Monocular 3D vision imaging method and system based on monochromatic visible light active projection

Abstract

The monocular 3D vision imaging method based on monochromatic visible light active projection comprises the steps that an imaging picture of a target object on a camera pixel grid is obtained, and each pixel point records RGB digital information of the picture; monochromatic visible light is projected to the target object, the colors of the target object are superposed, and a new color is formed; finding a new position of the pixel point in the pixel grid of the camera according to the RGB numerical value after the projection of the monochromatic visible light and the color superposition of the pixel point, and obtaining the pixel offset of the pixel point; wherein the offset comprises depth information about the distance from the camera to the camera; calculating a refractive index after color superposition according to the offset, performing pixel coordinate and image coordinate conversion on the pixel point according to the depth information offset, and calculating a depth value of a point on the target object corresponding to the pixel point through a camera imaging space coordinate system; and three-dimensional space coordinates of all points of the target object are obtained, and 3D reconstruction of the target object is completed.

Description

Technical Field

[0001] The present application relates to a visual imaging method, and more specifically to a monocular 3D visual imaging method and system based on active projection of monochromatic visible light. Background Art

[0002] For the recognition of the shape and spatial position of a specific object, the object needs to be reconstructed in 3D. The 3D reconstruction methods generally include binocular vision, laser scanning, structured light, and time-of-flight TOF. Among them, laser scanning, structured light, and time-of-flight TOF equipment are only used in some specific occasions due to the stringent requirements for use conditions, limited measurement distance, and high cost. Binocular vision is similar to the human eye. Based on the disparity value principle of the images taken by the left and right cameras, the coordinates of the depth direction of the image are obtained by calculation, thereby obtaining 3D reconstruction.

[0003] Binocular vision requires that the parameters of the left and right cameras are exactly the same and that they are located on the same plane. The calibration requirements are stringent and the calibration process is complex. The image correction process will also result in cumulative errors. In order to ensure measurement accuracy, the left and right cameras must maintain a certain baseline distance, which makes it difficult to achieve miniaturization. Summary of the invention

[0004] The purpose of the present application is to provide a monocular 3D visual imaging method and system based on active projection of monochromatic visible light, a method for calculating the depth of field of external objects using only a monocular camera, a method for determining the spatial depth coordinates of an object from slight changes in the refractive index and the imaging position offset by actively emitting monochromatic visible light to superimpose colors and change the propagation frequency of light.

[0005] In order to achieve the above-mentioned purpose, the present application provides, on the one hand, a monocular 3D visual imaging method based on active projection of monochromatic visible light, comprising: obtaining an imaging picture of a target object on a camera pixel network, wherein each pixel point records the RGB digital information of the picture; projecting monochromatic visible light to the target object so that the color of the target object is superimposed to form a new color; finding the new position of the pixel point in the camera pixel grid according to the RGB value after the projected monochromatic visible light and the color superposition of the pixel point, and obtaining the pixel offset of the pixel point; wherein the offset includes depth information of the distance from the camera head; calculating the refractive index after color superposition according to the offset, and converting the pixel point into pixel coordinates and image coordinates according to the depth information offset, and then calculating the depth value of a point on the target object corresponding to the pixel point through the camera imaging space coordinate system; obtaining the three-dimensional spatial coordinates of all points of the target object, and completing the 3D reconstruction of the target object.

[0006] Preferably, calculating the refractive index after color superposition includes: the refractive index of natural light from the camera is η, the refractive index after projecting monochromatic visible light is η', the actual refractive index obtained after eliminating the influence of the projected monochromatic visible light is η', the imaging distance of the target object is s, and the relationship between the focal length f of the lens camera and the actual refractive index η' is as follows: Where n is the geometric shape coefficient of the lens itself.

[0007] Preferably, according to the convex lens imaging object distance Z w , the relationship between image distance s and focal length f, Z w The calculation formula is as follows:

[0008] Preferably, the pixel coordinate and image coordinate conversion of the pixel point includes: the imaging point of the target object point P in the pixel coordinate system is p(u p , v p ), in the image coordinate system, the pixel coordinates of the origin of the image coordinate system (0,0) in the pixel coordinate system are (u o , v o ), the physical length of a single pixel in the image coordinate system is dx and dy, then the coordinates of the pixel point p in the image coordinate system (x p ,y p ) is calculated as follows:

[0009] Preferably, the influence of the projected monochromatic visible light is eliminated, and the imaging point of the target object point P in the pixel coordinate system is p'

[0010] (u p ',v p '), the coordinates in the image coordinate system (xp ',y p ') is calculated as follows:

[0011] Preferably, calculating the depth value of a point on the target object corresponding to the pixel point by using the camera imaging space coordinate system includes: a calculation formula in the three-dimensional space coordinate is as follows: The spatial coordinates of the 3D reconstruction point P are obtained as (X w , Y w , Z w ).

[0012] On the other hand, the present application provides a monocular 3D visual imaging system based on active projection of monochromatic visible light, using the above-mentioned monocular 3D visual imaging method based on active projection of monochromatic visible light, including a first acquisition module, configured to obtain an imaging picture of the target object on the camera pixel network, each pixel on the imaging picture records the RGB digital information of the picture; a projection module, configured to project monochromatic visible light to the target object; a second acquisition module, configured to find the new position of the pixel point in the camera pixel grid according to the RGB value after the projected monochromatic visible light and the color of the pixel point are superimposed, and obtain the pixel offset of the pixel point, and the offset includes the depth information of the distance from the camera head; a calculation module, configured to calculate the refractive index after color superposition according to the offset, and convert the pixel point into pixel coordinates and image coordinates according to the depth information offset, and then calculate the depth value of a point on the target object corresponding to the pixel point through the camera imaging space coordinate system to obtain the three-dimensional space coordinates of all points of the target object.

[0013] On the other hand, the present application provides a monocular 3D camera, which uses the above-mentioned monocular 3D visual imaging method based on active projection of monochromatic visible light, and the monocular 3D camera includes a monochromatic visible light projection light source.

[0014] On the other hand, the present application provides a computer device, comprising at least a processor and a memory, wherein the processor and the memory are communicatively connected, wherein one or more computer programs are stored in the memory, and when the computer program is executed by the processor, the above-mentioned monocular 3D visual imaging method based on active projection of monochromatic visible light is implemented.

[0015] On the other hand, the present application provides a computer-readable storage medium storing one or more computer programs, which, when executed, implement the above-mentioned monocular 3D visual imaging method based on active projection of monochromatic visible light.

[0016] This application can achieve the following beneficial effects:

[0017] The present invention adopts a monocular camera and adds actively projected monochromatic visible light. This imaging method avoids the requirement of consistency between internal and external parameters of a binocular camera and the restrictions of strict position requirements of left and right cameras, reduces calibration errors and image correction cumulative errors, and improves depth calculation accuracy. The equipment formed by the imaging method has the advantages of small size, simple calibration, reliability and durability, and high precision. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0019] Figure 1 It is a flow chart of a monocular 3D visual imaging method based on active projection of monochromatic visible light provided in an embodiment of the present application;

[0020] Figure 2 is a flow chart of a target object image acquisition and processing method provided in an embodiment of the present application;

[0021] Figure 3 is a schematic diagram of target object imaging and depth of field calculation provided by an embodiment of the present application;

[0022] Figure 4 is a flow chart of a depth of field calculation method provided in an embodiment of the present application;

[0023] Figure 5 is a flow chart of a three-dimensional space coordinate calculation method provided in an embodiment of the present application;

[0024] Figure 6 is a flow chart of a method for 3D reconstruction of a target object provided in an embodiment of the present application;

[0025] Figure 7 It is a structural schematic diagram of a monocular 3D visual imaging system based on active projection of monochromatic visible light provided in an embodiment of the present application;

[0026] Figure 8 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application;

[0027] Fig. 9 It is a schematic diagram of the structure of another electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] Hereinafter, embodiments will be described with reference to the drawings.

[0029] It should be noted that the disclosed content is only an example, and the invention is not limited by the content described in the following embodiments. Deformations that are easy for a person skilled in the art to think of are of course included in the scope of the disclosure. In order to make the description clearer, in the drawings, there are also cases where the size, shape, etc. of each part are changed relative to the actual implementation form and schematically represented. In multiple drawings, there are also cases where the same reference numerals are marked on corresponding elements and detailed descriptions are omitted.

[0030] This example provides a monocular 3D visual imaging method based on active projection of monochromatic visible light. Figure 1 As shown, the method includes steps S1 to S5, as shown below:

[0031] S1: Obtain an image of the target object on the camera pixel grid, where each pixel records the RGB digital information of the image.

[0032] Specifically, for digital cameras, external objects will be imaged as a two-dimensional picture on the camera pixel grid. The two-dimensional imaging picture of the target object is obtained through a monocular camera to ensure that the RGB number of each pixel can reflect the color information of the target object under the current lighting conditions.

[0033] like Figure 2 As shown, it includes steps S11 to S14, as shown below:

[0034] S11: Image acquisition; use a monocular camera to acquire images of the target object; it is understandable that during the image acquisition process, the position of the monocular camera needs to be fixed to avoid errors caused by jitter or angle changes; before using monochromatic light projection, obtain the original RGB image of the target object under natural light or ambient light, and record the initial color state of the target object; it is conceivable that this example selects an industrial-grade monocular camera, such as a 1080p or 4K-level resolution, which can provide higher pixel accuracy; use a standardized color card (such as a Macbeth color card) for preliminary calibration.

[0035] S12: Pixel grid construction: The imaging grid of the monocular camera is composed of CCD or CMOS photosensitive devices, and each pixel corresponds to a fixed imaging coordinate (u, v).

[0036] S13: Camera parameter setting and calibration; Setting the exposure time, white balance and ISO sensitivity of the monocular camera. Appropriate exposure time can avoid overexposure or underexposure of the image. Correcting the white balance can eliminate the influence of ambient light on the image color. Adjusting the sensitivity of the photoreceptor to adapt to the current lighting conditions. The camera calibration includes parameters such as focal length, principal point coordinates, and optical distortion.

[0037] S14: Data storage and preprocessing; losslessly compress and store the collected RGB images to retain the color information of each pixel; preprocessing includes denoising, color calibration, and image resolution optimization. Filtering algorithms (such as Gaussian filtering or median filtering) can be used to remove noise in the image to ensure accurate pixel color. Color calibration is performed to unify the color benchmark for changes in lighting. The image resolution is adjusted according to application requirements to balance accuracy and processing efficiency.

[0038] The original image of the target object is obtained through step S1, and the position (u, v) and RGB value of each pixel point are recorded in the camera pixel coordinate system.

[0039] S2: Projecting monochromatic visible light toward the target object, so that the color of the target object is superimposed to form a new color.

[0040] Specifically, a monochromatic visible light source (such as red light, with a wavelength of about 635-700 mm) is used as an active light source. The single wavelength characteristic of red light helps to form stable light reflection and color superposition on the surface of the target object. It is conceivable that the monochromatic light source can be generated by an LED light source, a laser light source or a specific filter to avoid interference from light of other wavelengths.

[0041] During operation, a monochromatic visible light source is projected onto the surface of the target object. By controlling the incident angle and light intensity of the projected light, the entire surface of the target object is evenly illuminated by the monochromatic light to avoid interference from shadow areas or overexposed areas. It can be imagined that for objects with complex shapes, a multi-angle or multi-projection source layout can be used so that all surfaces of the target object can receive monochromatic light.

[0042] It can be understood that when the target object does not receive monochromatic light projection, the color of the target object is determined by the light of a specific wavelength reflected from the surface of the target object, and the RGB value reflects the color information of the target object itself. When monochromatic visible light (such as red light) is projected onto the surface of the target object, the color of the light reflected from the target object will be superimposed. For example, the superimposed color = original RGB value + RGB value of the projected monochromatic light, where the interaction between the original color and the projected light will form a new color, and this new color has a specific RGB value.

[0043] Furthermore, projecting monochromatic visible light changes the color of the target object, causing the wavelength of the reflected light to change, and the refractive index to change accordingly. This slight change in the refractive index causes the refraction angle of the light to shift when it passes through the camera lens. When the refracted light forms an image on the camera's CCD or CMOS sensor, the position of the pixel will also shift to a certain extent. This pixel position shift caused by the change in the refraction angle is called "refractive difference", which corresponds to the parallax of binocular vision.

[0044] It can be understood that the size of the refraction difference is related to the color superposition result of the target object, the wavelength change of light and the refraction characteristics of the camera lens. The refraction difference is reflected as a slight position shift of the pixel point on the camera pixel grid, forming a pixel offset, and the offset can be calculated by an image processing algorithm.

[0045] The refraction difference is not only related to the color change of the object, but also to the depth distance (Z-axis coordinate) from each point on the surface of the target object to the camera. In the light propagation path, the farther the target object is from the camera, the more obvious the refraction deviation generated by the light during the propagation process. Therefore, the size of the refraction difference can be used as the basis for the depth information of the object.

[0046] In step S2, color superposition is introduced by projecting monochromatic visible light, which can effectively induce a change in the refractive index of light, so that the monocular camera can also perceive depth information.

[0047] S3: Find the new position of the pixel point in the camera pixel grid according to the RGB value of the superposition of the projected monochromatic visible light and the color of the pixel point, and obtain the pixel offset of the pixel point; wherein the offset includes the depth information of the distance from the camera head.

[0048] Specifically, without projecting monochromatic visible light, the light reflected from the surface of the target object is directly imaged by the camera to form the original RGB value of each pixel in the image; the original RGB value provides the color and brightness information of the object, and represents the initial position of the pixel on the CCD / CMOS sensor. No offset occurs at this time. When monochromatic visible light (such as red light) is projected, the surface color of the target object undergoes color superposition, and the RGB value of the reflected light changes accordingly. The superimposed RGB value not only reflects the interaction between the surface color of the object and the light source, but also indirectly shows the light offset phenomenon caused by the change in the refractive index on the light propagation path. The change in refractive index causes a slight change in the refraction angle of the light when it passes through the camera lens. This change ultimately affects the imaging position of the light on the camera pixel grid.

[0049] S4: calculating the refractive index after color superposition according to the offset, and converting the pixel coordinates and image coordinates of the pixel point according to the depth information offset, and then calculating the depth value of a point on the target object corresponding to the pixel point through the camera imaging space coordinate system;

[0050] Specifically, Figure 3 As shown, Figure 3This is a schematic diagram of the imaging of the target object and the calculation of the depth of field. The pixel coordinates represent the two-dimensional discrete position of the pixel in the image, which is represented by (u, v). The unit is usually pixels. The origin of the pixel coordinate system is located in the upper left corner of the image. The image coordinate system is the camera imaging plane coordinate system. The image coordinates are the pixel coordinates converted into continuous coordinates on the camera imaging plane. The unit is millimeters.

[0051] The camera imaging space coordinate system is a three-dimensional rectangular coordinate system, with the camera optical center as the origin, the optical axis direction as the Z axis, the horizontal direction as the X axis, and the vertical direction as the Y axis.

[0052] In one example, if Figure 4 As shown, it includes calculation steps S41 to S43, as shown below:

[0053] S41: The refractive index after color superposition is calculated as follows:

[0054] The refractive index of natural light in the camera is η, and the refractive index after projecting monochromatic visible light is η'. The actual refractive index after eliminating the influence of the projected monochromatic visible light is η'. The imaging distance of the target object is s, and the relationship between the focal length f of the lens camera and the actual refractive index η' is as follows:

[0055]

[0056] Where n is the geometric shape coefficient of the lens itself.

[0057] S42: The pixel coordinates and image coordinates are converted as follows:

[0058] refer to Figure 5 , the imaging point (pixel point) of the target object P in the pixel coordinate system is p(u p , v p ), in the image coordinate system, the pixel coordinates of the origin of the image coordinate system (0,0) in the pixel coordinate system are (u o , v o ), the target object point P forms a plane at the origin of the image coordinate system and the pixel point, which is the polar plane. According to the convex lens imaging principle, due to the change of refractive index, the position offset of the imaging pixel point p of the target object point P in the pixel coordinate system will move in the polar plane. The physical length of a single pixel in the image coordinate system is dx and dy, then the coordinates of the pixel point p in the image coordinate system (x p ,y p ) is calculated as follows:

[0059]

[0060] Eliminating the influence of the projected monochromatic visible light, the imaging point of the target object point P in the pixel coordinate system is p'(u p ',vp '), the coordinates in the image coordinate system (x p ',y p ') is calculated as follows:

[0061]

[0062] S43: Calculate the depth value of a point on the target object corresponding to the pixel point through the camera imaging space coordinate system, that is, the depth value of point P of the target object. The calculation formula in the three-dimensional space coordinate is as follows:

[0063]

[0064] The spatial coordinates of the 3D reconstruction point P are obtained as (X w , Y w , Z w ).

[0065] This step calculates the refractive index change caused by color superposition and corrects the pixel coordinates, and finally calculates the depth value of the target pixel point in combination with the camera imaging space coordinate system.

[0066] S5: Obtain the three-dimensional spatial coordinates of all points of the target object to complete the 3D reconstruction of the target object.

[0067] In step S4, the depth information of the target pixel has been calculated, and the three-dimensional coordinates in the spatial coordinate system have been obtained through coordinate transformation. The three-dimensional coordinate data corresponding to each target pixel is stored in order to form point cloud data. The point cloud data is a collection of a large number of three-dimensional coordinate points, which serves as the basic data form for subsequent three-dimensional reconstruction.

[0068] Specifically, for each valid pixel of the target object, its three-dimensional coordinates are calculated, and all points are grouped into point cloud data according to their spatial positions, which are represented as follows:

[0069] Q={(X1, Y1, Z1), (X2, Y2, X3), ..., (X n , Y n , Z n )};

[0070] Among them, Q represents the point cloud dataset of the target object, and n represents the number of all pixels on the surface of the target object.

[0071] The coordinate information of each point describes the position and outline of the target object in three-dimensional space. It is understandable that the density and distribution of the point cloud determine the fineness of the 3D reconstruction.

[0072] It is conceivable that in the process of generating point cloud data, some abnormal points may be introduced due to light reflection interference or noise. This example uses a point cloud denoising algorithm, such as statistical filtering, radius filtering or conditional filtering, to remove outliers.

[0073] like Figure 6 As shown, the 3D reconstruction includes steps S51 to S53, as shown below:

[0074] S51: Mesh reconstruction; convert the point cloud data set Q into a three-dimensional surface mesh model, and use a three-dimensional reconstruction algorithm. Based on the characteristics of the target object, you can choose Delaunay triangulation, Poisson surface reconstruction or Marching Cubes algorithm. Among them, Delaunay triangulation generates triangular facets through the adjacency relationship of three-dimensional coordinate points, Poisson surface reconstruction fits a continuous three-dimensional surface through point cloud data, and is suitable for processing high-density point clouds. The Marching Cubes algorithm converts point cloud data into a regular grid representation, which is suitable for the reconstruction of complex surface structures.

[0075] S52: Texture mapping: Using the RGB color information of each pixel recorded in step S1, the color data is mapped to the reconstructed three-dimensional model surface to generate a textured three-dimensional model.

[0076] The texture mapping step includes: color projection from point cloud to surface mesh, associating RGB values ​​with mesh vertices; smoothing to eliminate color mutations caused by pixel errors to enhance visual consistency.

[0077] S53: Model optimization; smoothing the generated 3D model and simplifying the mesh structure to reduce the amount of data while retaining the geometric features of the target object.

[0078] Finally, a complete three-dimensional model is output, which has the following characteristics: based on the calculated three-dimensional coordinates, the real three-dimensional outline of the target object is restored; the collected RGB color information is mapped to the surface to achieve a realistic visualization effect; through refractive index correction and coordinate transformation, the error is reduced and the reconstruction accuracy is improved.

[0079] According to application requirements, the 3D reconstructed model can be exported to a standard file format, such as PLY (Polygon FileFormat), OBJ (Wavefront Object) or STL (Stereolithography), where PLY is a point cloud and triangular patch data storage format, OBJ is a textured 3D model format, and STL is a surface mesh model format for 3D printing.

[0080] This example provides a monocular 3D visual imaging system 100 based on active projection of monochromatic visible light, using the monocular 3D visual imaging method based on active projection of monochromatic visible light provided in the above example, such as Figure 7 As shown, the imaging system 100 includes a first acquisition module 101 , a projection module 102 , a second acquisition module 103 and a calculation module 104 .

[0081] In one example, the first obtaining module 101 is configured to obtain an image of the target object on a camera pixel network, where each pixel on the image records RGB digital information of the image.

[0082] Specifically, the function of the first acquisition module 101 is to capture the initial image data of the target object, extract and record the color information of each pixel, and provide input data for subsequent steps. Its hardware configuration includes a monocular camera, which has high resolution and can capture high-resolution images. When the system starts, the monocular camera performs initial imaging of the target object and generates an imaging picture of the target object on the camera pixel network. Each pixel in the imaging picture records RGB color information, and each image is stored in a matrix form. Each element in the matrix contains the position of the pixel and the corresponding RGB value, which is expressed as follows:

[0083] Image = {(x, y, R, G, B)};

[0084] The imaging system 100 may be equipped with a standardized color card (such as a Macbeth color card) for preliminary color calibration to eliminate the interference of ambient light on the RGB value.

[0085] In one example, the projection module 102 is configured to project monochromatic visible light toward a target object.

[0086] Specifically, the function of the projection module 102 is to project monochromatic visible light (for example, red light) to the target object, causing superimposed changes in the color of the target object's surface. Its hardware configuration includes a light source emitting device that can project monochromatic visible light of a specific frequency (for example, red light with a wavelength of 650nm), and it is necessary to calibrate the light source projection angle and the camera imaging angle so that the surface of the target object receives light evenly. It also supports adjusting the intensity, wavelength and projection time of the light source to adapt to target objects of different materials and colors and optimize the imaging effect.

[0087] In one example, the second acquisition module 103 is configured to find the new position of the pixel point in the camera pixel grid based on the RGB value of the superposition of the projected monochromatic visible light and the color of the pixel point, and obtain the pixel offset of the pixel point, wherein the offset includes depth information of the distance from the camera head.

[0088] Specifically, the input data includes RGB information before projecting monochromatic visible light and RGB information after projecting monochromatic visible light. The second acquisition module 103 analyzes the change in light propagation frequency caused by color superposition by comparing the two sets of RGB information, and uses the change in refractive index caused by the frequency change, combined with the imaging model of the camera pixel grid, to calculate the new position of each pixel point, and then obtains the depth information of the pixel point based on the offset of the pixel point, combined with the camera intrinsic parameters (such as focal length, optical center position) and extrinsic parameters (such as rotation and translation of the camera relative to the target object), and outputs the offset of each pixel point and its corresponding depth information.

[0089] In one example, the calculation module 104 is configured to calculate the refractive index after color superposition based on the offset, and convert the pixel coordinates and image coordinates of the pixel point according to the depth information offset, and then calculate the depth value of a point on the target object corresponding to the pixel point through the camera imaging space coordinate system to obtain the three-dimensional space coordinates of all points of the target object.

[0090] Specifically, the function of the calculation module 104 is to calculate the refractive index after color superposition according to the pixel offset, perform coordinate system conversion, and finally calculate the depth value and three-dimensional space coordinates of the pixel point. The detailed calculation process is described in the above example.

[0091] The various modules of the system work together, from data acquisition, optical projection to calculation and three-dimensional reconstruction, to complete the spatial coordinate calculation and morphological reconstruction of the target object.

[0092] This example provides a monocular 3D camera, which uses the monocular 3D visual imaging method based on active projection of monochromatic visible light provided in the above example. The monocular 3D camera is a three-dimensional imaging device based on a single lens, which adopts the method of projecting monochromatic light, pixel offset analysis, refractive index calculation and depth reconstruction, and can perform three-dimensional spatial modeling of the target object. Its light source system includes a monochromatic light projection module 102 with a built-in controllable light source, which can emit monochromatic visible light of a specific wavelength (such as red light). Its imaging system adopts a CMOS or CCD image sensor, and its computing module 104 has a built-in high-performance embedded processor (such as ARM or DSP). Its storage module is equipped with high-speed memory and large-capacity storage module, and its communication interface supports high-speed data transmission interfaces such as USB 3.0, GigE, Wi-Fi, etc., and can be integrated with the software platform through a standard API interface to achieve extended functions. Its imaging method is as described in the above example.

[0093] In one example, if Figure 8 As shown, Figure 8The schematic diagram of the structure of an exemplary electronic device 200 is shown. The electronic device 200 includes: a memory 201 and a processor 202. The memory 201 stores a computer program that can be run on the processor 202. When the processor 202 executes the computer program, the method in the above example is implemented. The number of the memory 201 and the processor 202 can be one or more.

[0094] like Fig. 9 As shown, the electronic device 200 also includes:

[0095] The communication interface 203 is used to communicate with external devices and perform data exchange transmission.

[0096] If the memory 201, the processor 202 and the communication interface 203 are implemented independently, the memory 201, the processor 202 and the communication interface 203 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0097] Optionally, in a specific implementation, if the memory 201, the processor 202 and the communication interface 203 are integrated on a chip, the memory 201, the processor 202 and the communication interface 203 can communicate with each other through an internal interface.

[0098] The present application example provides a computer-readable storage medium storing a computer program, which implements the method provided in the present application example when the program is executed by the processor 202.

[0099] The example of the present application also provides a chip, which includes a processor 202, which is used to call and execute instructions stored in the memory 201 from the memory 201, so that a communication device equipped with the chip executes the method provided by the example of the present application.

[0100] The present application example also provides a chip, including: an input interface, an output interface, a processor 202 and a memory 201. The input interface, the output interface, the processor 202 and the memory 201 are connected through an internal connection path. The processor 202 is used to execute the code in the memory 201. When the code is executed, the processor 202 is used to execute the method provided in the application example.

[0101] It should be understood that the processor 202 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. It is worth noting that the processor may be a processor 202 that supports the Advanced RISC Machines (ARM) architecture.

[0102] Further, the above-mentioned memory 201 may include a read-only memory and a random access memory, and may also include a non-volatile random access memory. The memory 201 may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may include a random access memory (RAM), which is used as an external cache. By way of exemplary but not limiting description, many forms of RAM are available. For example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM) and direct memory bus random access memory (DR RAM).

[0103] Several embodiments of the present invention have been described, but these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other ways, and various omissions, substitutions, and changes can be made without departing from the scope of the invention. These embodiments and their variations are included in the scope and spirit of the invention, and are also included in the invention described in the claims and their equivalents.

[0104] List of reference numerals:

[0105] 100 Imaging System

[0106] 101 First Acquisition Module

[0107] 102 Projection Module

[0108] 103 Second Acquisition Module

[0109] 104 computing modules

[0110] 200 electronic equipment

[0111] 201 Memory

[0112] 202 processor

[0113] 203 communication interface.

Claims

1. A monocular 3D visual imaging method based on active projection of monochromatic visible light, characterized in that: include: Obtain an image of the target object on the camera pixel grid, where each pixel records the RGB digital information of the image; Projecting monochromatic visible light onto the target object so that the color of the target object is superimposed to form a new color; According to the RGB value after the projected monochromatic visible light and the color of the pixel point are superimposed, the new position of the pixel point in the camera pixel grid is found, and the pixel offset of the pixel point is obtained; The offset includes depth information of the distance from the camera head; Calculating the refractive index after color superposition according to the offset, and transforming the pixel point into pixel coordinates and image coordinates according to the depth information offset, and then calculating the depth value of a point on the target object corresponding to the pixel point through the camera imaging space coordinate system; The three-dimensional spatial coordinates of all points of the target object are obtained to complete the 3D reconstruction of the target object.

2. According to the method of claim 1, calculating the refractive index after color superposition comprises: The refractive index of natural light in the camera is η, and the refractive index after projecting monochromatic visible light is η'. The actual refractive index after eliminating the influence of the projected monochromatic visible light is η'. The imaging distance of the target object is s, and the relationship between the focal length f of the lens camera and the actual refractive index η' is as follows: Where n is the geometric shape coefficient of the lens itself.

3. The method according to claim 2, wherein the convex lens imaging object distance Z w , the relationship between image distance s and focal length f, Z w The calculation formula is as follows:

4. The method according to claim 3, converting pixel coordinates and image coordinates of the pixel points comprises: The imaging point of the target object P in the pixel coordinate system is p(u p , v p ), in the image coordinate system, the pixel coordinates of the origin of the image coordinate system (0,0) in the pixel coordinate system are (u o , v o ), the physical length of a single pixel in the image coordinate system is dx and dy, then the coordinates of the pixel point p in the image coordinate system (x p ,y p ) is calculated as follows:

5. According to the method of claim 4, the influence of the projected monochromatic visible light is eliminated, and the imaging point of the target object point P in the pixel coordinate system is p'(u p ',v p '), the coordinates in the image coordinate system (x p ',y p ') is calculated as follows:

6. The method according to claim 5, calculating the depth value of a point on the target object corresponding to the pixel point by using the camera imaging space coordinate system comprises: The calculation formula in three-dimensional space coordinates is as follows: The spatial coordinates of the 3D reconstruction point P are obtained as (X w , Y w , Z w ).

7. A monocular 3D visual imaging system based on active projection of monochromatic visible light, using the monocular 3D visual imaging method based on active projection of monochromatic visible light as described in any one of claims 1 to 6, comprising: A first acquisition module, wherein the first acquisition module is configured to obtain an image of the target object on a camera pixel grid, wherein each pixel point on the image records RGB digital information of the image; a projection module, wherein the projection module is configured to project monochromatic visible light toward a target object; A second acquisition module, wherein the second acquisition module is configured to find a new position of the pixel point in the camera pixel grid according to an RGB value obtained by superimposing the projected monochromatic visible light and the color of the pixel point, and obtain a pixel offset of the pixel point, wherein the offset includes depth information of the distance from the camera head; A calculation module is configured to calculate the refractive index after color superposition according to the offset, and transform the pixel coordinates and image coordinates of the pixel point according to the depth information offset, and then calculate the depth value of a point on the target object corresponding to the pixel point through the camera imaging space coordinate system to obtain the three-dimensional space coordinates of all points of the target object.

8. A monocular 3D camera, using the monocular 3D visual imaging method based on active projection of monochromatic visible light as described in any one of claims 1 to 6, wherein the monocular 3D camera comprises a monochromatic visible light projection light source.

9. A computer device comprising at least a processor and a memory, characterized in that: The processor is communicatively connected to the memory, wherein one or more computer programs are stored in the memory, and when the computer programs are executed by the processor, the monocular 3D visual imaging method based on active projection of monochromatic visible light as described in any one of claims 1-6 is implemented.

10. A computer-readable storage medium storing one or more computer programs, characterized in that: When the computer program is executed, the monocular 3D visual imaging method based on active projection of monochromatic visible light as described in any one of claims 1 to 6 is implemented.

Images