Non-contact testing method, system, storage medium and processor for skin mites

Abstract

An embodiment of the present invention provides a non-contact testing method, system, processor and storage medium for skin mites. The method includes: obtaining a skin picture to be measured; inputting the skin picture into a mite image recognition model, and extracting recognition features from the skin picture through the mite image recognition model to determine the mite recognition features included in the skin picture; using a single recognition feature segmentation method to determine the number of target recognition region image units containing a single mite recognition feature; segmenting the skin picture to be measured according to the single mite recognition feature principle, ensuring that the segmented image units only contain a single mite recognition feature, and determining the number of mites included in the skin picture based on the number of target recognition region image units segmented. In this way, it is possible to quickly, efficiently and accurately detect the number of mites, which is one of the skin surface defects, in a safe and efficient non-contact manner.

Description

Technical Field

[0001] The present invention relates to the technical field of medical aesthetic skin detection, and particularly to a non-contact testing method, system, storage medium and processor for skin mites. Background Art

[0002] There are mainly two types of mites parasitizing on the human body. One is the follicular mite, also known as Demodex hominis, and the other is the sebaceous gland mite, which parasitizes in the sebaceous glands on the human face. Generally, it is simply referred to as mites. Mites are usually divided into several categories such as dust mites, flour mites, leather mites, and chigger mites. They are tiny pests that are not easily visible to the naked eye. Mites are widely distributed in dark corners of the living room, carpets, mattresses, pillows, sofas, air conditioners, bamboo mats, etc. Among them, dust mites have the widest distribution and the greatest impact. The corpses, secretions and excreta of mites are all allergens, which can cause allergic dermatitis, asthma, bronchitis, nephritis, allergic rhinitis and other diseases, seriously endangering human health. People with a high degree of mite infection can get sick, and although a small number of mites do not cause disease, they also affect the beauty effect. It can be found under a microscope that there are often several mites parasitizing in a single skin hair follicle. These mites live and die in the human skin hair follicles. At a light level, they block pores, making it difficult for the skin to absorb the nutrients in skin care products, resulting in dullness, causing skin problems such as enlarged pores, rough and oily skin, acne, acne, and small red bumps; at a severe level, it can develop into chronic skin diseases. Therefore, many dermatologists emphasize that "to be beautiful, first remove mites". To safely and effectively remove mites, mite-removing daily chemical products produced by regular manufacturers verified by authoritative scientific research institutions should be selected. The conventional microscopic detection method is to take a little sebum secretion from the nose or relatively oily parts and observe it under a microscope. Mites are a type of parasite, not bacteria, so when using a microscope for detection, the operator does not require a high-power microscope.

[0003] However, although microscopic observation is intuitive and scientific, it also has certain limitations. It may not be possible to see mites in a single detection, and multiple repeated detections are required, resulting in low detection efficiency. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a non-contact testing method, system, storage medium and processor for skin mites.

[0005] To achieve the above purpose, the first aspect of the present invention provides a non-contact testing method for skin mites, including:

[0006] Obtain a skin image to be measured;

[0007] Input the skin image into a mite image recognition model, and extract recognition features from the skin image through the mite image recognition model to determine the mite recognition features included in the skin image;

[0008] Determine the number of target recognition region image units containing a single mite recognition feature using the single recognition feature segmentation method;

[0009] Segment the skin picture to be measured according to the single mite recognition feature principle, ensuring that the segmented image units only contain a single mite recognition feature, and determine the number of mites contained in the skin picture based on the number of target recognition region image units.

[0010] Optionally, the method further includes:

[0011] Obtain at least ten facial sample pictures and mark them, with each facial sample picture containing at least one mite;

[0012] Input the facial sample pictures into the mite image recognition model to be trained, and extract the features of the mites contained in each facial sample picture through the mite image recognition model to be trained to obtain corresponding sample recognition features;

[0013] Segment the sample pictures according to the sample recognition features and the single mite recognition feature principle to ensure that each segmented sample picture unit only contains a single recognition feature, and the number of segmented picture units is the number of mites contained in the sample picture;

[0014] Determine the prediction accuracy rate of the mite image recognition model to be trained according to the marked number of mites;

[0015] When the prediction accuracy rate is higher than the correct rate threshold, determine that the mite image recognition model to be trained is trained.

[0016] Optionally, determining the number of mites contained in the skin picture based on the number of segmented target recognition region image units includes: for each facial sample picture, integrate multiple target images occupied by the same mite according to the single recognition feature principle to determine the corresponding regional image unit; determine the number of regional image units of each facial sample picture as the number of mites contained in each facial sample picture.

[0017] Optionally, the method further includes: obtaining the skin picture to be measured through an image acquisition device, where the image acquisition device has an optical lens module with an automatic focusing function. When performing image acquisition, the image acquisition device automatically adjusts the lens focal length so that the object to be measured is perfectly imaged on the receiving element sensor plane, and accurately measures the distance from the object to be measured to the lens and the distance from the lens to the receiving plane.

[0018] Optionally, the method further includes: after determining the number of mites contained in the skin picture, determining the severity level of the skin according to the number of mites; determining the corresponding treatment plan according to the severity level.

[0019] In a second aspect of the present invention, a processor is provided, which is configured to execute the above non-contact skin mite testing method.

[0020] In a third aspect of the present invention, a non-contact skin mite testing system is provided, comprising:

[0021] the above-mentioned processor; and an image acquisition device for acquiring skin pictures to be measured.

[0022] Optionally, the system further includes a display device for displaying the number of mites contained in the skin picture and / or the treatment plan for the skin picture.

[0023] Optionally, the display device has a human-computer interaction function.

[0024] In a fourth aspect of the present invention, a machine-readable storage medium is provided, on which instructions are stored, and when the instructions are executed by a processor, the processor is configured to execute the above non-contact skin mite testing method.

[0025] In the above non-contact skin mite testing method, by acquiring a skin picture to be measured, inputting the skin picture into a mite image recognition model, extracting features from the skin picture through the mite image recognition model to determine the mite recognition features contained in the skin picture, after determining the number of target region image units containing a single mite recognition feature, determining the number of mites contained in the skin picture based on the number of target recognition region image units. In this way, it is possible to achieve a safe, efficient and non-contact method for quickly, efficiently and accurately detecting mites, which are one of the skin surface defects. The number of mites provides an almost perfect solution for detecting mites, one of the skin surface defects. The provided intrinsically safe product can give a fair and objective evaluation of mite data.

[0026] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. They are used together with the following specific implementation to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings:

[0028] Figure 1 Schematically shows a flowchart of the non-contact skin mite testing method according to an embodiment of the present invention;

[0029] Figure 2 Schematically shows a schematic diagram of mite testing according to an embodiment of the present invention;

[0030] Figure 3 Schematically shows the internal structure diagram of a computer device according to an embodiment of the present invention. Detailed implementation manners

[0031] The following will describe in detail the specific implementation manners of the embodiments of the present invention in conjunction with the accompanying drawings. It should be understood that the specific implementation manners described herein are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

[0032] Figure 1 Schematically shows the flowchart of a non-contact testing method for skin mites according to an embodiment of the present invention. As Figure 1 shown, in an embodiment of the present invention, taking a processor as an example, a non-contact testing method for skin mites is provided, including the following steps:

[0033] Step 101, obtain a skin picture to be measured.

[0034] Step 102, input the skin picture into a mite image recognition model, and extract recognition features from the skin picture through the mite image recognition model to determine the mite recognition features included in the skin picture.

[0035] Step 103, use the single recognition feature segmentation method to determine the number of target recognition region image units containing a single mite recognition feature.

[0036] Step 104, segment the skin picture to be measured according to the principle of single mite recognition feature, ensure that the segmented image units only contain a single mite recognition feature, and determine the number of mites included in the skin picture based on the number of target recognition region image units segmented.

[0037] The processor can obtain the skin picture of the skin to be recognized through an image acquisition device. Among them, the size of the image receiving sensor of the optical lens module, the vertical and horizontal distribution of pixels, and the size of pixel units determine the technical indicators of the detection function characteristics of the lens. The image acquisition device in this solution has an optical lens module with an automatic focusing function. First, automatically adjust the lens focal length so that the object to be measured is perfectly imaged on the sensor plane of the receiving element, and accurately measure the distance from the object to be measured to the lens (object distance) and the distance from the lens to the receiving plane (image distance), and indirectly obtain the imaging ratio of the lens as the technical basis for implementing this solution.

[0038] After obtaining the skin picture to be measured, the processor can input the skin picture into a mite image recognition model, extract features from the skin picture through the mite image recognition model to determine the mite features included in the skin picture. Then, the number of target region image units containing a single mite recognition feature can be determined, and the number of mites included in the skin picture can be determined based on the number of target region image units.

[0039] In one embodiment, the above method further includes a training step for the mite image recognition model. The training step includes: obtaining at least ten facial sample pictures and making labels, where each facial sample picture contains at least one mite; inputting the facial sample pictures into the mite image recognition model to be trained, and extracting the features of the mites contained in each facial sample picture through the mite image recognition model to be trained to obtain corresponding sample recognition features; segmenting the sample pictures according to the sample recognition features and the single mite recognition feature principle to ensure that each segmented sample picture unit contains only a single recognition feature, and the number of segmented picture units is the number of mites contained in the sample picture; determining the prediction accuracy rate of the mite image recognition model to be trained according to the marked number of mites; when the prediction accuracy rate is higher than the correct rate threshold, determining that the mite image recognition model to be trained is trained. Among them, the correct rate threshold can be set to 90%, indicating that when the prediction accuracy rate of the mite image recognition model to be trained is higher than 90%, it can be determined that the mite image recognition model to be trained is trained. To ensure the reliability of model training, the model can also be tested with a batch of test data. Different from the sample data during training, the test data can include facial pictures without mites to test the test accuracy rate of the model. When the test accuracy rate of the mite image recognition model to be trained is higher than 90%, it can be determined that the mite image recognition model to be trained is trained.

[0040] In one embodiment, determining the number of mites contained in the skin picture according to the number of target recognition region image units segmented includes: for each facial sample picture, integrating multiple target image pixels occupied by the same mite according to the single recognition feature principle to determine the corresponding region image unit; determining the number of region image units of each facial sample picture as the number of mites contained in each facial sample picture.

[0041] Mites belong to a class of tiny animals in the Arachnida, Eurypterida of the Arthropoda phylum. Their body size is as small as 0.1 mm, generally about 0.5 mm, and most species are less than 1 mm. The corresponding number of pixels is 6 - 60 pixels. Each recognition feature on the same picture is used as the counting identifier for one mite. In this way, the region image unit corresponding to each mite can be determined. Then, for each facial sample picture, the number of single recognition feature region image units contained in each facial sample picture can be counted. Since each region image unit corresponds to each mite, the number of region image units of each facial sample picture can be determined as the number of mites contained in each facial sample picture.

[0042] In one embodiment, the method further includes: after determining the number of mites included in the skin picture, determining the severity level of the skin according to the number of mites; and determining a corresponding treatment plan according to the severity level.

[0043] After detecting the skin problem corresponding to the skin picture to be measured, the severity level of the skin can also be determined according to the number of mites included in the picture, and a corresponding treatment plan can be determined according to the severity level of the skin. Further, the patient's condition data can also be given, such as the drugs to which the user is allergic, the user's working environment and living environment, to further optimize the treatment plan.

[0044] In a specific embodiment, Figure 2 A schematic diagram of mites according to an embodiment of the present invention is schematically shown. The image acquisition device uses a 1 / 1.65-inch image sensor with an effective diagonal length of 9.72 mm. Converting to an aspect ratio of 3 / 4 for the picture format, the true size of the image sensor can be obtained as 7.78x5.83 mm. The pixel array is 3648(H)×2736(V), and the pixel scale is 2.13 um. The first test subject is a healthy person: the number of mites is zero. The second test subject has facial blemishes: the number of mites is 13.

[0045] In the above non-contact test method for skin mites, by obtaining the skin picture to be measured, inputting the skin picture into the mite image recognition model, extracting features from the skin picture through the mite image recognition model to determine the mite recognition features included in the skin picture, using the single recognition feature segmentation method to divide a total of 13 target region image units containing single mite recognition features, and determining the number of mites included in the measured skin picture as 13 according to the number of target region image units. In this way, in a safe and efficient non-contact manner, for mites, which are one of the skin surface blemishes, the presence of mites can be quickly, efficiently and accurately determined and the number can be detected, providing an almost perfect solution for the detection of mites, one of the skin surface blemishes. The provided intrinsically safe product can give a fair and objective evaluation of mite data.

[0046] An embodiment of the present invention provides a processor, which is used to run a program. When the program runs, it executes the above non-contact test method for skin mites.

[0047] An embodiment of the present invention provides a storage medium, on which a program is stored. When the program is executed by a processor, it implements the above non-contact test method for skin mites.

[0048] In one embodiment, a non-contact test device for skin mites is further provided, including the above processor, which is used to run a program. When the program runs, it executes the above non-contact test method for skin mites.

[0049] In one embodiment, a non-contact testing system for skin mites is further provided. The system includes:

[0050] The above-mentioned processor; and an image acquisition device for acquiring skin pictures to be measured.

[0051] In one embodiment, the system further includes a display device for displaying the number of mites contained in the skin picture and / or a treatment plan for the skin picture to the user.

[0052] In one embodiment, the display device may further have a human-computer interaction function.

[0053] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in Figure 3 The figure. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure), and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store data such as skin pictures. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, it realizes a non-contact testing method for skin mites.

[0054] Those skilled in the art can understand that Figure 3 the structure shown in the figure is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0055] An embodiment of the present invention provides a device, which includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, the following steps are implemented: obtaining a skin picture to be measured; inputting the skin picture into a mite image recognition model, and extracting recognition features from the skin picture through the mite image recognition model to determine the mite recognition features included in the skin picture; using a single recognition feature segmentation method to determine the number of target recognition region image units containing a single mite recognition feature; segmenting the skin picture to be measured according to the single mite recognition feature principle, ensuring that the segmented image units only contain a single mite recognition feature, and determining the number of mites included in the skin picture based on the number of segmented target recognition region image units.

[0056] In one embodiment, the method further includes: obtaining at least ten facial sample pictures and marking them, with each facial sample picture containing at least one mite; inputting the facial sample pictures into a mite image recognition model to be trained, and extracting the features of the mites contained in each facial sample picture through the mite image recognition model to be trained to obtain corresponding sample recognition features; segmenting the sample pictures according to the sample recognition features and the single mite recognition feature principle to ensure that each segmented sample picture unit only contains a single recognition feature, and the number of segmented picture units is the number of mites contained in the sample picture; determining the prediction accuracy rate of the mite image recognition model to be trained according to the marked number of mites; and determining that the mite image recognition model to be trained is trained when the prediction accuracy rate is higher than the correct rate threshold.

[0057] In one embodiment, determining the number of mites included in the skin picture based on the number of segmented target recognition region image units includes: for each facial sample picture, integrating multiple target image pixels occupied by the same mite according to the single recognition feature principle to determine the corresponding region image unit; and determining the number of region image units of each facial sample picture as the number of mites included in each facial sample picture.

[0058] In one embodiment, the method further includes: obtaining the skin picture to be measured through an image acquisition device, where the image acquisition device has an optical lens module with an automatic focusing function. When performing image acquisition, the image acquisition device automatically adjusts the lens focal length so that the object to be measured is perfectly imaged on the receiving element sensor plane, and accurately measures the distance from the object to be measured to the lens and the distance from the lens to the receiving plane.

[0059] In one embodiment, the method further includes: after determining the number of mites included in the skin picture, determining the severity level of the skin according to the number of mites; and determining the corresponding treatment plan according to the severity level.

[0060] The present application also provides a computer program product which, when executed on a data processing device, is adapted to execute a program initialized with the following method steps: obtaining a skin picture to be measured; inputting the skin picture into a mite image recognition model, and extracting recognition features from the skin picture through the mite image recognition model to determine the mite recognition features included in the skin picture; using a single recognition feature segmentation method to determine the number of target recognition region image units containing a single mite recognition feature; segmenting the skin picture to be measured according to the single mite recognition feature principle, ensuring that the segmented image units only contain a single mite recognition feature, and determining the number of mites included in the skin picture based on the number of target recognition region image units segmented out.

[0061] In one embodiment, the method further includes: obtaining at least ten facial sample pictures and performing marking, where each facial sample picture contains at least one mite; inputting the facial sample pictures into a mite image recognition model to be trained, and extracting the features of the mites included in each facial sample picture through the mite image recognition model to be trained to obtain corresponding sample recognition features; segmenting the sample pictures according to the sample recognition features and the single mite recognition feature principle to ensure that each segmented sample picture unit only contains a single recognition feature, and the number of segmented picture units is the number of mites included in the sample picture; determining the prediction accuracy rate of the mite image recognition model to be trained according to the marked number of mites; and determining that the mite image recognition model to be trained is trained when the prediction accuracy rate is higher than the correct rate threshold.

[0062] In one embodiment, determining the number of mites included in the skin picture based on the number of target recognition region image units segmented out includes: for each facial sample picture, integrating multiple target image pixels occupied by the same mite according to the single recognition feature principle to determine the corresponding region image unit; and determining the number of region image units of each facial sample picture as the number of mites included in each facial sample picture.

[0063] In one embodiment, the method further includes: obtaining a skin picture to be measured through an image acquisition device, where the image acquisition device is equipped with an optical lens module with an automatic focusing function. When performing image acquisition, the image acquisition device automatically adjusts the lens focal length so that the object to be measured is perfectly imaged on the receiving element sensor plane, and accurately measures the distance from the object to be measured to the lens and the distance from the lens to the receiving plane.

[0064] In one embodiment, the method further includes: after determining the number of mites included in the skin picture, determining the severity level of the skin according to the number of mites; and determining a corresponding treatment plan according to the severity level.

[0065] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0066] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0067] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0068] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0069] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0070] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0071] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media such as modulated data signals and carrier waves.

[0072] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0073] The above are only examples of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A non-contact testing method for skin mites, characterized in that The method includes: Obtaining a skin picture to be measured; Obtaining a skin picture to be measured through an image acquisition device, wherein the image acquisition device is equipped with an optical lens module with an automatic focusing function. When performing image acquisition, the image acquisition device automatically adjusts the lens focal length so that the object to be measured is perfectly imaged on the receiving element sensor plane, and accurately measures the distance from the object to be measured to the lens and the distance from the lens to the receiving plane; Inputting the skin picture into a mite image recognition model, and extracting recognition features from the skin picture through the mite image recognition model to determine the mite recognition features included in the skin picture; Using a single recognition feature segmentation method to determine the number of target recognition area image units containing a single mite recognition feature; Segmenting the skin picture to be measured according to the principle of a single mite recognition feature to ensure that the segmented image units only contain a single mite recognition feature, and determining the number of mites included in the skin picture based on the number of target recognition area image units segmented; The method further includes: Obtaining at least ten facial sample pictures and performing marking, with at least one mite included in each facial sample picture; Inputting the facial sample pictures into a mite image recognition model to be trained, and extracting the features of the mites included in each facial sample picture through the mite image recognition model to be trained to obtain corresponding sample recognition features; Segmenting the sample pictures according to the sample recognition features and the principle of a single mite recognition feature to ensure that each segmented sample picture unit only contains a single recognition feature, and the number of segmented picture units is the number of mites included in the sample picture; Determining the prediction accuracy rate of the mite image recognition model to be trained according to the marked number of mites; When the prediction accuracy rate is higher than the correct rate threshold, determining that the mite image recognition model to be trained is trained; The determining the number of mites included in the skin picture based on the number of target recognition area image units segmented includes: For each facial sample picture, integrating multiple target image pixels occupied by the same mite according to the principle of a single recognition feature to determine the corresponding area image unit; Determining the number of area image units of each facial sample picture as the number of mites included in each facial sample picture.

2. The method according to claim 1, characterized in that, The method further includes: After determining the number of mites included in the skin picture, determining the severity level of the skin according to the number of mites; Determining a corresponding treatment plan according to the severity level.

3. A processor, characterized in that, Configured to execute the non-contact skin mite testing method according to any one of claims 1 to 2.

4. A non-contact testing system for skin mites, characterized in that, The system includes: A processor according to claim 3; An image acquisition device for acquiring a skin picture to be measured.

5. The system according to claim 4, wherein The system further includes a display device for displaying the number of mites included in the skin picture and / or the treatment plan for the skin picture.

6. The system according to claim 5, wherein The display device has a human-computer interaction function.

7. A machine-readable storage medium having instructions stored thereon, characterized in that, When the instruction is executed by the processor, the processor is configured to execute the non-contact skin mite testing method according to any one of claims 1 to 2.

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