Automatic Analysis Method and Device for AE Objective Index Based on Image Processing

Abstract

The present invention relates to the technical field of image processing, and discloses an automatic analysis method and device for AE objective indicators based on image processing; the method includes: obtaining a first digital image sample obtained by a photographing device photographing a gray scale card; extracting each image contour in the first digital image sample, and selecting a small rectangular pattern located in the central region therefrom, and continuously horizontally displacing the small rectangular pattern multiple times to the left and right sides respectively from its current position to draw a plurality of successively adjacent small rectangular patterns; selecting the total area covered by the small rectangular pattern with the smallest gray scale value and M-1 consecutive small rectangular patterns located on its left side as the gray scale area to be measured; converting the gray scale area to be measured into a grayscale image, calculating the gray scale values of each block, and analyzing to obtain the AEstepchart index. The embodiment of the present invention realizes the automatic selection of the gray scale area to be measured and each block therein, can effectively improve the test efficiency and reduce the test cost.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and particularly to an automatic analysis method and device for AE (AutoExposure) objective indicators based on image processing. Background Art

[0002] Image quality is one of the important indicators for evaluating the optical performance of a photographing device. In the evaluation of the photographing quality of a photographing device, in addition to the subjective feelings of the human eye, objective evaluation indicators such as color accuracy, color saturation, clarity, and signal-to-noise ratio are often used to quantitatively analyze the quality of the pictures taken by the photographing device. Therefore, to evaluate the evaluation indicators of the photographing device, a test chart needs to be used, and for different test items, the selected test charts are also different to achieve different test functions. Among them, the main function of the grayscale card is to test parameters such as noise, dynamic range, contrast, exposure accuracy, and lens flare.

[0003] Generally, the method steps for quantitatively analyzing the photographing quality of a photographing device through a grayscale card are as follows: First, use the photographing device to photograph the grayscale card to obtain multiple digital image samples; then manually frame the areas to be detected in the digital image samples, and then the terminal device calls professional analysis software to detect the areas to be detected one by one. This method of manually framing the areas to be detected is cumbersome in operation and low in test efficiency. Summary of the Invention

[0004] The purpose of the present invention is to provide an automatic analysis method and device for AE objective indicators based on image processing to overcome the defect of low efficiency in the existing method of manually framing the areas to be detected.

[0005] To achieve this purpose, the present invention adopts the following technical solutions:

[0006] An automatic analysis method for AE objective indicators based on image processing includes:

[0007] Obtain a first digital image sample obtained by the photographing device photographing the grayscale card;

[0008] Extract each image contour in the first digital image sample through image morphological operations;

[0009] Select a small rectangular pattern located in the central area from each of the image contours, and continuously horizontally displace the small rectangular pattern multiple times to the left and right sides from its current position to draw a plurality of sequentially adjacent small rectangular patterns in the first digital image sample, and the number of the small rectangular patterns is greater than the number of gray levels M of the grayscale card;

[0010] Select the total area covered by the small rectangular pattern with the smallest grayscale value and the consecutive M-1 small rectangular patterns on its left as the grayscale area to be measured;

[0011] Convert the grayscale area to be measured into a grayscale image, calculate the grayscale values of each block included in the grayscale image, and analyze the AEstepchart index accordingly.

[0012] Optionally, the automatic analysis method for the AE objective index further includes:

[0013] Obtain multiple second digital image samples with different brightness levels obtained by the imaging device shooting a grayscale card;

[0014] Convert the second digital image samples into grayscale images, and automatically analyze the AEshading index and AEbox index accordingly.

[0015] Optionally, before calculating the grayscale values of each block included in the grayscale image, it further includes:

[0016] After the grayscale area to be measured is aggregated with adjacent areas according to the grayscale value approximation degree, it is preliminarily cut to form multiple rectangular blocks arranged horizontally.

[0017] Optionally, before calculating the grayscale values of each block included in the grayscale image, if the number of rectangular blocks included in the grayscale area to be measured is less than the number of grayscale levels M of the grayscale card, it further includes:

[0018] Divide the rectangular blocks with a width greater than the preset width threshold until the number of rectangular blocks in the grayscale area to be measured is equal to the number of grayscale levels M of the grayscale card.

[0019] Optionally, in the step of continuously horizontally displacing the small rectangular pattern multiple times to the left and right from its current position, the displacement amount of the small rectangular pattern each time is the horizontal width of the small rectangular pattern.

[0020] Optionally, the method further includes: comparing the various AE indexes obtained by analysis with the target test standard, and generating an AE objective index test report according to the comparison result.

[0021] Optionally, the method for calculating the grayscale values of each block included in the grayscale image includes:

[0022] For each block, select the central area of the current block, calculate the grayscale value of the central area, and use the grayscale value of the central area as the grayscale value of the current block.

[0023] Optionally, the proportion of the central area in the current block is 30%.

[0024] An automatic analysis device for AE objective indicators based on image processing, which is used to implement the automatic analysis method for AE objective indicators based on image processing described in any one of the above, includes:

[0025] An image sample acquisition module, which is used to acquire the first digital image sample obtained by the shooting device shooting a grayscale card;

[0026] A grayscale area selection module, which is used to select a small rectangular pattern located in the central area from each of the image contours, continuously horizontally displace the small rectangular pattern to the left and right sides from its current position multiple times, so as to draw multiple adjacent small rectangular patterns in the first digital image sample, and the number of the small rectangular patterns is greater than the number of grayscale levels M of the grayscale card; select the total area covered by the small rectangular pattern with the smallest grayscale value and the consecutive M - 1 small rectangular patterns located on its left as the grayscale area to be measured;

[0027] A quality analysis module, which is used to convert the grayscale area to be measured into a grayscale image, calculate the grayscale values of each block included in the grayscale image, and analyze and obtain the AE step chart index accordingly.

[0028] A storage medium, which stores multiple instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the automatic analysis method for AE objective indicators based on image processing described in any one of the above.

[0029] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0030] In the embodiment of the present invention, first, a small rectangular pattern located in the central area is selected from the first digital image sample, and then multiple small rectangular patterns are obtained by continuously displacing the small rectangular pattern to its left and right sides multiple times. Since the number of these small rectangular patterns exceeds the number of grayscale levels of the grayscale card, the area covered by these small rectangular patterns will be slightly larger than the actual grayscale area; then, within the larger area covered by these small rectangular patterns, the area covered by the small rectangular pattern with the smallest grayscale value and the consecutive M - 1 small rectangular patterns located on its left is selected as the grayscale area to be measured, thus realizing the automatic selection of the grayscale area to be measured and each block inside it, which can replace the traditional manual method of selecting the measured block, effectively improve the test efficiency, and reduce the test cost. Description of the Drawings

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to these drawings without creative efforts.

[0032] Figure 1 This is the flowchart of the automatic analysis method for AE objective indicators based on image processing provided in the first embodiment of the present invention.

[0033] Figure 2 This is the flowchart of the automatic analysis method for AE objective indicators based on image processing provided in the second embodiment of the present invention.

[0034] Figure 3 This is an example diagram of the method for automatically selecting the gray-scale area to be measured from digital image samples provided in the second embodiment of the present invention. Detailed implementation manners

[0035] In order to make the objectives, features, and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the embodiments described below are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0036] Embodiment 1

[0037] In order to overcome the defect of time-consuming and laborious in the existing method of manually selecting the area to be measured during the image quality detection process, please refer to Figure 1 , the embodiment of the present invention provides an automatic analysis method for AE objective indicators based on image processing, including:

[0038] Step S11: Obtain the first digital image sample obtained by the shooting device shooting the gray-scale card.

[0039] Step S12: Extract each image contour in the current first digital image sample through image morphological operations.

[0040] Step S13: Select the small rectangular pattern located in the central area from each image contour, and continuously horizontally displace the small rectangular pattern to the left and right sides from its current position multiple times to draw a plurality of sequentially adjacent small rectangular patterns in the first digital image sample, and the number of small rectangular patterns is greater than the number of gray scales M of the gray-scale card.

[0041] Step S14: Select the total area covered by the small rectangular pattern with the smallest gray-scale value and the consecutive M-1 small rectangular patterns on its left as the gray-scale area to be measured.

[0042] Step S15: Convert the gray-scale area to be measured into a grayscale image, calculate the gray-scale values of each block included in the grayscale image, and analyze the AEstepchart index accordingly.

[0043] In the embodiment of the present invention, a small rectangular pattern located in the central region is first selected from the first digital image sample, and then a plurality of small rectangular patterns are obtained by continuously displacing the small rectangular pattern to its left and right sides multiple times. Since the number of these small rectangular patterns exceeds the number of gray levels of the gray scale card, the area covered by these small rectangular patterns will be slightly larger than the actual gray scale area. Then, within the larger area covered by these small rectangular patterns, the area covered by the small rectangular pattern with the minimum gray scale value and the consecutive M - 1 small rectangular patterns located on its left is selected as the gray scale area to be measured, thereby realizing the automatic selection of the gray scale area to be measured and each block therein, which can replace the traditional manual method of selecting the measured blocks, effectively improve the test efficiency, and reduce the test cost.

[0044] It should be noted that the main functions of the gray scale card are to test parameters such as noise, dynamic range, contrast, exposure accuracy, and lens flare. There are generally 5 - level gray scale cards, 7 - level gray scale cards, 9 - level gray scale cards, 11 - level gray scale cards, 13 - level gray scale cards, 20 - level gray scale cards, etc., which are a series of gray - scale combinations arranged in sequence. Exemplarily, the 20 - level gray scale card is composed of 20 gray levels with different tones from white to black.

[0045] The first digital image sample obtained by photographing the gray scale card in a laboratory test environment with good environmental configuration is relatively consistent with the standard gray scale card. However, the first digital image sample obtained by photographing the gray scale card in an office test environment with poor environmental configuration may be tilted compared with the standard gray scale card, resulting in a small deviation between each gray scale block selected in step S14 and the corresponding actual gray scale block.

[0046] Based on this, in order to reduce the adverse impact of this deviation on the test accuracy, in the step of calculating the gray scale values of each block included in the grayscale image, for each block, the central region of the current block is selected, the gray scale value of the central region is calculated, and the gray scale value of the central region is used as the gray scale value of the current block. In this way, when calculating the gray scale values of each block, the gray scale values of the edge regions of the current block are not included in the calculation range, which can reduce the influence degree of the deviation phenomenon to a certain extent and improve the test accuracy. The proportion of the central region within its block can be flexibly designed according to the actual situation. Exemplarily, the proportion is 30%.

[0047] Embodiment 2

[0048] Please refer to Figure 2 , this Embodiment 2 provides another automatic analysis method for AE objective indicators based on image processing, including:

[0049] Step S21: Obtain the first digital image sample obtained by photographing the gray scale card with the photographing device.

[0050] Step S22: Extract each image contour from the current first digital image sample through image morphological operations.

[0051] Step S23: Select small rectangular patterns located in the central region from each image contour, and continuously horizontally displace the small rectangular patterns multiple times to the left and right sides respectively from their current positions, so as to draw multiple adjacent small rectangular patterns in the first digital image sample, and the number of small rectangular patterns is greater than the number of gray levels M of the gray scale card.

[0052] Step S24: Select the total area covered by the small rectangular pattern with the smallest gray level value and the consecutive M - 1 small rectangular patterns on its left as the gray level area to be measured.

[0053] Step S25: After the gray level area to be measured is subjected to adjacent area aggregation according to the gray level value approximation degree, it is initially cut into multiple horizontally arranged rectangular blocks; if the number of rectangular blocks included in the gray level area to be measured is less than the number of gray levels M of the gray scale card, then the rectangular blocks with a width greater than the preset width threshold are divided until the number of rectangular blocks in the gray level area to be measured is equal to the number of gray levels M of the gray scale card.

[0054] Step S26: Convert the gray level area to be measured into a grayscale image, calculate the gray level values of each block included in the grayscale image, and analyze the AEstepchart index accordingly.

[0055] Compared with the AE objective index automatic analysis method in Embodiment 1, in this Embodiment 2, the gray level area to be measured is first subjected to adjacent area aggregation according to the gray level value approximation degree and then initially cut, and then divided according to the number and width of rectangular blocks, so that the gray level area to be measured is divided into blocks equal in number to the number of gray levels of the gray scale card. This block division method can accurately select each unequal - area gray level block in the first digital image sample obtained by photographing the gray scale card at an inclined shooting angle in a non - standard test environment.

[0056] Exemplarily, as Figure 3 shown, Figure 3 (1) and Figure 3 (2) are two first digital image samples obtained by photographing a 20 - level gray scale card in a well - configured laboratory test environment and a poorly - configured office test environment respectively. The image effect obtained by photographing in the laboratory test environment is more excellent than that in the office test environment, and is more conducive to subsequent accurate classification and analysis; Figure 3 (3) is Figure 3 the view after contour extraction of the first digital image sample shown in (1) obtained by photographing in the laboratory test environment, Figure 3 (4) is Figure 3The view of the first digital image sample taken in the office test environment as shown in (2) after contour extraction. It can be seen that due to the strict limitations such as thresholds in basic morphological operations, it may be impossible to accurately extract the contours of some gray-scale blocks. Figure 3 (5) and Figure 3 (6) are respectively the images obtained by first selecting a small rectangular pattern that can be accurately recognized and is located in the central area in Figure 3 (3) and Figure 3 (4), and then selecting the gray-scale area to be measured by shifting the small rectangular pattern left and right. It can be seen that the selected gray-scale area to be measured is relatively close to the actual gray-scale area. Figure 3 (7) and Figure 3 (8) are respectively the images of the gray-scale areas to be measured in Figure 3 (5) and Figure 3 (6) after being divided into multiple rectangular blocks.

[0057] In step S11, due to the strict limitations such as thresholds in basic morphological operations, it may be impossible to accurately extract the contours of all images, resulting in the missing of the contours of some images (such as some possible gray-scale blocks). Therefore, in step S12, only the small rectangular pattern that can be accurately recognized and is located in the central area is selected (since its contour approaches the contour of the gray-scale block, it can be initially assumed to be a gray-scale block, and then this gray-scale block is used as a reference to distinguish the entire gray-scale area). Then, by shifting the small rectangular pattern left and right, the gray-scale area to be measured can be selected on the basis of compensating for the missing images. As can be seen from Figure 3 (7) and Figure 3 (8), whether it is a laboratory test environment that meets higher requirements or an office test environment where the captured images are prone to tilting, overexposure or blurring due to poor environment, the above method can accurately select the gray-scale area to be measured and its respective gray-scale blocks from the first digital image sample.

[0058] Embodiment III

[0059] The embodiment of the present invention provides another automatic analysis method for AE objective indicators based on image processing. On the basis of Embodiment I or II, it further includes the following steps:

[0060] Obtain multiple second digital image samples with different brightnesses (such as LV4 - LV14) obtained by a photographing device shooting a gray-scale card;

[0061] Convert the second digital image samples into grayscale images, and automatically analyze the AEshading index and the AEbox index accordingly.

[0062] After that, the obtained AE indicators will be compared with the target test criteria, and an AE objective indicator test report will be generated according to the comparison results.

[0063] Embodiment 4

[0064] Based on the same inventive concept, an embodiment of the present invention further provides an automatic analysis device for AE objective indicators based on image processing, which is used to implement the above-mentioned automatic analysis method for AE objective indicators based on image processing, including:

[0065] An image sample acquisition module, configured to acquire a first digital image sample obtained by a photographing device photographing a gray scale card;

[0066] A gray scale area selection module, configured to select a small rectangular pattern located in the central area from each image contour, and continuously horizontally displace the small rectangular pattern multiple times to the left and right sides respectively from its current position, so as to draw a plurality of successively adjacent small rectangular patterns in the first digital image sample, and the number of small rectangular patterns is greater than the number of gray scales M of the gray scale card; select the total area covered by the small rectangular pattern with the smallest gray scale value and the consecutive M-1 small rectangular patterns on its left as the gray scale area to be measured;

[0067] A quality analysis module, configured to convert the gray scale area to be measured into a grayscale image, calculate the gray scale values of each block included in the grayscale image, and analyze the AEstepchart indicator accordingly.

[0068] Those of ordinary skill in the art can understand that all or part of the steps in the above-mentioned various methods can be completed by instructions, or by controlling relevant hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0069] For this reason, an embodiment of the present invention further provides a storage medium, in which multiple instructions are stored, and the instructions can be loaded by a processor to execute the steps in any of the automatic analysis methods for AE objective indicators based on image processing provided by the embodiments of the present invention.

[0070] Among them, the storage medium may include: a read-only memory (ROM, Read Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disc, etc.

[0071] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An automatic analysis method for AE objective indicators based on image processing, characterized in that, it includes: Obtain the first digital image sample obtained by the shooting device shooting a gray scale card; Through image morphological operations, extract each image contour in the first digital image sample; the area with the largest gray scale value in the first digital image sample is on the far left; Select a small rectangular pattern located in the central area from each of the image contours, and continuously horizontally displace the small rectangular pattern multiple times to the left and right sides respectively from its current position, so as to draw a plurality of successively adjacent small rectangular patterns in the first digital image sample, and the number of the small rectangular patterns is greater than the number of gray scales M of the gray scale card; Select the total area covered by the small rectangular pattern with the smallest gray scale value and the consecutive M - 1 small rectangular patterns on its left as the gray scale area to be measured; after clustering adjacent areas according to the gray scale value approximation degree for the gray scale area to be measured, initially crop to form a plurality of horizontally arranged rectangular blocks; If the number of rectangular blocks included in the gray scale area to be measured is less than the number of gray scales M of the gray scale card, it further includes: Divide the rectangular blocks with a width greater than the preset width threshold until the number of rectangular blocks in the gray scale area to be measured is equal to the number of gray scales M of the gray scale card; Convert the gray scale area to be measured into a grayscale image, calculate the gray scale values of each block included in the grayscale image, and analyze the AEstepchart indicator accordingly.

2. The automatic analysis method for AE objective indicators based on image processing according to claim 1, characterized in that, the automatic analysis method for AE objective indicators further includes: Obtain multiple second digital image samples with different brightnesses obtained by the shooting device shooting a gray scale card; Convert the second digital image samples into grayscale images, and automatically analyze the AEshading indicator and the AEbox indicator accordingly.

3. The automatic analysis method for AE objective indicators based on image processing according to claim 1, characterized in that, In the step of continuously horizontally displacing the small rectangular pattern multiple times to the left and right sides respectively from its current position, the displacement amount of each time of the small rectangular pattern is the horizontal width of the small rectangular pattern.

4. The automatic analysis method for AE objective indicators based on image processing according to claim 2, characterized in that, the method further includes: comparing the various AE indicators obtained by analysis with the target test standard, and generating an AE objective indicator test report according to the comparison result.

5. The automatic analysis method for AE objective indicators based on image processing according to claim 2, characterized in that, the method for calculating the gray scale values of each block included in the grayscale image includes: For each block, select the central area of the current block, calculate the gray scale value of the central area, and use the gray scale value of the central area as the gray scale value of the current block.

6. The automatic analysis method for AE objective indicators based on image processing according to claim 5, characterized in that, the proportion of the central area in the current block is 30%.

7. An automatic analysis device for AE objective indicators based on image processing, which is used to implement the automatic analysis method for AE objective indicators based on image processing according to any one of claims 1 to 6. Characterized in that, Comprising: An image sample acquisition module, configured to acquire a first digital image sample obtained by a photographing device photographing a gray scale card; A gray scale area selection module, configured to select a small rectangular pattern located in the central area from each of the image contours, continuously horizontally displace the small rectangular pattern multiple times to the left and right sides respectively from its current position, so as to draw a plurality of successively adjacent small rectangular patterns in the first digital image sample, and the number of the small rectangular patterns is greater than the number of gray scales M of the gray scale card; select the total area covered by the small rectangular pattern with the smallest gray scale value and the continuous M-1 small rectangular patterns on its left side as the gray scale area to be measured; A quality analysis module, configured to convert the gray scale area to be measured into a grayscale image, calculate the gray scale values of each block included in the grayscale image, and analyze the AEstepchart indicator accordingly.

8. A computer-readable storage medium, Characterized in that, The storage medium stores multiple instructions, and the instructions are suitable for being loaded by a processor to execute the steps in the automatic analysis method for AE objective indicators based on image processing according to any one of claims 1 to 6.

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