Semiconductor laser module far-field image detection method and device

Abstract

The present application relates to the field of semiconductor laser testing technology, and discloses a far-field image detection method and device for a semiconductor laser module. The method uses a camera to shoot the imaging of the light beam emitted by the semiconductor laser module on a perspective film to obtain a grayscale image; corrects and crops the grayscale image to obtain a target image; determines the pixel information of each pixel in the target image, and determines the center of mass position of the target image based on the pixel information; determines the target tangent based on the center of mass position and the size of the target image; then, calculates the uniformity within each grid on the target image, and calculates the uniformity on each target tangent. In the embodiment of the present application, the quality of the light beam emitted by the semiconductor laser module can be determined by the uniformity within each grid and the uniformity of each tangent to determine the performance of the semiconductor laser module.

Description

Technical Field

[0001] The present application relates to the field of semiconductor laser testing technology, and in particular to a method and device for detecting far-field images of a semiconductor laser module. Background Art

[0002] With the increasing maturity of semiconductor technology, semiconductor lasers, owing to their high conversion efficiency, compact size, light weight, high reliability, and direct modulation capabilities, have found increasingly widespread application in scientific research, industry, military, and medical fields. These applications have revolutionized many fields, generating enormous market demand and development potential. Far-field image analysis is a key method for evaluating semiconductor laser performance. The main objectives of far-field beam quality testing for semiconductor laser chips include verifying whether the chip's emission uniformity meets requirements, whether the window efficiency is within the specified range, and whether there are any irregularities in energy distribution. Therefore, far-field beam quality testing is crucial during production testing. Currently, measuring far-field emission beam images requires practical and effective measurement methods to obtain reliable test data for laser devices. Current testing methods, including vertical distance measurement and parallel profile measurement, can only roughly measure the laser's divergence angle, but cannot calculate beam quality parameters. These methods are also suitable for low-power lasers. Summary of the Invention

[0003] An embodiment of the present application provides a far-field image detection method for a semiconductor laser module to solve the problem in the prior art that the current vertical distance measurement method and parallel profile measurement method can only roughly measure the divergence angle of the laser but cannot calculate the beam quality parameters.

[0004] Correspondingly, the embodiments of the present application also provide a semiconductor laser module far-field image detection device, an electronic device, and a computer-readable storage medium to ensure the implementation and application of the above method.

[0005] In order to solve the above technical problems, the present invention discloses a method for detecting far-field images of a semiconductor laser module. The method includes:

[0006] Use a camera to capture the image of the light beam emitted by the semiconductor laser module on the transparent film to obtain a grayscale image;

[0007] Correcting and cropping the grayscale image to obtain a target image;

[0008] Determining pixel information of each pixel in the target image, and determining a centroid position of the target image based on the pixel information;

[0009] determining a target tangent according to the centroid position and the size of the target image;

[0010] Calculating the uniformity within each grid on the target image and calculating the uniformity on each target tangent line to determine the quality of the light beam emitted by the semiconductor laser module;

[0011] The grid is pre-divided according to the number of pixels of the target image.

[0012] Preferably, the step of correcting and cropping the grayscale image to obtain a target image includes:

[0013] Performing edge extraction on the grayscale image using a method for calculating an energy density ratio or a method for calculating a peak ratio to obtain a grayscale image copy and an image edge;

[0014] The grayscale image copy is adjusted to be horizontal, and the grayscale image copy is cropped according to the image edge to obtain the target image.

[0015] Preferably, adjusting the grayscale image copy to be horizontal comprises:

[0016] calculating a rotation angle according to the horizontal length and the vertical length of the grayscale image copy;

[0017] The horizontal length and the vertical length are processed using the Rotate function to obtain the rotation angle.

[0018] Preferably, the pixel information includes a grayscale value; and determining the pixel information of each pixel in the target image and determining the centroid position of the target image according to the pixel information includes:

[0019] Establishing a loop structure index two-dimensional array for the target image;

[0020] Traversing the two-dimensional array, calculating the grayscale value of each pixel and the row number of the pixel, and the grayscale value of each pixel and the column number of the pixel, to obtain the row weight and column weight of each pixel;

[0021] The row weights of all pixels and the column weights of all pixels are averaged to obtain the centroid position.

[0022] Preferably, the pixel information includes grayscale values; and the calculating of the uniformity on each target tangent line includes:

[0023] Generate a first curve based on all the pixels passing through the target tangent line, wherein the abscissa of the first curve is the divergence angle, and the ordinate is the grayscale value;

[0024] The variance of the grayscale values ​​of all pixels on the target tangent line is calculated according to the first curve as the uniformity of the target tangent line.

[0025] Preferably, the pixel information includes a grayscale value; and the calculating the uniformity within each grid on the target image includes:

[0026] The standard deviation of the grayscale values ​​of all pixels in the grid is calculated as the uniformity in the grid.

[0027] Preferably, before using a camera to capture the image of the light beam emitted by the semiconductor laser module on the transparent film to obtain a grayscale image, the method further comprises:

[0028] Controlling the semiconductor laser module to a specified position through chip motion instructions;

[0029] Adjust the camera vertically downward through a camera adjustment instruction, and adjust the distance between the camera and the semiconductor laser module to a first preset distance value;

[0030] The distance between the transparent film and the semiconductor laser module is adjusted to a second preset distance value through a transparent film adjustment instruction.

[0031] The present application also discloses a semiconductor laser module far-field image detection device, comprising:

[0032] An image acquisition module is used to use a camera to capture the image of the light beam emitted by the semiconductor laser module on the transparent film to obtain a grayscale image;

[0033] An image preprocessing module, used for correcting and cropping the grayscale image to obtain a target image;

[0034] An image processing module, configured to determine pixel information of each pixel in the target image, and determine a centroid position of the target image based on the pixel information;

[0035] The image processing module is further used to determine the target tangent according to the center of mass position and the size of the target image;

[0036] The image processing module is further used to calculate the uniformity within each grid on the target image and the uniformity on each target tangent line to determine the quality of the light beam emitted by the semiconductor laser module;

[0037] The grid is pre-divided according to the number of pixels of the target image.

[0038] In an embodiment of the present application, a camera is used to capture the image of the light beam emitted by the semiconductor laser module on a transparent film to obtain a grayscale image; the grayscale image is corrected and cropped to obtain a target image; the pixel information of each pixel in the target image is determined, and the center of mass position of the target image is determined based on the pixel information; the target tangent is determined based on the center of mass position and the size of the target image; then, the uniformity within each grid on the target image and the uniformity on each target tangent are calculated, wherein the grid is pre-divided according to the number of pixels of the target image. In an embodiment of the present application, the quality of the light beam emitted by the semiconductor laser module can be determined based on the uniformity within each grid and the uniformity of each tangent to determine the performance of the semiconductor laser module.

[0039] Additional aspects and advantages of the embodiments of the present application will be given in the following description, which will become apparent from the following description or be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0041] Figure 1 A flowchart of a far-field image detection method for a semiconductor laser module provided in an embodiment of the present application;

[0042] Figure 2 A specific flow chart for light beam analysis based on Labview software provided in an embodiment of the present application;

[0043] Figure 3 A schematic diagram of a grayscale image provided in an embodiment of the present application;

[0044] Figure 4 Schematic diagram of pseudo-color image and grid / tangent analysis results of the target image provided in the embodiment of the present application;

[0045] Figure 5 A schematic structural diagram of a far-field image detection device for a semiconductor laser module provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] The following describes embodiments of the present application in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application.

[0047] It will be understood by those skilled in the art that, unless expressly stated otherwise, the singular forms "a", "an", "said" and "the" used herein may also include the plural forms. It should be further understood that the term "comprising" used in the specification of this application refers to the presence of features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof. It should be understood that when we refer to an element as being "connected" or "coupled" to another element, it may be directly connected or coupled to the other element, or there may be intermediate elements. In addition, "connected" or "coupled" as used herein may include wireless connections or wireless couplings. The term "and / or" used herein includes all or any units and all combinations of one or more associated listed items.

[0048] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art in the art to which the present invention pertains. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art and, unless specifically defined as herein, will not be interpreted in an idealized or overly formal sense.

[0049] The solution provided in the embodiments of the present application can be executed by any electronic device, such as a terminal device or a server, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited to this. The terminal and the server can be directly or indirectly connected via wired or wireless communication, and this application does not limit this. With respect to the technical problems existing in the prior art, the semiconductor laser module far-field image detection method and device provided in this application are intended to solve at least one of the technical problems of the prior art.

[0050] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0051] The present application embodiment provides a possible implementation method, such as Figure 1 As shown, a flowchart of a semiconductor laser module far-field image detection method is provided. The solution can be executed by any electronic device, and optionally, can be executed on a server or terminal device.

[0052] like Figure 1 As shown in , the method may include the following steps:

[0053] Step 101 : Using a camera to capture the image of the light beam emitted by the semiconductor laser module on the transparent film to obtain a grayscale image.

[0054] The camera, semiconductor laser module and transparent film in the embodiment of the present application are all arranged horizontally and on the same vertical line, and the transparent film is arranged between the camera and the semiconductor laser module.

[0055] The light beam emitted by the semiconductor laser module of the embodiment of the present application is controlled by a source meter. The source meter emits a current pulse according to the input parameters. The current pulse acts on the semiconductor laser module, causing the semiconductor laser module to undergo energy level transition and emit a light beam. The light beam propagates freely in the air and forms an image on the transparent film. The camera shoots the transparent film and obtains the image on the transparent film as a grayscale image. The grayscale image is as follows: Figure 3 shown.

[0056] Step 102: Correct and crop the grayscale image to obtain a target image.

[0057] In the embodiment of the present application, a pre-designed system can be used to implement the specific methods in steps 102 to 105. The system includes an industrial computer, which is mainly used to process the grayscale image captured by the camera. In the embodiment of the present application, the configuration required for the semiconductor laser module test and the file save path can be set in the system program configuration interface. After the camera is controlled to capture the grayscale image, the grayscale image captured by the camera is converted into a discrete image signal and transmitted to the industrial computer, and saved in the corresponding folder according to different batch numbers. Select the required configuration and parameters, including edge standard, ratio, number of grids M*N (M refers to the number of horizontal grids, N refers to the number of vertical grids), the distance from the chip to the transmission film, the chip emission angle, the grid size ratio, and the calculation method.

[0058] For the saved grayscale image, the algorithm program saved in the industrial computer can be used to perform correction and cropping to obtain the target image.

[0059] Step 103: Determine pixel information of each pixel in the target image, and determine the centroid position of the target image according to the pixel information.

[0060] Step 104 : determining a target tangent according to the centroid position and the size of the target image.

[0061] The target tangents include a parallel tangent, a vertical tangent, and two oblique tangents between the parallel tangent and the vertical tangent that pass through the centroid.

[0062] Step 105 , calculating the uniformity within each grid on the target image and calculating the uniformity on each target tangent line to determine the quality of the light beam emitted by the semiconductor laser module.

[0063] The grid is pre-divided according to the number of pixels of the target image.

[0064] In the embodiment of the present application, different grids and target tangents are used to classify and identify semiconductor laser modules with different divergence angles, thereby improving the accuracy of judging the quality of the light beam emitted by the semiconductor laser module.

[0065] In an embodiment of the present application, a camera is used to capture the image of the light beam emitted by the semiconductor laser module on a transparent film to obtain a grayscale image; the grayscale image is corrected and cropped to obtain a target image; the pixel information of each pixel in the target image is determined, and the center of mass position of the target image is determined based on the pixel information; the target tangent is determined based on the center of mass position and the size of the target image; then, the uniformity within each grid on the target image and the uniformity on each target tangent are calculated, wherein the grid is pre-divided according to the number of pixels of the target image. In an embodiment of the present application, the quality of the light beam emitted by the semiconductor laser module can be determined based on the uniformity within each grid and the uniformity of each tangent to determine the performance of the semiconductor laser module.

[0066] In an optional embodiment, before using a camera to capture the image of the light beam emitted by the semiconductor laser module on the transparent film to obtain a grayscale image, the method further includes:

[0067] Controlling the semiconductor laser module to a specified position through chip motion instructions;

[0068] Adjust the camera vertically downward through a camera adjustment instruction, and adjust the distance between the camera and the semiconductor laser module to a first preset distance value;

[0069] The distance between the transparent film and the semiconductor laser module is adjusted to a second preset distance value through a transparent film adjustment instruction.

[0070] Figure 2 The specific flow chart of the beam analysis based on Labview software provided in the embodiment of this application. Figure 2In the embodiment of the present application, the semiconductor laser module is first driven to obtain a grayscale image. During this process, the focal length is adjusted and configuration parameters are selected. The focal length can be adjusted by adjusting the camera and the perspective film, and the configuration parameters can be selected using the laboratory virtual instrument engineering platform (Labview). After adjusting the focal length and obtaining the grayscale image using the camera, the grayscale image can be processed and analyzed based on the selected configuration parameters.

[0071] Optionally, adjust the focus as follows:

[0072] Set the trigger mode and exposure time for the camera to ensure that the camera's field of view can better cover the semiconductor laser module. Even if the camera illuminates the semiconductor laser module vertically, and the light intensity of the camera is set to ensure that the image of the light beam emitted by the semiconductor laser module on the perspective film is clear. You can use the front window of the program that controls the camera to observe the camera's field of view and confirm whether the image on the perspective film within the camera's viewing angle is clear. Based on the above, try to adjust the camera's working distance within the maximum field of view and maximum installation distance specified by the project requirements. The field of view of the camera when working is

[0073] FOV = L object +l dynamic +l buffer

[0074] Among them, L object is the maximum length of the detected target, l dynamic is the maximum distance the target may move, l buffer It is the reserved safety distance, usually equal to one tenth of L object This ensures that more pixels are used to represent the smallest features of the target, while ensuring that the camera captures the target. This results in a higher-resolution near-field image. If the camera acquisition time is long enough and a normal display is not visible, adjust the filter to obtain a clear image. The target mentioned above refers to the image on the transparent film.

[0075] The camera's movement is primarily accomplished through pre-set stepper commands (camera adjustment commands), which adjust the far-field industrial camera's focal length to determine the appropriate distance from the camera to the transmissive film. If the image is unclear, the mechanical device is adjusted using the pre-set stepper command interface. During motion control, pre-set stepper commands can be used to automatically control the camera's movement to the last adjusted position.

[0076] The mechanism for adjusting the see-through film in the embodiment of the present application is designed as a see-through film device that can be raised and lowered, and the lifting distance can be manually input as needed. Specifically, the see-through film is fixed by a robotic arm in a position parallel to the semiconductor laser module. The distance from the semiconductor laser module can be adjusted by a stepping instruction (see-through film adjustment instruction). By entering a suitable value, the motion system drives the connected robotic arm to move vertically up and down and record the distance between the see-through film and the semiconductor laser module. The final position requires that the light beam be clearly imaged on the see-through film to avoid errors in the collected signal caused by external ambient light.

[0077] After adjusting the camera and the transparent film as described above until a clear image is obtained, the semiconductor laser module is turned on to obtain an image at any angle on the transparent film. The image on the transparent film is then photographed with a camera to obtain a grayscale image.

[0078] In the embodiment of the present application, the semiconductor laser module can be automatically controlled to run to the test position, with a high degree of automation, reducing the time cost and position error caused by manual operation.

[0079] In an optional embodiment, the correcting and cropping the grayscale image to obtain the target image includes:

[0080] Performing edge extraction on the grayscale image using a method for calculating an energy density ratio or a method for calculating a peak ratio to obtain a grayscale image copy and an image edge;

[0081] The grayscale image copy is adjusted to be horizontal, and the grayscale image copy is cropped according to the image edge to obtain the target image.

[0082] In the image processing program on the industrial computer, the copy function is used to save a copy of the grayscale image from the acquired discrete image signal. The complete definition and pixel data of the grayscale image are copied to the copy. The border size of the copy is also modified to be equal to the border size of the grayscale image. The grayscale image also contains other information, such as calibration information, overlay information, or pattern matching information.

[0083] For the saved image copy, edge extraction is performed on the grayscale image copy using either the energy density ratio or peak ratio calculation method. The peak ratio uses the product of the maximum grayscale and the ratio to determine whether a pixel falls within the valid range of the far-field image. The energy density ratio uses a threshold method to determine whether a pixel falls within the valid range of the far-field image.

[0084] In combination with the above embodiments, refer to Figure 2,Since the semiconductor laser module cannot strictly maintain a horizontal angle during ,manual loading, in order to improve the anti-interference ,performance during mechanical movement, a grayscale image copy is generated after ,the edge extraction mentioned above, and the grayscale image copy is ,rotated to the horizontal.

[0085] The rotated grayscale image is cropped along the aforementioned edges. The Extract function in LabVIEW is called to reduce the grayscale image copy or the portion outside the grayscale image copy by adjusting the horizontal and vertical resolutions. Furthermore, the grayscale values ​​of the grayscale image are extracted and the target image is generated based on the grayscale values.

[0086] For example, use the Optional Rectangle port in LabVIEW to define a four-element array containing the left, top, right, and bottom coordinates of the region to be processed. The right and bottom values ​​are exclusive and fall outside the region. If the "Optional Rectangle" is empty or unconnected, the entire rotated image is retained. The Image to Array function is then used to extract and copy the pixels of the cropped image into a two-dimensional LabVIEW array. This array is encoded as 8-bit, 16-bit, or floating-point numbers, depending on the input grayscale image copy type. An accumulator is used to sum all grayscale values ​​in the two-dimensional array to obtain the sum of the grayscale values ​​of each pixel in the grayscale image copy, which is denoted as K. The detection function returns the number of particles detected in the grayscale image copy and a report array containing the most common particle measurements, yielding the top, bottom, left, and right boundary values. These values ​​are then subtracted to determine the boundary size of the grayscale image copy. By indexing the maximum and minimum values ​​of the two-dimensional array, the maximum and minimum grayscale values ​​of the cropped image are obtained. This value is combined with the grayscale image copy size information and the sum of the grayscale values ​​of each pixel to produce the target image.

[0087] In the embodiment of the present application, the program on the industrial computer runs based on the data flow of Labview, has high maintainability, strong compatibility, and can be used across platforms.

[0088] In an optional embodiment, adjusting the grayscale image copy to be horizontal includes:

[0089] calculating a rotation angle according to the horizontal length and the vertical length of the grayscale image copy;

[0090] The horizontal length and the vertical length are processed using the Rotate function to obtain the rotation angle.

[0091] In an optional embodiment, the pixel information includes a grayscale value; and determining the pixel information of each pixel in the target image and determining the centroid position of the target image based on the pixel information includes:

[0092] Establishing a loop structure index two-dimensional array for the target image;

[0093] Traversing the two-dimensional array, calculating the grayscale value of each pixel and the row number of the pixel, and the grayscale value of each pixel and the column number of the pixel, to obtain the row weight and column weight of each pixel;

[0094] The row weights of all pixels and the column weights of all pixels are averaged to obtain the centroid position.

[0095] Reference Figure 2 , calculating the centroid position according to the pixel information of the target image and establishing a two-dimensional plane coordinate system. Optionally, calculating the centroid position according to the pixel information of the target image includes:

[0096] Suppose a two-dimensional array with i rows and j columns, and the grayscale value of each pixel is x ij Indicates that we first traverse row by row and find 1*x 11 +2*x 12 +3*x 13 +……+j*x 1j , then perform the same operation row by row, add up the grayscale values ​​of each row to get the total weighted grayscale value, recorded as m;

[0097] Then traverse column by column, and similarly, find 1*x 11 +2*x 21 +3*x 31 +……+i*x i1 , then perform the same operation column by column, add up the values ​​of each column to get the total weighted gray value recorded as n; divide m and n by the sum of the total gray values. The coordinates of the center of mass position are: Denoted as (a, b).

[0098] The size of the target image in the actual space can be obtained by multiplying the pixel size by the number of rows and by multiplying the pixel size by the number of columns.

[0099] Optionally, establishing a two-dimensional plane coordinate system includes: combining the center of mass position coordinates obtained in the previous step, the distance between the preset transparent film and the semiconductor laser module, and the target image size to calculate a two-dimensional plane coordinate system with the center of mass as the coordinate origin, wherein each pixel in the two-dimensional plane coordinate system contains both pixel information (such as grayscale value) and coordinate information in actual space, which is used to determine the position of each pixel.

[0100] In addition, in the embodiment of the present application, different gray values ​​can be divided by setting several different colors, wherein red represents a higher gray value, while blue and cyan represent a lower gray value threshold. The target image is processed by the IMAG plug-in in Labview to generate a proportional pseudo-color image. The pseudo-color image is as follows: Figure 4 shown.

[0101] In an optional embodiment, the pixel information includes a grayscale value; and calculating the uniformity on each target tangent line includes:

[0102] Generate a first curve based on all the pixels passing through the target tangent line, wherein the abscissa of the first curve is the divergence angle, and the ordinate is the grayscale value;

[0103] The variance of the grayscale values ​​of all pixels on the target tangent line is calculated according to the first curve as the uniformity of the target tangent line.

[0104] Combine Figure 2 In the embodiment of the present application, a grid and a target tangent can be established to analyze the grid. The process of establishing the target tangent is analyzed as follows:

[0105] The actual aspect ratio of the target image can be determined based on the coordinate information of each pixel in the two-dimensional plane coordinate system established above. Based on the actual aspect ratio and boundary information of the target image, four target tangents are determined. For example, if the target image is an ellipse or a rectangle, the angle and starting position of the target tangent are determined on the outer boundary of the target image. Set four target tangents: horizontal, vertical, left oblique, and right oblique, such as Figure 4 As shown, the fluctuation state of the target image can be reflected from different angles, which is convenient for obtaining the beam quality distribution state under the uncertain model.

[0106] The grayscale values ​​of the pixels along the four target tangents form a first curve, with the centroid as the zero point and the divergence angles on either side. The abscissa of the first curve represents the divergence angle, and the ordinate represents the grayscale value. The peak and trough of the first curve represent the maximum and minimum grayscale values, respectively, along the target tangent. By calculating the uniformity along each target tangent, it can be verified that the chip's emitted light meets the design specifications.

[0107] In an optional embodiment, the pixel information includes a grayscale value; and calculating the uniformity within each grid on the target image includes:

[0108] The standard deviation of the grayscale values ​​of all pixels in the grid is calculated as the uniformity in the grid.

[0109] Combine Figure 2 The process of establishing the grid and the grid analysis are as follows:

[0110] If the target image is a rectangle, divide the target image into M*N blocks according to the preset number of M*N grids, such as Figure 4 The 3*3 grid shown in ( Figure 4 The grayscale value of each grid (block) is calculated for the grids numbered 1, 2, 3, 4, 5, 6, 7, 8, and 9. The specific process involves traversing each grid, extracting the grayscale value of each pixel in each grid, and calculating the mean, standard deviation, and variance for each row and column. The calculated standard deviation is used as the uniformity within each grid.

[0111] In addition, each grid records the grid information of the corresponding grid, which includes the total light intensity in the grid (which can be expressed as grayscale value), the standard deviation of the light intensity in the grid, and the ratio of the total light intensity in the grid to the grid area (i.e., window efficiency); the grid information can effectively reflect the light changes at different positions of the semiconductor laser module and the conversion efficiency within the unit range.

[0112] Based on the same principle as the method provided in the embodiment of the present application, the embodiment of the present application also provides a semiconductor laser module far-field image detection device, such as Figure 5 As shown, the device includes:

[0113] The image acquisition module 501 is used to use a camera to capture the image of the light beam emitted by the semiconductor laser module on the transparent film to obtain a grayscale image;

[0114] An image preprocessing module 502 is used to correct and crop the grayscale image to obtain a target image;

[0115] An image processing module 503 is configured to determine pixel information of each pixel in the target image, and determine a centroid position of the target image based on the pixel information;

[0116] The image processing module 503 is further configured to determine a target tangent according to the centroid position and the size of the target image;

[0117] The image processing module 503 is further configured to calculate the uniformity within each grid on the target image and the uniformity on each target tangent line to determine the quality of the light beam emitted by the semiconductor laser module;

[0118] The grid is pre-divided according to the number of pixels of the target image.

[0119] In an embodiment of the present application, the image acquisition module uses a camera to capture the image of the light beam emitted by the semiconductor laser module on the perspective film to obtain a grayscale image; the image preprocessing module corrects and crops the grayscale image to obtain a target image; the image processing module determines the pixel information of each pixel in the target image, and determines the center of mass position of the target image based on the pixel information; determines the target tangent based on the center of mass position and the size of the target image; then, calculates the uniformity within each grid on the target image, and calculates the uniformity on each target tangent, wherein the grid is pre-divided according to the number of pixels of the target image. In an embodiment of the present application, the quality of the light beam emitted by the semiconductor laser module can be determined by the uniformity within each grid and the uniformity of each tangent to determine the performance of the semiconductor laser module.

[0120] The semiconductor laser module far-field image detection device provided in the embodiment of the present application can achieve Figures 1 to 2 To avoid repetition, the various processes implemented in the method embodiment will not be described again here.

[0121] The semiconductor laser module far-field image detection device of the embodiment of the present application can execute the semiconductor laser module far-field image detection method provided by the embodiment of the present application, and its implementation principle is similar. The actions performed by each module and unit in the semiconductor laser module far-field image detection device in each embodiment of the present application correspond to the steps in the semiconductor laser module far-field image detection method in each embodiment of the present application. For the detailed functional description of each module of the semiconductor laser module far-field image detection device, please refer to the description in the corresponding semiconductor laser module far-field image detection method shown in the previous text, which will not be repeated here.

[0122] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A semiconductor laser module far-field image detection method, characterized in that: The method comprises: Use a camera to capture the image of the light beam emitted by the semiconductor laser module on the transparent film to obtain a grayscale image; Correcting and cropping the grayscale image to obtain a target image; Determining pixel information of each pixel in the target image, and determining a centroid position of the target image based on the pixel information; determining a target tangent according to the centroid position and the size of the target image; Calculating the uniformity within each grid on the target image and calculating the uniformity on each target tangent line to determine the quality of the light beam emitted by the semiconductor laser module; The grid is pre-divided according to the number of pixels of the target image; The pixel information includes a grayscale value; and the calculating of the uniformity on each target tangent line includes: Generate a first curve based on all the pixels passing through the target tangent line, wherein the abscissa of the first curve is the divergence angle, and the ordinate is the grayscale value; Calculate the variance of the grayscale values ​​of all pixels on the target tangent line according to the first curve as the uniformity of the target tangent line; Calculating the uniformity within each grid on the target image includes: The standard deviation of the grayscale values ​​of all pixels in the grid is calculated as the uniformity in the grid.

2. The semiconductor laser module far-field image detection method according to claim 1, characterized in that: The correcting and cropping the grayscale image to obtain a target image includes: Performing edge extraction on the grayscale image using a method for calculating an energy density ratio or a method for calculating a peak ratio to obtain a grayscale image copy and an image edge; The grayscale image copy is adjusted to be horizontal, and the grayscale image copy is cropped according to the image edge to obtain the target image.

3. The semiconductor laser module far-field image detection method according to claim 2, characterized in that: The step of adjusting the grayscale image copy to a horizontal level comprises: calculating a rotation angle according to the horizontal length and the vertical length of the grayscale image copy; The horizontal length and the vertical length are processed using the Rotate function to obtain the rotation angle.

4. The semiconductor laser module far-field image detection method according to claim 1, characterized in that: The pixel information includes a grayscale value; determining the pixel information of each pixel in the target image and determining the centroid position of the target image according to the pixel information includes: Establishing a loop structure index two-dimensional array for the target image; Traversing the two-dimensional array, calculating the grayscale value of each pixel and the row number of the pixel, and the grayscale value of each pixel and the column number of the pixel, to obtain the row weight and column weight of each pixel; The row weights of all pixels and the column weights of all pixels are averaged to obtain the centroid position.

5. The semiconductor laser module far-field image detection method according to claim 1, characterized in that: Before using a camera to capture the image of the light beam emitted by the semiconductor laser module on the transparent film to obtain a grayscale image, the method further includes: Controlling the semiconductor laser module to a specified position through chip motion instructions; Adjust the camera vertically downward through a camera adjustment instruction, and adjust the distance between the camera and the semiconductor laser module to a first preset distance value; The distance between the transparent film and the semiconductor laser module is adjusted to a second preset distance value through a transparent film adjustment instruction.

6. A semiconductor laser module far-field image detection device, characterized in that: The device comprises: An image acquisition module is used to use a camera to capture the image of the light beam emitted by the semiconductor laser module on the transparent film to obtain a grayscale image; An image preprocessing module, used for correcting and cropping the grayscale image to obtain a target image; An image processing module, configured to determine pixel information of each pixel in the target image, and determine a centroid position of the target image based on the pixel information; The image processing module is further used to determine the target tangent according to the center of mass position and the size of the target image; The image processing module is further used to calculate the uniformity within each grid on the target image and the uniformity on each target tangent line to determine the quality of the light beam emitted by the semiconductor laser module; The grid is pre-divided according to the number of pixels of the target image; The pixel information includes a grayscale value; and the calculating of the uniformity on each target tangent line includes: Generate a first curve based on all the pixels passing through the target tangent line, wherein the abscissa of the first curve is the divergence angle, and the ordinate is the grayscale value; Calculate the variance of the grayscale values ​​of all pixels on the target tangent line according to the first curve as the uniformity of the target tangent line; Calculating the uniformity within each grid on the target image includes: The standard deviation of the grayscale values ​​of all pixels in the grid is calculated as the uniformity in the grid.

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