CN116193271A - Gamma curve debugging method, gamma curve debugging device and computer storage medium - Google Patents
Gamma curve debugging method, gamma curve debugging device and computer storage medium Download PDFInfo
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Abstract
The application discloses a gamma curve debugging method, a gamma curve debugging device and a computer storage medium. The gamma curve debugging method comprises the following steps: under the same environment, respectively acquiring first image data of reference equipment and second image data of test equipment; acquiring a data difference value of the first image data and the second image data; acquiring a difference curve of the reference equipment and the test equipment by utilizing the data difference value and first image data of the reference equipment; and determining the gamma curve of the test equipment according to the difference curve and the gamma curve of the reference equipment. By means of the method, the gamma curve can be automatically calibrated, the problem of device variability can be effectively eliminated, and the brightness consistency of the dynamic range of the equipment is improved.
Description
Technical Field
The application relates to the technical field of image processing, in particular to a gamma curve debugging method, a gamma curve debugging device and a computer storage medium.
Background
In the current image processing technical field, because different devices have different hardware such as lenses, sensors and filters, the problem that local brightness is inconsistent can exist in images of different devices under the condition that the average brightness of the sensitization of the sensors is consistent in the same illumination environment, and the problem can be reflected on RAW data of the images. If the RAW data of the images are different due to the device variability problem, the difference of the RAW data of the images is further amplified when a subsequent series of image processing processes are performed, which results in the problems of brightness and dynamic range variability of the images on a plurality of devices when the multi-serialization device is developed.
Disclosure of Invention
The application provides a gamma curve debugging method, a gamma curve debugging device and a computer storage medium, so as to solve the problem that images on a plurality of devices have brightness and dynamic range differences.
In order to solve the technical problems, one technical scheme adopted by the application is as follows: provided is a gamma curve debugging method, comprising the following steps: under the same environment, respectively acquiring first image data of reference equipment and second image data of test equipment; acquiring a data difference value of the first image data and the second image data; acquiring a difference curve of the reference equipment and the test equipment by utilizing the data difference value and first image data of the reference equipment; and determining the gamma curve of the test equipment according to the difference curve and the gamma curve of the reference equipment.
The gamma curve debugging method further comprises the following steps: acquiring average brightness based on the initial image data; the amount of light input to the reference device and the test device is set according to the average luminance.
Under the same environment, respectively acquiring first image data of the reference device and second image data of the test device, wherein the method comprises the following steps: under the same environment, respectively acquiring third image data of the reference equipment and fourth image data of the test equipment; converting the third image data of the reference device into first image data; converting fourth image data of the test device into second image data; wherein the data type of the first image data and the second image data is RGB data, and the data type of the third image data and the fourth image data is RAW data.
Wherein acquiring a data difference of the first image data and the second image data includes: acquiring first channel data, second channel data and third channel data of first image data, and acquiring the first channel data, the second channel data and the third channel data of second image data; acquiring a first channel data difference value between first channel data of first image data and first channel data of second image data, acquiring a second channel data difference value between second channel data of the first image data and second channel data of the second image data, and acquiring a third channel data difference value between third channel data of the first image data and third channel data of the second image data.
The method for acquiring the difference curve of the reference equipment and the test equipment by utilizing the data difference value and the first image data of the reference equipment comprises the following steps:
acquiring first channel difference data by using the first channel data difference value and first channel data of the first image data, acquiring second channel difference data by using the second channel data difference value and second channel data of the first image data, and acquiring third channel difference data by using third channel data difference value and third channel data of the first image data; converting the first channel difference data, the second channel difference data and the third channel difference data into a two-dimensional dataset; and generating a difference curve of the reference equipment and the test equipment according to the two-dimensional data set.
Wherein the two-dimensional dataset comprises channel data and difference data; generating a difference curve of the reference device and the test device according to the two-dimensional data set, wherein the difference curve comprises the following steps: taking channel data in the two-dimensional data set as an abscissa and difference data as an ordinate, and fitting to generate a mapping two-dimensional map; acquiring an average value of a plurality of difference data corresponding to each channel data; and fitting and generating a difference curve of the reference equipment and the test equipment according to the data of each channel and the average value of the corresponding difference data.
Wherein, confirm the gamma curve of the test equipment according to the gamma curve of difference curve and reference equipment, include: and under the same channel data, performing linear multiplication compensation operation on the brightness value of the gamma curve of the reference equipment by using the difference data average value of the difference curve to obtain the brightness value of the gamma curve of the test equipment under the channel data.
Before the compensation operation of performing linear multiplication on the brightness value of the gamma curve of the reference device by using the difference data average value of the difference curve, the gamma curve method further comprises: acquiring the maximum channel data of a gamma curve of reference equipment; and linearly mapping the channel data of the difference curve according to the maximum channel data to unify the maximum channel data of the difference curve and the gamma curve of the reference equipment.
In order to solve the technical problems, one technical scheme adopted by the application is as follows: there is provided a gamma curve debugging apparatus comprising a memory for storing program data and a processor coupled to the memory for executing the program data to implement the gamma curve debugging method of any one of the above.
In order to solve the technical problems, another technical scheme adopted by the application is as follows: there is provided a computer storage medium having stored therein program instructions that are executed to implement the gamma curve debugging method of any one of the above.
The beneficial effects of this application are: different from the condition of the prior art, the gamma curve debugging method of the application firstly obtains the first image data of the reference equipment and the second image data of the test equipment respectively under the same environment; calculating a data difference value of the first image data and the second image data, so as to obtain a difference curve of the reference equipment and the test equipment by utilizing the data difference value and the first image data of the reference equipment; and finally, determining the gamma curve of the test equipment according to the difference curve and the gamma curve of the reference equipment.
Drawings
FIG. 1 is a flow chart of a first embodiment of a gamma curve debugging method provided in the present application;
FIG. 2 is a schematic diagram of one embodiment of a TC004HD test card provided herein;
FIG. 3 is a flow chart of a second thermal embodiment of the gamma curve debugging method provided by the present application;
FIG. 4 is a flowchart of the step S101 in FIG. 1;
FIG. 5 is a flowchart illustrating the step S102 in FIG. 1;
FIG. 6 is a flowchart illustrating step S103 in FIG. 1;
FIG. 7 is a flowchart of a specific implementation method of step S503 in FIG. 6;
FIG. 8 is a two-dimensional schematic of the mapping of RGB values to diffRGB values provided herein;
FIG. 9 is a schematic diagram of an embodiment of a difference curve provided herein;
FIG. 10 is a flowchart of the step S104 in FIG. 1;
FIG. 11 is a schematic diagram illustrating an embodiment of a gamma curve debugging apparatus according to the present disclosure;
fig. 12 is a schematic structural diagram of an embodiment of a computer storage medium provided in the present application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In the multi-serialization equipment development process, because different image pickup equipment is provided with different hardware such as lenses, sensors and filters, the problem that local brightness is inconsistent in the images acquired by the different image pickup equipment is caused even if the image pickup equipment is consistent with the average brightness of the sensitization of the sensors in the same illumination environment when the images are acquired.
The problem of inconsistent local brightness of the image is reflected on Raw data of the acquired image, wherein Raw refers to an original image file, in short, data information before the photosensitive element operates to generate the image, and the photographing device acquires light through a lens and interprets and calculates the light to form the image.
If the RAW data of the images are different due to the device variability problem, the difference of the RAW data of the images is further amplified when a subsequent series of image processing processes are performed, which results in the problems of brightness and dynamic range variability of the images on a plurality of devices when the multi-serialization device is developed. The dynamic range refers to the richness of dark part details and bright part details recorded by the image pickup equipment at the same time, and the higher the dynamic range is, the more abundant picture details can be recorded. In the image pickup apparatus, there occurs a deviation in brightness of an image actually output due to a graphic card or a display, and a gamma curve is used to correct the brightness deviation of the image.
In order to solve the problem of difference in image RAW data caused by the problem of device variability and acquire a new gamma curve of test equipment, the present application firstly proposes a gamma curve debugging method, please refer to fig. 1, fig. 1 is a flow diagram of a first embodiment of the gamma curve debugging method provided in the present application. As shown in fig. 1, the gamma curve debugging method of the present embodiment specifically includes steps S101 to S104:
step S101: under the same environment, the first image data of the reference device and the second image data of the test device are acquired respectively.
In order to eliminate the device variability of the test equipment and the reference equipment, first, the gamma curve debugging device needs to acquire first image data of the reference equipment under the same environment and second image data of the test equipment under the same environment.
For example, when the first image data of the reference device is acquired, RAW data of the reference device may be acquired in a specific color temperature environment and converted into first image data, where the first image data is RGB data of the reference device and gamma curve data of the reference device in the current environment, and when the gamma curve of the reference device and the first image data are acquired, the color temperature is not limited, and the color temperature may be a fixed color temperature or a full color temperature, depending on whether the reference device uses different gamma curves according to different color temperature environments.
After the first image data of the reference device is acquired, when the second image data of the test device is acquired, the RAW data of the test device under the same color temperature and the same environment are acquired and converted into the second image data, and the second image data is the RGB data of the test device. In this embodiment, the same demosaic algorithm may be used to convert RAW data into RGB data.
In addition, when the first image data of the reference device and the second image data of the test device are acquired, an imatestt TC004HD test card may be placed in the shooting areas of the reference device and the test device, as shown in fig. 2, and fig. 2 is a schematic diagram of an embodiment of the TC004HD test card provided in the present application. The Imatest TC004HD test card is placed, so that the color difference of the acquired image data is large, and the calculation efficiency of a subsequent method is improved. In other embodiments, other test cards may be placed within the capture area, without limitation.
Step S102: a data difference of the first image data and the second image data is acquired.
The gamma curve debugging device calculates RGB data difference values of the first image data and the second image data after acquiring the first image data of the reference device and the second image data of the test device under the same environment.
For example, in this embodiment, the remote image data of the reference device and the remote image data of the test device may be converted into RGB image data by using a remote algorithm, and then the data difference between the two may be calculated. Among them, demosaic is the most important ring in image processing, and its main function is to convert RAW format data obtained by a sensor into a complete RGB data format that can be seen by human eyes. In other embodiments, other algorithms may be used for format conversion, which is not limited herein.
Step S103: and acquiring a difference curve of the reference equipment and the test equipment by utilizing the data difference value and the first image data of the reference equipment.
After the gamma curve debugging device obtains the data difference value between the reference device and the test device in step S102, the data difference value and the first image data of the reference device are used to obtain the difference data of the three-color channels between the reference device and the test device, and finally, a difference curve between the reference device and the test device is generated by fitting the difference data based on the three-color channels.
Step S104: and determining the gamma curve of the test equipment according to the difference curve and the gamma curve of the reference equipment.
The gamma curve debugging device performs linear multiplication operation on the obtained difference curve and the gamma curve of the parameter equipment, and particularly, as described below, the gamma curve of the test equipment can be determined.
Different from the condition of the prior art, the gamma curve debugging method of the application firstly obtains the first image data of the reference equipment and the second image data of the test equipment respectively under the same environment; calculating a data difference value of the first image data and the second image data, so as to obtain a difference curve of the reference equipment and the test equipment by utilizing the data difference value and the first image data of the reference equipment; and finally, determining the gamma curve of the test equipment according to the difference curve and the gamma curve of the reference equipment.
Optionally, referring to fig. 3, fig. 3 is a flow chart of a second thermal embodiment of the gamma curve debugging method provided in the present application. As shown in fig. 3, the gamma curve debugging method of the present embodiment specifically includes steps S201 to S206:
step S201: based on the initial image data, an average luminance is obtained.
The gamma curve debugging device acquires first image data of the reference device and second image data of the test device, the reference device and the test device are firstly arranged in the same environment, and the input of the reference device and the test device should be the same.
In this embodiment, initial image data of an initial state of the reference device may be acquired first, the initial image data including RAW data of the reference device in the initial state, and the average brightness thereof may be acquired by calculation based on the acquired RAW data of the reference device in the initial state.
Step S202: the amount of light input to the reference device and the test device is set according to the average luminance.
After calculating the average luminance of RAW data for acquiring the initial state of the reference device, the light intake amount of the initial state of the reference device may be acquired based on the average luminance, so that the light intake amounts of the reference device and the test device may be set so that the light intake amounts of the reference device and the test device remain the same, before the step of acquiring the first image data of the reference device and the second image data of the test device.
Step S203: under the same environment, the first image data of the reference device and the second image data of the test device are acquired respectively.
Step S203 corresponds to step S101, and will not be described here.
Step S204: a data difference of the first image data and the second image data is acquired.
Step S204 corresponds to step S102, and will not be described here.
Step S205: and acquiring a difference curve of the reference equipment and the test equipment by utilizing the data difference value and the first image data of the reference equipment.
Step S205 corresponds to step S103, and will not be described here.
Step S206: and determining the gamma curve of the test equipment according to the difference curve and the gamma curve of the reference equipment.
Step S206 corresponds to step S104, and will not be described here.
Optionally, based on the above embodiment, a method for acquiring the first image data of the reference device and the second image data of the test device is shown in fig. 4, please refer to fig. 4, and fig. 4 is a flowchart of an embodiment of step S101 in fig. 1. In this embodiment, the data types of the first image data and the second image data are RGB data, and the data types of the third image data and the fourth image data are RAW data. The present embodiment may implement step S101 by the method shown in fig. 4, where the specific implementation steps include steps S301 to S303:
step S301: under the same environment, the third image data of the reference device and the fourth image data of the test device are acquired respectively.
Because of the device differences between the reference device and the test device, the image data in the RAW format acquired by the reference device and the test device will also have differences, so that the third image data in the RAW format of the reference device and the fourth image data in the RAW format of the test device should be acquired respectively under the same environment.
Step S302: third image data of the reference device is converted into first image data.
In order to facilitate the subsequent calculation of the difference in image data, the gamma curve debugging apparatus needs to convert the third image data of the reference device into the first image data, i.e., to convert RAW format data acquired from the reference device into RGB data that facilitates the calculation of the difference.
Step S303: fourth image data of the test device is converted into second image data.
Similarly, the gamma curve debugging device also needs to convert the fourth image data of the test device into the second image data, that is, the RAW format data acquired from the test device into RGB data that is favorable for calculating the difference.
Optionally, a method for acquiring the data difference between the first image data and the second image data is shown in fig. 5, please refer to fig. 5, fig. 5 is a flowchart of an embodiment of step S102 in fig. 1. The present embodiment may implement step S102 by the method shown in fig. 5, where the specific implementation steps include steps S401 to S404:
step S401: first channel data, second channel data and third channel data of the first image data are acquired, and first channel data, second channel data and third channel data of the second image data are acquired.
After converting RAW data of the reference device into first image data in RGB format, the gamma curve debugging device may acquire first channel data, second channel data and third channel data of the first image data based on the first image data in RGB format, respectively; the gamma curve debugging device may also acquire the first channel data, the second channel data, and the third channel data of the second image data based on the second image data in RGB format, respectively.
Step S402: a first channel data difference between first channel data of the first image data and first channel data of the second image data is acquired.
The gamma curve debugging means calculates a first channel data difference between the first channel data of the first image data and the first channel data of the second image data, and marks it as diffR.
Step S403: a second channel data difference between second channel data of the first image data and second channel data of the second image data is acquired.
The gamma curve debugging means calculates a second channel data difference value between the second channel data of the first image data and the second channel data of the second image data, and marks it as diffG.
Step S404: a third channel data difference between third channel data of the first image data and third channel data of the second image data is acquired.
The gamma curve debugging means calculates a third channel data difference between the third channel data of the first image data and the third channel data of the second image data, which is denoted as diffB.
In this embodiment, the calculated data difference diffR, diffG, diffB is consistent with the resolution of the RAW data.
Optionally, as shown in fig. 6, referring to fig. 6, fig. 6 is a flowchart of an embodiment of step S103 in fig. 1. The present embodiment may implement step S103 by a method as shown in fig. 6, where the specific implementation steps include steps S501 to S503:
step S501: the method comprises the steps of obtaining first channel difference data by using a first channel data difference value and first channel data of first image data, obtaining second channel difference data by using a second channel data difference value and second channel data of the first image data, and obtaining third channel difference data by using a third channel data difference value and third channel data of the first image data.
The gamma curve debugging device obtains first channel difference data by using a first channel data difference value and first channel data of first image data, obtains second channel difference data by using a second channel data difference value and second channel data of the first image data, and obtains third channel difference data by using a third channel data difference value and third channel data of the first image data.
When calculating the first channel difference data, first channel data of the first image data of the reference device, namely a pixel value R of the first channel, is obtained, and then a ratio of a difference value DiffR of the first channel data to the pixel value R of the first channel is obtained, wherein a calculation formula is DiffRatior=DiffR/R. The diffratio is expressed as first channel difference data, and the value range of the diffratio is 0-1.
The second channel data difference value and the third channel difference data are obtained as above, wherein the calculation formula of the second channel data difference value is diff ratio g=diffg/G, the calculation formula of the third channel data difference value is diff ratio b=diffb/B, wherein G is the second channel data of the first image data of the reference device, namely, the pixel value of the second channel, and diff ratio G is expressed as the second channel difference data; b is third channel data of the first image data of the reference device, i.e. pixel values of the third channel, diffratio being denoted as third channel difference data.
Step S502: and converting the first channel difference data, the second channel difference data and the third channel difference data into a two-dimensional data set.
The gamma curve debugging device acquires the channel data diffRatiorgb in the set by the acquired first channel difference data diffRatior, second channel difference data diffRatiog and third channel difference data diffRatiob and converts the channel data diffRatiorgb into a two-dimensional data set. Here, the pixel values at different physical locations have the same RGB values, but have different diffRGB values, so the obtained diffratio RGB will have a difference.
Step S503: and generating a difference curve of the reference equipment and the test equipment according to the two-dimensional data set.
The gamma curve debugging device acquires diffRatioRGB values corresponding to 0-255 continuous RGB values according to the acquired two-dimensional data set, averages the diffRatioRGB values, and then carries out linear fitting on the diffRatioRGB values, so as to acquire a difference curve of the reference equipment and the test equipment.
Optionally, referring to fig. 7, fig. 7 is a flow chart of a method for implementing step S503 in fig. 6. In this embodiment, the two-dimensional dataset includes channel data and variance data. As shown in fig. 7, the present embodiment may implement step S503 by the method shown in fig. 7, and the specific implementation steps include steps S601 to S603:
step S601: and fitting by taking channel data in the two-dimensional data set as an abscissa and difference data as an ordinate to generate a mapping two-dimensional map.
Referring to fig. 8, fig. 8 is a two-dimensional schematic diagram of mapping of RGB values and diffRGB values provided in the present application. As shown in fig. 8, the gamma curve debugging device generates a mapping two-dimensional map as shown in fig. 8 by fitting the gamma curve debugging device with the channel data in the two-dimensional data set as an abscissa and the difference data as an ordinate.
Step S602: and obtaining an average value of a plurality of difference data corresponding to each channel data.
The gamma curve debugging device obtains the average value of a plurality of difference data corresponding to each channel data, as shown in fig. 8, the abscissa is an RGB value, the ordinate is a corresponding diff ratio RGB value, and finally, after the mapping two-dimensional map shown in fig. 8 is generated by fitting, the average value of the diff RGB value of each RGB value corresponding to each difference data between 0 and 255 is obtained.
Step S603: and fitting and generating a difference curve of the reference equipment and the test equipment according to the data of each channel and the average value of the corresponding difference data.
Referring to fig. 9, fig. 9 is a schematic diagram of an embodiment of a difference curve provided in the present application. As shown in 9, the gamma curve debugging device fits and generates a difference curve of the reference equipment and the test equipment according to the data of each channel and the average value of the corresponding difference data. And taking average value of all diffRatioRGB values corresponding to RGB values with continuous 0-255, and then performing linear fitting to generate a difference curve of the reference equipment and the test equipment.
Optionally, as shown in fig. 10, referring to fig. 10, fig. 10 is a flowchart illustrating an embodiment of step S104 in fig. 1. The present embodiment may implement step S104 by the method shown in fig. 10, and the specific implementation steps include steps S701 to S703:
step S701: maximum channel data of a gamma curve of a reference device is acquired.
After the gamma curve debugging device obtains the difference curve between the reference equipment and the test equipment as shown in fig. 9, the difference curve can be multiplied by the gamma curve of the reference equipment linearly, so that a new gamma curve of the test equipment can be obtained. The calculation mode is as follows, firstly, the gamma curve debugging device needs to acquire the maximum channel data of the gamma curve of the reference equipment.
Step S702: and linearly mapping the channel data of the difference curve according to the maximum channel data to unify the maximum channel data of the difference curve and the gamma curve of the reference equipment.
And the gamma curve debugging device performs linear mapping on the channel data of the difference curve according to the maximum channel data so as to unify the difference curve and the maximum channel data of the gamma curve of the reference equipment.
Step S703: and under the same channel data, performing linear multiplication compensation operation on the brightness value of the gamma curve of the reference equipment by using the difference data average value of the difference curve to obtain the brightness value of the gamma curve of the test equipment under the channel data.
And finally, under the same channel data, the gamma curve debugging device performs linear multiplication compensation operation on the brightness value of the gamma curve of the reference equipment by utilizing the difference data average value of the difference curve so as to obtain the brightness value of the gamma curve of the test equipment under the channel data.
In this embodiment, the gamma curve debugging device performs linear mapping on the abscissa of the gamma curve of the reference device and the abscissa of the difference curve, and the maximum value is mapped correspondingly to unify the abscissa, and then performs linear multiplication compensation operation on the brightness value of the gamma curve of the reference device by using the average value of the difference data of the difference curve to obtain the brightness value of the gamma curve of the test device under the channel data, thereby finally obtaining the gamma curve of the test device.
Different from the condition of the prior art, the gamma curve debugging method of the application firstly obtains the first image data of the reference equipment and the second image data of the test equipment respectively under the same environment; calculating a data difference value of the first image data and the second image data, so as to obtain a difference curve of the reference equipment and the test equipment by utilizing the data difference value and the first image data of the reference equipment; and finally, determining the gamma curve of the test equipment according to the difference curve and the gamma curve of the reference equipment.
Optionally, referring to fig. 11, fig. 11 is a schematic structural diagram of an embodiment of a gamma curve debugging device provided in the present application, and the gamma curve debugging device 200 includes a processor 201 and a memory 202 coupled to the processor 201.
The processor 201 may also be referred to as a CPU (Central Processing Unit ). The processor 201 may be an integrated circuit chip with signal processing capabilities. Processor 201 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 202 is used to store program data required for the operation of the processor 201.
The processor 201 is also configured to execute program data stored in the memory 202 to implement the gamma curve debugging method described above.
Optionally, the present application further proposes a computer storage medium. Referring to fig. 12, fig. 12 is a schematic structural diagram of an embodiment of a computer storage medium provided in the present application.
The computer storage medium 300 of the embodiment of the present application stores therein the program instructions 310, and the program instructions 310 are executed to implement the gamma curve debugging method described above.
The program instructions 310 may form a program file stored in the storage medium in the form of a software product, so that an electronic device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) performs all or part of the steps of the methods according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, an optical disk, or other various media capable of storing program codes, or a terminal device such as a computer, a server, a mobile phone, a tablet, or the like.
The computer storage medium 300 of the present embodiment may be, but is not limited to, a usb disk, an SD card, a PD optical drive, a mobile hard disk, a high capacity floppy drive, a flash memory, a multimedia memory card, a server, etc.
In one embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer storage medium. The processor of the electronic device reads the computer instructions from the computer storage medium and executes the computer instructions to cause the electronic device to perform the steps of the method embodiments described above.
In addition, the above-described functions, if implemented in the form of software functions and sold or used as a separate product, may be stored in a mobile terminal-readable storage medium, that is, the present application also provides a storage device storing program data that can be executed to implement the method of the above-described embodiment, the storage device may be, for example, a U-disk, an optical disk, a server, or the like. That is, the present application may be embodied in a software product that includes instructions for causing a smart terminal to perform all or part of the steps of the methods described in the various embodiments.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing mechanisms, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., may be considered as a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device (which can be a personal computer, server, network device, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions). For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
The foregoing description is only exemplary embodiments of the present application and is not intended to limit the scope of the present application, and all equivalent structures or equivalent processes using the descriptions and the drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the present application.
Claims (10)
1. The gamma curve debugging method is characterized by comprising the following steps of:
under the same environment, respectively acquiring first image data of reference equipment and second image data of test equipment;
acquiring a data difference value of the first image data and the second image data;
acquiring a difference curve of the reference equipment and the test equipment by using the data difference value and first image data of the reference equipment;
and determining the gamma curve of the test equipment according to the difference curve and the gamma curve of the reference equipment.
2. The gamma curve debugging method of claim 1, wherein the gamma curve debugging method comprises the steps of,
the gamma curve debugging method further comprises the following steps:
acquiring average brightness based on the initial image data;
and setting the light inlet quantity of the reference equipment and the test equipment according to the average brightness.
3. The gamma curve debugging method according to claim 1 or 2, wherein,
the method for respectively acquiring the first image data of the reference device and the second image data of the test device under the same environment comprises the following steps:
under the same environment, respectively acquiring third image data of the reference equipment and fourth image data of the test equipment;
converting third image data of the reference device into the first image data;
converting fourth image data of the test device into the second image data;
the data types of the first image data and the second image data are RGB data, and the data types of the third image data and the fourth image data are RAW data.
4. The gamma curve debugging method of claim 1, wherein the gamma curve debugging method comprises the steps of,
the acquiring a data difference of the first image data and the second image data includes:
acquiring first channel data, second channel data and third channel data of the first image data, and acquiring the first channel data, the second channel data and the third channel data of the second image data;
acquiring a first channel data difference value between first channel data of the first image data and first channel data of the second image data, acquiring a second channel data difference value between second channel data of the first image data and second channel data of the second image data, and acquiring a third channel data difference value between third channel data of the first image data and third channel data of the second image data.
5. The gamma curve debugging method of claim 4, wherein the gamma curve debugging method comprises the steps of,
the obtaining a difference curve between the reference device and the test device by using the data difference and the first image data of the reference device includes:
acquiring first channel difference data by using the first channel data difference value and first channel data of the first image data, acquiring second channel difference data by using the second channel data difference value and second channel data of the first image data, and acquiring third channel difference data by using the third channel data difference value and third channel data of the first image data;
converting the first channel difference data, the second channel difference data and the third channel difference data into a two-dimensional dataset;
and generating a difference curve of the reference equipment and the test equipment according to the two-dimensional data set.
6. The gamma curve debugging method of claim 5, wherein the gamma curve debugging method comprises the steps of,
the two-dimensional dataset includes channel data and difference data;
the generating a difference curve of the reference device and the test device according to the two-dimensional data set comprises the following steps:
taking channel data in the two-dimensional data set as an abscissa and difference data as an ordinate, and fitting to generate a mapping two-dimensional map;
acquiring an average value of a plurality of difference data corresponding to each channel data;
and fitting and generating a difference curve of the reference equipment and the test equipment according to the data of each channel and the average value of the corresponding difference data.
7. The gamma curve debugging method of claim 6, wherein the gamma curve debugging method comprises the steps of,
the determining the gamma curve of the test device according to the difference curve and the gamma curve of the reference device comprises:
and under the same channel data, performing linear multiplication compensation operation on the brightness value of the gamma curve of the reference equipment by using the difference data average value of the difference curve to obtain the brightness value of the gamma curve of the test equipment under the channel data.
8. The gamma curve debugging method of claim 7, wherein the gamma curve debugging method comprises the steps of,
before the compensation operation of linearly multiplying the brightness value of the gamma curve of the reference device by the difference data average value of the difference curve, the gamma curve method further includes:
acquiring the maximum channel data of the gamma curve of the reference equipment;
and linearly mapping the channel data of the difference curve according to the maximum channel data to unify the maximum channel data of the difference curve and the gamma curve of the reference equipment.
9. A gamma curve debugging device, wherein the gamma curve debugging device comprises a memory and a processor coupled with the memory;
wherein the memory is for storing program data and the processor is for executing the program data to implement the gamma curve debugging method of any of claims 1 to 8.
10. A computer storage medium for storing program data which, when executed by a computer, is adapted to carry out the gamma curve debugging method of any one of claims 1 to 8.
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