Image acquisition method, image acquisition device, storage medium and program product

Abstract

The application provides an image acquisition method, an image acquisition device, a storage medium and a program product. The method is applied to an intelligent monitoring device and includes the following steps: acquiring a current exposure parameter of the intelligent monitoring device, wherein the current exposure parameter is an exposure parameter of a current picture collected by the intelligent monitoring device at a current time; determining whether the current exposure parameter is greater than a parameter threshold; in the case where the current exposure parameter is greater than the parameter threshold, adjusting a current brightness value of a target scene in the current picture to obtain a target brightness value, and adjusting the current exposure parameter according to the target brightness value and a prepared brightness value to obtain a target exposure parameter; in the case where the current exposure parameter is less than or equal to the parameter threshold, determining the target exposure parameter according to the current brightness value, the prepared brightness value and the parameter threshold; and collecting a target image according to the target exposure parameter. The application solves the problem that an intelligent monitoring camera in the prior art cannot accurately identify objects and details in a picture in a backlight environment.

Description

Technical Field

[0001] This application relates to the field of image acquisition, and more specifically, to an image acquisition method, an image acquisition device, a computer-readable storage medium, and a computer program product. Background Technology

[0002] Surveillance cameras possess advantages such as high sensitivity, resistance to strong light, low distortion, small size, long lifespan, and vibration resistance. In security systems, images are currently primarily generated by CCD cameras. They can also extract stored charges to change voltage, exhibiting vibration and impact resistance, and are widely used. Cameras are currently the fastest-growing type of sensor in the ADAS field, and the dynamic parameters of the camera's captured image are the foundation of the sensor's operation, directly determining the accuracy of the captured image. However, in practical use, existing intelligent surveillance cameras automatically adjust the amount of near light when capturing the image based on the overall scene. In normal environments, the image acquired by the surveillance camera is sufficient for subsequent processing to complete the corresponding functions. However, in backlit environments, due to the need to balance the brightness of the entire image, the main part of the image may be dark, making it impossible to identify objects in the image, let alone details, thus affecting the normal use of the surveillance camera and causing security risks.

[0003] Therefore, there is an urgent need for an image acquisition method that can overcome the above problems. Summary of the Invention

[0004] The main objective of this application is to provide an image acquisition method, an image acquisition device, a computer-readable storage medium, and a computer program product, so as to at least solve the problem of inaccurate identification of objects and details in the image by existing intelligent surveillance cameras in backlit environments.

[0005] According to one aspect of this application, an image acquisition method is provided, the method being applied to an intelligent monitoring device, the method comprising: acquiring current exposure parameters of the intelligent monitoring device, the current exposure parameters being the exposure parameters of the current image acquired by the intelligent monitoring device at the current moment; determining whether the current exposure parameters are greater than a parameter threshold; if the current exposure parameters are greater than the parameter threshold, adjusting the current brightness value of a target object in the current image to obtain a target brightness value, and adjusting the current exposure parameters according to the target brightness value and a preset brightness value to obtain a target exposure parameter, the current image including multiple objects, the target object being any one of the multiple objects, the preset brightness value being the brightness value of the other objects in the multiple objects excluding the target object; if the current exposure parameters are less than or equal to the parameter threshold, determining the target exposure parameter according to the current brightness value, the preset brightness value, and the parameter threshold; and acquiring a target image according to the target exposure parameter.

[0006] Optionally, when the current exposure parameter is greater than the parameter threshold, adjusting the current brightness value of the target object in the current frame to obtain a target brightness value includes: segmenting the current frame to obtain multiple segmented frames, wherein each segmented frame includes at least one object; obtaining the brightness value of each segmented frame to obtain multiple object brightness values; obtaining the brightness value of the same object in different historical frames to obtain multiple historical brightness values, wherein the historical frames are frames captured by the intelligent monitoring device before the current time; calculating the average of the multiple historical brightness values ​​to obtain a brightness threshold; and adjusting the current brightness value according to the brightness threshold and the current brightness value to obtain the target brightness value.

[0007] Optionally, adjusting the current brightness value based on the brightness threshold and the current brightness value to obtain the target brightness value includes: determining whether the absolute value of the difference between the current brightness value and the brightness threshold is greater than an absolute value threshold; if the absolute value of the difference between the current brightness value and the brightness threshold is greater than the absolute value threshold, adjusting the current brightness value closer to the brightness threshold to obtain the target brightness value; if the absolute value of the difference between the current brightness value and the brightness threshold is less than or equal to the absolute value threshold, determining the target exposure parameters based on the current brightness value, the prepared brightness value, and the parameter threshold.

[0008] Optionally, if the absolute value of the difference between the current brightness value and the brightness threshold is greater than the absolute value threshold, after adjusting the current brightness value to be closer to the brightness threshold to obtain the target brightness value, the method further includes: reducing the brightness value of the exposure point in the current image according to the target brightness value to obtain the exposure point brightness value.

[0009] Optionally, adjusting the current exposure parameters based on the target brightness value and the prepared brightness value to obtain the target exposure parameters includes: a first fusion step, fusing the target brightness value and the prepared brightness value to obtain a fused brightness value for the target scene, wherein the fusion process includes one of weighted averaging, histogram equalization, and local contrast enhancement, the segmented frame corresponding to the target scene is a target segmented frame, and the prepared brightness value is the brightness value of the segmented frames adjacent to the target segmented frame; a first repetition step, repeating the first fusion step at least once until all the segmented frames in the current frame are traversed to obtain multiple fused brightness values; and a first adjustment step, adjusting the current exposure parameters based on the average of the multiple fused brightness values ​​to obtain the target exposure parameters.

[0010] Optionally, when the current exposure parameter is less than or equal to the parameter threshold, determining the target exposure parameter based on the current brightness value, the prepared brightness value, and the parameter threshold includes: a second fusion step, performing a fusion process on the target brightness value, the prepared brightness value, and the parameter threshold to obtain a fused brightness value for the target scene, wherein the fusion process includes one of weighted averaging, histogram equalization, and local contrast enhancement, the segmented image corresponding to the target scene is a target segmented image, and the prepared brightness value is the brightness value of the segmented images adjacent to the target segmented image; a second repetition step, repeating the second fusion step at least once until all the segmented images in the current image are traversed to obtain multiple fused brightness values; and a second adjustment step, adjusting the current exposure parameter based on the average of the multiple fused brightness values ​​to obtain the target exposure parameter.

[0011] Optionally, acquiring a target image based on the target exposure parameters includes: determining the acquisition parameters of the intelligent monitoring device based on the target exposure parameters to acquire the target image, wherein the acquisition parameters include at least one of shutter speed, aperture size, and ISO sensitivity.

[0012] According to another aspect of this application, an image acquisition device is provided, the device being applied to an intelligent monitoring device, the device comprising: an acquisition unit, configured to acquire current exposure parameters of the intelligent monitoring device, the current exposure parameters being the exposure parameters of the current image acquired by the intelligent monitoring device at the current moment; a first determination unit, configured to determine whether the current exposure parameters are greater than a parameter threshold; an adjustment unit, configured to, when the current exposure parameters are greater than the parameter threshold, adjust the current brightness value of a target object in the current image to obtain a target brightness value, and adjust the current exposure parameters according to the target brightness value and a preset brightness value to obtain a target exposure parameter, the current image including multiple objects, the target object being any one of the multiple objects, the preset brightness value being the brightness value of the other objects in the multiple objects excluding the target object; a second determination unit, configured to, when the current exposure parameters are less than or equal to the parameter threshold, determine the target exposure parameter according to the current brightness value, the preset brightness value, and the parameter threshold; and an acquisition unit, configured to acquire a target image according to the target exposure parameter.

[0013] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium including a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform any of the methods described.

[0014] According to another aspect of this application, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the steps of any of the methods described.

[0015] This application provides an image acquisition method. First, the current exposure parameters of the intelligent monitoring device are obtained; these parameters represent the exposure values ​​of the current image captured by the device at the current moment. It is then determined whether the current exposure parameters exceed a parameter threshold. If the current exposure parameters exceed the threshold, the current brightness value of the target object in the current image is adjusted to obtain the target brightness value. Based on the target brightness value and a preset brightness value, the current exposure parameters are adjusted to obtain the target exposure parameters. Next, if the current exposure parameters are less than or equal to the parameter threshold, the target exposure parameters are determined based on the current brightness value, the preset brightness value, and the parameter threshold. Finally, the target image is acquired based on the target exposure parameters. By judging the relationship between the current exposure parameters and the parameter threshold, when the current exposure parameters exceed the threshold, the brightness values ​​of the objects in the current image are automatically adjusted to a reasonable range. The adjusted brightness values ​​are then fused with the brightness values ​​of other locations to automatically adjust the overall exposure parameters of the image. Acquiring the target image based on the adjusted exposure parameters improves image processing efficiency, shortens image correction time, further improves the image refresh rate and image smoothness, and ensures high-quality monitoring images can be acquired even in complex environments such as backlighting. This solves the problem of inaccurate recognition of objects and details in images by existing intelligent surveillance cameras in backlit environments. Attached Figure Description

[0016] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0017] Figure 1 A hardware structure block diagram of a mobile terminal performing an image acquisition method according to an embodiment of this application is shown;

[0018] Figure 2 A schematic flowchart of an image acquisition method according to an embodiment of this application is shown;

[0019] Figure 3 A structural block diagram of an image acquisition device provided according to an embodiment of this application is shown.

[0020] The above figures include the following reference numerals:

[0021] 102. Processor; 104. Memory; 106. Transmission device; 108. Input / output device. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of this application described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0025] As described in the background section, existing intelligent surveillance cameras are inaccurate in recognizing objects and details in backlit environments. To address this issue, embodiments of this application provide an image acquisition method, an image acquisition device, a computer-readable storage medium, and a computer program product.

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0027] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for an image acquisition method according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0028] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the image acquisition method in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via 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 transmission device 106 is used to receive or send data via a network. Specific examples of the aforementioned networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] This embodiment provides an image acquisition method that runs on a mobile terminal, computer terminal, or similar computing device. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Also, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0030] Figure 2 This is a flowchart of an image acquisition method according to an embodiment of this application. Figure 2 As shown, the above method is applied to intelligent monitoring equipment, and the method includes the following steps:

[0031] Step S201: Obtain the current exposure parameters of the intelligent monitoring device. The current exposure parameters are the exposure parameters of the current image captured by the intelligent monitoring device at the current moment.

[0032] Specifically, when monitoring the angle of the target position, the camera acquires and records the exposure parameters of the current image in real time.

[0033] Step S202: Determine whether the current exposure parameter is greater than the parameter threshold.

[0034] Specifically, the threshold settings for the above parameters are related to the type of intelligent monitoring equipment.

[0035] Step S203: When the current exposure parameter is greater than the parameter threshold, adjust the current brightness value of the target object in the current frame to obtain the target brightness value, and adjust the current exposure parameter according to the target brightness value and the prepared brightness value to obtain the target exposure parameter. The current frame includes multiple objects, the target object is any one of the multiple objects, and the prepared brightness value is the brightness value of the other objects in the multiple objects except the target object.

[0036] Specifically, when the current exposure parameters are greater than the aforementioned parameter thresholds, i.e. the current image is in a backlit state, the exposure parameters and brightness of the current image are much greater than the calculated exposure parameters and brightness values. At this time, the brightness of the objects in the current image that are the same as those in the previous frame will be automatically adjusted, so as to facilitate the adjustment of the overall brightness and exposure of the entire image as a reference.

[0037] Step S204: If the current exposure parameter is less than or equal to the parameter threshold, determine the target exposure parameter based on the current brightness value, the preparatory brightness value, and the parameter threshold.

[0038] Specifically, when the current exposure parameters are less than or equal to the above parameter thresholds, i.e., when there is no backlight in the current scene, the overall exposure parameters in the current scene are adjusted to improve the refresh rate and the smoothness of the scene.

[0039] Step S205: Acquire the target image according to the above target exposure parameters.

[0040] Specifically, once all target exposure parameters are set and the preview confirms they are correct, the camera begins capturing the target image. At this point, the camera captures the image according to the set exposure parameters, ensuring that the image's brightness, contrast, and detail are optimally presented.

[0041] This embodiment provides an image acquisition method. First, the current exposure parameters of the intelligent monitoring device are obtained; these parameters represent the exposure values ​​of the current image captured by the device at the current moment. It is then determined whether the current exposure parameters exceed a parameter threshold. If the current exposure parameters exceed the threshold, the current brightness value of the target object in the current image is adjusted to obtain the target brightness value. Based on the target brightness value and a preset brightness value, the current exposure parameters are adjusted to obtain the target exposure parameters. Next, if the current exposure parameters are less than or equal to the parameter threshold, the target exposure parameters are determined based on the current brightness value, the preset brightness value, and the parameter threshold. Finally, the target image is acquired based on the target exposure parameters. By judging the relationship between the current exposure parameters and the parameter threshold, when the current exposure parameters exceed the threshold, the brightness values ​​of the objects in the current image are automatically adjusted to a reasonable range. The adjusted brightness values ​​are then fused with the brightness values ​​of other locations to automatically adjust the overall exposure parameters of the image. Acquiring the target image based on the adjusted exposure parameters improves image processing efficiency, shortens image correction time, further improves the image refresh rate and image smoothness, and ensures high-quality monitoring images can be acquired even in complex environments such as backlighting. This solves the problem of inaccurate recognition of objects and details in images by existing intelligent surveillance cameras in backlit environments.

[0042] In specific implementation, step S203 can be achieved through the following steps: Step S2031, segmenting the current image to obtain multiple segmented images, wherein each segmented image includes at least one of the aforementioned objects; Step S2032, acquiring the brightness values ​​of each segmented image to obtain multiple object brightness values; Step S2033, acquiring the brightness values ​​of the same object in different historical images to obtain multiple historical brightness values, wherein the historical images are images captured by the intelligent monitoring device before the current moment; Step S2034, calculating the average of the multiple historical brightness values ​​to obtain a brightness threshold; Step S2035, adjusting the current brightness value based on the brightness threshold and the current brightness value to obtain the target brightness value. This method can further improve image processing efficiency, shorten image correction time, and improve the accuracy and efficiency of automatic adjustment.

[0043] Specifically, during the shooting process, the camera continuously divides the image into a grid, determining the brightness values ​​and exposure parameters of different grids. When encountering backlighting, the changes in brightness values ​​and exposure parameters across different grids reveal the exposure and range within the image, as well as the impact on the brightness of objects within each grid. This facilitates automatic adjustment of the brightness of objects within the grid and the overall exposure and range of the image in post-processing. Furthermore, the brightness and exposure of the next frame can be predicted and calculated in advance, allowing the camera to directly compare and adjust the captured image. The preset threshold for the brightness value of foreground objects is a certain range of values, representing the average brightness value of the same objects in previous frames. The brightness threshold for objects in the current frame is not static but changes with the overall environment of the current scene, without affecting the overall movement of the image or the accuracy of the recorded footage, thus improving correction efficiency and stability.

[0044] The above-mentioned step S2035 of this application can be implemented through the following steps: Step S20351, determining whether the absolute value of the difference between the current brightness value and the brightness threshold is greater than the absolute value threshold; Step S20352, if the absolute value of the difference between the current brightness value and the brightness threshold is greater than the absolute value threshold, adjusting the current brightness value to approach the brightness threshold to obtain the target brightness value; Step S20353, if the absolute value of the difference between the current brightness value and the brightness threshold is less than or equal to the absolute value threshold, determining the target exposure parameters based on the current brightness value, the prepared brightness value, and the parameter threshold. This method can further avoid the brightness of the scene changing with the brightness adjustment of other scenes, affecting the shooting accuracy of the scene, and improving the correction effect of the image.

[0045] Specifically, if the brightness value of an object in the current frame is greater than a preset threshold, the average brightness value of the objects in the current frame is compared with the brightness value of the same object in the current frame. The difference in brightness is then compared to determine whether the difference in brightness is greater than the absolute threshold. In other words, when backlighting causes the brightness of the objects in the current frame to be much greater than the average value calculated from the previous frame, the difference in brightness between the two values ​​is compared to determine whether the brightness of the object in the current frame needs to be adjusted.

[0046] The above step S20352 can also be achieved in other ways, for example: in step S20354, based on the above target brightness value, the brightness value of the exposure point in the current image is reduced to obtain the exposure point brightness value.

[0047] Specifically, since the exposure point is the part of the image with the highest exposure parameters, the brightness value of the exposure point should be reduced.

[0048] In some embodiments, step S203 can be implemented through the following steps: Step S2036, a first fusion step, fusing the target brightness value and the prepared brightness value to obtain the fused brightness value of the target scene, wherein the fusion process includes one of weighted averaging, histogram equalization, and local contrast enhancement, the segmented image corresponding to the target scene is the target segmented image, and the prepared brightness value is the brightness value of the segmented images adjacent to the target segmented image; Step S2037, a first repetition step, repeating the first fusion step at least once until all the segmented images in the current image are traversed to obtain multiple fused brightness values; Step S2038, a first adjustment step, adjusting the current exposure parameters according to the average of the multiple fused brightness values ​​to obtain the target exposure parameters. This method can further shorten the exposure time and improve the monitoring and recording effect.

[0049] Specifically, after the brightness of the foreground object is automatically adjusted, the required image brightness is directly obtained through image analysis and fusion processing. Combined with the predicted exposure brightness, the current exposure parameters are adjusted as a whole to improve the accuracy and precision of the adjustment, enhance the harmony of the image, and the target image is captured by the camera lens for real-time monitoring.

[0050] Step S204 above can be implemented through the following steps: Step S2041, second fusion step, fusing the target brightness value, the prepared brightness value, and the parameter threshold to obtain the fused brightness value of the target scene, wherein the fusion process includes one of weighted averaging, histogram equalization, and local contrast enhancement, the segmented image corresponding to the target scene is the target segmented image, and the prepared brightness value is the brightness value of the segmented images adjacent to the target segmented image; Step S2042, second repetition step, repeating the second fusion step at least once until all the segmented images in the current image are traversed to obtain multiple fused brightness values; Step S2043, second adjustment step, adjusting the current exposure parameters according to the average of the multiple fused brightness values ​​to obtain the target exposure parameters. This method can further quickly determine the target exposure parameters.

[0051] Specifically, weighted averaging is a method of fusing multiple images into a single image, where the pixel values ​​of each image are weighted and calculated according to their respective weights. These weights can be determined based on image quality, exposure levels, or importance in a specific scene. In surveillance camera applications, this means that the brightness values ​​of the current frame and other frames can be fused according to a certain weighted ratio to obtain a more stable and higher-quality final brightness value. For example, if the exposure parameters of the current frame are too high, the final brightness can be balanced by increasing the weight of historical frames to avoid overexposure. Histogram equalization is used to improve image contrast, especially when there are large variations in brightness in local areas of the image. By adjusting the distribution of image pixels to make the image histogram more uniform, the global contrast of the image is increased, making the image appear clearer. In smart surveillance cameras, histogram equalization can be used for overall or local areas to optimize image exposure and contrast, especially under low-light or high-light conditions. For example, if most of the monitored area is in shadow, histogram equalization can help increase the brightness of shadow areas, making details more visible. Local contrast enhancement improves the visual effect of an image by enhancing the contrast of local areas. This is typically achieved by applying local filters or Gaussian blur, then calculating and adjusting the brightness differences in local areas. In smart surveillance cameras, local contrast enhancement can be used to highlight details of key objects in the image, ensuring clear display of important information even in cases of overall underexposure or overexposure. For example, if a foreground object is much brighter than the background, local contrast enhancement can help adjust the brightness of the foreground while preserving background details, resulting in a more balanced and clear image.

[0052] Step S205 above can be achieved through the following steps: Step S2051, based on the target exposure parameters, determine the acquisition parameters of the intelligent monitoring device to acquire the target image. The acquisition parameters include at least one of shutter speed, aperture size, and ISO sensitivity. This method can further accelerate the acquisition of the target image.

[0053] Specifically, the steps for acquiring the target image are as follows: First, the system has calculated or adjusted the most suitable target exposure parameters for the current scene using algorithms. This includes shutter speed, aperture size, ISO sensitivity, and possible brightness and contrast adjustments. Next, camera parameters are set: the target exposure parameters are applied to the camera's hardware settings, including adjusting shutter speed, aperture size, and ISO sensitivity, to ensure the camera shoots according to the calculated parameters. Then, previewing and fine-tuning: before actual shooting, the camera may perform a preview shot to check if the target exposure parameters truly achieve the expected effect. If the preview image still has exposure issues, the system will fine-tune the parameters based on the actual situation until a satisfactory exposure effect is achieved.

[0054] This application also provides an image acquisition device. It should be noted that the image acquisition device of this application can be used to execute the image acquisition method provided in this application. This device is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0055] The image acquisition device provided in the embodiments of this application will be described below.

[0056] Figure 3 This is a schematic diagram of an image acquisition device according to an embodiment of this application. Figure 3 As shown, the above-mentioned device is applied to intelligent monitoring equipment, and the device includes:

[0057] The acquisition unit 10 is used to acquire the current exposure parameters of the intelligent monitoring device, wherein the current exposure parameters are the exposure parameters of the current image captured by the intelligent monitoring device at the current moment;

[0058] Specifically, when monitoring the angle of the target position, the camera acquires and records the exposure parameters of the current image in real time.

[0059] The first determining unit 20 is used to determine whether the current exposure parameter is greater than the parameter threshold.

[0060] Specifically, the threshold settings for the above parameters are related to the type of intelligent monitoring equipment.

[0061] The adjustment unit 30 is used to adjust the current brightness value of the target scene in the current frame when the current exposure parameter is greater than the parameter threshold, to obtain the target brightness value, and to adjust the current exposure parameter according to the target brightness value and the prepared brightness value to obtain the target exposure parameter. The current frame includes multiple scenes, the target scene is any one of the multiple scenes, and the prepared brightness value is the brightness value of the other scenes in the multiple scenes except the target scene.

[0062] Specifically, when the current exposure parameters are greater than the aforementioned parameter thresholds, i.e. the current image is in a backlit state, the exposure parameters and brightness of the current image are much greater than the calculated exposure parameters and brightness values. At this time, the brightness of the objects in the current image that are the same as those in the previous frame will be automatically adjusted, so as to facilitate the adjustment of the overall brightness and exposure of the entire image as a reference.

[0063] The second determining unit 40 is used to determine the target exposure parameter based on the current brightness value, the preparatory brightness value, and the parameter threshold when the current exposure parameter is less than or equal to the parameter threshold.

[0064] Specifically, when the current exposure parameters are less than or equal to the above parameter thresholds, i.e., when there is no backlight in the current scene, the overall exposure parameters in the current scene are adjusted to improve the refresh rate and the smoothness of the scene.

[0065] The acquisition unit 50 is used to acquire the target image according to the above-mentioned target exposure parameters.

[0066] Specifically, once all target exposure parameters are set and the preview confirms they are correct, the camera begins capturing the target image. At this point, the camera captures the image according to the set exposure parameters, ensuring that the image's brightness, contrast, and detail are presented optimally.

[0067] This embodiment provides an image acquisition device. An acquisition unit acquires the current exposure parameters of an intelligent monitoring device, which are the exposure parameters of the current image captured by the intelligent monitoring device at the current moment. A first determining unit determines whether the current exposure parameters are greater than a parameter threshold. If the current exposure parameters are greater than the parameter threshold, an adjusting unit adjusts the current brightness value of the target scene in the current image to obtain a target brightness value, and adjusts the current exposure parameters based on the target brightness value and a preset brightness value to obtain the target exposure parameters. If the current exposure parameters are less than or equal to the parameter threshold, a second determining unit determines the target exposure parameters based on the current brightness value, the preset brightness value, and the parameter threshold. The acquisition unit acquires the target image based on the target exposure parameters. By judging the relationship between the current exposure parameters and the parameter threshold, when the current exposure parameters exceed the parameter threshold, the brightness value of the scene in the current image is automatically adjusted to a reasonable range. Based on the adjusted scene brightness value, it is fused with the brightness values ​​of other locations to automatically adjust the exposure parameters of the entire image. Acquiring the target image based on the adjusted exposure parameters can improve image processing efficiency and shorten image correction time, further improving the image refresh rate and image smoothness, ensuring high-quality monitoring images can be acquired even in complex environments such as backlighting. This solves the problem of inaccurate recognition of objects and details in images by existing intelligent surveillance cameras in backlit environments.

[0068] In its specific implementation, the aforementioned adjustment unit includes a processing module, a first acquisition module, a second acquisition module, a calculation module, and a first adjustment module. The processing module segments the current image to obtain multiple segmented images, each segmented image including at least one of the aforementioned objects. The first acquisition module acquires the brightness values ​​of each segmented image to obtain multiple object brightness values. The second acquisition module acquires the brightness values ​​of the same object in different historical images to obtain multiple historical brightness values, where the historical images are those captured by the intelligent monitoring device before the current moment. The calculation module calculates the average of the multiple historical brightness values ​​to obtain a brightness threshold. The first adjustment module adjusts the current brightness value based on the brightness threshold and the current brightness value to obtain the target brightness value. This device can further improve image processing efficiency, shorten image correction time, and improve the accuracy and efficiency of automatic adjustment.

[0069] Specifically, during the shooting process, the camera continuously divides the image into a grid, determining the brightness values ​​and exposure parameters of different grids. When encountering backlighting, the changes in brightness values ​​and exposure parameters across different grids reveal the exposure and range within the image, as well as the impact on the brightness of objects within each grid. This facilitates automatic adjustment of the brightness of objects within the grid and the overall exposure and range of the image in post-processing. Furthermore, the brightness and exposure of the next frame can be predicted and calculated in advance, allowing the camera to directly compare and adjust the captured image. The preset threshold for the brightness value of foreground objects is a certain range of values, representing the average brightness value of the same objects in previous frames. The brightness threshold for objects in the current frame is not static but changes with the overall environment of the current scene, without affecting the overall movement of the image or the accuracy of the recorded footage, thus improving correction efficiency and stability.

[0070] The adjustment module of this application includes a first determining submodule, an adjusting submodule, and a second determining submodule. The first determining submodule determines whether the absolute value of the difference between the current brightness value and the brightness threshold is greater than the absolute value threshold. The adjusting submodule adjusts the current brightness value towards the brightness threshold to obtain the target brightness value when the absolute value of the difference between the current brightness value and the brightness threshold is greater than the absolute value threshold. The second determining submodule determines the target exposure parameters based on the current brightness value, the prepared brightness value, and the parameter threshold when the absolute value of the difference between the current brightness value and the brightness threshold is less than or equal to the absolute value threshold. This device can further prevent the brightness of the scene from changing with the brightness adjustment of other scenes, thus affecting the shooting accuracy of the scene and improving the image correction effect.

[0071] Specifically, if the brightness value of an object in the current frame is greater than a preset threshold, the average brightness value of the objects in the current frame is compared with the brightness value of the same object in the current frame. The difference in brightness is then compared to determine whether the difference in brightness is greater than the absolute threshold. In other words, when backlighting causes the brightness of the objects in the current frame to be much greater than the average value calculated from the previous frame, the difference in brightness between the two values ​​is compared to determine whether the brightness of the object in the current frame needs to be adjusted.

[0072] The aforementioned adjustment submodule is used to reduce the brightness value of the exposure point in the current image based on the aforementioned target brightness value, thereby obtaining the exposure point brightness value.

[0073] Specifically, since the exposure point is the part of the image with the highest exposure parameters, the brightness value of the exposure point should be reduced.

[0074] In some embodiments, the adjustment unit includes a first fusion module, a first repetition module, and a second adjustment module. The first fusion module performs a first fusion step, fusing the target brightness value and the prepared brightness value to obtain a fused brightness value for the target scene. The fusion process includes one of weighted averaging, histogram equalization, and local contrast enhancement. The segmented image corresponding to the target scene is a target segmented image, and the prepared brightness value is the brightness value of the segmented images adjacent to the target segmented image. The first repetition module performs a first repetition step, repeating the first fusion step at least once until all the segmented images in the current image are traversed, resulting in multiple fused brightness values. The second adjustment module performs a first adjustment step, adjusting the current exposure parameters based on the average of the multiple fused brightness values ​​to obtain the target exposure parameters. This device can further shorten the exposure time and improve the monitoring and recording effect.

[0075] Specifically, after the brightness of the foreground object is automatically adjusted, the required image brightness is directly obtained through image analysis and fusion processing. Combined with the adjusted and predicted exposure brightness, the current exposure parameters are adjusted as a whole to improve the accuracy and precision of the adjustment, enhance the harmony of the image, and the target image is captured by the camera lens for real-time monitoring.

[0076] The second determining unit includes a second fusion module, a second repetition module, and a third adjustment module. The second fusion module performs a second fusion step, fusing the target brightness value, the preliminary brightness value, and the parameter threshold to obtain a fused brightness value for the target scene. The fusion process includes one of weighted averaging, histogram equalization, and local contrast enhancement. The segmented frame corresponding to the target scene is the target segmented frame, and the preliminary brightness value is the brightness value of the segmented frames adjacent to the target segmented frame. The second repetition module performs a second repetition step, repeating the second fusion step at least once until all the segmented frames in the current frame are traversed, obtaining multiple fused brightness values. The third adjustment module performs a second adjustment step, adjusting the current exposure parameters based on the average of the multiple fused brightness values ​​to obtain the target exposure parameters. This device can further quickly determine the target exposure parameters.

[0077] Specifically, weighted averaging is a method of fusing multiple images into a single image, where the pixel values ​​of each image are weighted and calculated according to their respective weights. These weights can be determined based on image quality, exposure levels, or importance in a specific scene. In surveillance camera applications, this means that the brightness values ​​of the current frame and other frames can be fused according to a certain weighted ratio to obtain a more stable and higher-quality final brightness value. For example, if the exposure parameters of the current frame are too high, the final brightness can be balanced by increasing the weight of historical frames to avoid overexposure. Histogram equalization is used to improve image contrast, especially when there are large variations in brightness in local areas of the image. By adjusting the distribution of image pixels to make the image histogram more uniform, the global contrast of the image is increased, making the image appear clearer. In smart surveillance cameras, histogram equalization can be used for overall or local areas to optimize image exposure and contrast, especially under low-light or high-light conditions. For example, if most of the monitored area is in shadow, histogram equalization can help increase the brightness of shadow areas, making details more visible. Local contrast enhancement improves the visual effect of an image by enhancing the contrast of local areas. This is typically achieved by applying local filters or Gaussian blur, then calculating and adjusting the brightness differences in local areas. In smart surveillance cameras, local contrast enhancement can be used to highlight details of key objects in the image, ensuring clear display of important information even in cases of overall underexposure or overexposure. For example, if a foreground object is much brighter than the background, local contrast enhancement can help adjust the brightness of the foreground while preserving background details, resulting in a more balanced and clear image.

[0078] The aforementioned acquisition unit includes a determination module, used to determine the acquisition parameters of the intelligent monitoring device based on the aforementioned target exposure parameters, in order to acquire the aforementioned target image. The acquisition parameters include at least one of shutter speed, aperture size, and ISO sensitivity. This device can further acquire the aforementioned target image more quickly.

[0079] Specifically, the steps for acquiring the target image are as follows: First, the system has calculated or adjusted the most suitable target exposure parameters for the current scene using algorithms. This includes shutter speed, aperture size, ISO sensitivity, and possible brightness and contrast adjustments. Next, camera parameters are set: the target exposure parameters are applied to the camera's hardware settings, including adjusting shutter speed, aperture size, and ISO sensitivity, to ensure the camera shoots according to the calculated parameters. Then, previewing and fine-tuning: before actual shooting, the camera may perform a preview shot to check if the target exposure parameters truly achieve the expected effect. If the preview image still has exposure issues, the system will fine-tune the parameters based on the actual situation until a satisfactory exposure effect is achieved.

[0080] The aforementioned image acquisition device includes a processor and a memory. The acquisition unit, first determining unit, adjustment unit, second determining unit, and adjustment unit are all stored as program units in the memory. The processor executes these program units stored in the memory to achieve their respective functions. All of the above modules reside in the same processor; alternatively, the modules may be located in different processors in any combination.

[0081] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and images are acquired by adjusting the kernel parameters.

[0082] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0083] This invention provides a computer-readable storage medium including a stored program, wherein the program, when running, controls the device containing the computer-readable storage medium to execute the image acquisition method.

[0084] This invention provides a processor for running a program, wherein the program executes the image acquisition method described above.

[0085] This invention provides a device including a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements at least the steps of the image acquisition method described above. The device described herein can be a server, PC, PAD, mobile phone, etc.

[0086] This application also provides a computer program product that, when executed on a data processing device, is adapted to execute an initialization program having at least the following image acquisition method steps described above.

[0087] It is obvious to those skilled in the art that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. They can be implemented using computer-executable program code, and thus can be stored in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0088] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0089] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0090] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0091] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0092] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0093] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0094] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0095] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0096] As can be seen from the above description, the embodiments of this application achieve the following technical effects:

[0097] 1) The image acquisition method of this application first obtains the current exposure parameters of the intelligent monitoring device, which are the exposure parameters of the current image acquired by the intelligent monitoring device at the current moment; determines whether the current exposure parameters are greater than a parameter threshold; then, if the current exposure parameters are greater than the parameter threshold, adjusts the current brightness value of the target scene in the current image to obtain the target brightness value, and adjusts the current exposure parameters according to the target brightness value and the prepared brightness value to obtain the target exposure parameters; then, if the current exposure parameters are less than or equal to the parameter threshold, determines the target exposure parameters according to the current brightness value, the prepared brightness value, and the parameter threshold; finally, acquires the target image according to the target exposure parameters. By judging the relationship between the current exposure parameters and the parameter threshold, when the current exposure parameters exceed the parameter threshold, the brightness value of the scene in the current image is automatically adjusted to a reasonable range. Based on the adjusted scene brightness value, it is fused with the brightness values ​​of other positions to automatically adjust the exposure parameters of the entire image. Acquiring the target image according to the adjusted exposure parameters can improve image processing efficiency and shorten image correction time, further improving the image refresh rate and image smoothness, ensuring that high-quality monitoring images can be acquired even in complex environments such as backlighting. This solves the problem of inaccurate recognition of objects and details in images by existing intelligent surveillance cameras in backlit environments.

[0098] 2) The image acquisition device of this application includes an acquisition unit that acquires the current exposure parameters of the intelligent monitoring device, which are the exposure parameters of the current image acquired by the intelligent monitoring device at the current moment; a first determining unit that determines whether the current exposure parameters are greater than a parameter threshold; an adjustment unit that, if the current exposure parameters are greater than the parameter threshold, adjusts the current brightness value of the target scene in the current image to obtain a target brightness value, and adjusts the current exposure parameters according to the target brightness value and a preset brightness value to obtain the target exposure parameters; a second determining unit that, if the current exposure parameters are less than or equal to the parameter threshold, determines the target exposure parameters according to the current brightness value, the preset brightness value, and the parameter threshold; and an acquisition unit that acquires the target image according to the target exposure parameters. By judging the relationship between the current exposure parameters and the parameter threshold, when the current exposure parameters exceed the parameter threshold, the brightness value of the scene in the current image is automatically adjusted to a reasonable range. Based on the adjusted scene brightness value, it is fused with the brightness values ​​of other locations to automatically adjust the exposure parameters of the entire image. Acquiring the target image according to the adjusted exposure parameters can improve image processing efficiency and shorten image correction time, further improving the image refresh rate and image smoothness, ensuring high-quality monitoring images can be acquired even in complex environments such as backlighting. This solves the problem of inaccurate recognition of objects and details in images by existing intelligent surveillance cameras in backlit environments.

[0099] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. An image acquisition method, characterized in that, The method is applied to intelligent monitoring equipment, and the method includes: Obtain the current exposure parameters of the intelligent monitoring device, wherein the current exposure parameters are the exposure parameters of the current image captured by the intelligent monitoring device at the current moment; Determine whether the current exposure parameter is greater than the parameter threshold; When the current exposure parameter is greater than the parameter threshold and the absolute value of the difference between the current brightness value of the target object in the current image and the brightness threshold is greater than the absolute value threshold, the current brightness value of the target object in the current image is adjusted to obtain the target brightness value. Then, based on the target brightness value and the prepared brightness value, the current exposure parameter is adjusted to obtain the target exposure parameter. The current image includes multiple objects, and the target object is any one of the multiple objects. The prepared brightness value is the brightness value of the other objects in the multiple objects besides the target object. The current exposure parameter being greater than the parameter threshold is used to indicate that the current image is in a backlit state. Adjusting the current brightness value of the target object in the current frame to obtain the target brightness value includes: The current frame is segmented to obtain multiple segmented frames, wherein each segmented frame includes at least one of the objects; The brightness values ​​of each segmented image are obtained to obtain multiple scene brightness values; The brightness values ​​of the same scene are obtained in different historical images to obtain multiple historical brightness values, wherein the historical images are images captured by the intelligent monitoring device before the current moment; Calculate the average of the multiple historical brightness values ​​to obtain the brightness threshold; Based on the brightness threshold and the current brightness value, adjust the current brightness value to obtain the target brightness value; Under the following conditions, the target exposure parameter is determined based on the current brightness value, the prepared brightness value, and the parameter threshold. The current exposure parameter being less than or equal to the parameter threshold is used to characterize that the current image is in a non-backlight state. The condition is that the current exposure parameter is less than or equal to the parameter threshold, or that the current exposure parameter is greater than the parameter threshold and the absolute value of the difference between the current brightness value and the brightness threshold is less than or equal to the absolute value threshold. Acquire a target image based on the target exposure parameters.

2. The method according to claim 1, characterized in that, If the absolute value of the difference between the current brightness value and the brightness threshold is greater than the absolute value threshold, the current brightness value is adjusted according to the brightness threshold and the current brightness value to obtain the target brightness value, including: The current brightness value is adjusted to approach the brightness threshold to obtain the target brightness value.

3. The method according to claim 2, characterized in that, If the absolute value of the difference between the current brightness value and the brightness threshold is greater than the absolute value threshold, the method further includes adjusting the current brightness value to approach the brightness threshold to obtain the target brightness value: Based on the target brightness value, the brightness value of the exposure point in the current image is reduced to obtain the exposure point brightness value.

4. The method according to claim 1, characterized in that, Based on the target brightness value and the preset brightness value, the current exposure parameters are adjusted to obtain the target exposure parameters. The segmented image corresponding to the target scene is the target segmented image, including: The first fusion step involves fusing the target brightness value and the brightness values ​​of adjacent segmented images to obtain the fused brightness value of the target scene. The fusion process includes one of weighted averaging, histogram equalization, and local contrast enhancement. The first repeating step is to repeat the first fusion step at least once until all the segmented frames in the current frame are traversed to obtain multiple fused brightness values. The first adjustment step involves adjusting the current exposure parameters based on the average of the multiple fused brightness values ​​to obtain the target exposure parameters.

5. The method according to claim 1, characterized in that, When the current exposure parameter is less than or equal to the parameter threshold, the target exposure parameter is determined based on the current brightness value, the pre-set brightness value, and the parameter threshold. The segmented image corresponding to the target scene is the target segmented image, including: The second fusion step involves fusing the target brightness value, the brightness values ​​of adjacent segmented images of the target segmented image, and the parameter threshold to obtain the fused brightness value of the target scene. The fusion process includes one of weighted averaging, histogram equalization, and local contrast enhancement. The second repetition step involves repeating the second fusion step at least once until all the segmented frames in the current frame are traversed to obtain multiple fused brightness values. The second adjustment step involves adjusting the current exposure parameters based on the average of the multiple fused brightness values ​​to obtain the target exposure parameters.

6. The method according to claim 1, characterized in that, Based on the target exposure parameters, acquire the target image, including: Based on the target exposure parameters, the acquisition parameters of the intelligent monitoring device are determined to acquire the target image. The acquisition parameters include at least one of shutter speed, aperture size, and ISO sensitivity.

7. An image acquisition apparatus for performing the method according to any one of claims 1 to 6, characterized in that, The device is used in intelligent monitoring equipment, and the device includes: The acquisition unit is used to acquire the current exposure parameters of the intelligent monitoring device, wherein the current exposure parameters are the exposure parameters of the current image captured by the intelligent monitoring device at the current moment; The first determining unit is used to determine whether the current exposure parameter is greater than a parameter threshold. An adjustment unit is configured to adjust the current brightness value of a target object in the current frame when the current exposure parameter is greater than the parameter threshold, to obtain a target brightness value, and adjust the current exposure parameter according to the target brightness value and a preset brightness value to obtain a target exposure parameter. The current frame includes multiple objects, the target object is any one of the multiple objects, and the preset brightness value is the brightness value of the other objects in the multiple objects excluding the target object. The second determining unit is configured to determine the target exposure parameter based on the current brightness value, the prepared brightness value, and the parameter threshold when the current exposure parameter is less than or equal to the parameter threshold. The acquisition unit is used to acquire a target image based on the target exposure parameters.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device on which the computer-readable storage medium is located to perform the method according to any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method described in any one of claims 1 to 6.

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