WO2021051949A1 - 一种图像处理方法及装置、电子设备和存储介质 - Google Patents

一种图像处理方法及装置、电子设备和存储介质 Download PDF

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Publication number
WO2021051949A1
WO2021051949A1 PCT/CN2020/099580 CN2020099580W WO2021051949A1 WO 2021051949 A1 WO2021051949 A1 WO 2021051949A1 CN 2020099580 W CN2020099580 W CN 2020099580W WO 2021051949 A1 WO2021051949 A1 WO 2021051949A1
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Prior art keywords
brightness
region
interest
target image
image
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PCT/CN2020/099580
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English (en)
French (fr)
Inventor
高哲峰
李若岱
庄南庆
马堃
彭悦
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深圳市商汤科技有限公司
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Priority to KR1020217013139A priority Critical patent/KR20210065180A/ko
Priority to JP2021514431A priority patent/JP7152598B2/ja
Priority to SG11202112936XA priority patent/SG11202112936XA/en
Publication of WO2021051949A1 publication Critical patent/WO2021051949A1/zh
Priority to US17/455,909 priority patent/US20220076006A1/en

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/60—Extraction of image or video features relating to illumination properties, e.g. using a reflectance or lighting model
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/161—Detection; Localisation; Normalisation
    • G06V40/166—Detection; Localisation; Normalisation using acquisition arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/168—Feature extraction; Face representation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/10—Image acquisition
    • G06V10/12—Details of acquisition arrangements; Constructional details thereof
    • G06V10/14—Optical characteristics of the device performing the acquisition or on the illumination arrangements
    • G06V10/147—Details of sensors, e.g. sensor lenses

Definitions

  • the present disclosure relates to the field of computer vision technology, and relates to an image processing method and device, electronic equipment, and storage medium.
  • Computer vision technology is a technology that simulates human visual functions through equipment and can be used in many applications such as artificial intelligence and image processing. For example, in a face recognition scene, the identity corresponding to the face can be determined by performing face recognition on the captured image.
  • the image quality of the face is a main influencing factor, and higher image quality helps to improve the accuracy of face recognition.
  • the image quality of the face is relatively poor, which is not conducive to the recognition of the face image and the living body judgment.
  • the present disclosure proposes an image processing method and device, electronic equipment and storage medium.
  • an image processing method including:
  • a target parameter value used for image acquisition in the current scene is determined.
  • the determining the region of interest of the target image according to the human figure detection result of the target image includes:
  • the area of interest of the target image is determined according to the face area in the target image.
  • the determining the region of interest of the target image according to the face region in the target image includes:
  • the largest face region is determined as the region of interest of the target image.
  • the determining the region of interest of the target image according to the human figure detection result of the target image includes:
  • the central image area is determined as the interest area of the target image.
  • the brightness distribution of the region of interest is determined.
  • the determining the target parameter value for image acquisition in the current scene based on the brightness distribution of the region of interest includes:
  • the target parameter value corresponding to the target brightness is determined.
  • the determining the average brightness of the region of interest includes:
  • the average brightness of the region of interest is determined.
  • the determining the weight corresponding to each pixel in the region of interest includes:
  • the weight corresponding to each pixel point in the region of interest is determined; wherein, the pixel point and the region center of the region of interest are different from each other.
  • the distance between the pixels is positively correlated with the weights corresponding to the pixels.
  • the determining the boundary brightness of the region of interest according to the brightness distribution of the region of interest includes:
  • the number of pixels corresponding to the brightness reference value range is determined, and the brightness reference value range is the brightness range from the minimum brightness value in the brightness distribution to the brightness reference value, and
  • the brightness reference value is any brightness value in the brightness distribution;
  • the brightness reference value is determined as the boundary brightness of the region of interest.
  • the determining the target brightness of the region of interest according to the average brightness of the region of interest and the boundary brightness includes:
  • the method further includes:
  • image collection is performed on the current scene.
  • the target parameter value includes:
  • At least one of exposure value, exposure time, and gain At least one of exposure value, exposure time, and gain.
  • an image processing device including:
  • the detection module is configured to perform humanoid detection on the target image collected in real time in the current scene to obtain the humanoid detection result;
  • the first determining module is configured to determine the region of interest of the target image according to the human figure detection result of the target image
  • the second determining module is configured to determine a target parameter value for image acquisition in the current scene based on the brightness distribution of the region of interest.
  • the first determining module is further configured to:
  • the area of interest of the target image is determined according to the face area in the target image.
  • the first determining module is further configured to:
  • the largest face region is determined as the region of interest of the target image.
  • the first determining module is further configured to:
  • the central image area is determined as the interest area of the target image.
  • the first determining module is further configured to:
  • the brightness distribution of the region of interest was determined according to the brightness of each pixel in the region of interest of the target image.
  • the second determining module is further configured to:
  • the target parameter value corresponding to the target brightness is determined.
  • the second determining module is further configured to:
  • the average brightness of the region of interest is determined.
  • the second determining module is further configured to:
  • the weight corresponding to each pixel point in the region of interest is determined; wherein, the pixel point and the region center of the region of interest are different from each other.
  • the distance between the pixels is positively correlated with the weights corresponding to the pixels.
  • the second determining module is further configured to:
  • the number of pixels corresponding to the brightness reference value range is determined, and the brightness reference value range is the brightness range from the minimum brightness value in the brightness distribution to the brightness reference value, and
  • the brightness reference value is any brightness value in the brightness distribution;
  • the brightness reference value is determined as the boundary brightness of the region of interest.
  • the second determining module is further configured to:
  • the device further includes:
  • the acquisition module is configured to use the target parameter value to perform image acquisition on the current scene.
  • the target parameter value includes:
  • At least one of exposure value, exposure time, and gain At least one of exposure value, exposure time, and gain.
  • an electronic device including:
  • a memory configured to store executable instructions of the processor
  • the processor is configured to execute the above-mentioned image processing method.
  • a computer-readable storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the foregoing image processing method is implemented.
  • the target image collected in real time under the current scene can be acquired, and then the human figure detection is performed on the target image to obtain the human figure detection result, and then according to the human figure detection result of the target image, the region of interest included in the target image can be determined Finally, based on the determined brightness distribution of the region of interest, the acquisition parameter value for image acquisition in the current scene is determined.
  • the human figure detection results obtained from the human figure detection on the target image can be used to determine the appropriate acquisition parameter value in the current scene, so that the image acquisition device can determine the acquisition parameter value according to the determined acquisition parameter value.
  • Image collection of the current scene makes the collected image frames have higher face quality and improves the accuracy of subsequent face recognition.
  • Fig. 1 shows a flowchart of an example of an image processing method according to an embodiment of the present disclosure
  • Fig. 2 shows an application scenario diagram of an example of an image processing method according to an embodiment of the present disclosure
  • FIG. 3 shows a flowchart of an example of determining a target parameter value for image acquisition according to an embodiment of the present disclosure
  • Fig. 4 shows a flowchart of an example of an image processing method according to an embodiment of the present disclosure
  • Fig. 5 shows a block diagram of an example of an image processing device according to an embodiment of the present disclosure
  • Fig. 6 shows a block diagram of an example of an electronic device according to an embodiment of the present disclosure.
  • the image processing solution provided by the embodiments of the present disclosure can perform humanoid detection on a target image collected in real time in the current scene, and obtain a humanoid detection result.
  • the region of interest included in the target image can be determined.
  • the brightness distribution of the region of interest can be determined, and based on the brightness distribution of the region of interest, the acquisition parameter values used for image acquisition in the current scene can be determined.
  • the background brightness of the image frame is relatively large, the face area in the image frame is dark, and the face quality is poor, which may affect the effect of face recognition.
  • the image processing solution provided by the embodiments of the present disclosure is suitable for environments that are not conducive to shooting such as strong light, dark light, and backlight, and can improve the image quality of human faces in various environments.
  • Fig. 1 shows a flowchart of an image processing method according to an embodiment of the present disclosure.
  • the image processing method can be executed by a terminal device or other types of electronic devices.
  • terminal devices can be access control devices, user equipment (UE), mobile devices, user terminals, terminals, cellular phones, cordless phones, personal digital assistants (PDAs), handheld devices, computing devices, and vehicle-mounted devices. Equipment and wearable devices, etc.
  • the image processing method may be implemented by a processor invoking computer-readable instructions stored in the memory.
  • the image processing method according to the embodiment of the present disclosure will be described below by taking an image processing terminal as an execution subject as an example.
  • the image processing terminal may be the aforementioned terminal device or other types of electronic devices.
  • the image processing method shown may include the following steps:
  • S11 Perform human form detection on the target image collected in real time in the current scene to obtain a human form detection result.
  • the image processing terminal 1 can perform image collection in real time for the current scene, and obtain the target image collected in real time.
  • FIG. 2 shows an application scene diagram of an example of an image processing method according to an embodiment of the present disclosure.
  • the image processing terminal 1 can obtain real-time collected target images by receiving real-time collected or photographed target images transmitted by other equipment 2 through the network 3, for example, receiving image collection devices (such as cameras, image sensors) , Camera devices (such as cameras, monitors) and other equipment 2 real-time collection or shooting of target images to obtain real-time collected target images.
  • the target image can be a separate image, or the target image can be an image frame in a video stream.
  • the image processing terminal obtains the target image, performs human shape detection on the target image, and obtains the human shape detection result.
  • the human shape detection result may be the detection result of certain areas of the target image, for example, the detection result of the face area, the detection of the upper body area result.
  • the image processing terminal may use the constructed human form detection network to perform human form detection on the target image, and the human form detection network may be obtained by training the constructed neural network.
  • an existing neural network structure can be used to construct a neural network, or a neural network structure can be designed according to actual application scenarios to construct a neural network. Construct a neural network, input the training image into the constructed neural network, use the constructed neural network to perform humanoid detection on the training image, and obtain the humanoid detection result, and then compare the humanoid detection result with the annotation result of the training image to obtain the comparison result.
  • the comparison results use the comparison results to adjust the model parameters of the constructed neural network, so that the humanoid detection result of the constructed neural network model is consistent with the annotation result, so that the humanoid detection network can be obtained from the constructed neural network model.
  • images collected in harsh shooting environments such as strong light and dark light can be used as training images.
  • the human figure detection network can detect the human figure contour of the target image.
  • the human figure detection result obtained can be the detection result of the human face area.
  • S12 Determine the region of interest of the target image according to the result of the human form detection of the target image.
  • the image processing terminal can determine whether there is a face area in the target image according to the human shape detection result of the target image.
  • the area of interest of the target image can be determined in different ways. For example, if there is a face area in the target image, the face area can be regarded as the area of interest of the target image. There is no face area in the target image, and a certain part of the image area of the target image can be used as the area of interest of the target image, such as the upper half of the image area, the lower half of the image area, and other image areas as the target image.
  • the region of interest here can be understood as the image region that is of interest in the image processing process. Determining the region of interest of the target image can facilitate further image processing of the region.
  • the area of interest of the target image is determined according to the face area in the target image .
  • the face area can be regarded as the interest area of the target image.
  • the human figure detection result shows that there are multiple face areas in the target image, at least one face area can be selected from the multiple face areas, and the selected at least one face area is taken as the interest area of the target image, for example, in multiple face areas.
  • At least one face area located in the middle part of the target image is selected from the face area.
  • the largest face area among the multiple face areas may be determined, and then the largest face area may be determined as the entire face area.
  • the region of interest of the target image in the case where there are multiple face areas in the target image, the largest face area among the multiple face areas may be determined, and then the largest face area may be determined as the entire face area.
  • the sizes of multiple face regions can be compared, and then the largest face region among the multiple face regions can be determined according to the comparison result, so that the largest face region can be used as The region of interest of the target image.
  • one of the most concerned face regions can be selected as the region of interest among multiple face regions, so that other image regions outside the region of interest can be ignored in the image processing process, so that the efficiency and accuracy of image processing can be improved. improve.
  • the central image area of the target image may be determined, and then the central image area may be determined as the entire image area. The region of interest of the target image.
  • the face area is usually located in the center image area of the target image. Therefore, when the face area is not detected in the human form detection, the center image area of the target image can be used as the target image.
  • Region of interest For example, the target image can be divided into multiple image regions, such as dividing the target image into 9 or 25 regions on average, and then determining the center image region of the multiple regions as the target image of interest Area, for example, one image area located in the center of the target image among 9 image areas is taken as the area of interest. In this way, even if the face area is not detected in the target image, the area of interest of the target image can be determined, and further image processing can be performed on the determined area of interest, thereby improving the efficiency and accuracy of image processing.
  • the region of interest of the target image is determined, and the brightness distribution of the region of interest is obtained according to the brightness of each pixel in the region of interest of the target image.
  • the brightness distribution can be represented by a brightness histogram or the like.
  • the target parameter value for image acquisition in the current scene can be obtained.
  • the target parameter value is a parameter value suitable for the current shooting environment. Under the action of the target parameter value, a good exposure can be obtained , Images with better face quality, which can be adapted to various harsh shooting environments, such as strong light and dark light.
  • image acquisition parameters need to be used when performing image acquisition.
  • the image acquisition parameters can be shooting parameters set during the image acquisition process.
  • the target parameter value is the image acquisition parameter in the current scene.
  • the image acquisition parameter or target parameter value can include: exposure One or more of value, exposure time, and gain.
  • the exposure value is a parameter indicating the light-transmitting ability of the lens, and it can be a combination of the shutter speed value and the aperture value.
  • the exposure time may be the time interval from opening to closing of the shutter.
  • the gain can be a multiple when the collected video signal is amplified.
  • the image acquisition parameters can be set. When the image acquisition parameters are different, the images captured in the same scene are also different. Therefore, by adjusting the image acquisition parameters, an image with better image quality can be obtained.
  • the target parameter value in the current scene is determined, the image acquisition parameter is adjusted to the target parameter value, and the target parameter value is used to perform image acquisition on the current scene.
  • the image processing terminal may have an image acquisition function, and may shoot the current scene.
  • the image processing terminal determines the target parameter value for image acquisition in the current scene, sets the image acquisition parameter to the target parameter value, and continues to shoot the current scene under the action of the target parameter value to obtain the image captured after the target image
  • the image is an image obtained under the action of the image acquisition parameter as the target parameter value. Because the target parameter value is an optimized parameter value, the image has better image quality. In a face recognition scene, the image has Better face quality can improve the speed and accuracy of subsequent face recognition.
  • the image processing terminal may send the determined target parameter value to the image acquisition device, so that the image acquisition device can continue to capture the current scene using the target parameter value.
  • the image processing solution provided by the embodiments of the present disclosure can determine the target parameter value for image acquisition based on the brightness distribution of the region of interest, so as to solve the problem of poor face quality in scenes such as backlight, strong light, and low light. problem.
  • the embodiment of the present disclosure also provides an implementation manner for determining the target parameter value of the image acquisition parameter.
  • Fig. 3 shows a flowchart of an example of determining a target parameter value for image acquisition according to an embodiment of the present disclosure. As shown in FIG. 3, the above step S13 may include the following steps:
  • S131 Determine the average brightness of the region of interest.
  • the average brightness of the region of interest can be determined according to the brightness of each pixel in the region of interest. For example, the number of pixels included in the region of interest can be counted, and then the brightness of all pixels in the region of interest can be measured. Sum, get the total brightness of the region of interest, and then divide the total brightness by the number of pixels included in the region of interest to get the average brightness of the region of interest.
  • the weight corresponding to each pixel in the region of interest can be determined, and then the weight corresponding to each pixel in the region of interest and the brightness of each pixel can be determined.
  • a corresponding weight can be set for each pixel in the region of interest. For example, a larger weight can be set for the pixels included in the image part of the region of interest, so as to determine the region of interest. When the average brightness is higher, the part of the image that is focused on can contribute a larger proportion.
  • the same weight can be set for the pixels in the region of interest. For example, in the case where the region of interest is a face region, the same weight values can be set for the pixels in the region of interest.
  • the human figure detection result indicates that the target image has a human face
  • it may be determined according to the distance between the pixel point in the region of interest and the center of the region of interest
  • the weight corresponding to each pixel in the region of interest wherein, the distance between the pixel and the center of the region of interest is positively correlated with the weight corresponding to the pixel, and the pixel is positively related to the weight of the pixel of interest. The closer the distance between the center of the region, the greater the weight corresponding to the pixel.
  • the area of interest may be the center image area of the target image, and the distance between the pixel points in the area of interest and the center of the area of interest may be used.
  • the pixel weight of the outer part far from the center of the region is 4, and the pixel weight of the outermost part of the region of interest is 1.
  • the region of interest can be divided into multiple image parts, and the pixels in each image part can have the same weight. In this way, since the probability that the face area is located in the center of the target image is greater, the weight of the pixels in the middle part can be set larger, and the contribution of the pixels in the face area to the average brightness can be preserved as much as possible.
  • S132 Determine the boundary brightness of the region of interest according to the brightness distribution of the region of interest.
  • the brightness distribution of the region of interest can be represented by a brightness histogram.
  • the abscissa of the brightness histogram may be the brightness value, and the ordinate of the brightness histogram may be the number of pixels corresponding to the brightness value.
  • the boundary brightness of the region of interest can be determined.
  • the boundary brightness can be a brightness value, and the corresponding pixel points within the brightness value can include most of the pixel points of the region of interest.
  • the boundary brightness may be a brightness interval, and the corresponding pixel points in the brightness interval may include most of the pixels in the region of interest.
  • the number of pixels corresponding to the range of the brightness reference value can be determined, and then the number of pixels corresponding to the range of the brightness reference value can be determined.
  • the pixel point ratio of the total number of pixels in the region of interest in the case that the pixel point ratio is greater than or equal to the preset ratio, the pixel point ratio is greater than or equal to the brightness reference value corresponding to the preset ratio and determined as The boundary brightness of the region of interest.
  • the boundary brightness can be a brightness value.
  • any brightness value can be used as the brightness reference value, and then the number of corresponding pixels within the brightness reference value range can be counted.
  • the range of the brightness reference value can be from the minimum brightness value of the brightness histogram to the brightness range of the brightness reference value. If the ratio of the number of corresponding pixels in the brightness reference value range to the total number of pixels in the region of interest is greater than or equal to the preset Assuming the ratio, for example, the ratio of the number of corresponding pixels in the range of the luminance reference value to the total number of pixels reaches 99%, then the luminance reference value can be determined as the boundary luminance.
  • S133 Determine the target brightness of the region of interest according to the average brightness of the region of interest and the boundary brightness.
  • determine the average brightness and boundary brightness of the region of interest and determine a target brightness suitable for the region of interest according to the average brightness and boundary brightness of the region of interest.
  • the target brightness it can be considered that the pixels in the region of interest have reasonable The brightness value of, will not cause poor image quality due to overexposure or underexposure, so that the target parameter value of the image acquisition parameter can be determined according to the determined target brightness.
  • a preset desired boundary brightness can be obtained, and then the ratio of the desired boundary brightness to the boundary brightness is determined, and then according to the ratio of the desired boundary brightness to the boundary brightness and the The average brightness of the region of interest determines the target brightness of the region of interest.
  • the desired boundary brightness may be the boundary brightness determined when the image is well exposed, and may be set according to actual application scenarios.
  • the ratio of the desired boundary brightness to the boundary brightness can be calculated, and then the ratio can be multiplied by the average brightness of the region of interest to obtain the target brightness of the region of interest. For example, assuming that the desired boundary brightness is 200 and the boundary brightness of the region of interest is 100, it can indicate that the average brightness of the region of interest is low, and the image quality in the region of interest is poor. Face recognition is performed on the region of interest.
  • the ratio of the desired boundary brightness 200 to the boundary brightness 100 of the region of interest can be multiplied by the average brightness of the region of interest to obtain a target brightness, which is twice the average brightness, that is It can be shown that when the average brightness of the region of interest reaches the target brightness, the region of interest has better image quality, and then the target parameter value of the image acquisition parameter can be determined according to the determined target brightness, so as to shoot under the action of the target parameter value Images with better face quality.
  • S134 Determine a target parameter value corresponding to the target brightness based on the mapping relationship between the brightness and the image acquisition parameters.
  • the target parameter value corresponding to the target brightness can be determined according to the mapping relationship between the brightness of the image and the image acquisition parameters, for example, one or more of the exposure value, the exposure time, and the gain value can be determined, so that the image processing terminal can The image acquisition parameters are adjusted to the best exposure value.
  • the image processing solution provided by the embodiment of the present disclosure can determine the parameter value of the image collection suitable for the current scene according to the human figure detection result of the human figure detection of the target image. Even if the current scene is a backlit or strong light scene, the captured The image has better face quality, which improves the accuracy of subsequent face recognition.
  • Fig. 4 shows a flowchart of an example of an image processing method according to an embodiment of the present disclosure. As shown in FIG. 4, in an example, the image processing method may include the following steps:
  • the image processing terminal may have an image collection function, and can perform real-time shooting of the current scene. For example, in an access control scene, the image processing terminal collects real-time images of the user before the access control to obtain the target image.
  • S302 Perform human form detection on the target image by using the human form detection network to obtain a human form detection result.
  • the human figure detection network may be obtained by training the constructed neural network, and the human figure detection result obtained may be the detection result of the face region in the target image.
  • S304 In the case that there is a face area in the target image, the largest face area among the one or more face areas is taken as the area of interest, and S306 is executed.
  • S305 In the case that there is no face area in the target image, the central image area of the target image is taken as the area of interest, and S306 is executed.
  • the center image area may be the area where the center of the target image is located, for example, the target image is divided into 9 areas evenly, where the center image area may be the area in the middle of the 9 areas.
  • S306 Perform brightness histogram statistics on the region of interest to obtain a brightness histogram of the region of interest.
  • S307 Calculate the average brightness of the region of interest according to the brightness of the pixel in the brightness histogram and the weight set for the pixel.
  • S308 Calculate the brightness distribution within the brightness reference value range according to the brightness histogram, and determine the brightness reference value as the boundary brightness when the brightness distribution within the brightness reference value range reaches 99% of the total brightness distribution of the region of interest.
  • S309 Calculate the target brightness according to the boundary brightness, the preset expected boundary brightness, and the average brightness.
  • S310 Calculate the optimal exposure value and/or gain value to be configured according to the target brightness.
  • a proportional-integral-differential (PID) controller can be used to obtain the optimal exposure value and/or gain value from the target brightness.
  • S311 Configure the obtained optimal exposure value and/or gain value into the photosensitive chip, and execute S301.
  • the image signal processing (Image Signal Processing, ISP) unit can be used to configure the obtained optimal exposure value and/or gain value into the camera's photosensitive chip, and then use the optimal exposure value and/or gain value to continue to collect the next Target image.
  • ISP Image Signal Processing
  • the image processing solution provided by the embodiments of the present disclosure can use the human figure detection network to detect the face area in the target image, determine the area of interest, and then determine the area of interest according to the brightness of each pixel in the area of interest of the target image.
  • the brightness distribution of the area based on the brightness distribution of the area of interest to obtain the best exposure value, which can well cope with the face image acquisition and face detection of backlit, dark and strong light scenes, and does not require additional costs , Can improve user experience.
  • the present disclosure also provides image processing devices, electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the image processing methods provided in the present disclosure.
  • image processing devices electronic equipment, computer-readable storage media, and programs, all of which can be used to implement any of the image processing methods provided in the present disclosure.
  • the writing order of the steps does not mean a strict execution order but constitutes any limitation on the implementation process.
  • the specific execution order of each step should be based on its function and possibility.
  • the inner logic is determined.
  • Fig. 5 shows a block diagram of an image processing device according to an embodiment of the present disclosure. As shown in Fig. 5, the image processing device includes:
  • the detection module 41 is configured to perform humanoid detection on a target image collected in real time in the current scene, to obtain a humanoid detection result;
  • the first determining module 42 is configured to determine the region of interest of the target image according to the human figure detection result of the target image;
  • the second determining module 43 is configured to determine a target parameter value for image acquisition in the current scene based on the brightness distribution of the region of interest.
  • the first determining module 42 is further configured to:
  • the area of interest of the target image is determined according to the face area in the target image.
  • the first determining module 42 is further configured to:
  • the largest face region is determined as the region of interest of the target image.
  • the first determining module 42 is further configured to:
  • the central image area is determined as the interest area of the target image.
  • the first determining module 42 is further configured to determine the region of interest of the target image based on the human figure detection result of the target image, and the The brightness distribution of the region of interest, before determining the target parameter value for image acquisition in the current scene, determine the brightness of the region of interest according to the brightness of each pixel in the region of interest of the target image distributed.
  • the second determining module 43 is further configured to:
  • the target parameter value corresponding to the target brightness is determined.
  • the second determining module 43 is further configured to:
  • the average brightness of the region of interest is determined.
  • the second determining module 43 is further configured to:
  • the weight corresponding to each pixel point in the region of interest is determined; The distance is positively correlated with the weight corresponding to the pixel point.
  • the second determining module 43 is further configured to:
  • the number of pixels corresponding to the brightness reference value range is determined, and the brightness reference value range is the brightness range from the minimum brightness value in the brightness distribution to the brightness reference value, and
  • the brightness reference value is any brightness value in the brightness distribution;
  • the brightness reference value is determined as the boundary brightness of the region of interest.
  • the second determining module 43 is further configured to:
  • the device further includes:
  • the acquisition module is configured to use the target parameter value to perform image acquisition on the current scene.
  • the target parameter value includes:
  • At least one of exposure value, exposure time, and gain At least one of exposure value, exposure time, and gain.
  • the functions or modules included in the apparatus provided in the embodiments of the present disclosure may be configured to execute the methods described in the above method embodiments.
  • the functions or modules included in the apparatus provided in the embodiments of the present disclosure may be configured to execute the methods described in the above method embodiments.
  • the embodiments of the present disclosure also provide a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above-mentioned method when executed by a processor.
  • the computer-readable storage medium may be a non-volatile computer-readable storage medium.
  • An embodiment of the present disclosure also proposes an electronic device, including: a processor; a memory configured to store executable instructions of the processor; wherein the processor is configured to call instructions stored in the memory to execute the above method.
  • the electronic device can be provided as a terminal, server or other form of device.
  • Fig. 6 is a block diagram showing an electronic device 800 according to an exemplary embodiment.
  • the electronic device 800 may be a terminal such as a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, and a personal digital assistant.
  • the electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, And the communication component 816.
  • the processing component 802 generally controls the overall operations of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations.
  • the processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the foregoing method.
  • the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components.
  • the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.
  • the memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of these data include instructions for any application or method operated on the electronic device 800, contact data, phone book data, messages, pictures, and videos, and so on.
  • the memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable and Programmable read only memory (EPROM), programmable read only memory (PROM), read only memory (ROM), magnetic memory, flash memory and magnetic or optical disks.
  • SRAM static random access memory
  • EEPROM electrically erasable programmable read-only memory
  • EPROM erasable and Programmable read only memory
  • PROM programmable read only memory
  • ROM read only memory
  • magnetic memory magnetic memory
  • flash memory magnetic or optical disks.
  • the power supply component 806 provides power for various components of the electronic device 800.
  • the power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
  • the multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user.
  • the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user.
  • the touch panel includes one or more touch sensors to sense touch, sliding, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure related to the touch or slide operation.
  • the multimedia component 808 includes a front camera and/or a rear camera. When the electronic device 800 is in an operation mode, such as a shooting mode or a video mode, the front camera and/or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
  • the audio component 810 is configured to output and/or input audio signals.
  • the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal.
  • the received audio signal may be further stored in the memory 804 or transmitted via the communication component 816.
  • the audio component 810 further includes a speaker for outputting audio signals.
  • the I/O interface 812 provides an interface between the processing component 802 and a peripheral interface module.
  • the above-mentioned peripheral interface module may be a keyboard, a click wheel, a button, and the like. These buttons may include but are not limited to: home button, volume button, start button, and lock button.
  • the sensor component 814 includes one or more sensors for providing the electronic device 800 with various aspects of state evaluation.
  • the sensor component 814 can detect the on/off status of the electronic device 800 and the relative positioning of the components.
  • the component is the display and the keypad of the electronic device 800.
  • the sensor component 814 can also detect the electronic device 800 or the electronic device 800.
  • the position of the component changes, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration/deceleration of the electronic device 800, and the temperature change of the electronic device 800.
  • the sensor component 814 may include a proximity sensor configured to detect the presence of nearby objects when there is no physical contact.
  • the sensor component 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.
  • the sensor component 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
  • the communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices.
  • the electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 2G, or 3G, or a combination thereof.
  • the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication component 816 further includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field-available A programmable gate array (FPGA), controller, microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • ASIC application-specific integrated circuits
  • DSP digital signal processors
  • DSPD digital signal processing devices
  • PLD programmable logic devices
  • FPGA field-available A programmable gate array
  • controller microcontroller, microprocessor, or other electronic components are implemented to implement the above methods.
  • a non-volatile computer-readable storage medium such as the memory 804 including computer program instructions, which can be executed by the processor 820 of the electronic device 800 to complete the foregoing method.
  • the present disclosure may be a system, method and/or computer program product.
  • the computer program product may include a computer-readable storage medium loaded with computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.
  • the computer-readable storage medium may be a tangible device that can hold and store instructions used by the instruction execution device.
  • the computer-readable storage medium may be, for example, but is not limited to an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • Non-exhaustive list of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) Or flash memory), static random access memory (SRAM), portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanical encoding device, such as a printer with instructions stored thereon
  • RAM random access memory
  • ROM read-only memory
  • EPROM erasable programmable read-only memory
  • flash memory flash memory
  • SRAM static random access memory
  • CD-ROM compact disk read-only memory
  • DVD digital versatile disk
  • memory stick floppy disk
  • mechanical encoding device such as a printer with instructions stored thereon
  • the computer-readable storage medium used here is not interpreted as the instantaneous signal itself, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (for example, light pulses through fiber optic cables), or through wires Transmission of electrical signals.
  • the computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to various computing/processing devices, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may include copper transmission cables, optical fiber transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • the network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network, and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device .
  • the computer program instructions used to perform the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or in one or more programming languages.
  • Source code or object code written in any combination, the programming language includes object-oriented programming languages such as Smalltalk, C++, etc., and conventional procedural programming languages such as "C" language or similar programming languages.
  • Computer-readable program instructions can be executed entirely on the user's computer, partly on the user's computer, executed as a stand-alone software package, partly on the user's computer and partly executed on a remote computer, or entirely on the remote computer or server carried out.
  • the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to access the Internet). connection).
  • LAN local area network
  • WAN wide area network
  • an electronic circuit such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), can be customized by using the status information of the computer-readable program instructions.
  • the computer-readable program instructions are executed to realize various aspects of the present disclosure.
  • These computer-readable program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine that makes these instructions when executed by the processor of the computer or other programmable data processing device , A device that implements the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams is produced. It is also possible to store these computer-readable program instructions in a computer-readable storage medium. These instructions make computers, programmable data processing apparatuses, and/or other devices work in a specific manner. Thus, the computer-readable medium storing the instructions includes An article of manufacture, which includes instructions for implementing various aspects of the functions/actions specified in one or more blocks in the flowcharts and/or block diagrams.
  • each block in the flowchart or block diagram may represent a module, program segment, or part of an instruction, and the module, program segment, or part of an instruction contains one or more components for realizing the specified logical function.
  • Executable instructions may also occur in a different order than the order marked in the drawings. For example, two consecutive blocks can actually be executed substantially in parallel, or they can sometimes be executed in the reverse order, depending on the functions involved.
  • each block in the block diagram and/or flowchart, and the combination of the blocks in the block diagram and/or flowchart can be implemented by a dedicated hardware-based system that performs the specified functions or actions Or it can be realized by a combination of dedicated hardware and computer instructions.
  • the embodiments of the present disclosure obtain the target image collected in real time in the current scene, and then perform humanoid detection on the target image to obtain the humanoid detection result. Then, according to the humanoid detection result of the target image, the region of interest included in the target image is determined, and finally based on Determine the brightness distribution of the region of interest, and determine the acquisition parameter values used for image acquisition in the current scene. In this way, even in scenes such as backlighting or strong light, the human figure detection results obtained from the human figure detection on the target image can be used to determine the appropriate acquisition parameter value in the current scene, so that the image acquisition device can determine the acquisition parameter value according to the determined acquisition parameter value. Image collection of the current scene makes the collected image frames have higher face quality and improves the accuracy of subsequent face recognition.

Abstract

一种图像处理方法及装置、电子设备和存储介质，其中，所述方法包括：对在当前场景下实时采集的目标图像进行人形检测，得到人形检测结果（S11）；根据目标图像的人形检测结果，确定目标图像的感兴趣区域（S12）；基于感兴趣区域的亮度分布，确定在当前场景下用于进行图像采集的目标参数值（S13）。

Description

一种图像处理方法及装置、电子设备和存储介质
相关申请的交叉引用
本申请基于申请号为201910872325.1、申请日为2019年09月16日的中国专利申请提出，并要求该中国专利申请的优先权，该中国专利申请的全部内容在此引入本申请作为参考。
技术领域
本公开涉及计算机视觉技术领域，涉及一种图像处理方法及装置、电子设备和存储介质。
背景技术
计算机视觉技术是通过设备模拟人类的视觉功能的技术，可以应用在人工智能、图像处理等诸多应用中。例如，在人脸识别场景中，可以通过对拍摄的图像进行人脸识别，确定人脸对应的身份。
在人脸识别中，人脸的成像质量是一个主要的影响因素，较高的成像质量有助于提高人脸识别的准确度。但是，在逆光场景下，人脸的成像质量比较差，不利于人脸图像的识别和活体判断。
发明内容
本公开提出了一种图像处理方法及装置、电子设备和存储介质。
根据本公开的一方面，提供了一种图像处理方法，包括：
对在当前场景下实时采集的目标图像进行人形检测，得到人形检测结果；
根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域；
基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值。
在一种可能的实现方式中，所述根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域，包括：
在所述人形检测结果表明所述目标图像存在人脸区域的情况下，根据所述目标图像中的人脸区域，确定所述目标图像的所述感兴趣区域。
在一种可能的实现方式中，所述根据所述目标图像中的人脸区域，确定所述目标图像的所述感兴趣区域，包括：
在所述目标图像存在多个人脸区域的情况下，确定所述多个人脸区域中最大的人脸区域；
将所述最大的人脸区域确定为所述目标图像的所述感兴趣区域。
在一种可能的实现方式中，所述根据所述目标图像的人形检测结果，确定所述目标图像的所述感兴趣区域，包括：
在所述人形检测结果表明所述目标图像不存在人脸区域的情况下，确定 所述目标图像的中心图像区域；
将所述中心图像区域确定为所述目标图像的所述感兴趣区域。
在一种可能的实现方式中，在根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域之后，且所述基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值之前，还包括：
根据所述目标图像的感兴趣区域中每个像素点的亮度，确定所述感兴趣区域的亮度分布。
在一种可能的实现方式中，所述基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值，包括：
确定所述感兴趣区域的平均亮度；
根据所述感兴趣区域的亮度分布，确定所述感兴趣区域的边界亮度；
根据所述感兴趣区域的平均亮度以及所述边界亮度，确定所述感兴趣区域的目标亮度；
基于亮度与图像采集参数之间的映射关系，确定所述目标亮度所对应的目标参数值。
在一种可能的实现方式中，所述确定所述感兴趣区域的平均亮度，包括：
确定所述感兴趣区域中每个像素点对应的权重；
根据所述感兴趣区域中每个像素点对应的权重以及每个像素点的亮度，确定所述感兴趣区域的平均亮度。
在一种可能的实现方式中，所述确定所述感兴趣区域中每个像素点对应的权重，包括：
根据所述感兴趣区域中像素点与所述感兴趣区域的区域中心的距离，确定所述感兴趣区域中每个像素点对应的权重；其中，像素点和所述感兴趣区域的区域中心之间的距离，与所述像素点对应的权重正相关。
在一种可能的实现方式中，所述根据所述感兴趣区域的亮度分布，确定所述感兴趣区域的边界亮度，包括：
在所述感兴趣区域的亮度分布中，确定亮度参考值范围内对应的像素点个数，所述亮度参考值范围为所述亮度分布中的最小亮度值到亮度参考值的亮度范围，所述亮度参考值为所述亮度分布中的任意一个亮度值；
确定所述亮度参考值范围内对应的像素点个数占所述感兴趣区域的像素点总数的像素点比例；
在所述像素点比例大于或等于预设比例的情况下，将所述亮度参考值确定为所述感兴趣区域的边界亮度。
在一种可能的实现方式中，所述根据所述感兴趣区域的平均亮度以及所述边界亮度，确定所述感兴趣区域的目标亮度，包括：
获取预设的期望边界亮度；
确定所述期望边界亮度与所述边界亮度的比值；
根据所述期望边界亮度与所述边界亮度的比值以及所述感兴趣区域的平均亮度，确定所述感兴趣区域的目标亮度。
在一种可能的实现方式中，所述方法还包括：
采用所述目标参数值，对所述当前场景进行图像采集。
在一种可能的实现方式中，所述目标参数值包括：
曝光值、曝光时间和增益中的至少一种。
根据本公开的另一方面，提供了一种图像处理装置，包括：
检测模块，被配置为对在当前场景下实时采集的目标图像进行人形检测，得到人形检测结果；
第一确定模块，被配置为根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域；
第二确定模块，被配置为基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值。
在一种可能的实现方式中，所述第一确定模块，还被配置为，
在所述人形检测结果表明所述目标图像存在人脸区域的情况下，根据所述目标图像中的人脸区域，确定所述目标图像的所述感兴趣区域。
在一种可能的实现方式中，所述第一确定模块，还被配置为，
在所述目标图像存在多个人脸区域的情况下，确定所述多个人脸区域中最大的人脸区域；
将所述最大的人脸区域确定为所述目标图像的所述感兴趣区域。
在一种可能的实现方式中，所述第一确定模块，还被配置为，
在所述人形检测结果表明所述目标图像不存在人脸区域的情况下，确定所述目标图像的中心图像区域；
将所述中心图像区域确定为所述目标图像的所述感兴趣区域。
在一种可能的实现方式中，所述第一确定模块，还被配置为，
根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域之后，且所述基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值之前，根据所述目标图像的感兴趣区域中每个像素点的亮度，确定所述感兴趣区域的亮度分布。
在一种可能的实现方式中，所述第二确定模块，还被配置为，
确定所述感兴趣区域的平均亮度；
根据所述感兴趣区域的亮度分布，确定所述感兴趣区域的边界亮度；
根据所述感兴趣区域的平均亮度以及所述边界亮度，确定所述感兴趣区域的目标亮度；
基于亮度与图像采集参数之间的映射关系，确定所述目标亮度所对应的目标参数值。
在一种可能的实现方式中，所述第二确定模块，还被配置为，
确定所述感兴趣区域中每个像素点对应的权重；
根据所述感兴趣区域中每个像素点对应的权重以及每个像素点的亮度，确定所述感兴趣区域的平均亮度。
在一种可能的实现方式中，所述第二确定模块，还被配置为，
根据所述感兴趣区域中像素点与所述感兴趣区域的区域中心的距离，确定所述感兴趣区域中每个像素点对应的权重；其中，像素点和所述感兴趣区域的区域中心之间的距离，与所述像素点对应的权重正相关。
在一种可能的实现方式中，所述第二确定模块，还被配置为，
在所述感兴趣区域的亮度分布中，确定亮度参考值范围内对应的像素点个数，所述亮度参考值范围为所述亮度分布中的最小亮度值到亮度参考值的亮度范围，所述亮度参考值为所述亮度分布中的任意一个亮度值；
确定所述亮度参考值范围内对应的像素点个数占所述感兴趣区域的像素点总数的像素点比例；
在所述像素点比例大于或等于预设比例的情况下，将所述亮度参考值，确定为所述感兴趣区域的边界亮度。
在一种可能的实现方式中，所述第二确定模块，还被配置为，
获取预设的期望边界亮度；
确定所述期望边界亮度与所述边界亮度的比值；
根据所述期望边界亮度与所述边界亮度的比值以及所述感兴趣区域的平均亮度，确定所述感兴趣区域的目标亮度。
在一种可能的实现方式中，所述装置还包括：
采集模块，被配置为采用所述目标参数值，对所述当前场景进行图像采集。
在一种可能的实现方式中，所述目标参数值包括：
曝光值、曝光时间和增益中的至少一种。
根据本公开的另一方面，提供了一种电子设备，包括：
处理器；
被配置为存储处理器可执行指令的存储器；
其中，所述处理器被配置为：执行上述图像处理方法。
根据本公开的一方面，提供了一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述图像处理方法。
在本公开实施例中，可以获取在当前场景下实时采集的目标图像，然后对目标图像进行人形检测，得到人形检测结果，再根据目标图像的人形检测结果，确定目标图像所包括的感兴趣区域，最后基于确定的感兴趣区域的亮度分布，确定在当前场景下用于进行图像采集的采集参数值。这样，即使在逆光或强光等场景的情况下，也可以通过对目标图像进行人形检测得到的人形检测结果，确定当前场景中合适的采集参数值，从而图像采集装置可以根据确定的采集参数值对当前场景进行图像采集，使得采集的图像帧具有较高的人脸质量，提高了后续人脸识别的准确率。
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，而非限制本公开。
根据下面参考附图对示例性实施例的详细说明，本公开的其它特征及方面将变得清楚。
附图说明
此处的附图被并入说明书中并构成本说明书的一部分，这些附图示出了符合本公开的实施例，并与说明书一起用于说明本公开的技术方案。
图1示出根据本公开实施例的图像处理方法一示例的流程图；
图2示出根据本公开实施例的图像处理方法一示例的应用场景图；
图3示出根据本公开实施例的确定用于进行图像采集的目标参数值一示例的流程图；
图4示出根据本公开实施例的图像处理方法一示例的流程图；
图5示出根据本公开实施例的图像处理装置一示例的框图；
图6示出根据本公开实施例的电子设备一示例的框图。
具体实施方式
以下将参考附图详细说明本公开的各种示例性实施例、特征和方面。附图中相同的附图标记表示功能相同或相似的元件。尽管在附图中示出了实施例的各种方面，但是除非特别指出，不必按比例绘制附图。
在这里专用的词“示例性”意为“用作例子、实施例或说明性”。这里作为“示例性”所说明的任何实施例不必解释为优于或好于其它实施例。
本文中术语“和/或”，仅仅是一种描述关联对象的关联关系，表示可以存在三种关系，例如，A和/或B，可以表示：单独存在A，同时存在A和B，单独存在B这三种情况。另外，本文中术语“至少一种”表示多种中的任意一种或多种中的至少两种的任意组合，例如，包括A、B、C中的至少一种，可以表示包括从A、B和C构成的集合中选择的任意一个或多个元素。
另外，为了更好地说明本公开，在下文的具体实施方式中给出了众多的具体细节。本领域技术人员应当理解，没有某些具体细节，本公开同样可以实施。在一些实例中，对于本领域技术人员熟知的方法、手段、元件和电路未作详细描述，以便于凸显本公开的主旨。
本公开实施例提供的图像处理方案，可以对在当前场景下实时采集的目标图像进行人形检测，得到人形检测结果，根据该目标图像的人形检测结果，可以确定目标图像所包括的感兴趣区域，根据目标图像的感兴趣区域中每个像素点的亮度，可以确定感兴趣区域的亮度分布，基于感兴趣区域的亮度分布，可以确定在当前场景下用于进行图像采集的采集参数值，这样，可以通过对目标图像进行人形检测的人形检测结果，确定适合当前场景的采集参数值，从而可以根据确定的采集参数对当前场景进行图像采集，即使当前场景是逆光或强光场景，也可以根据确定的采集参数值调整采集参数，从而使拍摄到的图像具有较佳的人脸质量，提高后续人脸识别的准确率。
在相关技术中，在逆光场景下采集的图像帧，图像帧的背景亮度较大，图像帧中的人脸区域较暗，人脸质量较差，会影响人脸识别的效果。本公开实施例提供的图像处理方案，适用于强光、暗光和逆光等不利于拍摄的环境， 可以提高各种环境下人脸的成像质量。
图1示出根据本公开实施例的图像处理方法的流程图。该图像处理方法可以由终端设备或其它类型的电子设备执行。其中，终端设备可以为门禁设备、用户设备(User Equipment，UE)、移动设备、用户终端、终端、蜂窝电话、无绳电话、个人数字处理(Personal Digital Assistant，PDA)、手持设备、计算设备、车载设备和可穿戴设备等。
在一些可能的实现方式中，该图像处理方法可以通过处理器调用存储器中存储的计算机可读指令的方式来实现。下面以图像处理终端作为执行主体为例对本公开实施例的图像处理方法进行说明。图像处理终端可以是上述终端设备或其它类型的电子设备。
如图1所示，所示图像处理方法可以包括以下步骤：
S11，对在当前场景下实时采集的目标图像进行人形检测，得到人形检测结果。
在本公开实施例中，图像处理终端1可以针对当前场景实时进行图像采集，得到实时采集的目标图像。或者，图2示出根据本公开实施例的图像处理方法一示例的应用场景图。如图2所示，图像处理终端1可以通过接收其他设备2通过网络3传送的实时采集或拍摄到的目标图像，得到实时采集的目标图像，例如，接收图像采集装置(如相机、图像传感器)、摄像装置(如摄像机、监控器)等其他设备2实时采集或拍摄的目标图像，得到实时采集的目标图像。目标图像可以是单独的图像，或者，目标图像可以是视频流中的一个图像帧。图像处理终端得到目标图像，对目标图像进行人形检测，得到人形检测结果，该人形检测结果可以是针对目标图像的某些区域检测的检测结果，例如，人脸区域的检测结果、上半身区域的检测结果。
在一种可能的实现方式中，图像处理终端可以利用构建的人形检测网络对目标图像进行人形检测，人形检测网络可以是通过对构建的神经网络进行训练得到的。举例来说，可以利用现有的神经网络结构构建神经网络，也可以根据实际的应用场景设计神经网络结构，以构建神经网络。构建神经网络，将训练图像输入构建的神经网络，利用构建的神经网络对训练图像进行人形检测，并得到人形检测结果，然后将该人形检测结果与训练图像的标注结果进行比较，得到比较结果，并利用比较结果对构建的神经网络的模型参数进行调整，使构建的神经网络模型的人形检测结果与标注结果一致，这样，可以由构建的神经网络模型得到人形检测网络。这里，可以将在强光和暗光等恶劣拍摄环境下采集的图像作为训练图像。人形检测网络可以针对目标图像的人形轮廓进行检测，在人脸识别场景中，得到的人形检测结果可以是人脸区域的检测结果。
S12，根据目标图像的人形检测结果，确定目标图像的感兴趣区域。
在本公开实施例中，图像处理终端可以根据目标图像的人形检测结果，确定目标图像中是否存在人脸区域。根据目标图像中是否存在人脸区域的不同情况，可以根据不同方式确定目标图像的感兴趣区域，例如，如果目标图 像中存在人脸区域，可以将人脸区域作为目标图像的感兴趣区域，如果目标图像中不存在人脸区域，可以将目标图像的某部分图像区域作为目标图像的感兴趣区域，如上半部分图像区域、下半部分图像区域等图像区域作为目标图像的感兴趣区域。这里的感兴趣区域可以理解为图像处理过程中所关注的图像区域，确定目标图像的感兴趣区域可以便于对该区域进行进一步图像处理。
在一种可能的实现方式中，在所述人形检测结果表明所述目标图像存在人脸区域的情况下，根据所述目标图像中的人脸区域，确定所述目标图像的所述感兴趣区域。
在该实现方式中，目标图像中可以存在一个以上的人脸区域。如果人形检测结果表明目标图像中存在一个人脸区域，则可以将该人脸区域作为目标图像的感兴趣区域。如果人形检测结果表明目标图像中存在多个人脸区域，则可以在多个人脸区域中选择至少一个人脸区域，将选择的至少一个人脸区域作为目标图像的感兴趣区域，例如，在多个人脸区域中选择位于目标图像中间部分的至少一个人脸区域。这样，可以由目标图像中的人脸区域确定感兴趣区域，进而可以针对确定的感兴趣区域进行进一步的图像处理，提高图像处理的效率以及准确性。
在该实现方式的一个示例中，在所述目标图像存在多个人脸区域的情况下，可以确定所述多个人脸区域中最大的人脸区域，然后将所述最大的人脸区域确定为所述目标图像的所述感兴趣区域。
在该示例中，如果目标图像中存在多个人脸区域，可以比较多个人脸区域的大小，然后根据比较结果可以确定多个人脸区域中最大的人脸区域，从而可以将最大的人脸区域作为目标图像的感兴趣区域。这样，可以在多个人脸区域中选择一个最关注的人脸区域作为感兴趣区域，从而在图像处理过程中可以不考虑感兴趣区域之外的其他图像区域，使得图像处理的效率以及准确性可以提高。
在一种可能的实现方式中，在所述人形检测结果表明所述目标图像不存在人脸区域的情况下，可以确定所述目标图像的中心图像区域，然后将所述中心图像区域确定为所述目标图像的所述感兴趣区域。
在该实现方式中，在图像采集过程中，人脸区域通常位于目标图像的中心图像区域，因此，在人形检测中没有检测到人脸区域时，可以将目标图像的中心图像区域作为目标图像的感兴趣区域。举例来说，可以将目标图像划分为多个图像区域，如，将目标图像平均分为9个或25个等多个区域，然后将多个区域中的中心图像区域确定为目标图像的感兴趣区域，如，将9个图像区域中位于目标图像中心的一个图像区域作为感兴趣区域。这样，即使在目标图像中未检测到人脸区域，也可以确定目标图像的感兴趣区域，进而可以针对确定的感兴趣区域进行进一步的图像处理，提高图像处理的效率以及准确性。
在本公开实施例中，确定目标图像的感兴趣区域，根据目标图像的感兴 趣区域中每个像素点的亮度得到感兴趣区域的亮度分布，该亮度分布可以用亮度直方图等进行表示。
S13，基于感兴趣区域的亮度分布，确定在当前场景下用于进行图像采集的目标参数值。
基于感兴趣区域的亮度分布，可以得到在当前场景下用于进行图像采集的目标参数值，该目标参数值是适合当前拍摄环境的参数值，在该目标参数值的作用下，可以得到曝光良好、人脸质量较佳的图像，从而可以适应于各种恶劣的拍摄环境，如，强光和暗光等拍摄环境。
这里，进行图像采集时需要采用图像采集参数，该图像采集参数可以是图像采集过程中设置的拍摄参数，目标参数值为当前场景下的图像采集参数，图像采集参数或目标参数值可以包括：曝光值、曝光时间和增益中的一种或多种。其中，曝光值是表示镜头通光能力的一个参数，可以是快门速度值和光圈值的组合。曝光时间可以是快门打开到关闭的时间间隔。增益可以是对采集的视频信号进行放大时的倍数。图像采集参数可以进行设定，图像采集参数不同时，同一个场景中拍摄得到的图像也不同。因此，可以通过调整图像采集参数，得到图像质量较好的图像。
在一种可能的实现方式中，确定当前场景下的目标参数值，将所述图像采集参数调整为所述目标参数值，采用所述目标参数值，对所述当前场景进行图像采集。
在该实现方式中，图像处理终端可以具有图像采集功能，可以对当前场景进行拍摄。图像处理终端确定当前场景下用于进行图像采集的目标参数值，将图像采集参数设置为目标参数值，在目标参数值的作用下继续对当前场景进行拍摄，得到目标图像之后所采集到的图像，该图像是在图像采集参数为目标参数值的作用下得到的图像，由于目标参数值是经过优化的参数值，从而该图像具有较佳的图像质量，在人脸识别场景中，该图像具有较佳的人脸质量，可以提高后续人脸识别的速度以及准确率。
这里，如果图像处理终端不具有图像采集功能，图像处理终端可以将确定的目标参数值发送给图像采集装置，从而使图像采集装置可以采用目标参数值继续对当前场景进行拍摄。
本公开实施例提供的图像处理方案，可以基于感兴趣区域的亮度分布确定用于进行图像采集的目标参数值，从而可以解决逆光、强光和弱光等场景下拍摄的人脸质量较差的问题。本公开实施例还提供了确定图像采集参数的目标参数值的一种实现方式。
图3示出根据本公开实施例的确定用于进行图像采集的目标参数值一示例的流程图。如图3所示，上述步骤S13可以包括以下步骤：
S131，确定感兴趣区域的平均亮度。
这里，可以根据感兴趣区域中每个像素点的亮度确定感兴趣区域的平均亮度，例如，可以统计感兴趣区域中所包括的像素点个数，然后将感兴趣区域中所有像素点的亮度进行求和，得到感兴趣区域的总亮度，然后将总亮度 除以感兴趣区域中所包括的像素点个数，得到感兴趣区域的平均亮度。
在一种可能的实现方式中，可以确定所述感兴趣区域中每个像素点对应的权重，然后根据所述感兴趣区域中每个像素点对应的权重以及每个像素点的亮度，确定所述感兴趣区域的平均亮度。
在该实现方式中，可以为感兴趣区域中的每个像素点设置相应的权重，例如，为感兴趣区域中重点关注的图像部分包括的像素点设置较大的权重，从而在确定感兴趣区域的平均亮度时，可以使重点关注的图像部分贡献较大的比重。或者，还可以为感兴趣区域中的像素点设置相同的权重，例如，在感兴趣区域是人脸区域的情况下，可以为感兴趣区域中的像素点设置相同的权重值。确定感兴趣区域中每个像素点对应的权重，对每个像素点的亮度进行加权求和，再将加权求和得到的总亮度除以感兴趣区域中像素点的权重之和，可以得到感兴趣区域的平均亮度。
在该实现方式的一个示例中，在所述人形检测结果表明所述目标图像存在人脸的情况下，可以根据所述感兴趣区域中像素点与所述感兴趣区域的区域中心的距离，确定所述感兴趣区域中每个像素点对应的权重；其中，像素点和所述感兴趣区域的区域中心之间的距离，与所述像素点对应的权重正相关，像素点与所述感兴趣区域的区域中心的距离越近，所述像素点对应的权重越大。
在该示例中，如果人形检测结果表明所述目标图像不存在人脸区域，感兴趣区域可以是目标图像的中心图像区域，可以根据感兴趣区域中像素点与感兴趣区域的区域中心的距离，为感兴趣区域中的像素点设置相应的权重，像素点和所述感兴趣区域的区域中心之间的距离，与所述像素点对应的权重正相关，举例来说，可以为距离区域中心较近的像素点设置较大的权重，为距离区域中心较远的像素点设置较小的权重，即，越处于中间部分的像素点权重越大，例如，中间部分的像素点的权重是8，远离区域中心的外层部分的像素点权重是4，感兴趣区域内最外层部分的像素点权重是1。这里，可以将感兴趣区域划分为多个图像部分，每个图像部分中的像素点可以具有相同的权重。这样，由于人脸区域位于目标图像的中心的概率较大，从而可以将中间部分的像素点的权重设置的较大，尽可能地保留人脸区域的像素点对平均亮度的贡献。
S132，根据感兴趣区域的亮度分布，确定感兴趣区域的边界亮度。
这里，感兴趣区域的亮度分布可以用亮度直方图进行表示。亮度直方图的横坐标可以是亮度值，亮度直方图的纵坐标可以是亮度值对应的像素点个数。根据感兴趣区域的亮度分布，可以确定感兴趣区域的边界亮度，该边界亮度可以是一个亮度值，在该亮度值之内对应的像素点可以包括感兴趣区域大部分的像素点。或者，该边界亮度可以是一个亮度区间，在该亮度区间内对应的像素点可以包括感兴趣区域大部分的像素点。
在一个可能的实现方式中，可以在所述感兴趣区域的亮度分布中，确定亮度参考值范围内对应的像素点个数，然后确定所述亮度参考值范围内对应 的像素点个数占所述感兴趣区域的像素点总数的像素点比例，在所述像素点比例大于或等于预设比例的情况下，将所述像素点比例大于或等于预设比例所对应的亮度参考值，确定为所述感兴趣区域的边界亮度。
在该实现方式中，边界亮度可以是一个亮度值，针对感兴趣区域的亮度直方图，可以将任意一个亮度值作为亮度参考值，然后统计该亮度参考值范围内对应的像素点个数，该亮度参考值的范围可以是亮度直方图的最小亮度值到该亮度参考值的亮度范围，如果该亮度参考值范围内对应的像素点个数占感兴趣区域中像素点总数的比例大于或等于预设比例，例如，亮度参考值范围内对应的像素点个数占像素点总数的比例达到99％，则可以将该亮度参考值确定为边界亮度。
S133，根据感兴趣区域的平均亮度以及边界亮度，确定感兴趣区域的目标亮度。
这里，确定感兴趣区域的平均亮度以及边界亮度，根据感兴趣区域的平均亮度以及边界亮度，确定一个适合感兴趣区域的目标亮度，在该目标亮度下，可以认为感兴趣区域内像素点具有合理的亮度值，不会由于曝光过度或者曝光不足使得图像质量较差，从而可以根据确定的目标亮度确定图像采集参数的目标参数值。
在一种可能的实现方式中，可以获取预设的期望边界亮度，然后确定所述期望边界亮度与所述边界亮度的比值，再根据所述期望边界亮度与所述边界亮度的比值以及所述感兴趣区域的平均亮度，确定所述感兴趣区域的目标亮度。
在该实现方式中，期望边界亮度可以是图像在曝光良好的情况下确定的边界亮度，可以根据实际的应用场景进行设置。获取预设的期望边界亮度，可以计算期望边界亮度与边界亮度的比值，然后将该比值乘以感兴趣区域的平均亮度，可以得到感兴趣区域的目标亮度。举例来说，假设期望边界亮度是200，感兴趣区域的边界亮度是100，可以表明感兴趣区域的平均亮度较低，感兴趣区域内的图像质量较差，对该感兴趣区域进行人脸识别会存在一定的困难，从而可以将期望边界亮度200与感兴趣区域的边界亮度100的比值2，乘以感兴趣区域的平均亮度，得到一个目标亮度，该目标亮度是平均亮度的2倍，即可以表明，当感兴趣区域的平均亮度达到目标亮度时，感兴趣区域具有较佳的图像质量，进而可以根据确定的目标亮度确定图像采集参数的目标参数值，以在目标参数值的作用下拍摄人脸质量较好的图像。
S134，基于亮度与图像采集参数之间的映射关系，确定目标亮度所对应的目标参数值。
这里，图像的亮度与图像采集参数之间可以存在一定的映射关系，例如，图像的曝光时间越长，图像的亮度越大。从而可以根据图像的亮度与图像采集参数之间的映射关系，确定目标亮度所对应的目标参数值，例如，确定曝光值、曝光时间和增益值中的一个或多个，从而图像处理终端可以将图像采集参数调整到最佳的曝光值。
本公开实施例提供的图像处理方案，可以根据对目标图像进行人形检测的人形检测结果，确定适合当前场景的图像采集的参数值，即使当前场景是逆光或强光场景，也可以使拍摄到的图像具有较佳的人脸质量，提高后续人脸识别的准确率。
图4示出根据本公开实施例的图像处理方法一示例的流程图。如图4所示，在一个示例中，图像处理方法可以包括以下步骤：
S301，获取实时采集的目标图像。
这里，图像处理终端可以具有图像采集功能，可以对当前场景进行实时拍摄，例如，在门禁场景中，图像处理终端对门禁前的用户进行实时图像采集，得到目标图像。
S302，利用人形检测网络对目标图像进行人形检测，得到人形检测结果。
这里，人形检测网络可以是通过对构建的神经网络进行训练得到的，得到人形检测结果可以是目标图像中的人脸区域的检测结果。
S303，根据人形检测结果判断目标图像中是否存在人脸区域。
S304，在目标图像中存在人脸区域的情况下，将一个或多个人脸区域中最大的人脸区域作为感兴趣区域，执行S306。
S305，在目标图像中不存在人脸区域的情况下，将目标图像的中心图像区域作为感兴趣区域，执行S306。
这里，中心图像区域可以是目标图像的区域中心所在的区域，例如，将目标图像平均分为9个区域，其中，中心图像区域可以是9个区域中位于中间的区域。
S306，对感兴趣区域进行亮度直方图统计，得到感兴趣区域的亮度直方图。
S307，根据亮度直方图中像素点的亮度以及为像素点设置的权重，计算感兴趣区域的平均亮度。
S308，根据亮度直方图计算亮度参考值范围内的亮度分布，在亮度参考值范围内的亮度分布达到感兴趣区域的总亮度分布的99％时，将该亮度参考值确定为边界亮度。
S309，根据边界亮度、预设的期望边界亮度以及平均亮度，计算目标亮度。
S310，根据目标亮度计算需要配置的最佳曝光值和/或增益值。
这里，可以利用比例-积分-微分(Proportion-Integral-Differential，PID)控制器由目标亮度得到最佳曝光值和/或增益值。
S311，将得到的最佳曝光值和/或增益值配置到感光芯片中，执行S301。
这里，可以通过图像信号处理(Image Signal Processing，ISP)单元将得到的最佳曝光值和/或增益值配置到相机的感光芯片中，然后利用最佳曝光值和/或增益值继续采集下一个目标图像。
本公开实施例提供的图像处理方案，可以利用人形检测网络对目标图像中的人脸区域进行检测，确定感兴趣区域，然后根据目标图像的感兴趣区域 中每个像素点的亮度，确定感兴趣区域的亮度分布，基于感兴趣区域的亮度分布获得最佳的曝光值，从而可以很好的应对逆光、暗光和强光场景的人脸图像采集以及人脸检测，并且不需要增加额外的成本，可以提升用户体验。
可以理解，本公开提及的上述各个方法实施例，在不违背原理逻辑的情况下，均可以彼此相互结合形成结合后的实施例，限于篇幅，本公开不再赘述。
此外，本公开还提供了图像处理装置、电子设备、计算机可读存储介质和程序，上述均可用来实现本公开提供的任一种图像处理方法，相应技术方案和描述参见方法部分的相应记载，此处不再赘述。
本领域技术人员可以理解，在具体实施方式的上述方法中，各步骤的撰写顺序并不意味着严格的执行顺序而对实施过程构成任何限定，各步骤的具体执行顺序应当以其功能和可能的内在逻辑确定。
图5示出根据本公开实施例的图像处理装置的框图，如图5所示，所述图像处理装置包括：
检测模块41，被配置为对在当前场景下实时采集的目标图像进行人形检测，得到人形检测结果；
第一确定模块42，被配置为根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域；
第二确定模块43，被配置为基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值。
在一种可能的实现方式中，所述第一确定模块42，还被配置为，
在所述人形检测结果表明所述目标图像存在人脸区域的情况下，根据所述目标图像中的人脸区域，确定所述目标图像的所述感兴趣区域。
在一种可能的实现方式中，所述第一确定模块42，还被配置为，
在所述目标图像存在多个人脸区域的情况下，确定所述多个人脸区域中最大的人脸区域；
将所述最大的人脸区域确定为所述目标图像的所述感兴趣区域。
在一种可能的实现方式中，所述第一确定模块42，还被配置为，
在所述人形检测结果表明所述目标图像不存在人脸区域的情况下，确定所述目标图像的中心图像区域；
将所述中心图像区域确定为所述目标图像的所述感兴趣区域。
在一种可能的实现方式中，所述第一确定模块42，还被配置为，在根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域之后，且所述基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值之前，根据所述目标图像的感兴趣区域中每个像素点的亮度，确定所述感兴趣区域的亮度分布。
在一种可能的实现方式中，所述第二确定模块43，还被配置为，
确定所述感兴趣区域的平均亮度；
根据所述感兴趣区域的亮度分布，确定所述感兴趣区域的边界亮度；
根据所述感兴趣区域的平均亮度以及所述边界亮度，确定所述感兴趣区域的目标亮度；
基于亮度与图像采集参数之间的映射关系，确定所述目标亮度所对应的目标参数值。
在一种可能的实现方式中，所述第二确定模块43，还被配置为，
确定所述感兴趣区域中每个像素点对应的权重；
根据所述感兴趣区域中每个像素点对应的权重以及每个像素点的亮度，确定所述感兴趣区域的平均亮度。
在一种可能的实现方式中，所述第二确定模块43，还被配置为，
根据所述感兴趣区域中像素点与所述感兴趣区域的区域中心的距离，确定所述感兴趣区域中每个像素点对应的权重；其中，像素点和所述感兴趣区域的区域中心的距离，与所述像素点对应的权重正相关。
在一种可能的实现方式中，所述第二确定模块43，还被配置为，
在所述感兴趣区域的亮度分布中，确定亮度参考值范围内对应的像素点个数，所述亮度参考值范围为所述亮度分布中的最小亮度值到亮度参考值的亮度范围，所述亮度参考值为所述亮度分布中的任意一个亮度值；
确定所述亮度参考值范围内对应的像素点个数占所述感兴趣区域的像素点总数的像素点比例；
在所述像素点比例大于或等于预设比例的情况下，将所述亮度参考值确定为所述感兴趣区域的边界亮度。
在一种可能的实现方式中，所述第二确定模块43，还被配置为，
获取预设的期望边界亮度；
确定所述期望边界亮度与所述边界亮度的比值；
根据所述期望边界亮度与所述边界亮度的比值以及所述感兴趣区域的平均亮度，确定所述感兴趣区域的目标亮度。
在一种可能的实现方式中，所述装置还包括：
采集模块，被配置为采用所述目标参数值，对所述当前场景进行图像采集。
在一种可能的实现方式中，所述目标参数值包括：
曝光值、曝光时间和增益中的至少一种。
在一些实施例中，本公开实施例提供的装置具有的功能或包含的模块可以被配置为执行上文方法实施例描述的方法，其具体实现可以参照上文方法实施例的描述，为了简洁，这里不再赘述。
本公开实施例还提出一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现上述方法。计算机可读存储介质可以是非易失性计算机可读存储介质。
本公开实施例还提出一种电子设备，包括：处理器；被配置为存储处理器可执行指令的存储器；其中，所述处理器被配置为调用所述存储器存储的指令，以执行上述方法。
电子设备可以被提供为终端、服务器或其它形态的设备。
图6是根据一示例性实施例示出的一种电子设备800的框图。例如，电子设备800可以是移动电话，计算机，数字广播终端，消息收发设备，游戏控制台，平板设备，医疗设备，健身设备和个人数字助理等终端。
参照图6，电子设备800可以包括以下一个或多个组件：处理组件802，存储器804，电源组件806，多媒体组件808，音频组件810，输入/输出(I/O)接口812，传感器组件814，以及通信组件816。
处理组件802通常控制电子设备800的整体操作，诸如与显示，电话呼叫，数据通信，相机操作和记录操作相关联的操作。处理组件802可以包括一个或多个处理器820来执行指令，以完成上述的方法的全部或部分步骤。此外，处理组件802可以包括一个或多个模块，便于处理组件802和其他组件之间的交互。例如，处理组件802可以包括多媒体模块，以方便多媒体组件808和处理组件802之间的交互。
存储器804被配置为存储各种类型的数据以支持在电子设备800上的操作。这些数据的示例包括用于在电子设备800上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片和视频等。存储器804可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器和磁盘或光盘。
电源组件806为电子设备800的各种组件提供电力。电源组件806可以包括电源管理系统，一个或多个电源，及其他与为电子设备800生成、管理和分配电力相关联的组件。
多媒体组件808包括在所述电子设备800和用户之间提供一个输出接口的屏幕。在一些实施例中，屏幕可以包括液晶显示器(LCD)和触摸面板(TP)。如果屏幕包括触摸面板，屏幕可以被实现为触摸屏，以接收来自用户的输入信号。触摸面板包括一个或多个触摸传感器以感测触摸、滑动和触摸面板上的手势。所述触摸传感器可以不仅感测触摸或滑动动作的边界，而且还检测与所述触摸或滑动操作相关的持续时间和压力。在一些实施例中，多媒体组件808包括一个前置摄像头和/或后置摄像头。当电子设备800处于操作模式，如拍摄模式或视频模式时，前置摄像头和/或后置摄像头可以接收外部的多媒体数据。每个前置摄像头和后置摄像头可以是一个固定的光学透镜系统或具有焦距和光学变焦能力。
音频组件810被配置为输出和/或输入音频信号。例如，音频组件810包括一个麦克风(MIC)，当电子设备800处于操作模式，如呼叫模式、记录模式和语音识别模式时，麦克风被配置为接收外部音频信号。所接收的音频信号可以被进一步存储在存储器804或经由通信组件816发送。在一些实施例中，音频组件810还包括一个扬声器，用于输出音频信号。
I/O接口812为处理组件802和外围接口模块之间提供接口，上述外围接 口模块可以是键盘，点击轮和按钮等。这些按钮可包括但不限于：主页按钮、音量按钮、启动按钮和锁定按钮。
传感器组件814包括一个或多个传感器，用于为电子设备800提供各个方面的状态评估。例如，传感器组件814可以检测到电子设备800的打开/关闭状态，组件的相对定位，例如所述组件为电子设备800的显示器和小键盘，传感器组件814还可以检测电子设备800或电子设备800一个组件的位置改变，用户与电子设备800存在或不存在接触，电子设备800的方位或加速/减速和电子设备800的温度变化。传感器组件814可以包括接近传感器，被配置用来在没有任何的物理接触时检测附近物体的存在。传感器组件814还可以包括光传感器，如CMOS或CCD图像传感器，用于在成像应用中使用。在一些实施例中，该传感器组件814还可以包括加速度传感器，陀螺仪传感器，磁传感器，压力传感器或温度传感器。
通信组件816被配置为便于电子设备800和其他设备之间有线或无线方式的通信。电子设备800可以接入基于通信标准的无线网络，如WiFi，2G或3G，或它们的组合。在一个示例性实施例中，通信组件816经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实施例中，所述通信组件816还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
在示例性实施例中，电子设备800可以被一个或多个应用专用集成电路(ASIC)、数字信号处理器(DSP)、数字信号处理设备(DSPD)、可编程逻辑器件(PLD)、现场可编程门阵列(FPGA)、控制器、微控制器、微处理器或其他电子元件实现，用于执行上述方法。
在示例性实施例中，还提供了一种非易失性计算机可读存储介质，例如包括计算机程序指令的存储器804，上述计算机程序指令可由电子设备800的处理器820执行以完成上述方法。
本公开可以是系统、方法和/或计算机程序产品。计算机程序产品可以包括计算机可读存储介质，其上载有用于使处理器实现本公开的各个方面的计算机可读程序指令。
计算机可读存储介质可以是可以保持和存储由指令执行设备使用的指令的有形设备。计算机可读存储介质例如可以是，但不限于电存储设备、磁存储设备、光存储设备、电磁存储设备、半导体存储设备或者上述的任意合适的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：便携式计算机盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、静态随机存取存储器(SRAM)、便携式压缩盘只读存储器(CD-ROM)、数字多功能盘(DVD)、记忆棒、软盘、机械编码设备、例如其上存储有指令的打孔卡或凹槽内凸起结构、以及上述的任意合适的组合。这里所使用的计算机可读存储介质不被解释为瞬时信号本身，诸如无线电波或者其他自由传播的电磁波、通过波导或其他传输媒介 传播的电磁波(例如，通过光纤电缆的光脉冲)、或者通过电线传输的电信号。
这里所描述的计算机可读程序指令可以从计算机可读存储介质下载到各个计算/处理设备，或者通过网络、例如因特网、局域网、广域网和/或无线网下载到外部计算机或外部存储设备。网络可以包括铜传输电缆、光纤传输、无线传输、路由器、防火墙、交换机、网关计算机和/或边缘服务器。每个计算/处理设备中的网络适配卡或者网络接口从网络接收计算机可读程序指令，并转发该计算机可读程序指令，以供存储在各个计算/处理设备中的计算机可读存储介质中。
用于执行本公开操作的计算机程序指令可以是汇编指令、指令集架构(ISA)指令、机器指令、机器相关指令、微代码、固件指令、状态设置数据、或者以一种或多种编程语言的任意组合编写的源代码或目标代码，所述编程语言包括面向对象的编程语言—诸如Smalltalk、C++等，以及常规的过程式编程语言—诸如“C”语言或类似的编程语言。计算机可读程序指令可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络—包括局域网(LAN)或广域网(WAN)—连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。在一些实施例中，通过利用计算机可读程序指令的状态信息来个性化定制电子电路，例如可编程逻辑电路、现场可编程门阵列(FPGA)或可编程逻辑阵列(PLA)，该电子电路可以执行计算机可读程序指令，从而实现本公开的各个方面。
这里参照根据本公开实施例的方法、装置(系统)和计算机程序产品的流程图和/或框图描述了本公开的各个方面。应当理解，流程图和/或框图的每个方框以及流程图和/或框图中各方框的组合，都可以由计算机可读程序指令实现。
这些计算机可读程序指令可以提供给通用计算机、专用计算机或其它可编程数据处理装置的处理器，从而生产出一种机器，使得这些指令在通过计算机或其它可编程数据处理装置的处理器执行时，产生了实现流程图和/或框图中的一个或多个方框中规定的功能/动作的装置。也可以把这些计算机可读程序指令存储在计算机可读存储介质中，这些指令使得计算机、可编程数据处理装置和/或其他设备以特定方式工作，从而，存储有指令的计算机可读介质则包括一个制造品，其包括实现流程图和/或框图中的一个或多个方框中规定的功能/动作的各个方面的指令。
也可以把计算机可读程序指令加载到计算机、其它可编程数据处理装置、或其它设备上，使得在计算机、其它可编程数据处理装置或其它设备上执行一系列操作步骤，以产生计算机实现的过程，从而使得在计算机、其它可编程数据处理装置、或其它设备上执行的指令实现流程图和/或框图中的一个或多个方框中规定的功能/动作。
附图中的流程图和框图显示了根据本公开的多个实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段或指令的一部分，所述模块、程序段或指令的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个连续的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或动作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
以上已经描述了本公开的各实施例，上述说明是示例性的，并非穷尽性的，并且也不限于所披露的各实施例。在不偏离所说明的各实施例的范围和精神的情况下，对于本技术领域的普通技术人员来说许多修改和变更都是显而易见的。本文中所用术语的选择，旨在最好地解释各实施例的原理、实际应用或对市场中技术的技术改进，或者使本技术领域的其它普通技术人员能理解本文披露的各实施例。
工业实用性
本公开实施例通过获取在当前场景下实时采集的目标图像，然后对目标图像进行人形检测，得到人形检测结果，再根据目标图像的人形检测结果，确定目标图像所包括的感兴趣区域，最后基于确定的感兴趣区域的亮度分布，确定在当前场景下用于进行图像采集的采集参数值。这样，即使在逆光或强光等场景的情况下，也可以通过对目标图像进行人形检测得到的人形检测结果，确定当前场景中合适的采集参数值，从而图像采集装置可以根据确定的采集参数值对当前场景进行图像采集，使得采集的图像帧具有较高的人脸质量，提高了后续人脸识别的准确率。

Claims (26)

  1. 一种图像处理方法，包括：
    对在当前场景下实时采集的目标图像进行人形检测，得到人形检测结果；
    根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域；
    基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值。
  2. 根据权利要求1所述的方法，其中，所述根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域，包括：
    在所述人形检测结果表明所述目标图像存在人脸区域的情况下，根据所述目标图像中的人脸区域，确定所述目标图像的所述感兴趣区域。
  3. 根据权利要求2所述的方法，其中，所述根据所述目标图像中的人脸区域，确定所述目标图像的所述感兴趣区域，包括：
    在所述目标图像存在多个人脸区域的情况下，确定所述多个人脸区域中最大的人脸区域；
    将所述最大的人脸区域确定为所述目标图像的所述感兴趣区域。
  4. 根据权利要求1所述的方法，其中，所述根据所述目标图像的人形检测结果，确定所述目标图像的所述感兴趣区域，包括：
    在所述人形检测结果表明所述目标图像不存在人脸区域的情况下，确定所述目标图像的中心图像区域；
    将所述中心图像区域确定为所述目标图像的所述感兴趣区域。
  5. 根据权利要求1所述的方法，其中，在根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域之后，且所述基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值之前，还包括：
    根据所述目标图像的感兴趣区域中每个像素点的亮度，确定所述感兴趣区域的亮度分布。
  6. 根据权利要求1至5中任意一项所述的方法，其中，所述基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值，包括：
    确定所述感兴趣区域的平均亮度；
    根据所述感兴趣区域的亮度分布，确定所述感兴趣区域的边界亮度；
    根据所述感兴趣区域的平均亮度以及所述边界亮度，确定所述感兴趣区域的目标亮度；
    基于亮度与图像采集参数之间的映射关系，确定所述目标亮度所对应的目标参数值。
  7. 根据权利要求6所述的方法，其中，所述确定所述感兴趣区域的平均亮度，包括：
    确定所述感兴趣区域中每个像素点对应的权重；
    根据所述感兴趣区域中每个像素点对应的权重以及每个像素点的亮度，确定所述感兴趣区域的平均亮度。
  8. 根据权利要求7所述的方法，其中，所述确定所述感兴趣区域中每个像素点对应的权重，包括：
    根据所述感兴趣区域中像素点与所述感兴趣区域的区域中心的距离，确定所述感兴趣区域中每个像素点对应的权重；其中，像素点和所述感兴趣区域的区域中心之间的距离，与所述像素点对应的权重正相关。
  9. 根据权利要求6所述的方法，其中，所述根据所述感兴趣区域的亮度分布，确定所述感兴趣区域的边界亮度，包括：
    在所述感兴趣区域的亮度分布中，确定亮度参考值范围内对应的像素点个数，所述亮度参考值范围为所述亮度分布中的最小亮度值到亮度参考值的亮度范围，所述亮度参考值为所述亮度分布中的任意一个亮度值；
    确定所述亮度参考值范围内对应的像素点个数占所述感兴趣区域的像素点总数的像素点比例；
    在所述像素点比例大于或等于预设比例的情况下，将所述亮度参考值确定为所述感兴趣区域的边界亮度。
  10. 根据权利要求6所述的方法，其中，所述根据所述感兴趣区域的平均亮度以及所述边界亮度，确定所述感兴趣区域的目标亮度，包括：
    获取预设的期望边界亮度；
    确定所述期望边界亮度与所述边界亮度的比值；
    根据所述期望边界亮度与所述边界亮度的比值以及所述感兴趣区域的平均亮度，确定所述感兴趣区域的目标亮度。
  11. 根据权利要求1-5或7-10中任意一项所述的方法，其中，所述方法还包括：
    采用所述目标参数值，对所述当前场景进行图像采集。
  12. 根据权利要求11所述的方法，其中，所述目标参数值包括：
    曝光值、曝光时间和增益中的至少一种。
  13. 一种图像处理装置，包括：
    检测模块，被配置为对在当前场景下实时采集的目标图像进行人形检测，得到人形检测结果；
    第一确定模块，被配置为根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域；
    第二确定模块，被配置为基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值。
  14. 根据权利要求13所述的装置，其中，所述第一确定模块，还被配置为，
    在所述人形检测结果表明所述目标图像存在人脸区域的情况下，根据所述目标图像中的人脸区域，确定所述目标图像的所述感兴趣区域。
  15. 根据权利要求14所述的装置，其中，所述第一确定模块，还被配置为，
    在所述目标图像存在多个人脸区域的情况下，确定所述多个人脸区域中最大的人脸区域；
    将所述最大的人脸区域确定为所述目标图像的所述感兴趣区域。
  16. 根据权利要求13所述的装置，其中，所述第一确定模块，还被配置为，
    在所述人形检测结果表明所述目标图像不存在人脸区域的情况下，确定所述目标图像的中心图像区域；
    将所述中心图像区域确定为所述目标图像的所述感兴趣区域。
  17. 根据权利要求13所述的装置，其中，所述第一确定模块，还被配置为，在根据所述目标图像的人形检测结果，确定所述目标图像的感兴趣区域之后，且所述基于所述感兴趣区域的亮度分布，确定在所述当前场景下用于进行图像采集的目标参数值之前，根据所述目标图像的感兴趣区域中每个像素点的亮度，确定所述感兴趣区域的亮度分布。
  18. 根据权利要求13至17中任意一项所述的装置，其中，所述第二确定模块，还被配置为，
    确定所述感兴趣区域的平均亮度；
    根据所述感兴趣区域的亮度分布，确定所述感兴趣区域的边界亮度；
    根据所述感兴趣区域的平均亮度以及所述边界亮度，确定所述感兴趣区域的目标亮度；
    基于亮度与图像采集参数之间的映射关系，确定所述目标亮度所对应的目标参数值。
  19. 根据权利要求18所述的装置，其中，所述第二确定模块，还被配置为，
    确定所述感兴趣区域中每个像素点对应的权重；
    根据所述感兴趣区域中每个像素点对应的权重以及每个像素点的亮度，确定所述感兴趣区域的平均亮度。
  20. 根据权利要求19所述的装置，其中，所述第二确定模块，还被配置为，
    根据所述感兴趣区域中像素点与所述感兴趣区域的区域中心的距离，确定所述感兴趣区域中每个像素点对应的权重；其中，像素点和所述感兴趣区域的区域中心之间的距离，与所述像素点对应的权重正相关。
  21. 根据权利要求18所述的装置，其中，所述第二确定模块，还被配置为，
    在所述感兴趣区域的亮度分布中，确定亮度参考值范围内对应的像素点个数，所述亮度参考值范围为所述亮度分布中的最小亮度值到亮度参考值的亮度范围，所述亮度参考值为所述亮度分布中的任意一个亮度值；
    确定所述亮度参考值范围内对应的像素点个数占所述感兴趣区域的像素 点总数的像素点比例；
    在所述像素点比例大于或等于预设比例的情况下，将所述亮度参考值确定为所述感兴趣区域的边界亮度。
  22. 根据权利要求18所述的装置，其中，所述第二确定模块，还被配置为，
    获取预设的期望边界亮度；
    确定所述期望边界亮度与所述边界亮度的比值；
    根据所述期望边界亮度与所述边界亮度的比值以及所述感兴趣区域的平均亮度，确定所述感兴趣区域的目标亮度。
  23. 根据权利要求13-16或19-22中任意一项所述的装置，其中，所述装置还包括：
    采集模块，被配置为采用所述目标参数值，对所述当前场景进行图像采集。
  24. 根据权利要求23所述的装置，其中，所述目标参数值包括：
    曝光值、曝光时间和增益中的至少一种。
  25. 一种电子设备，包括：
    处理器；
    被配置为存储处理器可执行指令的存储器；
    其中，所述处理器被配置为调用所述存储器存储的指令，以执行权利要求1至12中任意一项所述的方法。
  26. 一种计算机可读存储介质，其上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现权利要求1至12中任意一项所述的方法。
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