Distance detection method, system, device and storage medium based on spot image

Abstract

The application provides a distance detection method and system based on a light spot image, a device and a storage medium, and comprises the following steps: acquiring a light spot image of a target person, the light spot image being collected by a depth camera; intercepting a pixel region of a preset size on the light spot image; calculating the light spot definition of the pixel region; and determining distance information between the target person and the depth camera according to the light spot definition and a preset distance information generation model associated with the light spot definition and distance. In the application, the light spot image of the target person is collected, the pixel region is intercepted on the light spot image, the light spot definition of the pixel region is calculated, and the distance information between the target person and the depth camera is determined according to the light spot definition and the preset distance information generation model associated with the light spot definition and distance. The depth information of the object can be obtained more quickly, and the application can be used in a mobile phone, a motion sensing game, a payment and other consumer products for acquiring the face depth information at a close distance.

Description

TECHNICAL FIELD

[0001] The present application relates to image detection, in particular to a distance detection method and system based on a light spot image, a device and a storage medium. BACKGROUND

[0002] 3D depth vision as a new technology has appeared in mobile phones, motion games, payments and other consumer products and gradually penetrated into new fields such as security and automatic driving. With the continuous progress of hardware technology and the continuous optimization of algorithms and software, the accuracy and practicality of 3D depth vision have been greatly improved. The current mature depth measurement method includes the TOF scheme.

[0003] TOF (time of flight) technology is a 3D imaging technology that emits a measurement light from a projector and reflects the measurement light back to a receiver through a target object, so as to obtain the spatial distance from the object to the sensor according to the propagation time of the measurement light in the propagation path. Common TOF technologies include single-point scanning projection method and surface light projection method.

[0004] However, the TOF technology has the problem of not being accurate enough for close-range testing, which is not convenient for close-range face depth information acquisition and not convenient for applying the TOF technology to consumer products such as mobile phones, motion games and payments. SUMMARY

[0005] In view of the defects in the prior art, the present application aims to provide a distance detection method and system based on a light spot image, a device and a storage medium.

[0006] The distance detection method based on a light spot image provided by the present application comprises the following steps:

[0007] Step S1: acquiring a light spot image of a target person, the light spot image being collected by a depth camera;

[0008] Step S2: intercepting a pixel region of a preset size on the light spot image;

[0009] Step S3: calculating the light spot definition of the pixel region, and determining the distance information between the target person and the depth camera according to the light spot definition and a pre-set distance information generation model associated with the distance of the light spot definition.

[0010] Preferably, the step S1 comprises the following steps:

[0011] Step S101: projecting dot array light to the target person through the light beam projector end of the depth camera;

[0012] Step S102: receiving the dot array light reflected by the target person through a detector end of the depth camera;

[0013] Step S103: generating a light spot image of the target person according to the dot array light received by the detector end of the depth camera.

[0014] Preferably, the step S2 comprises the following steps:

[0015] Step S201: performing face detection on the light spot image to determine a face region;

[0016] Step S202: obtaining a preset size of an image cropping frame, and moving the image cropping frame to a target region on the face region;

[0017] Step S203: cropping a pixel region of a preset size in the target region through the image cropping frame.

[0018] Preferably, the step S3 comprises the following steps:

[0019] Step S301: obtaining a gray value of each pixel point in the pixel region, and calculating a light spot sharpness of the pixel region according to the gray value of each pixel point;

[0020] Step S302: obtaining a distance information generation model associated with the light spot sharpness and the distance;

[0021] Step S303: inputting the light spot sharpness into the distance information generation model to generate distance information between the pixel point and the depth camera.

[0022] Preferably, the step S301 comprises the following steps:

[0023] Step S3011: defining a Laplace operator L;

[0024] Step S3012: performing convolution processing on the gray value of each pixel point according to the Laplace operator L to generate a convolution value of each pixel point;

[0025] Step S3013: generating a numerical value of the light spot sharpness according to an average value of the convolution values of all the pixel points.

[0026] Preferably, the Laplace operator L is:

[0027]

[0028] The convolution value is: G(x, y) = B * L

[0029] Wherein, B is a matrix composed of gray values of a center pixel and neighborhood pixels, G(x, y) is a value of the center pixel after convolution;

[0030] The value of the spot clarity is: C is the total number of pixels in the pixel region, and D(f) is the value of the spot clarity.

[0031] Preferably, the distance information generation model is generated by fitting a plurality of distance information corresponding to the spot clarity in the spot image and pre-acquired.

[0032] According to the present application, a distance detection system based on a spot image is provided, comprising the following modules:

[0033] A spot image acquisition module is configured to acquire a spot image of a target person, wherein the spot image is acquired by a depth camera.

[0034] An image intercepting module is configured to intercept a pixel region with a preset size on the spot image.

[0035] A distance calculation module is configured to calculate a spot clarity of the pixel region, and determine distance information between the target person and the depth camera according to the spot clarity and a distance information generation model in which the spot clarity is associated with distance.

[0036] According to the present application, a distance detection device based on a spot image is provided, comprising:

[0037] A processor;

[0038] A memory having executable instructions of the processor stored therein;

[0039] The processor is configured to execute the steps of the distance detection method based on a spot image by executing the executable instructions.

[0040] According to the present application, a computer readable storage medium is provided for storing a program, wherein the program is executed to implement the steps of the distance detection method based on a spot image.

[0041] Compared with the prior art, the present application has the following advantages:

[0042] In the present application, the depth information of an object can be obtained more quickly by acquiring a spot image of a target person, intercepting a pixel region on the spot image, calculating a spot clarity of the pixel region, and determining distance information between the target person and the depth camera according to the spot clarity and a distance information generation model in which the spot clarity is associated with distance. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort on the basis of the provided drawings. Other features, objects and advantages of the present application will become more apparent through reading the following detailed description of the non-limiting embodiments with reference to the following drawings:

[0044] Figure 1 A step flow chart of the face light spot image-based living body detection method in the embodiment of the present application;

[0045] Figure 2 A step flow chart of the face light spot image-based living body detection method in the embodiment of the present application;

[0046] Figure 3 A step flow chart of the face light spot image-based living body detection method in the embodiment of the present application;

[0047] Figure 4 A step flow chart of the face light spot image-based living body detection method in the embodiment of the present application;

[0048] Figure 5 A fitting data chart of the distance information generation model in the embodiment of the present application;

[0049] Figure 6 A step flow chart of the face light spot image-based living body detection method in the embodiment of the present application;

[0050] Figure 7 A module schematic diagram of the depth camera in the embodiment of the present application;

[0051] Figure 8 A module schematic diagram of the face light spot image-based living body detection system in the embodiment of the present application;

[0052] Figure 9 A structure schematic diagram of the face light spot image-based living body detection device in the embodiment of the present application; and

[0053] Figure 10 A structure schematic diagram of the computer readable storage medium in the embodiment of the present application. DETAILED DESCRIPTION

[0054] The application will be described in detail below with specific examples. The following examples will help those skilled in the art to further understand the application, but do not limit the application in any form. It should be pointed out that, for those skilled in the art, without departing from the concept of the application, a number of modifications and improvements can be made. These are within the scope of the application.

[0055] The terms "first", "second", "third", "fourth" and the like in the description and claims of the application, and above figures, if any, are used to distinguish similar objects, and are not necessarily used to describe a particular sequential or chronological order. It should be understood that the data thus used can be interchanged, where appropriate, to present an embodiment of the application described herein in any order, for example, other than those illustrated or described herein. In addition, the terms "comprise" and "have" and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product or apparatus that includes a list of steps or units is not necessarily limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products or apparatus.

[0056] The technical solutions of the application will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes may not be described in detail in some examples.

[0057] The distance detection method based on light spot image provided by the application aims to solve the problems in the prior art.

[0058] The technical solutions of the application and how the technical solutions of the application solve the above technical problems will be described in detail below with specific examples. The following specific examples can be combined with each other, and the same or similar concepts or processes may not be described in detail in some examples. The embodiments of the application will be described below with reference to the drawings.

[0059] Figure 1 For the step flow chart of the face light spot image-based living body detection method in the embodiments of the application, as shown in Figure 1 The face light spot image-based living body detection method provided by the application includes the following steps:

[0060] Step S1: acquiring a light spot image of a target person;

[0061] Figure 2 For the step flow chart of acquiring the light spot image of the target person in the embodiments of the application, as shown in Figure 2 The step S1 includes the following steps:

[0062] Step S101: projecting dot array light to the target person through a light beam projector end of the depth camera;

[0063] Step S102: receiving the dot array light reflected by the target person through a detector end of the depth camera;

[0064] Step S103: generating a light spot image of the target person according to the dot array light received by the detector end of the depth camera.

[0065] In the embodiment of the present application, the detector end is an infrared detector, and the dot array light reflected by the target person is received by the infrared detector.

[0066] The light spot image is collected when the depth camera is at a distance of 30-80 cm from the target person. The diameter of the light spot on the light spot image is 6-15 mm; when the depth camera is at a distance of 40 cm from the target person, the diameter of the light spot on the light spot image is about 8 mm; when the depth camera is at a distance of 60 cm from the target person, the diameter of the light spot on the light spot image is about 12 mm.

[0067] Step S2: intercepting a pixel region of a preset size on the light spot image;

[0068] Figure 3 The flow chart of the step of intercepting a pixel region on the light spot image in the embodiment of the present application is shown in FIG. 2. Figure 3 The step S2 includes the following steps as shown in FIG. 2.

[0069] Step S201: performing face detection on the light spot image to determine a face region;

[0070] Step S202: obtaining an image intercepting frame of the preset size, and moving the image intercepting frame to a target region on the face region;

[0071] Step S203: intercepting a pixel region of the preset size in the target region through the image intercepting frame.

[0072] In the embodiment of the present application, the preset size is a pixel region of 100 pixels x 100 pixels; the target region on the face region can be a middle region on the face region, or a lower left region, an upper left region, a lower right region, an upper right region, etc.

[0073] Step S3: calculating light spot sharpness of the pixel region, and determining distance information between the target person and the depth camera according to the light spot sharpness and a preset light spot sharpness-distance association model.

[0074] Figure 4 A flow chart of the step of judging the facial spot image of the living person according to the spot definition in the embodiment of the present application is shown in Fig. 3. Figure 4 As described above, the step S3 comprises the following steps:

[0075] Step S301: obtaining the gray value of each pixel point in the pixel region, and calculating the spot definition of the pixel region according to the gray value of each pixel point;

[0076] Step S302: obtaining the distance information generation model associated with the spot definition and the distance;

[0077] Step S303: inputting the spot definition into the distance information generation model to generate the distance information between the pixel point and the depth camera.

[0078] In the embodiment of the present application, the depth image can be further generated according to the distance information between the plurality of pixel points and the depth camera.

[0079] Figure 5 A fitting data graph of the distance information generation model in the embodiment of the present application is shown in Fig. 4. Figure 5 As shown in the figure, the distance information generation model is generated by fitting a plurality of distance information corresponding to the spot definition in the spot image and has good fitting effect.

[0080] In the embodiment of the present application, the distance information generation model is specifically as follows:

[0081] y = -1.101e -14 x 5 + 6.971e -11 x 4 - 1.719e -07 x 3 + 0.0002105x 2 - 0.1484x + 103

[0082] Wherein, x is the definition, and y is the actual distance, i.e. the distance information.

[0083] Figure 6 A flow chart of the step of calculating the spot definition of the pixel region in the embodiment of the present application is shown in Fig. 5. Figure 6 As shown in the figure, the step S301 comprises the following steps:

[0084] Step S3011: defining the Laplace operator L;

[0085] Step S3012: performing convolution processing on the gray value of each pixel point according to the Laplace operator L to generate the convolution value of each pixel point;

[0086] Step S3013: generating the value of the spot sharpness according to the average value of the convolution values of all the pixel points.

[0087] In the embodiment of the present application, the Laplace operator is:

[0088]

[0089] The convolution value is: G(x, y) = B * L

[0090] Wherein, B is a matrix composed of the gray values of a center pixel and 8 neighboring pixels, and G(x, y) is the value of the center pixel after convolution.

[0091] The value of the spot sharpness is: C is the total number of pixels in the pixel region, and D(f) is the value of the spot sharpness.

[0092] Figure 8 As shown in the module schematic diagram of the living body detection system based on the face spot image in the embodiment of the present application, Figure 8 The living body detection system based on the face spot image provided by the present application comprises the following modules:

[0093] The spot image acquisition module is used to acquire the spot image of the target person, and the spot image is collected by a depth camera.

[0094] The image intercepting module is used to intercept a pixel region of a preset size on the spot image.

[0095] The distance calculation module is used to calculate the spot sharpness of the pixel region, and the distance information between the target person and the depth camera is determined according to the spot sharpness and a distance information generation model in which the spot sharpness and the distance are associated.

[0096] In the embodiment of the present application, a living body detection device based on a face spot image is also provided, which comprises a processor and a memory having executable instructions of the processor stored therein. The processor is configured to execute the steps of the living body detection method based on the face spot image by executing the executable instructions.

[0097] As described above, in the embodiment, the spot image of the target person is collected, the pixel region is intercepted on the spot image, the spot sharpness of the pixel region is calculated, and the distance information between the target person and the depth camera is determined according to the spot sharpness and a distance information generation model in which the spot sharpness and the distance are associated. The depth information of the object can be obtained more quickly, and the method can be used in mobile phones, motion sensing games, payment and other consumer products for close-range face depth information acquisition.

[0098] Those skilled in the art can understand that each aspect of the present application can be implemented as a system, a method or a program product. Therefore, each aspect of the present application can be specifically implemented as follows: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "platform" here.

[0099] Figure 9 is a structural schematic diagram of a face light spot image-based living body detection device in an embodiment of the present application. The electronic device 600 according to this embodiment of the present application will be described below with reference to Figure 9 Figure 9 The electronic device 600 shown is merely an example and should not impose any limitation on the functions and use range of the embodiments of the present application.

[0100] As shown in Figure 9 , the electronic device 600 is in the form of a general computing device. The components of the electronic device 600 can include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0101] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present application described in the above face light spot image-based living body detection method part of the present specification. For example, the processing unit 610 can perform the steps as shown in Figure 1

[0102] The storage unit 620 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 6201 and / or a cache memory unit 6202, and can further include a read-only memory (ROM) 6203.

[0103] The storage unit 620 can further include a program / utility 6204 having a set of (at least one) program modules 6205, which include but are not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination thereof can include implementation of a network environment.

[0104] The bus 630 can represent one or more of several types of bus structures, including a storage unit bus or storage unit controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of a variety of bus structures.

[0105] ​​The electronic device 600 can also communicate with one or more external devices 700 such as a keyboard, a pointing device, a Bluetooth device, etc.; other devices that enable a user to interact with the electronic device 600; and / or any devices (e.g., a router, a modem, a peer device etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface 650. Still yet, the electronic device 600 can communicate with one or more networks, such as a local area network (LAN), a wide area network (WAN), and / or the Internet, through a network adapter 660. The network adapter 660 can be communicatively coupled to the other components of the electronic device 600 via a bus 630. It should be appreciated that the bus 630 can be one of any suitable type, including a bus system, a message bus, a PCI bus, a HyperTransport bus, a USB bus, etc. Figure 9 Other hardware and / or software modules that can be used in conjunction with the electronic device 600, but are not shown in FIG. 6, include but are not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data archival storage systems, etc.

[0106] The embodiment of the present application also provides a computer readable storage medium for storing a program, the program being executed to implement the steps of the living body detection method based on the facial light spot image. In some possible implementation manners, various aspects of the present application can also be implemented in the form of a program product, which includes program codes for causing the terminal device to execute the steps described in the above-mentioned living body detection method based on the facial light spot image part of the present application according to various exemplary embodiments of the present application when the program product is run on the terminal device.

[0107] As shown above, the program of the computer readable storage medium of the embodiment, when executed, can acquire the light spot image of the target person, cut a pixel region on the light spot image, calculate the light spot definition of the pixel region, and determine the distance information between the target person and the depth camera according to the light spot definition and the pre-set distance information generation model associated with the distance of the light spot definition, so as to obtain the depth information of the object more quickly and be used in the mobile phone, motion sensing game, payment and other consumer products for obtaining the facial depth information at a close distance.

[0108] Figure 10 is a structural schematic diagram of the computer readable storage medium in the embodiment of the present application. Referring to Figure 10As shown, a program product 800 for implementing the above-described method according to an embodiment of the present application is described, which can take the form of a portable compact disc read-only memory (CD-ROM) and includes a program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto, and in the present document, the readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device.

[0109] The program product can take any combination of one or more readable media. The readable media can be a readable signal medium or a readable storage medium. The readable storage medium, for example, can be, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any suitable combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0110] The computer readable storage medium can include a data signal carried by a carrier wave or a propagated signal, where the readable program code is carried by the data signal. Such a propagated signal can take any of a variety of forms, including but not limited to electro-magnetic, optical, or any suitable combination thereof. The readable storage medium can also be any readable medium that is not a storage medium and that can be used to carry or store program code in any form, where the program code can be utilized or directed by an instruction execution system, apparatus or device. Program code carried by the readable storage medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical fiber, RF, or any suitable combination of the above.

[0111] The program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server. In the latter scenario, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computing device, such as through the Internet using an Internet Service Provider.

[0112] In the embodiments of the present application, the distance information between the target person and the depth camera is determined by collecting the light spot image of the target person, intercepting a pixel region on the light spot image, calculating the light spot definition of the pixel region, and generating a model according to the light spot definition and pre-set distance information associated with the distance of the light spot definition, so that the depth information of the object can be obtained more quickly, and the depth information of the face in close distance can be obtained by the mobile phone, motion sensing game, payment and other consumer products.

[0113] The various embodiments are described in the present specification in progressive order, and each embodiment focuses on the difference from other embodiments. The same or similar parts between the embodiments can be mutually referred to. The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0114] The specific embodiments of the present application are described above. It should be understood that the present application is not limited to the specific embodiments described above, and various modifications or changes can be made by those skilled in the art within the scope of the claims, which do not affect the essential content of the present application.

Claims

1. A distance detection method based on a light spot image, characterized by, The method comprises the following steps: Step S1: acquiring a light spot image of a target person, the light spot image being collected by a depth camera; Step S2: intercepting a pixel region of a preset size on the light spot image; Step S3: calculating light spot definition of the pixel region, and determining distance information between the target person and the depth camera according to the light spot definition and a preset distance information generation model associated with the light spot definition and distance; The step S3 comprises the following steps: Step S301: acquiring a gray value of each pixel point in the pixel region, and calculating light spot definition of the pixel region according to the gray value of each pixel point; Step S302: acquiring a preset distance information generation model associated with the light spot definition and distance; Step S303: inputting the light spot definition into the distance information generation model to generate distance information between the pixel point and the depth camera, so as to determine the distance information between the target person and the depth camera.

2. The spot image-based distance detection method according to claim 1, characterized in that, The step S1 comprises the following steps: Step S101: projecting dot array light to the target person through a light beam projector end of the depth camera; Step S102: receiving the dot array light reflected by the target person through a detector end of the depth camera; Step S103: generating a light spot image of the target person according to the dot array light received by the detector end of the depth camera.

3. The spot image-based distance detection method according to claim 1, characterized in that, The step S2 comprises the following steps: Step S201: performing face detection on the light spot image to determine a face region; Step S202: acquiring an image intercepting frame of a preset size, and moving the image intercepting frame to a target region on the face region; Step S203: intercepting a pixel region of a preset size in the target region through the image intercepting frame.

4. The spot image-based distance detection method according to claim 1, characterized in that, The step S301 comprises the following steps: Step S3011: defining a Laplace operator L; Step S3012: performing convolution processing on the gray value of each pixel point according to the Laplace operator L to generate a convolution value of each pixel point; Step S3013: generating a numerical value of the light spot definition according to an average value of the convolution values of all the pixel points.

5. The spot image-based distance detection method according to claim 1, characterized in that, The distance information generation model is generated by fitting a plurality of distance information corresponding to light spot definition in a pre-collected light spot image.

6. A distance detection system based on a light spot image, characterized by The method comprises the following modules: A light spot image acquisition module, configured to acquire a light spot image of a target person, the light spot image being collected by a depth camera; An image intercepting module, configured to intercept a pixel region of a preset size on the light spot image; A distance calculation module, configured to calculate light spot definition of the pixel region, and determine distance information between the target person and the depth camera according to the light spot definition and a preset distance information generation model associated with the light spot definition and distance; The distance calculation module comprises the following steps when processing: Step S301: acquiring a gray value of each pixel point in the pixel region, and calculating light spot definition of the pixel region according to the gray value of each pixel point; Step S302: acquiring a preset distance information generation model associated with the light spot definition and distance; Step S303: inputting the light spot definition into the distance information generation model to generate distance information between the pixel point and the depth camera, so as to determine the distance information between the target person and the depth camera. Step S303: inputting the spot clarity to the distance information generation model to generate distance information between the pixel point and the depth camera to determine distance information between the target person and the depth camera.

7. A spot image-based distance detection apparatus, characterized by comprising: comprise: a processor; a memory having executable instructions of the processor stored therein; wherein the processor is configured to perform the steps of the spot image based distance detection method of any one of claims 1-5 via execution of the executable instructions.

8. A computer readable storage medium for storing a program, characterized in that, the program, when executed, implements the steps of the spot image based distance detection method of any one of claims 1-5.

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