Display method and device of three-dimensional LED screen, computer equipment and storage medium

Abstract

The invention relates to a three-dimensional LED screen display method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining a to-be-displayed image, segmenting the to-be-displayed image, and obtaining each segmented image of the to-be-displayed image; identifying each segmented image through a trained image processing model to obtain a screen adjustment parameter corresponding to each segmented image; determining the priority of each segmented image; and displaying the to-be-displayed image according to the priority and the screen adjustment parameter corresponding to each segmented image. The method and the device have the effect of improving the fluency of the LED screen during projection and playing.

Description

Technical Field

[0001] The present invention relates to the technical field of LED screens, and in particular to a display method, device, computer equipment and storage medium for a three-dimensional LED screen. Background Art

[0002] At present, LED display screens, as an important tool for modern information display, have been widely used in various occasions. However, traditional LED display screens are mostly flat displays and cannot provide three-dimensional visual effects, which limits the viewing experience. In some scenes, multiple display screens are required to form a three-dimensional display screen so that more viewers can watch. Multiple display screens need to be played synchronously to make smooth transitions at the joints to achieve better broadcasting effects. When the current three-dimensional LED display screen is used, the display screen is slow to recognize images with a large amount of complex color data, which will cause the image to flicker during imaging and fail to smoothly display the required image.

[0003] The above-mentioned prior art solutions have the following defects: the display screen is slow to recognize images with a large amount of complex color data, which will cause the image to flicker during imaging and fail to smoothly display the required image, so there is room for improvement. Summary of the invention

[0004] In order to improve the fluency of LED screen broadcasting, the present application provides a display method, device, computer equipment and storage medium for a three-dimensional LED screen.

[0005] The above-mentioned invention objective of the present application is achieved through the following technical solutions: A display method for a three-dimensional LED screen, the display method for the three-dimensional LED screen comprising: Acquire an image to be displayed, and segment the image to be displayed to obtain segmented images of the image to be displayed; Recognize each segmented image by using a trained image processing model to obtain screen adjustment parameters corresponding to each segmented image; Determining the priorities of the respective segmented images; The image to be displayed is displayed according to the priorities and the screen adjustment parameters corresponding to the respective segmented images.

[0006] By adopting the above technical solution, when the three-dimensional LED screen is working, the image to be displayed can be segmented and processed, and identification and detection can be performed separately. When the image to be displayed is transmitted into the three-dimensional LED screen display system, the image data to be displayed is obtained, and the image to be displayed is segmented into multiple parts or areas to obtain each segmented image of the image to be displayed, so as to help quickly identify the image to be displayed, improve the smoothness of the LED screen display, and better understand and process the data of the image to be displayed; when identifying the image to be displayed, each segmented image is input into the trained image processing model for identification to obtain the label or category information corresponding to each segmented image. According to these identification results, the screen parameters that need to be adjusted for each segmented image, such as brightness, contrast, color, etc., can be determined so that the image can be displayed more clearly or more in line with user needs. It helps to improve the accuracy and synchronization effect of image recognition; when processing a large number of segmented images, the grayscale value and color value of each segmented image can be analyzed to calculate its proportion in the entire image to be displayed, so as to determine the priority of each segmented image, which can make the image processing process more efficient and more targeted, ensure that key images can be processed and displayed in time, and improve the performance of the system and user experience; according to the priority of each segmented image determined before, they are sorted, and it is determined that the images with high priority should be processed first and cached in the system, and then, according to the screen adjustment parameters corresponding to each segmented image, such as brightness, contrast, color, etc., the display effect of the screen is adjusted. Finally, after all the segmented images of the image to be displayed are cached, the adjusted segmented images are displayed on the screen at the same time to present the overall image to be displayed.

[0007] In a preferred example, the present application may be further configured as follows: before the trained image processing model is used to identify the respective segmented images and obtain the screen adjustment parameters corresponding to the respective segmented images, obtaining the trained image processing model specifically includes: Obtaining a standard display image set and corresponding standard screen adjustment parameters; Clustering the standard display image set by using a k-means clustering algorithm to obtain a standard image feature value set; The initial model is trained according to the standard image feature value set and the corresponding standard screen adjustment parameters to obtain the trained image processing model.

[0008] By adopting the above technical solution, when using the image processing model to identify the image to be displayed, a set of standard display images is obtained to test and evaluate the performance and expression of the display device. The standard images include changes in different scenes, colors, contrast, etc., so as to fully optimize the processing capability of the image processing model. For each standard display image, there will be corresponding standard screen adjustment parameters. These parameters include brightness, contrast, color calibration, etc., which are used to ensure the consistency and accuracy of the image display on different LED screens. By obtaining the standard display image set and the corresponding standard screen adjustment parameters, the image display effect and consistency are improved; through the k-means clustering algorithm, the images in the standard display image set are grouped according to their characteristic attributes to form several clusters. The algorithm will divide each image into It is assigned to the cluster nearest to it until the specified number of clusters is reached. Finally, for each cluster, a set of numerical values ​​representing the characteristics of the cluster can be obtained, that is, a set of standard image feature values, which is helpful to divide and classify the images in the standard image set according to their common features, thereby improving the accuracy of image processing. The numerical value sets representing image features obtained by clustering the standard display image set are used as training data to train an image processing model. During the training process, the model will learn how to predict the corresponding screen adjustment parameters based on the image feature values, thereby realizing the function of automatically adjusting the screen effect. The trained image processing model can predict and adjust the screen parameters according to the characteristics of the input image, so as to improve the image display effect and user experience.

[0009] In a preferred example, the present application may be further configured as follows: the recognition of each segmented image by a trained image processing model to obtain the screen adjustment parameters corresponding to each segmented image includes: Based on the k-means clustering algorithm, clustering the segmented images to obtain segmented image feature values ​​corresponding to the segmented images, wherein the segmented image feature values ​​include segmented image grayscale values ​​and color values; The respective segmented image feature values ​​are input into the trained image processing model to obtain respective screen adjustment parameters corresponding to the respective segmented image feature values.

[0010] By adopting the above technical solution, based on the k-means clustering algorithm, each segmented image is clustered according to its grayscale value and color value. By extracting the grayscale value and color value of each segmented image and applying the k-means clustering algorithm, the segmented images can be divided into different groups, each group has similar features, and these segmented image feature values ​​can be used for subsequent image processing, classification or analysis tasks to improve the accuracy and efficiency of image processing; the trained image processing model can be used to process and analyze the input image feature values, and then the feature values ​​of each segmented image are input into the trained image processing model. The model will process and infer according to the input feature values ​​to predict the screen adjustment parameters corresponding to the segmented image. These screen adjustment parameters include brightness and color data, which are used to adjust the display screen effect to optimize the image display. The screen parameters are adjusted according to the characteristics of each segmented image to improve the display effect and user experience.

[0011] In a preferred example, the present application may be further configured as follows: determining the priority of each image to be displayed includes: Through the weight formula: Calculation is performed to obtain the weight scores of the characteristic values ​​of each segmented image in the image to be displayed, wherein α and β are weight coefficients, C is the weight score, I is the gray value of the segmented image, I0 is the gray value of the image to be displayed, F is the color value of the segmented image, F1 is the reference color value, and F0 is the color value of the image to be displayed; the weight scores are sorted from large to small, and the sorting result is determined as the priority of each segmented image.

[0012] By adopting the above technical solution, a weight score is calculated by a weight calculation formula, and the weight score can be used to determine the contribution of each segmented image to the final displayed image, so as to make appropriate adjustments and optimizations during the image display process. By performing weight calculation on the characteristic values ​​of each segmented image, the influence of different features on the overall image can be better understood, and the image processing and display fluency can be improved; according to the sorting result, the priority of each segmented image can be determined, that is, the segmented image with a high weight score is placed in front in the sorting order, and the image is processed first. Therefore, by determining the priority of the segmented image, each segmented image can be processed in an orderly manner during the synthesis or display process of the image, ensuring that important image parts can be displayed or processed first, thereby improving the efficiency and quality of image processing and display.

[0013] In a preferred example, the present application may be further configured as follows: displaying the image to be displayed according to the priority and the screen adjustment parameter corresponding to each segmented image includes: Allocating the corresponding segmented images to different threads of the system for caching according to the priorities, and generating a cached image set when the different threads of the system have processed the corresponding segmented images; The image to be displayed is displayed according to the cached image set.

[0014] By adopting the above technical solution, each segmented image is assigned to a different thread in the system for processing according to the priority order of the segmented images determined previously. By processing the segmented images in parallel in multiple threads, the processing efficiency and system performance can be improved. Each thread is responsible for processing one or more segmented images, and processes them one by one according to the determined priority order. After each thread processes the corresponding segmented image, the system will generate a cached image set, which contains the processing results of all segmented images. In this way, the advantages of parallel processing can be utilized to efficiently process a large amount of image data, speed up the processing speed and improve the processing capacity of the system. During the display process, the system can use the cached image set as input to display the image to be displayed according to user needs or system settings. Such a display process can cover the processing results of multiple segmented images to ensure that all image parts can be displayed. In this way, the image to be displayed can be displayed in a more efficient and accurate manner to show the final result of image processing.

[0015] In a preferred example, the present application may be further configured as follows: the display method of the three-dimensional LED screen further includes: obtaining a display time of the image to be displayed, and triggering a switching display instruction when the display time reaches a preset image display time; According to the switching display instruction, the image to be displayed at the next moment is transmitted to the display screen for display.

[0016] By adopting the above technical solution, the system will record the display time of each image to be displayed, that is, the length of time an image is displayed on the display screen, and then the system will set a preset image display time, that is, when the time a certain image is displayed reaches this preset time, it needs to be switched. When the display time of the image to be displayed reaches the preset image display time, the system will trigger the switch display instruction, that is, switch to the next image, which helps the system to automatically control the display time of the image to be displayed, ensure that the display time of the image on the screen meets the preset requirements, and realize the control and management of image display; once the system detects the instruction to switch the display image, that is, the switch display instruction is triggered, the system will immediately prepare the image to be displayed at the next moment, and then transmit the image to the display screen for display. In this process, the system will send the image data of the image to be displayed to the display screen device, and the display screen will decode and display the image. By timely transmitting and displaying the image to be displayed, it can be ensured that the next image can be displayed on the screen on time, and a smooth transition of image switching and display can be achieved.

[0017] The second object of the invention is achieved by the following technical solutions: A display device of a three-dimensional LED screen, the display device of the three-dimensional LED screen comprising: An image segmentation module is used to obtain an image to be displayed, segment the image to be displayed, and obtain each segmented image of the image to be displayed; A parameter acquisition module, used to identify each segmented image through a trained image processing model to obtain screen adjustment parameters corresponding to each segmented image; A priority determination module, used for determining the priority of each segmented image; A display module is used to display the image to be displayed according to the priority and the screen adjustment parameter corresponding to each segmented image.

[0018] By adopting the above technical solution, when the three-dimensional LED screen is working, the image to be displayed can be segmented and processed, and identification and detection can be performed separately. When the image to be displayed is transmitted into the three-dimensional LED screen display system, the image data to be displayed is obtained, and the image to be displayed is segmented into multiple parts or areas to obtain each segmented image of the image to be displayed, so as to help quickly identify the image to be displayed, improve the smoothness of the LED screen display, and better understand and process the data of the image to be displayed; when identifying the image to be displayed, each segmented image is input into the trained image processing model for identification to obtain the label or category information corresponding to each segmented image. According to these identification results, the screen parameters that need to be adjusted for each segmented image, such as brightness, contrast, color, etc., can be determined so that the image can be displayed more clearly or more in line with user needs. It helps to improve the accuracy and synchronization effect of image recognition; when processing a large number of segmented images, the grayscale value and color value of each segmented image can be analyzed to calculate its proportion in the entire image to be displayed, so as to determine the priority of each segmented image, which can make the image processing process more efficient and more targeted, ensure that key images can be processed and displayed in time, and improve the performance of the system and user experience; according to the priority of each segmented image determined before, they are sorted, and it is determined that the images with high priority should be processed first and cached in the system, and then, according to the screen adjustment parameters corresponding to each segmented image, such as brightness, contrast, color, etc., the display effect of the screen is adjusted. Finally, after all the segmented images of the image to be displayed are cached, the adjusted segmented images are displayed on the screen at the same time to present the overall image to be displayed.

[0019] The third objective of the present application is achieved through the following technical solutions: A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the display method of the three-dimensional LED screen are implemented.

[0020] The fourth objective of the present application is achieved through the following technical solutions: A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned three-dimensional LED screen display method.

[0021] In summary, the present application includes at least one of the following beneficial technical effects: 1. When the three-dimensional LED screen is working, the image to be displayed can be segmented and processed, and identification and detection can be performed separately. When the image to be displayed is transmitted into the three-dimensional LED screen display system, the image data to be displayed is obtained, and the image to be displayed is segmented into multiple parts or areas to obtain each segmented image of the image to be displayed, so as to help quickly identify the image to be displayed, improve the smoothness of the LED screen display, and better understand and process the data of the image to be displayed; when identifying the image to be displayed, each segmented image is input into the trained image processing model for identification to obtain the label or category information corresponding to each segmented image. According to these identification results, the screen parameters that need to be adjusted for each segmented image, such as brightness, contrast, color, etc., can be determined so that the image can be displayed more clearly or more in line with user needs, which helps to improve High image recognition accuracy and synchronization effect; when processing a large number of segmented images, the grayscale value and color value of each segmented image can be analyzed to calculate its proportion in the entire image to be displayed, so as to determine the priority of each segmented image, which can make the image processing process more efficient and more targeted, ensure that key images can be processed and displayed in time, and improve the performance of the system and user experience; according to the priority of each segmented image determined before, they are sorted, and it is determined that the image with high priority should be processed first and cached in the system, and then, according to the screen adjustment parameters corresponding to each segmented image, such as brightness, contrast, color, etc., the display effect of the screen is adjusted. Finally, after all the segmented images of the image to be displayed are cached, the adjusted segmented images are displayed on the screen at the same time to present the overall image to be displayed; 2. When using the image processing model to identify the image to be displayed, a set of standard display images is obtained to test and evaluate the performance and performance of the display device. The standard images include changes in different scenes, colors, contrast, etc., so as to fully optimize the processing capabilities of the image processing model. For each standard display image, there will be corresponding standard screen adjustment parameters. These parameters include brightness, contrast, color calibration, etc., which are used to ensure the consistency and accuracy of the image display on different LED screens. By obtaining a set of standard display images and corresponding standard screen adjustment parameters, the image display effect and consistency are improved; through the k-means clustering algorithm, the images in the standard display image set are grouped according to their characteristic attributes to form several clusters. The algorithm will assign each image to the cluster closest to it. The clusters are clustered in the nearest clusters until the specified number of clusters is reached. Finally, for each cluster, a set of numerical values ​​representing the characteristics of the cluster can be obtained, namely, a set of standard image feature values, which is helpful to divide and classify the images in the standard image set according to their common characteristics, thereby improving the accuracy of image processing. The numerical value sets representing the image characteristics obtained by clustering the standard display image set are used as training data to train an image processing model. During the training process, the model will learn how to predict the corresponding screen adjustment parameters according to the image feature values, thereby realizing the function of automatically adjusting the screen effect. The trained image processing model can predict and adjust the screen parameters according to the characteristics of the input image, thereby improving the image display effect and user experience. 3. Based on the k-means clustering algorithm, each segmented image is clustered according to its grayscale value and color value. By extracting the grayscale value and color value of each segmented image and applying the k-means clustering algorithm, the segmented images can be divided into different groups, each group having similar features. These segmented image feature values ​​can be used for subsequent image processing, classification or analysis tasks to improve the accuracy and efficiency of image processing; the trained image processing model can be used to process and analyze the input image feature values, and then the feature values ​​of each segmented image are input into the trained image processing model. The model will process and infer based on the input feature values ​​to predict the screen adjustment parameters corresponding to the segmented image. These screen adjustment parameters include brightness and color data, which are used to adjust the display screen effect to optimize the image display. The screen parameters are adjusted according to the characteristics of each segmented image to improve the display effect and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a flow chart of a display method of a three-dimensional LED screen in one embodiment of the present application; Figure 2 This is another implementation flow chart of step S20 in the display method of a three-dimensional LED screen in one embodiment of the present application; Figure 3 This is another implementation flow chart of step S20 in the display method of a three-dimensional LED screen in one embodiment of the present application; Figure 4 It is a flowchart for implementing step S30 in the display method of a three-dimensional LED screen in one embodiment of the present application; Figure 5 It is a flowchart for implementing step S40 in the display method of a three-dimensional LED screen in one embodiment of the present application; Figure 6 It is another implementation flow chart of the display method of the three-dimensional LED screen in one embodiment of the present application; Figure 7 This is a principle block diagram of a display device of a three-dimensional LED screen in one embodiment of the present application; Figure 8 It is a schematic diagram of a device in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The present application is further described in detail below in conjunction with the accompanying drawings.

[0024] In one embodiment, if Figure 1 As shown, the present application discloses a display method of a three-dimensional LED screen, which specifically includes the following steps: S10: Acquire the image to be displayed, segment the image to be displayed, and obtain segmented images of the image to be displayed.

[0025] In this embodiment, the image to be displayed refers to the image to be displayed when the three-dimensional LED screen is working.

[0026] Specifically, when the three-dimensional LED screen is working, in order to better understand and process the data of the image to be displayed, the image to be displayed can be segmented and processed, and identified and detected separately. Therefore, when the image to be displayed is transmitted into the three-dimensional LED screen display system, the image data to be displayed is obtained, and the image to be displayed is segmented into multiple parts or areas to obtain each segmented image of the image to be displayed, so as to help quickly identify the image to be displayed and improve the smoothness of the LED screen display.

[0027] S20: Recognize each segmented image through the trained image processing model to obtain screen adjustment parameters corresponding to each segmented image.

[0028] In this embodiment, the trained image processing model refers to a trained model that can process images displayed on an LED screen. The screen adjustment parameters refer to data for adjusting the brightness, color, and refresh frequency of the LED screen.

[0029] Specifically, when identifying the image to be displayed, in order to improve the accuracy and synchronization effect of image recognition, each segmented image is input into the trained image processing model for recognition to obtain the label or category information corresponding to each segmented image. Based on these recognition results, the screen parameters that need to be adjusted for each segmented image can be determined, such as brightness, contrast, color, etc., so as to display the image more clearly or better meet user needs.

[0030] S30: Determine the priority of each segmented image.

[0031] Specifically, when processing a large number of segmented images, it is necessary to determine based on some specific factors that some important and complex images should be processed with a higher priority. For example, the priority can be determined by analyzing the importance of the image, the complexity of the content, user needs, etc. Therefore, the grayscale value and color value of each segmented image can be analyzed, and its proportion in the entire image to be displayed can be calculated to determine the priority of each segmented image. This can make the image processing process more efficient and more targeted, ensure that key images can be processed and displayed in a timely manner, and improve system performance and user experience.

[0032] S40: Displaying the image to be displayed according to the priority and screen adjustment parameters corresponding to each segmented image.

[0033] Specifically, the segmented images are sorted according to their priorities previously determined, and images with high priorities are processed first and cached in the system. Then, the screen display effect is adjusted according to the screen adjustment parameters corresponding to each segmented image, such as brightness, contrast, color, etc. Finally, after all the segmented images of the image to be displayed are cached, the adjusted segmented images are displayed on the screen at the same time to present the overall image to be displayed.

[0034] Through the above technical solution, when the three-dimensional LED screen is working, the image to be displayed can be segmented and processed, and identification and detection can be performed separately. When the image to be displayed is transmitted into the three-dimensional LED screen display system, the image data to be displayed is obtained, and the image to be displayed is segmented into multiple parts or areas to obtain each segmented image of the image to be displayed, so as to help quickly identify the image to be displayed, improve the smoothness of the LED screen display, and better understand and process the data of the image to be displayed; when identifying the image to be displayed, each segmented image is input into the trained image processing model for identification to obtain the label or category information corresponding to each segmented image. According to these identification results, the screen parameters that need to be adjusted for each segmented image, such as brightness, contrast, color, etc., can be determined so that the image can be displayed more clearly or more in line with user needs. It helps to improve the accuracy and synchronization effect of image recognition; when processing a large number of segmented images, the grayscale value and color value of each segmented image can be analyzed to calculate its proportion in the entire image to be displayed, so as to determine the priority of each segmented image, which can make the image processing process more efficient and more targeted, ensure that key images can be processed and displayed in time, and improve the performance of the system and user experience; according to the priority of each segmented image determined before, they are sorted, and it is determined that the images with high priority should be processed first and cached in the system, and then, according to the screen adjustment parameters corresponding to each segmented image, such as brightness, contrast, color, etc., the display effect of the screen is adjusted. Finally, after all the segmented images of the image to be displayed are cached, the adjusted segmented images are displayed on the screen at the same time to present the overall image to be displayed.

[0035] In one embodiment, if Figure 2 As shown, before step S20, that is, before each segmented image is identified by the trained image processing model to obtain the screen adjustment parameters corresponding to each segmented image, obtaining the trained image processing model specifically includes: S201: Acquire a standard display image set and corresponding standard screen adjustment parameters.

[0036] In this embodiment, the standard display image set refers to a set of images used for testing and evaluating images to be displayed.

[0037] Specifically, when using the image processing model to identify the image to be displayed, in order to quickly and accurately identify and analyze the image transmitted into the model, a set of standard display images is obtained to test and evaluate the performance and expression of the display device. The standard image includes different scenes, colors, contrast and other changes, so as to fully optimize the processing capabilities of the image processing model. For each standard display image, there will be corresponding standard screen adjustment parameters, including brightness, contrast, color calibration, etc., which are used to ensure the consistency and accuracy of the image display on different LED screens. By obtaining a set of standard display images and the corresponding standard screen adjustment parameters, the image display effect and consistency are improved.

[0038] S202: Clustering the standard display image set by using a k-means clustering algorithm to obtain a standard image feature value set.

[0039] In this embodiment, the standard image feature value set refers to a set of feature value data used to test and evaluate an image to be displayed.

[0040] Specifically, through the k-means clustering algorithm, the images in the standard display image set are grouped according to their characteristic attributes to form several clusters. The algorithm will assign each image to the cluster closest to it until the specified number of clusters is reached. Finally, for each cluster, a set of numerical values ​​representing the characteristics of the cluster can be obtained, that is, the standard image feature value set, which helps to divide and classify the images in the standard image set according to their common characteristics, thereby improving the accuracy of image processing.

[0041] S203: Training the initial model according to the standard image feature value set and the corresponding standard screen adjustment parameters to obtain a trained image processing model.

[0042] Specifically, by clustering the standard display image set to obtain a set of numerical values ​​representing image features, these feature value sets and their corresponding standard screen adjustment parameters are used as training data to train an image processing model. During the training process, the model will learn how to predict the corresponding screen adjustment parameters based on the image feature values, thereby realizing the function of automatically adjusting the screen effect. The trained image processing model can predict and adjust the screen parameters according to the characteristics of the input image to improve the image display effect and user experience.

[0043] In one embodiment, if Figure 3 As shown, in step S20, each segmented image is identified by the trained image processing model to obtain the screen adjustment parameters corresponding to each segmented image, which specifically include: S21: Based on the k-means clustering algorithm, cluster each segmented image to obtain a segmented image feature value corresponding to each segmented image, wherein the segmented image feature value includes a segmented image grayscale value and a color value.

[0044] In this embodiment, the gray value refers to the brightness value of a pixel in an image, and the color value refers to the numerical representation of a color.

[0045] Specifically, based on the k-means clustering algorithm, each segmented image is clustered according to its grayscale value and color value. By extracting the grayscale value and color value of each segmented image and applying the k-means clustering algorithm, the segmented images can be divided into different groups, each group having similar features. These segmented image feature values ​​can be used for subsequent image processing, classification or analysis tasks to improve the accuracy and efficiency of image processing.

[0046] S22: Inputting each segmented image feature value into a trained image processing model to obtain each screen adjustment parameter corresponding to each segmented image feature value.

[0047] Specifically, the trained image processing model can be used to process and analyze the input image feature values. Then, the feature values ​​of each segmented image are input into the trained image processing model. The model will process and infer based on the input feature values ​​to predict the screen adjustment parameters corresponding to the segmented image. These screen adjustment parameters include brightness and color data, which are used to adjust the display screen effect to optimize the image display. The screen parameters are adjusted according to the characteristics of each segmented image to improve the display effect and user experience.

[0048] In one embodiment, if Figure 4 As shown, in step S30, the priority of each segmented image is determined, which specifically includes: S31: Through the weight formula: Calculation is performed to obtain the weight scores of each segmented image feature value in the image to be displayed, where α and β are weight coefficients, C is the weight score, I is the grayscale value of the segmented image, I0 is the grayscale value of the image to be displayed, F is the color value of the segmented image, F1 is the reference color value, and F0 is the color value of the image to be displayed.

[0049] Specifically, the weight score is calculated through the weight calculation formula, and the weight score can be used to determine the contribution of each segmented image to the final displayed image, so as to make appropriate adjustments and optimizations during the image display process. By performing weight calculation on the eigenvalues ​​of each segmented image, we can better understand the impact of different features on the overall image and improve the smoothness of image processing and display.

[0050] S32: sorting the weight scores from large to small, and determining the sorting result as the priority of each segmented image.

[0051] Specifically, according to the sorting results, the priority of each segmented image can be determined, that is, the segmented images with high weight scores are placed in front according to the sorting order, and the images are processed first. Therefore, by determining the priority of the segmented images, each segmented image can be processed in an orderly manner during the image synthesis or display process, ensuring that important image parts can be displayed or processed first, thereby improving the efficiency and quality of image processing and display.

[0052] In one embodiment, if Figure 5 As shown, in step S40, the image to be displayed is displayed according to the priority and screen adjustment parameters corresponding to each segmented image, specifically including: S41: Distributing the corresponding segmented images to different threads of the system for caching according to the priorities, and generating a cached image set when the different threads of the system have processed the corresponding segmented images.

[0053] Specifically, according to the priority order of the segmented images determined previously, each segmented image is assigned to a different thread in the system for processing. By processing the segmented images in parallel in multiple threads, the processing efficiency and system performance can be improved. Each thread is responsible for processing one or more segmented images, which are processed one by one in the determined priority order. After each thread processes the corresponding segmented image, the system generates a cached image set, which contains the processing results of all segmented images. In this way, the advantages of parallel processing can be utilized to efficiently process a large amount of image data, speed up the processing speed and improve the processing capacity of the system.

[0054] S42: Displaying the image to be displayed according to the cached image set.

[0055] Specifically, during the display process, the system can use the cached image set as input to display the image to be displayed according to user needs or system settings. Such a display process can cover the processing results of multiple segmented images to ensure that all image parts can be displayed. Through this method, the image to be displayed can be displayed in a more efficient and accurate manner to show the final result of image processing.

[0056] In one embodiment, if Figure 6 As shown, the display method of the three-dimensional LED screen also includes: S51: Obtaining the display time of the image to be displayed, and triggering a display switching instruction when the display time reaches a preset image display time.

[0057] In this embodiment, the switching display instruction refers to an instruction to switch the current image to be displayed to display the next image to be displayed.

[0058] Specifically, the system will record the display time of each image to be displayed, that is, the length of time an image is displayed on the display screen. Then, the system will set a preset image display time, that is, when an image is displayed for this preset time, it needs to be switched. When the display time of the image to be displayed reaches the preset image display time, the system will trigger a switch display instruction, that is, switch to the next image. This helps the system to automatically control the display time of the image to be displayed, ensure that the display time of the image on the screen meets the preset requirements, and realize the control and management of image display.

[0059] S52: According to the switching display instruction, the image to be displayed at the next moment is transmitted to the display screen for display.

[0060] Specifically, once the system detects an instruction to switch the displayed image, that is, the switching display instruction is triggered, the system will immediately prepare the image to be displayed at the next moment, and then transmit the image to the display screen for display. During this process, the system will send the image data of the image to be displayed to the display screen device, and the display screen will decode and display the image. By timely transmitting and displaying the image to be displayed, it can be ensured that the next image can be displayed on the screen on time, achieving a smooth transition of image switching and display.

[0061] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0062] In one embodiment, a display device of a three-dimensional LED screen is provided, and the display device of the three-dimensional LED screen corresponds one-to-one with the display method of the three-dimensional LED screen in the above embodiment. Figure 7 As shown, the display device of the three-dimensional LED screen includes an image segmentation module, a parameter acquisition module, a priority determination module and a display module. The detailed description of each functional module is as follows: The image segmentation module is used to obtain the image to be displayed, segment the image to be displayed, and obtain each segmented image of the image to be displayed; A parameter acquisition module is used to identify each segmented image through a trained image processing model to obtain screen adjustment parameters corresponding to each segmented image; A priority determination module, used for determining the priority of each segmented image; The display module is used to display the image to be displayed according to the priority and screen adjustment parameters corresponding to each segmented image.

[0063] Optionally, the display device of the three-dimensional LED screen further includes: A training set acquisition module is used to acquire a standard display image set and corresponding standard screen adjustment parameters; A standard eigenvalue acquisition module is used to cluster the standard display image set by using a k-means clustering algorithm to obtain a standard image eigenvalue set; The model training module is used to train the initial model according to the standard image feature value set and the corresponding standard screen adjustment parameters to obtain a trained image processing model.

[0064] A switching display module is used to obtain the display time of the image to be displayed, and trigger a switching display instruction when the display time reaches a preset image display time; The transmission module is used to transmit the image to be displayed at the next moment to the display screen for display according to the switching display instruction.

[0065] Optionally, the parameter acquisition module includes: A feature value submodule is obtained, which is used to cluster each segmented image based on a k-means clustering algorithm to obtain a segmented image feature value corresponding to each segmented image, wherein the segmented image feature value includes a segmented image grayscale value and a color value; The adjustment parameter obtaining submodule is used to input each segmented image feature value into the trained image processing model to obtain each screen adjustment parameter corresponding to each segmented image feature value.

[0066] Optionally, the prioritization module includes: The weight calculation submodule is used to calculate the weight value through the weight formula: Calculate and obtain the weight score of each segmented image feature value in the image to be displayed, where α and β are weight coefficients, C is the weight score, I is the gray value of the segmented image, I0 is the gray value of the image to be displayed, F is the color value of the segmented image, F1 is the reference color value, and F0 is the color value of the image to be displayed; The priority sorting submodule is used to sort the weight scores from large to small, and determine the sorting result as the priority of each segmented image.

[0067] Optionally, the display module includes: An image caching submodule, used for allocating corresponding segmented images to different threads of the system for caching according to the priority, and generating a cached image set when different threads of the system have processed the corresponding segmented images; The image display submodule is used to display the image to be displayed according to the cached image set.

[0068] For the specific definition of the display device of the three-dimensional LED screen, please refer to the definition of the display method of the three-dimensional LED screen in the above text, which will not be repeated here. Each module in the display device of the above-mentioned three-dimensional LED screen can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0069] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a display method of a three-dimensional LED screen is implemented.

[0070] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: Acquire an image to be displayed, segment the image to be displayed, and obtain segmented images of the image to be displayed; Recognize each segmented image through the trained image processing model to obtain the screen adjustment parameters corresponding to each segmented image; Determine the priority of each segmented image; The images to be displayed are displayed according to the priorities and screen adjustment parameters corresponding to the respective segmented images.

[0071] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented: Acquire an image to be displayed, segment the image to be displayed, and obtain segmented images of the image to be displayed; Recognize each segmented image through the trained image processing model to obtain the screen adjustment parameters corresponding to each segmented image; Determine the priority of each segmented image; The images to be displayed are displayed according to the priorities and screen adjustment parameters corresponding to the respective segmented images.

[0072] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0073] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0074] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A display method of a three-dimensional LED screen, characterized in that: The display method of the three-dimensional LED screen includes: Acquire an image to be displayed, and segment the image to be displayed to obtain segmented images of the image to be displayed; Recognize each segmented image by using a trained image processing model to obtain screen adjustment parameters corresponding to each segmented image; Determining the priorities of the respective segmented images; The image to be displayed is displayed according to the priority level and the screen adjustment parameter corresponding to each segmented image.

2. The display method of the three-dimensional LED screen according to claim 1, characterized in that: Before identifying each segmented image by using the trained image processing model to obtain the screen adjustment parameters corresponding to each segmented image, obtaining the trained image processing model specifically includes: Obtaining a standard display image set and corresponding standard screen adjustment parameters; Clustering the standard display image set by using a k-means clustering algorithm to obtain a standard image feature value set; The initial model is trained according to the standard image feature value set and the corresponding standard screen adjustment parameters to obtain the trained image processing model.

3. The display method of the three-dimensional LED screen according to claim 2, characterized in that: The identifying each segmented image by the trained image processing model to obtain the screen adjustment parameters corresponding to each segmented image comprises: clustering each segmented image based on the k-means clustering algorithm to obtain the segmented image feature values ​​corresponding to each segmented image, wherein the segmented image feature values ​​include the gray value and the color value of the segmented image; The respective segmented image feature values ​​are input into the trained image processing model to obtain respective screen adjustment parameters corresponding to the respective segmented image feature values.

4. The display method of the three-dimensional LED screen according to claim 3, characterized in that: Determining the priority of each of the images to be displayed includes: Through the weight formula: Calculate and obtain the weight scores of each segmented image feature value in the image to be displayed, wherein α and β are weight coefficients, C is the weight score, I is the gray value of the segmented image, I0 is the gray value of the image to be displayed, F is the color value of the segmented image, F1 is the reference color value, and F0 is the color value of the image to be displayed; The weight scores are sorted in order from large to small, and the sorting result is determined as the priority of each segmented image.

5. The display method of the three-dimensional LED screen according to claim 4, characterized in that: The displaying the image to be displayed according to the priority and the screen adjustment parameter corresponding to each segmented image comprises: Allocating the corresponding segmented images to different threads of the system for caching according to the priorities, and generating a cached image set when the different threads of the system have processed the corresponding segmented images; The image to be displayed is displayed according to the cached image set.

6. The display method of the three-dimensional LED screen according to claim 1, characterized in that: The display method of the three-dimensional LED screen also includes: Acquire the display time of the image to be displayed, and trigger a switching display instruction when the display time reaches a preset image display time; According to the switching display instruction, the image to be displayed at the next moment is transmitted to the display screen for display.

7. A three-dimensional LED screen display device, characterized in that: The display device of the three-dimensional LED screen includes: An image segmentation module is used to obtain an image to be displayed, segment the image to be displayed, and obtain each segmented image of the image to be displayed; A parameter acquisition module, used to identify each segmented image through a trained image processing model to obtain screen adjustment parameters corresponding to each segmented image; A priority determination module, used for determining the priority of each segmented image; A display module is used to display the image to be displayed according to the priority and the screen adjustment parameter corresponding to each segmented image.

8. The three-dimensional LED screen display device according to claim 7, characterized in that: The display device of the three-dimensional LED screen also includes: A training set acquisition module is used to acquire a standard display image set and corresponding standard screen adjustment parameters; A standard feature value acquisition module is used to cluster the standard display image set by using a k-means clustering algorithm to obtain a standard image feature value set; The model training module is used to train the initial model according to the standard image feature value set and the corresponding standard screen adjustment parameters to obtain the trained image processing model.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the display method of the three-dimensional LED screen as described in any one of claims 1 to 6 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the display method of the three-dimensional LED screen as described in any one of claims 1 to 6 are implemented.

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