WO2021052436A1 - Skin roughness measurement method and electronic device - Google Patents

Abstract

A skin roughness measurement method. According to a texture depth, a texture width, the density of wide texture, and a texture density extracted from a skin to be measured, a skin roughness measurement result is obtained by means of a method of using a machine learning model, and thus, a more accurate and more intuitive skin roughness measurement result is obtained.

Description

Method for detecting skin roughness and electronic equipment

Cross-references to related applications

This application claims the priority of a Chinese patent application filed with the Chinese Patent Office on September 18, 2019, the application number is 201910882738.8, and the application name is "a method for detecting skin roughness and electronic equipment", the entire content of which is incorporated by reference In this application.

Technical field

The embodiments of the present application relate to the field of image processing technology, and in particular, to a skin roughness detection method and electronic equipment.

Background technique

Due to the influence of age, disease, external environment and other factors on the surface of human skin, it is easy to cause the formation of peaks and valleys with different depths and directions, thus forming a variety of skin textures. Skin Roughness, as an important skin texture analysis method, can reflect the health of human body functions to a certain extent.

Currently, professional detection equipment is generally used to detect and evaluate skin conditions by analyzing skin images. The price of professional testing equipment is relatively high, and it is generally adopted by professional organizations, not suitable for the general public. Nowadays, with the improvement of the imaging capabilities of mobile terminals, skin detection based on mobile terminals has become possible, so that the general public can use mobile terminals to detect skin conditions. However, there is currently no effective method for detecting skin roughness suitable for mobile terminals.

Summary of the invention

The embodiments of the present application provide a skin roughness detection method and electronic device, which are used to detect the roughness of the skin through the electronic device.

In the first aspect, an embodiment of the present application provides a method for detecting skin roughness, which is applied to an electronic device or a chip in an electronic device. The method includes: acquiring a grayscale image of a skin image to be processed; The texture feature is extracted from the gray-scale image, and the texture feature includes at least one of texture depth, texture width, wide texture density, and texture density; the texture depth is used to characterize the depth of the lines on the skin, and the texture width is In order to characterize the width of the lines on the skin, the wide texture density is used to characterize the density of the lines in the skin whose line width reaches a preset threshold, and the texture density is used to characterize the density of the lines in the skin; further according to the texture The feature determines the roughness of the skin in the skin image to be processed. In the above solution, the electronic device is used to determine the roughness of the skin based on the texture feature. It is easy to use and does not use a simple parameter threshold to determine the roughness of the skin. It has strong stability and high accuracy.

In a possible design, the extraction of texture features in the grayscale image can be achieved by dividing the grayscale image into K image blocks, and obtaining the grayscale from the K image blocks N image blocks with an average value within a preset grayscale range, extract the texture features from the N image blocks, K and N are both positive integers, and N is less than or equal to K; or, the grayscale The image is divided into K image blocks, the gray scale average size of the K image blocks is sorted, N image blocks sorted within a preset ranking range are obtained, and the texture feature is extracted from the N image blocks . Through the above design, several image blocks are selected from multiple image blocks to extract texture features, which reduces the influence of ambient light on the extraction of texture features and improves accuracy.

In a possible design, the extraction of texture features from the N image blocks can be achieved by performing binarization processing on the N image blocks respectively to obtain N binarized images; N binarized images are subjected to connected domain analysis to obtain at least one first connected domain, where the at least one first connected domain is used to indicate the position of the skin texture area in the N image blocks; from the N images The texture feature is extracted from a region where the at least one first connected domain in the block is located. The above design provides a simple and effective way to extract texture features.

In a possible design, performing binarization processing on the N image blocks to obtain N binarized images respectively includes: filtering the N image blocks respectively, and performing the filtered N images Block binarization processing to obtain N binarized images. In the above design, filtering is performed before binarization, which can smooth and denoise the image.

In a possible design, the performing connected domain analysis on the N binarized images to obtain at least one first connected domain includes: performing corrosion and/or expansion processing on the N binarized images, The connected domain analysis is performed on the N binarized images after the corrosion and/or expansion processing to obtain the at least one first connected domain.

The above design can make the determined skin texture clearer and more accurate by performing corrosion and/or expansion processing on the binary image.

In a possible design, the texture depth may be determined according to the average gray value of the area where at least one first connected domain in the N image blocks is located and the average gray value of the N image blocks.

In a possible design, the texture depth can meet the requirements of the following formula:

F1=abs(M-M1)/M;

Wherein, F1 represents the texture depth, M1 represents the average gray value of the area where the at least one first connected domain in the N image blocks is located, and M represents the average gray value of the N image blocks. The above design provides a simple and effective way to extract texture depth.

In a possible design, extracting the texture width from the area where the at least one first connected domain in the N image blocks is located includes: according to the outer contour of the second connected domain in the at least one first connected domain The length and area determine the texture width; wherein the second connected domain is the first connected domain with the longest outer contour length or the largest area in the at least one first connected domain. The above design provides a simple and effective way to extract the texture width.

In a possible design, determining the texture width according to the length and area of the outer contour of the second connected domain in the at least one first connected domain can be implemented in the following manner:

When the second connected domain is a multi-connected domain, the texture width is determined by the following formula:

F2=F1×S1/(L1+L0); or, F2=S1/(L1+L0);

Wherein, F2 represents the texture width, F1 represents the texture depth, S1 represents the area of the second connected domain, L1 represents the length of the outer contour of the second connected domain, and L0 represents the sum of the length of the inner contour of the second connected domain;

When the second connected domain is a single connected domain, the texture width is determined by the following formula:

F2=F1×S1/L1; or, F2=S1/L1;

Wherein, F2 represents the texture width, F1 represents the texture depth, S1 represents the area of the second connected domain, and L1 represents the outer contour length of the second connected domain.

The above design provides a simple and effective way to extract the texture width.

In a possible design, extracting the wide texture density from the area where the at least one first connected domain in the N image blocks is located can be implemented in the following manner:

Determine from the at least one first connected domain at least one third connected domain whose outer contour length is greater than a preset threshold; determine K image blocks containing the second connected domain among N image blocks; determine the K image blocks The first ratio between the sum of the areas of the third connected domain included in the image block and the sum of the areas of the N image blocks; the wide texture density is determined by multiplying the first ratio by the texture depth Or, the first ratio is determined as the wide texture density. The above design provides a simple and effective way to extract wide texture density.

In a possible design, extracting the texture density from the area where the at least one first connected domain in the N image blocks is located may be implemented in the following manner: determining the first one included in the N image blocks The second ratio between the area sum of the connected domain and the area sum of the N image blocks; the second ratio is multiplied by the texture depth to determine the texture density, or the second ratio is Determined as the texture density. The above design provides a simple and effective way to extract texture density.

In a possible design, the extraction of the texture features in the grayscale image may be achieved in the following manner: performing a histogram equalization process on the grayscale image to obtain an equalized image, and extracting the equalized image The texture feature in, can prevent the influence of uneven illumination on the detection result.

In a possible design, the determination of the roughness of the skin in the skin image to be processed according to the texture feature can be implemented in the following manner: an integrated learning algorithm model is used to determine the skin image in the skin image to be processed according to the texture feature The roughness of the skin.

In a second aspect, an embodiment of the present application provides a skin roughness detection device, which includes units respectively used to execute the method according to the first aspect or any one of the designs of the first aspect.

In a third aspect, an embodiment of the present application provides an electronic device including a processor and a memory; wherein the processor is coupled with the memory; wherein the memory is used to store program instructions; the processor is used to read program instructions stored in the memory, To achieve the first aspect and any possible design methods.

In a fourth aspect, a computer storage medium provided by an embodiment of the present application, the computer storage medium stores program instructions, and when the program instructions run on an electronic device, the electronic device executes the first aspect and any of its possible designs. method.

In the fifth aspect, a computer program product provided by an embodiment of the present application, when the computer program product runs on an electronic device, causes the electronic device to execute the first aspect and any possible design method thereof.

The sixth aspect is a chip provided by an embodiment of the present application, which is coupled with a memory in an electronic device, and executes the first aspect and any possible design method thereof.

In addition, the technical effects brought by the second aspect to the sixth aspect can be referred to the description of the above-mentioned first aspect, which will not be repeated here.

It should be noted that “coupled” in the embodiments of the present application means that two components are directly or indirectly combined with each other.

Description of the drawings

FIG. 1 is a schematic structural diagram of a terminal device 100 in an embodiment of the application;

2 is a schematic flow chart of a method for detecting the roughness of the back of the hand in an embodiment of the application;

FIG. 3 is a schematic diagram of a preview user interface in an embodiment of the application;

4 is a schematic diagram of image blocks used for extracting texture features in an image of the back of the hand in an embodiment of the application;

Figure 5 is a schematic diagram of binarization processing in an embodiment of the application;

Figure 6 is a schematic diagram of connected domains in an embodiment of this application;

FIG. 7 and FIG. 8 are schematic diagrams of the detection result of the method provided by this application in the embodiment of the application;

Figure 9 is a schematic diagram of a skin roughness detection report in an embodiment of the application;

FIG. 10 is a schematic diagram of an electronic device 1000 provided by an embodiment of the application.

detailed description

The embodiment of the present application proposes a skin roughness detection solution, which is suitable for electronic equipment, and the electronic equipment may be a terminal device. The skin roughness detection function provided in the embodiments of the present application may be integrated in one or more applications of the terminal device, for example, integrated in a camera application. Taking a camera application as an example, the terminal device starts the camera application and displays a viewfinder interface. The viewfinder interface may include a control. When the control is activated, the terminal device can start the skin roughness detection function provided by the embodiment of the present application. The skin roughness detection function provided by the embodiments of the present application can also be integrated in an application dedicated to skin detection in a terminal device. As an example, the application for skin detection can not only realize the skin roughness detection function, but also can realize the detection of wrinkles, pores, blackheads, etc. of the facial skin. Skin roughness can be neck roughness, facial roughness, or back hand roughness. After completing the skin test, the skin test application can also provide the user with a test result report. Taking the roughness detection of the back of the hand as an example, the detection result report can include, but is not limited to, scoring of various features on the back of the hand, comprehensive analysis of the back of the hand, etc., and corresponding nursing or treatment suggestions can also be given according to the score of the back of the hand. It is understandable that the test result report can be presented to the user through the user interface.

In some embodiments of the present application, the terminal device may be a portable terminal device containing functions such as a personal digital assistant and/or a music player, such as a mobile phone, a tablet computer, a wearable device with wireless communication function (such as a smart watch), Vehicle equipment, etc. Exemplary embodiments of portable terminal equipment include, but are not limited to, carrying

Or portable terminal equipment with other operating systems. The aforementioned portable terminal device may also be, for example, a laptop computer with a touch-sensitive surface (such as a touch panel). It should also be understood that in some other embodiments of the present application, the aforementioned terminal device may also be a desktop computer with a touch-sensitive surface (such as a touch panel).

In other embodiments of the present application, the terminal device may also have algorithmic computing capabilities (capable of running the skin roughness detection algorithm provided in the embodiments of the present application) and communication functions, without the need for image acquisition functions. For example, a terminal device receives an image sent by another device, and then runs the skin roughness detection algorithm provided in this embodiment of the application to detect the roughness of the skin in the image. In the following, the terminal device itself has an image collection function and arithmetic operation function as an example.

FIG. 1 shows a schematic structural diagram of a possible terminal device 100. The terminal device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 2, wireless communication Module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, earphone interface 170D, sensor module 180, buttons 190, motor 191, indicator 192, camera 193, display screen 194 and so on. In other embodiments, the terminal device 100 in the embodiment of the present application may further include an antenna 1, a mobile communication module 150, a subscriber identification module (SIM) card interface 195, and so on.

The processor 110 may include one or more processing units. For example, the processor 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, and a memory. , Video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural network processor (neural-network processing unit, NPU), etc. Among them, the different processing units may be independent devices or integrated in one or more processors.

In some embodiments, a memory may also be provided in the processor 110 for storing instructions and data. For example, the memory in the processor 110 may be a cache memory. The memory can store instructions or data that the processor 110 has just used or used cyclically. If the processor 110 needs to use the instruction or data again, it can be directly called from the memory. Repeated accesses are avoided, the waiting time of the processor 110 is reduced, and the efficiency of the system is improved. The processor 110 may run the skin roughness detection algorithm provided in the embodiment of the present application to detect the roughness of the skin on the image.

In other embodiments, the processor 110 may further include one or more interfaces. For example, the interface may be a universal serial bus (USB) interface 130. For another example, the interface can also be an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, and a universal asynchronous transceiving transmission Universal asynchronous receiver/transmitter (UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, subscriber identity module (SIM) ) Interface, etc. It can be understood that the embodiments of the present application may connect different modules of the terminal device 100 through interfaces, so that the terminal device 100 can implement different functions. For example, taking pictures, processing, etc. It should be noted that the embodiment of the present application does not limit the connection mode of the interface in the terminal device 100.

Among them, the USB interface 130 is an interface that complies with the USB standard specification. For example, the USB interface 130 may include a Mini USB interface, a Micro USB interface, a USB Type C interface, and so on. The USB interface 130 can be used to connect a charger to charge the terminal device 100, and can also be used to transfer data between the terminal device 100 and peripheral devices. It can also be used to connect earphones and play audio through earphones. This interface can also be used to connect to other terminal devices, such as AR devices.

The charging management module 140 is used to receive charging input from the charger. Among them, the charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive the charging input of the wired charger through the USB interface 130. In some embodiments of wireless charging, the charging management module 140 may receive the wireless charging input through the wireless charging coil of the terminal device 100. While the charging management module 140 charges the battery 142, it can also supply power to the terminal device through the power management module 141.

The power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charging management module 140, and supplies power to the processor 110, the internal memory 121, the external memory, the display screen 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor battery capacity, battery cycle times, battery health status (leakage, impedance) and other parameters. In some other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may also be provided in the same device.

The wireless communication function of the terminal device 100 may be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor, and the baseband processor.

The antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the terminal device 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example: Antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna can be used in combination with a tuning switch.

The mobile communication module 150 may provide a wireless communication solution including 2G/3G/4G/5G and the like applied to the terminal device 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), and the like. The mobile communication module 150 can receive electromagnetic waves by the antenna 1, filter, amplify, etc. the received electromagnetic waves, and transmit them to the modem processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modem processor, and convert it into electromagnetic wave radiation via the antenna 1. In some embodiments, at least part of the functional modules of the mobile communication module 150 may be provided in the processor 110. In some embodiments, at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be provided in the same device.

The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low frequency baseband signal to be sent into a medium and high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. Then the demodulator transmits the demodulated low-frequency baseband signal to the baseband processor for processing. After the low-frequency baseband signal is processed by the baseband processor, it is passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays an image or video through the display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processor 110 and be provided in the same device as the mobile communication module 150 or other functional modules.

The wireless communication module 160 can provide applications on the terminal device 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), and global navigation satellites. System (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field communication technology (near field communication, NFC), infrared technology (infrared, IR) and other wireless communication solutions. The wireless communication module 160 may be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic wave signals via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110. The wireless communication module 160 may also receive the signal to be sent from the processor 110, perform frequency modulation, amplify it, and convert it into electromagnetic waves to radiate through the antenna 2.

In some embodiments, the antenna 1 of the terminal device 100 is coupled with the mobile communication module 150, and the antenna 2 is coupled with the wireless communication module 160, so that the terminal device 100 can communicate with the network and other devices through wireless communication technology. The wireless communication technology may include global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), broadband Code division multiple access (wideband code division multiple access, WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC , FM, and/or IR technology, etc. The GNSS may include global positioning system (GPS), global navigation satellite system (GLONASS), Beidou navigation satellite system (BDS), and quasi-zenith satellite system (quasi). -zenith satellite system, QZSS) and/or satellite-based augmentation systems (SBAS).

The terminal device 100 implements a display function through a GPU, a display screen 194, an application processor, and the like. The GPU is a microprocessor for image processing, connected to the display 194 and the application processor. The GPU is used to perform mathematical and geometric calculations for graphics rendering. The processor 110 may include one or more GPUs, which execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos, and the like. The display screen 194 includes a display panel. The display panel can adopt liquid crystal display (LCD), organic light-emitting diode (OLED), active matrix organic light-emitting diode or active-matrix organic light-emitting diode (active-matrix organic light-emitting diode). AMOLED, flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diode (QLED), etc. In some embodiments, the terminal device 100 may include one or N display screens 194, and N is a positive integer greater than one.

The terminal device 100 can implement a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, and an application processor.

The ISP is used to process the data fed back by the camera 193. For example, when taking a picture, the shutter is opened, the light is transmitted to the photosensitive element of the camera through the lens, the light signal is converted into an electrical signal, and the photosensitive element of the camera transmits the electrical signal to the ISP for processing and is converted into an image visible to the naked eye. ISP can also optimize the image noise, brightness, and skin color. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or videos. Generally, the camera 193 may include photosensitive elements such as a lens group and an image sensor, where the lens group includes a plurality of lenses (convex lens or concave lens) for collecting the light signal reflected by the object to be photographed (such as the back of the hand), and the collected light signal Passed to the image sensor. The image sensor generates an image of the object to be photographed (such as an image of the back of the hand) according to the light signal. Taking the image of the back of the hand as an example, after the camera 193 collects the image of the back of the hand, it can send the image of the back of the hand to the processor 110, and the processor 110 runs the skin roughness detection algorithm provided by the embodiment of the present application to detect the roughness of the back of the hand in the image of the back of the hand. After the processor 110 determines the roughness of the back of the hand image, the display screen 194 may display the detection report of the roughness of the back of the hand. The camera 193 shown in FIG. 1 may include 1-N cameras, and the number of cameras is not limited in this application.

Digital signal processors are used to process digital signals. In addition to digital image signals, they can also process other digital signals. For example, when the terminal device 100 selects the frequency point, the digital signal processor is used to perform Fourier transform on the energy of the frequency point.

Video codecs are used to compress or decompress digital video. The terminal device 100 may support one or more video codecs. In this way, the terminal device 100 can play or record videos in multiple encoding formats, such as: moving picture experts group (MPEG) 1, MPEG2, MPEG3, MPEG4, and so on.

NPU is a neural-network (NN) computing processor. By drawing on the structure of biological neural networks, for example, the transfer mode between human brain neurons, it can quickly process input information, and it can also continuously self-learn. Through the NPU, applications such as intelligent cognition of the terminal device 100 can be realized, such as image recognition, face recognition, voice recognition, text understanding, and so on.

The external memory interface 120 may be used to connect an external memory card (for example, a Micro SD card) to expand the storage capacity of the terminal device 100. The external memory card communicates with the processor 110 through the external memory interface 120 to realize the data storage function. For example, save music, video and other files in an external memory card.

The internal memory 121 may be used to store computer executable program code, where the executable program code includes instructions. The processor 110 executes various functional applications and data processing of the terminal device 100 by running instructions stored in the internal memory 121. The internal memory 121 may include a storage program area and a storage data area. Among them, the storage program area can store an operating system, at least one application program (such as a camera application, a skin detection application, etc.) required by a function, and the like. The data storage area can store data created during the use of the terminal device 100 (for example, images captured by a camera, etc.) and the like. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), and the like. The internal memory 121 may also store the code of the skin roughness detection algorithm provided in the embodiment of the present application. When the code of the skin roughness detection algorithm stored in the internal memory 121 is executed by the processor 110, the skin roughness detection function is realized. Of course, the code of the skin roughness detection algorithm provided in the embodiment of the present application can also be stored in an external memory. In this case, the processor 110 may run the code of the skin roughness detection algorithm stored in the external memory through the external memory interface 120 to implement the corresponding wrinkle detection function.

The terminal device 100 can implement audio functions through an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone interface 170D, an application processor, and the like. For example, music playback, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal for output, and is also used to convert an analog audio input into a digital audio signal. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be provided in the processor 110, or part of the functional modules of the audio module 170 may be provided in the processor 110.

The speaker 170A, also called "speaker", is used to convert audio electrical signals into sound signals. The terminal device 100 can listen to music or listen to a hands-free call through the speaker 170A.

The receiver 170B, also called "earpiece", is used to convert audio electrical signals into sound signals. When the terminal device 100 answers a call or voice message, it can receive the voice by bringing the receiver 170B close to the human ear.

The microphone 170C, also called "microphone", "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can make a sound by approaching the microphone 170C through the human mouth, and input the sound signal into the microphone 170C. The terminal device 100 may be provided with at least one microphone 170C. In other embodiments, the terminal device 100 may be provided with two microphones 170C, which can implement noise reduction functions in addition to collecting sound signals. In other embodiments, the terminal device 100 may also be provided with three, four or more microphones 170C to realize sound signal collection, noise reduction, sound source identification, and directional recording functions.

The earphone interface 170D is used to connect wired earphones. The earphone interface 170D can be a USB interface 130, or a 3.5mm open mobile terminal platform (OMTP) standard interface, a cellular telecommunications industry association (cellular telecommunications industry association of the USA, CTIA) standard interface, etc. .

The sensor module 180 includes an ambient light sensor 180L. In addition, the sensor module 180 may also include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, Bone conduction sensor 180M and so on.

The pressure sensor 180A is used to sense the pressure signal and can convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be provided on the display screen 194. There are many types of pressure sensors 180A, such as resistive pressure sensors, inductive pressure sensors, capacitive pressure sensors and so on. The capacitive pressure sensor may include at least two parallel plates with conductive material. When a force is applied to the pressure sensor 180A, the capacitance between the electrodes changes. The terminal device 100 determines the strength of the pressure according to the change in capacitance. When a touch operation acts on the display screen 194, the terminal device 100 detects the intensity of the touch operation according to the pressure sensor 180A. The terminal device 100 may also calculate the touched position based on the detection signal of the pressure sensor 180A. In some embodiments, touch operations that act on the same touch position but have different touch operation strengths may correspond to different operation instructions. For example, when a touch operation whose intensity of the touch operation is less than the first pressure threshold is applied to the short message application icon, an instruction to view the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold acts on the short message application icon, an instruction to create a new short message is executed.

The gyro sensor 180B may be used to determine the movement posture of the terminal device 100. The air pressure sensor 180C is used to measure air pressure. The magnetic sensor 180D includes a Hall sensor. The terminal device 100 may use the magnetic sensor 180D to detect the opening and closing of the flip holster. The acceleration sensor 180E can detect the magnitude of the acceleration of the terminal device 100 in various directions (generally three axes). Distance sensor 180F, used to measure distance. The terminal device 100 can measure the distance by infrared or laser. In some embodiments, when shooting a scene, the terminal device 100 may use the distance sensor 180F to measure the distance to achieve fast focusing. The proximity light sensor 180G may include, for example, a light emitting diode (LED) and a light detector such as a photodiode. The ambient light sensor 180L is used to sense the brightness of the ambient light. The fingerprint sensor 180H is used to collect fingerprints. The terminal device 100 can use the collected fingerprint characteristics to realize fingerprint unlocking, access application locks, fingerprint photographs, fingerprint answering calls, and so on. The temperature sensor 180J is used to detect temperature.

Touch sensor 180K, also called "touch panel". The touch sensor 180K may be disposed on the display screen 194, and the touch screen is composed of the touch sensor 180K and the display screen 194, which is also called a “touch screen”. The touch sensor 180K is used to detect touch operations acting on or near it. The touch sensor can pass the detected touch operation to the application processor to determine the type of touch event. The visual output related to the touch operation can be provided through the display screen 194. In other embodiments, the touch sensor 180K may also be disposed on the surface of the terminal device 100, which is different from the position of the display screen 194. The button 190 may include a power-on button, a volume button, and the like. The button 190 may be a mechanical button. It can also be a touch button. The terminal device 100 may receive key input, and generate key signal input related to user settings and function control of the terminal device 100.

The motor 191 can generate vibration prompts. The motor 191 can be used for incoming call vibration notification, and can also be used for touch vibration feedback. For example, touch operations that act on different applications (such as photographing, audio playback, etc.) can correspond to different vibration feedback effects. Acting on touch operations in different areas of the display screen 194, the motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminding, receiving information, alarm clock, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

The indicator 192 may be an indicator light, which may be used to indicate the charging status, power change, or to indicate messages, missed calls, notifications, and the like.

The SIM card interface 195 is used to connect to the SIM card. The SIM card can be inserted into the SIM card interface 195 or pulled out from the SIM card interface 195 to achieve contact and separation with the terminal device 100.

It can be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the terminal device 100. In other embodiments of the present application, the terminal device 100 may include more or fewer components than shown, or combine certain components, or split certain components, or arrange different components. The illustrated components can be implemented in hardware, software, or a combination of software and hardware.

In addition, it should be noted that in this application, "at least one" refers to one or more, and "multiple" refers to two or more. "And/or" describes the association relationship of the associated objects, indicating that there can be three relationships, for example, A and/or B, which can mean: A alone exists, A and B exist at the same time, and B exists alone, where A, B can be singular or plural. The character "/" generally indicates that the associated objects before and after are in an "or" relationship. "The following at least one item (a)" or similar expressions refers to any combination of these items, including any combination of a single item (a) or a plurality of items (a). For example, at least one item (a) of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple . In addition, it should be understood that in the description of this application, words such as "first" and "second" are only used for the purpose of distinguishing description, and cannot be understood as indicating or implying relative importance, nor can it be understood as indicating Or imply the order.

The single connected domain involved in the embodiments of this application refers to: the inside of any simple closed curve belonging to a region D belongs to D, then D is called a single connected region, and a single connected domain can also be described as follows: any closed curve in D The area enclosed by the curve contains only the points in D. More generally speaking, a singly connected area is an area without "holes". Multi-connected domain refers to a region B on the complex plane, if any simple closed curve is made in it, and the interior of the curve does not always belong to B, it is called multi-connected domain.

In order to facilitate the description of the skin roughness detection algorithm provided by the embodiments of the present application, the following takes the roughness detection of the back of the hand as an example for description. The method for detecting the roughness of the back of the hand may be executed by the terminal device 100, for example, by the processor 110 in the terminal device 100.

In some embodiments of the present application, referring to FIG. 2, detecting the roughness of the back of the hand through the terminal device 100 shown in FIG. 1 may be the following process:

S201: The terminal device 100 acquires a grayscale image of the back image of the hand to be processed. S201 can be implemented through the following two steps:

A1, get the image of the back of the hand to be processed.

The function of detecting the roughness of the back of the hand is integrated into the application dedicated to skin detection. For the convenience of description, the application dedicated to skin detection is called "skin measurement application". The skin measurement application can only integrate the skin roughness detection function. On the basis of the integrated skin roughness detection function, the skin detection of the face can also be integrated, such as blackhead detection, pore detection, erythema detection, and so on. The image of the back of the hand is acquired through the skin measurement application, and the skin detection method provided in the embodiment of the present application is executed based on the image of the back of the hand. As shown in FIG. 3, the display screen 194 of the terminal device 100 displays an icon 300 of the skin test application. The terminal device 100 detects the operation on the icon 300, and in response to the operation on the icon 300, displays the user interface 310 of the skin test application on the display screen 194. The user interface 310 of the skin test application includes a detection button 311. The terminal device 100 detects the operation of the detection button 311. In response to the operation of the detection button 311, the camera 193 is turned on, and the display screen 194 displays the photographing preview interface 320. The photo preview interface 320 is used to display the images collected by the camera 193. For example, the photo preview interface 320 may include a preview area 321, and the preview area 321 is used to display the image collected by the camera 193. It should be understood that the image collected by the camera 193 may be an image of the back of the user's hand. In addition, the camera 193 may be a front camera of the terminal device 100 or a rear camera of the terminal device 100. In some embodiments, in order to improve the quality of taking pictures, the camera 193 is the rear camera of the terminal device 100 when the pixels of the front camera are lower than the pixels of the rear camera. In order to further improve the quality of photographing, the terminal device 100 automatically photographs the image collected by the camera 193 to obtain the back image of the hand when the ambient light meets the photographing conditions. It should be noted that the detection button 311 in the embodiment of the present application may also be referred to as a photographing button, or other names, and the embodiment of the present application does not limit the name of the detection button 311.

In another possible example, the image of the back of the hand may also be an image already stored in the terminal device 100, for example, stored in the internal memory 121, so that the terminal device 100 obtains the image of the back of the hand from the internal memory 121. For another example, it is stored in an external memory, so that the terminal device 100 obtains the image of the back of the hand from the external memory through the external memory interface 120.

A2: After acquiring the image of the back of the hand, the terminal device 100 converts the image of the back of the hand into a grayscale image.

The image of the back of the hand collected by the terminal device 100 through the camera is a color image, and the color of the back of the hand image is converted into a grayscale image in A2.

S202: The terminal device 100 extracts texture features in the grayscale image, where the texture features include at least one of texture depth, texture width, wide texture density, and texture density;

Among them, the texture depth is used to characterize the depth of the lines on the back of the hand, the texture width is used to characterize the width of the lines on the back of the hand, the wide texture density is used to characterize the density of the lines on the back of the hand whose line width reaches a preset threshold, and the texture density is used to characterize the lines. Density in the back of the hand.

S203: The terminal device 100 determines the roughness of the back of the hand in the back of the hand image to be processed according to the texture feature.

As an example, when the terminal device 100 extracts texture features in a grayscale image, it may first preprocess the grayscale image, and then extract the texture feature from the preprocessed image. Preprocessing, for example, can be histogram equalization processing, which can prevent uneven illumination from affecting the extraction of texture features. For example, the grayscale image can be enlarged, reduced, or segmented. For example, the grayscale image can be divided into multiple Image block, and then select several image blocks with more obvious texture features from multiple image blocks for subsequent processing of extracting texture features.

Taking the terminal device 100 performing histogram equalization processing and segmentation processing on the grayscale image as an example, the process of preprocessing the grayscale image is as follows:

B1, the terminal device 100 performs histogram equalization processing on the gray image to obtain an equalized image.

B2. The terminal device 100 divides the equalized image into K image blocks, for example, divides the equalized image into 10×10 image blocks.

B3. The terminal device 100 sorts the average gray values of the K image blocks, and obtains N image blocks sorted within a preset ranking range. For example, the average gray value is sorted in the 9th image blocks from 20th to 28th, as shown in Fig. 4.

Exemplarily, when the terminal device 100 obtains N image blocks from K image blocks, in addition to the method shown in B3, obtains N images with gray average values within a preset range from the K image blocks. Piece. If there are less than N image blocks with an average gray value within the preset range, the actual number of image blocks can be used as the reference for subsequent extraction of texture features. If there are more than N image blocks with the mean gray value in the preset range, several image blocks can be randomly removed to make the number reach N. Of course, if the image blocks with the gray mean value in the preset range exceed N , The actual number of image blocks can also be used as the reference for subsequent extraction of texture features.

B4. The terminal device 100 extracts the texture feature from the N image blocks, K and N are both positive integers, and N is less than or equal to K.

When extracting texture features from N image blocks, it can be achieved through the following steps:

C1. The terminal device 100 performs binarization processing on the N image blocks respectively to obtain N binarized images.

Optionally, when the N image blocks are binarized to obtain N binarized images, the N image blocks may be filtered first for smoothing and denoising the N image blocks. . The filtering method can be average filtering, median filtering, Gaussian filtering, bilateral filtering and other methods. Taking mean filtering as an example, the fuzzy kernel 91×91 can be used for mean filtering. Then binarization is performed on the filtered N image blocks to obtain N binarized images. After the binarization process, the gray value of the grain area is set to 255, and the gray value of other areas is set to 0. For example, see FIG. 5, which is a schematic diagram of a binarized image after the image block has been binarized.

C2. The terminal device 100 performs connected domain analysis on the N binarized images to obtain at least one first connected domain, where the at least one first connected domain is used to indicate that the skin texture area is in the N image blocks position.

Optionally, when performing connected domain analysis on the N binarized images to obtain at least one first connected domain, the N binarized images may be corroded and/or expanded first, and then the corroded and /Or the connected domain analysis is performed on the N binarized images after the expansion process to obtain the at least one first connected domain. For example, a 5×5 corrosion core may be used to respectively perform corrosion processing on the N binarized images, and then perform connected component analysis on the N binarized images after the corrosion processing to obtain at least one first connected component.

C3. The terminal device 100 extracts the texture feature from the area where the at least one first connected domain is located in the N image blocks.

The following exemplarily describes the implementation of determining the texture depth, texture width, wide texture density, or texture density in the texture feature.

1) Texture depth:

The terminal device 100 may determine according to the average gray value of the area where the at least one first connected domain in the N image blocks is located and the average gray value of the N image blocks. For example, the texture depth can be determined by the following formula (1):

F1=abs(M-M1)/M Formula (1)

Wherein, F1 represents texture depth, M1 represents the average gray value of the area where the at least one first connected domain in the N image blocks is located, and M represents the average gray value of the N image blocks.

When extracting the texture depth in the grayscale image, the terminal device 100 may extract the texture depth of each of the N image blocks, and then use the average value of the texture depths of the N image blocks as the texture depth of the grayscale image. . For example, taking the first image block as an example, the first image block is any one of the N image blocks.

The texture depth of the first image block can be determined by the following formula (2):

E1=abs(X-X1)/X Formula (2)

Wherein, E1 represents the texture depth of the first image block, X represents the average gray value of the area where the first connected domain in the first image block is located, and X1 represents the average gray value of the first image block. After the texture depths of the N image blocks are respectively determined by the above formula (2), the average value of the texture depths of the N image blocks is determined.

When the terminal device 100 extracts the texture depth in the grayscale image, it may also regard the N image blocks as a whole, directly determine the grayscale mean value of the area where the first connected domain of the N image blocks is located, and determine the N images The gray average value of the block, thereby determining the texture depth of the gray image based on the above formula (1).

2) Texture width:

The terminal device 100 may determine the texture width according to the length and area of the outer contour of the second connected domain in the at least one first connected domain. Wherein, the second connected domain is the first connected domain with the longest outer contour length or the largest area in the at least one first connected domain.

For example, when the second connected domain is a multi-connected domain, the texture width can be determined by the following formula (3) or formula (4):

F2=F1×S1/(L1+L0) Formula (3)

F2=S1/(L1+L0) Formula (4)

Wherein, F2 represents the texture width, F1 represents the texture depth, S1 represents the area of the second connected domain, L1 represents the length of the outer contour of the second connected domain, and L0 represents the sum of the length of the inner contour of the second connected domain.

As shown in Figure 6 (1), the white area is the second connected domain, then L2 is the length of the outer ring of the white area, and L0 is equal to the length of the inner ring of the white area, that is, the length of the black ellipse surrounded by the white area. The area of the first connected domain is the area of the white area, that is, the area of the ring area. As shown in (2) in Figure 6, the white area is the second connected domain, then L2 is the length of the outer ring of the white area, and L0 is equal to the sum of the lengths of the two black ellipses surrounded by the white area, that is, the sum of L3 and L4.

When the first connected domain is simply connected, the texture width can be determined by the following formula (5) or formula (6):

F2=F1×S1/L1 Formula (5)

F2=S1/L1 Formula (6)

Among them, F2 represents the texture width, F1 represents the texture depth, S1 represents the area of the second connected domain, and L1 represents the outer contour length of the second connected domain. For example, see (3) in Figure 6, where S1 is the white area. Area, L1 is the outline perimeter of the white area.

3) Wide texture density:

The terminal device 100 may determine from the at least one first connected domain at least one third connected domain whose outer contour length is greater than a preset threshold, and then determine K image blocks containing the third connected domain among the N image blocks, Furthermore, the area sum of the third connected domain included in each image block in the K image blocks is determined separately, and the ratio of the area sum to the area sum of the N image blocks obtains K ratios; finally, the average value of the K ratios is multiplied The above texture depth is determined as the wide texture density, or the average of the K ratios is determined as the wide texture density.

For example, the wide texture density can be determined by the following formula (7) or formula (8):

F3=F1×S2/S Formula (7)

F3=S2/S Formula (8)

Among them, F3 represents the wide texture density, and S2 represents the area sum of the third connected domain included in the K image blocks. For example, there are 3 image blocks including the third connected domain, which are image block 1, image block 2, and image block 3. Image block 1 includes 2 third connected domains, and image block 2 includes 1 third connected domain. 3 includes 3 third connected domains, then S2 is the area of the 2 third connected domains in image block 1, the area of 1 third connected domain in image block 2, and the 3 third connected domains in image block 3. The sum of the area of the domain, that is, the sum of the area of the 6 third connected domains. S represents the area sum of N image blocks.

3) Texture density:

Determine the second ratio between the sum of the areas of the first connected domains included in the N image blocks and the sum of the areas of the N image blocks; and then multiply the second ratio by the texture depth to determine as The texture density, or, the second ratio is determined as the wide texture density.

For example, the texture density can be determined by the following formula (9) or formula (10):

F4=F1×S3/S Formula (9)

F4=S3/S Formula (10)

Wherein, F4 represents the texture density, S3 represents the sum of the areas of the first connected domain included in the N image blocks, and S is the sum of the areas of the N image blocks. For example, N is 4, which are image block 1-image block 4, image block 1 includes 2 first connected domains, image block 2 includes 3 first connected domains, image block 3 includes 3 first connected domains, image block 4 includes 1 first connected domain, then S3 is the area of the 2 first connected domains in image block 1, the area of the 3 first connected domains in image block 2, and the 3 first connected domains in image block 3. The area of the domain, and the sum of the area of one first connected domain in the image block 4, that is, the sum of the area of the nine first connected domains. S represents the area sum of N image blocks.

As an example, when the terminal device 100 does not segment the gray image, the texture feature of the gray image may be determined in the following manner. Perform mean filtering on the grayscale image, and perform binarization processing on the grayscale image after the mean filtering, perform connected domain analysis on the binarized image to obtain at least one connected domain, and then extract the area where at least one connected domain is located Texture characteristics. For example, when determining the texture depth, the ratio of the gray value of the connected domain position in the gray image to the gray value of the gray image can be used as the texture depth. When determining the texture width, it can be determined according to at least one connected domain with the largest area or the longest outer contour length in at least one connected domain. For a specific determination method, refer to formula (3) or formula (4). When determining the wide texture density, a connected domain whose outer contour length is greater than a preset threshold can be determined from the at least one connected domain, and the sum of the area of the connected domain whose outer contour length is greater than the preset threshold can be determined as compared with the gray scale image. The ratio between the areas is the density of the wide texture, or the sum of the areas of connected domains whose outer contour length is greater than a preset threshold is determined, and the ratio between the area and the area of the gray image multiplied by the depth of the texture is determined to be the wide texture density. When determining the texture density, the ratio between the area sum of at least one connected domain and the area of the gray-scale image may be determined as the texture density, or the area sum of at least one connected domain may be determined as the texture density. The ratio between the areas of the image multiplied by the above-mentioned texture depth is determined as the texture density.

As an example, when the terminal device 100 determines the roughness of the skin in the skin image to be processed according to the texture feature, it may use an integrated learning algorithm model to determine the roughness of the skin in the skin image to be processed according to the texture feature. The integrated learning algorithm can be, for example, Adaboost, or Boosting, or bagging (bootstrap aggregation, bagging).

The ensemble learning algorithm model can be trained in the following way:

Collect images of the backs of the hands of people of multiple age groups to form training samples. Multiple "skin experts" respectively determine the roughness of the back of the hands of each person in the crowd, that is, score the back of the hands, and can use a 100-point system, a 10-point system, or a 1-point system. The average score of a person by multiple skin experts is used as a label for the roughness of the back of a person's hand. Based on the training samples and the corresponding roughness label of the back of the hand, the preset integrated learning algorithm model is trained, and the integrated learning algorithm model obtained through training can be used as a model for detecting the roughness of the skin in the skin image to be processed in the embodiment of the present application.

The applicant conducted the experiment with a population of 150 people. According to the "skin expert" scoring system, three human experts were selected to blindly evaluate the roughness of the back of the hands of 150 people. The average score of the three human experts corresponding to each person was taken as The roughness score of the individual. 150 people’s back images were used as training samples, and 94 people were used as verification samples for model verification. The experiment uses AdaBoost to obtain a correlation coefficient of 0.88 in the test set through cross-validation, which preliminarily verifies the feasibility of the algorithm. The test results are shown in Figure 7 and Figure 8. FIG. 7 shows the test result of the method of dividing the image block, and FIG. 8 shows the test result of the method of not using the image block. The x-axis in Figures 7 and 8 represents the scoring result of the test set data by the skin expert, and the y-axis is the model test result. The lines in Figure 7 and Figure 8 are ideal fitting results. The closer the black point set is to the red line, the higher the correlation. The correlation test result in Fig. 7 is 0.73, and the correlation test result in Fig. 8 is 0.88. The method of segmenting image blocks is better than the method of non-segmenting image blocks.

Further, after determining the roughness of the back of the hand in the image of the back of the hand, the terminal device 100 displays the roughness value (that is, the scoring result) on the display screen, and can also display care suggestions for the back of the hand. For example, see Figure 9.

The various embodiments mentioned above can be used in combination with each other or used alone.

In the above-mentioned embodiments provided in the present application, the method provided in the embodiments of the present application is introduced from the perspective of an electronic device as an execution subject. In order to realize the functions in the methods provided in the above embodiments of the present application, the electronic device may include a hardware structure and/or a software module, and realize the above functions in the form of a hardware structure, a software module, or a hardware structure plus a software module. Whether a certain function of the above-mentioned functions is executed by a hardware structure, a software module, or a hardware structure plus a software module depends on the specific application and design constraint conditions of the technical solution.

Based on the same concept, FIG. 10 shows an electronic device 1000 provided in this application. For example, the electronic device 1000 includes at least one processor 1010 and a memory 1020, and may also include a display screen 1030 and a camera 1040. The processor 1010 is coupled with the memory 1020, the display screen 1030, and the camera 1040. The coupling in the embodiment of the present application is an indirect coupling or communication connection between devices, units, or modules, and may be in electrical, mechanical or other forms. Used for information exchange between devices, units or modules.

Specifically, the memory 1020 is used to store program instructions, the camera 1040 is used to capture images, and the display screen 1030 is used to display a photo preview interface when the camera 1040 starts shooting. The photo preview interface includes the images collected by the camera 1040. The display screen 1030 may also be used to display the user interfaces involved in the above embodiments, such as the user interface shown in FIG. 3, the interface shown in FIG. 9, and so on. The processor 1010 is configured to call and execute the program instructions stored in the memory 1020, and execute the steps in the color spot detection method shown in FIG. 3 above.

It should be understood that the electronic device 1000 can be used to implement the skin roughness detection method shown in FIG. 2 in the embodiment of the present application, and related features can be referred to the above, which will not be repeated here.

Those skilled in the art can clearly understand that the embodiments of the present application can be implemented by hardware, firmware, or a combination of them. When implemented by software, the above-mentioned functions can be stored in a computer-readable medium or transmitted as one or more instructions or codes on the computer-readable medium. The computer-readable medium includes a computer storage medium and a communication medium, where the communication medium includes any medium that facilitates the transfer of a computer program from one place to another. The storage medium may be any available medium that can be accessed by a computer. Take this as an example but not limited to: computer-readable media can include RAM, ROM, electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory, CD- ROM) or other optical disk storage, magnetic disk storage media or other magnetic storage devices, or any other media that can be used to carry or store desired program codes in the form of instructions or data structures and that can be accessed by a computer. In addition. Any connection can suitably become a computer-readable medium. For example, if the software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL), or wireless technology such as infrared, radio, and microwave, Then coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, wireless, and microwave are included in the fixing of the media. As used in the embodiments of this application, disks and discs include compact discs (CDs), laser discs, optical discs, digital video discs (digital video discs, DVDs), floppy discs, and Blu-ray discs. Disks usually copy data magnetically, while disks use lasers to copy data optically. The above combination should also be included in the protection scope of the computer-readable medium.

In short, the above descriptions are only examples of the present application, and are not used to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made according to the disclosure of this application shall be included in the protection scope of this application.

Claims (17)

1. A method for detecting skin roughness, which is characterized in that it is applied to an electronic device and includes:

   Acquiring a grayscale image of the skin image to be processed;

   Extracting texture features in the grayscale image, where the texture features include at least one of texture depth, texture width, wide texture density, and texture density;

   Wherein, the texture depth is used to characterize the depth of the lines on the skin, the texture width is used to characterize the width of the lines on the skin, and the wide texture density is used to characterize the density of the lines in the skin whose line width reaches a preset threshold. The texture density is used to characterize the density of lines in the skin;

   The roughness of the skin in the skin image to be processed is determined according to the texture feature.
2. The method of claim 1, wherein extracting texture features in the grayscale image comprises:

   Divide the gray-scale image into K image blocks, obtain N image blocks with a gray average value within a preset gray-scale range from the K image blocks, and extract the N image blocks from the N image blocks. Texture feature, K and N are both positive integers, and N is less than or equal to K; or,

   Divide the gray image into K image blocks, sort the average gray values of the K image blocks, obtain N image blocks sorted within a preset ranking range, and select from the N image blocks The texture feature is extracted.
3. 3. The method of claim 2, wherein extracting texture features from the N image blocks comprises:

   Performing binarization processing on the N image blocks respectively to obtain N binarized images;

   Performing connected domain analysis on the N binarized images to obtain at least one first connected domain, where the at least one first connected domain is used to indicate the position of the skin texture area in the N image blocks;

   The texture feature is extracted from the area where the at least one first connected domain is located in the N image blocks.
4. The method according to claim 3, wherein the binarization process is performed on the N image blocks to obtain N binarized images, comprising:

   The N image blocks are respectively filtered, and the filtered N image blocks are binarized to obtain N binarized images.
5. The method according to claim 3 or 4, wherein the performing connected component analysis on the N binarized images to obtain at least one first connected component comprises:

   Corrosion and/or expansion processing is performed on the N binarized images, and connected domain analysis is performed on the N binarized images after the corrosion and/or expansion processing to obtain the at least one first connected domain.
6. The method according to any one of claims 3-5, wherein the texture depth is based on the gray average value of the area where at least one first connected domain is located in the N image blocks and the N image blocks The mean value of gray is determined.
7. The method of claim 6, wherein the texture depth meets the requirements of the following formula:

   F1=abs(M-M1)/M;

   Wherein, F1 represents the texture depth, M1 represents the average gray value of the area where the at least one first connected domain in the N image blocks is located, and M represents the average gray value of the N image blocks.
8. 7. The method according to any one of claims 3-7, wherein extracting the texture width from the area where the at least one first connected domain in the N image blocks is located comprises:

   Determining the texture width according to the outer contour length and area of the second connected domain in the at least one first connected domain;

   Wherein, the second connected domain is the first connected domain with the longest outer contour length or the largest area in the at least one first connected domain.
9. The method according to claim 8, wherein the determining the texture width according to the outer contour length and the area of the second connected domain in the at least one first connected domain comprises:

   When the second connected domain is a multi-connected domain, the texture width meets the requirements of the following formula:

   F2=F1×S1/(L1+L0); or, F2=S1/(L1+L0);

   Wherein, F2 represents the texture width, F1 represents the texture depth, S1 represents the area of the second connected domain, L1 represents the length of the outer contour of the second connected domain, and L0 represents the sum of the length of the inner contour of the second connected domain;

   When the second connected domain is a single connected domain, the texture width meets the requirements of the following formula:

   F2=F1×S1/L1; or, F2=S1/L1;

   Wherein, F2 represents the texture width, F1 represents the texture depth, S1 represents the area of the second connected domain, and L1 represents the outer contour length of the second connected domain.
10. 9. The method according to any one of claims 3-9, wherein extracting the wide texture density from the area where the at least one first connected domain in the N image blocks is located comprises:

    Determine, from the at least one first connected domain, at least one third connected domain whose outer contour length is greater than a preset threshold;

    Determining K image blocks of the second connected domain among the N image blocks;

    Determining a first ratio between the sum of the areas of the third connected domains included in the K image blocks and the sum of the areas of the N image blocks;

    The first ratio multiplied by the texture depth is determined as the wide texture density, or the first ratio is determined as the wide texture density.
11. The method according to any one of claims 3-10, wherein extracting the texture density from the area where the at least one first connected domain in the N image blocks is located comprises:

    Determining a second ratio between the sum of the areas of the first connected domains included in the N image blocks and the sum of the areas of the N image blocks;

    The second ratio multiplied by the texture depth is determined as the texture density, or the second ratio is determined as the wide texture density.
12. The method according to any one of claims 1-11, wherein the extracting the texture feature in the grayscale image comprises:

    Performing a histogram equalization process on the grayscale image to obtain an equalized image, and extract texture features in the equalized image.
13. The method according to any one of claims 1-12, wherein determining the roughness of the skin in the skin image to be processed according to the texture feature comprises:

    Using an integrated learning algorithm model according to the texture feature to determine the roughness of the skin in the skin image to be processed.
14. A terminal device, characterized by comprising a processor and a memory; wherein the processor is coupled with the memory;

    The memory is used to store program instructions;

    The processor is configured to read the program instructions stored in the memory to implement the method according to any one of claims 1 to 13 in an embodiment of the present application.
15. A computer storage medium, wherein the computer storage medium stores program instructions, and when the program instructions run on an electronic device, the electronic device executes the method according to any one of claims 1 to 13.
16. A computer program product, characterized in that, when the computer program product runs on an electronic device, the electronic device is caused to execute the method according to any one of claims 1 to 13.
17. A chip, characterized in that the chip is coupled with a memory in an electronic device, and when the chip reads and executes the program instructions stored in the memory, the electronic device executes any one of claims 1 to 13 The method described.

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