LED lamp testing method, device, testing equipment, testing system and storage medium

Abstract

The application relates to the technical field of LED lamp testing, and discloses an LED lamp testing method, device, testing equipment, testing system and storage medium, the method comprising the following steps: generating corresponding adjustment parameters based on preset adjustment instructions, and sending the adjustment parameters to a to-be-tested device, so that the to-be-tested device adjusts the LED lamp and a camera according to the adjustment parameters; sending a shooting instruction to the to-be-tested device, so that the to-be-tested device uses the adjusted LED lamp to emit light to irradiate a standard color card, and obtains a target image by shooting the standard color card through the adjusted camera; and obtaining a corresponding test result of the LED lamp according to the target image. Through synchronous adjustment and collection of the cooperative output of illumination and imaging under controllable conditions, integrated performance evaluation of the LED lamp on the to-be-tested device is realized, separate LED lamps are not needed to be disassembled and tested, and the test process is simplified.

Description

Technical Field

[0001] This application relates to the field of LED lamp testing technology, and in particular to an LED lamp testing method, apparatus, testing equipment, testing system and storage medium. Background Technology

[0002] With societal development, LED lights have become ubiquitous in people's lives, making LED light testing a crucial step in ensuring their quality, performance consistency, and user experience. LED light testing refers to the process of systematically evaluating and verifying the key performance indicators of light-emitting diodes (LEDs), their modules, and lighting fixtures through a series of physical, optical, and electrical measurement methods.

[0003] Most existing methods employ offline measurement, which requires disassembling LEDs already assembled in end devices (such as cameras) to obtain an independent light source under test, and then measuring them separately using the aforementioned large, specialized testing equipment. This offline testing method is cumbersome and inefficient, and cannot perform rapid, non-destructive direct testing of integrated LEDs in a production line environment, resulting in high testing costs and complex processes. Summary of the Invention

[0004] The main objective of this application is to provide an LED lamp testing method that addresses the technical problem of existing methods that require disassembling the assembled device under test to obtain the LED lamp for individual measurement, resulting in a complex testing process.

[0005] To achieve the above objectives, this application proposes an LED light testing method. The method is applied to a testing device in a testing system. The testing system further includes a standard color card, which is set in the shooting direction of the camera of the device under test. The device under test also includes an LED light set on the same side as the camera. The light emitted by the LED light illuminates the standard color card. The testing device is also connected to the device under test.

[0006] The method includes:

[0007] Based on a preset adjustment command, corresponding adjustment parameters are generated and sent to the device under test, so that the device under test adjusts the LED light and the camera according to the adjustment parameters;

[0008] A shooting command is sent to the device under test, so that the device under test uses an adjusted LED light to emit light onto the standard color card, and takes a picture of the standard color card through the adjusted camera to obtain a target image;

[0009] The test results corresponding to the LED light are obtained based on the target image.

[0010] In one embodiment, the standard color chart includes at least two color patch areas of different colors;

[0011] The step of obtaining the test result corresponding to the LED light based on the target image includes:

[0012] The target image is detected by a preset image feature detection algorithm, and the area in the target image corresponding to the standard color card is taken as the detection area.

[0013] The detection area is divided based on the color block layout corresponding to the standard color card to obtain the color block detection area corresponding to each color block area;

[0014] The RGB data corresponding to each pixel in each of the color block detection areas is obtained, and the RGB data is processed by arithmetic mean to obtain the RGB data of each color block detection area. The test result corresponding to the LED light is obtained based on the RGB data of each color block.

[0015] In one embodiment, the step of obtaining the test result corresponding to the LED light based on preset measurement parameters and RGB data of each color block includes:

[0016] Based on the standard conversion formula, the RGB data of each color block is converted to obtain the YUV color space data and the Lab color space data corresponding to each color block region.

[0017] The average brightness is calculated based on the YUV color space data to obtain the brightness characteristic value, and the brightness deviation value is determined based on the preset typical brightness value and the brightness characteristic value.

[0018] Color temperature feature values ​​are obtained based on the Lab color space data, and color temperature deviation values ​​are determined based on preset typical color temperature values ​​and the color temperature feature values.

[0019] If the brightness deviation value is less than the preset brightness tolerance and the color temperature deviation value is less than the preset color temperature tolerance, the test is considered qualified as the test result for the LED light.

[0020] In one embodiment, the Lab color space data includes: red-green hue components and yellow-blue hue components;

[0021] The step of obtaining color temperature feature values ​​based on the Lab color space data includes:

[0022] A first arithmetic mean is obtained by performing arithmetic mean processing on the red-green hue components corresponding to each of the color block regions, and a second arithmetic mean is obtained by performing arithmetic mean processing on the yellow-blue hue components corresponding to each of the color block regions.

[0023] The color temperature feature value is obtained based on the first arithmetic mean and the second arithmetic mean.

[0024] In one embodiment, the test device in the test system is also connected to at least one standard device;

[0025] The standard color chart is positioned in the shooting direction of the standard camera of the standard device. The standard device also includes a standard LED light positioned on the same side as the standard camera, and the light emitted by the standard LED light illuminates the standard color chart.

[0026] Before the step of sending the shooting command to the device under test, the method further includes:

[0027] The adjustment parameters are sent to each of the standard devices so that each of the standard devices adjusts the standard LED light and the standard camera according to the adjustment parameters;

[0028] Test commands are sent to each of the standard devices, so that each of the standard devices uses an adjusted standard LED light to emit light onto the standard color card, and takes a picture of the standard color card through the adjusted standard camera to obtain a standard image;

[0029] Based on the standard image, obtain the preset typical brightness value, preset typical color temperature value, preset brightness tolerance, and preset color temperature tolerance.

[0030] In one embodiment, the step of obtaining a preset typical brightness value, a preset typical color temperature value, a preset brightness tolerance, and a preset color temperature tolerance based on the standard image includes:

[0031] Each of the standard images is identified to obtain the standard RGB data corresponding to each standard image;

[0032] The standard RGB data of each standard image is converted into standard YUV color space data, and the average brightness is calculated based on the standard YUV color space data to obtain the standard brightness feature value corresponding to each standard image.

[0033] A preset typical brightness value is obtained by averaging the standard brightness characteristic values ​​and a preset brightness tolerance is obtained by standard deviation processing based on the standard brightness characteristic values.

[0034] The standard RGB data of each standard image is converted into standard Lab color space data, and the standard color temperature feature value corresponding to each standard image is obtained based on the standard Lab color space data.

[0035] The preset color temperature typical value and preset color temperature tolerance are obtained based on the standard color temperature characteristic values ​​through the preset geometric center algorithm.

[0036] Furthermore, to achieve the above objectives, this application also proposes an LED lamp testing device, the device comprising:

[0037] The parameter adjustment module is used to generate corresponding adjustment parameters based on preset adjustment instructions and send the adjustment parameters to the device under test, so that the device under test adjusts the LED lights and the camera according to the adjustment parameters;

[0038] The image acquisition module is used to send a shooting command to the device under test, so that the device under test uses an adjusted LED light to emit light to illuminate the standard color card, and takes a picture of the standard color card through the adjusted camera to obtain a target image;

[0039] The result acquisition module is used to obtain the test results corresponding to the LED light based on the target image.

[0040] In addition, to achieve the above objectives, this application also proposes a testing device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the LED lamp testing method described above.

[0041] In addition, to achieve the above objectives, this application also proposes a testing system, which includes: a standard color card as described above and testing equipment.

[0042] In addition, to achieve the above objectives, this application also proposes a storage medium that is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the LED lamp testing method described above.

[0043] This application discloses an LED lamp testing method, apparatus, testing equipment, testing system, and storage medium. The method is applied to the testing equipment in the testing system, which also includes a standard color card. The standard color card is positioned in the shooting direction of the camera of the device under test (DUT). The DUT also includes an LED lamp positioned on the same side as the camera. The light emitted by the LED lamp illuminates the standard color card. The testing equipment is also connected to the DUT. The method includes: generating corresponding adjustment parameters based on a preset adjustment command and sending the adjustment parameters to the DUT, so that the DUT adjusts the LED lamp and the camera according to the adjustment parameters; sending a shooting command to the DUT, so that the DUT uses the adjusted LED lamp to emit light that illuminates the standard color card and takes a picture of the standard color card through the adjusted camera to obtain a target image; and obtaining the test result corresponding to the LED lamp based on the target image.

[0044] This application's LED lamp testing method and system can be set up with a testing environment including a standard color chart and testing equipment connected to the device under test (DUT). The testing equipment can generate adjustment parameters to synchronously control the LED lamp and camera in the DUT. During actual testing, the testing equipment sends adjustment parameters to the DUT to adjust the state of the LED lamp and camera, then controls the camera to capture an image of the standard color chart after illumination by the LED lamp, and analyzes the image to derive the LED lamp performance test results. Compared to existing testing methods that can only test individual LED lamps, this application, by synchronously adjusting and acquiring the coordinated output of illumination and imaging under controllable conditions, can achieve objective and integrated performance evaluation of LED lamps in real-world application scenarios. Furthermore, users can directly obtain quantitative evaluation data reflecting the actual usage effect when acquiring test results, simplifying the testing process. Attached Figure Description

[0045] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a structural diagram of the LED lamp testing method test system and the device under test proposed in the embodiments of this application;

[0048] Figure 2This is a flowchart of the first embodiment of the LED lamp testing method proposed in this application;

[0049] Figure 3 This is a flowchart of the second embodiment of the LED lamp testing method proposed in this application;

[0050] Figure 4 This is a flowchart of the third embodiment of the LED lamp testing method proposed in this application;

[0051] Figure 5 A diagram of an LED lamp testing device provided in an embodiment of this application;

[0052] Figure 6 This is a schematic diagram of the structure of a test device suitable for implementing the embodiments of this application.

[0053] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0054] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not intended to limit this application.

[0055] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0056] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0057] Understandably, with societal development, LED lights have become ubiquitous in people's lives, making LED light testing a crucial step in ensuring their quality, performance consistency, and user experience. LED light testing refers to the process of systematically evaluating and verifying the key performance indicators of light-emitting diodes (LEDs), their modules, and lighting fixtures through a series of physical, optical, and electrical measurement methods.

[0058] Most existing methods employ offline measurement, which requires disassembling LEDs already assembled in end devices (such as cameras) to obtain an independent light source under test, and then measuring them separately using the aforementioned large, specialized testing equipment. This offline testing method is cumbersome and inefficient, and cannot perform rapid, non-destructive direct testing of integrated LEDs in a production line environment, resulting in high testing costs and complex processes.

[0059] Therefore, to address the complex testing process caused by the need to disassemble and individually measure LEDs in existing systems for assembled devices under test (DUTs), this embodiment proposes an LED testing method. This method is applied to a testing system that includes a standard color chart positioned in the shooting direction of the camera on the DUT. The DUT also includes an LED light positioned on the same side as the camera, emitting light that illuminates the standard color chart. The testing system is also connected to the DUT. The method includes: generating corresponding adjustment parameters based on preset adjustment instructions and sending these parameters to the DUT, causing the DUT to adjust the LED light and camera according to the adjustment parameters; sending a shooting instruction to the DUT, causing the DUT to use the adjusted LED light to emit light that illuminates the standard color chart and capture a target image of the standard color chart using the adjusted camera; and obtaining the test result corresponding to the LED light based on the target image.

[0060] This embodiment of the LED lamp testing method and system includes a testing environment with a standard color chart and testing equipment connected to the device under test (DUT). The testing equipment generates adjustment parameters to synchronously control the LED lamp and camera in the DUT. During actual testing, the testing equipment sends adjustment parameters to the DUT to adjust the state of the LED lamp and camera. Then, it controls the camera to capture an image of the standard color chart after illumination by the LED lamp, and analyzes this image to derive the LED lamp performance test results. Compared to existing testing methods that can only test individual LED lamps, this embodiment, by synchronously adjusting and collecting the coordinated output of illumination and imaging under controllable conditions, enables objective and integrated performance evaluation of LED lamps in real-world application scenarios. Furthermore, users can directly obtain quantitative evaluation data reflecting the actual usage effect when acquiring test results, simplifying the testing process.

[0061] For ease of understanding, the following is combined with Figures 1 to 6 The LED lamp testing method provided in the embodiments of this application, as well as the LED lamp testing method, apparatus, testing equipment, testing system and storage medium provided in the following embodiments, will be described in detail.

[0062] This application provides an LED lamp testing method, referring to... Figure 1 , Figure 1This is a structural diagram of the LED lamp testing method, testing system, and device under test proposed in this application.

[0063] like Figure 1 As shown, the testing system includes a standard color chart and testing equipment. The standard color chart is set in the shooting direction of the camera of the device under test. The device under test also includes an LED light set on the same side as the camera. The light emitted by the LED light shines on the standard color chart. The testing equipment is also connected to the device under test.

[0064] It is understood that the LED light testing method in this embodiment is mainly aimed at LED lights already assembled on the device under test. The device under test can be a terminal product that integrates a camera and an LED fill light, such as a smartphone, webcam, or dashcam. The camera can be an optical sensor module on the device under test for capturing images, such as a CMOS image sensor. The LED light can be a light-emitting diode light source on the device under test that provides illumination for the camera, such as a flash or a constantly lit fill light located next to the camera module. The standard color chart can be a physical chart with known and stable reflectivity and color values, such as the internationally used 24-color chart or ColorChecker color chart.

[0065] Furthermore, considering the influence of ambient light, in this embodiment, the aforementioned standard color chart and the device under test are placed in a dark box for measurement, but this does not limit this embodiment.

[0066] refer to Figure 2 , Figure 2 This is a flowchart of the first embodiment of the LED lamp testing method proposed in this application. Figure 2 As shown, the method includes:

[0067] Step S10: Generate corresponding adjustment parameters based on preset adjustment instructions, and send the adjustment parameters to the device under test, so that the device under test adjusts the LED light and the camera according to the adjustment parameters.

[0068] It should be noted that the executing entity in this embodiment can be a multifunctional machine or device capable of testing LED lights, such as a testing device, or a device capable of performing the aforementioned functions. The aforementioned testing device can be a computer for control and analysis, such as an industrial control computer connected to a testing station on the production line. This embodiment uses a testing device (hereinafter referred to as the device) for description, but does not impose specific limitations on this embodiment.

[0069] It should also be noted that the aforementioned preset adjustment commands can be a set of predefined operation commands used to configure the hardware status of the device under test, such as a script or configuration file containing commands such as "set LED power to 50%" and "lock camera exposure parameters". The aforementioned adjustment parameters can be specific numerical or status-based control data generated based on the preset adjustment commands, such as LED current value, camera shutter speed, ISO value, white balance gain value, etc.

[0070] In its implementation, the aforementioned test equipment first generates adjustment parameters based on user requirements or preset script instructions. Then, via a physical interface or wireless link, it sends a data packet containing the LED power value and camera operating parameters to the connected device under test (DUT). Upon receiving the data packet, the DUT parses the instructions and invokes its internal driver to set the LED drive current according to the parameter requirements. Simultaneously, it configures parameters such as the camera's exposure mode, shutter speed, ISO, and white balance coefficient.

[0071] For ease of understanding, the following example illustrates the concept, but does not impose specific limitations on this embodiment: Assume the test device is an industrial control computer, whose preset adjustment command requires setting the LED brightness to medium (corresponding to a drive current of 150mA) and the camera to manual mode (shutter speed 1 / 60 second, ISO 200, color temperature 5500K, corresponding to R / G / B gain). Based on this command, the industrial control computer software generates a structured parameter data packet and sends it to the network camera under test via USB. Upon receiving the data, the network camera's main control chip parses the data packet and sends control signals to the LED drive circuit and camera sensor respectively, adjusting the LED current to 150mA and simultaneously setting the camera's imaging parameters to the specified manual mode values.

[0072] Step S20: Send a shooting command to the device under test, so that the device under test uses the adjusted LED light to emit light to illuminate the standard color card, and takes a picture of the standard color card through the adjusted camera to obtain a target image.

[0073] It should be noted that the aforementioned shooting command can be a software command or hardware signal that triggers the device under test (DUT) to perform an image capture operation, such as a "CAPTURE" command sent via serial port or a specific level pulse. The aforementioned adjusted LED light can be an LED light source with a specific luminous power set according to the received adjustment parameters, such as a fill light with a current set to 100mA. The aforementioned adjusted camera can be an image sensor module configured with specific exposure parameters and white balance mode according to the received adjustment parameters, such as a camera operating in manual exposure mode, with a shutter speed of 1 / 30 second and an ISO sensitivity of 400. The aforementioned target image can be a digital image file generated by the camera of the DUT capturing a standard color chart under specific LED lighting conditions, such as a JPEG or RAW format image containing the color chart.

[0074] In its implementation, the aforementioned testing equipment sends a shooting command to the device under test (DUT) after confirming that the parameters have been adjusted. Upon receiving this command, the main control unit of the DUT first drives its LED driver circuit, causing the adjusted LEDs to emit light at the set power, illuminating the standard color chart in front. Subsequently, the DUT drives its adjusted camera to acquire an image of the currently illuminated color chart, completing photoelectric conversion and data readout, generating a digital image, and storing this image data as the target image in a cache or transmitting it back via an interface.

[0075] Step S30: Obtain the test results corresponding to the LED light based on the target image.

[0076] It should be noted that the test results for the LED lights mentioned above can be qualitative or quantitative conclusions about whether the LED light performance meets the preset standards, derived from the analysis of the target image. For example, a judgment string such as "brightness is qualified and color temperature is within tolerance range" or specific quantitative data such as brightness deviation value and color temperature deviation value.

[0077] In its implementation, the aforementioned testing equipment receives or reads the target image returned by the device under test. The testing equipment runs an image processing program, performing steps such as color card region recognition, color block segmentation, color value extraction, and color space conversion on the target image to obtain feature data characterizing the brightness and color temperature of the LED light. The calculated feature data is then compared with a pre-stored acceptable standard range. Based on the comparison result, the testing equipment generates a clear conclusion indicating whether the LED light is qualified, and outputs or records this conclusion.

[0078] Furthermore, to obtain accurate test results, the standard color chart includes at least two color patch areas of different colors. The reflectance spectra of these color patch areas are pre-calibrated and known, and the colors presented by the different color patch areas are visually and spectrally distinct from each other; for example, a red patch and a green patch, or a white patch and a black patch. This ensures that when an LED light illuminates the color chart, the different color patch areas exhibit different light reflection characteristics, thus creating color differences in the image captured by the camera.

[0079] Accordingly, the step of obtaining the test result corresponding to the LED light based on the target image includes:

[0080] Step S31: Detect the target image using a preset image feature detection algorithm, and take the area in the target image corresponding to the standard color card as the detection area.

[0081] It is understood that the aforementioned preset image feature detection algorithm can be an image processing program or method based on computer vision that can recognize specific patterns or structures, such as algorithms based on template matching, edge detection (e.g., Canny operator), feature point detection (e.g., SIFT, ORB), or deep learning object detection models (e.g., YOLO, SSD). The aforementioned detection region can be a pixel range encompassing the entire standard color chart, automatically selected in the target image by the algorithm, such as an image region enclosed by a rectangular bounding box.

[0082] In practical use, after acquiring the target image, the aforementioned testing equipment calls a preset image feature detection algorithm to scan and analyze the image. By comparing the known visual features of the color chart (such as specific corner vertices, outline shape, internal color block arrangement pattern, or special positioning marks) with the image content, the precise location and range of the color chart in the target image are determined and marked. Finally, a set of coordinates or a polygonal region is output, which is the detection area in the target image corresponding to the physical standard color chart.

[0083] Step S32: Divide the detection area based on the color block layout corresponding to the standard color card to obtain the color block detection area corresponding to each color block area.

[0084] It should be noted that the color block layout corresponding to the above standard color card can be a predefined geometric position and order of the color blocks on the standard color card, such as the layout information of "6 rows and 4 columns of 24 rectangular color blocks of equal size, arranged from left to right and from top to bottom".

[0085] In its implementation, after acquiring the detection area representing the entire color chart, the aforementioned testing equipment geometrically divides the detection area based on the pre-known and stored standard color chart color block layout information. The division process subdivides the detection area into several smaller sub-regions of the same size or conforming to a preset ratio, according to the number of rows and columns, relative positions, and size ratios of the color blocks defined in the layout. Each sub-region precisely corresponds to a specific color block region on the physical color chart; these sub-regions are called the color block detection regions corresponding to each color block region. After division, each color block detection region contains the set of pixels in the target image belonging to that specific color block.

[0086] Step S33: Obtain the RGB data corresponding to each pixel in each of the color block detection areas, and perform arithmetic mean processing on the RGB data to obtain the color block RGB data corresponding to each of the color block detection areas, and obtain the test result corresponding to the LED light based on the color block RGB data.

[0087] It is important to emphasize that the RGB data mentioned above can be a data format used to represent the color of each pixel in a digital image. It typically includes intensity values ​​for three channels: Red, Green, and Blue, with each channel's value ranging from, for example, an integer from 0 to 255. The arithmetic mean processing described above can be a statistical calculation method, where the average value is obtained by adding a set of values ​​and dividing by the number of values ​​in the set. For example, summing the R channel values ​​of all pixels within a certain area and dividing by the total number of pixels yields the average R value for that area. The RGB data for color patches mentioned above can be a set of three values ​​representing the average color of the color patch, obtained by performing arithmetic mean processing on the RGB data of all pixels within the color patch detection area, for example, (R_avg, G_avg, B_avg).

[0088] In a specific implementation, the above-mentioned test device processes each divided color block detection area. For any specified color block detection area, the test device traverses all pixel points within the area and reads the values of the R, G, and B channels of each pixel point. Subsequently, the test device calculates the arithmetic mean of the R-channel values of all pixel points respectively to obtain the average R value of the color block detection area; the same operations are performed on the G-channel values and B-channel values to obtain the average G value and average B value respectively. These three average values together constitute the color block RGB data corresponding to the color block detection area. After obtaining the color block RGB data of all color block detection areas, the test device obtains the average RGB value of the current test based on this set of complete color data set, and determines the preset standard RGB typical value according to the image obtained by the standard device that has been detected as qualified. The comprehensive deviation in the RGB space is calculated based on the average RGB value and the standard RGB typical value. Finally, it is compared with the comprehensive deviation threshold. If the comprehensive deviation is less than the comprehensive deviation threshold, the detection result is that the LED lamp is qualified; otherwise, the detection result is that the LED lamp is unqualified.

[0089] For ease of understanding, the following is illustrated by way of example, but no specific restrictions are imposed on this embodiment: In a specific test, the test device obtains the color card image of the device to be tested and divides it into 24 color block detection areas, and 24 sets of color block RGB data are obtained through calculation. The test device calculates the average values of the R, G, and B channels of these 24 sets of data respectively, and obtains that the average RGB value of the current test is (185, 125, 95). On the other hand, the preset standard RGB typical value determined according to historical data or by comparing with the standard device is (180, 120, 90). The test device calculates the comprehensive deviation between the current average RGB value and the typical value. For example, using the Euclidean distance formula, the deviation value is calculated as √[(185 - 180)² + (125 - 120)² + (95 - 90)²] = √[25 + 25 + 25] ≈ 8.66. The preset comprehensive deviation threshold is 15. Since 8.66 is less than 15, the test device determines that the LED lamp is detected as qualified. If the calculated comprehensive deviation is greater than 15, for example, it reaches 20, it is determined as unqualified.

[0090] This embodiment establishes a test environment containing a standard color chart and a test device connected to the device under test (DUT). The test device generates adjustment parameters to synchronously control the LED lights and camera within the DUT. During actual testing, the test device sends these adjustment parameters to the DUT, causing it to adjust the states of the LED lights and camera. The camera then captures an image of the standard color chart illuminated by the LED lights, and the performance of the LED lights is analyzed based on this image. Compared to existing testing methods that can only test individual LED lights, this embodiment, by synchronously adjusting and acquiring the coordinated output of illumination and imaging under controllable conditions, enables an objective and integrated performance evaluation of LED lights in real-world application scenarios. Users can directly obtain quantitative evaluation data reflecting actual usage effects when acquiring test results, simplifying the testing process.

[0091] Based on the first embodiment, in the second embodiment, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 3 , Figure 3 This is a flowchart of the second embodiment of the LED lamp testing method proposed in this application. Further, considering that the RGB space is not sensitive to differences in color temperature and brightness, in order to obtain more accurate LED lamp test results, the above-mentioned testing device, after acquiring the RGB data of the color blocks, can also perform color space conversion on the RGB data of the color blocks, and obtain the test results corresponding to the LED lamp based on the space-converted data. The step of obtaining the test results corresponding to the LED lamp based on preset measurement parameters and the RGB data of each color block includes:

[0092] Step S331: Convert the RGB data of each color block based on the standard conversion formula to obtain the YUV color space data and the Lab color space data corresponding to each color block region.

[0093] It should be noted that the aforementioned standard conversion formulas can be a set of internationally recognized formal calculation equations or algorithms used for mathematical mapping between different color spaces. For example, the RGB to YUV conversion formulas specified in the ITU-R BT.601 or BT.709 standards defined by the International Telecommunication Union (ITU), or the RGB to Lab conversion formulas defined by the International Commission on Illumination (CIE) (usually requiring conversion via the XYZ color space). The aforementioned YUV color space data can be a data representation that separates color information into a luminance component (Y) and two chromaticity components (U, V), such as a data tuple containing the values ​​of Y, U, and V. The aforementioned Lab color space data can be a data representation designed based on the characteristics of human visual perception, containing a lightness component (L) and two opposing chromaticity components (a, b), such as a data tuple containing the values ​​of L, a, and b.

[0094] In its implementation, the aforementioned testing equipment performs mathematical operations on each set of previously calculated RGB data (R_avg, G_avg, B_avg) according to the standard conversion formula preset in the program or configuration file. For a set of RGB data, the testing equipment first applies the RGB-to-YUV conversion formula, calculating the corresponding Y, U, and V values ​​through a linear matrix transformation. These three values ​​together constitute the YUV color space data corresponding to the color patch area. Subsequently, the testing equipment applies the RGB-to-Lab conversion formula, which is typically performed in two steps: first converting the RGB data to XYZ color space data, and then converting the XYZ data to Lab color space data. Through this series of calculations, the testing equipment obtains the L, a, and b values ​​corresponding to the color patch area, which together constitute the Lab color space data corresponding to the color patch area. The testing equipment repeats this process for all color patches, ultimately obtaining a complete set of YUV and Lab data containing all color patches.

[0095] Step S332: Calculate the average brightness based on the YUV color space data to obtain the brightness characteristic value, and determine the brightness deviation value based on the preset typical brightness value and the brightness characteristic value.

[0096] It should be noted that the above average brightness calculation can be a statistical method, obtained by adding multiple brightness values ​​and dividing by the total number of values ​​to obtain their arithmetic mean. For example, summing the Y component values ​​of 24 color blocks and dividing by 24 yields a single value representing the overall brightness. The above brightness characteristic value can be a quantitative indicator used to characterize the overall or average luminous brightness of the LED under test, obtained through average brightness calculation; for example, a specific Y_avg value. The above preset typical brightness value can be a value determined in advance through testing and statistics, representing the ideal or average brightness level that a qualified LED should achieve; for example, a Y_typical value measured and calculated from multiple standard devices. The above brightness deviation value can be a quantitative indicator used to represent the magnitude of the difference between the current brightness characteristic value of the LED under test and the preset typical brightness value; for example, a value obtained by calculating the absolute value of the difference between the two.

[0097] In its implementation, the device extracts the Y component of each data point from the YUV color space data corresponding to all color blocks. The device sums these extracted Y component values, then divides the sum by the total number of these components to calculate the average brightness. The result is the brightness characteristic value. Subsequently, the device reads a preset typical brightness value from the storage unit. The device subtracts the calculated brightness characteristic value from the preset typical brightness value and determines the brightness deviation value by the absolute value of the result (or performs other predefined mathematical processing, such as difference or percentage).

[0098] For ease of understanding, the following example illustrates the concept, but does not limit the scope of this embodiment: The device has obtained YUV data corresponding to 24 color blocks, each containing three values: Y, U, and V. The device extracts 24 Y values, for example: 120, 121, 118, ..., 119. The device adds these 24 Y values, assuming the sum is 2856. Then, the device divides this sum by 24 to calculate the average luminance value, i.e., the luminance characteristic value Y_avg = 2856 / 24 = 119. Assume that the preset typical luminance value Y_typical, determined by testing multiple standard devices and pre-stored in the system, is 120. The device calculates the luminance deviation value, for example, using the absolute difference: |Y_avg - Y_typical| = |119 - 120| = 1. Therefore, the luminance deviation value in this test is 1.

[0099] Step S333: Obtain color temperature feature values ​​based on the Lab color space data, and determine color temperature deviation values ​​based on preset typical color temperature values ​​and the color temperature feature values.

[0100] It should be noted that the aforementioned color temperature characteristic value can be a quantitative indicator used to characterize the emitted color of the LED lamp under test. This indicator is calculated based on its Lab color space data, for example, a two-dimensional coordinate point composed of average chromaticity values ​​(a_avg, b_avg). The aforementioned preset typical color temperature value can be a quantitative indicator determined in advance through testing and statistics, representing the ideal or average color level that a qualified LED lamp should achieve, for example, a two-dimensional coordinate point (a_typical, b_typical) defined in Lab space. The aforementioned color temperature deviation value can be a quantitative indicator used to represent the magnitude of the difference between the current color temperature characteristic value of the LED lamp under test and the preset typical color temperature value, for example, a value obtained by calculating the Euclidean distance between two two-dimensional coordinate points.

[0101] In its implementation, the device extracts the 'a' and 'b' components from the Lab color space data corresponding to all color blocks. The device then calculates the arithmetic mean of all extracted 'a' component values ​​to obtain the average 'a' value. Similarly, it calculates the arithmetic mean of all extracted 'b' component values ​​to obtain the average 'b' value. The two-dimensional coordinate point formed by the calculated average 'a' and average 'b' values ​​is the color temperature characteristic value. Subsequently, the device reads a preset typical color temperature value from the storage unit. This typical value is also a two-dimensional coordinate point defined in the same color space. The device uses specific mathematical methods (such as calculating the Euclidean distance between two points) to calculate the degree of difference between the color temperature characteristic value and the preset typical color temperature value, and determines the calculated value as the color temperature deviation value.

[0102] Furthermore, the Lab color space data includes: red-green hue components and yellow-blue hue components;

[0103] The step of obtaining color temperature feature values ​​based on the Lab color space data includes:

[0104] A first arithmetic mean is obtained by performing arithmetic mean processing on the red-green hue components corresponding to each of the color block regions, and a second arithmetic mean is obtained by performing arithmetic mean processing on the yellow-blue hue components corresponding to each of the color block regions.

[0105] The color temperature feature value is obtained based on the first arithmetic mean and the second arithmetic mean.

[0106] It is understandable that the aforementioned red-green hue components can be the numerical values ​​used in Lab color space data to represent the degree of color shift along the red-green axis, i.e., the a component, where positive values ​​lean towards red and negative values ​​lean towards green. Similarly, the aforementioned yellow-blue hue components can be the numerical values ​​used in Lab color space data to represent the degree of color shift along the yellow-blue axis, i.e., the b component, where positive values ​​lean towards yellow and negative values ​​lean towards blue.

[0107] In its implementation, the device extracts the red-green hue component (a component) from the Lab color space data corresponding to each color patch area. The device then calculates the first arithmetic mean by averaging the values ​​of all extracted red-green hue components. Simultaneously, the device extracts the yellow-blue hue component (b component) from the same Lab color space data. The device then calculates the second arithmetic mean by averaging the values ​​of all extracted yellow-blue hue components. Finally, the device combines the first and second arithmetic means to form a two-dimensional coordinate point, which represents the color temperature characteristic value.

[0108] For ease of understanding, the following example illustrates the concept, but does not limit the scope of this embodiment: Assume there are three color patch regions with Lab color space data as follows: (L1, a1, b1) = (50, -2.0, 1.0), (L2, a2, b2) = (52, -1.8, 1.2), (L3, a3, b3) = (49, -2.2, 0.8). The device first extracts all a components: a1 = -2.0, a2 = -1.8, a3 = -2.2. These values ​​are then averaged: First arithmetic mean = (-2.0 + (-1.8) + (-2.2)) / 3 = -6.0 / 3 = -2.0. Then, the device extracts all b components: b1 = 1.0, b2 = 1.2, b3 = 0.8. The values ​​are then averaged: Second arithmetic mean = (1.0 + 1.2 + 0.8) / 3 = 3.0 / 3 = 1.0. Finally, the device obtains the color temperature characteristic value (-2.0, 1.0) based on the first arithmetic mean (-2.0) and the second arithmetic mean (1.0).

[0109] Step S334: If the brightness deviation value is less than the preset brightness tolerance and the color temperature deviation value is less than the preset color temperature tolerance, the test pass is taken as the test result corresponding to the LED lamp.

[0110] It should be noted that the aforementioned preset brightness tolerance can be a pre-set upper limit of the allowable deviation range used to determine whether the brightness is qualified. For example, a specific positive value ΔY_max represents the maximum allowable absolute difference between the brightness characteristic value and the preset typical brightness value. Similarly, the aforementioned preset color temperature tolerance can be a pre-set upper limit of the allowable deviation range used to determine whether the color temperature is qualified. For example, a specific positive value ΔE_max represents the maximum allowable Euclidean distance between the color temperature characteristic value and the preset typical color temperature value.

[0111] In its implementation, the device compares the calculated brightness deviation value with a preset brightness tolerance. Simultaneously, it compares the calculated color temperature deviation value with a preset color temperature tolerance. The device performs a logical judgment. If the comparison results simultaneously satisfy both of the following conditions: first, the brightness deviation value is less than the preset brightness tolerance; second, the color temperature deviation value is less than the preset color temperature tolerance, then the device generates a judgment conclusion. This conclusion is "Test Passed" as the test result for the LED light, which is then output or recorded. If either of the above two conditions is not met, the device generates a "Test Failed" conclusion.

[0112] Based on the first and second embodiments, in the third embodiment, the content that is the same as or similar to that in Embodiments 1 and 2 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 , Figure 4This is a flowchart of the third embodiment of the LED lamp testing method proposed in this application. Further, the testing equipment in the testing system is also connected to at least one standard device.

[0113] The standard color chart is positioned in the shooting direction of the standard camera of the standard device. The standard device also includes a standard LED light positioned on the same side as the standard camera, and the light emitted by the standard LED light illuminates the standard color chart.

[0114] Understandably, the aforementioned standard equipment can be one or a group of reference devices that have undergone rigorous screening and verification, and whose LED light and camera performance has been confirmed to be qualified or in an ideal state. For example, it could be a device selected from a mass-production batch with median performance, or a device calibrated with high-precision instruments. The aforementioned standard camera can be a camera module used for image acquisition on standard equipment, whose imaging performance is known and stable. The aforementioned standard LED light can be an LED light source used to provide illumination on standard equipment, whose luminous characteristics are known and stable, serving as a reference benchmark for judging the performance of the LED light in the device under test.

[0115] like Figure 4 As shown, before the step of sending the shooting command to the device under test, the method further includes:

[0116] Step S01: Send the adjustment parameters to each of the standard devices so that each of the standard devices adjusts the standard LED light and the standard camera according to the adjustment parameters;

[0117] Step S02: Send test instructions to each of the standard devices, so that each of the standard devices uses the adjusted standard LED light to emit light to illuminate the standard color card, and takes a picture of the standard color card through the adjusted standard camera to obtain a standard image;

[0118] Step S03: Obtain the preset typical brightness value, preset typical color temperature value, preset brightness tolerance, and preset color temperature tolerance based on the standard image.

[0119] It should be noted that the aforementioned test command can be a command that triggers the standard device to perform image acquisition operations, such as a specific command code sent via a communication protocol. The aforementioned standard image can be a digital image generated by the standard device under specific adjustment parameters and used as a performance benchmark, such as a JPEG image.

[0120] In its implementation, the aforementioned testing equipment generates and uses the same set of adjustment parameters to adjust the device under test, and sends these parameters to each connected standard device via a communication link. Upon receiving these parameters, each standard device sets the driving power (or current) of its internal standard LED lights and the exposure parameters (such as shutter speed and ISO) and white balance gain of its standard camera accordingly. After all standard devices have completed their adjustments, the testing equipment sends test commands to each standard device sequentially or simultaneously. Upon receiving the test command, each standard device activates its pre-adjusted standard LED lights to illuminate the standard color chart and drives its pre-adjusted standard camera to capture an image of the color chart. After capturing the image, each standard device sends the captured image back to the testing equipment as a standard image. After collecting all the standard images, the testing equipment executes a preset image processing and analysis algorithm. Based on the color data contained in these standard images, it calculates and determines the benchmark values ​​used to determine whether the device under test is qualified, namely, the preset typical brightness value, the preset typical color temperature value, and the allowable deviation range, namely, the preset brightness tolerance and the preset color temperature tolerance.

[0121] For ease of understanding, the following example illustrates the concept, but does not impose specific limitations on this embodiment: An industrial control computer (testing equipment) plans to establish a benchmark for the LED light's "50% brightness" test. It first generates adjustment parameters: "LED power = medium, shutter speed = 1 / 60 second, ISO = 200." Then, the industrial control computer sends these parameters to three mobile phones as standard devices via USB. Upon receiving the parameters, each of the three mobile phones adjusts its own fill light brightness to medium, sets its camera to manual mode, and applies a 1 / 60 second shutter speed and ISO 200. Subsequently, the industrial control computer sends a "take a picture" command to each of the three standard mobile phones. Each phone turns on its own fill light, takes a picture of the color chart in front of it, and transmits the photo (standard image) back to the industrial control computer. After receiving the three standard images, the industrial control computer performs the same processing flow as the device under test for each image: identifying the color chart, calculating the average RGB of each color block, converting to YUV and Lab space, and calculating the average Y value (brightness characteristic) and average (a, b) value (color temperature characteristic) of the entire image. Assume we have three luminance values: 118, 120, and 122; and three color temperature points: (a, b) with values ​​of (-1.5, 1.2), (-1.48, 1.22), and (-1.52, 1.18), respectively. The industrial computer calculates the average of these three luminance values ​​(120) as the preset typical luminance value, and calculates their standard deviation (e.g., approximately 1.63) or sets a range (e.g., ±3) based on experience as the preset luminance tolerance. Simultaneously, it calculates the geometric center of the three color temperature points (e.g., (-1.5, 1.2)) as the preset typical color temperature value, and calculates the distance from each point to the center, taking the maximum distance or setting an empirical value (e.g., 0.05) as the preset color temperature tolerance.

[0122] Furthermore, the step of obtaining the preset typical brightness value, preset typical color temperature value, preset brightness tolerance, and preset color temperature tolerance based on the standard image includes:

[0123] Step S041: Recognize each of the standard images to obtain the standard RGB data corresponding to each standard image;

[0124] Step S042: Convert the standard RGB data of each standard image into standard YUV color space data, and calculate the average brightness based on the standard YUV color space data to obtain the standard brightness feature value corresponding to each standard image.

[0125] Step S043: Averaging the standard brightness characteristic values ​​to obtain a preset typical brightness value, and standard deviation processing to obtain a preset brightness tolerance.

[0126] Step S044: Convert the standard RGB data of each standard image into standard Lab color space data, and obtain the standard color temperature feature value corresponding to each standard image based on the standard Lab color space data;

[0127] Step S045: Obtain the preset color temperature typical value and preset color temperature tolerance based on the standard color temperature feature values ​​using the preset geometric center algorithm.

[0128] It should be noted that the aforementioned standard RGB data can be a set of RGB values ​​extracted from a standard image, representing the average color of each color patch area on a standard color chart, such as a list containing 24 sets of (R_avg, G_avg, B_avg) data. The aforementioned standard YUV color space data can be a set of data containing luminance (Y) and chromaticity (U, V) components, obtained by converting standard RGB data using a standard formula. The aforementioned standard luminance feature value can be a single value representing the overall luminance of the standard device's LED light, obtained by averaging the Y components of all color patches in a standard image, such as a Y_avg value. The aforementioned standard color temperature feature value can be a two-dimensional coordinate point representing the overall color of the standard device's LED light, obtained by processing the Lab data of all color patches in a standard image (e.g., calculating the average of the a and b components), such as a (a_avg, b_avg) coordinate point. The aforementioned preset geometric center algorithm can be a method for calculating the center position of a set of two-dimensional data points, for example, calculating the arithmetic mean of these points along the a-axis and b-axis directions to obtain the geometric center coordinates.

[0129] In its implementation, the aforementioned testing equipment processes each collected standard image. First, the equipment uses an image recognition algorithm to locate the standard color chart region in each standard image and divides it into color block regions based on the color chart layout. The equipment calculates the average RGB value of pixels within each color block region, thereby obtaining the standard RGB data corresponding to that standard image. Next, the equipment converts this standard RGB data into standard YUV color space data using a standard formula. The equipment extracts the Y component of all color blocks from the converted YUV data and averages these Y components to obtain a standard luminance feature value representing the overall brightness of the image. After obtaining the standard luminance feature values ​​for all standard images, the equipment performs an arithmetic mean on these feature values, and the result is the preset typical brightness value. Simultaneously, the equipment calculates the standard deviation of these standard luminance feature values ​​and sets the result (or a value determined according to certain rules based on this result) as the preset luminance tolerance. On the other hand, the equipment converts the standard RGB data of each standard image into standard Lab color space data using a standard formula. Based on the converted Lab data (e.g., calculating the average of the a component and the average of the b component of all color blocks), the equipment obtains the standard color temperature feature value corresponding to each standard image. Then, the device applies a preset geometric center algorithm to process this set of standard color temperature feature values ​​(two-dimensional point set). This algorithm typically calculates the average value of all points along the a-axis and b-axis to obtain the geometric center point, which is then determined as the preset typical color temperature value. At the same time, the device calculates the distance from each standard color temperature feature value point to the geometric center point and determines the preset color temperature tolerance based on these distances (e.g., taking the maximum value, the average value, or according to the standard deviation rule).

[0130] For ease of understanding, the following example illustrates the concept, but does not impose specific limitations on this embodiment: The test equipment collected five standard images taken by five standard devices under the same parameters. The first image was processed to obtain its standard RGB data, and after conversion, its standard luminance characteristic value was calculated to be Y1=121, and its standard color temperature characteristic value was (a1, b1)=(-1.52, 1.21). Similarly, the other four sets of data were obtained: Y2=119, (a2, b2)=(-1.48, 1.18); Y3=120, (a3, b3)=(-1.50, 1.20); Y4=122, (a4, b4)=(-1.49, 1.19); Y5=118, (a5, b5)=(-1.51, 1.22).

[0131] The preset typical brightness value = (121 + 119 + 120 + 122 + 118) / 5 = 120. Calculate the standard deviation of these 5 Y values, assuming it is 1.58. The device can be set to a preset brightness tolerance of twice the standard deviation, i.e., 3.16 (or rounded down to 3).

[0132] For color temperature, a preset geometric center algorithm is applied: Calculate the average value a_typ in direction a = [(-1.52) + (-1.48) + (-1.50) + (-1.49) + (-1.51)] / 5 = -1.50; calculate the average value b_typ in direction b = (1.21 + 1.18 + 1.20 + 1.19 + 1.22) / 5 = 1.20. Therefore, the typical preset color temperature is (-1.50, 1.20). Then calculate the distance from each point to the center: ΔE1≈0.022, ΔE2≈0.028, ΔE3≈0.0, ΔE4≈0.014, ΔE5≈0.022. The device can take the maximum value of these distances, 0.028, as the preset color temperature tolerance, or set a threshold slightly larger than this value, such as 0.05.

[0133] This embodiment also provides a first embodiment of an LED lamp testing device; please refer to [reference needed]. Figure 5 , Figure 5 This is a diagram of an LED lamp testing device provided in an embodiment of this application. The LED lamp testing device includes:

[0134] The parameter adjustment module is used to generate corresponding adjustment parameters based on preset adjustment instructions and send the adjustment parameters to the device under test, so that the device under test adjusts the LED lights and the camera according to the adjustment parameters;

[0135] The image acquisition module is used to send a shooting command to the device under test, so that the device under test uses an adjusted LED light to emit light to illuminate the standard color card, and takes a picture of the standard color card through the adjusted camera to obtain a target image;

[0136] The result acquisition module is used to obtain the test result corresponding to the LED light based on the target image;

[0137] The result acquisition module is further configured to detect the target image using a preset image feature detection algorithm, and take the area corresponding to the standard color card in the target image as the detection area; divide the detection area based on the color block layout corresponding to the standard color card to obtain the color block detection area corresponding to each color block area; acquire the RGB data corresponding to each pixel in each color block detection area, and perform arithmetic mean processing on the RGB data to obtain the color block RGB data corresponding to each color block detection area, and obtain the test result corresponding to the LED light based on the color block RGB data.

[0138] Referring to the first embodiment of the LED lamp testing device, this embodiment also proposes a second embodiment of the LED lamp testing device. The contents that are the same as or similar to those in the first embodiment of the LED lamp testing device can be referred to the above description, and will not be repeated hereafter.

[0139] The result acquisition module is further configured to convert the RGB data of each color block based on a standard conversion formula to obtain the YUV color space data and the Lab color space data corresponding to each color block region; calculate the average brightness based on the YUV color space data to obtain a brightness feature value, and determine a brightness deviation value based on a preset typical brightness value and the brightness feature value; obtain a color temperature feature value based on the Lab color space data, and determine a color temperature deviation value based on a preset typical color temperature value and the color temperature feature value; if the brightness deviation value is less than a preset brightness tolerance and the color temperature deviation value is less than a preset color temperature tolerance, the test is considered qualified as the test result corresponding to the LED light.

[0140] The result acquisition module is further used in the step of obtaining color temperature feature values ​​based on each of the Lab color space data, including: performing arithmetic mean processing on the red-green hue components corresponding to each of the color block regions to obtain a first arithmetic mean, and performing arithmetic mean processing on the yellow-blue hue components corresponding to each of the color block regions to obtain a second arithmetic mean; obtaining color temperature feature values ​​based on the first arithmetic mean and the second arithmetic mean.

[0141] Referring to the first embodiment and the second embodiment of the LED lamp testing device, this embodiment also proposes a third embodiment of the LED lamp testing device. The contents that are the same as or similar to the first embodiment and the second embodiment of the LED lamp testing device can be referred to the above description, and will not be repeated hereafter.

[0142] The image acquisition module is further configured to send the adjustment parameters to each of the standard devices, so that each of the standard devices adjusts the standard LED and the standard camera according to the adjustment parameters; send test commands to each of the standard devices, so that each of the standard devices uses the adjusted standard LED to emit light to illuminate the standard color chart, and takes a picture of the standard color chart through the adjusted standard camera to obtain a standard image; and obtain a preset typical brightness value, a preset typical color temperature value, a preset brightness tolerance, and a preset color temperature tolerance based on the standard image.

[0143] The image acquisition module is further configured to: identify each of the standard images to obtain standard RGB data corresponding to each standard image; convert the standard RGB data of each standard image into standard YUV color space data, and calculate the average brightness based on the standard YUV color space data to obtain standard brightness feature values ​​corresponding to each standard image; perform averaging processing based on the standard brightness feature values ​​to obtain a preset typical brightness value, and perform standard deviation processing based on the standard brightness feature values ​​to obtain a preset brightness tolerance; convert the standard RGB data of each standard image into standard Lab color space data, and obtain standard color temperature feature values ​​corresponding to each standard image based on the standard Lab color space data; and obtain a preset typical color temperature value and a preset color temperature tolerance based on the standard color temperature feature values ​​using a preset geometric center algorithm.

[0144] The LED lamp testing device provided in this embodiment, employing the LED lamp testing method described in the above embodiments, solves the problem of complex testing processes caused by the need to disassemble assembled devices under test (DUTs) to obtain individual LEDs for measurement. Compared with the prior art, the beneficial effects of the LED lamp testing device provided in this embodiment are the same as those of the LED lamp testing method described in the above embodiments, and other technical features of the LED lamp testing device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0145] This embodiment provides a testing device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the LED lamp testing method in the first embodiment described above.

[0146] The following is for reference. Figure 6 , Figure 6 This is a schematic diagram of the structure of a test device suitable for implementing the embodiments of this application. The test device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), etc., as well as fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The test equipment shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0147] like Figure 6As shown, the test device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the test device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the test equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows test equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0148] Specifically, according to this embodiment, the process described above with reference to the flowchart can be implemented as a computer software program. For example, this embodiment includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the disclosed embodiments of this embodiment.

[0149] The testing equipment provided in this embodiment, employing the LED lamp testing method described in the above embodiments, solves the technical problem of existing methods that require disassembling the assembled device under test (DUT) to obtain the LED lamp for individual measurement, resulting in a complex testing process. Compared with the prior art, the beneficial effects of the testing equipment provided in this embodiment are the same as those of the LED lamp testing method provided in the above embodiments, and other technical features of this testing equipment are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0150] It should be understood that the various parts disclosed in this embodiment can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0151] The above description is merely a specific implementation of this embodiment, but the protection scope of this embodiment is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this embodiment should be included within the protection scope of this embodiment. Therefore, the protection scope of this embodiment should be determined by the protection scope of the claims.

[0152] This embodiment provides a testing system, which includes: a standard color chart as described above and testing equipment.

[0153] This embodiment provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the LED lamp testing method in the above embodiment.

[0154] The computer-readable storage medium provided in this embodiment may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0155] The aforementioned computer-readable storage medium may be included in the test equipment or may exist independently without being assembled into the test equipment.

[0156] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the test device, cause the test device to perform LED light testing.

[0157] Computer program code for performing the operations of this embodiment can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0158] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this embodiment. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0159] The modules described in this embodiment can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0160] The readable storage medium provided in this embodiment is a computer-readable storage medium. This medium stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned LED lamp testing method. This solves the problem of existing technologies that require disassembling the assembled device under test (DUT) to obtain the LED lamp for individual measurement, leading to a complex testing process. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this embodiment are the same as those of the LED lamp testing method provided in the above embodiments, and will not be repeated here.

[0161] The above descriptions are only some embodiments and do not limit the patent scope of this embodiment. All equivalent structural transformations made based on the technical concept of this application and the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A method for testing LED lights, characterized in that, The method is applied to a test device in a test system. The test system also includes a standard color card, which is set in the shooting direction of the camera of the device under test. The device under test also includes an LED light set on the same side as the camera. The light emitted by the LED light illuminates the standard color card. The test device is also connected to the device under test. The method includes: Based on a preset adjustment command, corresponding adjustment parameters are generated and sent to the device under test, so that the device under test adjusts the LED light and the camera according to the adjustment parameters; A shooting command is sent to the device under test, so that the device under test uses an adjusted LED light to emit light onto the standard color card, and takes a picture of the standard color card through the adjusted camera to obtain a target image; The test results corresponding to the LED light are obtained based on the target image; The standard color chart includes at least two color block areas of different colors; The step of obtaining the test result corresponding to the LED light based on the target image includes: The target image is detected by a preset image feature detection algorithm, and the area in the target image corresponding to the standard color card is taken as the detection area. The detection area is divided based on the color block layout corresponding to the standard color card to obtain the color block detection area corresponding to each color block area; The RGB data corresponding to each pixel in each of the color block detection areas is obtained, and the RGB data is processed by arithmetic mean to obtain the color block RGB data corresponding to each of the color block detection areas. The test result corresponding to the LED light is obtained based on the color block RGB data. The step of obtaining the test results corresponding to the LED light based on the RGB data of each color block includes: Based on the standard conversion formula, the RGB data of each color block is converted to obtain the YUV color space data and the Lab color space data corresponding to each color block region. The average brightness is calculated based on the YUV color space data to obtain the brightness characteristic value, and the brightness deviation value is determined based on the preset typical brightness value and the brightness characteristic value. Color temperature feature values ​​are obtained based on the Lab color space data, and color temperature deviation values ​​are determined based on preset typical color temperature values ​​and the color temperature feature values. If the brightness deviation value is less than the preset brightness tolerance and the color temperature deviation value is less than the preset color temperature deviation, the test is considered qualified as the test result for the LED light.

2. The method as described in claim 1, characterized in that, The Lab color space data includes: red-green hue components and yellow-blue hue components; The step of obtaining color temperature feature values ​​based on the Lab color space data includes: A first arithmetic mean is obtained by performing arithmetic mean processing on the red-green hue components corresponding to each of the color block regions, and a second arithmetic mean is obtained by performing arithmetic mean processing on the yellow-blue hue components corresponding to each of the color block regions. The color temperature feature value is obtained based on the first arithmetic mean and the second arithmetic mean.

3. The method as described in claim 1, characterized in that, The test equipment in the test system is also connected to at least one standard device; The standard color chart is positioned in the shooting direction of the standard camera of the standard device. The standard device also includes a standard LED light positioned on the same side as the standard camera, and the light emitted by the standard LED light illuminates the standard color chart. Before the step of sending the shooting command to the device under test, the method further includes: The adjustment parameters are sent to each of the standard devices so that each of the standard devices adjusts the standard LED light and the standard camera according to the adjustment parameters; Test commands are sent to each of the standard devices, so that each of the standard devices uses an adjusted standard LED light to emit light onto the standard color card, and takes a picture of the standard color card through the adjusted standard camera to obtain a standard image; Based on the standard image, obtain the preset typical brightness value, preset typical color temperature value, preset brightness tolerance, and preset color temperature tolerance.

4. The method as described in claim 3, characterized in that, The steps of obtaining the preset typical brightness value, preset typical color temperature value, preset brightness tolerance, and preset color temperature tolerance based on the standard image include: Each of the standard images is identified to obtain the standard RGB data corresponding to each standard image; The standard RGB data of each standard image is converted into standard YUV color space data, and the average brightness is calculated based on the standard YUV color space data to obtain the standard brightness feature value corresponding to each standard image. A preset typical brightness value is obtained by averaging the standard brightness characteristic values ​​and a preset brightness tolerance is obtained by standard deviation processing based on the standard brightness characteristic values. The standard RGB data of each standard image is converted into standard Lab color space data, and the standard color temperature feature value corresponding to each standard image is obtained based on the standard Lab color space data. The preset color temperature typical value and preset color temperature tolerance are obtained based on the standard color temperature characteristic values ​​through the preset geometric center algorithm.

5. An LED lamp testing device, characterized in that, The device includes: The parameter adjustment module is used to generate corresponding adjustment parameters based on preset adjustment instructions and send the adjustment parameters to the device under test, so that the device under test adjusts the LED lights and the camera according to the adjustment parameters; The image acquisition module is used to send a shooting command to the device under test, so that the device under test uses an adjusted LED light to emit light to illuminate the standard color card, and takes a picture of the standard color card through the adjusted camera to obtain a target image; The result acquisition module is used to obtain the test result corresponding to the LED light based on the target image; The standard color chart includes at least two color block areas of different colors; The result acquisition module is used to detect the target image using a preset image feature detection algorithm, taking the area corresponding to the standard color chart in the target image as the detection area; dividing the detection area based on the color block layout corresponding to the standard color chart to obtain the color block detection area corresponding to each color block area; acquiring the RGB data corresponding to each pixel in each color block detection area, and performing arithmetic mean processing on the RGB data to obtain the color block RGB data corresponding to each color block detection area, and obtaining the test result corresponding to the LED light based on the color block RGB data; The result acquisition module is further configured to convert the RGB data of each color block based on a standard conversion formula to obtain the YUV color space data and the Lab color space data corresponding to each color block region; calculate the average brightness based on the YUV color space data to obtain a brightness characteristic value, and determine a brightness deviation value based on a preset typical brightness value and the brightness characteristic value; obtain a color temperature characteristic value based on the Lab color space data, and determine a color temperature deviation value based on a preset typical color temperature value and the color temperature characteristic value; if the brightness deviation value is less than a preset brightness tolerance and the color temperature deviation value is less than a preset color temperature deviation, the test is considered qualified as the test result corresponding to the LED light.

6. A testing device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and capable of running on the processor, the computer program being configured to implement the steps of the LED lamp testing method as described in any one of claims 1 to 4.

7. A testing system, characterized in that, The system includes: a standard color chart and the testing equipment as described in claim 6, wherein the standard color chart includes at least two color block areas of different colors.

8. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the LED lamp testing method as described in any one of claims 1 to 4.

Images