Human body height detection method and clothes airing machine control method and system

Abstract

The invention discloses a human body height detection method and a clothes airing machine control method and system.The human body height detection method is applied to a clothes airing machine provided with an image acquisition unit and comprises the steps that image information acquired by the image acquisition unit is acquired; extracting a face portrait from the image information, and performing depth map calculation; and calculating the body height according to the depth map calculation result. In the control process of the clothes airing machine, for a target person entering the airing induction area, the body height of the target person is detected through a lost body height detection method; finally, according to the body height of the target person, the descending stroke of the airing rod of the clothes airing machine is controlled. The clothes airing machine can detect the height of the target person entering the airing induction area of the clothes airing machine in real time, so that the clothes airing machine can adaptively control the descending height of the airing rod based on the height of a user, and the user can air clothes conveniently.

Description

Technical Field

[0001] The present application relates to the technical field of drying control, and in particular to a method for detecting human height based on a clothes drying machine, and a method and system for controlling the clothes drying machine. Background Art

[0002] Electric clothes drying machines are becoming more and more popular because they can be automatically raised and lowered and have integrated functions such as lighting, drying, and sterilization. They have become an indispensable part of smart home products.

[0003] At present, when electric clothes dryers leave the factory, the manufacturer generally provides an adjustable lower height range for the height of the clothes dryer rod, such as 80cm to 140cm. When installing the clothes dryer, the installer can choose the height of the clothes dryer rod according to the installation environment, such as the height of the user's balcony, or can also choose a suitable height according to the height of the person who will use the clothes dryer in the user's home; therefore, after the clothes dryer is installed, the height of the clothes dryer controlled by the user will remain fixed. If the height of the clothes dryer needs to be readjusted, the installer needs to be on site to readjust it.

[0004] With the development of Internet of Things technology, many electric clothes dryers are now equipped with application (APP) functions. Based on this, users can also adjust the descent height of the clothes dryer. For example, the descent height of the clothes dryer can be set for users of different heights. This control method requires setting the descent height of the clothes dryer for different users in advance through the APP, and during use, it is necessary to identify the user before controlling the clothes dryer to descend to the corresponding height. It cannot adaptively descend to the corresponding travel height according to the height of different users; in addition, after the user's height changes (for example, after the child grows taller), it is also necessary to adjust the descent height and travel of the clothes dryer through the APP again. Summary of the invention

[0005] The first purpose of the present invention is to overcome the shortcomings and deficiencies of the prior art and to provide a human body height detection method, which can enable a clothes dryer to detect the height of a target person entering a drying sensing area of ​​the clothes dryer in real time, thereby enabling the clothes dryer to adaptively control the descent height of the drying rod based on the user's height.

[0006] The second object of the present invention is to provide a method for controlling a clothes drying machine, by which the height of the clothes drying rod can be adaptively adjusted according to the height of a human body, so as to facilitate different users to dry and collect clothes.

[0007] A third object of the present invention is to provide a control device for a clothes drying machine.

[0008] A fourth object of the present invention is to provide a storage medium.

[0009] A fifth object of the present invention is to provide a computing device.

[0010] The first object of the present invention is achieved by the following technical solution: A method for detecting human height is applied to a clothes drying machine equipped with an image acquisition unit, comprising:

[0011] Acquiring image information acquired by an image acquisition unit;

[0012] Extract the face image from the image information and calculate the depth map;

[0013] Calculate the height h of the human body based on the depth map calculation result:

[0014] h=M-(Z i cosα+Y i sinα);

[0015] in:

[0016] M is the installation height of the image acquisition unit on the clothes drying machine, and α is the inclination angle of the image acquisition unit installation;

[0017] Z i and Y i is the depth map calculation result, Z i is the coordinate value of the target point i of the face portrait at depth, Y i It is the coordinate value of the target point i of the face portrait in height.

[0018] Preferably, the method further includes a human body height compensation step, which is achieved by any of the following methods:

[0019] For the face image area extracted from the image information, determine the pixel point j at the highest position of the face image in the image, then calculate the distance h" between the face image target point i and the pixel point j in the image, and add the h value to h" as the final detection result of the human height;

[0020] Set h" as a fixed compensation value, and add the h value to h" as the final detection result of the human body height.

[0021] Preferably, the image acquisition unit is installed at an inclination angle α of 10° to 85°, where the inclination angle α refers to the angle between the central axis of the image acquisition unit and the plumb line;

[0022] The face portrait target point is the center of gravity of the face portrait area in the image or the center point of the face portrait area.

[0023] Preferably, the image acquisition unit is a binocular camera, including a first camera and a second camera; wherein:

[0024] Respectively obtain the left image information and the right image information collected by the first camera and the second camera, extract the face avatar therein, and obtain the left face avatar and the right face avatar respectively;

[0025] For the face portraits on the left and right, depth map calculation is performed to determine the coordinate values ​​of the face portrait target point i in the image depth and height.

[0026] The second object of the present invention is achieved by the following technical solution: A clothes drying machine control method, comprising:

[0027] For a target person who enters the drying sensing area, the target person's height is detected by the human height detection method described in the first object of the present invention;

[0028] According to the height of the target person, the descending stroke of the clothes drying machine's drying rod is controlled.

[0029] Preferably, the method further includes a target person determination step, which specifically includes: determining the target person based on a face image extracted from the image information collected by the image acquisition unit;

[0030] In the target person determination step, when multiple face images are extracted from the image information, the target person is determined by any of the following methods:

[0031] Obtaining the depth coordinate values ​​of the target points of each face avatar in the image information, and determining the depth information of each face avatar through the depth coordinate values ​​of the target points of each face avatar; then comparing the depth information of each face avatar, and determining the target person based on the face avatar with the smallest depth distance;

[0032] Obtain the coordinate values ​​of the height of each face avatar target point in the image information, and determine the height information of each face avatar through the coordinate values ​​of the height of each face avatar target point; then compare the height information of each face avatar, and determine the target person based on the face avatar with the smallest height distance;

[0033] For each face image extracted from the image information, the corresponding human height is detected by the human height detection method described in any one of claims 1 to 4; the human heights corresponding to the face images are compared, and the target person is determined based on the face image with the shortest human height.

[0034] Furthermore, it also includes a target person determination step; the image acquisition unit is a binocular camera;

[0035] The target person determination steps are:

[0036] Get the left image and the right image captured by the binocular camera, and determine whether the facial portraits extracted from the left image and the right image meet the following conditions: in the pixel coordinates (x, y) of the top corner of the face frame, |y| is less than the set value; if so, the facial portraits extracted from the left image and the right image are determined as the target person, otherwise it is determined that there is no target person in the image.

[0037] Preferably, it also includes a gesture recognition step, and the specific process is as follows:

[0038] Acquire image information acquired by the image acquisition unit, and detect the target gesture from the image information;

[0039] Identify the detected target gesture and determine the gesture action;

[0040] Controlling the clothes drying machine to perform corresponding functions according to the determined gesture action;

[0041] Among them, if the function matched by the currently determined gesture action is the lowering function of the clothes drying rod of the clothes drying machine, the clothes drying rod is controlled to be lowered to the corresponding height according to the height of the target person.

[0042] Furthermore, before detecting the target gesture from the image information, the method further includes: determining that a face image is extracted from the image information;

[0043] Detecting a target gesture from the image information includes: when multiple gestures are detected in the image information, calculating the distance between each gesture in the image and the face avatar, and selecting the gesture closest to the face avatar as the target gesture.

[0044] Preferably, before acquiring the image information acquired by the image acquisition unit, the method further includes:

[0045] The human body sensing unit is used to detect whether anyone enters the drying sensing area;

[0046] If yes, the image acquisition unit is started to collect image information.

[0047] The third object of the present invention is achieved by the following technical solution: a clothes drying machine control system, including a controller, an image processor and an image acquisition unit;

[0048] The image acquisition unit is used to acquire image information;

[0049] The image processor is connected to the image acquisition unit, and is used to execute the human body height detection method described in the first object of the present invention, and send the detected human body height information to the controller;

[0050] The controller is connected to the image processor and is used to control the descending stroke of the clothes drying rod of the clothes drying machine according to the received human body height information.

[0051] Preferably, it also includes a human body sensing unit;

[0052] The human body sensing unit is connected to the controller and is used to detect whether a person enters the drying sensing area. When a person enters, the controller starts the image acquisition unit to collect image information.

[0053] The fourth purpose of the present invention is achieved through the following technical solution: a storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the human height detection method described in the first purpose of the present invention and / or the clothes drying machine control method described in the second purpose of the present invention are implemented.

[0054] The fifth purpose of the present invention is achieved through the following technical solution: an electronic device, comprising a processor and a memory for storing programs executable by the processor, characterized in that when the processor executes the program stored in the memory, the human height detection method described in the first purpose of the present invention and / or the clothes drying machine control method described in the second purpose of the present invention are implemented.

[0055] Compared with the prior art, the present invention has the following advantages and effects:

[0056] (1) The human height detection method of the present invention is applied to a clothes drying machine equipped with an image acquisition unit. First, the image information collected by the image acquisition unit is obtained, and the face image is extracted from the image information and a depth map is calculated. The depth and height coordinate values ​​of the face image target point are determined by the depth map calculation. Finally, the human height is calculated based on the depth and height coordinate values ​​of the face image target point and the installation height and tilt angle of the image acquisition unit. Based on this, the detection method can enable the clothes drying machine to detect the height of the target person entering the clothes drying sensing area of ​​the clothes drying machine in real time and quickly.

[0057] (2) The human height detection method of the present invention also includes a compensation method for height calculation, which is specifically based on the face portrait area extracted from the image information (the left image or the right image captured by the binocular camera). The human height calculation result can be added to the distance h" between the face portrait target point i and the pixel point j at the highest position of the face portrait in the left image or the right image, and the h value and h" are added as the final detection result of the human height; based on this, the human height calculated by the method of the present invention can be closer to the actual height, further improving the accuracy of height detection. Alternatively, h" can be set as a fixed compensation value, and the h value and h" are added as the final detection result of the human height. This compensation method reduces the amount of calculation and speeds up the human height detection process.

[0058] (3) In the human height detection method of the present invention, the image acquisition unit installed on the clothes drying machine can be a binocular camera, specifically including a first camera and a second camera. Combining the left-image human face portrait and the right-image human face portrait captured by the two cameras, the coordinate value of the human face portrait target point i in the image depth and height can be accurately determined through depth calculation. Based on this, the human height calculated by the detection method of the present invention has a smaller calculation error than the actual height. Since the image acquisition unit of the present invention can only start height detection when a human face portrait is captured, it can avoid the situation where the user does not raise his head, that is, does not have the intention to control the clothes drying machine, and thus starts the height detection and starts the control of the clothes drying machine.

[0059] (4) In the method for detecting human height of the present invention, the image acquisition unit can be installed at an angle of inclination of 10° to 85°, which can be adjusted according to the range of the drying sensing area, wherein the image acquisition unit can preferably have an inclination angle of 30 degrees or about 30 degrees. The image acquisition unit installed at an inclination can control the image acquisition area to be located on one side of the clothes drying machine, such as the side of the balcony close to the living room, so that when the target person walks from the living room to the balcony and enters the image acquisition area, the human height can be detected. In addition, the image acquisition unit installed at an inclination of the present invention allows the user to capture the face image only when raising his head at a certain angle, that is, the user can start the height detection only when he consciously controls the clothes drying machine, which can effectively avoid the situation where the clothes drying machine is triggered by mistake.

[0060] (5) The clothes drying machine control method of the present invention detects the height of the target person who enters the drying sensing area through the above-mentioned human height detection method of the present invention, and controls the clothes drying machine rod to descend to a corresponding appropriate height according to the target person's height; that is, when it is necessary to control the clothes drying machine rod to descend, the clothes drying machine rod is controlled to descend to a corresponding height according to the target person's height. Based on this, the present invention can adaptively adjust the height of the clothes drying rod according to the height of the human body, so as to facilitate different users to dry and collect clothes.

[0061] (6) In the clothes drying machine control method of the present invention, the target person can be determined based on the face portrait in the image information, and the height of the person can be further determined based on the depth calculation result of the face portrait in the image information. Therefore, the method of the present invention achieves the purpose of simultaneously realizing the detection of the target person and the measurement of the height of the person through the image acquisition unit, and effectively reduces the additional hardware cost of implementing the method. In addition, when the image information includes multiple face portraits, the present invention can determine the final target person based on the depth information, height information of each face portrait or the comparison result of the height of the person corresponding to each face portrait, so that the target person can be automatically identified when multiple people enter the drying sensing area, thereby realizing adaptive control of the height of the clothes drying machine's drying rod.

[0062] (7) In the clothes drying machine control method of the present invention, the target gesture can also be detected based on the image information collected by the image acquisition unit, and the target gesture can be recognized, so as to determine the gesture action of the target person, so as to be able to further control the clothes drying machine to perform corresponding functions through the gesture action, including the rising and falling of the clothes drying machine, the turning on and off of functions such as lighting and disinfection, etc. When it is recognized that the function corresponding to the gesture action is the lowering of the clothes drying rod of the clothes drying machine, the clothes drying rod is controlled to be lowered to the corresponding height according to the height of the target person. Therefore, the present invention combines the user's gesture and height to realize the control of the clothes drying machine, which brings greater convenience to the user in using the clothes drying machine.

[0063] (8) In the clothes drying machine control method of the present invention, it can be arranged that the above-mentioned target gesture detection step is started only when a face portrait is extracted from the image information, so as to prevent the situation where a person enters the drying sensing area but the corresponding function of the clothes drying machine is falsely triggered when there is no control demand (wherein the tilted image acquisition unit allows the user to be photographed only when he / she intentionally raises his / her head toward the image acquisition unit. Therefore, if a person merely enters the drying sensing area but no face portrait is extracted from the image information, the present invention can determine that the user has no control demand). When multiple gestures are detected in the image information, the gesture closest to the face portrait is used as the target gesture, which can effectively ensure that the recognized gesture comes from the target person and reduce the situation of false triggering as much as possible.

[0064] (9) In the clothes drying machine control method of the present invention, human body detection is performed before acquiring the image information collected by the image acquisition unit. Specifically, the human body sensing unit is used to detect whether a person has entered the drying sensing area. The image acquisition unit is started to perform the acquisition work only when a person has entered. This can effectively reduce the power consumption of the image acquisition unit and extend its service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the human body height detection principle of the present invention.

[0066] Figure 2a , 2b 2c and 2c are schematic diagrams of depth calculation under binocular vision.

[0067] Figure 3 It is a flow chart of the clothes drying machine control method in the present invention.

[0068] Figure 4 It is a detailed flow chart of the clothes drying machine control method in embodiment 1 of the present invention.

[0069] Figure 5 This is a flow chart of the control system of the clothes drying machine in Example 2 of the present invention. DETAILED DESCRIPTION

[0070] The embodiments of the present application are described below in conjunction with the drawings in the present application. It should be understood that the implementation methods described below in conjunction with the drawings are exemplary descriptions for explaining the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions of the embodiments of the present application.

[0071] Example 1

[0072] At present, the lowering height of the clothes drying rod of an electric clothes drying machine is generally adjusted by the installer during installation, and the user cannot adjust it by himself after the installation is completed; or, for some clothes drying machines configured with application programs, the lowering height of the clothes drying rod can be configured by the application program for users of corresponding heights. This method requires the lowering height of the clothes drying rod to be configured in advance for users of different heights, and the specific user identity needs to be identified during use. In addition, the configuration needs to be performed again as the user's height changes, and it is not possible to adaptively adjust the height of different users so that the clothes drying rod can be lowered to the corresponding height.

[0073] Based on the problems existing in the prior art, this embodiment provides a method for detecting human height, which is applied to a clothes drying machine equipped with an image acquisition unit.

[0074] The clothes drying machine referred to in this embodiment generally includes a main unit and a drying rod. The main unit is generally installed on the ceiling, and the drying rod is arranged below the main unit through a steel wire rope or other lifting parts. The main unit is provided with a controller (such as an MCU), a motor and other devices. The controller is connected to the motor through a motor driver, and the steel wire rope or the lifting part is controlled by the motor to perform lifting and lowering movements, thereby realizing automatic lifting and lowering of the drying rod. In addition, the clothes drying machine may also be equipped with functional modules such as lighting, drying, and disinfection, and the controller can issue instructions to realize the control of each functional template.

[0075] In this embodiment, the image acquisition unit can be installed on the main unit of the clothes drying machine, specifically, it can be installed on the main unit housing, or embedded in the main unit housing. Generally, a camera can be selected as the image acquisition unit. In this embodiment, in order to detect the height of a human body more accurately and make the detection range wider, the image acquisition unit can use a binocular camera, which includes a first camera and a second camera, and the first camera and the second camera simultaneously perform image acquisition. After the image acquisition unit is installed on the clothes drying machine, an image acquisition area will be formed, that is, objects in the image acquisition area can be captured by the image acquisition unit. In this embodiment, the image acquisition area can be defined as a drying sensing area.

[0076] The human height detection method of this embodiment comprises the following specific steps:

[0077] S101. Acquire image information acquired by an image acquisition unit.

[0078] In this embodiment, when the image acquisition unit uses a binocular camera, image information captured by the first camera and the second camera is obtained respectively, which are defined as a left image and a right image.

[0079] S102: extracting facial features from the image information and calculating a depth map.

[0080] In this embodiment, the face image is extracted from the image information captured by the first camera and the second camera respectively to obtain the left face image and the right face image; the depth map is calculated for the left face image and the right face image to determine the coordinate value Z of the face image target point i at depth. i and the coordinate value Y at height i .

[0081] S103, calculating the height h of the human body according to the calculation result of the depth map:

[0082] h=M-(Z i cosα+Y i sinα);

[0083] in:

[0084] M is the installation height of the image acquisition unit on the clothes drying machine, which is generally about 3 meters. α is the inclination angle of the image acquisition unit. In this embodiment, the inclination angle α of the image acquisition unit can be 10° to 85°. The inclination angle α refers to the central axis of the image acquisition unit (such as Figure 1 The angle between the vertical line and the dotted arrow (in the middle), where the plumb line refers to the line perpendicular to the horizontal plane, that is, the line in the direction of human body height. In this embodiment, the preferred inclination angle is 30° or about 30°. When it is 30°, the drying sensing area can be a rectangular area of ​​2m*1.2m.

[0085] Z i and Y i is the depth map calculation result, Z i is the coordinate value of the target point i of the face portrait at depth, Y iIt is the coordinate value of the face portrait target point i in height. The above-mentioned face portrait target point can be the center of gravity of the face portrait area in the image, the center point of the face portrait area (specifically, it can be the center point of the face frame) or other specified suitable points (such as points taken in the nose area, eye area or forehead area); the center of gravity of the above-mentioned face portrait area refers to the grayscale center of gravity of the face portrait area. In the above step S102 of this embodiment, based on the image captured by the binocular camera, the depth coordinate value Z and the height coordinate value Y of each pixel point in the face portrait can be calculated by the existing depth map calculation method, which correspond to the two coordinate values ​​in the three-dimensional coordinates (X, Y, Z) of each pixel point of the face portrait. Specifically, the height coordinate value Y and the depth coordinate value Z of each pixel point of the face portrait can be obtained by depth calculation, respectively:

[0086]

[0087]

[0088] Among them, Figures 2a to 2c As shown in the figure, X is the alignment axis, Y is the height, and Z represents the depth. The line connecting the optical centers of the first camera CL and the second camera CR is aligned with the X axis (parallel). The coordinate value of the pixel point of the face portrait corresponding to the X axis in the left image is x L , the coordinate value of the corresponding X-axis in the right image is x R ; The coordinate value of the face pixel point on the left image corresponding to the Y axis is y L , the corresponding Y-axis coordinate value in the right image is y R b is the baseline, which is the distance between the two cameras, f is the focal length of the first camera and the second camera, and the parallax between the left and right cameras is d=|x L -x R |. The formulas for Z and Y in the above depth calculation are existing formulas, and the derivation principles will not be repeated here.

[0089] In this embodiment, if the target point of the above-mentioned face portrait is the center of gravity of the face portrait area, after obtaining the left image and the right image captured by the first camera and the second camera, the left face portrait and the right face portrait are extracted, and then the height coordinate value and the depth coordinate value of each pixel in the face portrait are determined by the above formula in combination with the internal parameters of the binocular camera (including f, b, etc.), and finally the height coordinate value Y of the center of gravity i of the face portrait can be determined based on the grayscale center of gravity method i and the depth coordinate value Z i When the target point of the face avatar is the center point of the face avatar, the height coordinate value and depth coordinate value of the center pixel of the face avatar can also be directly determined as the height coordinate value Y of the target point i of the face avatar i and depth coordinate value Zi .

[0090] In the above step S103 of this embodiment, if Figure 1 As shown in , the calculation principle of the formula for human height h is as follows, where Figure 1 The human body at position 1 on the left is on the left side of the central axis of the binocular camera (dashed arrow), and the height coordinate value Y1 is positive. The human body at position 2 on the right is on the right side of the central axis of the binocular camera, and the height coordinate value Y2 is negative.

[0091] After obtaining the depth coordinate value and height coordinate value of the target point of the face portrait, for the human body at position 1 (face portrait No. 1) in the figure, auxiliary lines OC1, A1C1, A1B1, B1C1, etc. are drawn in the figure, where point B1 is the target point of face portrait No. 1, point O is located in the plane where the binocular camera is located (i.e., the plane formed by the X and Y coordinate axes) and is located on the central axis of the binocular camera or on a line parallel to the central axis. In this embodiment, O is the coordinate origin of the plane where the Y and Z axes are located, and C1 is the coordinate origin of B1 in the plane formed by the X and Y coordinate axes. The projection points of the plane; B1C1=Z1; Point A1 is the intersection of the first straight line passing through point C1 and perpendicular to the horizontal plane and the second straight line passing through point B1 and parallel to the horizontal plane, wherein the first straight line and the second straight line are perpendicular; Point D1 is the intersection of the third straight line passing through point O and perpendicular to the horizontal plane and the fourth straight line passing through point C1 and parallel to the horizontal plane, and the third straight line and the fourth straight line are perpendicular; Point O1 is the intersection of the fifth straight line passing through point O and parallel to the horizontal plane and the sixth straight line passing through B1 and perpendicular to the horizontal plane, and the fifth straight line and the sixth straight line are perpendicular.

[0092] Based on the above auxiliary line calculation Figure 1 The vector length of the midline segment A1C1 is:

[0093] A1C1=Z1cosα;

[0094] calculate Figure 1 The length of the midline segment OD1 vector is:

[0095] OD1 = Y1sinα;

[0096] Based on this, the distance |O1B1| from the face avatar target point i to the plane where the binocular camera is installed can be calculated as:

[0097] |O1B1|=Z1cosα+Y1sinα;

[0098] Since the binocular camera is installed at a height of M, the human height h can be obtained as:

[0099] h=M-(Z1cosα+Y1sinα);

[0100] Based on the above principle, this embodiment can realize the detection of human height.

[0101] Similarly, for the human body at position 2 (face avatar No. 2) in the figure, auxiliary lines B2C2, B2A2, B2O2, A2O2, etc. are drawn in the figure, among which point B2 is the target point of face avatar No. 2, C2 is the projection point of B2 on the plane formed by the X and Y coordinate axes; point A2 is the intersection of a straight line passing through point C2 and parallel to the horizontal plane and a straight line passing through point B2 and perpendicular to the horizontal plane, and the above two straight lines are perpendicular; point O2 is the intersection of a straight line passing through point O and parallel to the horizontal plane and a straight line passing through point B2 and perpendicular to the horizontal plane, and the above two straight lines are also perpendicular.

[0102] Based on the above auxiliary line calculation Figure 1 The vector length of the midline segment B2A2 is:

[0103] B2A2=Z2cosα;

[0104] calculate Figure 1 The length of the midline segment A2O2 vector is:

[0105] A2O2=Y2sinα;

[0106] Based on this, the distance |O2B2| from the face avatar target point i to the plane where the binocular camera is installed can be calculated as:

[0107] |O2B2|=|Z2cosα+Y2sinα; where Y2 is a negative value.

[0108] Since the binocular camera is installed at a height of M, the human height h can be obtained as:

[0109] h=M-(Z2cosα+Y2sinα).

[0110] The principle of the above-mentioned human height calculation formula in this embodiment can be used to deduce that the height detection in this embodiment is achievable and accurate.

[0111] After step S103 in the present embodiment, considering that the height of the target point i of the face portrait is calculated in step S103, and the point may be the grayscale center of gravity or the center point in the face portrait, in order to make the human height detected by the method of the present embodiment more accurate, a compensation process of the human height is performed in step S104, which is as follows:

[0112] S104. For the face avatar area extracted from the image information (left image or right image), determine the pixel point j at the highest position of the face avatar, then calculate the distance h" between the face avatar target point i and the pixel point j, and add the h value calculated in step S103 to h" as the final detection result H of the human body height.

[0113] in:

[0114]

[0115] H=h+h".

[0116] (x i ,y i ) is the coordinate of the target point i in the face portrait in the image (left image or right image), (x j ,y j ) is the coordinate of the pixel point j at the highest position of the face portrait in the image (left image or right image). The above coordinates are all the coordinates of the pixel points on the left image or the right image, and do not need to be obtained through depth calculation. In this embodiment, the pixel point j at the highest position of the face portrait can be a point corresponding to the top of the head of the face portrait, or a point on the upper border of the face portrait box. If the pixel point j is a point on the upper border of the face portrait box, then the pixel point j is the projection of the target point i on the upper border of the face portrait box, that is, the line connecting the pixel points i and j is perpendicular to the upper border of the face portrait box.

[0117] In this embodiment, h" may also be set as a fixed compensation value, that is, after calculating the height h of the human body in step S103, the h value is added to the fixed value h" as the final detection result of the height of the human body. In this embodiment, if the target point i of the face avatar is the center of gravity or center of the face avatar, the distance from the target point of the face avatar of multiple people to the highest position of the face avatar can be obtained, and the average is taken as the fixed compensation value.

[0118] Based on the height detection method of this embodiment, the clothes dryer can detect the height of the target person entering the drying sensing area of ​​the clothes dryer in real time and quickly. When the clothes drying rod of the clothes dryer needs to be lowered, the lowering stroke and the lowering height of the clothes drying rod can be controlled according to the height of the target person. When the target person is taller, the lowering stroke is usually shorter, so that the clothes drying rod is farther from the ground; when the target person is shorter, the lowering stroke is usually longer, so that the clothes drying rod is closer to the ground, so as to meet the drying needs of users of different heights. The specific lowering stroke of the clothes drying rod can be set according to actual conditions, and can also be referred to the description in Example 2.

[0119] This embodiment 2

[0120] This embodiment discloses a method for controlling a clothes drying machine. Figure 3 As shown in , the steps include:

[0121] S1. For a target person entering a drying sensing area, the height of the target person is detected by the human height detection method described in Example 1.

[0122] In this embodiment, before performing step S103 of calculating the height of the target person in embodiment 1, a target person determination step is also included, specifically:

[0123] The target person is determined according to the face portrait extracted from the image information collected by the image collection unit, that is, when the image collection unit collects the face portrait, the collected face portrait is directly determined as the face portrait of the target person.

[0124] Alternatively, in this embodiment, when the image acquisition unit is a binocular camera, for the left image and the right image captured by the binocular camera, it is determined whether the extracted face portraits meet the following conditions: in the pixel coordinates (x, y) of the top corner of the face frame, |y| is less than a set value; if the face portraits extracted from the left image and the right image both meet the above conditions, the face portraits extracted from the left image and the right image are determined as the target person, otherwise it is determined that there is no target person in the image. Among them, when a face portrait is extracted from the image information, the face portrait will be framed in the form of a box. After the binocular camera acquires the left image and the right image, the face portraits in the left image and the right image are framed in a box. The box is defined as a face frame, and the top corner of the face frame is the upper left corner, upper right corner, lower left corner or lower right corner of the box.

[0125] In this embodiment, the |y| in the coordinates of the top corners of the face frame corresponding to the face portrait extracted from the left image and the right image is compared with the set value. If both satisfy |y| is less than the set value, the face portrait extracted from the left image and the right image is used as the target person, otherwise it is determined that there is no target person in the image, and the subsequent height detection work is not performed. In this embodiment, for the left image or the right image, before the above determination is made, multiple face portraits may be extracted from it. In this case, one of the face portraits is first determined based on the target person determination method under the following multiple face portraits, and then based on this face portrait, combined with the left image and the right image, the above determination is made to determine whether the target person exists. Based on the above operation, the method of this embodiment can control the face portrait within a certain range captured by the binocular camera as the target face portrait, improve the flexibility of face portrait acquisition, and adapt to various installation environments; the above setting value can be set according to the actual shooting range requirements.

[0126] When multiple face images are extracted from the image information, the target person can be identified by any of the following methods:

[0127] The first one:

[0128] First, the coordinate value of each face avatar target point in the image information at depth is obtained. Specifically, the coordinate value of each face avatar target point at depth can be determined by calculating the depth map shown in Example 1.

[0129] Then, the depth information of each face image is determined by the coordinate value of each face image target point at depth; the above depth information corresponds to the depth distance, such as Figure 1 As shown in , since the depth coordinate values ​​of the target point of the face portrait, such as Z1 and Z2, are positive values, the depth distance of the face portrait corresponds to the depth coordinate value of the target point of the face image.

[0130] Finally, the depth information of each face image is compared, and the target person is determined based on the face image with the smallest depth distance. Figure 1 The two face portraits shown in the figure correspond to face portrait target point B1 No. 1 and face portrait target point B2 No. 2 respectively. The depth coordinate values ​​of face portrait target point B1 and face portrait target point B2 No. 2 are calculated, that is, corresponding to Z1 and Z2. If the value of Z2 is greater than the value of Z1, the target person is determined based on face portrait No. 1.

[0131] Second type:

[0132] The coordinate values ​​of the height of each face avatar target point in the image information are obtained, and the height information of each face avatar is determined through the coordinate values ​​of the height of each face avatar target point; then the height information of each face avatar is compared, and the target person is determined based on the face avatar with the smallest height distance.

[0133] For example, the image information is extracted at the same time Figure 1 The two face portraits shown in the figure correspond to face portrait target point B1 No. 1 and face portrait target point B2 No. 2 respectively. The height coordinate values ​​of face portrait target point B1 and face portrait target point B2 No. 2 are calculated, that is, corresponding to Y1 and Y2. If the value of Y2 is greater than the value of Y1, the target person is determined based on face portrait No. 1.

[0134] The third type:

[0135] For each face portrait extracted from the image information, the corresponding human height is detected by the human height detection method described in Example 1; the human heights corresponding to the face portraits are compared, and the target person is determined based on the face portrait with the shortest human height.

[0136] For example, the image information is extracted at the same time Figure 1 The two human face avatars shown in the figure correspond to target point B1 of human face avatar No. 1 and target point B2 of human face avatar No. 2, respectively. After depth map calculation is performed to determine the coordinate values ​​in height and depth for target point B1 of human face avatar No. 1 and target point B2 of human face avatar No. 2, the human heights h1 and h2 corresponding to human face avatar No. 1 and human face avatar No. 2 are calculated respectively through step S103 in Example 1, and the sizes of h1 and h2 are compared. If h1 is smaller than h2, the target person is determined based on human face avatar No. 1.

[0137] S2. Control the height of the clothes drying rod of the clothes drying machine according to the height of the target person. In this embodiment, corresponding lowering strokes of the clothes drying rod are configured for different heights of the target person, for example, as follows:

[0138] If the height of the person is less than 165cm, the lowering stroke of the clothes drying rod is 100%, for example, the corresponding lowering height can be 120cm;

[0139] The height of a person is 165cm to 170cm, and the descending stroke of the drying pole is 96%, for example, the corresponding descending height can be 115cm;

[0140] The height of a person is 170cm to 175cm, and the descending stroke of the clothes-drying rod is 92%, for example, the corresponding descending height can be 110cm;

[0141] The height of a person is 175cm to 180cm, and the descending stroke of the clothes-drying rod is 88%, for example, the corresponding descending height can be 105cm;

[0142] The height of a person is 180cm to 185cm, and the descending stroke of the drying pole is 83%, for example, the corresponding descending height can be 100cm;

[0143] The height of a person is 185cm to 190cm, and the descending stroke of the drying pole is 79%, for example, the corresponding descending height can be 95cm;

[0144] The height of a person is 190cm to 195cm, and the descending stroke of the clothes-drying rod is 75%, for example, the corresponding descending height can be 90cm;

[0145] The height of a person is 195cm to 200cm, and the descending stroke of the drying pole is 71%, for example, the corresponding descending height can be 85cm;

[0146] When the height of a person is higher than 200 cm, the descending stroke of the clothes drying pole is 66%, for example, the corresponding descending height may be 80 cm.

[0147] The above is only one of the data configuration methods. Specifically, the corresponding pole lowering stroke, i.e., the corresponding lowering height, can be configured for different human heights according to the actual installation environment of the clothes drying machine (e.g., the height of the balcony) or user needs. Based on the above steps, this embodiment can adaptively adjust the lowering height of the pole according to the height of the human body, making it convenient for different users to dry and collect clothes.

[0148] The clothes drying machine control method of this embodiment further includes a gesture detection step, and the specific process is as follows:

[0149] Sa. Detect the target gesture based on the image information collected by the image acquisition unit.

[0150] In this embodiment, with respect to the image information collected by the image collection unit, gesture features in the image information are extracted to detect the target gesture.

[0151] In this embodiment, the gesture detection step can be started only when a face image is extracted from the image information collected by the image acquisition unit, that is, for the image information collected by the image acquisition unit, if a face image is extracted from it, this step Sa is started to identify the target gesture. Based on this, gesture recognition can be performed only when the user intends to control the clothes drying machine, and the algorithm power consumption is reduced.

[0152] If multiple gestures are detected when the image information is detecting the target gesture, the distance between each gesture and the face avatar is calculated, and the gesture closest to the face avatar is selected as the target gesture; based on this, it is possible to avoid the situation where other gestures are recognized and cause miscontrol. The details are as follows:

[0153] Sa1, obtain the coordinates of the face portrait target point i in the image, and obtain the coordinates of each gesture target point s in the image. The gesture target point refers to the center of gravity, center point or other specified appropriate point of the gesture area in the image (such as a point on a finger, back of hand, or palm).

[0154] Sa2, the distance between each gesture and the face avatar is calculated by the following formula:

[0155]

[0156] Among them, (x i ,y i ) is the coordinate of the target point i in the face image in the image, (x s ,y s ) is the coordinate of the gesture target point s in the image; D is is the distance between the gesture target point s and the face avatar target point i in the image. The above coordinates are obtained from the left image or the right image captured by the binocular camera, that is, the coordinates of the face avatar target point i and the gesture target point s in the left image or the right image.

[0157] Sa3, compare the distances between each gesture and the face avatar, and select the gesture corresponding to the shortest distance as the target gesture. That is, in this embodiment, the gesture closest to the face avatar is used as the target gesture.

[0158] Sb. Identify the detected target gesture and determine the gesture action.

[0159] In this embodiment, a gesture recognition model can be trained based on a neural network. Specifically, multiple groups of gesture features can be obtained, and each group of gesture features can be labeled. The label corresponds to a specific gesture action, and a certain number of training samples are obtained. The neural network model (such as a convolutional neural network model) constructed based on the training samples is trained to obtain a gesture recognition model. In this step, the gesture features corresponding to the target gesture extracted from the image information in step Sa are input into the gesture recognition model, and the gesture action is classified and recognized by the gesture recognition model. The gesture action may include raising one or more fingers, making a fist, OK gesture, stretching out the palm, etc. The gesture action to be recognized can be selected according to the actual control requirements.

[0160] Sc. Control the clothes drying machine to perform a corresponding function according to the determined gesture action. If the function matched by the currently determined gesture action is a clothes drying rod lowering function, the lowering stroke of the clothes drying rod is controlled according to the height of the target person.

[0161] In this embodiment, before executing step Sc, different functions of the clothes drying machine need to be configured for different gesture actions. For example, the first gesture action is configured with a function of raising the clothes drying rod, the second gesture action is configured with a function of lowering the clothes drying rod, the third gesture action is configured with a function of turning on the clothes drying machine lighting, the fourth gesture action is configured with a function of turning off the clothes drying machine lighting, the fifth gesture action is configured with a function of turning on the clothes drying machine disinfection, the sixth gesture action is configured with a function of turning off the clothes drying machine disinfection, and so on. Based on this, in this step, when step Sb recognizes that the target gesture is the second gesture action, the clothes drying rod can be controlled to descend at this time, and the specific descending stroke is controlled according to the height range of the human body.

[0162] In the clothes drying machine control method of this embodiment, the following steps are also included before acquiring the image information collected by the image collection unit:

[0163] The human body sensing unit is used to detect whether anyone enters the drying sensing area; if so, the image acquisition unit is started to collect image information, otherwise the image information collection work is not started, which can avoid the waste of power consumption caused by the image acquisition unit being in working state all the time.

[0164] like Figure 4 As shown in , the following specifically illustrates one complete implementation process of the clothes drying machine control method of this embodiment:

[0165] S201, the human body sensing unit installed on the clothes drying machine monitors whether there is anyone entering the drying sensing area, if yes, the image acquisition unit is started to perform image acquisition, and then the process proceeds to step S202. In this embodiment, the binocular camera is started to start image capture.

[0166] S202, acquiring image information acquired by the image acquisition unit in the drying sensing area. In this embodiment, the left image and the right image acquired by the first camera and the second camera in the binocular camera are acquired.

[0167] S203, extracting facial features from the image information collected by the image collection unit, and entering step S204 after the facial features are extracted.

[0168] In this embodiment, the left face image and the right face image are extracted from the left image and the right image respectively. If the image information includes multiple face images, one of the face images is determined as the face image of the target person based on the above step S1 of this embodiment.

[0169] S204, starting a gesture detection step to detect whether the image information collected by the image acquisition unit includes gesture features. If the gesture features can be extracted, gesture recognition is performed to determine the gesture action.

[0170] In this embodiment, if multiple gestures are extracted from the image information, the distance between each gesture and the facial avatar is calculated based on the above steps Sa1 to Sa3 of this embodiment, and the gesture with the shortest distance to the facial avatar is selected as the target gesture. Gesture recognition is performed on the gesture features corresponding to the target gesture to determine the final gesture action.

[0171] S205, according to the gesture action, the clothes drying machine is controlled to perform a corresponding function. For example, when the gesture action matches the lighting on, the clothes drying machine lighting is controlled to turn on; when the gesture action matches the clothes drying rod rising, the clothes drying rod is controlled to rise. When the gesture action matches the clothes drying rod lowering, the clothes drying rod is controlled to lower.

[0172] In this embodiment, if the function matched to the gesture action is lowering the clothes drying rod of the clothes drying machine, the clothes drying rod is controlled to be lowered by executing step S206.

[0173] S206, issuing a control instruction for lowering the clothes drying rod, and obtaining the height of the person detected by the human height detection method of Example 1 for the face image extracted from the image information. That is, the face image extracted from the image information is used as the target person, and the height of the target person calculated based on the method of Example 1 is obtained, and the lowering stroke of the clothes drying rod is controlled according to the height of the target person.

[0174] In this embodiment, in step S203, it can be arranged that when the facial information in the image information is extracted, when the corresponding target person is determined, the height of the person is detected by using embodiment 1. As time goes by, multiple height detections may be performed before the clothes-drying rod is controlled to descend. When it is determined that the function matched by the gesture action is the descent of the clothes-drying rod, or when there are other triggering methods to trigger the descent of the clothes-drying rod, the height of the person detected at this moment can be used as the final detection result, and the descent stroke of the clothes-drying rod is controlled based on the final detection result. Of course, in this embodiment, it is also possible to control that only when a trigger for the descent of the clothes-drying rod is received, including when triggered by a gesture action or other methods such as a remote control, an APP, etc., the method of embodiment 1 is executed to detect the height of the person, and finally the descent stroke of the clothes-drying rod is determined based on the detection result.

[0175] The above steps S201 to 206 of this embodiment are only one of the implementation processes. In the actual control process of the clothes drying machine, there may be no gesture recognition process. When the clothes drying rod is lowered by other triggers, the lowering stroke of the clothes drying rod is controlled based on the human body height obtained in this embodiment.

[0176] Those skilled in the art will appreciate that all or part of the steps in the method of the present embodiment can be completed by instructing the relevant hardware through a program, and the corresponding program can be stored in a computer-readable storage medium. It should be noted that although the method operation of the present embodiment is described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in this specific order, or that all the operations shown must be performed to achieve the desired result. On the contrary, the steps depicted can change the order of execution, some steps can also be performed simultaneously, additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution.

[0177] Example 3

[0178] This embodiment discloses a clothes drying machine control system, which is applied to the clothes drying machine. Figure 5 As shown in , it includes a controller, an image processor, an image acquisition unit and a human body sensing unit. The controller can be a controller in the clothes drying machine host, or another controller set separately. If it is the latter, the controller is connected to the clothes drying machine main control board and exchanges information with the clothes drying machine main control board. Among them:

[0179] In this embodiment, the human body sensing unit is connected to the controller to detect whether a person enters the drying sensing area. When a person enters, the image acquisition unit can be activated to start image acquisition. The human body sensing unit can be a sensor that can sense the human body, such as an infrared sensor.

[0180] In this embodiment, the image acquisition unit is connected to the image processor to acquire image information of the drying sensing area and send the acquired image information to the image processor for image processing. In this embodiment, the image acquisition unit uses a binocular camera, which can be installed on the main housing of the clothes drying machine or embedded in the main housing.

[0181] In this embodiment, the binocular camera is installed tilted, and the tilt angle can be 10° to 85°. The tilt angle refers to the angle between the central axis of the binocular camera and the plumb line. Figure 1 In this embodiment, the preferred tilt angle is 30° or about 30°. When the tilt angle is 30°, the drying sensing area of ​​the binocular camera can be a rectangular area of ​​2m*1.2m. The image acquisition unit installed at a certain tilt angle can make it possible for a person who enters the drying sensing area to have his or her face captured only when he or she looks up at the image acquisition unit. This can effectively avoid false triggering when there is no intention to dry the clothes.

[0182] In this embodiment, the binocular camera is installed on the side of the clothes drying machine main body shell. For the clothes drying machine installed on the balcony, the binocular camera shoots in the direction of the balcony, which is usually located on the side of the long side of the clothes drying machine main body shell. The tilted binocular camera ensures that an area on the side of the clothes drying machine is a drying sensing area, and objects entering this area will be photographed by the binocular camera. In addition, the installation position of the above-mentioned human body sensing unit can also be consistent with the image acquisition unit, and the sensing range of the human body sensing unit is greater than or equal to the drying sensing area of ​​the image acquisition unit. It is best that the two can be as consistent as possible.

[0183] In this embodiment, the image processor is connected to the image acquisition unit, and is used to receive the image information acquired by the image acquisition unit and perform image processing. In this embodiment, the image processor receives the image information acquired by the image acquisition unit, and can execute the human height detection method described in Example 1, and detect the human height through the detection method described in Example 1. At the same time, the image processor can also execute the gesture detection step described in Example 2, detect the target gesture from the image information and perform gesture action recognition. The human height information detected by the image processor and the recognized gesture action information can be sent to the controller.

[0184] In this embodiment, the controller is connected to the image processor and can receive human height information detected by the image processor and gesture action information recognized by the image processor; further, the controller can control the clothes drying machine to perform corresponding operations based on the function matched by the gesture action. If the matched function is the lowering of the clothes drying rod, the controller can further control the lowering stroke of the clothes drying rod based on the human height information sent by the image processor.

[0185] In this embodiment, the image acquisition unit (such as a binocular camera), the image processor and the human body sensing unit can be arranged in a structural member, for example, all arranged in a shell, and the shell is installed on one side of the long side of the clothes drying machine main body shell. After the shell is installed, the angle between the central axis of the binocular camera image and the plumb line, that is, the tilt angle, reaches 10° to 85°; the corresponding connecting wires of the image processor, including the power connecting wire and the signal connecting wire, pass through the shell and the clothes drying machine main body shell, and are connected to the controller in the clothes drying machine main body. In addition, a controller can also be arranged in the shell, and the corresponding connecting wires of the controller pass through the shell and the clothes drying machine main body shell, and are connected to the main control board in the clothes drying machine main body.

[0186] Example 4

[0187] This embodiment discloses a storage medium storing a program. When the program is executed by a processor, the method for detecting human height described in Embodiment 1 is implemented, including:

[0188] Acquire image information acquired by an image acquisition unit, wherein the image acquisition unit is installed on the clothes drying machine;

[0189] Extract the face image from the image information and calculate the depth map;

[0190] Calculate the height h of the human body based on the depth map calculation result:

[0191] h=M-(Z i cosα+Y i sinα);

[0192] in:

[0193] M is the installation height of the image acquisition unit on the clothes drying machine, and α is the inclination angle of the image acquisition unit installation;

[0194] Z i and Y i is the depth map calculation result, Z i is the coordinate value of the target point i of the face portrait at depth, Y i It is the coordinate value of the target point i of the face portrait in height.

[0195] The specific implementation process of the above steps is shown in Example 1 and will not be repeated here.

[0196] and / or,

[0197] Implementing the clothes drying machine control method described in Example 2 includes:

[0198] For a target person who enters the drying sensing area, the target person's height is detected by the human height detection method described in Example 1;

[0199] According to the height of the target person, the descending stroke of the clothes drying machine's drying rod is controlled.

[0200] In addition, the human body detection step and gesture detection step described in Example 2 may also be included. The specific process is shown in Example 2 and will not be repeated here.

[0201] In this embodiment, the storage medium may be a disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), a USB flash drive, a mobile hard disk, or the like.

[0202] Example 5

[0203] This embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the human height detection method described in Embodiment 1, including:

[0204] Acquire image information acquired by an image acquisition unit, wherein the image acquisition unit is installed on the clothes drying machine;

[0205] Extract the face image from the image information and calculate the depth map;

[0206] Calculate the height h of the human body based on the depth map calculation result:

[0207] h=M-(Z i cosα+Y i sinα);

[0208] in:

[0209] M is the installation height of the image acquisition unit on the clothes drying machine, and α is the inclination angle of the image acquisition unit installation;

[0210] Z i and Y i is the depth map calculation result, Z i is the coordinate value of the target point i of the face portrait at depth, Y i It is the coordinate value of the target point i of the face portrait in height.

[0211] The specific implementation process of the above steps is shown in Example 1 and will not be repeated here.

[0212] and / or,

[0213] Implementing the clothes drying machine control method described in Example 2 includes:

[0214] For a target person who enters the drying sensing area, the target person's height is detected by the human height detection method described in Example 1;

[0215] According to the height of the target person, the descending stroke of the clothes drying machine's drying rod is controlled.

[0216] In addition, the human body detection step and gesture detection step described in Example 2 may also be included. The specific process is shown in Example 2 and will not be repeated here.

[0217] In this embodiment, the electronic device can be a device with the above-mentioned functions set on a clothes drying machine, including: a processor, a memory, a bus and a communication interface, the processor, the communication interface and the memory are connected through the bus; the processor is configured to execute an executable module stored in the memory, such as a computer program.

[0218] The above is only an optional implementation method for some implementation scenarios of the present application. It should be pointed out that for ordinary technicians in this technical field, without departing from the technical concept of the solution of the present application, other similar implementation methods based on the technical ideas of the present application are also within the protection scope of the embodiments of the present application.

Claims

1. A method for detecting human height, characterized in that: Applicable to a clothes drying machine equipped with an image acquisition unit, including: Acquiring image information acquired by an image acquisition unit; Extract the face image from the image information and calculate the depth map; Calculate the height h of the human body based on the depth map calculation result: h=M-(Z i cosα+Y i sinα); in: M is the installation height of the image acquisition unit on the clothes drying machine, and α is the inclination angle of the image acquisition unit installation; Z i and Y i is the depth map calculation result, Z i is the coordinate value of the target point i of the face portrait at depth, Y i It is the coordinate value of the target point i of the face portrait in height.

2. The method for detecting human height according to claim 1, characterized in that: It also includes a human height compensation step, which is achieved by any of the following methods: For the face image area extracted from the image information, determine the pixel point j at the highest position of the face image in the image, then calculate the distance h" between the face image target point i and the pixel point j in the image, and add the h value to h" as the final detection result of the human height; Set h" as a fixed compensation value, and add the h value to h" as the final detection result of the human body height.

3. The method for detecting human height according to claim 1, characterized in that: The image acquisition unit is installed at an inclination angle α of 10° to 85°, where the inclination angle α refers to the angle between the central axis of the image acquisition unit and the plumb line; The face portrait target point is the center of gravity of the face portrait area in the image or the center point of the face portrait area.

4. The method for detecting human height according to claim 1, characterized in that: The image acquisition unit is a binocular camera, including a first camera and a second camera; wherein: Respectively obtain the left image information and the right image information collected by the first camera and the second camera, extract the face avatar therein, and obtain the left face avatar and the right face avatar respectively; For the face portraits on the left and right, depth map calculation is performed to determine the coordinate values ​​of the face portrait target point i in the image depth and height.

5. A clothes drying machine control method, characterized in that: include: For a target person who enters the drying sensing area, the target person's height is detected by the human height detection method described in any one of claims 1 to 4; According to the height of the target person, the descending stroke of the clothes drying machine's drying rod is controlled.

6. The clothes drying machine control method according to claim 5, characterized in that: The step of determining a target person is also included, specifically including: determining the target person based on a face image extracted from the image information collected by the image collection unit; In the target person determination step, when multiple face images are extracted from the image information, the target person is determined by any of the following methods: Obtaining the depth coordinate values ​​of the target points of each face avatar in the image information, and determining the depth information of each face avatar through the depth coordinate values ​​of the target points of each face avatar; then comparing the depth information of each face avatar, and determining the target person based on the face avatar with the smallest depth distance; Obtain the coordinate values ​​of the height of each face avatar target point in the image information, and determine the height information of each face avatar through the coordinate values ​​of the height of each face avatar target point; then compare the height information of each face avatar, and determine the target person based on the face avatar with the smallest height distance; For each face image extracted from the image information, the corresponding human height is detected by the human height detection method described in any one of claims 1 to 4; the human heights corresponding to the face images are compared, and the target person is determined based on the face image with the shortest human height.

7. The clothes drying machine control method according to claim 5, characterized in that: The step of determining the target person is also included; the image acquisition unit is a binocular camera; The target person determination steps are: Get the left image and the right image captured by the binocular camera, and determine whether the facial portraits extracted from the left image and the right image meet the following conditions: in the pixel coordinates (x, y) of the top corner of the face frame, |y| is less than the set value; if so, the facial portraits extracted from the left image and the right image are determined as the target person, otherwise it is determined that there is no target person in the image.

8. The method for controlling a clothes drying machine according to claim 5, characterized in that: It also includes gesture recognition steps, the specific process is as follows: Acquire image information acquired by the image acquisition unit, and detect the target gesture from the image information; Identify the detected target gesture and determine the gesture action; Controlling the clothes drying machine to perform corresponding functions according to the determined gesture action; Among them, if the function matched by the currently determined gesture action is the lowering function of the clothes drying rod of the clothes drying machine, the clothes drying rod is controlled to be lowered to the corresponding height according to the height of the target person.

9. The clothes drying machine control method according to claim 8, characterized in that: Before detecting the target gesture from the image information, the method further includes: determining that a face image is extracted from the image information; Detecting a target gesture from the image information includes: when multiple gestures are detected in the image information, calculating the distance between each gesture in the image and the face avatar, and selecting the gesture closest to the face avatar as the target gesture.

10. The method for controlling a clothes drying machine according to any one of claims 5 to 9, characterized in that: Before acquiring the image information acquired by the image acquisition unit, the method further includes: The human body sensing unit is used to detect whether anyone enters the drying sensing area; If yes, the image acquisition unit is started to collect image information.

11. A clothes drying machine control system, characterized in that: It includes a controller, an image processor and an image acquisition unit; The image acquisition unit is used to acquire image information; The image processor is connected to the image acquisition unit, and is used to execute the human body height detection method according to any one of claims 1 to 4, and send the detected human body height information to the controller; The controller is connected to the image processor and is used to control the descending stroke of the clothes drying rod of the clothes drying machine according to the received human body height information.

12. The clothes drying machine control system according to claim 11, characterized in that: It also includes a human body sensing unit; The human body sensing unit is connected to the controller and is used to detect whether a person enters the drying sensing area. When a person enters, the controller starts the image acquisition unit to collect image information.

13. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the human body height detection method according to any one of claims 1 to 4 and / or the clothes drying machine control method according to any one of claims 5 to 10 are implemented.

14. An electronic device comprising a processor and a memory for storing a program executable by the processor, characterized in that: When the processor executes the program stored in the memory, the human body height detection method according to any one of claims 1 to 4 and / or the clothes drying machine control method according to any one of claims 5 to 10 are implemented.

Images