CN113099064A - Method and apparatus for image parameter determination - Google Patents
Method and apparatus for image parameter determination Download PDFInfo
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- CN113099064A CN113099064A CN202110376787.1A CN202110376787A CN113099064A CN 113099064 A CN113099064 A CN 113099064A CN 202110376787 A CN202110376787 A CN 202110376787A CN 113099064 A CN113099064 A CN 113099064A
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Abstract
The invention discloses a method and a device for image parameter determination. The method comprises the following steps: acquiring a first image of a first object, wherein a second object in the first image is an image of the first object; acquiring a second parameter of the second object; the first parameters of the first object are compared with the second parameters of the second object to determine the image parameters of the first image, so that the precision and the resolution of the image acquisition equipment can be accurately judged, and the image acquired by the image acquisition equipment has a good application effect when being applied to OCR (optical character recognition) and the like.
Description
Technical Field
Embodiments of the present disclosure relate to the field of image processing, and in particular, to a method and apparatus for image parameter determination.
Background
Along with the development of image acquisition technology, there are multiple image acquisition equipment in the market now, and each image acquisition equipment leads to the collection precision all different because of the problem of production machining precision, and then has reduced image acquisition equipment's image availability factor.
For a high-speed camera with better application prospect in the market, image acquisition equipment such as the high-speed camera (also called as a quick-speed camera) can complete one-second high-speed scanning, has an OCR character recognition function, can recognize and convert a scanned image into an editable word document, and can also perform operations such as photographing, video recording, copying, network paperless faxing, electronic book making, edge cutting and righting and the like. Because of the production and processing precision problem, high-speed cameras dpi (Dots Per Inch) of the same brand and the same model are different, and the inconsistency of image resolution causes the reduction of the OCR recognition accuracy, i.e. the image use efficiency, when performing the OCR recognition, for example.
Disclosure of Invention
At least one embodiment of the present disclosure provides a method and apparatus for image parameter determination. The method for determining the image parameters at least solves the problem of low image use efficiency of the image acquisition equipment, and has the advantages of better precision and resolution for accurately judging the image acquisition equipment and better application effect when the image acquired by the image acquisition equipment is applied to OCR recognition and the like.
According to an aspect of the disclosure, at least one embodiment provides a method for image parameter determination, the method comprising: acquiring a first image of a first object, wherein a second object in the first image is an image of the first object; acquiring a second parameter of the second object; and comparing the first parameter of the first object with the second parameter of the second object to determine the image parameter of the first image.
According to another aspect of the present disclosure, at least one embodiment also provides an apparatus for image parameter determination, comprising: a processor; and a memory configured to store computer program instructions adapted to be loaded by the processor and to perform the above-described method for image parameter determination.
According to another aspect of the present disclosure, at least one embodiment also provides a method for image parameter determination, comprising: the device for image parameter determination is described above.
According to another aspect of the present disclosure, at least one embodiment also provides a non-volatile storage medium readable by a computer, storing computer program instructions which, when executed by the computer, perform the above-described method for image parameter determination.
With the above embodiment of the present disclosure, a first image of a first object is acquired, wherein a second object in the first image is an image of the first object; acquiring a second parameter of the second object; the first parameters of the first object are compared with the second parameters of the second object to determine the image parameters of the first image, so that the precision and the resolution of the image acquisition equipment can be accurately judged, and the image acquired by the image acquisition equipment has a good application effect when being applied to OCR (optical character recognition) and the like.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly described below, and it is apparent that the drawings in the following description only relate to some embodiments of the present invention and are not limiting on the present invention.
FIG. 1 is a flow chart of a method for image parameter determination according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of an apparatus for image parameter determination according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an image correction card according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present disclosure better understood by those skilled in the art, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only some embodiments of the present disclosure, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
At least one embodiment of the present disclosure provides a system for image parameter determination, which may include: apparatus for image parameter determination. At least one embodiment of the present disclosure provides an apparatus for image parameter determination, as shown in fig. 2, comprising: a processor 201; and a memory 202 configured to store computer program instructions adapted to be loaded by the processor and to carry out the method of storing a groomed garment as developed in the present invention (to be described in detail later). Optionally, at least one embodiment of the present disclosure provides a non-transitory storage medium readable by a computer, storing computer program instructions, which when executed by the computer, perform the method for image parameter determination (described in detail below) developed by the present invention.
The processor 201 may be any suitable processor, such as a central processing unit, a microprocessor, an embedded processor, and the like, and may employ an architecture such as X86, ARM, and the like. The memory 202 may be a variety of suitable storage devices, such as non-volatile storage devices, including but not limited to magnetic storage devices, semiconductor storage devices, optical storage devices, and the like, and may be arranged as a single storage device, an array of storage devices, or a distributed storage device, which are not limited by embodiments of the present disclosure.
It will be understood by those skilled in the art that the above-described structure of the apparatus for image parameter determination is merely illustrative, and does not limit the structure of the apparatus for image parameter determination. For example, the apparatus for image parameter determination may also include more or fewer components (e.g., transmission devices) than shown in FIG. 2. The above-mentioned transmission device is used for receiving or transmitting data via a network. Examples of the network may include a wired network and a wireless network. In one example, the transmission device includes a network adapter (NIC) that can be connected to the router via a network cable and other network devices to communicate with the internet or a local area network. In one example, the transmission device is a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
In the above operating environment, at least one embodiment of the present disclosure provides a flowchart of a method for image parameter determination as shown in fig. 1, which can be applied to an apparatus for image parameter determination, loaded and executed by the processor 201, to at least solve the problem of clothes being stacked into a uniform size. It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein. As shown in fig. 1, this may include the steps of:
step S101, acquiring a first image of a first object, wherein a second object in the first image is the image of the first object;
step S102, acquiring a second parameter of a second object;
step S103, comparing the first parameter of the first object with the second parameter of the second object to determine the image parameter of the first image.
By the above embodiment of the present disclosure, a first image of a first object is acquired, wherein a second object in the first image is an image of the first object; acquiring a second parameter of a second object; the first parameter of the first object is compared with the second parameter of the second object to determine the image parameter of the first image, so that the beneficial effects that the precision and the resolution of the image acquisition equipment are accurately judged, and the application effect of the image acquired by the image acquisition equipment is good when OCR (optical character recognition) and other applications are carried out are better achieved.
In step S101, a first image of a first object is acquired, wherein a second object in the first image is an image of the first object. The first object is an image correction card (as shown in fig. 3). The image correction card is preset with a first parameter, and the first parameter comprises a horizontal size and a vertical size.
In step S102, a second parameter of the second object is acquired. The second parameter may include a number of horizontal pixels and a number of vertical pixels, and the acquiring the second parameter of the second object may include: performing deviation correction and black edge removal processing on the first image to obtain a second image; by means of a second object of the second image, the number of horizontal pixels and the number of vertical pixels of the second object are determined.
Optionally, the deviation rectifying method includes: projection method, projecting the image onto a line after rotating a direction, dropping all pixel values, measuring the length of the colored part of the line meeting a certain threshold value to obtain a line length table of each angle arrangement, wherein the angle is the angle of deviation correction because the image is rectangular and two obvious concave points can be seen with an interval of 90 degrees; the fuzzy principal component analysis method, namely binarizing an image, wherein colored points can be regarded as distribution points, and searching the orthogonal principal component direction by using a data analysis method (covariance matrix in probability); the hough transform "sets an angle as a transform coefficient". In the prior art, the deviation rectifying method is mature, and is not described herein again.
Optionally, black edge removal processing, such as threshold screening, globally detects black edges. The black border removal process in the prior art is mature, and is not described herein again.
Here, determining the number of horizontal pixels and the number of vertical pixels of the second object may include: inputting a second image into the trained image processing model, wherein the image processing model outputs the number of horizontal pixels and the number of vertical pixels based on the scale space representation sequence; and acquiring the number of horizontal pixels and the number of vertical pixels output by the image processing model. For example, the image processing model extracts contour information of the second object in the second image in scale space; the image processing model detects edges and corners and determines characteristics according to the main contour information; the image processing model outputs the number of horizontal pixels and the number of vertical pixels of the second object using the determined features.
Extraction of contour information in a scale space needs to be achieved by using Gaussian filtering, the Gaussian filtering is a process of carrying out weighted average on the whole image, and the value of each pixel point is obtained by carrying out weighted average on the value of each pixel point and other pixel values in a neighborhood. The gaussian filter uses a function which is a common gaussian function, and when calculating a discrete approximation of the gaussian function, pixels beyond approximately 3 distances can be considered to be invalid, and the calculation of the pixels can be ignored. In addition, a parameter regarded as a scale is introduced into the image processing model, a scale space representation sequence under the multi-scale is obtained by continuously changing the scale parameter, the contour extraction of a second object in a second image in the scale space is carried out on the sequences, the contour is used as a feature vector, the edge and corner detection, the feature extraction on different resolutions and the like are realized, and finally the horizontal pixel number and the vertical pixel number of the second object are output.
In step S103, the first parameter of the first object is compared with the second parameter of the second object to determine the image parameter of the first image. For example, the horizontal size of the first object is compared with the number of horizontal pixels of the second object, the vertical size of the first object is compared with the number of vertical pixels of the second object, and finally the image resolution of the first image is determined according to the horizontal size, the number of horizontal pixels, the vertical size, and the number of vertical pixels.
In the above manner, a first image of a first object is acquired, wherein a second object in the first image is an image of the first object; acquiring a second parameter of a second object; the first parameter of the first object is compared with the second parameter of the second object to determine the image parameter of the first image, so that the beneficial effects that the precision and the resolution of the image acquisition equipment are accurately judged, and the application effect of the image acquired by the image acquisition equipment is good when OCR (optical character recognition) and other applications are carried out are better achieved.
It should be noted that, for simplicity of description, the above-mentioned embodiments of the system, apparatus and method are all described as a series of acts or a combination of modules, but those skilled in the art should understand that the present disclosure is not limited by the described order of acts or the connection of modules, as some steps may be performed in other orders or simultaneously and some modules may be connected in other ways according to the present disclosure.
Those skilled in the art should also appreciate that the embodiments described in this specification are all one embodiment, and the above-described embodiment numbers are merely for description, and the acts and modules involved are not necessarily essential to the disclosure.
In the above embodiments of the present disclosure, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present disclosure, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present disclosure may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present disclosure. The storage medium includes a volatile storage medium or a non-volatile storage medium, such as a usb disk, a Read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, which can store program codes.
The foregoing is merely a preferred embodiment of the present disclosure, and it should be noted that modifications and embellishments could be made by those skilled in the art without departing from the principle of the present disclosure, and these should also be considered as the protection scope of the present disclosure.
Claims (10)
1. Method for image parameter determination, characterized in that the method comprises:
acquiring a first image of a first object, wherein a second object in the first image is an image of the first object;
acquiring a second parameter of the second object;
and comparing the first parameter of the first object with the second parameter of the second object to determine the image parameter of the first image.
2. The method of claim 1, wherein the second parameters comprise a number of horizontal pixels and a number of vertical pixels, and wherein obtaining the second parameters for the second object comprises:
performing deviation correction and black edge removal processing on the first image to obtain a second image;
determining, by a second object of the second image, a horizontal pixel count and a vertical pixel count of the second object.
3. The method of claim 2, wherein determining the number of horizontal pixels and the number of vertical pixels of the second object comprises:
inputting the second image into a trained image processing model, wherein the image processing model outputs a horizontal pixel count and a vertical pixel count based on a scale space representation sequence;
and acquiring the number of horizontal pixels and the number of vertical pixels output by the image processing model.
4. The method of claim 3, wherein the image processing model outputting a horizontal pixel count and a vertical pixel count comprises:
the image processing model extracts contour information of an object in an image in a scale space;
the image processing model detects edges and corners and determines characteristics according to the main contour information;
the image processing model outputs the number of horizontal pixels and the number of vertical pixels of the object using the determined features.
5. The method of claim 2, wherein the first parameters comprise a horizontal dimension and a vertical dimension, and wherein comparing the first parameters of the first object to the second parameters of the second object comprises:
comparing the horizontal size of the first object with the horizontal pixel number of the second object;
and comparing the vertical size of the first object with the vertical pixel number of the second object.
6. The method of claim 5, the image parameter being a resolution, wherein determining the image parameter for the first image comprises:
determining an image resolution of the first image based on the horizontal size, the number of horizontal pixels, the vertical size, and the number of vertical pixels.
7. The method of any of claims 1-6, wherein the first object is an image correction card.
8. Apparatus for image parameter determination, comprising:
a processor; and
a memory configured to store computer program instructions adapted to be loaded by the processor and to perform the method for image parameter determination as claimed in claims 1 to 7.
9. For use in an image parameter determination system, comprising: an apparatus for image parameter determination as claimed in claim 8.
10. A non-volatile storage medium readable by a computer, storing computer program instructions which, when executed by the computer, perform the method for image parameter determination as claimed in claims 1 to 7.
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Application publication date: 20210709 |