A fast area mapping heterospectral structured light measurement method

Abstract

The application discloses a fast area mapping heterospectral structured light measurement method, which can realize the area contour acquisition of a measured object only according to a square grid pattern, and then complete the area coordinate mapping of a projection device plane. Compared with the current method for acquiring the phase through horizontal and vertical stripes, a large number of stripe images are no longer needed for coordinate conversion, the efficiency can be significantly improved, and the fast measurement of a dynamic scene can be realized. Different regions are set to different spectrums for simultaneous projection, compared with the current method for sequentially projecting different regions, the measurement efficiency can be obviously improved. The application provides the feasibility for the dynamic measurement of a mutual reflection scene.

Description

Technical Field

[0001] This invention relates to the field of three-dimensional measurement technology, and in particular to a method for measuring heterospectral structured light using rapid region mapping. Background Technology

[0002] Structured light 3D measurement technology is widely used in industries such as industry, medicine, and entertainment due to its advantages of being non-contact, highly accurate, and fast. However, traditional structured light measurement methods have limitations when the measured scene contains both diffuse and specular reflective objects. Because the reflected light from the surface of a specular reflective object may coincidentally illuminate the surface of a diffuse reflective object at certain angles, mutual reflection between the objects occurs, resulting in aliasing interference in the structured light images acquired by the image acquisition device. This affects the accuracy of phase extraction and 3D reconstruction.

[0003] To overcome this problem, a method of regional projection is commonly used. However, achieving region localization requires pre-projecting horizontal and vertical stripes onto the surface of the object being measured to obtain the phase of the object in two directions, and then mapping the corresponding object region onto the projection device surface based on the phase. When the position of the object being measured moves, a large number of horizontal and vertical stripes need to be reprojected to obtain the region outline, so this method cannot meet dynamic requirements. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a fast region mapping heterospectral structured light measurement method. This method can significantly improve efficiency, reduce phase error, and is suitable for high-precision measurement in dynamic scenes.

[0005] To achieve the above objectives, the technical solution provided by this invention is as follows:

[0006] A fast region mapping method for heterospectral structured light measurement includes:

[0007] S1. Project a preset multi-colored grid pattern onto the surface of the target object using a projection device;

[0008] S2. Use an image acquisition device to acquire a multi-color grid image modulated by the object surface;

[0009] S3. It adopts RGB three-channel separation technology and combines color coding to identify multiple color blocks; then, it marks feature points according to preset rules, and establishes a precise mapping relationship between the coordinate system of the projection device and the coordinate system of the image acquisition device through the spatial distribution relationship of these feature points.

[0010] S4. Based on the feature matching algorithm, dynamically generate a binary projection mask that matches the geometric features of the object surface;

[0011] S5. Divide the projection mask area into two groups according to the spatial relationship, and generate longitudinal sinusoidal phase shift fringe patterns in these two groups of mask areas.

[0012] S6. Encode the two sets of sinusoidal phase-shifting stripes into the red and blue channels of the RGB image respectively, while retaining the green channel for color balance, to form a heterospectral phase-shifting stripe pattern.

[0013] S7. Project the encoded striped image onto the target object at a set refresh rate via the HDMI interface, and simultaneously trigger the image acquisition device to capture the image.

[0014] S8. Use the acquired heterospectral phase-shifting fringe pattern to perform phase extraction and three-dimensional topography reconstruction.

[0015] Furthermore, the multi-colored checkered pattern projected by the projection device onto the surface of the target object is a seven-color checkered pattern. This seven-color checkered pattern has an m×n pixel resolution, and its vertical direction is divided into seven color gamut blocks according to the principle of equal division. The height of each block is [missing information]. Pixel;

[0016] The color gamut arrangement and color composition of the seven-color checkered pattern are as follows:

[0017] First color gamut block: pure red (R, RGB value [255,0,0]);

[0018] Second color gamut block: pure green (G, RGB value [0,255,0]);

[0019] Third color gamut block: pure blue (B, RGB value [0,0,255]);

[0020] Fourth color gamut block: Red-green mixed color (RG / Yellow, RGB value [255,255,0]);

[0021] Fifth color gamut block: Red / Blue mixed color (RB / Magenta, RGB value [255,0,255]);

[0022] Sixth color gamut block: Green-blue mixed color (GB / Cyan, RGB value [0,255,255]);

[0023] The seventh color gamut block: red, green and blue mixed color (RGB / White, RGB value [255,255,255]).

[0024] Furthermore, in step S3, RGB three-channel separation technology is used, combined with color encoding, to identify the seven color blocks. The process includes:

[0025] The image is subjected to RGB three-channel separation, and the red (R), green (G), and blue (B) channels are extracted to generate three binary images; based on these three binary images, each High-pixel color gamut blocks can be uniquely identified by their corresponding 3-bit binary codes. The code for a pure red (R) block is 100 (R=1, G=0, B=0), the code for a pure green (G) block is 010 (R=0, G=1, B=0), the code for a pure blue (B) block is 001 (R=0, G=0, B=1), the code for a red-green (RG / Yellow) block is 110 (R=1, G=1, B=0), the code for a red-blue (RB / Magenta) block is 101 (R=1, G=0, B=1), the code for a green-blue (GB / Cyan) block is 011 (R=0, G=1, B=1), and the code for a red-green-blue (RGB / White) block is 111 (R=1, G=1, B=1). By scanning the 3-bit codes of image pixels and matching them with a preset coding sequence, the color gamut block to which a feature point belongs can be accurately located.

[0026] Further, in step S3, feature points are marked according to preset rules, and a precise mapping relationship between the projection device coordinate system and the image acquisition device coordinate system is established based on the spatial distribution relationship of these feature points. The process includes:

[0027] The m×n pixel seven-color square pattern is divided into seven equal-height regions according to the color gamut using RGB three-channel separation technology (the height of each of the seven regions is equal). Each area is constructed by alternating between a primary color and an 8x8 pixel black grid. A regular grid matrix; then each color block region (containing The grid is divided into 7 sub-regions along the row direction (each region contains...). The system is divided into seven sub-regions (each containing a grid of squares), with a circular feature marker placed at the center of each sub-region. The color configuration of the markers must meet the following requirements: 1) using one of the seven colors specified in the color coding above; 2) each of the seven markers corresponds to a feature of one of the other seven main color regions; 3) the color arrangement strictly follows the top-to-bottom spatial distribution rule of the seven color regions. The center coordinates of the feature markers are represented using the grid normalization method, with the first marker located at the geometric center of the first sub-region, at coordinates [coordinates missing]. Subsequent markers of the same color are arranged horizontally. A grid of squares (equivalent to the horizontal direction) The pixels are evenly spaced at intervals, with vertical directions of... A grid of squares (equivalent to the vertical direction) The feature points (number of pixels) are evenly spaced, and the center coordinates of the feature points within the first color block region are as follows: Where k is the result of color encoding, which is numerically equal to the binary number corresponding to the color area. The 7 feature points in the first color block correspond one-to-one with these 7 coordinates.

[0028] Furthermore, the process of step S4 includes:

[0029] First, the multi-color grid image modulated by the object surface, acquired by the image acquisition device, is sequentially separated into red, green, and blue color channels to obtain I. R I G I B , will I R I G I B Binarize and encode in RGB order to obtain sequentially encoded I R I G I B The three-digit code corresponds one-to-one with the color block region, thus obtaining the horizontal coordinate of the feature point. Where k1 is the decimal color code value corresponding to the color block area;

[0030] Then, the red, green, and blue color channels of the central circle within the feature point are separated to obtain C. R C G C B and for C R C G C B Binarization and encoding according to RGB order yield C R C G C B Based on the color coding, the feature point is identified as its nth feature point within the color block area, thus obtaining the ordinate of the feature point. Where k2 is the decimal color code value of the feature point corresponding to the color block area;

[0031] Finally, create a new m×n grid, identify the feature points closest to the contour points, and fill all contour points into the grid based on the coordinates of the feature points. For any contour point E... i =(x i ,y i Its grid coordinates (m) i ,n i Calculated via projection:

[0032]

[0033] E k Let v be the coordinates of the nearest feature point to the corner, where Δx = Δy = 8, v x and v y Let v be the reference vector, satisfying orthogonality.x v y =0;

[0034]

[0035] Where E1 is the feature point E k The nearest corner point in the horizontal direction, E2 is the feature point E k The nearest corner point in the vertical direction; obtain the grid coordinates (m) of all contour points. i ,n i After that, they are connected together to form a binary projection mask M of the target object. p .

[0036] Further, step S5 includes:

[0037] All projection mask regions are labeled with connected components. Then, the projection mask regions are divided into two groups according to the connected component index. The connected component indexes of these two groups are guaranteed to be discontinuous.

[0038] The first group of mask regions is named M. R The second set of mask regions is named M. B In M respectively R and M B The three-step phase-shifting fringe pattern P is generated. i R and P i B Both are calculated using the following formula:

[0039]

[0040] (u p ,v p ) represents the coordinates of the projected pixel, A and B are preset constants, F represents the fringe frequency, and i represents the i-th fringe pattern in the three-step phase-shifting method, i = 1, 2, 3.

[0041] Further, step S6 includes:

[0042] First, create a 24-bit completely black background image. i_color Using (0,0,0) as a carrier, the first group of eight-bit deep three-step phase-shifted fringe patterns P i R Encoded to the red channel, second group of three-step phase-shift fringe pattern P i B Encode the image into the blue channel, leaving the green channel at zero, to form the composite image I. i_color =(P i R ,0,P i B Three sets of such images were generated using the three-step phase-shifting method.i_color =(P i R ,0,P i B ).

[0043] Further, step S8 includes:

[0044] First, the spectral channels are separated to obtain stripe images for the red and blue monochromatic channels. Then, phase extraction is performed independently on the red and blue monochromatic channel images, as shown in the following formula:

[0045]

[0046] Among them, I1, I2, and I3 are three fringe patterns with different phase shifts in the monochrome channel. The wrap-around phase of this channel is calculated using the I1, I2, and I3 phase shift fringe patterns. Finally, the phases calculated from the two different color channel images are fused and then calibrated using the phase-depth formula.

[0047]

[0048] To obtain the final three-dimensional shape.

[0049] Compared with existing technologies, the principles and advantages of this technical solution are as follows:

[0050] 1. The proposed fast region mapping method can obtain the outline of the measured object region based on only one grid pattern, and then complete the region coordinate mapping of the projection device surface. Compared with the current method of obtaining phase through horizontal and vertical stripes, it no longer requires a large number of stripe images for coordinate transformation, which can significantly improve efficiency and realize rapid measurement of dynamic scenes.

[0051] 2. The proposed heterospectral projection method simultaneously projects different spectra onto different regions, significantly improving measurement efficiency compared to current methods that project different regions sequentially. This provides feasibility for dynamic measurements in mutually reflective scenarios. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the services required in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0053] Figure 1 This is a schematic diagram illustrating the working principle of the structured light projection three-dimensional measurement system used in this invention.

[0054] Figure 2 This is a flowchart illustrating the principle of a fast region mapping heterospectral structured light measurement method according to the present invention.

[0055] Figure 3 It is a seven-color grid image;

[0056] Figure 4 Color-coded images;

[0057] Figure 5 A feature point image within a single region;

[0058] Figure 6 To extract grayscale images of the R, G, and B channels of a seven-color grid. Detailed Implementation

[0059] The present invention will be further described below with reference to specific embodiments:

[0060] This example uses an 896×1120 pixel image as the projected image. Before that, the structured light projection three-dimensional measurement system used in the fast region mapping heterospectral structured light measurement method described in this embodiment of the invention will be specifically explained:

[0061] like Figure 1 As shown, the structured light projection 3D measurement system includes a computer 1, a projection optical engine 2, a color industrial camera 3, and the object to be measured 4.

[0062] like Figure 2 As shown in the figure, the heterospectral structured light measurement method for rapid region mapping described in this embodiment is implemented using a structured light projection three-dimensional measurement system, and its working process is as follows:

[0063] S1. A preset multi-color grid pattern (generated by computer 1) is projected onto the surface of the object under test 4 by the projection optical engine 2;

[0064] like Figure 3 As shown, the seven-color grid pattern projected by the projector 2 onto the surface of the object 4 has a resolution of 896×1120 pixels. Its vertical direction is divided into seven color gamut blocks according to the principle of equal division, and each block has a height of 160 pixels.

[0065] The color gamut arrangement and color composition of the seven-color checkered pattern are as follows:

[0066] First color gamut block: pure red (R, RGB value [255,0,0]);

[0067] Second color gamut block: pure green (G, RGB value [0,255,0]);

[0068] Third color gamut block: pure blue (B, RGB value [0,0,255]);

[0069] Fourth color gamut block: Red-green mixed color (RG / Yellow, RGB value [255,255,0]);

[0070] Fifth color gamut block: Red / Blue mixed color (RB / Magenta, RGB value [255,0,255]);

[0071] Sixth color gamut block: Green-blue mixed color (GB / Cyan, RGB value [0,255,255]);

[0072] The seventh color gamut block: red, green and blue mixed color (RGB / White, RGB value [255,255,255]).

[0073] S2. Use a color industrial camera 3 to acquire a multi-color grid image modulated on the surface of the object under test 4;

[0074] S3, Computer 1 uses RGB three-channel separation technology and color coding to identify multiple color blocks; then, feature points are marked according to preset rules, and through the spatial distribution relationship of these feature points, a precise mapping relationship is established between the coordinate system of the projection optical engine 2 and the coordinate system of the color industrial camera 3;

[0075] The specific process of this step includes:

[0076] S3-1. Extract the red (R), green (G), and blue (B) channels to generate three binary images. Based on these three binary images, each 160-pixel high color gamut block can be uniquely identified by its corresponding 3-bit binary code. The pure red (R) block is encoded as 100 (R=1, G=0, B=0), the pure green (G) block is encoded as 010 (R=0, G=1, B=0), and the pure blue (B) block is encoded as 001 (R=0, G=0, B=1). The red-green gamut (RG / Y) block is also encoded as... The color gamut block is encoded as follows: 110 (R=1, G=1, B=0) for the yellow color, 101 (R=1, G=0, B=1) for the red / blue (RB / Magenta) color, 011 (R=0, G=1, B=1) for the green / blue (GB / Cyan) color, and 111 (R=1, G=1, B=1) for the red / green / blue (RGB / White) color. By scanning the 3-bit code of the image pixels and matching it with the preset encoding sequence, the color gamut block to which the feature point belongs can be accurately located. The encoding diagram is shown below. Figure 4 As shown.

[0077] S3-2. Using RGB three-channel separation technology, the 896×1120 pixel seven-color square pattern is divided into seven equal-height regions (each region is 160 pixels high) according to the color gamut. Each region is constructed into a regular square matrix of 128 rows × 160 columns by alternating filling of the main color and 8×8 pixel black squares. Then, each color block region (containing 128×20 square blocks) is subdivided into 7 sub-regions along the row direction (each region contains 16×20 square blocks), and a circular feature marker is set at the center of each sub-region. The color configuration of the marker must meet the following requirements: (1) one of the seven colors of the above color coding, (2) the seven markers correspond to the features of the other seven main color regions respectively, and (3) the color arrangement The order strictly follows the top-down spatial distribution rule of the seven-color regions; the center coordinates of the feature markers are represented by a grid normalization method. The first marker is located at the geometric center of the first sub-region (containing 16×20 grids), with coordinates (8, 10). Subsequent markers of the same color are equally spaced in the vertical direction at intervals of 20 grids (equivalent to 160 pixels in the vertical direction) and in the horizontal direction at intervals of 16 grids (equivalent to 128 pixels in the vertical direction). The center coordinates of the feature points in the first color block are (8+16k, 10), where k is the result obtained by color encoding, which is numerically equal to the binary number corresponding to the color region. The seven feature points in the first color block correspond one-to-one with these seven coordinates.

[0078] S4. Computer 1 dynamically generates a binary projection mask that matches the geometric features of the object's surface based on a feature matching algorithm.

[0079] The specific process of this step includes:

[0080] S4-1. First, the multi-color grid image modulated by the object surface obtained by the color industrial camera 3 is sequentially separated into R, G, and B color channels to obtain I... R I G I B , will I R I G I B Binarize and encode in RGB order (if the region is not 0, I will be encoded). R I G I B Consider it as 1), and we get I. R I G I B The three-digit code corresponds one-to-one with the color block area, thus obtaining the horizontal coordinate of the feature point as 8+16k1, where k1 is the decimal color code value corresponding to the color block area.

[0081] S4-2. Then, perform R, G, and B color channel separation on the center circle inside the feature point to obtain C.R C G C B Also for C R C G C B Binarization, and encoding according to RGB order (when the region is not 0, C is used). R C G C B Considering it as 1), we get C. R C G C B Based on the color code, the feature point is identified as the nth feature point in the color block area, thus obtaining the ordinate of the feature point 10+20k2, where k2 is the decimal color code value of the feature point in the color block area.

[0082] S4-3. Create a new 896×1120 grid map, identify the feature points closest to the contour points, and fill all contour points into the grid based on the coordinates of the feature points. For any contour point E... i =(x i ,y i Its grid coordinates (m) i ,n i )Calculated by projection:

[0083]

[0084] Among them, E k Let v be the coordinates of the nearest feature point to the corner, where Δx = Δy = 8, v x and v y Let v be the reference vector, satisfying orthogonality. x v y =0.

[0085]

[0086] Where E1 is the feature point E k The nearest corner point in the horizontal direction, E2 is the feature point E k The nearest corner point in the vertical direction. Obtain the grid coordinates (m) of all contour points. i ,n i Then, connect them to form a binary projection mask M for the target object. p .

[0087] S5. Computer 1 divides the projection mask area into two groups according to the spatial relationship, and generates a longitudinal sinusoidal phase shift fringe pattern in these two groups of mask areas.

[0088] This step specifically includes:

[0089] All projection mask regions are labeled with connected components. Then, the projection mask regions are divided into two groups according to the connected component index. The connected component indexes of these two groups are guaranteed to be discontinuous.

[0090] The first group of mask regions is named M. R The second set of mask regions is named M. B In M respectively R and M B The three-step phase-shifting fringe pattern P is generated. i R and P i B Both are calculated using the following formula:

[0091]

[0092] (u p ,v p ) represents the coordinates of the projected pixel, A and B are preset constants, F represents the fringe frequency, and i represents the i-th fringe pattern in the three-step phase-shifting method, i = 1, 2, 3.

[0093] S6. Computer 1 encodes the two sets of sinusoidal phase-shifting stripes into the red and blue channels of the RGB image, respectively, while the green channel is retained for color balance, forming a heterospectral phase-shifting stripe pattern.

[0094] This step specifically includes:

[0095] First, create a 24-bit completely black background image. i_color Using (0,0,0) as a carrier, the first group of eight-bit deep three-step phase-shifted fringe patterns P i R Encoded to the red channel, second group of three-step phase-shift fringe pattern P i B Encode the image into the blue channel, leaving the green channel at zero, to form the composite image I. i_color =(P i R ,0,P i B Three sets of such images were generated using the three-step phase-shifting method. i_color =(P i R ,0,P i B ).

[0096] S7. The projector 2 projects the encoded stripe image onto the object under test 4 at a refresh rate of ≥60Hz, and simultaneously triggers the color industrial camera 3 to capture the image.

[0097] S8. Use the acquired heterospectral phase-shifting fringe pattern to perform phase extraction and three-dimensional topography reconstruction.

[0098] This step specifically includes:

[0099] First, the spectral channels are separated to obtain stripe images for the red and blue monochromatic channels. Then, phase extraction is performed independently on the red and blue monochromatic channel images, as shown in the following formula:

[0100]

[0101] Among them, I1, I2, and I3 are three fringe patterns with different phase shifts in the monochrome channel. The wrap-around phase of this channel is calculated using the I1, I2, and I3 phase shift fringe patterns. Finally, the phases calculated from the two different color channel images are fused and then calibrated using the phase-depth formula.

[0102]

[0103] To obtain the final three-dimensional shape.

[0104] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Therefore, any changes made in accordance with the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for measuring heterospectral structured light using rapid region mapping, characterized in that, include: S1. Project a preset multi-colored grid pattern onto the surface of the target object using a projection device; S2. Use an image acquisition device to acquire a multi-color grid image modulated by the object surface; S3. It adopts RGB three-channel separation technology and combines color coding to identify multiple color blocks; then, it marks feature points according to preset rules, and establishes a precise mapping relationship between the coordinate system of the projection device and the coordinate system of the image acquisition device through the spatial distribution relationship of these feature points. S4. Based on the feature matching algorithm, dynamically generate a binary projection mask that matches the geometric features of the object surface; S5. Divide the projection mask area into two groups according to the spatial relationship, and generate longitudinal sinusoidal phase shift fringe patterns in these two groups of mask areas. S6. Encode the two sets of sinusoidal phase-shifting stripes into the red and blue channels of the RGB image respectively, while retaining the green channel for color balance, to form a heterospectral phase-shifting stripe pattern. S7. Project the encoded stripe image onto the target object at a set refresh rate, and simultaneously trigger the image acquisition device to capture the image. S8. Use the acquired heterospectral phase-shifting fringe pattern to perform phase extraction and three-dimensional topography reconstruction; Step S4 includes the following process: First, the multi-color grid image modulated by the object surface, acquired by the image acquisition device, is sequentially separated into red, green, and blue color channels. , , ,Will , , Binarize and encode in RGB order to obtain sequentially encoded results. The three-digit code corresponds one-to-one with the color block region, thus obtaining the horizontal coordinate of the feature point. + ,in This is the decimal color code value corresponding to the color block area; Then, the red, green, and blue color channels of the central circle within the feature point are separated to obtain... , , and on , , Binarization and encoding according to RGB order yield the following results: Based on the color coding, the feature point is identified as its nth feature point within the color block area, thus obtaining the ordinate of the feature point. + ,in This is the decimal color code value of the feature point corresponding to the color block area; Finally, create a new one. The grid map identifies the nearest feature points to the contour points. Based on the coordinates of these feature points, all contour points are filled into the grid. (This refers to any contour point.) = Its grid coordinates Calculated by projection: ； for The coordinates of the nearest feature point, where = =8, and As the reference vector, it satisfies the orthogonality. =0; , ; in For feature points The nearest corner point in the horizontal direction. For feature points The nearest corner point in the vertical direction; obtain the grid coordinates of all contour points. Then, they are connected together to form a binary projection mask of the target object. .

2. The method for rapid region mapping heterospectral structured light measurement according to claim 1, characterized in that, The multi-colored checkered pattern projected by the projection device onto the surface of the target object is a seven-color checkered pattern, which has... The pixel resolution is divided vertically into seven color gamut blocks, each with a height of [missing information]. Pixel; The color gamut arrangement and color composition of the seven-color checkered pattern are as follows: First color gamut block: pure red; Second color gamut block: pure green; Third color gamut area: pure blue; Fourth color gamut block: red-green mixed color; Fifth color gamut block: Red and blue mixed colors; Sixth color gamut block: green-blue mixed color; The seventh color gamut block: a mixture of red, green and blue.

3. The method for rapid region mapping heterospectral structured light measurement according to claim 2, characterized in that, In step S3, RGB three-channel separation technology is used, combined with color encoding, to identify the seven color blocks. The process includes: The image is subjected to RGB three-channel separation, and the red, green, and blue channels are extracted to generate three binary images; based on these three binary images, each... Each high-pixel color gamut block is uniquely identified by its corresponding 3-bit binary code. Pure red blocks are coded as 100, pure green blocks as 010, pure blue blocks as 001, red-green mixed blocks as 110, red-blue mixed blocks as 101, green-blue mixed blocks as 011, and red-green-blue mixed blocks as 111. By scanning the 3-bit codes of image pixels and matching them with a preset coding sequence, the color gamut block to which the feature point belongs can be accurately located.

4. The method for rapid region mapping heterospectral structured light measurement according to claim 2, characterized in that, In step S3, feature points are marked according to preset rules, and a precise mapping relationship between the projection device coordinate system and the image acquisition device coordinate system is established based on the spatial distribution relationship of these feature points. The process includes: Using RGB three-channel separation technology The pixel's seven-color checkered pattern is divided into seven equal-height areas according to the color gamut. Each area is constructed by alternating between the main color and 8×8 pixel black squares. Line × The columns are arranged in a regular square matrix; then each color block area is subdivided into 7 sub-regions along the row direction, and a circular feature marker is set at the center of each sub-region. The color configuration of the markers must meet the following requirements: 1) Use one of the seven colors mentioned above for color coding; 2) The seven markers correspond to the features of the other seven main color areas respectively; 3) The color arrangement strictly follows the spatial distribution rule of the seven color areas from top to bottom; The center coordinates of the feature markers are represented by the square block normalization method, and the first marker is located at the geometric center of the first sub-region, with coordinates ( , Subsequent markers of the same color will be arranged horizontally. The squares are evenly spaced, with vertical spacing of [missing information]. The squares are evenly spaced, and the center coordinates of the feature points within the first color block area are as follows: ( ) , + ),in The result obtained by color encoding is numerically equal to the binary number corresponding to the color area. The seven feature points in the first color block correspond one-to-one with these seven coordinates.

5. The method for rapid region mapping heterospectral structured light measurement according to claim 2, characterized in that, Step S5 includes: All projection mask regions are labeled with connected components. Then, the projection mask regions are divided into two groups according to the connected component index. The connected component indexes of these two groups are guaranteed to be discontinuous. The first group of mask areas is named The second group of mask areas is named , respectively in and Generate a three-step phase-shift fringe pattern and Both are calculated using the following formula: ； ( , ) represents the projected pixel coordinates. and All are preset constants. Indicates the fringe frequency. This represents the first step in the three-step phase shift method. Zhang striped pattern, .

6. The method for rapid region mapping heterospectral structured light measurement according to claim 5, characterized in that, Step S6 includes: First, create a 24-bit all-black background image. Using (0,0,0) as a carrier, the first set of eight-bit deep three-step phase-shifted fringe patterns are displayed. Encoding to the red channel, second set of three-step phase-shift fringe pattern Encode the image into the blue channel, leaving the green channel at zero, to create a composite image. =( ,0, Three sets of such images were generated using a three-step phase-shifting method. =( ,0, ).

7. The method for rapid region mapping heterospectral structured light measurement according to claim 1, characterized in that, Step S8 includes: First, the spectral channels are separated to obtain stripe images for the red and blue monochromatic channels. Then, phase extraction is performed independently on the red and blue monochromatic channel images, as shown in the following formula: ； in, , , These are three fringe patterns with different phase shifts in a monochrome channel. For those passing through this channel , , The phase-shifting fringe pattern is used to calculate the wrap-around phase of the channel. Finally, the phases calculated from the two different color channel images are fused and then calibrated using the phase-depth formula. ； To obtain the final three-dimensional shape.

Images