A screen brightness detection method and system based on spectral waveform

Abstract

The application discloses a screen brightness detection method and system based on a spectrum waveform, and is used for improving the precision of screen brightness detection. A sample machine type is lighted; a sample picture of the sample machine type is subjected to spectrum detection, and exposure sequence images with different exposure times are collected; brightness data is measured and calculated; fitting is performed according to the brightness data of sample spectrum waveform data, the gray scale data of the exposure sequence images and the exposure time, so as to generate a calibration fitting data group; the sample spectrum waveform data and the calibration fitting data group are stored in a database; image collection and spectrum detection are performed on a to-be-detected screen, so as to generate a shooting image and target spectrum waveform data, record an actual exposure time, match the target spectrum waveform data and the sample spectrum waveform data; target calibration fitting data corresponding to the sample spectrum waveform data matched with the target spectrum waveform data is determined; and target brightness data is generated according to the gray scale data of the shooting image, the target calibration fitting data and the actual exposure time.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of display screen detection, and in particular to a screen brightness detection method and system based on spectral waveform. BACKGROUND

[0002] As a core link of the display screen industry chain, the quality detection technology of the display screen has been continuously valued. The brightness detection of the micron-level pixels on the surface of the display screen has become a key element determining the quality of the display screen product. Especially, Micro LED display technology, as a new generation of display technology, has the advantages of high pixel density, high resolution, small volume, low energy consumption, wide color gamut, etc., and is regarded as an important development direction in the future display field. However, with the continuous innovation of Micro LED in structure, the complexity of the screen structure is continuously improved, and in order to be applied to specific fields, the existing Micro LED also continuously increases the specific structure for adapting to new display screen equipment in different fields. This makes it more likely for panel products to have various brightness display defects such as dark spots, micro-darkness, bright spots, etc. during the production and manufacturing process of new Micro LED, which seriously affects the display effect of Micro LED, and further affects the display effect of the display screen product in specific fields. Therefore, accurate measurement of the brightness of Micro LED is crucial for product quality detection and subsequent repair, and has become a key element determining the quality of the display screen product.

[0003] In the production and detection process of Micro LED, accurate measurement of its brightness is a key link to ensure product quality and performance. When conducting brightness detection on different models of Micro LED, the traditional luminance meter does not adapt and optimize the light source characteristics of specific models, but still uses the general light separation measurement method of filter. Such a method has many problems for new Micro LED models.

[0004] Firstly, for new Micro LED models, the spectral response of the industrial camera has inherent nonlinearity (such as differences in light sensitivity of the detector, fluctuations in filter transmittance), which reduces the accuracy of brightness measurement of new Micro LED models. Moreover, due to the differences in manufacturing processes and inherent differences in specific structures of different models of Micro LED, the differences in their light source characteristics are becoming larger and larger. Using conventional fixed specification filters for detection cannot accurately match the exclusive optical characteristics of new Micro LED models, resulting in the inability to accurately capture the real light signal of new Micro LED models during brightness measurement, further reducing the accuracy of brightness measurement of new Micro LED models. SUMMARY

[0005] The application discloses a screen brightness detection method and system based on a spectral waveform, which is used to improve the accuracy of display screen brightness detection, and is especially suitable for brightness detection of a Micro LED screen.

[0006] In a first aspect, the embodiments of the application provide a screen brightness detection method based on a spectral waveform, comprising:

[0007] The sample screen is turned on to display different sample pictures;

[0008] In the visible light band, the different sample pictures of the sample screen are subjected to spectral detection to generate a plurality of sample spectral waveform data, and a group of exposure sequence images with different exposure times are collected for the corresponding sample pictures during each detection of the sample spectral waveform data;

[0009] The brightness data of each sample spectral waveform data is measured and calculated;

[0010] The brightness data corresponding to the different sample spectral waveform data and the gray scale data and exposure time of the exposure sequence images are respectively fitted to generate a calibration fitting data group corresponding to the different sample spectral waveform data;

[0011] The different sample spectral waveform data and the corresponding calibration fitting data group are stored in a database;

[0012] The image of the turned-on screen to be detected is collected and subjected to spectral detection to generate a shooting image and target spectral waveform data, the actual exposure time is recorded, and the target spectral waveform data and the plurality of sample spectral waveform data are matched;

[0013] The target calibration fitting data corresponding to the sample spectral waveform data most matched with the target spectral waveform data is determined;

[0014] The target brightness data is generated according to the gray scale data of the shooting image, the actual exposure time and the target calibration fitting data.

[0015] Optionally, after the step of, in the visible light band, the different sample pictures of the sample screen are subjected to spectral detection to generate a plurality of sample spectral waveform data, and a group of exposure sequence images with different exposure times are collected for the corresponding sample pictures during each detection of the sample spectral waveform data, and before the step of, the image of the turned-on screen to be detected is collected and subjected to spectral detection to generate a shooting image and target spectral waveform data, the actual exposure time is recorded, and the target spectral waveform data and the sample spectral waveform data in the database are matched, the screen brightness detection method further comprises:

[0016] Fitting the gray scale data and exposure time of each set of exposure sequence images to generate corresponding rate of change data and store in the database, and a sample spectral waveform data corresponds to a rate of change data;

[0017] According to the gray scale data and exposure time of each set of exposure sequence images and the corresponding rate of change data and sample spectral waveform data, a spectral response function is generated and stored in the database, and the spectral response function is used to correct the gray scale data of the captured image.

[0018] Optionally, after the steps of image acquisition and spectral detection of the lit screen to be tested, generating captured images and target spectral waveform data, recording the actual exposure time, matching the target spectral waveform data with the sample spectral waveform data in the database, and before the step of generating target brightness data according to the gray scale data of the captured image and the actual exposure time and the target calibration fitting data, the screen brightness detection method further comprises:

[0019] Based on the sample spectral waveform data most matched with the target spectral waveform data, the corresponding target rate of change data is determined;

[0020] According to the gray scale data of the captured image, the actual exposure time, the target spectral waveform data, the target rate of change and the spectral response function, the captured image is processed for gray scale correction.

[0021] Optionally, the step of processing the captured image for gray scale correction according to the gray scale data of the captured image, the actual exposure time, the target spectral waveform data, the target rate of change and the spectral response function specifically comprises:

[0022] According to the target spectral waveform data, the actual exposure time, the spectral response function and the corresponding rate of change data, target gray scale data of the captured image is generated;

[0023] According to the target gray scale data and the measured gray scale data of the captured image, a gray scale correction coefficient is generated;

[0024] According to the gray scale correction coefficient, the captured image is processed for gray scale correction.

[0025] Optionally, the sample spectral waveform data includes a plurality of red spectral waveform data, a plurality of green spectral waveform data and a plurality of blue spectral waveform data; wherein in the step of fitting the gray scale data and exposure time of each set of exposure sequence images to generate corresponding rate of change data and store in the database, the plurality of red spectral waveform data, the plurality of green spectral waveform data and the plurality of blue spectral waveform data correspond to generate a plurality of red light rate of change data, a plurality of green light rate of change data and a plurality of blue light rate of change data;

[0026] The step of generating the spectral response function according to the gray scale data and the exposure time of each set of exposure sequence images and the corresponding rate data and the sample spectral waveform data and storing the spectral response function into the database specifically comprises:

[0027] A target exposure image is determined from each set of exposure sequence images of each red spectral waveform data respectively to generate a target red light exposure image group; and a red spectral response function is generated according to the exposure time and the gray scale data of each target exposure image in the target red light exposure image group and the corresponding red spectral waveform data and red light rate data;

[0028] A target exposure image is determined from each set of exposure sequence images of each green spectral waveform data respectively to generate a target green light exposure image group; and a green spectral response function is generated according to the exposure time and the gray scale data of each target exposure image in the target green light exposure image group and the corresponding green spectral waveform data and green light rate data;

[0029] A target exposure image is determined from each set of exposure sequence images of each blue spectral waveform data respectively to generate a target blue light exposure image group; and a blue spectral response function is generated according to the exposure time and the gray scale data of each target exposure image in the target blue light exposure image group and the corresponding blue spectral waveform data and blue light rate data;

[0030] The red spectral response function, the green spectral response function and the blue spectral response function are merged to generate an RGB spectral response function, and the RGB spectral response function is stored into the database.

[0031] Optionally, in the visible light band, different sample pictures of the sample species are subjected to spectral detection to generate a plurality of sample spectral waveform data, and a set of exposure sequence images with different exposure times are collected for the corresponding sample picture each time the sample spectral waveform data is detected.

[0032] Before each spectral detection, the temperature of the detection environment is controlled to be stable at a target collection temperature, and different sample pictures of the sample species are subjected to spectral detection to generate a plurality of sample spectral waveform data.

[0033] Optionally, in the visible light band, different sample pictures of the sample species are subjected to spectral detection to generate a plurality of sample spectral waveform data, and a set of exposure sequence images with different exposure times are collected for the corresponding sample picture each time the sample spectral waveform data is detected.

[0034] Before spectral detection, the sample species is placed in a detection environment with adjustable temperature and capable of maintaining a certain temperature unchanged;

[0035] In a preset temperature range, by adjusting at each target temperature, the spectral detection of different sample pictures of the sample species is completed to generate several sample spectral waveform data.

[0036] Optionally, the image acquisition and spectral detection of the lit screen to be measured are performed to generate a photographed image and target spectral waveform data, the actual exposure time is recorded, and the matching of the target spectral waveform data and the sample spectral waveform data specifically includes:

[0037] The target spectral waveform data is imported into a database, and the target spectral waveform data and the saved several sample spectral waveform data are matched in terms of waveform categories through cosine similarity calculation, and the cosine similarity calculation formula is as follows:

[0038]

[0039] wherein x and y are waveform vectors of the target spectral waveform data and the sample spectral waveform data respectively, n indicates a total number of equal divisions of a spatial interval of adjacent same-phase points in a spectral waveform period, i is a positive integer not greater than n, is the cosine similarity.

[0040] Optionally, before the step of lighting the screen of the sample species to make the sample species display different sample pictures, the screen brightness detection method further includes:

[0041] The flat field coefficient map is obtained and is used for flat field correction of the acquired image in the subsequent image acquisition process.

[0042] In a second aspect, an embodiment of the present application provides a screen brightness detection system based on spectral waveform, comprising:

[0043] The lighting unit is used for lighting the screen of the sample species, so that the sample species displays different sample pictures; the first spectrum detection unit is used for performing spectrum detection on different sample pictures of the sample species in a visible light band to generate a plurality of sample spectrum waveform data; the brightness measurement unit is used for measuring brightness data of each sample spectrum waveform data; the first image acquisition unit is used for acquiring a group of exposure sequence images with different exposure times for the corresponding sample picture each time the spectrum detection unit detects the sample spectrum waveform data; the fitting unit is used for fitting respectively according to the brightness data corresponding to different sample spectrum waveform data and the gray data and exposure time of the exposure sequence images, to generate a calibration fitting data group corresponding to different sample spectrum waveform data; the database unit is used for storing different sample spectrum waveform data and the corresponding calibration fitting data group; the second spectrum detection unit is used for performing spectrum detection on the lit screen to be measured to generate target spectrum waveform data; the second image acquisition unit is used for performing image acquisition on the screen to be measured to generate a shooting image and record an actual exposure time; the matching unit is used for matching the target spectrum waveform data with a plurality of sample spectrum waveform data; the feedback unit is used for determining target calibration fitting data corresponding to the sample spectrum waveform data most matched with the target spectrum waveform data; and the brightness generation unit is used for generating target brightness data according to the gray data of the shooting image, the actual exposure time and the target calibration fitting data.

[0044] From the above technical solution, it can be seen that the embodiments of the present application have the following advantages:

[0045] In the present application, first, the screen of the sample species is lit, so that the sample species displays different sample pictures. Then, spectrum detection is performed on different sample pictures of the sample species in a visible light band to generate a plurality of sample spectrum waveform data, and the brightness data of each sample spectrum waveform data is measured. A group of exposure sequence images with different exposure times are acquired for the corresponding sample picture each time the sample spectrum waveform data is detected. According to the brightness data corresponding to different sample spectrum waveform data and the gray data and exposure time of the exposure sequence images, fitting is performed respectively to generate a calibration fitting data group corresponding to different sample spectrum waveform data. Different sample spectrum waveform data and the corresponding calibration fitting data group are stored in the database. Image acquisition and spectrum detection are performed on the lit screen to be measured to generate a shooting image and target spectrum waveform data, and the actual exposure time is recorded. The target spectrum waveform data is matched with a plurality of sample spectrum waveform data. The target calibration fitting data corresponding to the sample spectrum waveform data most matched with the target spectrum waveform data is determined; and target brightness data is generated according to the gray data of the shooting image, the actual exposure time and the target calibration fitting data.

[0046] The sample machine type screen outputs different spectral waveforms, truly restores the light source characteristics of the sample machine type screen, detects each sample spectral waveform, collects a group of exposure sequence images with different exposure times for the sample machine type screen under each sample spectral waveform, and records the gray scale data. The exposure time and gray scale data of each group of exposure sequence images are fitted with the corresponding brightness data of the corresponding sample spectral waveform to generate a calibration fitting data group corresponding to different sample spectral waveform data, and the calibration fitting data group and the sample spectral waveforms are bound and stored in the database. When the to-be-tested screen is photographed, the spectrum at the time is detected, the detected target spectral waveform data is input into the database, the sample spectral waveform data with the highest similarity to the target spectral waveform data is determined by matching the sample spectral waveforms, the calibration fitting data associated with the sample spectral waveform data is determined, and the calibration fitting data is used as the target calibration fitting data. Then, the target calibration fitting data is used to calculate the brightness, and finally, the accurate target brightness data of the to-be-tested screen is generated. BRIEF DESCRIPTION OF DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0048] Figure 1 A schematic diagram of the screen brightness detection method based on spectral waveform of the present application;

[0049] Figure 2 A schematic diagram of the method for generating spectral response function of the present application;

[0050] Figure 3 A schematic diagram of the method for gray scale correction processing of the photographed image of the present application;

[0051] Figure 4 Another schematic diagram of the method for gray scale correction processing of the photographed image of the present application;

[0052] Figure 5 A schematic diagram of another method for generating spectral response function of the present application;

[0053] Figure 6 A schematic diagram of the method for collecting exposure sequence images of the present application;

[0054] Figure 7 Another schematic diagram of the method for collecting exposure sequence images of the present application;

[0055] Figure 8A schematic diagram of a method for matching spectral waveform data of the present application;

[0056] Figure 9 A schematic diagram of a method for image processing of the present application. DETAILED DESCRIPTION

[0057] In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and

[0058] It is to be understood that the terminology "includes", "has", "holds", "contains" or variants thereof, when utilized within the present specification and claims, denotes the presence of the stated feature but not the exclusion after the stated feature or addition of further features.

[0059] It is also to be understood that the terminology "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items, and that the term "at least one of' denotes one, or a plurality of, or any combination of the listed items.

[0060] As used in the present specification and claims, the term "if' can be interpreted as meaning "when" or "once" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once it is determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]", depending on the context.

[0061] In addition, in the description and the appended claims of the present specification, the terms "first", "second", "third", etc. are used merely for identifying different stages and cannot be interpreted as indicating or implying relative importance.

[0062] Reference within the specification to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment. The appearances of the phrase "in one embodiment" or "in some embodiments" in various places within specifications are not necessarily all referring to the same embodiment, however, are meant to signify that "one or more, but not all embodiments" have the feature, structure or characteristic. The terms "including," "comprising," "having," and variations thereof are meant to encompass the items listed thereafter, but do not exclude other items from also being present. The terms "a" and "an" are meant to encompass both the singular and the plural.

[0063] In the prior art, during the production and detection of Micro LED, accurate measurement of its brightness is a key link to ensure product quality and performance. When detecting the brightness of different types of Micro LED, the traditional luminance meter does not adapt and optimize the light source characteristics of the specific type, but still uses the general filter to separate the light measurement method. Such a way has many problems for new types of Micro LED.

[0064] Firstly, for new types of Micro LED, the spectral response of the industrial camera has inherent nonlinearity (such as the difference in light sensitivity of the detector, the fluctuation of the filter transmittance), which reduces the accuracy of brightness measurement of new types of Micro LED. Moreover, due to the differences in preparation process and inherent differences in specific structure of different types of Micro LED, the differences in light source characteristics are becoming larger and larger. Using conventional fixed specification filters for detection cannot accurately match the exclusive optical characteristics of new types of Micro LED, resulting in the inability to accurately capture the real light signal of new types of Micro LED during brightness measurement, further reducing the accuracy of brightness measurement of new types of Micro LED.

[0065] Based on this, the present application discloses a brightness detection method and system based on screen spectral waveform, which is used to improve the accuracy of screen brightness detection.

[0066] The technical solutions in the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0067] The method of the present application can be applied to a server, a device, a terminal or other device with logical processing capability, and the present application is not limited thereto. For the convenience of description, the following will be described by taking the terminal as an example.

[0068] Referring to Figure 1 The present application provides an embodiment of a screen brightness detection method based on a spectral waveform, comprising:

[0069] 101. Turn on the screen of the sample species, so that the sample species displays different sample pictures;

[0070] In this embodiment, the terminal can select one or more sample species screens, such as the screen of a Micro LED pixel, and the present application is not limited thereto. For each sample species, a plurality of sample pictures (such as single-color pictures of different colors) are determined. After the detection environment is set, the sample species is placed on the detection station, and the PG device is used to turn on the sample species, so that the screen of the sample species displays different sample pictures determined as described above, for subsequent spectral waveform detection and image acquisition.

[0071] 102. Perform spectral detection on different sample pictures of the sample species in the visible light band to generate a plurality of sample spectral waveform data, and acquire a group of exposure sequence images with different exposure times for the corresponding sample picture each time the sample spectral waveform data is detected;

[0072] 103. Measure the brightness data of each sample spectral waveform data;

[0073] The terminal processes the screens of different sample species to be detected, so that the screen of each sample species displays different sample pictures determined as described above. In the detection link, the spectrometer can obtain n spectral waveforms based on a plurality of sample pictures wherein i is a positive integer from 1 to n, representing the number of sample spectral waveforms; n is a positive integer, representing the preset number of sample pictures set according to actual detection requirements, which is also the preset number of sample spectral waveform data, and the greater the value, the higher the measurement accuracy requirement; λ represents different light waves. In the actual detection process, in addition to adjusting the sample pictures, the acquisition environment parameters can also be adjusted, specifically, the acquisition environment parameters that can affect the output of the sample pictures of the sample species screen are adjusted, and such parameters will have a certain influence on the spectral waveform output by the sample pictures. The same sample picture can be used for image acquisition and spectral waveform detection under different acquisition environment parameters.

[0074] There are two ways to obtain the sample spectrum waveform data. After lighting the screen of a sample species, the spectrum of a predetermined different sample picture of the sample species is detected by the spectrometer at the same temperature, and a plurality of sample spectrum waveform data is obtained accordingly. If there are multiple sample species, after the screen of a sample species is collected according to the foregoing method, the screen of another sample species is replaced and the same operation is performed.

[0075] In addition, the sample species can be placed in a detection environment with adjustable and constant temperature. After lighting the screen of a sample species, the spectrum of a predetermined different sample picture of the sample species is detected by the spectrometer at each predetermined target temperature by gradually adjusting the temperature within a predetermined temperature range. If there are multiple sample species, after the screen of a sample species is collected according to the foregoing method, the screen of another sample species is replaced and the same operation is performed. In this embodiment, the sample picture of the sample species can be set in a uniform color gradient from one end value to another end value in the visible light range, and / or the target temperature of the sample species can be set in a uniform temperature gradient from one end value to another end value in the predetermined temperature range, and the application is not limited thereto. Thus, a plurality of sample spectrum waveform data is obtained based on the screen of the sample species.

[0076] In this embodiment, during the detection of a sample spectrum waveform, an industrial camera can be selected without limitation in the image acquisition unit, and d screen exposure sequence images under the sample spectrum waveform (when the sample picture remains unchanged) are synchronously photographed and collected, and the sample spectrum waveform data of the sample species screen at this time is detected and recorded by the spectrometer, so as to subsequently calculate the corresponding brightness data. .

[0077] The terminal collects a group of exposure sequence images with different exposure times for the sample picture displayed on the screen of the sample species each time the sample spectrum waveform data is detected. Specifically, taking the sample spectrum waveform data detected by the terminal for the ith (i≤n) as an example, during the implementation of the spectrum waveform detection, d times of exposure shooting can be performed within Tdi=100ms. For example, when d=100, the first picture is photographed from T1i=1ms, each subsequent shooting is 1ms longer than the previous one to obtain an image, until T100i=100ms, and accordingly 100 exposure sequence images with different exposure times are obtained.

[0078] This makes one detected sample spectrum waveform data corresponding to a group of exposure times , d exposure sequence images and a corresponding brightness data that can be calculated .

[0079] At each time of detecting the sample spectral waveform data, the brightness data of each sample spectral waveform data is also calculated, which specifically needs to multiply the measured sample spectral waveform data (i.e. spectrum) with the brightness function CIE (Y) in the color space and then integrate to generate the brightness data L corresponding to each spectral waveform.

[0080] 104. The brightness data corresponding to different sample spectral waveform data and the gray data of the exposure sequence image and the exposure time are respectively fitted to generate the calibration fitting data corresponding to different sample spectral waveform data.

[0081] The terminal generates the calibration fitting data by fitting the brightness data corresponding to the sample spectral waveform data, the gray data of the exposure sequence image and the exposure time. Specifically, the terminal fits the n collected spectral waveforms according to the following formula. One spectral waveform The corresponding brightness data is The corresponding exposure time is The corresponding exposure sequence image is The gray value of the corresponding exposure sequence image is The gray value of the image can be the average gray value of the region of interest (ROI) in the center of the image, which is not limited in the present application. The fitting through the following relationship provides a basis for further brightness test.

[0082]

[0083] to The first sample spectral waveform is The gray value of the corresponding d sequence images (the average gray value of the ROI region corresponding to the d screen exposure sequence images respectively), to The first spectral waveform is The corresponding d exposure time (the exposure time corresponding to the d screen exposure sequence images respectively); similarly, to The second sample spectral waveform is The gray value of the corresponding d sequence images (the average gray value of the ROI region corresponding to the d screen exposure sequence images respectively), to The second spectral waveform is The corresponding d exposure time (the exposure time corresponding to the d screen exposure sequence images respectively); and so on.

[0084] wherein, and The calibration fitting data set corresponds to n spectral waveforms respectively. That is, for the n sample spectral waveforms to be detected, there are two calibration fitting data k and b, and for n spectral waveforms, there are n pairs of k and b, and then a data set is formed.

[0085] 105, store different sample spectral waveform data and corresponding calibration fitting data set in the database;

[0086] The terminal stores all sample spectral waveform data and n sets of corresponding calibration fitting data and Binding and storing together in the database, providing the basis for the brightness calculation in the subsequent test.

[0087] 106, image acquisition and spectral detection are performed on the lit screen to be tested, target spectral waveform data and sample spectral waveform data are generated, the actual exposure time is recorded, and the target spectral waveform data and the sample spectral waveform data are matched;

[0088] The terminal performs image acquisition and spectral detection on the lit screen to be tested, generates a shooting image and target spectral waveform data, records the actual exposure time of the shooting image, and matches the target spectral waveform data and the sample spectral waveform data. Specifically, the terminal can use an industrial camera to perform image acquisition on the lit screen to be tested without limitation in the image acquisition unit, generate a shooting image, calculate the average gray value of the shooting image (for example, taking the average gray value of the ROI region in the center of the image), and record the actual exposure time when shooting. These data are used for subsequent brightness calculation. And use the spectrometer to measure the spectral waveform data (that is, the target spectral waveform data), import the spectral waveform data into the database and match the sample spectral waveform data in the waveform category, and finally determine the sample spectral waveform data that best matches the spectral waveform data. The waveform category can be matched according to the cosine similarity, and the specific matching method is described in detail in the subsequent embodiments.

[0089] 107, determine the target calibration fitting data corresponding to the sample spectral waveform data that best matches the target spectral waveform data;

[0090] After the terminal determines the best matching target spectral waveform, the target calibration fitting data is determined from the database, that is, the k and b data corresponding to the sample spectral waveform that matches the target spectral waveform are determined from the generated calibration fitting data set. The k and b data are the target calibration fitting data.

[0091] 108, generate target brightness data according to the gray data of the shooting image, the actual exposure time and the target calibration fitting data.

[0092] In this embodiment, the gray data of the photographed image can be combined with the target calibration fitting data (k and b determined in the foregoing step) and the actual exposure time to calculate the target brightness data. The target brightness data can be calculated by substituting the target calibration fitting data into the formula shown in step 104, and details are not described herein. The target brightness data can be understood as the overall brightness average of the to-be-tested screen, the brightness average of the ROI region of the to-be-tested screen, or the pixel-level brightness value of the to-be-tested screen, which is determined according to the detection requirement.

[0093] Therefore, the terminal can further measure the pixel-level brightness of the target model screen based on the foregoing method. Taking the pixel-level brightness measurement of the Micro LED screen as an example, the brightness measurement of the Micro LED pixel can be basically realized by extracting the corresponding ROI region on the Micro LED screen of the target model. At present, if the required accuracy of the ROI region is high enough and the to-be-tested screen emits light uniformly, the measured brightness data can be equivalent to the brightness data of a single pixel in the region. Of course, with the development of detection accuracy, it is also possible to directly detect the brightness of a single pixel in the Micro LED screen by using the present application solution.

[0094] In this embodiment, first, the sample model is lit to display different sample pictures. Then, in the visible light band, the different sample pictures of the sample model are detected to generate a plurality of sample spectral waveform data, and the brightness data of each sample spectral waveform data is calculated. A set of exposure sequence images with different exposure times are collected for the corresponding sample picture during each detection of the sample spectral waveform data. The brightness data corresponding to different sample spectral waveform data and the gray data and exposure time of the exposure sequence images are fitted respectively to generate a calibration fitting data group corresponding to different sample spectral waveform data. Different sample spectral waveform data and corresponding calibration fitting data groups are stored in a database. The lit to-be-tested screen is collected and detected to generate photographed images and target spectral waveform data, and the actual exposure time is recorded. The target spectral waveform data and the sample spectral waveform data in the database are matched. The target calibration fitting data corresponding to the sample spectral waveform data most matched with the target spectral waveform data is determined; and the target brightness data is generated according to the gray data of the photographed image, the actual exposure time, and the target calibration fitting data.

[0095] The sample species screen outputs different spectral waveforms, truly restores the light source characteristics of the sample species screen, detects each sample spectral waveform, collects a group of exposure sequence images with different exposure times under each sample spectral waveform, and records the gray scale data. The exposure time and gray scale data of each group of exposure sequence images are fitted with the corresponding brightness data of the sample spectral waveform to generate a calibration fitting data set corresponding to different sample spectral waveform data, and the calibration fitting data set and the sample spectral waveform are stored in the database. When the to-be-tested screen is photographed, the spectrum at the time is detected, the detected target spectral waveform data is input into the database, the sample spectral waveform data with the highest similarity to the target spectral waveform data is determined by matching the sample spectral waveform, and the calibration fitting data associated with the sample spectral waveform data is determined as the target calibration fitting data, and then the target calibration fitting data is used to calculate the brightness, and finally the accurate target brightness data of the to-be-tested screen is generated.

[0096] Secondly, the embodiment also has the following beneficial effects: improving the measurement efficiency. The embodiment adopts automatic data processing and automatic matching spectral waveform calculation mode, without complex manual operation and tedious calculation process, can quickly complete pixel-level brightness test for the to-be-tested screen, especially for the Micro LED screen, greatly improves the measurement efficiency, and is suitable for rapid detection in large-scale production.

[0097] Please refer to Figure 2 An embodiment of a method for generating a spectral response function is provided, after step 102 and before step 106, further comprising:

[0098] 201, fitting the gray scale data and the exposure time of each group of exposure sequence images to generate corresponding change rate data and store them in the database. A plurality of sample spectral waveform data corresponds to one change rate data.

[0099] 202, generating a spectral response function according to the gray scale data and the exposure time of each group of exposure sequence images, the corresponding change rate data, and the sample spectral waveform data, and storing the spectral response function in the database. The spectral response function is used to correct the gray scale data of the photographed image.

[0100] In the embodiment, the terminal generates the gray scale-exposure time change rate data for each group of exposure sequence images, i.e. performs gray scale-exposure time relationship fitting to generate the change rate data of the gray scale with the exposure time. A plurality of sample spectral waveform data corresponds to one change rate data. Specifically, the exposure time of the sample screen under the i-th sample spectral waveform is fitted with the corresponding change rate data of the gray scale with the exposure time. and exposure sequence images gray scale is fitted, wherein the gray scale value of the image can take the mean gray scale value of the ROI region at the center of the image, which is not specifically limited by the present application. The relationship between the exposure time and the gray scale of the image is fitted according to the following formula:

[0101]

[0102] G is the gray scale, t is the exposure time, c is the intercept, and a is the rate data of the change of the gray scale with the exposure time under the i-th sample spectral waveform.

[0103] The terminal determines the exposure time of the screen under the n sample spectral waveforms according to the collected and exposure sequence images , selects one of the exposure sequence images under the preset exposure saturation as the target exposure image (for example, the target exposure image can be one image taken at 50% exposure saturation, and taking 12-bit pixel values as an example, the half exposure saturation is 2048 generated sensing), or selects one of the exposure sequence images at the middle exposure time point as the target exposure image. After selection, the gray scale of the target exposure image under different sample spectral waveforms is recorded respectively, and the corresponding exposure time is calculated according to the formula , and then the spectral response function corresponding to each sample spectral waveform data for the image acquisition unit (such as the aforementioned industrial camera) can be obtained according to the following formula, and is saved.

[0104]

[0105] wherein, is the spectral response function of the camera, is the exposure time of the target exposure image under the sample spectral waveform, is the gray scale of the target exposure image corresponding to the sample spectral waveform, the superscript T indicates transposition of the matrix, and a is the aforementioned rate data of the change of the gray scale of the sequence image generated under the corresponding sample spectral waveform data with the exposure time. The spectral response function is used to represent the corresponding relationship between a plurality of spectral response values and a plurality of sample spectral waveform data.

[0106] Please refer to Figure 3 , the present application further provides an embodiment of a method for performing gray scale correction processing on a photographed image, after step 106 and before step 108, further comprising:

[0107] 301. determining the corresponding target rate data based on the sample spectral waveform data that best matches the target spectral waveform data;

[0108] 302、According to the gray data of the photographed image and the actual exposure time, the target spectral waveform data, the target change rate data and the spectral response function, the gray correction processing is performed on the photographed image.

[0109] In this embodiment, the terminal determines the change rate data corresponding to the sample spectral waveform data most matched with the target spectral waveform data from each change rate data stored in the database, and takes the change rate data as the target change rate data; determines the spectral response value corresponding to the sample spectral waveform data most matched with the target spectral waveform data from the spectral response function stored in the database, and takes the spectral response value as the target spectral response value. Then, the detected target spectral waveform data and the actual exposure time of the photographed image are combined to perform the gray correction processing on the photographed image. First, the target gray of the photographed image is calculated, and the measured gray of the photographed image is calculated, wherein the gray value of the image can take the gray mean value of the center ROI region of the image, which is not specifically limited by the present application. The measured gray and the target gray are used to correct the gray of the photographed image as a whole, and the specific correction method will be described in detail later.

[0110] Please refer to Figure 4 , the present application further provides an embodiment of a method for performing gray correction processing on a photographed image, step 302 specifically includes:

[0111] 401、According to the target spectral waveform data, the actual exposure time, the spectral response function and the corresponding change rate data, the target gray data of the photographed image is generated;

[0112] In this embodiment, the terminal performs the gray correction processing on the photographed image according to the target spectral waveform data, the change rate data of the gray of the sequence image generated under the target spectral waveform data, the actual exposure time and the spectral response function. Specifically, the terminal generates the target gray of the photographed image according to the target spectral waveform data , the corresponding change rate data a, the actual exposure time and the target spectral response value corresponding to the target spectral waveform data in the spectral response function . .

[0113]

[0114] 402、According to the target gray data and the measured gray data of the photographed image, the gray correction coefficient is generated;

[0115] Before calculating the correction coefficient, the terminal needs to first perform the denoising processing on the photographed image, and the terminal generates the gray correction coefficient according to the target gray and the measured gray of the photographed image. The correction coefficient A can be directly calculated according to the ratio of the target gray and the measured gray mean value.

[0116]

[0117] 403、According to the gray correction coefficient, the gray data of the photographed image is corrected.

[0118] After the terminal calculates the photographed image gray correction coefficient, the photographed image gray data can be corrected using the gray correction coefficient A, that is, the photographed image gray data is obtained after the gray correction coefficient A is multiplied by the photographed image gray data.

[0119] The embodiment also has the following beneficial effects:

[0120] 1. The light sensitivity fluctuation of the image acquisition unit in the visible light spectrum range can be accurately compensated, and the absolute error of the brightness measurement is controlled within the measurement level detection requirement through targeted response correction.

[0121] 2. The above-mentioned response correction scheme can ensure the continuity of the spectral response in the visible light spectrum range, and the overall correction can completely capture the spectral details of the Micro LED with complex spectral distribution, avoid information loss caused by spectral exceeding a specific range, and adapt to complex spectral light sources. Moreover, the response correction of this method only needs to calculate the correction coefficient in a specific waveband range, which can accurately match the inherent spectral response characteristics of the waveband, directly calibrate the original data (gray data) of the waveband, and reduce the error caused by unnecessary calculation.

[0122] Please refer to Figure 5 The present application provides another embodiment of a method for generating a spectral response function. In the visible light waveband, the obtained sample spectral waveform data further includes red spectral waveform data, green spectral waveform data and blue spectral waveform data.

[0123] 501. In the foregoing step 201, the red spectral waveform data, green spectral waveform data and blue spectral waveform data correspondingly generate red light change rate data, green light change rate data and blue light change rate data.

[0124] First, in terms of the red waveband, the red light change rate data of the gray-exposure time relationship is generated according to the gray data and exposure time of each group of exposure sequence images corresponding to the red spectral waveform data. Specifically, the exposure time of the screen under the jth red spectral waveform data is recorded and the gray of the exposure sequence image The image grayscale value is fitted, where the grayscale value can be taken as the average grayscale value of the central ROI region of the image; this invention does not specifically limit this. The relationship between exposure time and image grayscale is fitted according to the following formula:

[0125]

[0126] G represents grayscale, and t represents exposure time. The intercept is... This represents the rate of change of grayscale with exposure time under the j-th red spectral waveform.

[0127] 502. Determine a target exposure image from each set of exposure sequence images of each red spectral waveform data to generate a target red light exposure image group; generate a red spectral response function based on the exposure time and grayscale data of each target exposure image in the target red light exposure image group and the corresponding red spectral waveform data and red light change rate data.

[0128] The terminal determines a target exposure image from each exposure sequence image of each set of red spectral waveform data, generates a target red light exposure image group, and then generates a red spectral response function based on the exposure time, grayscale mean data, and corresponding red spectral waveform data and red light change rate data of each target exposure image in the target red light exposure image group. .

[0129] 503. Determine a target exposure image from each set of exposure sequence images of each green spectral waveform data to generate a target green light exposure image group; generate a green spectral response function based on the exposure time and grayscale data of each target exposure image in the target green light exposure image group and the corresponding green spectral waveform data and green light change rate data;

[0130] 504. Determine a target exposure image from each set of exposure sequence images of each blue spectral waveform data to generate a target blue light exposure image group; generate a blue spectral response function based on the exposure time and grayscale data of each target exposure image in the target blue light exposure image group and the corresponding blue spectral waveform data and blue light change rate data.

[0131] Steps 503-504 are similar to step 502, ultimately generating the green spectral response function. and blue spectral response function .

[0132] 505. Merge the red, green, and blue spectral response functions to generate an RGB spectral response function, and store the RGB spectral response function in the database. R represents the red band, G represents the green band, and B represents the blue band.

[0133] The terminal merges the red spectral response function, the green spectral response function and the blue spectral response function to generate an RGB spectral response function for correcting the gray scale of the photographed image.

[0134] In the present application, two correction methods are provided. One is to use the overall spectral response function of the visible light band to correct the response of the image acquisition unit. The other is to obtain the red spectral response function, the green spectral response function and the blue spectral response function respectively, then merge them into an RGB spectral response function, and finally use the RGB spectral response function obtained by merging to correct the response of the image acquisition unit.

[0135] Using the visible light spectral response function without band division fitting has the beneficial effect of higher precision in measuring mixed color light. Using the RGB spectral response function obtained by fitting and then integrating has the beneficial effect of higher precision in measuring monochromatic light.

[0136] Referring to Figure 6 An embodiment of the present application provides a method for collecting exposure sequence images, comprising:

[0137] 601. Before each spectral acquisition, the temperature of the detection environment is controlled to be stable at a target acquisition temperature, and spectral detection is performed on different sample pictures of the sample species to generate a plurality of sample spectral waveform data.

[0138] To obtain the sample spectral waveform data, different collection schemes can be used according to the requirement difference of the detection environment temperature. In this embodiment, the detection environment temperature of the sample species is first determined, and the detection environment temperature is used as the target temperature of the entire detection link. The detection environment needs to be stabilized at the target temperature. In the visible light band, when the sample picture of the sample species is input, the sensor senses whether the detection temperature is at the target temperature. If it is determined that the temperature is at the target temperature and is stable, spectral detection is performed on the sample species to generate a plurality of sample spectral waveform data. The sample picture of the sample species can be set in a color uniform gradient manner from one endpoint value to another endpoint value in the visible light range. In an optional embodiment, when there are multiple sample species, after the screen of one sample species is detected according to the foregoing method, another sample species can be replaced to perform the operation as described above to obtain a plurality of sample spectral waveform data.

[0139] Referring to Figure 7 Another embodiment of the present application provides a method for collecting exposure sequence images, comprising:

[0140] 701. Before spectral acquisition, the sample species is placed in a detection environment with adjustable temperature and capable of maintaining a certain temperature unchanged;

[0141] 702、In the preset temperature range, by adjusting at each target temperature, the spectral detection of different sample pictures of the sample species is completed to generate several sample spectral waveform data.

[0142] In this embodiment, in order to detect the running condition of the sample species at different temperatures, the sample species is placed in a detection environment with adjustable and constant temperature before spectral detection, and several target temperatures are set for the sample species, so that the spectral waveform detection of the sample species is performed at different target temperatures.

[0143] For example, first, a sample species is placed in a temperature-controllable detection environment, the temperature of the detection environment is controlled at the first target temperature in the preset temperature range, and then the sample species is lit to display the predetermined sample picture. Before each collection, the target temperature is detected by the sensor for stability, and when the collection temperature is stable, the spectral waveform detection is performed. After the spectral waveform collection at the first target temperature is completed, the temperature of the detection environment is controlled by the temperature control device to the second target temperature in the preset temperature range, and the sample species is lit and the spectral waveform detection is repeated, until the spectral waveform of all sample pictures of the sample species is completely detected at all target temperatures. The sample picture of the sample species can be set in a color uniform gradient from one end value to another end value in the visible light range. In an optional embodiment, when there are multiple sample species, after the screen of a sample species is detected by the foregoing method, another sample species can be replaced to perform the foregoing operation to obtain several sample spectral waveform data.

[0144] Please refer to Figure 8 An embodiment of a spectral waveform data matching method is provided, which comprises:

[0145] 801、The target spectral waveform data is imported into a database, and the target spectral waveform data and the saved sample spectral waveform data are matched in waveform category by cosine similarity calculation, and the cosine similarity calculation formula is as follows:

[0146]

[0147] Wherein, x and y are waveform vectors of the target spectral waveform data and the sample spectral waveform data, n is the total number of equal division of the spatial interval of adjacent same phase points in the spectral waveform period, i is a positive integer not greater than n, is the cosine similarity.

[0148] In this embodiment, the terminal uses a spectrometer to perform spectral detection on the lit screen to be measured, generates target spectral waveform data, and then imports the target spectral waveform data into a database, and performs waveform category matching on the target spectral waveform data and the saved sample spectral waveform data through cosine similarity calculation. Specifically, the target spectral waveform data is imported into the obtained database and the adaptive waveform category is determined, and the waveform category is matched according to the cosine similarity, as shown in the following formula, where x and y are the waveform vectors of the target spectral waveform data and the calibrated spectral waveform data, respectively.

[0149]

[0150] According to the calculation The sample spectral waveform data most similar to the target spectral waveform data is matched, and the calibration fitting data corresponding to the most matched sample spectral waveform data is determined accordingly and ), and the calibration fitting data is used as the target calibration fitting data for subsequent implementation of brightness detection of the screen to be measured.

[0151] Please refer to Figure 9 , the present application provides an embodiment of a method for image processing, comprising:

[0152] 901, obtain a flat field coefficient map, which is used for flat field correction of the collected image in the subsequent image acquisition process.

[0153] In this embodiment, the terminal first installs an image acquisition unit, such as an industrial camera, but not limited to this, in front of the uniform light source, and then fixes the camera. Then set the aperture, focal length and other parameters of the camera. The camera first shoots an image of the uniform light source, and performs flat field calibration according to the following formula to obtain a flat field coefficient map. Wherein FFC is the flat field coefficient map, is the image of the shot uniform light source, is the gray scale of the uniform light source image, wherein the gray scale value of the image can take the gray scale mean value of the center ROI region of the image, which is not limited in this embodiment.

[0154]

[0155] Next, the terminal will perform flat field correction processing on the image collected by the camera. After the terminal obtains the flat field coefficient map of the camera, it performs flat field correction processing on each collected image, and performs flat field correction processing on all exposure sequence images through the preset flat field coefficient map. After the screen to be measured is collected and the shot image is generated, the camera will automatically perform flat field correction processing on the shot image through the preset flat field coefficient map.

[0156] The application further provides an embodiment of a screen brightness detection system based on spectral waveforms, comprising:

[0157] a lighting unit configured to light up the screen of the sample species so that the sample species displays different sample pictures;

[0158] a first spectral detection unit configured to perform spectral detection on the different sample pictures of the sample species in a visible light band to generate a plurality of sample spectral waveform data;

[0159] a brightness calculation unit configured to calculate brightness data of each sample spectral waveform data;

[0160] a first image acquisition unit configured to acquire a set of exposure sequence images with different exposure times for the corresponding sample picture each time the spectral detection unit detects the sample spectral waveform data;

[0161] a fitting unit configured to respectively fit the brightness data corresponding to the different sample spectral waveform data and the grayscale data and exposure time of the exposure sequence images to generate a set of calibration fitting data corresponding to the different sample spectral waveform data;

[0162] a database unit configured to store the different sample spectral waveform data and the corresponding set of calibration fitting data;

[0163] a second spectral detection unit configured to perform spectral detection on the lighted screen to be detected to generate target spectral waveform data;

[0164] a second image acquisition unit configured to acquire images of the screen to be detected to generate a shooting image and record an actual exposure time;

[0165] a matching unit configured to match the target spectral waveform data with the plurality of sample spectral waveform data;

[0166] a feedback unit configured to determine target calibration fitting data corresponding to the sample spectral waveform data that is most matched with the target spectral waveform data;

[0167] a brightness generation unit configured to generate target brightness data according to the grayscale data of the shooting image and the actual exposure time and the target calibration fitting data.

[0168] The first spectral detection unit and the second spectral detection unit are only for convenient description, and in fact, they can be two independent devices with the same function or the same device used in different steps. The first image acquisition unit and the second image acquisition unit are understood in the same way.

[0169] As to the implementation of the screen brightness detection system, it corresponds to all the embodiments listed in the aforementioned screen brightness detection method. The system can also further implement the scheme contents of steps 201-202, steps 301-302, steps 401-403, steps 501-505, step 601, steps 701-702, step 801 and step 901 by introducing corresponding functional units. Accordingly, a brief description is as follows, and for other parts not mentioned, please refer to the relevant description of the aforementioned screen brightness detection method.

[0170] The system also includes a rate generation unit and a function generation unit. In steps 201-202 corresponding to an embodiment of the aforementioned method, the rate generation unit is used to fit the gray-exposure time relationship of the gray data and exposure time of each set of exposure sequence images, and generate corresponding rate data and store them in the database unit. Among them, each sample spectral waveform data corresponds to a rate data. The function generation unit is used to generate a spectral response function according to the gray data and exposure time of each set of exposure sequence images and the corresponding rate data, sample spectral waveform data, and store the spectral response function in the database unit. Among them, the spectral response function is used to correct the gray data of the photographed image.

[0171] In combination with the foregoing, the system also includes a correction processing unit. In steps 301-302 corresponding to an embodiment of the aforementioned method, the correction processing unit is used to determine the corresponding target rate data based on the sample spectral waveform data that best matches the target spectral waveform data, and to perform gray correction processing on the photographed image according to the gray data of the photographed image, the actual exposure time, the target spectral waveform data, the target rate data and the spectral response function.

[0172] In combination with the foregoing, in steps 401-403 corresponding to an embodiment of the aforementioned method, the correction processing unit is also specifically used to generate target gray data of the photographed image according to the target spectral waveform data, the actual exposure time, the spectral response function and the corresponding rate data, and to generate a gray correction coefficient according to the target gray data and the measured gray data of the photographed image, and to perform gray data correction processing on the photographed image according to the gray correction coefficient.

[0173] In combination with the foregoing, in the steps 501-505 corresponding to an embodiment of the foregoing method, the rate of change generation unit is specifically further configured to correspondingly generate a plurality of red light rate of change data, a plurality of green light rate of change data, and a plurality of blue light rate of change data for a plurality of red spectrum waveform data, a plurality of green spectrum waveform data, and a plurality of blue spectrum waveform data. The function generation unit is further configured to determine a target exposure image from each set of exposure sequence images of each red spectrum waveform data, generate a target red light exposure image set, and generate a red spectrum response function according to the exposure time, the gray scale data, and the corresponding red spectrum waveform data and red light rate of change data of each target exposure image in the target red light exposure image set; and / or determine a target exposure image from each set of exposure sequence images of each green spectrum waveform data, generate a target green light exposure image set, and generate a green spectrum response function according to the exposure time, the gray scale data, and the corresponding green spectrum waveform data and green light rate of change data of each target exposure image in the target green light exposure image set; and / or determine a target exposure image from each set of exposure sequence images of each blue spectrum waveform data, generate a target blue light exposure image set, and generate a blue spectrum response function according to the exposure time, the gray scale data, and the corresponding blue spectrum waveform data and blue light rate of change data of each target exposure image in the target blue light exposure image set.

[0174] In combination with the foregoing, in the step 601 corresponding to an embodiment of the foregoing method, the first spectrum detection unit is further configured to complete spectrum detection on different sample pictures of the sample species to generate the plurality of sample spectrum waveform data when the temperature of the detection environment is stabilized at the target temperature before each spectrum detection.

[0175] In combination with the foregoing, in the step 801 corresponding to an embodiment of the foregoing method, the matching unit is further configured to import the target spectrum waveform data into the database unit, and match the target spectrum waveform data and the plurality of saved sample spectrum waveform data in terms of waveform category through cosine similarity calculation. The cosine similarity calculation formula is as follows:

[0176]

[0177] Wherein, x and y are waveform vectors of target spectrum waveform data and sample spectrum waveform data respectively, n is the total number of equal division of spatial interval of adjacent same phase points in spectrum waveform period, i is a positive integer not greater than n, is a cosine similarity.

[0178] In combination with the foregoing, the system further includes a flat field correction unit. In step 901 of an embodiment corresponding to the foregoing method, the flat field correction unit is configured to obtain a flat field coefficient map in advance, and perform flat field correction on the acquired image in the image acquisition process of the present application.

[0179] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

[0180] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0181] The units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the present embodiment scheme.

[0182] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0183] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

Claims

1. A screen brightness detection method based on spectral waveforms, characterized in that, include: The screen of the sample machine is turned on so that the sample machine displays different sample images; In the visible light band, spectral detection is performed on different sample images of the sample model to generate several sample spectral waveform data, and a set of exposure sequence images with different exposure times are acquired for the corresponding sample image each time the sample spectral waveform data is detected. Calculate the brightness data of the spectral waveform data for each sample; Based on the brightness data and grayscale data of the exposure sequence image corresponding to the spectral waveform data of different samples, respectively fitting is performed to generate calibration fitting data sets corresponding to the spectral waveform data of different samples. Store the spectral waveform data of different samples and the corresponding calibration and fitting data sets in the database; Image acquisition and spectral detection are performed on the illuminated screen to be tested to generate captured images and target spectral waveform data, the actual exposure time is recorded, and the target spectral waveform data is matched with several sample spectral waveform data. The target calibration fitting data corresponding to the sample spectral waveform data that best matches the target spectral waveform data is determined. Target brightness data is generated based on the grayscale data of the captured image, the actual exposure time, and the target calibration fitting data; The screen brightness detection method further includes the following steps: after performing spectral detection on different sample images of the sample model in the visible light band to generate several sample spectral waveform data, and acquiring a set of exposure sequence images with different exposure times for the corresponding sample image each time the sample spectral waveform data is detected; and before performing image acquisition and spectral detection on the lit screen to be tested to generate captured images and target spectral waveform data, recording the actual exposure time, and matching the target spectral waveform data with several sample spectral waveform data. For each set of exposure sequence images, the grayscale data and exposure time are fitted to fit the grayscale-exposure time relationship, and the corresponding rate of change data are generated and stored in the database. The sample spectral waveform data correspond one-to-one with the rate of change data. A corresponding spectral response function is generated based on the grayscale data and exposure time of each set of exposure sequence images, as well as the corresponding rate of change data and sample spectral waveform data. The spectral response function is then stored in the database. The spectral response function is used to correct the grayscale data of the captured images.

2. The screen brightness detection method according to claim 1, characterized in that, After the steps of acquiring and spectrally detecting the illuminated screen under test, generating captured images and target spectral waveform data, recording the actual exposure time, and matching the target spectral waveform data with several sample spectral waveform data, and before the step of generating target brightness data based on the grayscale data of the captured image, the actual exposure time, and the target calibration fitting data, the screen brightness detection method further includes: Based on the sample spectral waveform data that best matches the target spectral waveform data, the corresponding target rate of change data is determined. The spectral response value corresponding to the sample spectral waveform data that best matches the target spectral waveform data is determined from the spectral response function stored in the database, and this spectral response value is used as the target spectral response value. The captured image is subjected to grayscale correction processing based on the grayscale data of the captured image, the actual exposure time, the target spectral waveform data, the target rate of change data, and the target spectral response value.

3. The screen brightness detection method according to claim 2, characterized in that, The step of performing grayscale correction processing on the captured image based on the grayscale data of the captured image and the actual exposure time, the target spectral waveform data, the target rate of change data, and the target spectral response value specifically includes: The target grayscale data of the captured image is generated based on the target spectral waveform data, the actual exposure time, the target spectral response value, and the corresponding target rate of change data. A grayscale correction coefficient is generated based on the target grayscale data and the measured grayscale data of the captured image; The captured image is processed to correct its grayscale data according to the grayscale correction coefficient.

4. The screen brightness detection method according to claim 1, characterized in that, The sample spectral waveform data includes several red spectral waveform data, several green spectral waveform data, and several blue spectral waveform data; wherein, in the step of fitting the grayscale data and exposure time of each set of exposure sequence images to the grayscale data and exposure time, generating corresponding change rate data and storing them in the database, the several red spectral waveform data, several green spectral waveform data, and several blue spectral waveform data correspondingly generate several red light change rate data, several green light change rate data, and several blue light change rate data; The step of generating a corresponding spectral response function based on the grayscale data and exposure time of each exposure sequence image, as well as the corresponding rate of change data and sample spectral waveform data, and storing the spectral response function in the database specifically includes: A target exposure image is determined from each set of exposure sequence images of each red spectral waveform data to generate a target red light exposure image group; a red spectral response function is generated based on the exposure time and grayscale data of each target exposure image in the target red light exposure image group and the corresponding red spectral waveform data and red light change rate data. A target exposure image is determined from each exposure sequence image of each green spectral waveform data to generate a target green light exposure image group; a green spectral response function is generated based on the exposure time and grayscale data of each target exposure image in the target green light exposure image group and the corresponding green spectral waveform data and green light change rate data. A target exposure image is determined from each set of exposure sequence images of each blue spectral waveform data to generate a target blue light exposure image group; a blue spectral response function is generated based on the exposure time and grayscale data of each target exposure image in the target blue light exposure image group and the corresponding blue spectral waveform data and blue light change rate data. The red spectral response function, the green spectral response function, and the blue spectral response function are merged to generate an RGB spectral response function, which is then stored in the database.

5. The screen brightness detection method according to any one of claims 1 to 4, characterized in that, The step of performing spectral detection on different sample images of the sample model in the visible light band to generate several sample spectral waveform data, and acquiring a set of exposure sequence images with different exposure times for the corresponding sample image during each detection of sample spectral waveform data, specifically further includes: Before each spectral detection, the temperature of the detection environment is controlled to be stabilized at the target temperature. Spectral detection is performed on different sample images of the sample model to generate several sample spectral waveform data.

6. The screen brightness detection method according to any one of claims 1 to 4, characterized in that, The step of performing spectral detection on different sample images of the sample model in the visible light band to generate several sample spectral waveform data, and acquiring a set of exposure sequence images with different exposure times for the corresponding sample image during each detection of sample spectral waveform data, specifically further includes: Before spectral detection, the sample model is placed in a detection environment with adjustable temperature that can maintain a constant temperature. Within a preset temperature range, by adjusting the temperature at each target temperature, spectral detection is performed on different sample images of the sample model to generate the spectral waveform data of the several samples.

7. The screen brightness detection method according to any one of claims 1 to 4, characterized in that, The steps of acquiring images and performing spectral detection on the illuminated screen to generate captured images and target spectral waveform data, recording the actual exposure time, and matching the target spectral waveform data with several sample spectral waveform data specifically include: The target spectral waveform data is imported into the database. Cosine similarity calculation is then used to match the target spectral waveform data with several saved sample spectral waveform data for waveform categories. The cosine similarity calculation formula is as follows: Where x and y are the waveform vectors of the target spectral waveform data and the sample spectral waveform data, respectively, n refers to the total number of equal divisions of the spatial interval between adjacent in-phase points in the spectral waveform period, and i is a positive integer not greater than n. Let be the cosine similarity.

8. The screen brightness detection method according to any one of claims 1 to 4, characterized in that, Before the step of lighting up the screen of the sample unit to display different sample images, the screen brightness detection method further includes: Obtain the flat field coefficient map, which will be used to perform flat field correction on the acquired images during subsequent image acquisition.

9. A screen brightness detection system based on spectral waveforms, characterized in that, include: The lighting unit is used to light up the screen of the sample machine so that the sample machine can display different sample images; The first spectral detection unit is used to perform spectral detection on different sample images of the sample model in the visible light band to generate several sample spectral waveform data. A brightness measurement unit is used to measure the brightness data of each sample spectral waveform data. The first image acquisition unit is used to acquire a set of exposure sequence images with different exposure times for the corresponding sample image each time the first spectral detection unit detects sample spectral waveform data. The fitting unit is used to fit the brightness data and grayscale data and exposure time of the exposure sequence image corresponding to the spectral waveform data of different samples respectively, and generate calibration fitting data sets corresponding to the spectral waveform data of different samples. The database unit is used to store the spectral waveform data of different samples and the corresponding calibration fitting data set; The rate of change generation unit is used to fit the grayscale data and exposure time of each set of exposure sequence images to the grayscale-exposure time relationship, generate corresponding rate of change data and store them in the database unit. Among them, several sample spectral waveform data correspond one-to-one with one rate of change data. The function generation unit is used to generate a corresponding spectral response function based on the grayscale data, exposure time, corresponding rate of change data, and sample spectral waveform data of each set of exposure sequence images, and to store the spectral response function in the database unit; The second spectral detection unit is used to perform spectral detection on the lit screen under test to generate target spectral waveform data; The second image acquisition unit is used to acquire images of the screen under test, generate captured images, and record the actual exposure time. The spectral response function is used to correct the grayscale data of the captured image. A matching unit is used to match the target spectral waveform data with several sample spectral waveform data. The feedback unit is used to determine the target calibration fitting data corresponding to the sample spectral waveform data that best matches the target spectral waveform data; A brightness generation unit is used to generate target brightness data based on the grayscale data of the captured image, the actual exposure time, and the target calibration fitting data.

Images