and the corresponding mask concentration value is y=ln (1+3 (x 100-0.1)) ×ln (1+3 (x 100-0.1)) ×10, and the mapping relationship between the specific gray-scale value (0 to 255) and the corresponding mask concentration value may refer to fig. 1, where the horizontal axis in fig. 1 is the value of x 100 and the vertical axis is the value of y.
The liquid diffusion coefficient of the nutrient solution on the target mask substrate is measured in advance, and the specific calculation process of the liquid diffusion coefficient of the nutrient solution on the target mask substrate is as follows: determining a diffusion coefficient formula: the calculation formula of the liquid diffusion coefficient of the nutrient solution on the target mask substrate is d= (Q/a)/(deltac/deltav), and each parameter in the formula is determined: d represents the diffusion coefficient of the liquid, Q represents the mass of the substance passing through diffusion per unit time, a represents the diffusion area, Δc represents the variation of the substance concentration, and Δv represents the diffusion distance, which can be measured and substituted into the corresponding values for calculation. Through the steps, the liquid diffusion coefficient of the nutrient solution on the target mask substrate can be calculated.
Then the collected mask image is processed into a gray scale image according to the collected mask image, and the specific process of processing the image into the gray scale image is as follows: first, the image file to be processed needs to be read, and the color space is converted: converting an image from a color space (e.g., RGB) to a gray space typically involves converting each pixel of the image to a gray value, and calculating the gray value: the gray value is obtained by performing specific calculation on the RGB values of the original pixels, and a common calculation method is to perform weighted average on the values of three channels R, G, B to obtain a single gray value, and store a gray image: the calculated gray value is saved as a new image file, which is the gray image. The gray level map is processed into a plurality of divided image units (the gray level map is processed into a plurality of divided image units, namely the gray level map is divided into a plurality of small area images with equal areas in average, for example, a plurality of identical rectangular area images are divided, and a nutrient solution high concentration area and a nutrient solution low concentration area on each divided image unit are calculated according to a mapping relation, specifically, a target sampling point mask concentration value on the divided image units is calculated according to the mapping relation firstly, then the average value of the concentration of all target sampling point masks is determined, and the area with the concentration larger than the average value of the concentration of the target sampling point mask is the nutrient solution high concentration area, otherwise, the nutrient solution low concentration area is obtained;
then calculate the concentrated area and the omission area of spraying of nutrient solution on each divided image unit when spraying according to the liquid diffusion coefficient of the target nutrient solution and the time length L from the completion of spraying to the acquisition of the image, after calculating the concentrated area and the omission area of spraying of nutrient solution on each divided image unit, determine the diffusion direction of the nutrient solution as the diffusion of the nutrient solution from the concentrated area to the omission area, then calculate the concentrated area and the omission area of nutrient solution on each divided image unit when spraying according to the liquid diffusion coefficient of the target nutrient solution and the time length L from the completion of spraying to the acquisition of the image, namely the concentrated area of nutrient solution spraying on the divided image unit, namely the omission area of nutrient solution on the divided image unit;
then determining a concentrated area and a missing area of the whole image nutrient solution spraying of the collected mask, mutually splicing all adjacent concentrated areas of the whole image nutrient solution spraying of the collected mask, namely the concentrated area of the whole image nutrient solution spraying of the collected mask, mutually splicing all adjacent missing areas of the whole image nutrient solution spraying of the collected mask, namely the missing area of the whole image nutrient solution spraying of the collected mask, evaluating the concentrated area and the missing area of the spraying, and evaluating the uniformity of the spraying, thereby adjusting the spraying area.
Therefore, the method and the device can calculate the high concentration area and the low concentration area of the nutrient solution on each divided image unit according to the mapping relation, calculate the concentration area and the missing area of the nutrient solution spraying on each divided image unit when the spraying is finished and the image is acquired according to the liquid diffusion coefficient of the target nutrient solution and the time length L when the spraying is finished, and can accurately realize and feed back the control the mask nutrient solution spraying.
Embodiments of the present application that require protection include:
the mask nutrient solution spraying method based on image processing comprises the steps that firstly, in the spraying process of each mask, the total mass of nutrient solution sprayed on each mask is controlled, and the total mass value is recorded; then after spraying is finished, acquiring an image of the mask, recording the time length L from the completion of spraying to the acquisition of the image, and pre-establishing a mapping relation between gray scale values of gray scale images of the mask and the corresponding concentration of nutrient solution of the mask; measuring the liquid diffusion coefficient of the nutrient solution on the target mask substrate in advance; processing the acquired mask image into a gray level image according to the acquired mask image, processing the gray level image into a plurality of divided image units, and calculating a nutrient solution high concentration area and a nutrient solution low concentration area on each divided image unit according to a mapping relation;
then calculating a concentrated region and a missing region of nutrient solution spraying on each divided image unit during spraying according to the liquid diffusion coefficient of the target nutrient solution and the time length L from the completion of spraying to the acquisition of an image; and then determining a concentrated area and a missing area of the whole nutrient solution spraying of the acquired mask image, evaluating the concentrated area and the missing area of the spraying, and evaluating the uniformity of the spraying so as to adjust the spraying area.
Preferably, the mapping relationship between the gray scale value and the corresponding mask concentration value is that,
gray values y=ln (a+b (x 100-C))ln (a+b (x 100-C))d, where y is a gray value, x is a mask concentration value, A, B, C, D are constants, a is a section adjustment constant fixed to 1, B is a mask concentration value range adjustment constant, C is a number retention adjustment constant, and D is a gray value range adjustment constant.
Preferably, the mapping relationship between the gray scale value and the corresponding mask concentration value is that,
gray values y=ln (a+b (x 100-C))ln (a+b (x 100-C))d, where y is a gray value, x is a mask concentration value, A, B, C, D are constants, a is an interval adjustment constant fixed to 1, B is a mask concentration value range adjustment constant, C is a number retention adjustment constant, D is a gray value range adjustment constant, b=3, c=0.1, d=10, and x is a value range (C/100, 10/100).
Preferably, the gray scale map is processed into a plurality of divided image units, i.e. the gray scale map is divided equally into a number of small area images of equal area.
Preferably, a high concentration area and a low concentration area of the nutrient solution on each divided image unit are calculated according to the mapping relation, specifically, a concentration value of a target sampling point mask on each divided image unit is calculated according to the mapping relation, then an average value of the concentration of the mask of all the target sampling points is determined, a region with the concentration larger than the average value of the concentration of the mask of the target sampling points is a high concentration area of the nutrient solution, and otherwise, the region with the concentration larger than the average value of the concentration of the mask of the target sampling points is a low concentration area of the nutrient solution; after the high concentration area and the low concentration area of the nutrient solution on each of the divided image units are calculated, the diffusion direction of the nutrient solution is determined to be the diffusion from the high concentration area to the low concentration area of the nutrient solution, then the original high concentration area and the low concentration area of the nutrient solution on each of the divided image units during spraying can be calculated according to the liquid diffusion coefficient of the target nutrient solution and the time length L from the completion of spraying to the acquisition of the image, the original high concentration area of the nutrient solution on the divided image units is the concentrated area of the nutrient solution spraying on the divided image units, and the original low concentration area of the nutrient solution on the divided image units is the missing area of the nutrient solution spraying on the divided image units.
Preferably, adjacent concentrated areas sprayed by all nutrient solutions are spliced together, namely concentrated areas sprayed by the whole nutrient solution of the collected facial mask, and adjacent missing areas sprayed by all nutrient solutions are spliced together, namely missing areas sprayed by the whole nutrient solution of the collected facial mask.
The embodiment of the application also provides a computer device, which can comprise a terminal device or a server, and the data computing program of the facial mask nutrient solution spraying method based on image processing can be configured in the computer device. The computer device is described below.
If the computer device is a terminal device, the embodiment of the present application provides a terminal device, taking the terminal device as a mobile phone as an example:
the mobile phone comprises: radio Frequency (RF) circuitry, memory, input unit, display unit, sensors, audio circuitry, wireless fidelity (Wireless Fidelity, wiFi) module, processor, and power supply.
The RF circuit can be used for receiving and transmitting signals in the process of receiving and transmitting information or communication, particularly, after receiving downlink information of the base station, the downlink information is processed by the processor; in addition, the data of the design uplink is sent to the base station. Typically, RF circuitry includes, but is not limited to, antennas, at least one amplifier, transceivers, couplers, low noise amplifiers (Low NoiseAmplifier, LNA for short), diplexers, and the like. In addition, the RF circuitry may also communicate with networks and other devices via wireless communications. The wireless communication may use any communication standard or protocol, including but not limited to global system for mobile communications (Global System ofMobile communication, GSM for short), general packet radio service (GeneralPacket Radio Service, GPRS for short), code division multiple access (Code Division MultipleAccess, CDMA for short), wideband code division multiple access (Wideband Code Division MultipleAccess, WCDMA for short), long term evolution (Long Term Evolution, LTE for short), email, short message service (Short Messaging Service, SMS for short), and the like.
The memory may be used to store software programs and modules, and the processor executes the software programs and modules stored in the memory to perform various functional applications and data processing of the handset. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.
The input unit may be used to receive input numeric or character information and to generate key signal inputs related to user settings and function control of the handset. In particular, the input unit may include a touch panel and other input devices. The touch panel, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations thereon or thereabout by a user using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a predetermined program. Alternatively, the touch panel may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor, and can receive and execute commands sent by the processor. In addition, the touch panel may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. The input unit may include other input devices in addition to the touch panel. In particular, other input devices may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.
The display unit may be used to display information input by a user or information provided to the user and various menus of the mobile phone. The display unit may include a display panel, which may be optionally configured in the form of a liquid crystal display (LiquidCrystal Display, LCD) or an Organic Light-Emitting Diode (OLED) or the like. Further, the touch panel may overlay the display panel, and upon detection of a touch operation thereon or thereabout, the touch panel is transferred to the processor to determine the type of touch event, and the processor then provides a corresponding visual output on the display panel in accordance with the type of touch event. Although in the figures the touch panel and the display panel are shown as two separate components to implement the input and output functions of the cell phone, in some embodiments the touch panel and the display panel may be integrated to implement the input and output functions of the cell phone.
The handset may also include at least one sensor, such as a light sensor, a motion sensor, and other sensors. In particular, the light sensor may include an ambient light sensor that may configure the brightness of the display panel according to the brightness of ambient light, and a proximity sensor that may turn off the display panel and/or backlight when the phone is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and direction when stationary, and can be used for applications of recognizing the gesture of a mobile phone (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may also be configured with the handset are not described in detail herein.
Audio circuitry, speakers, and microphone may provide an audio interface between the user and the handset. The audio circuit can transmit the received electric signal after the audio data conversion to a loudspeaker, and the loudspeaker converts the electric signal into a sound signal to be output; on the other hand, the microphone converts the collected sound signals into electrical signals, which are received by the audio circuit and converted into audio data, which are processed by the audio data output processor and sent via the RF circuit to, for example, another mobile phone, or which are output to a memory for further processing.
WiFi belongs to a short-distance wireless transmission technology, and a mobile phone can help a user to send and receive an email, browse a webpage, access streaming media and the like through a WiFi module, so that wireless broadband Internet access is provided for the user. Although a WiFi module is illustrated, it is understood that it does not belong to the necessary configuration of the handset, and can be omitted entirely as needed within the scope of not changing the essence of the invention.
The processor is a control center of the mobile phone, and is connected with various parts of the whole mobile phone by various interfaces and lines, and executes various functions and processes data of the mobile phone by running or executing software programs and/or modules stored in the memory and calling data stored in the memory, so that the mobile phone is monitored integrally. In the alternative, the processor may include one or more processing units; preferably, the processor may integrate an application processor and a modem processor, wherein the application processor primarily handles operating systems, user interfaces, applications, etc., and the modem processor primarily handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor.
The handset further includes a power source (e.g., a battery) for powering the various components, preferably in logical communication with the processor through a power management system, such that functions such as managing charge, discharge, and power consumption are performed by the power management system.
Although not shown, the mobile phone may further include a camera, a bluetooth module, etc., which will not be described herein.
In this embodiment, the processor included in the terminal device further has the following functions:
and executing a data calculation program of the facial mask nutrient spraying method based on image processing.
If the computer device is a server, the embodiments of the present application further provide a server, where the server may generate a relatively large difference due to different configurations or performances, and may include one or more central processing units (Central Processing Units, abbreviated as CPUs) (e.g., one or more processors) and a memory, one or more storage media (e.g., one or more mass storage devices) storing application programs or data. The memory and storage medium may be transitory or persistent. The program stored on the storage medium may include one or more modules, each of which may include a series of instruction operations on the server. Still further, the central processor may be configured to communicate with a storage medium and execute a series of instruction operations on the storage medium on a server.
The server may also include one or more power supplies, one or more wired or wireless network interfaces, one or more input/output interfaces, and/or one or more operating systems, such as Windows Server, mac OS XTM, unixTM, linuxTM, freeBSDTM, etc.
In addition, the embodiment of the application also provides a storage medium for storing a computer program for executing the method provided by the embodiment.
The present embodiments also provide a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method provided by the above embodiments.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, where the above program may be stored in a computer readable storage medium, and when the program is executed, the program performs steps including the above method embodiments; and the aforementioned storage medium may be at least one of the following media: read-only Memory (ROM), RAM, magnetic disk or optical disk, etc.
It should be noted that, in the present specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment is mainly described in a different point from other embodiments. In particular, for the apparatus and system embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, with reference to the description of the method embodiments in part. The apparatus and system embodiments described above are merely illustrative, in which elements illustrated as separate elements may or may not be physically separate, and elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
Claims (6)
1. The mask nutrient solution spraying method based on image processing is characterized by comprising the steps of firstly, controlling the total mass of nutrient solution sprayed on each mask in the spraying process of each mask, and recording the total mass value; then after spraying is finished, acquiring an image of the mask, recording the time length L from the completion of spraying to the acquisition of the image, and pre-establishing a mapping relation between gray scale values of gray scale images of the mask and the corresponding concentration of nutrient solution of the mask; measuring the liquid diffusion coefficient of the nutrient solution on the target mask substrate in advance; processing the acquired mask image into a gray level image according to the acquired mask image, processing the gray level image into a plurality of divided image units, and calculating a nutrient solution high concentration area and a nutrient solution low concentration area on each divided image unit according to a mapping relation;
then calculating a concentrated region and a missing region of nutrient solution spraying on each divided image unit during spraying according to the liquid diffusion coefficient of the target nutrient solution and the time length L from the completion of spraying to the acquisition of an image; and then determining a concentrated area and a missing area of the whole nutrient solution spraying of the acquired mask image, evaluating the concentrated area and the missing area of the spraying, and evaluating the uniformity of the spraying so as to adjust the spraying area.
2. The method for spraying mask nutrient solution based on image processing according to claim 1, wherein the mapping relationship between gray values and corresponding mask concentration values is that gray values y=ln (a+b (x 100-C))xn (a+b (x 100-C))d, where y is a gray value, x is a mask concentration value, A, B, C, D are constants, a is an interval adjustment constant fixed to 1, B is a mask concentration value range adjustment constant, C is a number retention adjustment constant, and D is a gray value range adjustment constant.
3. The method for spraying mask nutrient solution based on image processing as claimed in claim 1, wherein the mapping relationship between the gray scale value and the corresponding mask concentration value is that,
gray values y=ln (a+b (x 100-C))ln (a+b (x 100-C))d, where y is a gray value, x is a mask concentration value, A, B, C, D are constants, a is an interval adjustment constant fixed to 1, B is a mask concentration value range adjustment constant, C is a number retention adjustment constant, D is a gray value range adjustment constant, b=3, c=0.1, d=10, and x is a value range (C/100, 10/100).
4. The image processing-based mask nutrient solution spraying method according to claim 1, wherein the gray scale image is processed into a plurality of divided image units, i.e. the gray scale image is divided into a plurality of small area images with equal areas on average.
5. The mask nutrient solution spraying method based on image processing according to claim 1, wherein a nutrient solution high concentration area and a nutrient solution low concentration area on each divided image unit are calculated according to a mapping relation, specifically, a target sampling point mask concentration value on each divided image unit is calculated according to the mapping relation, then an average value of all target sampling point mask concentrations is determined, and a region with a concentration larger than the average value of the target sampling point mask concentrations is a nutrient solution high concentration area, otherwise, a nutrient solution low concentration area; after the high concentration area and the low concentration area of the nutrient solution on each of the divided image units are calculated, the diffusion direction of the nutrient solution is determined to be the diffusion from the high concentration area to the low concentration area of the nutrient solution, then the original high concentration area and the low concentration area of the nutrient solution on each of the divided image units during spraying can be calculated according to the liquid diffusion coefficient of the target nutrient solution and the time length L from the completion of spraying to the acquisition of the image, the original high concentration area of the nutrient solution on the divided image units is the concentrated area of the nutrient solution spraying on the divided image units, and the original low concentration area of the nutrient solution on the divided image units is the missing area of the nutrient solution spraying on the divided image units.
6. The image processing-based mask nutrient solution spraying method according to claim 1, wherein adjacent concentrated areas of all nutrient solution sprays are spliced together, namely concentrated areas of the whole image nutrient solution spray of the collected mask, and adjacent missing areas of all nutrient solution sprays are spliced together, namely missing areas of the whole image nutrient solution spray of the collected mask.
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