CN113723539A - Test question information acquisition method and device - Google Patents
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Abstract
The invention provides a method and a device for collecting test question information, wherein the method comprises the following steps: acquiring a first image sequence of a first test question; processing the first image sequence to generate a first High Dynamic Range (HDR) image; acquiring image features of the first HDR image; and matching the image characteristics with a preset image characteristic model, and setting the first image in a first question bank when the matching is successful. Therefore, the definition of the collected test questions is ensured.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a test question information acquisition method and device.
Background
The question bank data is an important teaching resource, in daily learning, in order to improve the learning increasing rate, learning knowledge is generally consolidated in a way of making test questions, and students often improve and stabilize the proficiency of the existing knowledge points through a large number of exercise questions.
Therefore, how to collect a sufficient number of test questions and ensure the clarity of the collected test questions becomes an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention aims to provide a method and a device for acquiring test question information, so as to solve the problem that the existing acquired test questions are unclear.
In order to solve the above problem, in a first aspect, the present invention provides a method for collecting test question information, where the method includes:
acquiring a first image sequence of a first test question;
processing the first image sequence to generate a first High Dynamic Range (HDR) image;
acquiring image features of the first HDR image;
and matching the image characteristics with a preset image characteristic model, and setting the first image in a first question bank when the matching is successful.
Preferably, when the matching is successful, before the first image is arranged in the first question bank, the method further comprises:
comparing the matching result with a first threshold value;
when the matching result is larger than a first threshold value, the matching is successful;
and when the matching result is not larger than the first threshold value, the matching is failed.
Preferably, the first image sequence comprises: a reference picture, a first non-reference picture, and a second non-reference picture; the exposure duration of the first test question of the first non-reference image, the reference image and the second non-reference image is sequentially increased.
Preferably, the processing the first image sequence to generate a first HDR image comprises:
acquiring a first motion region and a second motion region of an image sequence; the first motion area is an area where the first non-reference image has a gray value difference relative to the reference image, and the second motion area is an area where the second non-reference image has a gray value difference relative to the reference image;
determining a target motion area according to a comparison result between the first proportion and a second threshold value and a comparison result between the second proportion and the second threshold value; wherein the first proportion is a proportion occupied by the first motion region in the first non-reference image, the second proportion is a proportion occupied by the second motion region in the second non-reference image, and the target motion region includes at least one connected region;
obtaining a first HDR image according to the first weight value and the second weight value; the first weighted value is the sum of the product of the saturation, the contrast and the exposure degree of each pixel point in the first non-reference image and the product of the saturation, the contrast and the exposure degree of each pixel point in the second non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the first non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the second non-reference image; the second weight value is the product of the saturation, the contrast and the exposure degree of each pixel point in the reference image.
In a second aspect, the present invention provides a test question information collecting device, including:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a first image sequence of a first test question;
a processing unit for processing the first sequence of images to generate a first high dynamic range, HDR, image;
the acquiring unit is further used for acquiring image characteristics of the first HDR image;
and the matching unit is used for matching the image characteristics with a preset image characteristic model, and when the matching is successful, the first image is arranged in a first question bank.
Preferably, the apparatus further comprises: a comparison unit;
the comparison unit is used for comparing the matching result with a first threshold value;
when the matching result is larger than a first threshold value, the matching is successful;
and when the matching result is not larger than the first threshold value, the matching is failed.
Preferably, the first image sequence comprises: a reference picture, a first non-reference picture, and a second non-reference picture; the exposure duration of the first test question of the first non-reference image, the reference image and the second non-reference image is sequentially increased.
Preferably, the acquiring unit is further configured to acquire a first motion region and a second motion region of the image sequence; the first motion area is an area where the first non-reference image has a gray value difference relative to the reference image, and the second motion area is an area where the second non-reference image has a gray value difference relative to the reference image;
a determination unit configured to determine a target motion region according to a comparison result between the first ratio and a second threshold and a comparison result between the second ratio and the second threshold; wherein the first proportion is a proportion occupied by the first motion region in the first non-reference image, the second proportion is a proportion occupied by the second motion region in the second non-reference image, and the target motion region includes at least one connected region;
the determining unit is further configured to obtain a first HDR image according to the first weight value and the second weight value; the first weighted value is the sum of the product of the saturation, the contrast and the exposure degree of each pixel point in the first non-reference image and the product of the saturation, the contrast and the exposure degree of each pixel point in the second non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the first non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the second non-reference image; the second weight value is the product of the saturation, the contrast and the exposure degree of each pixel point in the reference image.
Therefore, by applying the test question information acquisition method provided by the invention, a first image sequence of a first test question is obtained; processing the first image sequence to generate a first High Dynamic Range (HDR) image; acquiring image features of the first HDR image; and matching the image characteristics with a preset image characteristic model, and setting the first image in a first question bank when the matching is successful. The definition of the collected test questions is ensured.
Drawings
Fig. 1 is a flowchart of a test question information collection method according to an embodiment of the present invention;
FIG. 2 is a flowchart of step 120 provided by an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a test question information collecting device according to an embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Fig. 1 is a flowchart of a test question information acquisition method according to an embodiment of the present invention. As shown in fig. 1, the test question information collection method includes the following steps:
The mobile terminal can shoot the test questions into images by using a shooting function and store the images into a memory of the mobile terminal, and the first image sequence comprises: a reference picture, a first non-reference picture, and a second non-reference picture; the exposure duration of the first test question of the first non-reference image, the reference image and the second non-reference image is sequentially increased. In the prior art, whether an acquired image is clear needs to be judged, and when the image is not clear, shooting needs to be carried out again. In the method, the plurality of images about the first test question are acquired and synthesized, so that the problem that the images are not clear due to shaking of the test question image caused by shaking of hands of a test question photographer or turning of the test question by a third person is solved, and the efficiency is improved.
Where image features are similar to image fingerprints and can be uniquely identified as "interesting parts" of other images, its precise definition is often dictated by the particular problem or application type. Repeatable detectability is the most important characteristic of image features: the extracted features should be the same for the same image regardless of angle, displacement, and shading. Common image features include color features, texture features, shape features, and spatial relationship features.
A color feature, a global feature, describes the surface properties of a scene corresponding to an image or image region. Such as a grey histogram, etc. Texture features, a global feature, describe the surface properties of the scene corresponding to an image or image region. Such as entropy, angular second moment, and local stationarity based on co-occurrence matrices. The shape feature is a local feature describing the shape property of the object in the local area. Such as boundary features, etc. The spatial relationship feature refers to a spatial position or a relative directional relationship between a plurality of objects divided in an image. These relationships may also be classified into join/abut relationships, overlap/repeat relationships, and contain/subsist relationships, among others.
And 140, matching the image characteristics with a preset image characteristic model, and setting the first image in a first question bank when the matching is successful.
Wherein, when the matching is successful, before the first image is arranged in the first question bank, the method further comprises: comparing the matching result with a first threshold value; when the matching result is larger than a first threshold value, the matching is successful; and when the matching result is not larger than the first threshold value, the matching is failed.
The first threshold may be set as needed, for example, 95%, and the higher the first threshold is set, the lower the matching success rate is. The preset image feature model may be established according to a convolutional neural network.
Therefore, the HDR image is obtained by processing the collected multiple test question pictures, and the definition of the collected test questions is ensured.
Step 120 is described in detail below. Fig. 2 is a flowchart of step 120 provided by the embodiment of the present invention. As shown in fig. 2, step 120 includes the following specific steps:
The first motion area is an area where the first non-reference image has a difference in gray value with respect to the reference image, and the second motion area is an area where the second non-reference image has a difference in gray value with respect to the reference image.
It should be noted that, the present application only takes three images as an example for description, it is understood that the number of the images may be more than three, when the number of the images is more than three, the exposure time lengths of the first non-reference image and the second non-reference image are closest to the reference image, the exposure time length of the first non-reference image is less than that of the reference image, and the exposure time length of the second non-reference image is greater than that of the reference image.
Next, how to acquire the first non-reference image, the reference image, and the second non-reference image will be described in detail.
First, three images with gradually increasing exposure time of a target scene (test question), namely, an original first non-reference image, an original reference image and an original second non-reference image, may be captured by a terminal device, where the terminal device may be a device with a camera, including but not limited to a camera (e.g., a Digital camera), a video camera, a mobile phone (e.g., a smart phone), a tablet computer (Pad), a Personal Digital Assistant (PDA), a portable device (e.g., a portable computer), a wearable device, and the like, and this is not particularly limited in the embodiments of the present invention.
Next, the RGB images of the original first non-reference image, the original reference image, and the original second non-reference image are converted into grayscale images, respectively.
Then, on a brightness channel, Histogram Matching (HM) is performed on the gray scale map of the original first non-reference image by taking the gray scale map of the original reference image as a standard, the gray scale map of the original first non-reference image is brightened, HM is performed on the gray scale map of the original second non-reference image by taking the gray scale map of the original reference image as a standard, and the original second non-reference image is darkened.
And finally, acquiring homography matrixes of the brightened original first non-reference image, the original reference image and the dimmed original second non-reference image by using a surf characteristic point detection algorithm, and mapping the original first non-reference image, the original reference image and the original second non-reference image by using the homography matrixes respectively to align the images, so that the movement caused by the hand shake of an operator or the influence of test questions on the environment (such as the influence of wind) is eliminated, and the first non-reference image, the reference image and the second non-reference image are obtained.
Acquiring a first motion region and a second motion region of an image sequence specifically comprises:
the RGB maps of the first non-reference image, the reference image and the second non-reference image are converted into grayscale maps, respectively. And performing HM on a brightness channel, performing HM on the gray scale map of the first non-reference image by taking the gray scale map of the reference image as a standard, turning up the gray scale map of the first non-reference image, performing histogram matching on the gray scale map of the second non-reference image, and turning down the gray scale map of the second non-reference image. Finally, comparing the gray value difference between the gray level image of the first non-reference image after the brightness adjustment and the gray level image of the reference image, determining all the pixels with the gray level difference larger than the preset threshold value as the first motion area, correspondingly, comparing the gray value difference between the gray level image of the second non-reference image after the brightness adjustment and the gray level image of the reference image, determining all the pixels with the gray level difference larger than the preset threshold value as the second motion area, for example, the gray value of the first pixel in the gray level image of the second non-reference image after the brightness adjustment is 200, the gray value of the first pixel in the gray level image of the reference image is 100, and the gray value difference between the two is 100, and when the preset threshold is 50, the difference between the gray values of the two is larger than the preset threshold, so that the pixel value of the first pixel point is 1, and after all the pixel points with the pixel values of 1 are obtained, the dried second motion area is obtained through threshold filtering and corrosion expansion operation.
The first proportion is the proportion occupied by the first motion area in the first non-reference image, the second proportion is the proportion occupied by the second motion area in the second non-reference image, and the target motion area comprises at least one connected area.
The first weighted value is the sum of the product of the saturation, the contrast and the exposure degree of each pixel point in the first non-reference image and the product of the saturation, the contrast and the exposure degree of each pixel point in the second non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the first non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the second non-reference image; the second weight value is the product of the saturation, the contrast and the exposure degree of each pixel point in the reference image.
The first proportion is the proportion of a first motion area in the first non-reference image, the second proportion is the proportion of a second motion area in the second non-reference image, and the target motion area comprises at least one connected area. In the field of image processing, Connected Component generally refers to an image area composed of foreground pixels with the same pixel value and adjacent positions in an image,
further, determining the target motion region according to a comparison result between the first proportion and a first threshold and a comparison result between the second proportion and the first threshold, including:
when the first proportion and the second proportion are not larger than the first threshold value, overlapping the first motion area and the second motion area, and determining the overlapped area as a target motion area; alternatively, the first and second electrodes may be,
when the first ratio is not larger than the first threshold value and the second ratio is larger than the first threshold value, determining that the first motion area is a target motion area; alternatively, the first and second electrodes may be,
and when the first ratio is larger than a first threshold value and the second ratio is not larger than the first threshold value, determining the second motion area as a target motion area.
Fig. 3 is a schematic structural diagram of a test question information collecting device according to an embodiment of the present invention. As shown in fig. 3, the test question information collecting apparatus 300 includes: an acquisition unit 310, a processing unit 320, a matching unit 330, a comparison unit 340 and a determination unit 350.
The acquiring unit 310 is configured to acquire a first image sequence of a first test question.
The processing unit 320 is configured to process the first sequence of images to generate a first high dynamic range HDR image.
The obtaining unit 310 is further configured to obtain an image feature of the first HDR image.
And the matching unit 330 is configured to match the image feature with a preset image feature model, and when the matching is successful, set the first image in a first question bank.
Further, the comparing unit 340 is configured to compare the matching result with a first threshold; when the matching result is larger than a first threshold value, the matching is successful; and when the matching result is not larger than the first threshold value, the matching is failed.
Further, the first image sequence includes: a reference picture, a first non-reference picture, and a second non-reference picture; the exposure duration of the first test question of the first non-reference image, the reference image and the second non-reference image is sequentially increased.
Further, the obtaining unit 310 is further configured to obtain a first motion region and a second motion region of the image sequence.
The first motion area is an area where the first non-reference image has a difference in gray value with respect to the reference image, and the second motion area is an area where the second non-reference image has a difference in gray value with respect to the reference image.
The determining unit 350 is configured to determine the target motion region according to a comparison result between the first ratio and the second threshold and a comparison result between the second ratio and the second threshold.
The first proportion is the proportion occupied by the first motion area in the first non-reference image, the second proportion is the proportion occupied by the second motion area in the second non-reference image, and the target motion area comprises at least one connected area.
The determining unit 350 is further configured to obtain a first HDR image according to the first weight value and the second weight value.
The first weighted value is the sum of the product of the saturation, the contrast and the exposure degree of each pixel point in the first non-reference image and the product of the saturation, the contrast and the exposure degree of each pixel point in the second non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the first non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the second non-reference image; the second weight value is the product of the saturation, the contrast and the exposure degree of each pixel point in the reference image.
Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A test question information acquisition method is characterized by comprising the following steps:
acquiring a first image sequence of a first test question;
processing the first image sequence to generate a first High Dynamic Range (HDR) image;
acquiring image features of the first HDR image;
and matching the image characteristics with a preset image characteristic model, and setting the first image in a first question bank when the matching is successful.
2. The method for collecting question information according to claim 1, wherein said step of, when the matching is successful, setting said first image in a first question bank is preceded, further comprises:
comparing the matching result with a first threshold value;
when the matching result is larger than a first threshold value, the matching is successful;
and when the matching result is not larger than the first threshold value, the matching is failed.
3. The test question information acquisition method according to claim 1, characterized in that said first sequence of images comprises: a reference picture, a first non-reference picture, and a second non-reference picture; the exposure duration of the first test question of the first non-reference image, the reference image and the second non-reference image is sequentially increased.
4. The test question information acquisition method of claim 3, wherein the processing the first image sequence to generate a first HDR image comprises:
acquiring a first motion region and a second motion region of an image sequence; the first motion area is an area where the first non-reference image has a gray value difference relative to the reference image, and the second motion area is an area where the second non-reference image has a gray value difference relative to the reference image;
determining a target motion area according to a comparison result between the first proportion and a second threshold value and a comparison result between the second proportion and the second threshold value; wherein the first proportion is a proportion occupied by the first motion region in the first non-reference image, the second proportion is a proportion occupied by the second motion region in the second non-reference image, and the target motion region includes at least one connected region;
obtaining a first HDR image according to the first weight value and the second weight value; the first weighted value is the sum of the product of the saturation, the contrast and the exposure degree of each pixel point in the first non-reference image and the product of the saturation, the contrast and the exposure degree of each pixel point in the second non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the first non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the second non-reference image; the second weight value is the product of the saturation, the contrast and the exposure degree of each pixel point in the reference image.
5. An examination question information collecting apparatus, characterized in that the apparatus comprises:
the device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring a first image sequence of a first test question;
a processing unit for processing the first sequence of images to generate a first high dynamic range, HDR, image;
the acquiring unit is further used for acquiring image characteristics of the first HDR image;
and the matching unit is used for matching the image characteristics with a preset image characteristic model, and when the matching is successful, the first image is arranged in a first question bank.
6. The test question information collecting apparatus according to claim 5, characterized in that the apparatus further comprises: a comparison unit;
the comparison unit is used for comparing the matching result with a first threshold value;
when the matching result is larger than a first threshold value, the matching is successful;
and when the matching result is not larger than the first threshold value, the matching is failed.
7. The test question information acquisition apparatus according to claim 5, wherein the first image sequence includes: a reference picture, a first non-reference picture, and a second non-reference picture; the exposure duration of the first test question of the first non-reference image, the reference image and the second non-reference image is sequentially increased.
8. The test question information acquisition apparatus according to claim 7, wherein the acquisition unit is further configured to acquire a first motion region and a second motion region of the image sequence; the first motion area is an area where the first non-reference image has a gray value difference relative to the reference image, and the second motion area is an area where the second non-reference image has a gray value difference relative to the reference image;
a determination unit configured to determine a target motion region according to a comparison result between the first ratio and a second threshold and a comparison result between the second ratio and the second threshold; wherein the first proportion is a proportion occupied by the first motion region in the first non-reference image, the second proportion is a proportion occupied by the second motion region in the second non-reference image, and the target motion region includes at least one connected region;
the determining unit is further configured to obtain a first HDR image according to the first weight value and the second weight value; the first weighted value is the sum of the product of the saturation, the contrast and the exposure degree of each pixel point in the first non-reference image and the product of the saturation, the contrast and the exposure degree of each pixel point in the second non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the first non-reference image; or, the first weight value is a product of saturation, contrast and exposure of each pixel point in the second non-reference image; the second weight value is the product of the saturation, the contrast and the exposure degree of each pixel point in the reference image.
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