Light beam control method and device based on spatial light modulation and action perception

Abstract

The invention discloses a light beam control method and device based on spatial light modulation and action perception, and the method comprises the steps: modulating a laser beam, and enabling the modulated laser beam to enter a phase-type spatial light modulator; the method comprises the following steps: acquiring a real-time video frame stream of a hand action, preprocessing the real-time video frame stream, inputting the preprocessed real-time video frame stream into a neural network, extracting features by the neural network through convolution, activation and pooling, and outputting the category, confidence and bounding box coordinates of a target; non-maximum suppression is carried out in a multi-thread mode, overlapped bounding boxes are eliminated, and normalized coordinates of the bounding boxes are converted into absolute coordinates on an original image; performing four times of exponential movement smoothing combined with median filtering processing on the absolute coordinates, taking the center of the video frame as a reference point of Cartesian coordinates, and converting the absolute coordinates into relative coordinates of a Cartesian coordinate plane; based on the relative coordinates, a holographic phase diagram consistent with the target light spot is obtained; and the phase-type spatial light modulator modulates the laser based on the holographic phase diagram, outputs a corresponding modulation light spot, and monitors and adjusts the motion trail of the light beam.

Description

Technical Field

[0001] The present invention belongs to the field of optical technologies, and particularly relates to a beam control method and device based on spatial light modulation and motion perception. Background Art

[0002] In recent years, light field modulation technologies have been successfully applied to many fields such as optical micro-manipulation, optical super-resolution imaging, laser processing, holographic imaging, and optical communication. For example, the Bessel beam generated by spatial light modulation can perform non-destructive in-vivo operations on targets without affecting the surrounding environment, realizing the grasping and control of cells. Through spatial light modulation, three-dimensional laser processing of micro-nano structures can also be achieved.

[0003] Currently, beam control is a core technology in optical micro-manipulation and laser micro-nano manufacturing technologies, mainly divided into two methods:

[0004] (1) Moving the beam: By means of a scanning galvanometer, a digital micromirror array, or a phase-type spatial light modulator, etc., the direction of the beam entering the objective lens is controlled to achieve the movement of the beam on the sample. This method is restricted by the numerical aperture of the objective lens, and the beam movement range is small. In addition, usually only one beam can be controlled by a scanning galvanometer. Although a digital micromirror array can achieve multiple beams, it is difficult to achieve complex spot structures, and the efficiency of light field modulation is also low. The phase-type spatial light modulator can not only simultaneously generate various different beams, but also has a high modulation efficiency.

[0005] (2) Moving the sample: Fixing the beam and moving the sample on the translation stage to achieve the relative movement of the beam. This method can achieve a large range of beam movement, but the movement speed is low, resulting in low efficiency of micro-nano manufacturing. In optical micro-operations, due to the acceleration generated by the movement of the translation stage, the positioning of the sample will be seriously affected, and in-situ optical micro-operations are difficult to achieve.

[0006] Whether it is optical micro-manipulation or laser micro-nano manufacturing, the functions are mainly realized by means of preset programs. Not only are the beam functions and structures single, with few degrees of freedom, but it is also difficult to perform real-time intervention on the processes of optical micro-manipulation and laser micro-nano manufacturing. The industrial and academic communities both need a light field regulation technology that can be real-time, what you see is what you get, and has multiple degrees of freedom, which can perform real-time monitoring and arbitrary modification on the process of laser micro-nano manufacturing, and can also perform collaborative operations and control on multiple cells, particles, and drug carriers. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide a beam control method and device based on spatial light modulation and motion perception that are reasonably designed, have real-time response, flexible operation, and high precision.

[0008] The technical solution adopted to solve the above technical problems is as follows: A beam control method based on spatial light modulation and motion perception, comprising the following steps:

[0009] Step 1. After performing energy adjustment, beam expansion, filtering, and polarization modulation on the laser beam, it is incident on a phase-type spatial light modulator;

[0010] Step 2. Collect the real-time video frame stream of the hand movement and convert it into a digital signal for preprocessing, and obtain the number, shape, and position information of the target light spot through the hand movement;

[0011] Step 3. Input the preprocessed data into a neural network, and the neural network extracts features through operations of convolution, activation, and pooling, and outputs the category, confidence, and bounding box coordinates of the target;

[0012] Step 4. Perform non-maximum suppression in a multi-threaded manner to eliminate overlapping bounding boxes, and convert the normalized coordinates of the bounding boxes into absolute coordinates on the original image;

[0013] Step 5. Perform a process of combining four-time exponential moving average with median filtering on the absolute coordinate information for coordinate correction and conversion, and output the corrected absolute coordinate information;

[0014] The four-time exponential moving average is as follows: Calculate the exponential moving average values from the first layer to the fourth layer in sequence through a recurrence formula. Each smoothing is based on the previous result, and a smoothing coefficient is used to weigh the influence of the current value on the historical value;

[0015] The median filtering is as follows: Apply median filtering to the result of the fourth smoothing. At each time point, select a window of size 2k + 1, where k is a positive integer, and calculate the median of the data within the window to further remove residual spike noise, and finally output a smoothing result that retains the main trend of the data and eliminates short-term fluctuations;

[0016] Step 6. Use the center of the video frame as the reference point (0, 0) of the Cartesian coordinate, calculate the position offset of the target point relative to the reference point, and convert the absolute coordinates into relative coordinates in the Cartesian coordinate plane;

[0017] Step 7. Based on the relative coordinates, obtain a holographic phase diagram that is consistent with the number, shape, and position of the target light spot through a non-iterative algorithm;

[0018] Step 8. Load the holographic phase diagram onto the phase-type spatial light modulator. The phase-type spatial light modulator modulates the laser input in Step 1 based on the holographic phase diagram, outputs the corresponding modulated light spot, and detects the real-time motion trajectory of the modulated light spot for monitoring and adjustment of the beam motion trajectory.

[0019] As a preferred technical solution, in step 4, the method for non-maximum suppression in the multi-threaded manner is as follows: create a graphic matrix storing all categories, traverse all bounding boxes, group them by category, create threads according to the number of categories, start a thread for each category to perform non-maximum suppression, and wait for all threads to complete and then merge the results of all categories.

[0020] The present invention also provides a beam control device based on spatial light modulation and motion perception, which includes a laser. Along the optical path direction of the output light of the laser, a laser energy control module, a laser beam expander, a laser spatial filtering module, a polarization modulation module, and a spatial light modulation module are sequentially arranged. A computer is connected to the spatial light modulation module, and a motion perception device is connected to the computer;

[0021] The laser energy control module is used to adjust the laser output power;

[0022] The laser beam expander is used to expand the diameter of the laser beam and generate parallel light to ensure that the beam can fully cover the effective area of the spatial light modulation module;

[0023] The polarization modulation module is used to adjust the polarization state of the passed Gaussian beam to a linearly polarized state;

[0024] The motion perception device is used to capture the real-time motion process of the hand movement and input it into the computer in the form of a video frame stream;

[0025] The computer is used to process the data input by the motion perception device according to steps 3 to 7 of the method as described in claim 1, obtain the number, shape, and position of the modulated light spots, generate the corresponding holographic phase diagram, load it onto the spatial light modulation module, and at the same time perform zero-order suppression modulation on the laser entering the spatial light modulation module, wherein the entire calculation process of the non-iterative algorithm is carried out in the GPU video memory and accelerated by CUDA;

[0026] The spatial light modulation module is used to modulate the linearly polarized light output by the polarization modulation module based on the holographic phase diagram and output the corresponding modulated light spots.

[0027] As a preferred technical solution, the spatial light modulation module includes a phase-type spatial light modulator, a beam selector, and a beam collimator connected in sequence; the phase-type spatial light modulator receives the linearly polarized light and performs phase modulation according to the holographic phase diagram to generate modulated light; the beam selector is used to filter the zero-order light in the modulated light; the beam collimator is used to collimate the modulated light.

[0028] As a preferred technical solution, the entire calculation process of the non-iterative algorithm is accelerated by CUDA in the GPU video memory. Specifically: in the computer host memory, use OpenCV to create and initialize the graphic matrix and vector required by the non-iterative algorithm, and set a specific size and data type; then upload this data from the host memory to the GPU video memory, and pass the input matrix and vector to the GPU as parameters through the CUDA framework, specify the output matrix, and use the GPU to perform matrix multiplication, addition and normalization operations to achieve non-iterative calculation of the holographic phase diagram; after the calculation is completed, download the result from the GPU video memory back to the host memory, and finally use OpenCV to convert the matrix data into a picture format and save it to the local folder.

[0029] As a preferred technical solution, a camera is also connected to the computer. The camera is used to collect the motion trajectory video stream of the modulated light spot and feed it back to the computer, and the computer optimizes and adjusts the holographic phase diagram according to the feedback information.

[0030] As a preferred technical solution, the method for the computer to perform zero-order suppression modulation on the laser entering the spatial light modulation module is: increase the voltage of the dark pixels and decrease the voltage of the bright pixels to balance the modulation efficiency δ and the zero-order light spot suppression efficiency η. Among them, the modulation efficiency δ is:

[0031]

[0032] In the formula, I m (V H ,V L ) is the modulated light intensity, V H is the voltage of the bright pixels, V L is the voltage of the dark pixels, Ω1 is the integration region of the modulated light, Ω is the integration region of the total light intensity, and I(V H ,V L ) is the total light intensity value output by the laser;

[0033] The zero-order light spot suppression efficiency η is:

[0034]

[0035] In the formula, I z (V H ,V L ) is the zero-order light spot intensity, and Ω2 is the integration region of the zero-order light spot.

[0036] The beneficial effects of the present invention are as follows:

[0037] Through the combination of motion perception and spatial light modulation, the present invention realizes the real-time regulation of the light field and enhances the human-computer interaction experience. Users can directly control the generation and movement of light spots through gesture actions without complex preset programs, achieving the light field regulation effect of "what you see is what you get", allowing users to intervene and modify at any time during the operation, and improving the flexibility and adaptability of the operation.

[0038] The present invention adopts a coordinate correction method that combines four-time exponential moving average and median filtering, effectively eliminating the influence of position coordinate jitter, improving the stability of the recognition result, and ensuring that the light spot can accurately follow the movement of the action.

[0039] The present invention is based on a high-resolution holographic phase map fast generation method accelerated by OpenCV and CUDA, significantly improving the generation speed of the holographic phase map, without the need for deep learning training process, having higher computational efficiency, lower resource consumption, and stronger interpretability.

[0040] When performing non-maximum suppression on the output result of the neural network model inference, the present invention uses a multi-threaded method for processing, significantly shortening the post-processing time and improving the speed of the entire recognition process.

[0041] The present invention can not only be applied to the visualization and flexible manufacturing of complex micro-nano structures and their chips, but also realize the multi-target and multi-degree-of-freedom control and processing of cells and particles, as well as the research on the interaction between biology and drugs, and between cells and cancer cells, which has important significance and wide application scenarios in the fields of optical micro-nano processing, micro-nano fluidics, and biomedicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic flow chart of the beam control method based on spatial light modulation and motion perception of the present invention.

[0043] Figure 2 is a numerical simulation comparison chart before and after coordinate processing.

[0044] Figure 3 is a schematic structural diagram of the beam control device based on spatial light modulation and motion perception of the present invention.

[0045] In the figure: laser 1, laser energy control module 2, laser beam expander 3, laser spatial filtering module 4, polarization modulation module 5, spatial light modulation module 6, computer 7, camera 8, motion perception device 9. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0046] The following further describes the present invention in detail with reference to the drawings and embodiments, but the present invention is not limited to the following embodiments.

[0047] The beam control method based on spatial light modulation and motion perception in this embodiment includes the following steps:

[0048] Step 1. After performing energy adjustment, beam expansion, filtering, and polarization modulation on the laser beam, it is incident on the phase-type spatial light modulator 6-1.

[0049] Step 2. Collect the real-time video frame stream of the hand movement and convert it into a digital signal for preprocessing, and obtain the number, shape, and position information of the target light spot through the hand movement.

[0050] Among them, the data preprocessing includes: normalizing and adjusting the pixel range to be suitable for the input of the neural network, using bilinear interpolation to scale the image to the input size required by the neural network, subtracting the mean value to match the distribution of the training data, and converting the channel order from BGR to RGB.

[0051] Step 3. Input the preprocessed data into the neural network, and the neural network extracts features through operations such as convolution, activation, and pooling, and outputs the category, confidence, and bounding box coordinates of the target.

[0052] Step 4. Perform non-maximum suppression in a multi-threaded manner to eliminate overlapping bounding boxes, and convert the normalized coordinates of the bounding boxes into absolute coordinates on the original image.

[0053] Among them, the method of performing non-maximum suppression in a multi-threaded manner is: create a graphical matrix storing all categories, traverse all bounding boxes, group them by category, create threads according to the number of categories, start a thread for each category to perform non-maximum suppression, and wait for all threads to complete before merging the results of all categories.

[0054] Step 5. Perform a process of four-time exponential moving average combined with median filtering on the absolute coordinate information for coordinate correction and conversion, and output the corrected absolute coordinate information;

[0055] The four-time exponential moving average is: calculate the exponential moving average values from the first layer to the fourth layer in turn through a recurrence formula, and each smoothing is based on the previous result, using a smoothing coefficient to weigh the influence of the current value on the historical value;

[0056] The median filtering is: apply median filtering to the result of the fourth smoothing, select a window of size 2k + 1 at each time point, where k is a positive integer, calculate the median of the data within the window to further remove residual spike noise, and finally output a smoothing result that retains the main trend of the data and eliminates short-term fluctuations.

[0057] Step 6. Take the center of the video frame as the reference point (0, 0) of the Cartesian coordinate, calculate the position offset of the target point relative to the reference point, and convert the absolute coordinates into relative coordinates in the Cartesian coordinate plane, such as Figure 2。

[0058] Step 7. Based on the relative coordinates, obtain a holographic phase map that is consistent with the number, shape, and position of the target light spots through a non-iterative algorithm.

[0059] Step 8. Load the holographic phase map onto the phase-type spatial light modulator 6-1. The phase-type spatial light modulator 6-1 modulates the laser input in Step 1 based on the holographic phase map, outputs the corresponding modulated light spots, and detects the real-time movement trajectory of the modulated light spots for monitoring and adjusting the movement trajectory of the light beam.

[0060] In Figure 3 this embodiment, the beam control device based on spatial light modulation and motion perception includes a laser 1. Along the optical path direction of the light output by the laser 1, a laser energy control module 2, a laser beam expander 3, a laser spatial filtering module 4, a polarization modulation module 5, and a spatial light modulation module 6 are sequentially arranged. A computer 7 is connected to the spatial light modulation module 6, and an action perception device 9 and a camera 8 are connected to the computer 7.

[0061] The laser 1 emits a laser with a wavelength of 780 nm. The laser energy control module 2 is used to adjust the laser output power and adjust the laser power to be greater than 15 mW. The laser beam expander 3 expands the diameter of the laser beam and generates parallel light, making it tangent to the liquid crystal panel of the phase-type spatial light modulator 6-1, providing a light beam with a suitable size for subsequent spatial light modulation, ensuring that the light beam can fully cover the effective area of the spatial light modulator, and improving the modulation efficiency. The polarization modulation module 5 is a half-wave plate, which adjusts the polarization state of the passed Gaussian beam to a linearly polarized state, ensuring that the polarization direction is consistent with the long axis direction of the liquid crystal panel of the phase-type spatial light modulator 6-1, enabling the light beam to be effectively modulated on the spatial light modulator, and improving the modulation contrast and efficiency.

[0062] The action perception device 9 is a camera, which is used to capture the real-time movement process of hand movements and input it into the computer 7 in the form of a video frame stream;

[0063] The computer 7 is used to process the data input by the action perception device 9, obtain the number, shape, and position of the modulated light spots, generate the corresponding holographic phase map, load it onto the spatial light modulation module 6, and simultaneously perform zero-order suppression modulation on the laser entering the spatial light modulation module 6.

[0064] Among them, the method for the computer 7 to process the data input by the motion perception device 9 and obtain the number, shape, and position of the modulated light spots and generate the corresponding holographic phase diagram is as follows: The computer 7 sequentially completes the preparation work of CUDA environment configuration, OpenCV library loading, neural network model initialization, and SDK integration of the phase-type spatial light modulator 6-1. The computer 7 receives the video frame stream output by the motion perception device 9, and displays the real-time motion process of the hand motion in real time through the OpenCV interface. After the neural network detects the position of the hand joints, it outputs the relative coordinate data of the hand joints, which is optimized by four-time exponential moving average median filtering and then passed to the non-iterative algorithm for processing to generate the corresponding holographic phase diagram. The entire calculation process of the non-iterative algorithm is carried out in the GPU video memory and accelerated by CUDA operation.

[0065] The operation for the entire calculation process of the non-iterative algorithm to be carried out in the GPU video memory and accelerated by CUDA is as follows: Use OpenCV in the computer 7 host memory to create and initialize the graphic matrix and vector required by the non-iterative algorithm, and set a specific size and data type; then upload the data from the host memory to the GPU video memory, and pass the input matrix and vector to the GPU as parameters through the CUDA framework, specify the output matrix, and use the GPU to perform matrix multiplication, addition, and normalization operations to achieve the non-iterative calculation of the holographic phase diagram; after the calculation is completed, download the result from the GPU video memory back to the host memory, and finally convert the matrix data into a picture format with the help of OpenCV and save it to the local folder.

[0066] The method for the computer 7 to perform zero-order suppression modulation on the laser entering the spatial light modulation module 6 is as follows: Increase the dark pixel voltage and decrease the bright pixel voltage to balance the modulation efficiency δ and the zero-order light spot suppression efficiency η, that is, the modulation efficiency δ reaches a higher value and the zero-order light spot suppression efficiency η reaches a lower value. Among them, the modulation efficiency δ is:

[0067]

[0068] In the formula, I m (V H , V L ) is the modulated light intensity, V H is the bright pixel voltage, V L is the dark pixel voltage, Ω1 is the integration region of the modulated light, Ω is the integration region of the total light intensity, I(V H , V L ) is the total light intensity of the laser output by the laser 1;

[0069] The zero-order light spot suppression efficiency η is:

[0070]

[0071] In the formula, Iz (V H , V L ) is the intensity of the zero-order spot, and Ω2 is the integration region of the zero-order spot.

[0072] The specific operation is as follows:

[0073] Initial setting: Select an initial V L and V H value. For example, V L = Vc, where Vc is the threshold voltage, and V H = V c + V0, where V0 is the excess voltage;

[0074] Gradual adjustment: Gradually increase V L , and at the same time gradually decrease V H , and record the modulated light intensity I l , the intensity of the zero-order spot I z and the total light intensity I after each adjustment;

[0075] Performance evaluation: Calculate the modulation efficiency δ and the zero-order spot suppression efficiency η after each adjustment, and evaluate the performance change;

[0076] Find the balance point: Find the V L and V H values that achieve the best balance between the modulation efficiency and the zero-order spot suppression efficiency.

[0077] The spatial light modulation module 6 includes a phase-type spatial light modulator 6-1, a beam selector 6-2, and a beam collimator 6-3 connected in sequence. The phase-type spatial light modulator 6-1 receives linearly polarized light and performs phase modulation according to the holographic phase diagram to generate modulated light. The beam selector 6-2 is used to secondarily filter the zero-order light in the modulated light. The beam collimator 6-3 is used to collimate the modulated light.

[0078] The camera 8 collects the video stream of the movement trajectory of the modulated spot and feeds it back to the computer 7. The computer 7 optimizes and adjusts the holographic phase diagram according to the feedback information.

Claims

1. A beam control method based on spatial light modulation and motion perception, characterized in that, It includes the following steps: Step 1. After performing energy adjustment, beam expansion, filtering, and polarization modulation on the laser beam, it is incident on the phase-type spatial light modulator; Step 2. Collect the real-time video frame stream of the hand movement and convert it into a digital signal for preprocessing. Obtain the number, shape, and position information of the target spot through the hand movement; Step 3. Input the preprocessed data into the neural network. The neural network extracts features through operations such as convolution, activation, and pooling, and outputs the category, confidence, and bounding box coordinates of the target; Step 4. Perform non-maximum suppression in a multi-threaded manner to eliminate overlapping bounding boxes, and convert the normalized coordinates of the bounding boxes into absolute coordinates on the original image; Step 5. Perform a process of combining four-time exponential moving average with median filtering on the absolute coordinate information for coordinate correction and conversion, and output the corrected absolute coordinate information; The four-time exponential moving average is: Calculate the exponential moving average values from the first layer to the fourth layer in sequence through a recurrence formula. Each smoothing is based on the previous result, and a smoothing coefficient is used to weigh the influence of the current value on the historical value; The median filtering is: Apply median filtering to the result of the fourth smoothing. Select a window of size 2k + 1 at each time point, where k is a positive integer, and calculate the median of the data within the window to further remove residual spike noise. Finally, output a smoothed result that retains the main trend of the data and eliminates short-term fluctuations; Step 6. Use the center of the video frame as the reference point (0, 0) of the Cartesian coordinate system, calculate the position offset of the target point relative to the reference point, and convert the absolute coordinates into relative coordinates in the Cartesian coordinate plane; Step 7. Based on the relative coordinates, obtain a holographic phase diagram that is consistent with the number, shape, and position of the target spot through a non-iterative algorithm; Step 8. Load the holographic phase diagram onto the phase-type spatial light modulator. The phase-type spatial light modulator modulates the laser input in Step 1 based on the holographic phase diagram, outputs the corresponding modulated spot, and detects the real-time motion trajectory of the modulated spot for monitoring and adjusting the motion trajectory of the light beam.

2. The beam control method based on spatial light modulation and motion perception according to claim 1, wherein In Step 4, the method of performing non-maximum suppression in a multi-threaded manner is: Create a graphic matrix storing all categories, traverse all bounding boxes, group them by category, create threads according to the number of categories, start a thread for each category to perform non-maximum suppression, and wait for all threads to complete before merging the results of all categories.

3. A beam control device based on spatial light modulation and motion perception, characterized in that It includes a laser, and along the optical path direction of the output light of the laser, a laser energy control module, a laser beam expander, a laser spatial filtering module, a polarization modulation module, and a spatial light modulation module are sequentially arranged. A computer is connected to the spatial light modulation module, and an action sensing device is connected to the computer; The laser energy control module is used to adjust the laser output power; The laser beam expander is used to expand the diameter of the laser beam and generate parallel light to ensure that the beam can fully cover the effective area of the spatial light modulation module; The polarization modulation module is used to adjust the polarization state of the passed Gaussian beam to a linearly polarized state; The action sensing device is used to capture the real-time motion process of the hand movement and input it into the computer in the form of a video frame stream; The computer is configured to process the data input by the motion perception device according to steps 3 to 7 of the method described in claim 1, obtain the number, shape, and position of the modulated light spots, generate a corresponding holographic phase map, load it onto the spatial light modulation module, and simultaneously perform zero-order suppression modulation on the laser entering the spatial light modulation module. The entire calculation process of the non-iterative algorithm is carried out in the GPU video memory and accelerated by CUDA. The spatial light modulation module is configured to modulate the linearly polarized light output by the polarization modulation module based on the holographic phase map and output corresponding modulated light spots.

4. The beam control device based on spatial light modulation and motion perception according to claim 3, wherein The spatial light modulation module includes a phase-type spatial light modulator, a beam selector, and a beam collimator connected in sequence. The phase-type spatial light modulator receives the linearly polarized light and performs phase modulation according to the holographic phase map to generate modulated light. The beam selector is used to filter out the zero-order light in the modulated light. The beam collimator is used to collimate the modulated light.

5. The beam control device based on spatial light modulation and motion perception according to claim 3, wherein The entire calculation process of the non-iterative algorithm is accelerated by CUDA in the GPU video memory. Specifically, in the computer host memory, OpenCV is used to create and initialize the graphic matrices and vectors required by the non-iterative algorithm, and specific sizes and data types are set. Subsequently, this data is uploaded from the host memory to the GPU video memory, and the input matrices and vectors are passed as parameters to the GPU through the CUDA framework, specifying the output matrix. Matrix multiplication, addition, and normalization operations are performed using the GPU to achieve non-iterative calculation of the holographic phase map. After the calculation is completed, the result is downloaded back from the GPU video memory to the host memory, and finally the matrix data is converted into a picture format and saved to the local folder with the help of OpenCV.

6. The beam control device based on spatial light modulation and motion perception according to claim 3, wherein A camera is also connected to the computer. The camera is used to collect the motion trajectory video stream of the modulated light spots and feedback it to the computer. The computer optimizes and adjusts the holographic phase map according to the feedback information.

7. The beam control device based on spatial light modulation and motion perception according to claim 3, wherein The method for the computer to perform zero-order suppression modulation on the laser entering the spatial light modulation module is as follows: increase the voltage of the dark pixels and decrease the voltage of the bright pixels to balance the modulation efficiency δ and the zero-order light spot suppression efficiency η. Among them, the modulation efficiency δ is: Where, I m (V H , V L ) is the modulated light intensity, V H is the bright pixel voltage, V L is the dark pixel voltage, Ω1 is the integration region of the modulated light, Ω is the integration region of the total light intensity, I(V H , V L ) is the total light intensity value output by the laser; The zero-order light spot suppression efficiency η is: where I z (V H , V L ) is the light intensity of the zero-order spot, and Ω2 is the integration region of the zero-order spot.

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