Method, device, medium and equipment for detecting camera module test environment

Abstract

The present invention provides a method, device, medium and equipment for detecting the test environment of a camera module. The method includes: when a calibration test of a target parameter of a camera module is performed at a burning station, a first test value corresponding to the target parameter is obtained; when the target parameter of the camera module is tested at a detection station, a second test value corresponding to the target parameter is obtained; a test environment detection value is determined according to the first test value and the second test value; and an abnormality detection is performed on the test environment of the camera module according to the test environment detection value; in this way, whether the test environment is abnormal can be verified according to the test value of each calibration test of the target parameter, and then the abnormal test environment can be calibrated in time to ensure the test accuracy of the camera module and improve the yield of the camera module.

Description

Technical Field

[0001] The present application relates to the field of camera module testing technology, and in particular to a method, device, medium and equipment for detecting a camera module testing environment. Background Art

[0002] Currently, there are many test items based on camera modules. Usually, some test items are divided into two stations: burning test and detection test, and the quality of camera modules is controlled through these two stations.

[0003] However, when there are abnormalities in the test environment of the burning test and the detection test, when the camera module is tested in this environment, the test accuracy of the camera module cannot be ensured, which will affect the yield of the camera module.

[0004] Therefore, it is important to determine whether there is any abnormality in the test environment of the camera module and to further improve the accuracy of the test environment of the camera module. Summary of the Invention

[0005] In response to the problems existing in the prior art, the embodiments of the present invention provide a method, device, medium and equipment for detecting the test environment of a camera module, so as to solve or partially solve the technical problems in the prior art that it is impossible to determine whether there is an abnormality in the test environment of the camera module, and it is impossible to ensure the test accuracy of the camera module, thereby affecting the yield of the camera module.

[0006] The technical solution of the present invention is achieved as follows:

[0007] A first aspect of the present invention provides a method for detecting a camera module test environment, the method comprising:

[0008] When a calibration test of a target parameter is performed on the camera module at the burning station, a first test value corresponding to the target parameter is obtained;

[0009] When the target parameter is tested on the camera module at the detection station, a second test value corresponding to the target parameter is obtained;

[0010] Determine a test environment detection value according to the first test value and the second test value;

[0011] An abnormality detection is performed on the test environment of the camera module according to the test environment detection value.

[0012] In the above solution, obtaining a first test value of a target parameter of the camera module includes:

[0013] When the target parameter is a white balance parameter, determining a first color type of a target area in a first image; the first image is an original image captured by the camera module in the burning station based on a test condition corresponding to the white balance parameter;

[0014] Sequentially extracting first test values ​​of the target area in the first image based on the first color type of the target area in the first image and the color arrangement order of the first image; the first test value is the brightness value of each color of the target area in the first image; or

[0015] When the target parameter is a lens shading correction parameter, each color of the second image is extracted to form a sub-image corresponding to four-channel colors; the second image is an original image taken by the camera module in the burning station based on the test conditions corresponding to the lens shading correction parameter,

[0016] Each of the sub-images is divided into a corresponding number of blocks according to the test platform parameters, and a first test value of each block in each sub-image is determined; the first test value is the average brightness value of the corresponding color of each block in each sub-image.

[0017] In the above solution, obtaining the second test value corresponding to the target parameter includes:

[0018] When the target parameter is a white balance parameter, determining a first color type of the target area in a third target image; the third target image is an original image taken by the camera module in the detection station based on the preset white balance parameter;

[0019] Sequentially extracting second test values ​​of the target area in the third target image based on the first color type of the target area in the third image and the color arrangement order of the third image; the second test value is the brightness value of each color of the target area in the third image; or

[0020] When the target parameter is a lens shading correction parameter, each color of the fourth image is extracted respectively to form a sub-image corresponding to four-channel colors; the fourth image is an original image taken by the camera module in the detection station based on the test conditions corresponding to the lens shading correction parameter,

[0021] Divide each sub-image into a corresponding number of blocks according to the test platform parameters, and determine a second test value for each block in each sub-image; the second test value is the average brightness value of the color corresponding to each block. In the above solution, determining the test environment detection value based on the first test value and the second test value includes:

[0022] When the target parameter is a white balance parameter, obtaining the average brightness value of the R color, the average brightness value of the Gr color, the average brightness value of the Gb color, and the average brightness value of the B color in the first test value;

[0023] Determine the average brightness value of the R color, the average brightness value of the Gr color, the average brightness value of the Gb color, and the average brightness value of the B color in the second test value;

[0024] Determine the test environment detection value.

[0025] In the above solution, determining the test environment detection value according to the first test value and the second test value includes:

[0026] Determine the brightness difference value A of the color corresponding to each block n ; The colors include: R color, Gr color, Gb color and B color;

[0027] Determine the average brightness of the corresponding colors of all tiles;

[0028] Determine the reference test environment detection values ​​corresponding to R color, Gr color, Gb color and B color;

[0029] The maximum reference test environment detection value is used as the final test environment detection value.

[0030] In the above solution, the abnormality detection of the test environment of the camera module according to the test environment detection value includes:

[0031] If it is determined that the test environment detection value exceeds a preset threshold range, it is determined that the test environment of the camera module is abnormal.

[0032] A second aspect of the present invention provides a device for detecting a camera module test environment, the device comprising:

[0033] A first acquiring unit, configured to obtain a first test value corresponding to a target parameter when a calibration test of the target parameter is performed on the camera module at the burning station;

[0034] A second acquiring unit is configured to obtain a second test value corresponding to the target parameter when the target parameter is tested on the camera module at the detection station;

[0035] a determining unit, configured to determine a test environment detection value according to the first test value and the second test value;

[0036] A detection unit is used to perform abnormality detection on the test environment of the camera module according to the test environment detection value.

[0037] In the above solution, the first acquiring unit is specifically configured to:

[0038] When the target parameter is a white balance parameter, determining a first color type of a target area in a first picture; the first picture is a first picture taken by the camera module in the burning station based on the preset white balance parameter;

[0039] Sequentially extracting first test values ​​of the first target area based on the first color type of the target area in the first image and the color arrangement order of the first image; the first test values ​​are four-channel color values ​​of each pixel in the target area in the first image; or

[0040] When the target parameter is a lens shading correction parameter, each color of the second image is extracted to form a sub-image corresponding to four-channel colors; the second image is an original image taken by the camera module in the burning station based on the test conditions corresponding to the lens shading correction parameter,

[0041] Each of the sub-images is divided into a corresponding number of blocks according to the test platform parameters, and a first test value of each block in each sub-image is determined; the first test value is the average brightness value of the corresponding color of each block in each sub-image.

[0042] According to a third aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the steps of any one of the methods described in the first aspect are implemented.

[0043] According to a fourth aspect of the present invention, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any one of the methods described in the first aspect are implemented.

[0044] The present invention provides a method, device, medium and equipment for detecting the test environment of a camera module. The method includes: when a calibration test of a target parameter of a camera module is performed at a burning station, a first test value corresponding to the target parameter is obtained; when the target parameter of the camera module is tested at a detection station, a second test value corresponding to the target parameter is obtained; a test environment detection value is determined according to the first test value and the second test value; and an abnormality detection is performed on the test environment of the camera module according to the test environment detection value; in this way, whether the test environment is abnormal can be verified according to the test value of each calibration test of the target parameter, and then the abnormal test environment can be calibrated in time to ensure the test accuracy of the camera module and improve the yield of the camera module. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0046] Figure 1 A schematic diagram of a method for detecting a camera module test environment according to an embodiment of the present invention is shown;

[0047] Figure 2 A schematic diagram showing the color arrangement order in a RAW format image according to an embodiment of the present invention is shown;

[0048] Figure 3 FIG2 shows a schematic diagram of the brightness value of the B color in each block when the number of blocks is 225 according to an embodiment of the present invention;

[0049] Figure 4 1 shows a schematic diagram of the brightness value of the Gr color in each block when the number of blocks is 221 according to an embodiment of the present invention;

[0050] Figure 5 FIG2 shows a schematic diagram of the brightness value of the Gr color in each block when the number of blocks is 825 according to an embodiment of the present invention;

[0051] Figure 6 A schematic diagram of the structure of a device for detecting a camera module test environment according to an embodiment of the present invention is shown;

[0052] Figure 7 A schematic diagram showing the structure of a computer device according to an embodiment of the present invention is shown;

[0053] Figure 8 A schematic diagram of the structure of a computer-readable storage medium according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0054] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0055] This embodiment provides a method for detecting a camera module test environment, such as Figure 1 As shown, the method includes the following steps:

[0056] S110, when performing a calibration test of a target parameter on the camera module at the burning station, obtaining a first test value corresponding to the target parameter;

[0057] To better understand the technical solution of this application, let's first introduce the camera module testing process. Generally speaking, the camera module is first burned at the burning station, and then tested at the testing station. At the burning station and the testing station, the camera module is used to capture images under different parameter conditions. This embodiment mainly uses these images to test the test environment.

[0058] Specifically, when testing a camera module, different target parameters are tested at both the burn-in station and the inspection station. These target parameters may include: Auto White Balance (AWB) parameters, Lens Shading Correction (LSC) parameters, and other parameters.

[0059] When testing the camera module at both the burning station and the testing station, the same test equipment (e.g., the same light source) and test conditions (e.g., illumination and color temperature) are used. However, different target parameters may correspond to different test equipment and test conditions. This embodiment primarily detects differences in the test environments of the burning station and the testing station to determine whether the test environment is abnormal.

[0060] It is worth noting that in this embodiment, when the camera module is calibrated for the target parameter at the burning station, the first test value corresponding to the target parameter is obtained. When the camera module is calibrated for the target parameter at the testing station, the second test value corresponding to the target parameter is obtained. In other words, the test values ​​obtained by the burning station are all first test values, and the test values ​​obtained by the testing station are all second test values.

[0061] In one embodiment, obtaining a first test value of a target parameter of a camera module includes:

[0062] When the target parameter is a white balance parameter, determine the first color type of the target area in the first image; the first image is the original image captured by the camera module in the burning station based on the test conditions corresponding to the white balance parameter. Based on the first color type of the target area in the first image and the color arrangement order of the first image, extract the first test value of the target area in the first image; the first test value is the brightness value of each color in the target area in the first image.

[0063] Alternatively, when the target parameter is a lens shading correction parameter, each color of the second image is extracted separately to form a sub-image corresponding to the four-channel color; the second image is the original image taken by the camera module in the burning station based on the test conditions corresponding to the lens shading correction parameter.

[0064] Since different test platforms require different numbers of blocks, each sub-image is divided into a corresponding number of blocks according to the test platform parameters, and the first test value of each block in each sub-image is determined; the first test value is the average brightness of the corresponding color of each block in each sub-image.

[0065] Specifically, when the target parameter is AWB, the color temperature and illumination of the light source in the burn station are adjusted to the conditions required for the AWB test. The camera module then takes a picture of the preset background to obtain a first image. This first image is the first image captured by the camera module in the burn station based on the test conditions corresponding to the white balance parameters.

[0066] Similarly, when the target parameter is LSC, the color temperature and illumination of the light source in the test station are adjusted to the conditions required for the LSC test. Then, the camera module is used to capture a picture of the preset background to obtain a second image. In other words, the second image is the second image captured by the camera module in the burning station based on the test conditions corresponding to the LSC parameter.

[0067] In order to ensure the test accuracy, after obtaining the first image, this embodiment will determine the target area of ​​the first image based on a preset image division strategy.

[0068] For example, the image segmentation strategy can be: starting from the center of the image and expanding outward until it reaches 1 / 10 or 1 / 5 of the entire image, thus obtaining the target area. In other words, the target area is the area between the reference point and the 1 / 10 or 1 / 5 position of the entire image.

[0069] After determining the target area of ​​the first image, the brightness values ​​of R, Gr, Gb and B colors of the target area in the first image are sequentially extracted based on the first color type of the target area in the first image and the color arrangement order of the first image.

[0070] Here, the first color type of the target area can be determined based on the image sensor type of the camera module. Different sensors have different first color types. For example, the first color type corresponding to sensorA is R, while the first color type corresponding to sensorB is B.

[0071] After the first color type is determined, the brightness value of each color can be extracted in sequence based on the color arrangement order of the first image. The color arrangement order can be determined according to the image format. The original image format in this embodiment is RAW format. The color arrangement order corresponding to the RAW format can be as follows: Figure 2 shown.

[0072] When the target parameter is a lens shading correction parameter, the method for obtaining the first test value is implemented as follows:

[0073] First, the brightness values ​​of the four colors of the second image are extracted respectively. Based on the color type, the brightness values ​​of each color are summarized. In this way, four sub-images can be obtained, and each sub-image has only one color.

[0074] Each sub-image is then divided into a corresponding number of blocks according to the test platform parameters. The number of blocks n needs to be determined according to the test platform parameters. The test platform is mainly used to test the images taken by the camera module and generate a first test value.

[0075] For example, the number of blocks n corresponding to the test platform A is 225. Taking the color B in the first test value as an example, the brightness value of the color B in each block can be as follows: Figure 3 shown.

[0076] The number of blocks n corresponding to the test platform B is 221. Taking the Gr color in the first test value as an example, the brightness value of the Gr color in each block can be as follows: Figure 4 shown.

[0077] The number of blocks n corresponding to the test platform C is 825. Taking the Gr color in the first test value as an example, the brightness value of the Gr color in each block can be as follows: Figure 5 shown.

[0078] Then, for each block, the average brightness value of the corresponding color in the block is determined (a block may contain multiple brightness values ​​of the color), and finally the first test value of each block of each sub-image in the second image is determined.

[0079] For example, for the sub-image corresponding to the R color, assuming that the first block of the sub-image corresponding to the R color contains 3 R colors, then the first test value of the first block is the average brightness of the 3 R colors.

[0080] In this way, the first test value corresponding to the target parameter is obtained.

[0081] S111, when testing the target parameter on the camera module at the testing station, obtaining a second test value corresponding to the target parameter;

[0082] In one embodiment, obtaining a second test value corresponding to a target parameter includes:

[0083] When the target parameter is a white balance parameter, determining a first color type of the target area in the third target image; the third target image is an original image taken by the camera module in the detection station based on the preset white balance parameter;

[0084] Sequentially extract the second test value of the target area in the third target image based on the first color type of the target area in the third image and the color arrangement order of the third image; the second test value is the brightness value of each color in the target area in the third image; or

[0085] When the target parameter is the lens shading correction parameter, each color of the fourth image is extracted to form a sub-image corresponding to the four-channel color; the fourth image is the original image taken by the camera module in the detection station based on the test conditions corresponding to the lens shading correction parameter.

[0086] Each sub-image is divided into a corresponding number of blocks according to the test platform parameters, and a second test value of each block in each sub-image is determined; the second test value is the average brightness value of the color corresponding to each block.

[0087] Specifically, when the target parameter is LSC, the color temperature and illumination of the light source in the burning station are adjusted to the conditions required for the LSC test. Then, the camera module takes a picture of the preset background to obtain a third image. In other words, the third image is the original image captured by the camera module in the burning station based on the test conditions corresponding to the LSC parameters.

[0088] Similarly, when the target parameter is LSC, the color temperature and illumination of the light source in the testing station are adjusted to the conditions required for the LSC test. The camera module then takes a picture of the preset background to obtain the fourth image. This fourth image is the original image captured by the camera module in the testing station based on the test conditions corresponding to the LSC parameters.

[0089] In order to ensure the test accuracy, after obtaining the third image, this embodiment determines the target area of ​​the third image based on a preset image division strategy.

[0090] For example, the image segmentation strategy can be: starting from the center of the image and expanding outward to 1 / 10 or 1 / 5 of the entire image to obtain the target area. In other words, the target area is the area between the reference point and the 1 / 10 or 1 / 5 position of the entire image.

[0091] After determining the target area of ​​the third image, the brightness values ​​of R, Gr, Gb and B colors of the target area in the third image are sequentially extracted based on the first color type of the target area in the third image and the color arrangement order of the third image.

[0092] Similarly, the first color type of the target area can be determined based on the image sensor type of the camera module. Different sensors have different first color types. For example, the first color type corresponding to sensorA is R, while the first color type corresponding to sensorB is B.

[0093] After the first color type is determined, the brightness value of each color in each block can be extracted in sequence based on the color arrangement order of the first image. The color arrangement order can be determined according to the image format. The original image format in this embodiment is RAW format. The color arrangement order corresponding to the RAW format can be as follows: Figure 2 shown.

[0094] Similarly, when the target parameter is a lens shading correction parameter, the method for obtaining the second test value is implemented as follows:

[0095] First, the brightness values ​​of the four colors of the fourth image are extracted respectively. Based on the color type, the brightness values ​​of each color are summarized. In this way, four sub-images can be obtained, and each sub-image has only one color.

[0096] Each sub-image is then divided into a corresponding number of blocks according to the test platform parameters. The number of blocks n needs to be determined according to the test platform parameters. The test platform is also used to test the images taken by the camera module and generate a second test value.

[0097] For example, the number of blocks n corresponding to the test platform A is 225. Taking the color B in the first test value as an example, the brightness value of the color B in each block can be as follows: Figure 3 shown.

[0098] The number of blocks n corresponding to the test platform B is 221. Taking the Gr color in the first test value as an example, the brightness value of the Gr color in each block can be as follows: Figure 4 shown.

[0099] The number of blocks n corresponding to the test platform C is 825. Taking the Gr color in the first test value as an example, the brightness value of the Gr color in each block can be as follows: Figure 5 shown.

[0100] Then, for each block, the average brightness value of the corresponding color in the block is determined (a block may contain multiple brightness values ​​of the color), and finally the second test value of each block of each sub-image in the fourth image is determined.

[0101] For example, for the sub-image corresponding to the R color, assuming that the first image block of the sub-image corresponding to the R color contains 3 R colors, then the second test value of the first image block is the average brightness of the 3 R colors.

[0102] In this way, the second test value corresponding to the target parameter is obtained.

[0103] S112, determining a test environment detection value according to the first test value and the second test value;

[0104] After the first test value and the second test value are determined, the test environment detection value can be determined according to the first test value and the second test value.

[0105] It should be noted that if the target parameters are different, the method of determining the test environment detection value will also be different.

[0106] In one embodiment, determining the test environment detection value according to the first test value and the second test value includes:

[0107] When the target parameter is a white balance parameter, the average brightness value of the R color, the average brightness value of the Gr color, the average brightness value of the Gb color, and the average brightness value of the B color in the first test value are obtained;

[0108] Determine the average brightness value of the R color, the average brightness value of the Gr color, the average brightness value of the Gb color, and the average brightness value of the B color in the second test value;

[0109] According to the formula Determine the test environment detection value S; where,

[0110] R1 is the average brightness of all R colors in the first test value, R2 is the average brightness of all R colors in the second test value, Gr1 is the average brightness of all Gr colors in the first test value; Gr2 is the average brightness of all Gr colors in the second test value; Gb1 is the average brightness of all Gb colors in the first test value; Gb2 is the average brightness of all Gb colors in the second test value; B1 is the average brightness of all B colors in the first test value; B2 is the average brightness of all B colors in the second test value.

[0111] In one embodiment, determining the test environment detection value according to the first test value and the second test value includes:

[0112] When the target parameter is the lens shading correction parameter, according to the formula Determine the brightness difference value A of the color corresponding to each block n ; Colors include: R color, Gr color, Gb color and B color;

[0113] According to the formula Determine the average brightness value Avg1 of the corresponding colors of all blocks in each sub-image;

[0114] According to the formula Determine the reference test environment detection values ​​S corresponding to R color, Gr color, Gb color, and B color respectively;

[0115] The maximum reference test environment detection value is used as the final test environment detection value; among them,

[0116] The n is any block in each sub-image, X1(n) is the average brightness value of the color corresponding to the n-th block in the third image, and X2(n) is the average brightness value of the color corresponding to the n-th block in the fourth image.

[0117] Specifically, the fourth picture includes 4 sub-pictures, and each sub-picture corresponds to a different color. Therefore, when determining the A corresponding to the R color, n When , X1(n) is the average brightness value of the color R of the n-th block in the third image, and X2(n) is the average brightness value of the color R of the n-th block in the fourth image. The other colors are similar and will not be repeated here.

[0118] In this way, four reference test environment detection values ​​S are eventually determined. In this embodiment, the maximum value of the four reference test environment detection values ​​is used as the final test environment detection value.

[0119] S113: Perform abnormality detection on the test environment of the camera module according to the test environment detection value.

[0120] After determining the test environment detection value, perform an abnormality detection on the test environment of the camera module according to the test environment detection value, including:

[0121] If it is determined that the test environment detection value exceeds the preset threshold range, it is determined that the test environment of the camera module is abnormal.

[0122] In this embodiment, the threshold range for the test environment detection value exceeding the preset threshold value varies for different target parameters. For example, if the target parameter is a white balance parameter, the threshold range is 0-20%. That is, when the test environment detection value is greater than 20% or less than 0, it is determined that the test environment is abnormal.

[0123] If the target parameter is a lens shading correction parameter, the threshold range is 0 to 30%, that is, when the test environment detection value is greater than 30% or less than 0, it is determined that the test environment is abnormal.

[0124] In this embodiment, since the test environment includes the test environment of the burning station and the test environment of the detection station, when it is determined that the test environment is abnormal, it is necessary to further check whether the abnormality occurs in the test environment of the detection station or the test environment of the burning station.

[0125] For example, the test conditions (color temperature, illumination, etc.) of the burning station and the testing station and the test equipment can be checked item by item to see if they meet the test requirements. If not, it means that there is an abnormality in the corresponding test environment.

[0126] Based on the same inventive concept as in the above embodiment, this embodiment also provides a device for detecting the test environment of a camera module, such as Figure 6 As shown, the device includes:

[0127] A first obtaining unit 61 is configured to obtain a first test value corresponding to a target parameter when a calibration test of a target parameter is performed on the camera module at the burning station;

[0128] A second acquiring unit 62 is configured to obtain a second test value corresponding to the target parameter when the target parameter is tested on the camera module at the testing station;

[0129] a determining unit 63, configured to determine a test environment detection value according to the first test value and the second test value;

[0130] The detection unit 64 is used to detect abnormalities in the test environment of the camera module according to the test environment detection value.

[0131] In one embodiment, the first acquiring unit 61 is specifically configured to:

[0132] When the target parameter is a white balance parameter, determining a first color type of a target area in a first picture; the first picture is a first picture taken by the camera module in the burning station based on the preset white balance parameter;

[0133] Sequentially extracting first test values ​​of the first target area based on the first color type of the target area in the first image and the color arrangement order of the first image; the first test values ​​are four-channel color values ​​of each pixel in the target area in the first image; or

[0134] When the target parameter is a lens shading correction parameter, each color of the second image is extracted to form a sub-image corresponding to four channels of color; the second image is an original image captured by the camera module in the burning station based on the test conditions corresponding to the lens shading correction parameter;

[0135] Each of the sub-images is divided into a corresponding number of blocks according to the test platform parameters, and a first test value of each block in each sub-image is determined; the first test value is the average brightness value of the corresponding color of each block in each sub-image.

[0136] Since the device described in the embodiment of the present invention is a device used to implement the method for detecting the camera module test environment of the embodiment of the present invention, based on the method described in the embodiment of the present invention, those skilled in the art can understand the specific structure and deformation of the device, so they are not described in detail here. All devices used in the method of the embodiment of the present invention fall within the scope of protection of the present invention.

[0137] Based on the same inventive concept, this embodiment provides a computer device 700, such as Figure 7As shown, it includes a memory 710, a processor 720, and a computer program 711 stored in the memory 710 and executable on the processor 720. When the processor 720 executes the computer program 711, any step of the method described above is implemented.

[0138] Based on the same inventive concept, this embodiment provides a computer-readable storage medium 700, such as Figure 7 As shown, a computer program 711 is stored thereon, and when the computer program 711 is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0139] Through one or more embodiments of the present invention, the present invention has the following beneficial effects or advantages:

[0140] The present invention provides a method, device, medium and equipment for detecting the test environment of a camera module. The method includes: when a calibration test of a target parameter of a camera module is performed at a burning station, a first test value corresponding to the target parameter is obtained; when the target parameter of the camera module is tested at a detection station, a second test value corresponding to the target parameter is obtained; a test environment detection value is determined according to the first test value and the second test value; and an abnormality detection is performed on the test environment of the camera module according to the test environment detection value; in this way, whether the test environment is abnormal can be verified according to the test value of each calibration test of the target parameter, and then the abnormal test environment can be calibrated in time to ensure the test accuracy of the camera module and improve the yield of the camera module.

[0141] The algorithm and display provided herein are not inherently related to any particular computer, virtual system or other device. Various general-purpose systems can also be used together with the teachings based on this. According to the above description, it is obvious that the structure required for constructing this type of system. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages ​​can be utilized to realize the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the best mode of the present invention.

[0142] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0143] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0144] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and in addition may be divided into multiple submodules or subunits or subcomponents. All features disclosed in this specification (including the accompanying claims, abstracts and drawings) and all processes or units of any method or device disclosed herein may be combined in any combination, except that at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstracts and drawings) may be replaced by an alternative feature providing the same, equivalent or similar purpose.

[0145] Furthermore, those skilled in the art will appreciate that although some embodiments herein include certain features included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, in the claims below, any of the claimed embodiments may be used in any combination.

[0146] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It should be understood by those skilled in the art that a microprocessor or digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the gateway, proxy server, or system according to an embodiment of the present invention. The present invention can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for executing part or all of the methods described herein. Such a program implementing the present invention can be stored on a computer-readable medium, or can have the form of one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or provided in any other form.

[0147] It should be noted that the above embodiments illustrate rather than limit the invention, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0148] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0149] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting a camera module test environment, characterized in that: The method comprises: When a calibration test of a target parameter is performed on the camera module at the burning station, a first test value corresponding to the target parameter is obtained; When the target parameter is tested on the camera module at the detection station, a second test value corresponding to the target parameter is obtained; Determine a test environment detection value according to the first test value and the second test value; According to the test environment detection value, the test environment of the camera module is detected for abnormality; wherein, The determining of the test environment detection value according to the first test value and the second test value includes: When the target parameter is a white balance parameter, obtaining the average brightness value of the R color, the average brightness value of the Gr color, the average brightness value of the Gb color, and the average brightness value of the B color in the first test value; Determine the average brightness value of the R color, the average brightness value of the Gr color, the average brightness value of the Gb color, and the average brightness value of the B color in the second test value; According to the formula Determining the test environment detection value; R1 is the average brightness of all R colors in the first test value, R2 is the average brightness of all R colors in the second test value, Gr1 is the average brightness of all Gr colors in the first test value; Gr2 is the average brightness of all Gr colors in the second test value; Gb1 is the average brightness of all Gb colors in the first test value; Gb2 is the average brightness of all Gb colors in the second test value; B1 is the average brightness of all B colors in the first test value; B2 is the average brightness of all B colors in the second test value.

2. The method according to claim 1, wherein The obtaining of a first test value of the target parameter comprises: When the target parameter is a white balance parameter, determining a first color type of a target area in a first image; the first image is an original image captured by the camera module in the burning station based on a test condition corresponding to the white balance parameter; Sequentially extracting first test values ​​of the target area in the first image based on the first color type of the target area in the first image and the color arrangement order of the first image; the first test value is the average brightness value of each color in the target area in the first image; or When the target parameter is a lens shading correction parameter, each color of the second image is extracted to form a sub-image corresponding to four channels of color; the second image is an original image captured by the camera module in the burning station based on the test conditions corresponding to the lens shading correction parameter; Each of the sub-images is divided into a corresponding number of blocks according to the test platform parameters, and a first test value of each block in each sub-image is determined; the first test value is the average brightness value of the corresponding color of each block in each sub-image.

3. The method according to claim 1, wherein The obtaining of a second test value corresponding to the target parameter includes: When the target parameter is a white balance parameter, determining a first color type of the target area in a third image; the third image is an original image taken by the camera module in the detection station based on the preset white balance parameter; Sequentially extracting second test values ​​of the target area in the third image based on the first color type of the target area in the third image and the color arrangement order of the third image; the second test value is the average brightness value of each color in the target area in the third image; or When the target parameter is a lens shading correction parameter, each color of the fourth image is extracted respectively to form a sub-image corresponding to four-channel colors; the fourth image is an original image taken by the camera module in the detection station based on the test conditions corresponding to the lens shading correction parameter, Each of the sub-images is divided into a corresponding number of blocks according to the test platform parameters, and a second test value of each block in each sub-image is determined; the second test value is the average brightness value of the color corresponding to each block.

4. The method according to claim 1, wherein The determining of the test environment detection value according to the first test value and the second test value includes: When the target parameter is a lens shading correction parameter, according to the formula Determine the brightness difference value A of the color corresponding to each block n ; The colors include: R color, Gr color, Gb color and B color; Determine the average brightness value Avg1 of the corresponding colors of all blocks in each sub-image; According to the formula Determine the reference test environment detection values ​​S corresponding to R color, Gr color, Gb color, and B color respectively; The maximum reference test environment detection value is used as the final test environment detection value; Where n is the total number of blocks in each sub-image, X1(n) is the average brightness value of the color corresponding to the n-th block in the second image, and X2(n) is the average brightness value of the color corresponding to the n-th block in the fourth image.

5. The method according to claim 1, wherein The performing abnormality detection on the test environment of the camera module according to the test environment detection value includes: If it is determined that the test environment detection value exceeds a preset threshold range, it is determined that the test environment of the camera module is abnormal.

6. A device for detecting a camera module test environment, characterized in that: The device comprises: A first acquiring unit, configured to obtain a first test value corresponding to a target parameter when a calibration test of the target parameter is performed on the camera module at the burning station; A second acquiring unit is configured to obtain a second test value corresponding to the target parameter when the target parameter is tested on the camera module at the detection station; a determining unit, configured to determine a test environment detection value according to the first test value and the second test value; A detection unit is used to detect abnormalities in the test environment of the camera module according to the test environment detection value; wherein, The determining of the test environment detection value according to the first test value and the second test value includes: When the target parameter is a white balance parameter, obtaining the average brightness value of the R color, the average brightness value of the Gr color, the average brightness value of the Gb color, and the average brightness value of the B color in the first test value; Determine the average brightness value of the R color, the average brightness value of the Gr color, the average brightness value of the Gb color, and the average brightness value of the B color in the second test value; According to the formula Determining the test environment detection value; R1 is the average brightness of all R colors in the first test value, R2 is the average brightness of all R colors in the second test value, Gr1 is the average brightness of all Gr colors in the first test value; Gr2 is the average brightness of all Gr colors in the second test value; Gb1 is the average brightness of all Gb colors in the first test value; Gb2 is the average brightness of all Gb colors in the second test value; B1 is the average brightness of all B colors in the first test value; B2 is the average brightness of all B colors in the second test value.

7. The device according to claim 6, characterized in that The first acquiring unit is specifically configured to: When the target parameter is a white balance parameter, determining a first color type of a target area in a first image; the first image is an original image captured by the camera module in the burning station based on a test condition corresponding to the white balance parameter; Sequentially extracting first test values ​​of the target area in the first image based on the first color type of the target area in the first image and the color arrangement order of the first image; the first test value is the average brightness value of each color in the target area in the first image; or When the target parameter is a lens shading correction parameter, each color of the second image is extracted to form a sub-image corresponding to four-channel colors; the second image is an original image taken by the camera module in the burning station based on the test conditions corresponding to the lens shading correction parameter, Each of the sub-images is divided into a corresponding number of blocks according to the test platform parameters, and a first test value of each block in each sub-image is determined; the first test value is the average brightness value of the corresponding color of each block in each sub-image.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the method according to any one of claims 1 to 5 are implemented.

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