Scalp grease testing method, device and equipment based on test paper and medium

Abstract

The invention relates to a scalp grease testing method, device and equipment based on test paper and a medium, and the method comprises the following steps: acquiring a used test paper image; inputting the used test paper image into an object segmentation model to obtain a test paper mask area; performing background removal processing on the test paper mask area to obtain a test area with a white background; and inputting the test area with the white background into a grease quantity analysis model to obtain scalp grease quantity data of the user. According to the invention, the economic cost and time cost of the test can be reduced, and the home data observation of the subject can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of scalp health detection, and in particular to a scalp oil testing method, device, equipment and medium based on test paper. Background Art

[0002] At present, scalp oil detection mainly relies on the following methods:

[0003] Laboratory testing: Scalp samples are collected and the oil content is quantitatively analyzed using chemical reagents. While this method is highly accurate, it requires specialized laboratory equipment and personnel. The testing process is cumbersome, time-consuming, and expensive, making it unsuitable for daily use.

[0004] Visual assessment by a dermatologist: Doctors assess oiliness by visually inspecting the scalp's shine and oil production. This method is highly subjective, lacks objective quantitative criteria, and struggles to accurately distinguish subtle differences in oil production.

[0005] Electronic instrument testing: Some electronic instruments assess oil content by measuring the electrical conductivity of the scalp or the optical reflectivity of oil. While these instruments can provide some quantitative data, they are bulky, expensive, and require regular calibration, making them unsuitable for home use by the average consumer. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a scalp oil testing method, device, equipment and medium based on test paper, which can reduce the economic cost and time cost of the test and realize the home data observation of the subject.

[0007] The technical solution adopted by the present invention to solve the technical problem is to provide a scalp oil testing method based on a test paper, comprising the following steps:

[0008] Obtaining an image of the test strip after use;

[0009] Inputting the used test paper image into an object segmentation model to obtain a test paper mask area;

[0010] Performing background removal processing on the test paper mask area to obtain a test area with a white background;

[0011] The test area with a white background is input into the oil amount analysis model to obtain the user's scalp oil amount data.

[0012] The object segmentation model is obtained by training the test papers in the training set after contour annotation.

[0013] The background removal process is performed on the test paper mask area to obtain a test area with a white background, specifically comprising:

[0014] Constructing a minimum circumscribed rectangle for the outer contour of the test paper mask area, and calculating the direction of the main axis of the minimum circumscribed rectangle;

[0015] Constructing a mask according to the main axis direction, wherein the mask is used to indicate the negative side of the main axis direction vector;

[0016] Obtaining an image of a test area according to the mask, and affine transforming the image of the test area into a blank rectangular image, wherein the size of the blank rectangular image is the same as the minimum circumscribed rectangle;

[0017] A blank image is created and the inverted mask is applied to the blank image to obtain a first image. Background removal is performed on the blank rectangular image of the image with the test area, and the mask is applied to the background-removed image to obtain a second image. The first image and the second image are ORed to obtain a test area with a white background.

[0018] The calculation of the main axis direction of the minimum circumscribed rectangle is specifically as follows: determining the centroid of the minimum circumscribed rectangle, determining the center of mass of the test paper mask area, and taking the direction in which the centroid of the minimum circumscribed rectangle points to the center of mass of the test paper mask area as the main axis direction of the minimum circumscribed rectangle.

[0019] The oil content analysis model is trained on a dataset of more than 400 test area images and Sebumeter values ​​at adjacent hairline locations. The scalp oil content data output by the model includes the ratio of the grayscale value integral of the test paper print to the test area area, the ratio of the test paper print area to the test area area, the area ratio of the top 10 independent points on the test paper print, and the ratio of the top 5-10 to the top 20-50 points on the test paper print.

[0020] The ratio of the grayscale value integral of the test paper print to the area of ​​the test area refers to the calculation of the basic segmentation threshold by the maximum entropy algorithm, and then taking three step thresholds in combination with the step threshold method, determining four area data under different step threshold ranges based on the obtained step thresholds, integrating the grayscale of the test area over the four areas, and dividing them by the area of ​​the test area to obtain four ratios, and performing a first weighted processing on the four ratios; the ratio of the grayscale value integral of the test paper print to the area of ​​the test area refers to the calculation of the basic segmentation threshold by the maximum entropy algorithm, and then taking three step thresholds in combination with the step threshold method, determining four area data under different step threshold ranges based on the obtained step thresholds, integrating the grayscale of the test area over the four areas, and dividing them by the area of ​​the test area. The four ratios obtained are subjected to a second weighted processing; the area ratio of the top 10 independent points of the test paper print refers to multiple contours obtained by performing medium threshold segmentation on the print of the test paper area, and sorting all independent contours in descending order of area, taking the top 10 contours as the sum of areas, and calculating the ratio of the summed area to the total area; the ratio of the top 5-10 to the top 20-50 of the test paper print refers to multiple contours obtained by performing medium threshold segmentation on the print of the test paper area, and sorting all independent contours in descending order of area, taking the 5th to 10th contours as the sum of areas, and the 20th to 50th contours as the sum of areas, and calculating the ratio of the sum of the areas of the 5th to 10th contours to the sum of the areas of the 20th to 50th contours.

[0021] Before processing the test area with a white background, the grease content analysis model further includes: dividing the test area with a white background into four intervals from the center, calculating the ratio of the test paper imprint area to the test area area of ​​each interval, and selecting the interval with the largest ratio of the test paper imprint area to the test area area for analysis.

[0022] The technical solution adopted by the present invention to solve the technical problem is to provide a scalp oil testing device based on a test paper, comprising:

[0023] An acquisition module, used to acquire an image of the test strip after use;

[0024] a segmentation module, configured to input the used test paper image into an object segmentation model to obtain a test paper mask area;

[0025] a processing module, configured to perform background removal processing on the test paper mask area to obtain a test area with a white background;

[0026] The analysis module is used to input the test area with a white background into the oil amount analysis model to obtain the user's scalp oil amount data.

[0027] The technical solution adopted by the present invention to solve its technical problem is: providing an electronic device, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned test paper-based scalp oil testing method are implemented.

[0028] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned test paper-based scalp oil testing method are implemented.

[0029] Beneficial effects

[0030] Due to the adoption of the above-mentioned technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention uses an object segmentation model to identify and segment the test paper mask area from the test paper image after use, performs an analysis on the test paper mask area to obtain a test area with a white background, and then uses the oil content analysis model to analyze the test area to obtain oil content data. This method only requires the user to take a photo of the test paper after use and upload it to a terminal or platform to obtain accurate oil content data. The entire detection process does not require complicated operating steps, professional equipment or professional guidance. Ordinary consumers can easily complete the test in a home environment, and the detection time is only 1-2 minutes, which greatly improves the convenience and practicality of the test. Compared with traditional laboratory testing and electronic instrument testing, the test paper detection method of the present invention is extremely low-cost, with a single detection cost of only a few yuan, which lowers the detection threshold, makes scalp oil detection more popular, and is suitable for large-scale promotion and use. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is a flow chart of a scalp oil testing method based on a test paper according to a first embodiment of the present invention;

[0032] Figure 2 Schematic diagram of an image of a test paper after use in the first embodiment of the present invention. DETAILED DESCRIPTION

[0033] Below in conjunction with specific embodiment, further set forth the present invention.Should be understood that these embodiments are only used to illustrate the present invention and are not used in limiting the scope of the present invention.In addition, should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall equally within the scope limited by the appended claims of the application.

[0034] The first embodiment of the present invention relates to a scalp oil testing method based on a test paper, such as Figure 1 As shown, the following steps are included:

[0035] Step 1: Get the image of the test paper after use (see Figure 2 );

[0036] Step 2: Input the used test strip image into an object segmentation model to obtain a test strip mask area. In this step, the object segmentation model is trained by annotating the test strips in the training set. This test strip mask area can be used to eliminate the impact of cluttered environment on subsequent analysis.

[0037] Step 3: Perform background removal on the test paper mask area to obtain a test area with a white background. This step specifically includes:

[0038] A minimum circumscribed rectangle is constructed for the outer contour of the test paper mask area, and the direction of the main axis of the minimum circumscribed rectangle is calculated; wherein the direction of the main axis of the minimum circumscribed rectangle is obtained by:

[0039] First, determine the centroid of the minimum circumscribed rectangle. The centroid is the center point in a geometric sense. For a rectangle, the coordinates of the centroid can be determined by the average value of the coordinates of the rectangle vertices. Second, determine the center of mass of the test paper mask area. The center of mass can be calculated and determined based on the weighted average of the positions of each pixel in the test paper mask area. Finally, the direction of the centroid of the minimum circumscribed rectangle pointing to the center of mass of the test paper mask area is used as the main axis direction of the minimum circumscribed rectangle. That is, the direction vector is obtained by subtracting the centroid of the minimum circumscribed rectangle from the center of mass of the test paper mask area. The direction vector is the main axis direction of the minimum circumscribed rectangle.

[0040] A mask is constructed based on the principal axis direction, the mask being used to represent the side where the principal axis direction vector is negative. In this step, a mask is constructed based on the principal axis direction to represent one side of the principal axis direction. Here, the side where the direction vector is negative is selected. This method of constructing a mask limits the mask to the measurement area of ​​the test paper (i.e., at the tip of the test paper), while excluding the gripping end of the test paper (i.e., the thick end).

[0041] An image of the test area is obtained based on the mask, and the image of the test area is affine transformed into a blank rectangular image, wherein the size of the blank rectangular image is the same as the minimum bounding rectangle. In this step, when performing the image affine transformation, the minimum bounding rectangle is obtained, and then its length and width are calculated, and then the image of the test area is affine transformed into a blank rectangular image of the same size as the minimum bounding rectangle.

[0042] A blank image is created and the inverted mask is applied to the blank image to obtain a first image. Background removal is performed on the blank rectangular image of the image with the test area, and the mask is applied to the background-removed image to obtain a second image. The first image and the second image are ORed to obtain a test area with a white background.

[0043] In step 4, the white background test area is input into a scalp oil content analysis model to obtain the user's scalp oil content data. The scalp oil content analysis model in this step is trained using a dataset of over 400 test area images and Sebumeter readings from adjacent hairline areas. The scalp oil content data output by the scalp oil content analysis model includes the ratio of the grayscale integral of the paper print to the test area area, the ratio of the test paper print area to the test area area, the area ratio of the top 10 individual points on the test paper print, and the ratio of the top 5-10 to the top 20-50 points on the test paper print.

[0044] Among them, the ratio of the integral of the grayscale value of the paper print to the area of ​​the test area refers to calculating the basic segmentation threshold by the maximum entropy algorithm, and then taking three step thresholds in combination with the step threshold method. According to the obtained step thresholds, four area data under different step threshold ranges are determined, and the grayscale of the test area is integrated over the four areas respectively, and the four ratios are divided by the area of ​​the test area respectively, and the four ratios are subjected to the first weighted processing.

[0045] The ratio of the test paper print area to the test area area refers to calculating the basic segmentation threshold through the maximum entropy algorithm, and then taking three step thresholds in combination with the step threshold method. According to the obtained step thresholds, four area data under different step threshold ranges are determined, and the grayscale of the test area is integrated over the four areas respectively, and the four ratios are divided by the area of ​​the test area respectively, and the four ratios are subjected to a second weighted processing.

[0046] The area ratio of the top 10 independent points on the test strip imprint refers to the multiple contours obtained by performing medium threshold segmentation on the test strip imprint. All independent contours are sorted in descending order of area, the areas of the top 10 contours are summed, and the ratio of this summed area to the total area is calculated.

[0047] The ratio of the top 5-10 to top 20-50 test strip traces refers to the multiple contours obtained by performing medium threshold segmentation on the test strip trace. All independent contours are sorted in descending order of area, and the areas of the 5th to 10th contours and the 20th to 50th contours are summed. The ratio of the sum of the areas of the 5th to 10th contours to the sum of the areas of the 20th to 50th contours is calculated.

[0048] In order to reduce the detection error caused by uneven imprinting, the oil amount analysis model also includes, before processing the test area with a white background: dividing the test area with a white background into four intervals from the center, calculating the ratio of the test paper imprint area to the test area area of ​​each interval, and selecting the interval with the largest ratio of the test paper imprint area to the test area area for analysis, that is, for the selected interval, calculating the ratio of the paper imprint grayscale value integral to the test area area, the ratio of the test paper imprint area to the test area area, the area ratio of the top 10 independent points of the test paper imprint, and the ratio of the top 5-10 to the top 20-50 of the test paper imprint. The above indicators calculated for this interval are used as the user's scalp oil amount data.

[0049] It is not difficult to find that the present invention uses an object segmentation model to identify and segment the test paper mask area from the used test paper image, performs a test on the test paper mask area, obtains a test area with a white background, and then uses the oil content analysis model to analyze the test area to obtain oil content data. This method only requires the user to take a photo of the used test paper and upload it to a terminal or platform to obtain accurate oil content data. The entire detection process does not require complex operating steps, professional equipment, or professional guidance. Ordinary consumers can easily complete the test in a home environment, and the detection time is only 1-2 minutes, greatly improving the convenience and practicality of the test. Compared with traditional laboratory testing and electronic instrument testing, the test paper detection method of the present invention is extremely low-cost, with a single test cost of only a few yuan, lowering the detection threshold, making scalp oil detection more popular, and suitable for large-scale promotion and use.

[0050] A second embodiment of the present invention relates to a scalp oiliness testing device based on a test paper, comprising:

[0051] An acquisition module, used to acquire an image of the test strip after use;

[0052] a segmentation module, configured to input the used test paper image into an object segmentation model to obtain a test paper mask area;

[0053] a processing module, configured to perform background removal processing on the test paper mask area to obtain a test area with a white background;

[0054] The analysis module is used to input the test area with a white background into the oil amount analysis model to obtain the user's scalp oil amount data.

[0055] The object segmentation model is obtained by training the test papers in the training set after contour annotation.

[0056] The processing module includes:

[0057] A circumscribed rectangle calculation unit, configured to construct a minimum circumscribed rectangle for the outer contour of the test paper mask area and calculate the direction of the main axis of the minimum circumscribed rectangle;

[0058] a construction unit, configured to construct a mask according to the main axis direction, wherein the mask is used to indicate a side where the main axis direction vector is negative;

[0059] an image affine transformation unit, configured to obtain an image of a test area according to the mask, and affine transform the image of the test area into a blank rectangular image, wherein the size of the blank rectangular image is the same as that of the minimum bounding rectangle;

[0060] The processing unit is configured to create a new blank image, apply the inverted mask to the blank image to obtain a first image, perform background removal processing on the blank rectangular image of the image with the test area, apply the mask to the image after background removal to obtain a second image, and perform an OR operation on the first image and the second image to obtain a test area with a white background.

[0061] When calculating the main axis direction of the minimum circumscribed rectangle, the circumscribed rectangle calculation unit determines the centroid of the minimum circumscribed rectangle, determines the center of mass of the test paper mask area, and takes the direction in which the centroid of the minimum circumscribed rectangle points to the center of mass of the test paper mask area as the main axis direction of the minimum circumscribed rectangle.

[0062] The oil content analysis model is trained on a dataset of more than 400 test area images and Sebumeter values ​​at adjacent hairline locations. The scalp oil content data output by the model includes the ratio of the grayscale value integral of the test paper print to the test area area, the ratio of the test paper print area to the test area area, the area ratio of the top 10 independent points on the test paper print, and the ratio of the top 5-10 to the top 20-50 points on the test paper print.

[0063] The ratio of the grayscale value integral of the test paper print to the area of ​​the test area refers to the calculation of the basic segmentation threshold by the maximum entropy algorithm, and then taking three step thresholds in combination with the step threshold method, determining four area data under different step threshold ranges based on the obtained step thresholds, integrating the grayscale of the test area over the four areas, and dividing them by the area of ​​the test area to obtain four ratios, and performing a first weighted processing on the four ratios; the ratio of the grayscale value integral of the test paper print to the area of ​​the test area refers to the calculation of the basic segmentation threshold by the maximum entropy algorithm, and then taking three step thresholds in combination with the step threshold method, determining four area data under different step threshold ranges based on the obtained step thresholds, integrating the grayscale of the test area over the four areas, and dividing them by the area of ​​the test area. The four ratios obtained are subjected to a second weighted processing; the area ratio of the top 10 independent points of the test paper print refers to multiple contours obtained by performing medium threshold segmentation on the print of the test paper area, and sorting all independent contours in descending order of area, taking the top 10 contours as the sum of areas, and calculating the ratio of the summed area to the total area; the ratio of the top 5-10 to the top 20-50 of the test paper print refers to multiple contours obtained by performing medium threshold segmentation on the print of the test paper area, and sorting all independent contours in descending order of area, taking the 5th to 10th contours as the sum of areas, and the 20th to 50th contours as the sum of areas, and calculating the ratio of the sum of the areas of the 5th to 10th contours to the sum of the areas of the 20th to 50th contours.

[0064] Before processing the test area with a white background, the grease content analysis model further includes: dividing the test area with a white background into four intervals from the center, calculating the ratio of the test paper imprint area to the test area area of ​​each interval, and selecting the interval with the largest ratio of the test paper imprint area to the test area area for analysis.

[0065] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the test paper-based scalp oil testing method of the first embodiment are implemented.

[0066] A fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the computer program implements the steps of the test paper-based scalp oil testing method of the first embodiment.

[0067] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) that contain computer-usable program code.

[0068] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0069] These computer program instructions may 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, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction method, which is implemented in the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0071] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A scalp oil testing method based on test paper, characterized in that: The following steps are involved: Obtaining an image of the test strip after use; Inputting the used test paper image into an object segmentation model to obtain a test paper mask area; Performing background removal processing on the test paper mask area to obtain a test area with a white background; The test area with a white background is input into the oil amount analysis model to obtain the user's scalp oil amount data.

2. The scalp oil testing method based on test paper according to claim 1, characterized in that: The object segmentation model is obtained by training the test papers in the training set after contour annotation.

3. The scalp oil testing method based on test paper according to claim 1, characterized in that: The background removal process is performed on the test paper mask area to obtain a test area with a white background, specifically comprising: Constructing a minimum circumscribed rectangle for the outer contour of the test paper mask area, and calculating the direction of the main axis of the minimum circumscribed rectangle; Constructing a mask according to the main axis direction, wherein the mask is used to indicate the negative side of the main axis direction vector; Obtaining an image of a test area according to the mask, and affine transforming the image of the test area into a blank rectangular image, wherein the size of the blank rectangular image is the same as the minimum circumscribed rectangle; A blank image is created and the inverted mask is applied to the blank image to obtain a first image. Background removal is performed on the blank rectangular image of the image with the test area, and the mask is applied to the background-removed image to obtain a second image. The first image and the second image are ORed to obtain a test area with a white background.

4. The scalp oil testing method based on test paper according to claim 3, characterized in that: The calculation of the main axis direction of the minimum circumscribed rectangle is specifically as follows: determining the centroid of the minimum circumscribed rectangle, determining the center of mass of the test paper mask area, and taking the direction in which the centroid of the minimum circumscribed rectangle points to the center of mass of the test paper mask area as the main axis direction of the minimum circumscribed rectangle.

5. The scalp oil testing method based on test paper according to claim 1, characterized in that: The oil content analysis model is trained on a dataset of more than 400 test area images and Sebumeter values ​​at adjacent hairline locations. The scalp oil content data output by the model includes the ratio of the grayscale value integral of the test paper print to the test area area, the ratio of the test paper print area to the test area area, the area ratio of the top 10 independent points on the test paper print, and the ratio of the top 5-10 to the top 20-50 points on the test paper print.

6. The scalp oil testing method based on test paper according to claim 5, characterized in that: The ratio of the grayscale value integral of the test paper print to the area of ​​the test area refers to the calculation of the basic segmentation threshold by the maximum entropy algorithm, and then taking three step thresholds in combination with the step threshold method, determining four area data under different step threshold ranges based on the obtained step thresholds, integrating the grayscale of the test area over the four areas, and dividing them by the area of ​​the test area to obtain four ratios, and performing a first weighted processing on the four ratios; the ratio of the grayscale value integral of the test paper print to the area of ​​the test area refers to the calculation of the basic segmentation threshold by the maximum entropy algorithm, and then taking three step thresholds in combination with the step threshold method, determining four area data under different step threshold ranges based on the obtained step thresholds, integrating the grayscale of the test area over the four areas, and dividing them by the area of ​​the test area. The four ratios obtained are subjected to a second weighted processing; the area ratio of the top 10 independent points of the test paper print refers to multiple contours obtained by performing medium threshold segmentation on the print of the test paper area, and sorting all independent contours in descending order of area, taking the top 10 contours as the sum of areas, and calculating the ratio of the summed area to the total area; the ratio of the top 5-10 to the top 20-50 of the test paper print refers to multiple contours obtained by performing medium threshold segmentation on the print of the test paper area, and sorting all independent contours in descending order of area, taking the 5th to 10th contours as the sum of areas, and the 20th to 50th contours as the sum of areas, and calculating the ratio of the sum of the areas of the 5th to 10th contours to the sum of the areas of the 20th to 50th contours.

7. The scalp oil testing method based on test paper according to claim 5, characterized in that: Before processing the test area with a white background, the grease content analysis model further includes: dividing the test area with a white background into four intervals from the center, calculating the ratio of the test paper imprint area to the test area area of ​​each interval, and selecting the interval with the largest ratio of the test paper imprint area to the test area area for analysis.

8. A scalp oil testing device based on a test paper, characterized in that: include: An acquisition module, used to acquire an image of the test strip after use; a segmentation module, configured to input the used test paper image into an object segmentation model to obtain a test paper mask area; a processing module, configured to perform background removal processing on the test paper mask area to obtain a test area with a white background; The analysis module is used to input the test area with a white background into the oil amount analysis model to obtain the user's scalp oil amount data.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the scalp oil testing method based on a test paper as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the scalp oil testing method based on a test paper as claimed in any one of claims 1 to 7 are implemented.

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