Focusing discrimination method and focusing discrimination system

Abstract

A focus discrimination method is suitable for a focus discrimination system. The focus discrimination method includes: capturing a projection picture to generate a captured picture; dividing the captured picture into a plurality of image regions; calculating a plurality of sharpness values corresponding to the plurality of image regions according to image data of the plurality of image regions; and displaying the plurality of sharpness values on the corresponding plurality of image regions to generate a first focus discrimination picture. In addition, the present application also discloses a focus discrimination system using the above focus discrimination method. The focus discrimination method and the focus discrimination system using the same can improve the efficiency of remote maintenance.

Description

Technical Field

[0001] This invention relates to a focus identification method, and more particularly to a focus identification method for the sharpness of a projected image. Background Technology

[0002] Traditional projectors use an image capture device to capture the projected image and adjust the position of the focusing lens based on the sharpness of the captured image. Generally, prior art calculates the correspondence between the focusing lens position and the sharpness of the entire captured image, thus determining the optimal focusing lens position for the best sharpness. However, because the projected image exhibits varying sharpness in different areas due to lens uniformity, the optimal sharpness calculated by prior art may not necessarily reflect the best projected image quality, leading to inaccurate assessment of focus quality. Furthermore, remote maintenance personnel cannot easily assess the projector's focus quality based on the captured image obtained by the image capture device.

[0003] The "Background Art" paragraph is only used to help understand the content of this invention. Therefore, the content disclosed in the "Background Art" paragraph may include some prior art that is not known to those skilled in the art. The content disclosed in the "Background Art" paragraph does not mean that the content or the problem to be solved by one or more embodiments of this invention was known or recognized by those skilled in the art before this application was filed. Summary of the Invention

[0004] This invention provides a focus identification method and a focus identification system that can display corresponding sharpness values ​​on each image area of ​​the captured image, so that remote maintenance personnel can easily identify the focus quality.

[0005] Other objects and advantages of the present invention can be further understood from the technical features disclosed herein. To achieve one or more of the above objects, or other objects, an embodiment of the present invention provides a focus identification method applicable to a focus identification system. The focus identification method includes: capturing a projected image to generate an image frame; dividing the image frame into multiple image regions; calculating multiple sharpness values ​​corresponding to the multiple image regions based on image data of the multiple image regions; and displaying the multiple sharpness values ​​on the corresponding multiple image regions to generate a first focus identification image.

[0006] Another embodiment of the present invention provides a focus identification system, which includes a projection device, an image capturing device, a projection target, and a processor. The projection device is configured to project a projection image onto the projection target. The image capturing device is coupled to the projection device and configured to capture the projection image onto the projection target to generate an image. The processor is coupled to the projection device and the image capturing device, and configured to divide the image into multiple image regions, calculate multiple sharpness values ​​corresponding to the multiple image regions based on the image data of the multiple image regions, and display the multiple sharpness values ​​on the corresponding multiple image regions to generate a first focus identification image.

[0007] Based on the above, the embodiments of the present invention have at least one of the following advantages or effects. The focus identification method of the embodiments of the present invention can calculate multiple sharpness values ​​of the image image in sections, and display the multiple sharpness values ​​on each image area of ​​the image image, so that remote maintenance personnel can easily identify the focus quality and identify the differences before and after maintenance and correction of the projector.

[0008] To make the above features and advantages of the present invention more apparent and understandable, specific embodiments are described below in conjunction with the accompanying drawings. Attached Figure Description

[0009] Figure 1 A schematic diagram illustrating a focus identification system according to an embodiment of the present invention is shown.

[0010] Figure 2 A flowchart illustrating a focus identification method according to an embodiment of the present invention is shown.

[0011] Figure 3 A schematic diagram illustrating a focus identification method according to an embodiment of the present invention is shown.

[0012] Figure 4 A schematic diagram illustrating the maintenance judgment of a focus identification method according to an embodiment of the present invention is shown.

[0013] Figure 5 A schematic diagram illustrating the maintenance judgment of a focus identification method according to another embodiment of the present invention is shown. Detailed Implementation

[0014] The foregoing and other technical contents, features, and effects of the present invention will be clearly presented in the following detailed description of a preferred embodiment with reference to the accompanying drawings. The directional terms mentioned in the following embodiments, such as up, down, left, right, front, or back, are merely for reference to the directions in the accompanying drawings. Therefore, the directional terms used are for illustrative purposes and not for limiting the present invention.

[0015] Figure 1 A schematic diagram illustrating a focus identification system according to an embodiment of the present invention is shown. Please refer to... Figure 1The projection system 10 of this embodiment includes a projection device 110, an image capturing device 120, a projection target 130, a processor 140, and a storage device 150. The projection device 110 is, for example, a projector, which also includes optical elements for projection, such as a light source, a light valve, and a projection lens. The projection device 110 projects a projection image onto the projection target 130 to generate a projection image. The image capturing device 120 is, for example, a camera, a video camera, or other image capturing device. The image capturing device 120 is coupled to the projection device 110 and can capture the projection image on the projection target 130 to generate an image. The projection target 130 is, for example, a projection screen. The processor 140 is coupled to the projection device 110 and the image capturing device 120. The processor 140 receives the image captured from the image capturing device 120 and divides the image captured into multiple image regions, such as nine image regions. The number of image regions can be designed differently depending on the actual situation, such as 16, 25, or other numbers. Here, nine image regions are used as an example. The processor 140 calculates multiple sharpness values ​​for each of the multiple image regions based on the image data of each region and displays the multiple sharpness values ​​on the corresponding multiple image regions to generate a focus identification screen. In this embodiment, the focus identification screen is equivalent to displaying multiple sharpness values ​​on multiple image regions of the image captured, which will be described in detail later. Storage device 150 is coupled to processor 140. Storage device 150 includes, for example, read-only memory (ROM), hard disk drive and / or random access memory (RAM) to store focus identification images at different times so that processor 140 can determine whether the focus identification images at different times are consistent.

[0016] In one embodiment, the processor 140 may be designed using a hardware description language (HDL) or any other design method for digital circuits familiar to those skilled in the art, and may be a hardware circuit implemented using a field programmable gate array (FPGA), a complex programmable logic device (CPLD), or an application-specific integrated circuit (ASIC). In this embodiment, the processor 140 may be configured in a remote device or a projection device 110, but is not limited thereto.

[0017] Regarding the calculation of sharpness values, the processor 140 can calculate the sharpness values ​​of image regions using methods such as convolutional neural networks, Sobel edge detection, Laplacian edge detection, and image edge gradient contrast detection to quantify image quality. The above-mentioned sharpness calculation methods are merely examples and are not limited to these.

[0018] Figure 2 A flowchart illustrating a focus identification method according to an embodiment of the present invention is shown. Figure 3 A schematic diagram illustrating a focus identification method according to an embodiment of the present invention is shown. Please also refer to... Figure 1 , Figure 2 and Figure 3 In step S210, the image capturing device 120 captures the projected image from the projection target 130 to generate an image frame 310. Next, in step S220, the processor 140 receives the image frame 310 from the image capturing device 120 and divides the image frame 310 into image regions A1 to A9, as shown below. Figure 3 The image capture frame 320. Specifically, the processor 140 can set the recognition range of the image capture frame 310, which includes the recognition start coordinates START (X1, Y1) and the recognition end coordinates END (XN, YN). Based on the recognition start coordinates START (X1, Y1) and the recognition end coordinates END (XN, YN), the length between coordinates X1 and XN is divided into three equal parts, and the width between coordinates Y1 and YN is divided into three equal parts, thereby cutting the image capture frame 310 into image areas A1 to A9.

[0019] In step S230, the processor 140 calculates multiple sharpness values ​​for each of the image regions A1 to A9 based on the image data. Then, in step S240, the processor 140 displays the multiple sharpness values ​​on the corresponding image regions A1 to A9 to generate a focus identification screen 330. For example, in the focus identification screen 330, the sharpness value for image region A1 is 27075, the sharpness value for image region A2 is 43352, the sharpness value for image region A3 is 31603, the sharpness value for image region A4 is 29434, the sharpness value for image region A5 is 19320, and the sharpness value for image region A6 is 28993. The sharpness value of image area A7 is 27824, the sharpness value of image area A8 is 44373, and the sharpness value of image area A9 is 30673.

[0020] Figure 4 A schematic diagram illustrating the maintenance judgment of a focus identification method according to an embodiment of the present invention is shown. Please refer to... Figure 4At time T1 (first time), processor 140 generates focus identification screen 400 (first focus identification screen). From time T2 to time T6 (second time), processor 140 sequentially and periodically generates multiple corresponding focus identification screens (multiple second focus identification screens, not shown). In time T1, the sharpness values ​​of image areas A1 to A9 in focus identification screen 400 are 90, 95, 100, 100, 100, 100, 90, 95, and 100, respectively, corresponding to an image area sharpness value of 410, which can be used as a comparison basis. Then, from time T2 to time T6, the multiple focus identification screens (multiple second focus identification screens, not shown) have image area sharpness values ​​from 420 to 460. It must be noted that... Figure 4 In the image, the distribution of image areas with sharpness values ​​from 410 to 460 corresponds to image areas A1 to A9 in the focus discrimination image 400. For the sake of brevity, in... Figure 4 Image areas A1 to A9 are not marked in the image area sharpness values ​​410 to 460; please refer to the focus identification screen 400. In this embodiment, time T1 is, for example, 8:00 AM on the first day, time T2 is, for example, 8:00 AM on the second day, time T3 is, for example, 8:00 AM on the third day, time T4 is, for example, 8:00 AM on the fourth day, time T5 is, for example, 8:00 AM on the fifth day, and time T6 is, for example, 8:00 AM on the sixth day. The time intervals can be set according to the actual situation, and this case is not limited to this.

[0021] At time T2, processor 140 compares image region sharpness values ​​410 and 420 to generate a comparison result. In this embodiment, processor 140 compares image region sharpness values ​​based on trends. The sharpness values ​​of image regions A1 to A3, where image region sharpness value 410 is 410, are 90, 95, and 100 respectively, showing an increasing trend. The sharpness values ​​of image regions A4 to A6, where image region sharpness value 410 is 410, are 410, are 410, and are 100 respectively, showing a fixed trend. The sharpness values ​​of image regions A7 to A9, where image region sharpness value 410 is 410, are 410, are 410, are 410, and are 410 respectively, showing an increasing trend. On the other hand, the sharpness values ​​of image areas A1 to A3, with image area sharpness value 420, are 90, 95, and 100 respectively, and their sharpness values ​​also show an increasing trend. The sharpness values ​​of image areas A4 to A6, with image area sharpness value 420, are 100, 100, and 100 respectively, and their sharpness values ​​show a fixed trend. The sharpness values ​​of image areas A7 to A9, with image area sharpness value 420, are 90, 95, and 100 respectively, and their sharpness values ​​show an increasing trend. In other words, the trend of sharpness values ​​in image area sharpness value 410 is the same as that in image area sharpness value 420. The processor 140 can determine, based on the comparison results, not to perform autofocus at time T2 to correct sharpness variations, and there is no need to provide maintenance prompts.

[0022] In time T3, time T4, and time T6, since the image region sharpness value 410 is the same as the image region sharpness values ​​430, 440, and 460, the comparison results and the judgment process of the change trend of their sharpness values ​​are the same as those in time T2, and will not be repeated here.

[0023] At time T5, processor 140 compares the image region sharpness value 410 with the image region sharpness value 450 to generate a comparison result. In this embodiment, the sharpness values ​​of image regions A1 to A3 with image region sharpness values ​​450 are 100, 95, and 100, respectively, and the trend of the sharpness values ​​is a decreasing trend followed by an increasing trend. The sharpness values ​​of image regions A4 to A6 with image region sharpness values ​​450 are 90, 90, and 90, respectively, and the trend of the sharpness values ​​is a fixed trend. The sharpness values ​​of image regions A7 to A9 with image region sharpness values ​​450 are 100, 95, and 100, respectively, and the trend of the sharpness values ​​is a decreasing trend followed by an increasing trend. In other words, the trends in sharpness values ​​410 and 450 are not entirely the same. It is evident that the focus identification screen (second focus identification screen, not shown) at time T5 has changed compared to the focus identification screen 400 (first focus identification screen) at time T1. The processor 140 can determine, based on the comparison results, to perform automatic focusing at time T5 to correct the sharpness variation. Furthermore, the processor 140 can provide maintenance prompts to the user or remote maintainer through various display methods, such as displaying graphics or text messages on the display screen of the projection device 110 or on the display screen of a remote device, to remind the user or remote maintainer that maintenance is needed for the sharpness variation.

[0024] In another embodiment, the processor 140 can determine whether to perform autofocus and provide maintenance prompts based on whether multiple sharpness values ​​of two compared focus identification images are completely identical. For example, since the sharpness values ​​of multiple image regions in image region sharpness value 410 and image region sharpness value 420 are completely identical, the processor 140 can determine, based on the comparison result, not to perform autofocus at time T2 to correct sharpness variations and provide maintenance prompts. Since the sharpness values ​​of multiple image regions in image region sharpness value 410 and image region sharpness value 450 are not completely identical, the processor 140 can determine, based on the comparison result, to perform autofocus at time T5 and provide maintenance prompts.

[0025] Figure 5 A schematic diagram illustrating the maintenance judgment of a focus identification method according to another embodiment of the present invention is shown. Please refer to... Figure 5At time T1 (first time), processor 140 generates focus identification screen 500 (first focus identification screen). From time T2 to time T6 (second time), processor 140 sequentially and periodically generates multiple focus identification screens (multiple second focus identification screens, not shown). In time T1, the sharpness values ​​of image areas A1 to A9 in focus identification screen 500 are 90, 95, 100, 100, 100, 100, 90, 95, and 100, respectively, corresponding to an image area sharpness value of 510, which can be used as a comparison basis. Then, from time T2 to time T6, the multiple focus identification screens (multiple second focus identification screens, not shown) have image area sharpness values ​​from 520 to 560. It must be noted that... Figure 5 In the image, the distribution of image areas with sharpness values ​​from 510 to 560 corresponds to image areas A1 to A9 in the focus discrimination image 500. For the sake of brevity, in... Figure 5 Image areas A1 to A9 are not marked in the image area sharpness values ​​510 to 560. Please refer to the focus identification screen 500.

[0026] At time T2, processor 140 compares image region sharpness values ​​510 and 520 to generate a comparison result. In this embodiment, processor 140 compares the image region sharpness values ​​based on their distribution locations. The highest sharpness value in image region sharpness value 510 is 100, and it is distributed in image regions A3, A4, A5, A6, and A9. The highest sharpness value in image region sharpness value 520 is also 100, and it is similarly distributed in image regions A3, A4, A5, A6, and A9. In other words, since the distribution locations of the highest sharpness values ​​in image region sharpness values ​​510 and 520 are exactly the same, processor 140 can determine that autofocus is not performed at time T2 to correct sharpness variations, and no maintenance prompt is required.

[0027] In times T3, T5, and T6, since the distribution of the highest sharpness values ​​among image region sharpness values ​​510, 530, 550, and 560 is exactly the same, the comparison results and judgment process for their sharpness values ​​are the same as in time T2 and will not be repeated here. It is worth noting that, although in time T3, the sharpness values ​​of image region A1 (95), A2 (85), A7 (80), and A8 (90) in image region sharpness value 530 are different from the corresponding image region sharpness values ​​in image region sharpness value 510, the highest sharpness values ​​in image region sharpness value 530 are distributed across image regions A3, A4, A5, A6, and A9, and their distribution positions are exactly the same as in image region sharpness value 510. Therefore, in this comparison logic, although the sharpness values ​​of each image area of ​​the two are not exactly the same, the processor 140 can still determine that autofocus is not performed at time T3 to correct the sharpness variation, and there is no need to provide maintenance prompts.

[0028] In time T4, processor 140 compares image region sharpness value 510 with image region sharpness value 540 to generate a comparison result. In this embodiment, the highest sharpness value of image region sharpness value 540 is 100, which is distributed in image regions A1, A3, A7, and A9. In other words, the distribution positions of the highest sharpness values ​​in image region sharpness value 510 and image region sharpness value 540 are not exactly the same. It can be seen that the focus identification screen (second focus identification screen, not shown) in time T4 has changed compared to the focus identification screen 500 (first focus identification screen) in time T1. Processor 140 can determine to perform autofocus in time T4 to correct the sharpness variation based on the comparison result. Furthermore, the processor 140 can provide maintenance prompts to users or remote maintainers through various display methods, such as displaying graphics or text messages on the display screen of the projection device 110 or on the display screen of a remote device, to remind users or remote maintainers that maintenance is needed for changes in sharpness.

[0029] In summary, the embodiments of the present invention have at least one of the following advantages or effects. In the embodiments of the present invention, multiple sharpness values ​​of the image capture screen can be calculated in sections, and these values ​​are displayed on different image areas of the image capture screen, allowing remote maintenance personnel to easily identify focus quality and recognize differences before and after maintenance and correction of the projection device. Furthermore, focus assessment screens can be generated periodically; by comparing the sharpness values ​​of each image area in the focus assessment screens at different times, it can be determined whether automatic focusing is needed and maintenance prompts can be provided to remote maintenance personnel, thereby improving remote maintenance efficiency.

[0030] The above description is merely a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All simple equivalent variations and modifications made according to the claims and description of the invention are still within the scope of this invention. Furthermore, no embodiment or claim of the present invention needs to achieve all the objectives, advantages, or features disclosed in the invention. In addition, the abstract and title are merely for assisting in patent document searches and are not intended to limit the scope of the invention. Moreover, the terms "first," "second," etc., used in this specification or claims are only used to name elements or distinguish different embodiments or scopes, and are not intended to limit the upper or lower limit of the number of elements.

Claims

1. A focus identification method, applicable to a focus identification system, characterized in that, The focus identification method includes: Capture the projected image to generate an image; The captured image is divided into multiple image regions; Calculate multiple sharpness values ​​corresponding to the multiple image regions based on the image data of the multiple image regions respectively; The multiple sharpness values ​​are displayed on the corresponding multiple image areas to generate a focus identification screen, which includes a first focus identification screen generated at a first time and a second focus identification screen generated at a second time. Compare the plurality of sharpness values ​​of the first focus identification image with the plurality of sharpness values ​​of the second focus identification image to generate a comparison result; and Based on the comparison results, it is determined whether to perform autofocus and provide maintenance prompts, wherein: When the changing trends of the plurality of sharpness values ​​in the first focus identification image and the plurality of sharpness values ​​in the second focus identification image are the same, the autofocus is not performed and the maintenance prompt is not provided. When the changing trends of the plurality of sharpness values ​​in the first focus identification image and the plurality of sharpness values ​​in the second focus identification image are different, the autofocus is performed and the maintenance prompt is provided, or When the plurality of sharpness values ​​in the first focus identification image are exactly the same as the plurality of sharpness values ​​in the second focus identification image, the autofocus is not performed and the maintenance prompt is not provided. When the plurality of sharpness values ​​in the first focus identification image are not exactly the same as the plurality of sharpness values ​​in the second focus identification image, the autofocus is performed and the maintenance prompt is provided, or When at least one highest sharpness value in the first focus identification image is exactly the same as the distribution position of at least one highest sharpness value in the second focus identification image, the autofocus is not performed and the maintenance prompt is not provided. When the distribution position of at least one highest sharpness value in the first focus identification image is not exactly the same as that of at least one highest sharpness value in the second focus identification image, the autofocus is performed and the maintenance prompt is provided.

2. The focus identification method according to claim 1, characterized in that, Also includes: After the first focus identification image is generated, multiple second focus identification images are generated sequentially and periodically at other times.

3. The focus identification method according to claim 1, characterized in that, The step of dividing the captured image into the multiple image regions includes: The recognition range of the captured image is defined, wherein the recognition range includes the coordinates of the recognition start point and the coordinates of the recognition end point; and The image is divided into multiple image regions based on the coordinates of the identification start point and the coordinates of the identification end point.

4. A focus identification system, characterized in that, include: A projection device configured to project a projection image onto a projection target; An image-capturing device, coupled to the projection device, is configured to capture the projected image on the projection target to generate an image-capturing image; A processor, coupled to the projection device and the image capturing device, is configured to divide the image capture screen into multiple image regions, calculate multiple sharpness values ​​corresponding to the multiple image regions based on image data of each of the multiple image regions, and display the multiple sharpness values ​​on the corresponding multiple image regions to generate a focus identification screen. The focus identification screen includes a first focus identification screen generated at a first time and a second focus identification screen generated at a second time. The processor compares the multiple sharpness values ​​of the first focus identification screen with the multiple sharpness values ​​of the second focus identification screen to generate a comparison result, and determines whether to perform automatic focusing and provides maintenance prompts based on the comparison result. When the processor determines that the multiple sharpness values ​​of the first focus identification image and the multiple sharpness values ​​of the second focus identification image have the same trend of change, the projection device does not perform the autofocus and does not provide the maintenance prompt. When the processor determines that the changing trends of the plurality of sharpness values ​​in the first focus identification image and the plurality of sharpness values ​​in the second focus identification image are different, the projection device performs the automatic focusing and provides the maintenance prompt, or... When the processor determines that the plurality of sharpness values ​​of the first focus identification image are exactly the same as the plurality of sharpness values ​​of the second focus identification image, the projection device does not perform the autofocus or provide the maintenance prompt. When the processor determines that the plurality of sharpness values ​​of the first focus identification image are not exactly the same as the plurality of sharpness values ​​of the second focus identification image, the projection device performs the automatic focusing and provides the maintenance prompt, or... When the processor determines that at least one highest sharpness value in the first focus identification image is exactly the same as the distribution position of at least one highest sharpness value in the second focus identification image, the projection device does not perform the autofocus or provide the maintenance prompt. When the processor determines that the distribution position of the at least one highest sharpness value in the first focus identification image is not exactly the same as that of the at least one highest sharpness value in the second focus identification image, the projection device performs the automatic focusing and provides the maintenance prompt.

5. The focus identification system according to claim 4, characterized in that, After generating the first focus identification image, the processor sequentially and periodically generates multiple second focus identification images at other times.

6. The focus identification system according to claim 4, characterized in that, The processor sets the recognition range in the image frame, including the recognition start coordinates and the recognition end coordinates, and cuts the image frame into the multiple image regions according to the recognition start coordinates and the recognition end coordinates.

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