WO2020228781A1 - 一种图像亮度调节方法、装置及无人机 - Google Patents

一种图像亮度调节方法、装置及无人机 Download PDF

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Publication number
WO2020228781A1
WO2020228781A1 PCT/CN2020/090269 CN2020090269W WO2020228781A1 WO 2020228781 A1 WO2020228781 A1 WO 2020228781A1 CN 2020090269 W CN2020090269 W CN 2020090269W WO 2020228781 A1 WO2020228781 A1 WO 2020228781A1
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candidate
image
predefined
proportion
gamma curve
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PCT/CN2020/090269
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English (en)
French (fr)
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姜德飞
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深圳市道通智能航空技术有限公司
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Publication of WO2020228781A1 publication Critical patent/WO2020228781A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64C—AEROPLANES; HELICOPTERS
    • B64C39/00—Aircraft not otherwise provided for
    • B64C39/02—Aircraft not otherwise provided for characterised by special use
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U10/00—Type of UAV
    • B64U10/10—Rotorcrafts
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/70—Circuitry for compensating brightness variation in the scene
    • H04N23/76—Circuitry for compensating brightness variation in the scene by influencing the image signals
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
    • H04N23/80—Camera processing pipelines; Components thereof
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B64U2101/30—UAVs specially adapted for particular uses or applications for imaging, photography or videography

Definitions

  • This application relates to the field of image processing technology, and in particular to an image brightness adjustment method, device and drone.
  • UAV is an unmanned aerial vehicle operated by radio remote control equipment or its own program control device, and is often used for aerial photography. Due to the uncertainty of the drone's navigation route, drone aerial photography is not always carried out when the shooting environment is in good condition, and often encounters insufficient light or backlight. At present, when adjusting the brightness of video images taken by drones, no matter what the shooting environment, the same preset gamma curve is used, so that the video images taken by the drone under the condition of insufficient light or backlighting pass the brightness After adjustment, there are still areas with abnormal brightness, resulting in loss of details, and the effect of drone aerial photography cannot be guaranteed.
  • the embodiments of the present invention aim to provide an image brightness adjustment method, device and drone, which can more effectively adjust the image brightness.
  • a technical solution adopted in the embodiments of the present invention is to provide an image brightness adjustment method used in an image acquisition device of a drone, and the method includes:
  • the brightness of the image to be processed is adjusted.
  • each of the predefined gray scale ranges corresponds to at least two candidate proportion index intervals
  • each candidate proportion index interval in the at least two candidate proportion index intervals corresponds to at least two candidate shooting parameters
  • Each of the at least two candidate shooting parameters corresponds to a candidate gamma curve
  • Said selecting an optimal gamma curve matching the current shooting parameter, the predefined gray scale range and the proportion according to the current shooting parameter, the predefined gray scale range and the proportion include:
  • the candidate gamma curve corresponding to the target shooting parameter is selected as the optimal gamma curve.
  • the method further includes:
  • the interpolation calculation method calculate the target gamma curve corresponding to the current shooting parameter, and select The target gamma curve serves as the optimal gamma curve.
  • the calculating the target gamma curve corresponding to the current shooting parameter according to the interpolation calculation method includes:
  • a target gamma curve corresponding to the current shooting parameter is calculated.
  • the determining the proportion of the number of pixels whose gray values are within a predefined grayscale range in the total number of pixels in the image to be processed in the image to be processed includes:
  • the percentage of the number of pixels whose gray values are within a predefined grayscale range in the image to be processed is determined by the grayscale histogram among the total number of pixels in the image to be processed.
  • the predefined grayscale range includes a predefined bright area grayscale range or a predefined dark area grayscale range.
  • the greater the maximum endpoint value of the candidate proportion index interval the greater the value of the candidate gamma curve corresponding to the candidate proportion index interval The greater the gamma value.
  • the greater the maximum endpoint value of the candidate proportion index interval the greater the value of the candidate gamma curve corresponding to the candidate proportion index interval The smaller the gamma value.
  • the current shooting parameter includes exposure and/or sensitivity.
  • an image brightness adjustment device used in an image capture device of a drone including:
  • An acquisition module for acquiring the image to be processed and the current shooting parameters when the image acquisition device collects the image to be processed
  • a determining module configured to determine the proportion of the number of pixels whose gray values are within a predefined grayscale range in the image to be processed in the total number of pixels in the image to be processed;
  • the selection module is configured to select an optimal image matching the current shooting parameter, the predefined gray scale range and the proportion according to the current shooting parameter, the predefined gray scale range and the proportion.
  • the adjustment module is configured to adjust the brightness of the image to be processed according to the optimal gamma curve.
  • each of the predefined gray scale ranges corresponds to at least two candidate proportion index intervals
  • each candidate proportion index interval in the at least two candidate proportion index intervals corresponds to at least two candidate shooting parameters
  • Each of the at least two candidate shooting parameters corresponds to a candidate gamma curve
  • the selection module is specifically used for:
  • the candidate gamma curve corresponding to the target shooting parameter is selected as the optimal gamma curve.
  • the selection module is also used for:
  • the interpolation calculation method calculate the target gamma curve corresponding to the current shooting parameter, and select The target gamma curve serves as the optimal gamma curve.
  • the selection module is specifically used for:
  • a target gamma curve corresponding to the current shooting parameter is calculated.
  • the determining module is specifically configured to:
  • the percentage of the number of pixels whose gray values are within a predefined grayscale range in the image to be processed is determined by the grayscale histogram among the total number of pixels in the image to be processed.
  • the predefined grayscale range includes a predefined bright area grayscale range or a predefined dark area grayscale range.
  • the greater the maximum endpoint value of the candidate proportion index interval the greater the value of the candidate gamma curve corresponding to the candidate proportion index interval The greater the gamma value.
  • the greater the maximum endpoint value of the candidate proportion index interval the greater the value of the candidate gamma curve corresponding to the candidate proportion index interval The smaller the gamma value.
  • the current shooting parameter includes exposure and/or sensitivity.
  • a drone including:
  • An arm connected to the fuselage
  • the power plant is arranged on the arm;
  • An image acquisition device connected to the fuselage
  • the image acquisition device includes:
  • At least one processor At least one processor
  • the device can be used to perform the image brightness adjustment method described above.
  • another technical solution adopted by the embodiments of the present invention is to provide a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores computer-executable instructions.
  • the computer-executable instructions are used to make the image acquisition device of the drone execute the image brightness adjustment method described above.
  • the embodiments of the present invention provide an image brightness adjustment method, device, and drone.
  • the image to be processed is acquired and the image is collected.
  • the device collects the current shooting parameters of the image to be processed, it determines the proportion of the number of pixels with gray values within the predefined grayscale range in the total number of pixels in the image to be processed in the image to be processed, and The current shooting parameters, the determined proportion and the predefined grayscale range when the proportion is determined, select the optimal gamma curve to match the brightness of the image to be processed, and can adaptively select the image brightness according to the actual shooting situation
  • the adjusted gamma curve enables the images taken in different shooting environments to get good brightness correction, ensuring the effect of drone aerial photography.
  • Figure 1 is a schematic structural diagram of an unmanned aerial vehicle provided by an embodiment of the present invention.
  • FIG. 2 is a schematic flowchart of an image brightness adjustment method provided by an embodiment of the present invention.
  • FIG. 3 is a schematic structural diagram of an image brightness adjustment device provided by an embodiment of the present invention.
  • Fig. 4 is a schematic diagram of the hardware structure of a drone provided by an embodiment of the present invention.
  • the present invention provides an image brightness adjustment method and device.
  • the method and device are applied to the image acquisition equipment of the drone, so that the image acquisition equipment of the drone can adaptively select the optimal image according to the actual shooting situation after aerial photography.
  • the gamma curve adjusts the brightness of the aerial video images, so that the video images shot in different shooting environments can be well corrected for brightness, ensuring the effect of drone aerial photography.
  • the UAV can be any suitable type of high-altitude UAV or low-altitude UAV equipped with image acquisition equipment for aerial photography, including fixed-wing UAV, rotary wing UAV, para-wing UAV or flapping wing. Drones and so on.
  • FIG. 1 is an unmanned aerial vehicle 100 provided by one embodiment of the present invention, including a fuselage 10, an arm 20, a power unit 30, an image acquisition device 40, a landing gear 50, and a flight control system (not shown) ).
  • the arm 20, the image acquisition device 40 and the landing gear 50 are all connected to the fuselage 10, the flight control system is arranged in the fuselage 10, and the power unit 30 is arranged on the arm 20.
  • the power unit 30, the image acquisition device 40, and the landing gear 50 are all communicatively connected to the flight control system, so that the flight control system can control the flight of the UAV 100 through the power device 30, and the flight control system can also control the image acquisition device 40 carries out aerial photography and controls the opening and closing of the landing gear 50.
  • the number of arms 20 is 4, evenly distributed around the fuselage 10 for carrying the power device 30.
  • the power device 30 includes a motor and a propeller connected to the motor shaft.
  • the motor can drive the propeller to rotate to provide lift for the drone 100 to achieve flight; the motor can also change the flying direction of the drone 100 by changing the speed and direction of the propeller.
  • the flight control system can control the flight of the drone 100 by controlling the motor.
  • the power device 30 is arranged at an end of the arm 20 that is not connected to the fuselage 10, and is connected to the arm 20 through a motor.
  • a power device 30 is provided on the four arms of the drone 100 so that the drone 100 can fly smoothly.
  • the image acquisition device 40 may be a device capable of shooting video images, such as a camera or a video camera, which is arranged at the bottom of the fuselage 10 and can perform aerial photography under the control of the flight control system, that is, shooting video images.
  • the image acquisition device 40 can also be set on the bottom of the fuselage 10 via a pan/tilt, so as to rotate with the rotation of the pan/tilt, so as to be able to perform aerial photography in all directions and shoot video images with different perspectives.
  • the brightness of the video images captured by the image capture device 40 is different.
  • the image capture device 40 is also used to perform the image brightness adjustment method according to the actual shooting.
  • the optimal gamma curve is selected adaptively to adjust the brightness of the aerial video images, so that the video images shot in different shooting environments can be well corrected in brightness to ensure the effect of drone aerial photography.
  • the landing gear 50 is arranged on opposite sides of the bottom of the fuselage 10 and is connected to the fuselage 10 through a driving device.
  • the landing gear 50 can be opened and retracted under the driving of the driving device.
  • the driving device controls the landing gear 50 to open so that the drone 100 contacts the ground through the landing gear 50; during the flight of the drone 100, the driving device controls the landing gear 50 to retract, To prevent the landing gear 50 from affecting the flight of the drone 100.
  • the flight control system can control the opening and closing of the landing gear 50 by controlling the driving device.
  • the flight control system communicates with the power unit 30, the image acquisition device 40, and the landing gear 50 through a wired connection or a wireless connection.
  • wireless connections include but are not limited to: WiFi, Bluetooth, ZigBee, etc.
  • the image capture device 40 executes the image brightness adjustment method, which specifically includes: after the image capture device 40 captures a video image, acquiring the image to be processed and the current shooting parameters when the image to be processed is captured.
  • the image to be processed is composed of a number of pixels arranged in rows, and each pixel corresponds to a color value.
  • the image to be processed may be an image frame of a video captured by the image capture device 40, or may be an image captured by the image capture device 40.
  • the current shooting parameter is the shooting parameter set when the image capture device 40 captures the image to be processed.
  • the shooting parameter includes exposure and/or sensitivity (ISO).
  • ISO exposure and/or sensitivity
  • the actual shooting situation of the image to be processed can be determined by the current shooting parameter.
  • the exposure amount can be calculated according to the gain and shutter of the image acquisition device 40, for example, the exposure amount can be calculated by the product of the number of exposure lines and the gain; the sensitivity can be obtained according to the setting parameters of the image acquisition device 40.
  • the image acquisition device 40 After the image acquisition device 40 acquires the image to be processed and the current shooting parameters when shooting the image to be processed, it determines that in the image to be processed, the number of pixels whose grayscale values are within the predefined grayscale range is the number of all pixels in the image to be processed For example, in some embodiments, the image acquisition device 40 uses the grayscale histogram to determine that in the image to be processed, the number of pixels whose grayscale values are within a predefined grayscale range is among all the pixels of the image to be processed. Percentage of the number.
  • the grayscale histogram is a statistical map of the distribution of all the pixels of the image to be processed in each grayscale value.
  • the grayscale histogram can determine the total number of pixels in the image to be processed and the The number of pixels of the value.
  • the grayscale histogram the sum of the number of pixels corresponding to each grayscale value in the predefined grayscale range of the image to be processed can be counted, so as to determine the grayscale value of the image to be processed within the predefined grayscale range. The number of pixels.
  • the dark area of the image to be processed can be represented by a predefined grayscale range Or bright area.
  • the predefined gray range is the predefined dark gray range
  • the gray value in the image to be processed is determined to be within the predefined dark gray range
  • the proportion of the number of pixels in the total number of pixels in the image to be processed can determine the brightness of the dark area
  • the predefined gray scale range is the predefined bright gray scale range
  • the gray scale value in the image to be processed is determined to be within the predefined bright gray scale range
  • the proportion of the number of pixels in the total number of pixels in the image to be processed can determine the brightness of the bright area.
  • an image to be processed includes several pixels with different gray values, and the gray value of each pixel may be the same or different, and the gray value of each pixel falls within the range of 0 to 255.
  • the grayscale histogram to count the pixels of the image to be processed, it is assumed that the total number of pixels in the image to be processed is 4096, and the number of pixels whose grayscale value lies in the predefined grayscale range of 0 to 32 is 1316.
  • the number of pixels with a degree value in the predefined gray range of 33 to 191 is 2600, and the number of pixels with a gray value in the predefined gray range of 192 to 255 is 180.
  • the predefined grayscale range is 0 to 32, the grayscale value is not greater than 50, so the predefined grayscale range 0 to 32 is the predefined dark area grayscale range.
  • the proportion of the number of pixels in the pre-defined dark area grayscale range in the total number of pixels of the image to be processed can determine the brightness of the dark area of the image to be processed.
  • the brightness of the dark area of the image to be processed includes "The number of pixels in the image to be processed with a gray value in the predefined gray range of 0 to 32 accounts for more than 30% of the total number of pixels in the image to be processed
  • the proportion of the number of pixels with gray values in the predefined gray range of 0 to 32 in the image to be processed in the total number of pixels in the image to be processed is 32.1% is greater than 30%. Therefore, it is determined that the brightness of the dark area of the image to be processed is too dark.
  • the predefined grayscale range is 192 to 255, the grayscale value is not less than 192, so the predefined grayscale range 192 to 255 is the predefined bright area grayscale range, by determining the grayscale value in the image to be processed By pre-defining the proportion of the number of pixels in the gray range of the bright area in the total number of pixels in the image to be processed, the brightness of the bright area of the image to be processed can be determined.
  • the brightness of the bright area of the image to be processed includes "The number of pixels in the image to be processed whose gray value is within the predefined gray range of 192 to 255 accounts for more than 25% of the total number of pixels in the image to be processed
  • the number of pixels with gray values in the pre-defined gray range of 192 to 255 in the image to be processed accounts for the total number of pixels in the image to be processed 4.4% is less than 25%. Therefore, it is determined that the brightness of the bright area of the image to be processed is normal.
  • determining the brightness of the dark area or the brightness of the bright area can determine the optimal gamma curve for brightness adjustment, so it can be determined that the gray value of the image to be processed is within the gray range of the predefined dark area.
  • the determined current shooting parameters are The proportion and the predefined gray scale range when the proportion is determined, and the optimal gamma curve that matches the current shooting parameter, the proportion and the predefined gray scale range is selected.
  • the proportion determined by the image acquisition device 40 is the proportion of the number of pixels in the image to be processed whose gray values are within the grayscale range of the predefined dark area in the total number of pixels in the image to be processed, the current shooting parameters , Proportion and predefined dark area grayscale range, select the first optimal gamma curve that matches the current shooting parameters, proportion, and predefined dark area grayscale range, the first optimal gamma curve is used to adjust the Process the brightness of pixels whose grayscale value is less than 128.
  • the proportion determined by the image acquisition device 40 is the proportion of the number of pixels in the image to be processed whose gray values are within the grayscale range of the predefined bright area in the total number of pixels in the image to be processed
  • the current shooting parameters , Proportion and predefined bright area grayscale range select the second optimal gamma curve that matches the current shooting parameters, proportion, and predefined bright area grayscale range. This second optimal gamma curve is used to adjust the Process the brightness of pixels whose gray value is not less than 128.
  • each predefined gray scale range corresponds to at least two candidate proportion index intervals
  • each of the at least two candidate proportion index intervals corresponds to at least two candidate proportion index intervals.
  • Each of the at least two candidate shooting parameters corresponds to a candidate gamma curve.
  • the pre-defined dark area grayscale range under the same candidate shooting parameters, the greater the maximum endpoint value of the candidate proportion index interval, the smaller the gamma value of the candidate gamma curve corresponding to the candidate proportion index interval;
  • each candidate proportion index interval corresponds to three candidate shooting parameters
  • Section 1 corresponds to ISO1, ISO2 and ISO3
  • Section 2 corresponds to ISO4, ISO5 and ISO6
  • each candidate shooting parameter corresponds to a candidate gamma curve
  • ISO1 corresponds to Gamma1
  • ISO2 corresponds to Gamma2
  • ISO3 corresponds to Gamma3
  • each candidate shooting parameter corresponds to a candidate gamma curve
  • ISO4 corresponds to Gamma4
  • ISO5 corresponds to Gamma5
  • ISO6 corresponds to Gamma6.
  • the predefined gray scale range 192-255 corresponds to two candidate proportion index intervals: interval 3 and interval 4.
  • each candidate proportion index interval corresponds to three candidate shooting parameters
  • interval 3 corresponds to ISO7 , ISO8 and ISO9
  • interval 4 corresponds to ISO10, ISO11 and ISO12
  • among the candidate shooting parameters ISO7, ISO8 and ISO9 corresponding to interval 3 each candidate shooting parameter corresponds to a candidate gamma curve
  • ISO7 corresponds to Gamma7
  • ISO8 corresponds to Gamma8
  • ISO9 corresponds to Gamma9
  • each candidate shooting parameter corresponds to a candidate gamma curve
  • ISO10 corresponds to Gamma10
  • ISO11 corresponds to Gamma11
  • ISO12 corresponds to Gamma12.
  • the image acquisition device 40 can select the optimal gamma curve according to the acquired current shooting parameters, the determined proportion, and the predefined grayscale range when the proportion is determined. For example, first, the image acquisition device 40 determines the candidate proportion index interval containing the proportion in the candidate proportion index interval corresponding to the predefined grayscale range as the target proportion index interval. For example, the image capture device 40 determines that the number of pixels in the image to be processed whose gray values are within the gray range of the predefined dark area 0-32 accounts for 25% of the total number of pixels in the image to be processed, as shown in Table 1. In, determine the candidate proportion index interval corresponding to the predefined dark area gray range 0-32 as interval 1 and interval 2. In interval 1 and interval 2, it is determined that interval 1 contains 25%, so interval 1 is determined as The target percentage index range.
  • the image capture device 40 determines the candidate shooting parameter matching the current shooting parameter among the candidate shooting parameters corresponding to the determined target ratio index interval as the target shooting parameter.
  • the current shooting parameter ISO is 100.
  • determine the target ratio index interval-the candidate shooting parameters corresponding to interval 1 are ISO1, ISO2, and ISO3.
  • ISO1, ISO2, and ISO3 determine ISO1 and the current shooting parameters 100 matches, so ISO1 is determined as the target shooting parameter.
  • the image acquisition device 40 selects the candidate gamma curve corresponding to the determined target shooting parameter as the optimal gamma curve.
  • the candidate gamma curve corresponding to the target shooting parameter ISO1 is Gamma1
  • Gamma1 is selected as the optimal gamma curve.
  • the target gamma curve corresponding to the current shooting parameter is calculated according to the interpolation calculation method, and the target gamma curve is selected As the optimal gamma curve.
  • the current shooting parameter ISO is 150.
  • determine the target ratio index interval-the candidate shooting parameters corresponding to interval 1 are ISO1, ISO2, and ISO3.
  • ISO1, ISO2, and ISO3 ISO1 is 100, and ISO2 is 200, ISO3 is 300, neither match with the current shooting parameter 150. Therefore, it is determined that there is no candidate shooting parameter matching the current shooting parameter among the candidate shooting parameters corresponding to the target proportion index interval.
  • calculate and The target gamma curve corresponding to the current shooting parameter, and the target gamma curve is selected as the optimal gamma curve.
  • the image capture device 40 determines at least two candidate shooting parameters as a reference among the candidate shooting parameters corresponding to the target proportion index interval Shooting parameters. For example, in the target ratio index interval-the candidate shooting parameters ISO1, ISO2, and ISO3 corresponding to interval 1, determine at least two of ISO1, ISO2, and ISO3 as reference shooting parameters, including determining ISO1 and ISO2 as reference shooting parameters, or , Determine ISO1 and ISO3 as reference shooting parameters, or determine ISO2 and ISO3 as reference shooting parameters, or determine ISO1, ISO2, and ISO3 as reference shooting parameters.
  • the image acquisition device 40 constructs an interpolation function according to the determined reference shooting parameter and the gamma value of the candidate gamma curve corresponding to the reference shooting parameter. For example, when the image capture device 40 determines ISO1 and ISO2 as the reference shooting parameters, the image capture device 40 determines the gamma value of the candidate gamma curve Gamma1 corresponding to ISO1, and determines the gamma value of the candidate gamma curve Gamma2 corresponding to ISO2, where , The determined gamma value of Gamma1 is ⁇ 1, and the gamma value of Gamma2 is ⁇ 2, so two sets of relationships (ISO1, ⁇ 1) and (ISO2, ⁇ 2) are formed.
  • the image acquisition device 40 selects the optimal gamma curve, it adjusts the brightness of the image to be processed according to the selected optimal gamma curve.
  • the image acquisition device 40 of the UAV 100 implements an image brightness adjustment method to adaptively select the optimal gamma curve to adjust the brightness of the aerial video image according to the actual shooting situation after aerial photography, so that The video images taken in different shooting environments can be well calibrated for brightness, ensuring the aerial photography effect.
  • FIG. 2 is a schematic flow chart of an image brightness adjustment method provided by one embodiment of the present invention, which is applied to a drone.
  • the drone is the drone 100 described in the above-mentioned embodiment, and the present invention
  • the method provided in the embodiment is executed by the above-mentioned image acquisition device 40, and is used to adaptively select the optimal gamma curve to adjust the brightness of the aerial video image according to the actual shooting situation to ensure the aerial photography effect.
  • the image brightness adjustment method includes:
  • S100 Acquire an image to be processed and current shooting parameters when the image acquisition device collects the image to be processed.
  • the aforementioned "image to be processed” consists of a number of pixels arranged in rows, and each pixel corresponds to a color value.
  • the image to be processed may be an image frame of a video captured by the image capture device 40, or may be an image captured by the image capture device 40.
  • the aforementioned "current shooting parameters" are shooting parameters set when the image capture device 40 captures the image to be processed.
  • the shooting parameters include exposure and/or sensitivity (ISO).
  • the actual shooting conditions of the image to be processed can be determined by the current shooting parameters.
  • the exposure amount can be calculated according to the gain and shutter of the image acquisition device 40, for example, the exposure amount can be calculated by the product of the number of exposure lines and the gain; the sensitivity can be obtained according to the setting parameters of the image acquisition device 40.
  • the image to be processed is acquired locally from the image processing device 40 and the current shooting parameters when the image to be processed is shot.
  • S200 Determine the proportion of the number of pixels whose gray values are within a predefined grayscale range in the image to be processed in the total number of pixels in the image to be processed.
  • the grayscale histogram is used to determine the proportion of the number of pixels with grayscale values within a predefined grayscale range in the total number of pixels in the image to be processed in the image to be processed.
  • the grayscale histogram is a statistical map of the distribution of all the pixels of the image to be processed in each grayscale value.
  • the grayscale histogram can determine the total number of pixels in the image to be processed and the The number of pixels of the value.
  • the grayscale histogram the sum of the number of pixels corresponding to each grayscale value in the predefined grayscale range of the image to be processed can be counted, so as to determine the grayscale value of the image to be processed within the predefined grayscale range. The number of pixels.
  • the dark area of the image to be processed can be represented by a predefined grayscale range Or bright area.
  • the predefined gray range is the predefined dark gray range
  • the gray value in the image to be processed is determined to be within the predefined dark gray range
  • the proportion of the number of pixels in the total number of pixels in the image to be processed can determine the brightness of the dark area
  • the predefined gray scale range is the predefined bright gray scale range
  • the gray scale value in the image to be processed is determined to be within the predefined bright gray scale range
  • the proportion of the number of pixels in the total number of pixels in the image to be processed can determine the brightness of the bright area.
  • an image to be processed includes several pixels with different gray values, and the gray value of each pixel may be the same or different, and the gray value of each pixel falls within the range of 0 to 255.
  • the grayscale histogram to count the pixels of the image to be processed, it is assumed that the total number of pixels in the image to be processed is 4096, and the number of pixels whose grayscale value lies in the predefined grayscale range of 0 to 32 is 1316.
  • the number of pixels with a degree value in the predefined gray range of 33 to 191 is 2600, and the number of pixels with a gray value in the predefined gray range of 192 to 255 is 180.
  • the predefined grayscale range is 0 to 32, the grayscale value is not greater than 50, so the predefined grayscale range 0 to 32 is the predefined dark area grayscale range.
  • the proportion of the number of pixels in the pre-defined dark area grayscale range in the total number of pixels of the image to be processed can determine the brightness of the dark area of the image to be processed.
  • the brightness of the dark area of the image to be processed includes "The number of pixels in the image to be processed with a gray value in the predefined gray range of 0 to 32 accounts for more than 30% of the total number of pixels in the image to be processed
  • the proportion of the number of pixels with gray values in the predefined gray range of 0 to 32 in the image to be processed in the total number of pixels in the image to be processed is 32.1% is greater than 30%. Therefore, it is determined that the brightness of the dark area of the image to be processed is too dark.
  • the predefined grayscale range is 192 to 255, the grayscale value is not less than 192, so the predefined grayscale range 192 to 255 is the predefined bright area grayscale range, by determining the grayscale value in the image to be processed By pre-defining the proportion of the number of pixels in the gray range of the bright area in the total number of pixels in the image to be processed, the brightness of the bright area of the image to be processed can be determined.
  • the brightness of the bright area of the image to be processed includes "The number of pixels in the image to be processed whose gray value is within the predefined gray range of 192 to 255 accounts for more than 25% of the total number of pixels in the image to be processed
  • the number of pixels with gray values in the pre-defined gray range of 192 to 255 in the image to be processed accounts for the total number of pixels in the image to be processed 4.4% is less than 25%. Therefore, it is determined that the brightness of the bright area of the image to be processed is normal.
  • determining the brightness of the dark area or the brightness of the bright area can determine the optimal gamma curve for brightness adjustment, so it can be determined that the gray value of the image to be processed is within the gray range of the predefined dark area.
  • S300 Select an optimal gamma curve that matches the current shooting parameter, the predefined grayscale range, and the proportion based on the current shooting parameter, the predefined grayscale range, and the proportion.
  • the current shooting parameters, proportion and pre- Define the gray range of the dark area, select the first optimal gamma curve that matches the current shooting parameters, proportions, and predefined dark area gray range, the first optimal gamma curve is used to adjust the gray value of the image to be processed The brightness of pixels less than 128.
  • the determined proportion is the proportion of the number of pixels in the pre-defined bright area grayscale range in the image to be processed in the total number of pixels in the image to be processed.
  • the second optimal gamma curve is used to adjust the grayscale value of the image to be processed The brightness of pixels not less than 128.
  • each predefined gray scale range corresponds to at least two candidate proportion index intervals
  • each of the at least two candidate proportion index intervals corresponds to at least two candidate proportion index intervals.
  • Each of the at least two candidate shooting parameters corresponds to a candidate gamma curve.
  • the pre-defined dark area grayscale range under the same candidate shooting parameters, the greater the maximum endpoint value of the candidate proportion index interval, the smaller the gamma value of the candidate gamma curve corresponding to the candidate proportion index interval;
  • each candidate proportion index interval corresponds to three candidate shooting parameters
  • Section 1 corresponds to ISO1, ISO2 and ISO3
  • Section 2 corresponds to ISO4, ISO5 and ISO6
  • each candidate shooting parameter corresponds to a candidate gamma curve
  • ISO1 corresponds to Gamma1
  • ISO2 corresponds to Gamma2
  • ISO3 corresponds to Gamma3
  • each candidate shooting parameter corresponds to a candidate gamma curve
  • ISO4 corresponds to Gamma4
  • ISO5 corresponds to Gamma5
  • ISO6 corresponds to Gamma6.
  • the predefined gray scale range 192-255 corresponds to two candidate proportion index intervals: interval 3 and interval 4; in interval 3 and interval 4, each candidate proportion index interval corresponds to three candidate shooting parameters, and interval 3 corresponds to ISO7 , ISO8 and ISO9, interval 4 corresponds to ISO10, ISO11 and ISO12; among the candidate shooting parameters ISO7, ISO8 and ISO9 corresponding to interval 3, each candidate shooting parameter corresponds to a candidate gamma curve, ISO7 corresponds to Gamma7, ISO8 corresponds to Gamma8, ISO9 corresponds to Gamma9; among the candidate shooting parameters ISO10, ISO11 and ISO12 corresponding to interval 4, each candidate shooting parameter corresponds to a candidate gamma curve, ISO10 corresponds to Gamma10, ISO11 corresponds to Gamma11, and ISO12 corresponds to Gamma12.
  • the optimal gamma curve can be selected according to the acquired current shooting parameters, the determined proportion, and the predefined grayscale range when the proportion is determined. For example, first, in the candidate proportion index interval corresponding to the predefined grayscale range, the candidate proportion index interval containing the proportion is determined as the target proportion index interval. For example, it is determined that the number of pixels in the image to be processed whose gray value is within the gray range of the predefined dark area 0-32 accounts for 25% of the total number of pixels in the image to be processed. In Table 1, the predetermined Define the candidate proportion index intervals corresponding to the dark gray range 0-32 as interval 1 and interval 2. In interval 1 and interval 2, it is determined that interval 1 contains 25%, so interval 1 is determined as the target proportion index Interval.
  • the candidate shooting parameter matching the current shooting parameter is determined as the target shooting parameter.
  • the current shooting parameter ISO is 100.
  • determine the target ratio index interval-the candidate shooting parameters corresponding to interval 1 are ISO1, ISO2, and ISO3.
  • ISO1, ISO2, and ISO3 determine ISO1 and the current shooting parameters 100 matches, so ISO1 is determined as the target shooting parameter.
  • the candidate gamma curve corresponding to the determined target shooting parameter is selected as the optimal gamma curve.
  • the candidate gamma curve corresponding to the target shooting parameter ISO1 is Gamma1
  • Gamma1 is selected as the optimal gamma curve.
  • the target corresponding to the current shooting parameter is calculated according to the interpolation calculation method.
  • Gamma curve and select the target gamma curve as the optimal gamma curve.
  • the current shooting parameter ISO is 150.
  • determine the target ratio index interval-the candidate shooting parameters corresponding to interval 1 are ISO1, ISO2, and ISO3.
  • ISO1, ISO2, and ISO3 ISO1 is 100, and ISO2 is 200, ISO3 is 300, neither match with the current shooting parameter 150. Therefore, it is determined that there is no candidate shooting parameter matching the current shooting parameter among the candidate shooting parameters corresponding to the target proportion index interval.
  • calculate and The target gamma curve corresponding to the current shooting parameter, and the target gamma curve is selected as the optimal gamma curve.
  • At least two candidate shooting parameters are determined as reference shooting parameters. For example, in the target ratio index interval-the candidate shooting parameters ISO1, ISO2, and ISO3 corresponding to interval 1, determine at least two of ISO1, ISO2, and ISO3 as reference shooting parameters, including determining ISO1 and ISO2 as reference shooting parameters, or , Determine ISO1 and ISO3 as reference shooting parameters, or determine ISO2 and ISO3 as reference shooting parameters, or determine ISO1, ISO2, and ISO3 as reference shooting parameters.
  • an interpolation function is constructed according to the determined reference shooting parameter and the gamma value of the candidate gamma curve corresponding to the reference shooting parameter. For example, when determining ISO1 and ISO2 as the reference shooting parameters, determine the gamma value of the candidate gamma curve Gamma1 corresponding to ISO1, and determine the gamma value of the candidate gamma curve Gamma2 corresponding to ISO2, where the determined gamma of Gamma1 The gamma value of ⁇ 1 and Gamma2 is ⁇ 2, so two sets of relationships (ISO1, ⁇ 1) and (ISO2, ⁇ 2) are formed.
  • the optimal gamma curve to be matched is selected to adjust the brightness of the image to be processed, so that the image capture
  • the device can adaptively select the optimal gamma curve to adjust the brightness of the aerial video image according to the actual shooting situation, ensuring that the video images shot in different shooting environments can be well corrected for brightness.
  • module is a combination of software and/or hardware that can implement predetermined functions.
  • devices described in the following embodiments can be implemented by software, implementation by hardware or a combination of software and hardware is also possible.
  • FIG. 3 is an image brightness adjustment device provided by one of the embodiments of the present invention.
  • the device is applied to a drone.
  • the drone is the drone 100 described in the foregoing embodiment.
  • the functions of each module of the device provided in the example are executed by the above-mentioned image acquisition device 40, which is used to adaptively select the optimal gamma curve to adjust the brightness of the aerial video image according to the actual shooting situation to ensure the aerial photography effect.
  • the image brightness adjustment device includes :
  • An acquiring module 200 which is used to acquire an image to be processed and the current shooting parameters when the image acquisition device acquires the image to be processed;
  • the determining module 300 is configured to determine the proportion of the number of pixels whose gray values are within a predefined grayscale range in the image to be processed in the total number of pixels in the image to be processed;
  • the selection module 400 is configured to select the current shooting parameter, the predefined grayscale range, and the occupied ratio according to the current shooting parameter, the predefined grayscale range, and the account ratio.
  • the adjustment module 500 is configured to adjust the brightness of the image to be processed according to the optimal gamma curve.
  • each of the predefined grayscale ranges corresponds to at least two candidate proportion index intervals
  • each of the at least two candidate proportion index intervals corresponds to at least Two candidate shooting parameters
  • each of the at least two candidate shooting parameters corresponds to a candidate gamma curve
  • the selection module 400 is specifically used for:
  • the candidate gamma curve corresponding to the target shooting parameter is selected as the optimal gamma curve.
  • the selection module 400 is further configured to:
  • the interpolation calculation method calculate the target gamma curve corresponding to the current shooting parameter, and select The target gamma curve serves as the optimal gamma curve.
  • the selection module 400 is specifically configured to:
  • a target gamma curve corresponding to the current shooting parameter is calculated.
  • the determining module 300 is specifically configured to:
  • the percentage of the number of pixels whose gray values are within a predefined grayscale range in the image to be processed is determined by the grayscale histogram among the total number of pixels in the image to be processed.
  • the predefined grayscale range includes a predefined bright area grayscale range or a predefined dark area grayscale range.
  • the greater the maximum endpoint value of the candidate proportion index interval the greater the maximum endpoint value of the candidate proportion index interval
  • the greater the maximum endpoint value of the candidate proportion index interval the greater the maximum endpoint value of the candidate proportion index interval
  • the gamma value of the candidate gamma curve is smaller.
  • the current shooting parameter includes exposure and/or sensitivity.
  • the above-mentioned acquisition module 200, determination module 300, selection module 400, and adjustment module 500 may be the image processing chip of the image acquisition device 40.
  • the content of the device embodiment can be quoted from the method embodiment on the premise that the content does not conflict with each other, which will not be repeated here.
  • the optimal gamma curve to be matched is selected to adjust the brightness of the image to be processed, so that the image capture
  • the device can adaptively select the optimal gamma curve to adjust the brightness of the aerial video image according to the actual shooting situation, ensuring that the video images shot in different shooting environments can be well corrected for brightness.
  • FIG. 4 is a schematic diagram of the hardware structure of an unmanned aerial vehicle provided by one of the embodiments of the present invention.
  • the hardware module provided in this embodiment of the present invention can be integrated into the image acquisition device 40 described in the above-mentioned embodiment, so that the unmanned aerial vehicle
  • the image acquisition device 40 of 100 can execute an image brightness adjustment method described in the above embodiment, and can also implement the functions of each module of an image brightness adjustment device described in the above embodiment.
  • the drone 100 includes:
  • processors 110 and memory 120. Among them, one processor 110 is taken as an example in FIG. 4.
  • the processor 110 and the memory 120 may be connected through a bus or in other ways.
  • the connection through a bus is taken as an example.
  • the memory 120 can be used to store non-volatile software programs, non-volatile computer-executable programs and modules, such as an image brightness adjustment method in the above-mentioned embodiment of the present invention
  • non-volatile software programs for example, acquisition module 200, determination module 300, selection module 400, adjustment module 500, etc.
  • the processor 110 executes various functional applications and data processing of an image brightness adjustment method by running non-volatile software programs, instructions, and modules stored in the memory 120, that is, implements an image in the above method embodiment.
  • the brightness adjustment method and the function of each module in the above device embodiment are examples of the image brightness adjustment method.
  • the memory 120 may include a storage program area and a storage data area.
  • the storage program area may store an operating system and an application program required by at least one function; the storage data area may store data created according to the use of an image brightness adjustment device, etc. .
  • the storage data area also stores preset data, including a predefined gray scale range, candidate shooting parameters, candidate ratio index interval, candidate gamma curve, and the like.
  • the memory 120 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
  • the memory 120 may optionally include memories remotely provided with respect to the processor 110, and these remote memories may be connected to the processor 110 through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.
  • the program instructions and one or more modules are stored in the memory 120, and when executed by the one or more processors 110, each step of an image brightness adjustment method in any of the foregoing method embodiments is executed, Or, realize the functions of each module of an image brightness adjustment device in any of the foregoing device embodiments.
  • the above-mentioned product can execute the method provided in the above-mentioned embodiment of the present invention, and has corresponding functional modules and beneficial effects for the execution method.
  • the above-mentioned product can execute the method provided in the above-mentioned embodiment of the present invention, and has corresponding functional modules and beneficial effects for the execution method.
  • the embodiment of the present invention also provides a non-volatile computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, the computer-executable instructions are executed by one or more processors, for example, FIG. 4
  • a processor 110 in any of the foregoing method embodiments may enable a computer to execute each step of an image brightness adjustment method in any of the foregoing method embodiments, or implement the functions of various modules of an image brightness adjustment device in any of the foregoing device embodiments.
  • the embodiment of the present invention also provides a computer program product, the computer program product includes a computer program stored on a non-volatile computer-readable storage medium, the computer program includes program instructions, when the program instructions are Or multiple processors, such as a processor 110 in FIG. 4, can cause a computer to execute each step of an image brightness adjustment method in any of the foregoing method embodiments, or implement any of the foregoing device embodiments The function of each module of the image brightness adjustment device.
  • the device embodiments described above are merely illustrative.
  • the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in One place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the objectives of the solutions of the embodiments.
  • each embodiment can be implemented by software plus a general hardware platform, and of course, it can also be implemented by hardware.
  • Those of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented by computer programs instructing relevant hardware.
  • the programs can be stored in a computer readable storage medium, and the program is executed At the time, it may include the flow of the implementation method of each method as described above.
  • the storage medium may be a magnetic disk, an optical disc, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM), etc.

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Abstract

应用于图像处理技术领域，公开一种图像亮度调节方法、装置及无人机。其中，图像亮度调节方法用于无人机的图像采集设备，包括：获取待处理图像及图像采集设备采集待处理图像时的当前拍摄参数；确定待处理图像中，灰度值处于预定义灰度范围内的像素个数在待处理图像的全部像素个数中的占比；根据当前拍摄参数、预定义灰度范围以及占比，选择与当前拍摄参数、预定义灰度范围以及占比匹配的最优伽马曲线；根据最优伽马曲线，调节所述待处理图像的亮度。通过上述方式，能够根据实际拍摄情况适应性地选择用于图像亮度调节的伽马曲线，使得不同拍摄环境下拍摄的图像均能够得到很好的亮度校正，保证无人机航拍的效果。

Description

一种图像亮度调节方法、装置及无人机
本申请要求于2019年5月15日提交中国专利局、申请号为201910407153.0、申请名称为“一种图像亮度调节方法、装置及无人机”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及图像处理技术领域，特别是涉及一种图像亮度调节方法、装置及无人机。
背景技术
无人机是一种由无线电遥控设备或自身程序控制装置操纵的无人驾驶飞行器，常用于航拍。由于无人机航行路线上的不确定性，无人机航拍并不总是在拍摄环境状况良好的情况下进行，时常遇到光线不足或者逆光的情况。而目前对无人机航拍的视频图像进行亮度调节时，无论何种拍摄环境，均使用同一条预置的伽马曲线，使得无人机在光线不足或者逆光的情况下拍摄的视频图像经过亮度调节后，仍存在亮度异常区域，导致细节丢失，无法保证无人机航拍的效果。
发明内容
本发明实施例旨在提供一种图像亮度调节方法、装置及无人机，能够更有效地调节图像亮度。
为解决上述技术问题，本发明实施例采用的一个技术方案是：提供一种图像亮度调节方法，用于无人机的图像采集设备，所述方法包括：
获取待处理图像及所述图像采集设备采集所述待处理图像时的当前拍摄参数；
确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比；
根据所述当前拍摄参数、所述预定义灰度范围以及所述占比，选择与所述当前拍摄参数、所述预定义灰度范围以及所述占比匹配的最优伽马曲线；
根据所述最优伽马曲线，调节所述待处理图像的亮度。
可选地，每个所述预定义灰度范围对应至少两个候选占比索引区间，所述至少两个候选占比索引区间中的每一个候选占比索引区间均对应至少两个候选拍摄参数，所述至少两个候选拍摄参数中的每一个候选拍摄参数均对应一条候选伽马曲线；则，
所述根据所述当前拍摄参数、所述预定义灰度范围以及所述占比，选择与所述当前拍摄参数、所述预定义灰度范围以及所述占比匹配的最优伽马曲线，包括：
在所述预定义灰度范围对应的候选占比索引区间中，确定包含所述占比的候选占比索引区间作为目标占比索引区间；
在所述目标占比索引区间对应的候选拍摄参数中，确定与所述当前拍摄参数匹配的候选拍摄参数作为目标拍摄参数；
选择所述目标拍摄参数对应的候选伽马曲线作为最优伽马曲线。
可选地，所述方法还包括：
若所述目标占比索引区间对应的候选拍摄参数中不存在与所述当前拍摄参数匹配的候选拍摄参数，则根据插值计算方法，计算与所述当前拍摄参数对应的目标伽马曲线，并选择所述目标伽马曲线作为最优伽马曲线。
可选地，所述根据插值计算方法，计算与所述当前拍摄参数对应的目标伽马曲线，包括：
在所述目标占比索引区间对应的候选拍摄参数中确定至少两个候选拍摄参数作为参考拍摄参数；
根据所述参考拍摄参数以及所述参考拍摄参数对应的候选伽马曲线的伽马值，构建插值函数；
根据所述插值函数和所述当前拍摄参数，计算与所述当前拍摄参数对应的目标伽马曲线。
可选地，所述确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比，包括：
通过灰度直方图确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比。
可选地，所述预定义灰度范围包括预定义亮区灰度范围或预定义暗区灰度范围。
可选地，在所述预定义亮区灰度范围内，同一候选拍摄参数下，所述候选占比索引区间的最大端点值越大，所述候选占比索引区间对应的候选伽马曲线的伽马值越大。
可选地，在所述预定义暗区灰度范围内，同一候选拍摄参数下，所述候选占比索引区间的最大端点值越大，所述候选占比索引区间对应的候选伽马曲线的伽马值越小。
可选地，所述当前拍摄参数包括曝光量和/或感光度。
为解决上述技术问题，本发明实施例采用的另一个技术方案是：提供一种图像亮度调节装置，用于无人机的图像采集设备，所述装置包括：
获取模块，用于获取待处理图像及所述图像采集设备采集所述待处理图像时的当前拍摄参数；
确定模块，用于确定所述待处理图像中，灰度值处于预定义灰度范围内的 像素个数在所述待处理图像的全部像素个数中的占比；
选择模块，用于根据所述当前拍摄参数、所述预定义灰度范围以及所述占比，选择与所述当前拍摄参数、所述预定义灰度范围以及所述占比匹配的最优伽马曲线；
调节模块，用于根据所述最优伽马曲线，调节所述待处理图像的亮度。
可选地，每个所述预定义灰度范围对应至少两个候选占比索引区间，所述至少两个候选占比索引区间中的每一个候选占比索引区间均对应至少两个候选拍摄参数，所述至少两个候选拍摄参数中的每一个候选拍摄参数均对应一条候选伽马曲线；则，
所述选择模块具体用于：
在所述预定义灰度范围对应的候选占比索引区间中，确定包含所述占比的候选占比索引区间作为目标占比索引区间；
在所述目标占比索引区间对应的候选拍摄参数中，确定与所述当前拍摄参数匹配的候选拍摄参数作为目标拍摄参数；
选择所述目标拍摄参数对应的候选伽马曲线作为最优伽马曲线。
可选地，所述选择模块还用于：
若所述目标占比索引区间对应的候选拍摄参数中不存在与所述当前拍摄参数匹配的候选拍摄参数，则根据插值计算方法，计算与所述当前拍摄参数对应的目标伽马曲线，并选择所述目标伽马曲线作为最优伽马曲线。
可选地，所述选择模块具体用于：
在所述目标占比索引区间对应的候选拍摄参数中确定至少两个候选拍摄参数作为参考拍摄参数；
根据所述参考拍摄参数以及所述参考拍摄参数对应的候选伽马曲线的伽马值，构建插值函数；
根据所述插值函数和所述当前拍摄参数，计算与所述当前拍摄参数对应的目标伽马曲线。
可选地，所述确定模块具体用于：
通过灰度直方图确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比。
可选地，所述预定义灰度范围包括预定义亮区灰度范围或预定义暗区灰度范围。
可选地，在所述预定义亮区灰度范围内，同一候选拍摄参数下，所述候选占比索引区间的最大端点值越大，所述候选占比索引区间对应的候选伽马曲线的伽马值越大。
可选地，在所述预定义暗区灰度范围内，同一候选拍摄参数下，所述候选占比索引区间的最大端点值越大，所述候选占比索引区间对应的候选伽马曲线的伽马值越小。
可选地，所述当前拍摄参数包括曝光量和/或感光度。
为解决上述技术问题，本发明实施例采用的另一个技术方案是：提供一种无人机，包括：
机身；
机臂，与所述机身相连；
动力装置，设于所述机臂；以及
图像采集设备，与所述机身相连；
其中，所述图像采集设备包括：
至少一个处理器；以及
与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够用于执行以上所述的图像亮度调节方法。
为解决上述技术问题，本发明实施例采用的另一个技术方案是：提供一种非易失性计算机可读存储介质，所述非易失性计算机可读存储介质存储有计算机可执行指令，所述计算机可执行指令用于使无人机的图像采集设备执行以上所述的图像亮度调节方法。
本发明实施例的有益效果是：区别于现有技术的情况下，本发明实施例提供一种图像亮度调节方法、装置及无人机，在图像亮度调节方法中，获取待处理图像及图像采集设备采集待处理图像时的当前拍摄参数后，确定待处理图像中，灰度值处于预定义灰度范围内的像素个数在待处理图像的全部像素个数中的占比，并根据所获取的当前拍摄参数、所确定的占比以及确定该占比时的预定义灰度范围选择匹配的最优伽马曲线对待处理图像进行亮度调节，能够根据实际拍摄情况适应性地选择用于图像亮度调节的伽马曲线，使得不同拍摄环境下拍摄的图像均能够得到很好的亮度校正，保证无人机航拍的效果。
附图说明
一个或多个实施例通过与之对应的附图中的图片进行示例性说明，这些示例性说明并不构成对实施例的限定，附图中具有相同参考数字标号的元件表示为类似的元件，除非有特别申明，附图中的图不构成比例限制。
图1是本发明一实施例提供的一种无人机的结构示意图；
图2是本发明一实施例提供的一种图像亮度调节方法的流程示意图；
图3是本发明一实施例提供的一种图像亮度调节装置的结构示意图；
图4是本发明一实施例提供的一种无人机的硬件结构示意图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整的描述，显然， 所描述的实施例是本发明一部分实施例，而不是全部的实施例。应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。基于本发明中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
需要说明的是，当元件被表述“固定于”另一个元件，它可以直接在另一个元件上、或者其间可以存在一个或多个居中的元件。当一个元件被表述“连接”另一个元件，它可以是直接连接到另一个元件、或者其间可以存在一个或多个居中的元件。本说明书所使用的术语“垂直的”、“水平的”、“左”、“右”以及类似的表述只是为了说明的目的。
此外，下面所描述的本发明各个实施例中所涉及到的技术特征只要彼此之间未构成冲突就可以相互组合。
本发明提供了一种图像亮度调节方法及装置，该方法及装置应用于无人机的图像采集设备，从而使得该无人机的图像采集设备航拍后能够根据实际拍摄情况适应性地选择最优伽马曲线对航拍的视频图像进行亮度调节，以使得不同拍摄环境下拍摄的视频图像均能得到很好的亮度校正，保证了无人机航拍的效果。其中，无人机可以是任何合适类型的搭载有用于航拍的图像采集设备的高空无人机或者低空无人机，包括固定翼无人机、旋翼无人机、伞翼无人机或者扑翼无人机等。
下面，将通过具体实施例对本发明进行阐述。
实施例一
请参阅图1，是本发明其中一实施例提供的一种无人机100，包括机身10、机臂20、动力装置30、图像采集设备40、起落架50以及飞控系统(图未示)。机臂20、图像采集设备40以及起落架50均与机身10连接，飞控系统则设置于机身10内，动力装置30则设置于机臂20上。其中，动力装置30、图像采集设备40以及起落架50均与飞控系统通信连接，使得飞控系统能够通过动力装置30来控制无人机100的飞行，并且飞控系统还能够控制图像采集设备40进行航拍以及控制起落架50打开与收起。
优选地，机臂20数量为4，均匀分布于机身10四周，用于承载动力装置30。
动力装置30包括电机以及与电机轴连接的螺旋桨，电机能够带动螺旋桨旋转以为无人机100提供升力，实现飞行；电机还能够通过改变螺旋桨的转速及方向来改变无人机100的飞行方向。当动力装置30与飞控系统通信连接时，飞控系统能够通过控制电机来控制无人机100的飞行。
该动力装置30设置于机臂20未与机身10连接的一端，并通过电机连接机臂20。
优选地，在无人机100的4个机臂上均设置有动力装置30，以使无人机100能够平稳飞行。
图像采集设备40则可以为照相机、摄像机等能够拍摄视频图像的设备，设置于机身10底部，能够在飞控系统的控制下进行航拍，即拍摄视频图像。其中，该图像采集设备40还能够通过云台设置于机身10底部，以随着云台的转动而转动，进而能够全方位进行航拍，拍摄不同视角的视频图像。
进一步地，在无人机100飞行过程中的不同拍摄环境下，图像采集设备40拍摄的视频图像亮度不同，为了保证航拍效果，图像采集设备40还用于执行图像亮度调节方法，以根据实际拍摄情况适应性地选择最优伽马曲线对航拍的视频图像进行亮度调节，以使得不同拍摄环境下拍摄的视频图像均能得到很好的亮度校正，保证无人机航拍的效果。
起落架50则设置于机身10底部相对两侧，通过驱动装置连接于机身10，起落架50在驱动装置的驱动下能够进行打开与收起。在无人机100与地面接触时，驱动装置控制起落架50打开，以使无人机100通过起落架50与地面接触；在无人机100飞行过程中，驱动装置控制起落架50收起，以避免起落架50影响无人机100飞行。当起落架50与飞控系统通信连接时，飞控系统能够通过控制驱动装置来控制起落架50的打开与收起。
飞控系统则与动力装置30、图像采集设备40以及起落架50通过有线连接或者无线连接的方式进行通信连接。其中，无线连接包括但不限于：WiFi、蓝牙、ZigBee等。
其中，图像采集设备40执行图像亮度调节方法，具体包括：图像采集设备40拍摄视频图像后，获取待处理图像以及拍摄该待处理图像时的当前拍摄参数。
其中，待处理图像由若干排成行列的像素组成，每个像素均对应色彩数值。该待处理图像可以为图像采集设备40拍摄的视频的图像帧，也可以为图像采集设备40拍摄的图像。
当前拍摄参数则为图像采集设备40拍摄该待处理图像时设置的拍摄参数，拍摄参数包括曝光量和/或感光度(ISO)，通过当前拍摄参数能够确定该待处理图像的实际拍摄情况。其中，曝光量能够根据图像采集设备40的增益和快门计算得到，比如：通过曝光行数与增益的乘积计算得到曝光量；感光度则能够根据图像采集设备40的设置参数得到。
图像采集设备40获取待处理图像以及拍摄该待处理图像时的当前拍摄参数后，确定待处理图像中，灰度值处于预定义灰度范围内的像素个数在待处理图像的全部像素个数中的占比，例如，在一些实施例中，图像采集设备40通过灰度直方图确定待处理图像中，灰度值处于预定义灰度范围内的像素个数在待处理图像的全部像素个数中的占比。
其中，灰度直方图为待处理图像的全部像素个数在各个灰度值的分布情况的统计图，通过灰度直方图能够确定待处理图像的全部像素个数以及待处理图像在各个灰度值的像素个数。
因此，使用灰度直方图，可以统计待处理图像中预定义灰度范围内各个灰 度值对应的像素个数之和，以便确定待处理图像中灰度值处于该预定义灰度范围内的像素个数。
由于灰度直方图中的灰度值为0至255的整数，且灰度值由0至255表征亮度由黑至白的变化规律，故能够通过预定义灰度范围表示待处理图像的暗区或亮区。
当预定义灰度范围的灰度值取值不大于50时，该预定义灰度范围为预定义暗区灰度范围，通过确定待处理图像中灰度值处于该预定义暗区灰度范围的像素个数在待处理图像的全部像素个数中的占比能够确定暗区的亮度情况；
当预定义灰度范围的灰度值取值不小于192时，该预定义灰度范围为预定义亮区灰度范围，通过确定待处理图像中灰度值处于该预定义亮区灰度范围的像素个数在待处理图像的全部像素个数中的占比能够确定亮区的亮度情况。
举例而言，一张待处理图像包括不同灰度值的若干像素，每个像素的灰度值可相同，亦可不同，每个像素的灰度值皆落在0至255范围内。使用灰度直方图统计该待处理图像的像素情况时，假设该待处理图像的全部像素个数为4096个，灰度值位于预定义灰度范围0至32的像素个数为1316个，灰度值位于预定义灰度范围33至191的像素个数为2600个，灰度值位于预定义灰度范围192至255的像素个数为180个。
由于预定义灰度范围为0至32时，灰度值取值不大于50，故预定义灰度范围0至32为预定义暗区灰度范围，通过确定待处理图像中灰度值处于该预定义暗区灰度范围的像素个数在待处理图像的全部像素个数中的占比，便能够确定该待处理图像暗区的亮度情况。例如，假设确定待处理图像暗区的亮度情况包括“待处理图像中灰度值处于预定义灰度范围0至32的像素个数在待处理图像的全部像素个数中的占比大于30％时确定待处理图像暗区亮度过暗”时，由于当前待处理图像中灰度值位于预定义灰度范围0至32的像素个数在该待处理图像的全部像素个数中的占比为32.1％，大于30％，因此，确定该待处理图像暗区亮度过暗。
由于预定义灰度范围为192至255时，灰度值取值不小于192，故预定义灰度范围192至255为预定义亮区灰度范围，通过确定待处理图像中灰度值处于该预定义亮区灰度范围的像素个数在待处理图像的全部像素个数中的占比，便能够确定该待处理图像亮区的亮度情况。例如，假设确定待处理图像亮区的亮度情况包括“待处理图像中灰度值处于预定义灰度范围192至255的像素个数在待处理图像的全部像素个数中的占比大于25％时确定待处理图像亮区亮度过亮”时，由于当前待处理图像中灰度值位于预定义灰度范围192至255的像素个数在该待处理图像的全部像素个数中的占比为4.4％，小于25％，因此，确定该待处理图像亮区亮度正常。
在本发明中，确定暗区的亮度情况或亮区的亮度情况均能够确定用于亮度调节的最优伽马曲线，故能够确定待处理图像中灰度值处于预定义暗区灰度范围的像素个数在待处理图像的全部像素个数中的占比，或，待处理图像中灰度 值处于预定义亮区灰度范围的像素个数在待处理图像的全部像素个数中的占比。即预定义灰度范围包括预定义暗区灰度范围或预定义亮区灰度范围。
图像采集设备40确定待处理图像中，灰度值处于预定义灰度范围内的像素个数在待处理图像的全部像素个数中的占比后，根据所获取的当前拍摄参数、所确定的占比以及确定该占比时的预定义灰度范围，选择与该当前拍摄参数、该占比以及该预定义灰度范围匹配的最优伽马曲线。
当图像采集设备40所确定的占比为待处理图像中灰度值处于预定义暗区灰度范围的像素个数在待处理图像的全部像素个数中的占比时，则根据当前拍摄参数、占比以及预定义暗区灰度范围，选择与当前拍摄参数、占比以及预定义暗区灰度范围匹配的第一最优伽马曲线，该第一最优伽马曲线用于调节待处理图像灰度值小于128的像素的亮度。
当图像采集设备40所确定的占比为待处理图像中灰度值处于预定义亮区灰度范围的像素个数在待处理图像的全部像素个数中的占比时，则根据当前拍摄参数、占比以及预定义亮区灰度范围，选择与当前拍摄参数、占比以及预定义亮区灰度范围匹配的第二最优伽马曲线，该第二最优伽马曲线用于调节待处理图像灰度值不小于128的像素的亮度。
在一些实施例中，请参阅表1，由于每个预定义灰度范围对应至少两个候选占比索引区间，至少两个候选占比索引区间中的每一个候选占比索引区间均对应至少两个候选拍摄参数，至少两个候选拍摄参数中的每一个候选拍摄参数均对应一条候选伽马曲线。其中，在预定义暗区灰度范围内，同一候选拍摄参数下，候选占比索引区间的最大端点值越大，候选占比索引区间对应的候选伽马曲线的伽马值越小；在预定义亮区灰度范围内，同一候选拍摄参数下，候选占比索引区间的最大端点值越大，候选占比索引区间对应的候选伽马曲线的伽马值越大。
表1
Figure PCTCN2020090269-appb-000001
由表1可知，预定义灰度范围0-32对应两个候选占比索引区间：区间1和区间2；在区间1和区间2中，每一个候选占比索引区间均对应三个候选拍摄参数，区间1对应ISO1、ISO2和ISO3，区间2对应ISO4、ISO5和ISO6；在区间1对应的候选拍摄参数ISO1、ISO2和ISO3中，每一个候选拍摄参数均对应一条候选伽马曲线，ISO1对应Gamma1、ISO2对应Gamma2、ISO3对应Gamma3；在区间2对应的候选拍摄参数ISO4、ISO5和ISO6中，每一个候选拍摄参数均对应一条候选伽马曲线，ISO4对应Gamma4、ISO5对应Gamma5、ISO6对应Gamma6。
预定义灰度范围192-255对应两个候选占比索引区间：区间3和区间4；在区间3和区间4中，每一个候选占比索引区间均对应三个候选拍摄参数，区间3对应ISO7、ISO8和ISO9，区间4对应ISO10、ISO11和ISO12；在区间3对应的候选拍摄参数ISO7、ISO8和ISO9中，每一个候选拍摄参数均对应一条候选伽马曲线，ISO7对应Gamma7、ISO8对应Gamma8、ISO9对应Gamma9；在区间4对应的候选拍摄参数ISO10、ISO11和ISO12中，每一个候选拍摄参数均对应一条候选伽马曲线，ISO10对应Gamma10、ISO11对应Gamma11、ISO12对应Gamma12。
在表1中，在预定义暗区灰度范围0-32内，ISO1和ISO4为同一候选拍摄参数，ISO4对应的区间2的最大端点值大于ISO1对应的区间1的最大端点 值，故区间2对应的Gamma4的伽马值小于区间1对应的Gamma1的伽马值。其中，对于候选占比索引区间——区间1：0％-30％，30％为该区间的最大端点值。
同理可得，在预定义亮区灰度范围192-255内，ISO8和ISO11为同一候选拍摄参数，ISO8对应的区间3的最大端点值大于ISO11对应的区间4的最大端点值，故区间3对应的Gamma8的伽马值大于区间4对应的Gamma11的伽马值。其中，对于候选占比索引区间——区间4：25.1％-40％，40％为该区间的最大端点值。
于是，图像采集设备40便可以根据所获取的当前拍摄参数、所确定的占比以及确定该占比时的预定义灰度范围，选择最优伽马曲线。举例而言，首先，图像采集设备40在预定义灰度范围对应的候选占比索引区间中，确定包含占比的候选占比索引区间作为目标占比索引区间。例如，图像采集设备40确定待处理图像中灰度值处于预定义暗区灰度范围0-32内的像素个数在待处理图像的全部像素个数中的占比为25％，在表1中，确定预定义暗区灰度范围0-32对应的候选占比索引区间为区间1和区间2，在区间1和区间2中，确定区间1包含占比25％，故将区间1确定为目标占比索引区间。
其次，图像采集设备40在所确定的目标占比索引区间对应的候选拍摄参数中，确定与当前拍摄参数匹配的候选拍摄参数作为目标拍摄参数。例如，当前拍摄参数ISO为100，在表1中，确定目标占比索引区间——区间1对应的候选拍摄参数为ISO1、ISO2和ISO3，在ISO1、ISO2和ISO3中，确定ISO1与当前拍摄参数100匹配，故将ISO1确定为目标拍摄参数。
最后，图像采集设备40选择所确定的目标拍摄参数对应的候选伽马曲线作为最优伽马曲线。例如，在表1中，目标拍摄参数ISO1对应的候选伽马曲线为Gamma1，则选择Gamma1作为最优伽马曲线。
反之，若目标占比索引区间对应的候选拍摄参数中不存在与当前拍摄参数匹配的候选拍摄参数，则根据插值计算方法，计算与当前拍摄参数对应的目标伽马曲线，并选择目标伽马曲线作为最优伽马曲线。例如，当前拍摄参数ISO为150，在表1中，确定目标占比索引区间——区间1对应的候选拍摄参数为ISO1、ISO2和ISO3，在ISO1、ISO2和ISO3中，ISO1为100，ISO2为200，ISO3为300，均不与当前拍摄参数150匹配，故确定目标占比索引区间对应的候选拍摄参数中不存在与当前拍摄参数匹配的候选拍摄参数，此时，根据插值计算方法，计算与当前拍摄参数对应的目标伽马曲线，并选择目标伽马曲线作为最优伽马曲线。
举例而言，根据插值计算方法，计算与当前拍摄参数对应的目标伽马曲线时，首先，图像采集设备40在目标占比索引区间对应的候选拍摄参数中，确定至少两个候选拍摄参数作为参考拍摄参数。例如，在目标占比索引区间——区间1对应的候选拍摄参数ISO1、ISO2和ISO3中，确定ISO1、ISO2和ISO3中至少两个作为参考拍摄参数，包括确定ISO1和ISO2作为参考拍摄参数，或者，确定ISO1和ISO3作为参考拍摄参数，或者，确定ISO2和ISO3作为参考 拍摄参数，或者，确定ISO1、ISO2和ISO3作为参考拍摄参数。
其次，图像采集设备40根据所确定的参考拍摄参数以及参考拍摄参数对应的候选伽马曲线的伽马值，构建插值函数。例如，当图像采集设备40确定ISO1和ISO2为参考拍摄参数时，图像采集设备40确定ISO1对应的候选伽马曲线Gamma1的伽马值、确定ISO2对应的候选伽马曲线Gamma2的伽马值，其中，所确定的Gamma1的伽马值为γ1、Gamma2的伽马值为γ2，于是，形成两组关系(ISO1，γ1)和(ISO2，γ2)。
最后，图像采集设备40根据所构建的插值函数和当前拍摄参数，计算与当前拍摄参数对应的目标伽马曲线。例如，在(ISO1，γ1)和(ISO2，γ2)以及当前拍摄参数150中，由于当前拍摄参数150＝(ISO1+ISO2)/2，于是，当前拍摄参数150对应的目标伽马曲线的伽马值γ3＝(γ1+γ2)/2，根据所计算出的伽马值γ3则能够得到目标伽马曲线。
图像采集设备40选择最优伽马曲线后，根据所选择的最优伽马曲线，调节待处理图像的亮度。
在本发明实施例中，无人机100的图像采集设备40通过执行图像亮度调节方法，实现航拍后根据实际拍摄情况适应性地选择最优伽马曲线对航拍的视频图像进行亮度调节，以使得不同拍摄环境下拍摄的视频图像均能得到很好的亮度校正，保证了航拍效果。
实施例二
请参阅图2，是本发明其中一实施例提供的一种图像亮度调节方法的流程示意图，应用于无人机，该无人机为上述实施例中所述的无人机100，而本发明实施例提供的方法由上述图像采集设备40执行，用于根据实际拍摄情况适应性地选择最优伽马曲线对航拍的视频图像进行亮度调节，保证航拍效果，该图像亮度调节方法包括：
S100：获取待处理图像及所述图像采集设备采集所述待处理图像时的当前拍摄参数。
上述“待处理图像”由若干排成行列的像素组成，每个像素均对应色彩数值。该待处理图像可以为图像采集设备40拍摄的视频的图像帧，也可以为图像采集设备40拍摄的图像。
上述“当前拍摄参数”为图像采集设备40拍摄该待处理图像时设置的拍摄参数，拍摄参数包括曝光量和/或感光度(ISO)，通过当前拍摄参数能够确定该待处理图像的实际拍摄情况。其中，曝光量能够根据图像采集设备40的增益和快门计算得到，比如：通过曝光行数与增益的乘积计算得到曝光量；感光度则能够根据图像采集设备40的设置参数得到。
其中，从图像处理设备40本地获取待处理图像及拍摄该待处理图像时的当前拍摄参数。
S200：确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比。
具体地，通过灰度直方图确定待处理图像中，灰度值处于预定义灰度范围内的像素个数在待处理图像的全部像素个数中的占比。
其中，灰度直方图为待处理图像的全部像素个数在各个灰度值的分布情况的统计图，通过灰度直方图能够确定待处理图像的全部像素个数以及待处理图像在各个灰度值的像素个数。
因此，使用灰度直方图，可以统计待处理图像中预定义灰度范围内各个灰度值对应的像素个数之和，以便确定待处理图像中灰度值处于该预定义灰度范围内的像素个数。
由于灰度直方图中的灰度值为0至255的整数，且灰度值由0至255表征亮度由黑至白的变化规律，故能够通过预定义灰度范围表示待处理图像的暗区或亮区。
当预定义灰度范围的灰度值取值不大于50时，该预定义灰度范围为预定义暗区灰度范围，通过确定待处理图像中灰度值处于该预定义暗区灰度范围的像素个数在待处理图像的全部像素个数中的占比能够确定暗区的亮度情况；
当预定义灰度范围的灰度值取值不小于192时，该预定义灰度范围为预定义亮区灰度范围，通过确定待处理图像中灰度值处于该预定义亮区灰度范围的像素个数在待处理图像的全部像素个数中的占比能够确定亮区的亮度情况。
举例而言，一张待处理图像包括不同灰度值的若干像素，每个像素的灰度值可相同，亦可不同，每个像素的灰度值皆落在0至255范围内。使用灰度直方图统计该待处理图像的像素情况时，假设该待处理图像的全部像素个数为4096个，灰度值位于预定义灰度范围0至32的像素个数为1316个，灰度值位于预定义灰度范围33至191的像素个数为2600个，灰度值位于预定义灰度范围192至255的像素个数为180个。
由于预定义灰度范围为0至32时，灰度值取值不大于50，故预定义灰度范围0至32为预定义暗区灰度范围，通过确定待处理图像中灰度值处于该预定义暗区灰度范围的像素个数在待处理图像的全部像素个数中的占比，便能够确定该待处理图像暗区的亮度情况。例如，假设确定待处理图像暗区的亮度情况包括“待处理图像中灰度值处于预定义灰度范围0至32的像素个数在待处理图像的全部像素个数中的占比大于30％时确定待处理图像暗区亮度过暗”时，由于当前待处理图像中灰度值位于预定义灰度范围0至32的像素个数在该待处理图像的全部像素个数中的占比为32.1％，大于30％，因此，确定该待处理图像暗区亮度过暗。
由于预定义灰度范围为192至255时，灰度值取值不小于192，故预定义灰度范围192至255为预定义亮区灰度范围，通过确定待处理图像中灰度值处于该预定义亮区灰度范围的像素个数在待处理图像的全部像素个数中的占比，便能够确定该待处理图像亮区的亮度情况。例如，假设确定待处理图像亮区的亮度情况包括“待处理图像中灰度值处于预定义灰度范围192至255的像素个数在待处理图像的全部像素个数中的占比大于25％时确定待处理图像亮区亮 度过亮”时，由于当前待处理图像中灰度值位于预定义灰度范围192至255的像素个数在该待处理图像的全部像素个数中的占比为4.4％，小于25％，因此，确定该待处理图像亮区亮度正常。
在本发明中，确定暗区的亮度情况或亮区的亮度情况均能够确定用于亮度调节的最优伽马曲线，故能够确定待处理图像中灰度值处于预定义暗区灰度范围的像素个数在待处理图像的全部像素个数中的占比，或，待处理图像中灰度值处于预定义亮区灰度范围的像素个数在待处理图像的全部像素个数中的占比。即预定义灰度范围包括预定义暗区灰度范围或预定义亮区灰度范围。
S300：根据所述当前拍摄参数、所述预定义灰度范围以及所述占比，选择与所述当前拍摄参数、所述预定义灰度范围以及所述占比匹配的最优伽马曲线。
当所确定的占比为待处理图像中灰度值处于预定义暗区灰度范围的像素个数在待处理图像的全部像素个数中的占比时，则根据当前拍摄参数、占比以及预定义暗区灰度范围，选择与当前拍摄参数、占比以及预定义暗区灰度范围匹配的第一最优伽马曲线，该第一最优伽马曲线用于调节待处理图像灰度值小于128的像素的亮度。
当所确定的占比为待处理图像中灰度值处于预定义亮区灰度范围的像素个数在待处理图像的全部像素个数中的占比时，则根据当前拍摄参数、占比以及预定义亮区灰度范围，选择与当前拍摄参数、占比以及预定义亮区灰度范围匹配的第二最优伽马曲线，该第二最优伽马曲线用于调节待处理图像灰度值不小于128的像素的亮度。
在一些实施例中，请参阅表1，由于每个预定义灰度范围对应至少两个候选占比索引区间，至少两个候选占比索引区间中的每一个候选占比索引区间均对应至少两个候选拍摄参数，至少两个候选拍摄参数中的每一个候选拍摄参数均对应一条候选伽马曲线。其中，在预定义暗区灰度范围内，同一候选拍摄参数下，候选占比索引区间的最大端点值越大，候选占比索引区间对应的候选伽马曲线的伽马值越小；在预定义亮区灰度范围内，同一候选拍摄参数下，候选占比索引区间的最大端点值越大，候选占比索引区间对应的候选伽马曲线的伽马值越大。
由表1可知，预定义灰度范围0-32对应两个候选占比索引区间：区间1和区间2；在区间1和区间2中，每一个候选占比索引区间均对应三个候选拍摄参数，区间1对应ISO1、ISO2和ISO3，区间2对应ISO4、ISO5和ISO6；在区间1对应的候选拍摄参数ISO1、ISO2和ISO3中，每一个候选拍摄参数均对应一条候选伽马曲线，ISO1对应Gamma1、ISO2对应Gamma2、ISO3对应Gamma3；在区间2对应的候选拍摄参数ISO4、ISO5和ISO6中，每一个候选拍摄参数均对应一条候选伽马曲线，ISO4对应Gamma4、ISO5对应Gamma5、ISO6对应Gamma6。
预定义灰度范围192-255对应两个候选占比索引区间：区间3和区间4； 在区间3和区间4中，每一个候选占比索引区间均对应三个候选拍摄参数，区间3对应ISO7、ISO8和ISO9，区间4对应ISO10、ISO11和ISO12；在区间3对应的候选拍摄参数ISO7、ISO8和ISO9中，每一个候选拍摄参数均对应一条候选伽马曲线，ISO7对应Gamma7、ISO8对应Gamma8、ISO9对应Gamma9；在区间4对应的候选拍摄参数ISO10、ISO11和ISO12中，每一个候选拍摄参数均对应一条候选伽马曲线，ISO10对应Gamma10、ISO11对应Gamma11、ISO12对应Gamma12。
在表1中，在预定义暗区灰度范围0-32内，ISO1和ISO4为同一候选拍摄参数，ISO4对应的区间2的最大端点值大于ISO1对应的区间1的最大端点值，故区间2对应的Gamma4的伽马值小于区间1对应的Gamma1的伽马值。其中，对于候选占比索引区间——区间1：0％-30％，30％为该区间的最大端点值。
同理可得，在预定义亮区灰度范围192-255内，ISO8和ISO11为同一候选拍摄参数，ISO8对应的区间3的最大端点值大于ISO11对应的区间4的最大端点值，故区间3对应的Gamma8的伽马值大于区间4对应的Gamma11的伽马值。其中，对于候选占比索引区间——区间4：25.1％-40％，40％为该区间的最大端点值。
于是，便可以根据所获取的当前拍摄参数、所确定的占比以及确定该占比时的预定义灰度范围，选择最优伽马曲线。举例而言，首先，在预定义灰度范围对应的候选占比索引区间中，确定包含占比的候选占比索引区间作为目标占比索引区间。例如，确定待处理图像中灰度值处于预定义暗区灰度范围0-32内的像素个数在待处理图像的全部像素个数中的占比为25％，在表1中，确定预定义暗区灰度范围0-32对应的候选占比索引区间为区间1和区间2，在区间1和区间2中，确定区间1包含占比25％，故将区间1确定为目标占比索引区间。
其次，在所确定的目标占比索引区间对应的候选拍摄参数中，确定与当前拍摄参数匹配的候选拍摄参数作为目标拍摄参数。例如，当前拍摄参数ISO为100，在表1中，确定目标占比索引区间——区间1对应的候选拍摄参数为ISO1、ISO2和ISO3，在ISO1、ISO2和ISO3中，确定ISO1与当前拍摄参数100匹配，故将ISO1确定为目标拍摄参数。
最后，选择所确定的目标拍摄参数对应的候选伽马曲线作为最优伽马曲线。例如，在表1中，目标拍摄参数ISO1对应的候选伽马曲线为Gamma1，则选择Gamma1作为最优伽马曲线。
进一步地，在本发明另一实施例中，若目标占比索引区间对应的候选拍摄参数中不存在与当前拍摄参数匹配的候选拍摄参数，则根据插值计算方法，计算与当前拍摄参数对应的目标伽马曲线，并选择目标伽马曲线作为最优伽马曲线。例如，当前拍摄参数ISO为150，在表1中，确定目标占比索引区间——区间1对应的候选拍摄参数为ISO1、ISO2和ISO3，在ISO1、ISO2和ISO3中，ISO1为100，ISO2为200，ISO3为300，均不与当前拍摄参数150匹配，故确 定目标占比索引区间对应的候选拍摄参数中不存在与当前拍摄参数匹配的候选拍摄参数，此时，根据插值计算方法，计算与当前拍摄参数对应的目标伽马曲线，并选择目标伽马曲线作为最优伽马曲线。
举例而言，根据插值计算方法，计算与当前拍摄参数对应的目标伽马曲线时，首先，在目标占比索引区间对应的候选拍摄参数中，确定至少两个候选拍摄参数作为参考拍摄参数。例如，在目标占比索引区间——区间1对应的候选拍摄参数ISO1、ISO2和ISO3中，确定ISO1、ISO2和ISO3中至少两个作为参考拍摄参数，包括确定ISO1和ISO2作为参考拍摄参数，或者，确定ISO1和ISO3作为参考拍摄参数，或者，确定ISO2和ISO3作为参考拍摄参数，或者，确定ISO1、ISO2和ISO3作为参考拍摄参数。
其次，根据所确定的参考拍摄参数以及参考拍摄参数对应的候选伽马曲线的伽马值，构建插值函数。例如，当确定ISO1和ISO2为参考拍摄参数时，确定ISO1对应的候选伽马曲线Gamma1的伽马值、确定ISO2对应的候选伽马曲线Gamma2的伽马值，其中，所确定的Gamma1的伽马值为γ1、Gamma2的伽马值为γ2，于是，形成两组关系(ISO1，γ1)和(ISO2，γ2)。
最后，根据所构建的插值函数和当前拍摄参数，计算与当前拍摄参数对应的目标伽马曲线。例如，在(ISO1，γ1)和(ISO2，γ2)以及当前拍摄参数150中，由于当前拍摄参数150＝(ISO1+ISO2)/2，于是，当前拍摄参数150对应的目标伽马曲线的伽马值γ3＝(γ1+γ2)/2，根据所计算出的伽马值γ3则能够得到目标伽马曲线。
S400：根据所述最优伽马曲线，调节所述待处理图像的亮度。
在本发明实施例中，通过所获取的当前拍摄参数、所确定的占比以及确定该占比时的预定义灰度范围选择匹配的最优伽马曲线对待处理图像进行亮度调节，使得图像采集设备能够根据实际拍摄情况适应性地选择最优伽马曲线对航拍的视频图像进行亮度调节，保证不同拍摄环境下拍摄的视频图像均能得到很好的亮度校正。
实施例三
以下所使用的术语“模块”为可以实现预定功能的软件和/或硬件的组合。尽管以下实施例所描述的装置可以以软件来实现，但是硬件，或者软件和硬件的组合的实现也是可能被构想的。
请参阅图3，是本发明其中一实施例提供的一种图像亮度调节装置，该装置应用于无人机，该无人机为上述实施例中所述的无人机100，而本发明实施例提供的装置各个模块的功能由上述图像采集设备40执行，用于根据实际拍摄情况适应性地选择最优伽马曲线对航拍的视频图像进行亮度调节，保证航拍效果，该图像亮度调节装置包括：
获取模块200，所述获取模块200用于获取待处理图像及所述图像采集设备采集所述待处理图像时的当前拍摄参数；
确定模块300，所述确定模块300用于确定所述待处理图像中，灰度值处 于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比；
选择模块400，所述选择模块400用于根据所述当前拍摄参数、所述预定义灰度范围以及所述占比，选择与所述当前拍摄参数、所述预定义灰度范围以及所述占比匹配的最优伽马曲线；
调节模块500，所述调节模块500用于根据所述最优伽马曲线，调节所述待处理图像的亮度。
在本发明的一实施例中，每个所述预定义灰度范围对应至少两个候选占比索引区间，所述至少两个候选占比索引区间中的每一个候选占比索引区间均对应至少两个候选拍摄参数，所述至少两个候选拍摄参数中的每一个候选拍摄参数均对应一条候选伽马曲线；则，
所述选择模块400具体用于：
在所述预定义灰度范围对应的候选占比索引区间中，确定包含所述占比的候选占比索引区间作为目标占比索引区间；
在所述目标占比索引区间对应的候选拍摄参数中，确定与所述当前拍摄参数匹配的候选拍摄参数作为目标拍摄参数；
选择所述目标拍摄参数对应的候选伽马曲线作为最优伽马曲线。
在本发明的一实施例中，所述选择模块400还用于：
若所述目标占比索引区间对应的候选拍摄参数中不存在与所述当前拍摄参数匹配的候选拍摄参数，则根据插值计算方法，计算与所述当前拍摄参数对应的目标伽马曲线，并选择所述目标伽马曲线作为最优伽马曲线。
在本发明的一实施例中，所述选择模块400具体用于：
在所述目标占比索引区间对应的候选拍摄参数中确定至少两个候选拍摄参数作为参考拍摄参数；
根据所述参考拍摄参数以及所述参考拍摄参数对应的候选伽马曲线的伽马值，构建插值函数；
根据所述插值函数和所述当前拍摄参数，计算与所述当前拍摄参数对应的目标伽马曲线。
在本发明的一实施例中，所述确定模块300具体用于：
通过灰度直方图确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比。
在本发明的一实施例中，所述预定义灰度范围包括预定义亮区灰度范围或预定义暗区灰度范围。
在本发明的一实施例中，在所述预定义亮区灰度范围内，同一候选拍摄参数下，所述候选占比索引区间的最大端点值越大，所述候选占比索引区间对应的候选伽马曲线的伽马值越大。
在本发明的一实施例中，在所述预定义暗区灰度范围内，同一候选拍摄参数下，所述候选占比索引区间的最大端点值越大，所述候选占比索引区间对应的候选伽马曲线的伽马值越小。
在本发明的一实施例中，所述当前拍摄参数包括曝光量和/或感光度。
当然，在其他一些可替代实施例中，上述获取模块200、确定模块300、选择模块400、调节模块500可以为图像采集设备40的图像处理芯片。
由于装置实施例和方法实施例是基于同一构思，在内容不互相冲突的前提下，装置实施例的内容可以引用方法实施例的，在此不再一一赘述。
在本发明实施例中，通过所获取的当前拍摄参数、所确定的占比以及确定该占比时的预定义灰度范围选择匹配的最优伽马曲线对待处理图像进行亮度调节，使得图像采集设备能够根据实际拍摄情况适应性地选择最优伽马曲线对航拍的视频图像进行亮度调节，保证不同拍摄环境下拍摄的视频图像均能得到很好的亮度校正。
实施例四
请参阅图4，是本发明其中一实施例提供的一种无人机的硬件结构示意图，本发明实施例提供的硬件模块能够集成于上述实施例所述的图像采集设备40，使得无人机100的图像采集设备40能够执行以上实施例所述的一种图像亮度调节方法，还能实现以上实施例所述的一种图像亮度调节装置的各个模块的功能。该无人机100包括：
一个或多个处理器110以及存储器120。其中，图4中以一个处理器110为例。
处理器110和存储器120可以通过总线或者其他方式连接，图4中以通过总线连接为例。
存储器120作为一种非易失性计算机可读存储介质，可用于存储非易失性软件程序、非易失性计算机可执行程序以及模块，如本发明上述实施例中的一种图像亮度调节方法对应的程序指令以及一种图像亮度调节装置对应的模块(例如，获取模块200、确定模块300、选择模块400和调节模块500等)。处理器110通过运行存储在存储器120中的非易失性软件程序、指令以及模块，从而执行一种图像亮度调节方法的各种功能应用以及数据处理，即实现上述方法实施例中的一种图像亮度调节方法以及上述装置实施例的各个模块的功能。
存储器120可以包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需要的应用程序；存储数据区可存储根据一种图像亮度调节装置的使用所创建的数据等。
所述存储数据区还存储有预设的数据，包括预定义灰度范围、候选拍摄参数、候选占比索引区间以及候选伽马曲线等。
此外，存储器120可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实施例中，存储器120可选包括相对于处理器110远程设置的存储器，这些远程存储器可以通过网络连接至处理器110。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
所述程序指令以及一个或多个模块存储在所述存储器120中，当被所述一 个或者多个处理器110执行时，执行上述任意方法实施例中的一种图像亮度调节方法的各个步骤，或者，实现上述任意装置实施例中的一种图像亮度调节装置的各个模块的功能。
上述产品可执行本发明上述实施例所提供的方法，具备执行方法相应的功能模块和有益效果。未在本实施例中详尽描述的技术细节，可参见本发明上述实施例所提供的方法。
本发明实施例还提供了一种非易失性计算机可读存储介质，所述计算机可读存储介质存储有计算机可执行指令，该计算机可执行指令被一个或多个处理器执行，例如图4中的一个处理器110，可使得计算机执行上述任意方法实施例中的一种图像亮度调节方法的各个步骤，或者，实现上述任意装置实施例中的一种图像亮度调节装置的各个模块的功能。
本发明实施例还提供了一种计算机程序产品，所述计算机程序产品包括存储在非易失性计算机可读存储介质上的计算机程序，所述计算机程序包括程序指令，当所述程序指令被一个或多个处理器执行，例如图4中的一个处理器110，可使得计算机执行上述任意方法实施例中的一种图像亮度调节方法的各个步骤，或者，实现上述任意装置实施例中的一种图像亮度调节装置的各个模块的功能。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的模块可以是或者也可以不是物理上分开的，作为模块显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
通过以上的实施例的描述，本领域普通技术人员可以清楚地了解到各实施例可借助软件加通用硬件平台的方式来实现，当然也可以通过硬件。本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程是可以通过计算机程序指令相关的硬件来完成，所述的程序可存储于一计算机可读取存储介质中，该程序在执行时，可包括如上述各方法的实施方法的流程。其中，所述存储介质可为磁碟、光盘、只读存储记忆体(Read-Only Memory,ROM)或随机存储记忆体(RandomAccessMemory,RAM)等。
以上所述仅为本发明的实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；在本发明的思路下，以上实施例或者不同实施例中的技术特征之间也可以进行组合，步骤可以以任意顺序实现，并存在如上所述的本发明的不同方面的许多其它变化，为了简明，它们没有在细节中提供；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述 各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的范围。

Claims (20)

  1. 一种图像亮度调节方法，用于无人机的图像采集设备，其特征在于，所述方法包括：
    获取待处理图像及所述图像采集设备采集所述待处理图像时的当前拍摄参数；
    确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比；
    根据所述当前拍摄参数、所述预定义灰度范围以及所述占比，选择与所述当前拍摄参数、所述预定义灰度范围以及所述占比匹配的最优伽马曲线；
    根据所述最优伽马曲线，调节所述待处理图像的亮度。
  2. 根据权利要求1所述的方法，其特征在于，每个所述预定义灰度范围对应至少两个候选占比索引区间，所述至少两个候选占比索引区间中的每一个候选占比索引区间均对应至少两个候选拍摄参数，所述至少两个候选拍摄参数中的每一个候选拍摄参数均对应一条候选伽马曲线；则，
    所述根据所述当前拍摄参数、所述预定义灰度范围以及所述占比，选择与所述当前拍摄参数、所述预定义灰度范围以及所述占比匹配的最优伽马曲线，包括：
    在所述预定义灰度范围对应的候选占比索引区间中，确定包含所述占比的候选占比索引区间作为目标占比索引区间；
    在所述目标占比索引区间对应的候选拍摄参数中，确定与所述当前拍摄参数匹配的候选拍摄参数作为目标拍摄参数；
    选择所述目标拍摄参数对应的候选伽马曲线作为最优伽马曲线。
  3. 根据权利要求2所述的方法，其特征在于，所述方法还包括：
    若所述目标占比索引区间对应的候选拍摄参数中不存在与所述当前拍摄参数匹配的候选拍摄参数，则根据插值计算方法，计算与所述当前拍摄参数对应的目标伽马曲线，并选择所述目标伽马曲线作为最优伽马曲线。
  4. 根据权利要求3所述的方法，其特征在于，所述根据插值计算方法，计算与所述当前拍摄参数对应的目标伽马曲线，包括：
    在所述目标占比索引区间对应的候选拍摄参数中确定至少两个候选拍摄参数作为参考拍摄参数；
    根据所述参考拍摄参数以及所述参考拍摄参数对应的候选伽马曲线的伽马值，构建插值函数；
    根据所述插值函数和所述当前拍摄参数，计算与所述当前拍摄参数对应的目标伽马曲线。
  5. 根据权利要求1至4中任一项所述的方法，其特征在于，所述确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比，包括：
    通过灰度直方图确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比。
  6. 根据权利要求1至5中任一项所述的方法，其特征在于，所述预定义灰度范围包括预定义亮区灰度范围或预定义暗区灰度范围。
  7. 根据权利要求6所述的方法，其特征在于，
    在所述预定义亮区灰度范围内，同一候选拍摄参数下，所述候选占比索引区间的最大端点值越大，所述候选占比索引区间对应的候选伽马曲线的伽马值越大。
  8. 根据权利要求6所述的方法，其特征在于，
    在所述预定义暗区灰度范围内，同一候选拍摄参数下，所述候选占比索引区间的最大端点值越大，所述候选占比索引区间对应的候选伽马曲线的伽马值越小。
  9. 根据权利要求1至8中任一项所述的方法，其特征在于，所述当前拍摄参数包括曝光量和/或感光度。
  10. 一种图像亮度调节装置，用于无人机的图像采集设备，其特征在于，所述装置包括：
    获取模块，用于获取待处理图像及所述图像采集设备采集所述待处理图像时的当前拍摄参数；
    确定模块，用于确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比；
    选择模块，用于根据所述当前拍摄参数、所述预定义灰度范围以及所述占比，选择与所述当前拍摄参数、所述预定义灰度范围以及所述占比匹配的最优伽马曲线；
    调节模块，用于根据所述最优伽马曲线，调节所述待处理图像的亮度。
  11. 根据权利要求10所述的装置，其特征在于，每个所述预定义灰度范围对应至少两个候选占比索引区间，所述至少两个候选占比索引区间中的每一个候选占比索引区间均对应至少两个候选拍摄参数，所述至少两个候选拍摄参数中的每一个候选拍摄参数均对应一条候选伽马曲线；则，
    所述选择模块具体用于：
    在所述预定义灰度范围对应的候选占比索引区间中，确定包含所述占比的候选占比索引区间作为目标占比索引区间；
    在所述目标占比索引区间对应的候选拍摄参数中，确定与所述当前拍摄参数匹配的候选拍摄参数作为目标拍摄参数；
    选择所述目标拍摄参数对应的候选伽马曲线作为最优伽马曲线。
  12. 根据权利要求11所述的装置，其特征在于，所述选择模块还用于：
    若所述目标占比索引区间对应的候选拍摄参数中不存在与所述当前拍摄参数匹配的候选拍摄参数，则根据插值计算方法，计算与所述当前拍摄参数对应的目标伽马曲线，并选择所述目标伽马曲线作为最优伽马曲线。
  13. 根据权利要求12所述的装置，其特征在于，所述选择模块具体用于：
    在所述目标占比索引区间对应的候选拍摄参数中确定至少两个候选拍摄参数作为参考拍摄参数；
    根据所述参考拍摄参数以及所述参考拍摄参数对应的候选伽马曲线的伽马值，构建插值函数；
    根据所述插值函数和所述当前拍摄参数，计算与所述当前拍摄参数对应的目标伽马曲线。
  14. 根据权利要10至13中任一项所述的装置，其特征在于，所述确定模块具体用于：
    通过灰度直方图确定所述待处理图像中，灰度值处于预定义灰度范围内的像素个数在所述待处理图像的全部像素个数中的占比。
  15. 根据权利要求10至14中任一项所述的装置，其特征在于，所述预定义灰度范围包括预定义亮区灰度范围或预定义暗区灰度范围。
  16. 根据权利要求15所述的装置，其特征在于，
    在所述预定义亮区灰度范围内，同一候选拍摄参数下，所述候选占比索引区间的最大端点值越大，所述候选占比索引区间对应的候选伽马曲线的伽马值越大。
  17. 根据权利要求15所述的装置，其特征在于，
    在所述预定义暗区灰度范围内，同一候选拍摄参数下，所述候选占比索引区间的最大端点值越大，所述候选占比索引区间对应的候选伽马曲线的伽马值越小。
  18. 根据权利要求10至17中任一项所述的装置，其特征在于，所述当前拍摄参数包括曝光量和/或感光度。
  19. 一种无人机，其特征在于，包括：
    机身；
    机臂，与所述机身相连；
    动力装置，设于所述机臂；以及
    图像采集设备，与所述机身相连；
    其中，所述图像采集设备包括：
    至少一个处理器；以及
    与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够用于执行如权利要求1至9中任一项所述的图像亮度调节方法。
  20. 一种非易失性计算机可读存储介质，其特征在于，所述非易失性计算机可读存储介质存储有计算机可执行指令，所述计算机可执行指令用于使无人机的图像采集设备执行如权利要求1至9中任一项所述的图像亮度调节方法。
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