CN113554615A - Image refinement processing method and device, electronic equipment and storage medium - Google Patents
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Abstract
The application provides an image refinement processing method, an image refinement processing device, electronic equipment and a storage medium, wherein the method comprises the following steps: aiming at each target image in an image library, carrying out size adjustment and rasterization processing on the target image to obtain a first image; screening layers meeting preset characteristic conditions from all layers of the first image, and integrating the layers to obtain a second image; carrying out graying, color space standardization and color gradation processing on the second image to obtain a third image; dividing the third image into a plurality of pixel grids, and comparing a combination vector obtained by combining the color gradation data in the pixel grids with a characteristic vector of a characteristic image; and if the combined vector is the same as the feature vector, outputting the target image. The method and the device can improve the filtering precision of the target image and reduce the workload of image processing.
Description
Technical Field
The present disclosure relates to the field of image processing technologies, and in particular, to an image refinement method and apparatus, an electronic device, and a storage medium.
Background
Currently, image screening is a technology that is always required, and a specific image is retrieved from an image library according to features to reduce the retrieval time of a user, and the method is often applied to aspects of search engines, advertisement promotion and the like.
In the existing image refinement processing scheme, special filters on the surface of an image, fuzzy operation and the like bring inconvenience to the feature recognition of the image, and the retrieval precision of the feature image is not high. Moreover, the image library has more data, which increases the workload of image processing.
Disclosure of Invention
In view of the above, an object of the present application is to provide an image refinement method, an image refinement apparatus, an electronic device, and a storage medium, which can improve the filtering accuracy of a target image and reduce the workload of image processing.
In a first aspect, an embodiment of the present application provides an image refinement processing method, including:
aiming at each target image in an image library, carrying out size adjustment and rasterization processing on the target image to obtain a first image;
screening layers meeting preset characteristic conditions from all layers of the first image, and integrating the layers to obtain a second image;
carrying out graying, color space standardization and color gradation processing on the second image to obtain a third image;
dividing the third image into a plurality of pixel grids, and comparing a combination vector obtained by combining the color gradation data in the pixel grids with a characteristic vector of a characteristic image;
and if the combined vector is the same as the feature vector, outputting the target image.
In a possible implementation, the step of resizing and rasterizing each target image in the image library to obtain a first image comprises:
for each target image in an image library, zooming the target image into a preset size range according to the preset size range;
and converting the format of the scaled target image from a vector image to a raster image to obtain a first image.
In a possible implementation manner, the step of screening out layers satisfying a preset characteristic condition from each layer of the first image, and integrating a plurality of layers to obtain a second image includes:
for each layer of the first image, comparing a first aspect ratio of the image features in the layer with a second aspect ratio of the image features in the feature image to obtain a confidence coefficient;
if the confidence coefficient exceeds a target confidence coefficient threshold value, deleting the layer;
and integrating the layers with the confidence degrees not exceeding the target confidence degree threshold value to obtain a second image.
In one possible embodiment, the method further comprises:
and training the confidence level threshold based on a non-maximum suppression algorithm to obtain a target confidence level threshold.
In one possible embodiment, the step of performing graying, color space normalization and color gradation processing on the second image to obtain a third image includes:
carrying out graying processing on the second image to obtain a grayscale image;
carrying out color space standardization processing on the gray scale map to remove pixels with brightness lower than first brightness and pixels with brightness higher than second brightness in the gray scale map to obtain a standard map;
and carrying out color gradation processing on the standard image to obtain a third image.
In a possible implementation manner, the step of dividing the third image into a plurality of pixel cells and comparing a combination vector obtained by combining the grayscale data of the color levels in the pixel cells with the feature vector of the feature image includes:
dividing the third image into a number of square pixel cells;
acquiring color level gray data in each square pixel grid;
combining the color gradation data in the square pixel grids to obtain a combined vector;
and comparing the combination vector with the feature vector of the feature image.
In a possible implementation, the step of combining the grayscale data of the color levels in the square pixel cells to obtain a combination vector includes:
counting a gradient histogram of each square pixel grid based on the color gradation data of each square pixel grid to form a feature descriptor of each square pixel grid;
forming pixel blocks by each square pixel grid with preset number;
connecting the feature descriptors of a preset number of square pixel grids in each pixel block in series to obtain the feature descriptors of each pixel block;
and connecting the feature descriptors of all the pixel blocks in series to obtain a combined vector.
In one possible embodiment, the method further comprises:
storing a plurality of characteristic images containing characteristics as an image library;
updating the image library based on an update operation.
In one possible embodiment, the method further comprises:
temporarily storing the image in the image refinement processing process into a cache image library;
and deleting the temporarily stored images in the cache image library according to a preset period.
In a second aspect, an embodiment of the present application further provides an image refinement processing apparatus, including:
the preprocessing module is used for carrying out size adjustment and rasterization processing on each target image in the image library to obtain a first image;
the integration module is used for screening out layers meeting preset characteristic conditions from all layers of the first image and integrating the layers to obtain a second image;
the processing module is used for carrying out graying, color space standardization and color gradation processing on the second image to obtain a third image;
the comparison module is used for dividing the third image into a plurality of pixel grids and comparing a combination vector obtained by combining the color gradation data in the pixel grids with the characteristic vector of the characteristic image;
and the output module is used for outputting the target image if the combined vector is the same as the characteristic vector.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor, a memory and a bus, the memory storing machine-readable instructions executable by the processor, the processor and the memory communicating via the bus when the electronic device is running, the machine-readable instructions when executed by the processor performing the steps of the first aspect described above, or any possible implementation of the first aspect.
In a fourth aspect, this application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps in the first aspect or any one of the possible implementation manners of the first aspect.
According to the image refinement processing method provided by the embodiment of the application, firstly, for each target image in an image library, size adjustment and rasterization processing are carried out on the target image to obtain a first image; screening layers meeting preset characteristic conditions from all layers of the first image, and integrating the layers to obtain a second image; therefore, the image layer which does not meet the preset characteristic condition can be removed, the identification difficulty brought to image identification by special filters, fuzzy operation and the like on the surface of the image can be effectively reduced, and the workload of image processing is reduced. Then, carrying out graying, color space standardization and color gradation processing on the second image to obtain a third image; therefore, highlight and over-dark pixels in the gray-scale image can be removed, and the difficulty of image recognition can be effectively reduced. Finally, dividing the third image into a plurality of pixel grids, and comparing a combination vector obtained by combining the color gradation data in the pixel grids with the characteristic vector of the characteristic image; and if the combined vector is the same as the feature vector, outputting the target image. In this way, since the third image is divided into several pixel cells and processed separately, the workload of image processing can be reduced. Therefore, the method and the device for filtering the target image can improve the filtering precision of the target image and reduce the workload of image processing.
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart illustrating an image refinement processing method provided in an embodiment of the present application;
FIG. 2 is a schematic structural diagram illustrating an image refinement processing apparatus according to an embodiment of the present application;
fig. 3 shows a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the drawings in the present application are for illustrative and descriptive purposes only and are not used to limit the scope of protection of the present application. Additionally, it should be understood that the schematic drawings are not necessarily drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of the present application. It should be understood that the operations of the flow diagrams may be performed out of order, and steps without logical context may be performed in reverse order or simultaneously. One skilled in the art, under the guidance of this application, may add one or more other operations to, or remove one or more operations from, the flowchart.
In addition, the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that in the embodiments of the present application, the term "comprising" is used to indicate the presence of the features stated hereinafter, but does not exclude the addition of further features.
In the existing image refinement processing scheme, special filters on the surface of an image, fuzzy operation and the like all bring inconvenience to the feature recognition of the image, and the retrieval accuracy of the feature image is not high. Moreover, the image library has more data, which increases the workload of image processing. Based on this, embodiments of the present application provide an image refinement processing method, an image refinement processing apparatus, an electronic device, and a storage medium, which are described below with reference to embodiments.
To facilitate understanding of the present embodiment, a detailed description will be given first of all of an image refinement processing method disclosed in the embodiments of the present application.
Referring to fig. 1, fig. 1 is a flowchart illustrating an image refinement processing method according to an embodiment of the present disclosure. As shown in fig. 1, the method may include the steps of:
s101, aiming at each target image in an image library, carrying out size adjustment and rasterization processing on the target image to obtain a first image;
s102, screening layers meeting preset characteristic conditions from all layers of the first image, and integrating the layers to obtain a second image;
s103, carrying out graying, color space standardization and color gradation processing on the second image to obtain a third image;
s104, dividing the third image into a plurality of pixel grids, and comparing a combination vector obtained by combining the color gradation data in the pixel grids with a feature vector of a feature image;
and S105, if the combined vector is the same as the feature vector, outputting the target image.
In step S101, the target image is an image stored in an image library. In a specific implementation, a plurality of characteristic images containing characteristics are stored as an image library; updating the image library based on an update operation. The first image is an image obtained after the target image is subjected to size adjustment and rasterization processing.
In the step, for each target image in an image library, zooming the target image into a preset size range according to the preset size range; and converting the format of the scaled target image from a vector image to a raster image to obtain a first image.
In step S102, the layers that do not satisfy the preset characteristic condition are deleted, and the plurality of layers that satisfy the preset characteristic condition are recombined to obtain a second image. Therefore, the image layer which does not meet the preset characteristic condition can be removed, the identification difficulty brought to image identification by special filters, fuzzy operation and the like on the surface of the image can be effectively reduced, and the workload of image processing is reduced.
In this step, for each layer of the first image, comparing a first aspect ratio of the image feature in the layer with a second aspect ratio of the image feature in the feature image to obtain a confidence; if the confidence coefficient exceeds a target confidence coefficient threshold value, deleting the layer; and integrating the layers with the confidence degrees not exceeding the target confidence degree threshold value to obtain a second image.
Wherein, the confidence threshold is trained based on a Non-Maximum Suppression algorithm (NMS) to obtain a target confidence threshold.
In step S103, performing graying processing on the second image to obtain a grayscale image; carrying out color space standardization processing on the gray scale map to remove pixels with brightness lower than first brightness and pixels with brightness higher than second brightness in the gray scale map to obtain a standard map; and carrying out color gradation processing on the standard image to obtain a third image. Therefore, highlight and over-dark pixels in the gray-scale image can be removed, and the difficulty of image recognition can be effectively reduced.
The specific process of the color space normalization process includes: the gray image is regarded as a (x, y, z) three-dimensional image, and the gray image is subjected to color space standardization processing by adopting a Gamma correction method, so that the contrast of the gray image can be adjusted, the influence caused by local shadow and illumination change of the gray image can be reduced, and the noise interference can be inhibited.
In step S104, the feature image is an image to be retrieved, and a target image having the same feature vector as the feature image is retrieved from the image library.
In this step, the third image is divided into a plurality of pixel cells, and a combination vector obtained by combining the gradation data of the color levels in the pixel cells is compared with the feature vector of the feature image. In this way, since the third image is divided into several pixel cells and processed separately, the workload of image processing can be reduced.
Specifically, step S104 may include the following sub-steps:
s1041, dividing the third image into a plurality of square pixel grids;
s1042, obtaining the gray scale data of the color level in each square pixel grid;
s1043, combining the color gradation data in the square pixel grids to obtain a combined vector;
and S1044, comparing the combined vector with the feature vector of the feature image.
In step S1041, the pixel cells are square pixel cells. For example, the third image is divided into 6 by 6 square pixel cells.
In step S1042, color gradation data of each square pixel cell is obtained by performing color gradation digitization on each square pixel cell.
In step S1043, a gradient histogram of each square pixel cell is counted based on the grayscale data of each square pixel cell to form a feature descriptor (descriptor) of each square pixel cell; in this way, contour information can be captured while further mitigating the interference of illumination. Grouping each preset number (e.g., every 9) of square pixel cells into pixel blocks; connecting the feature descriptors of a preset number of square pixel grids in each pixel block in series to obtain the feature descriptors of each pixel block; and connecting the feature descriptors of all the pixel blocks in series to obtain a combined vector for classification.
In one possible embodiment, the method further comprises: temporarily storing the image in the image refinement processing process into a cache image library; and deleting the temporarily stored images in the cache image library according to a preset period.
According to the image refinement processing method provided by the embodiment of the application, firstly, for each target image in an image library, size adjustment and rasterization processing are carried out on the target image to obtain a first image; screening layers meeting preset characteristic conditions from all layers of the first image, and integrating the layers to obtain a second image; therefore, the image layer which does not meet the preset characteristic condition can be removed, the identification difficulty brought to image identification by special filters, fuzzy operation and the like on the surface of the image can be effectively reduced, and the workload of image processing is reduced. Then, carrying out graying, color space standardization and color gradation processing on the second image to obtain a third image; therefore, highlight and over-dark pixels in the gray-scale image can be removed, and the difficulty of image recognition can be effectively reduced. Finally, dividing the third image into a plurality of pixel grids, and comparing a combination vector obtained by combining the color gradation data in the pixel grids with the characteristic vector of the characteristic image; and if the combined vector is the same as the feature vector, outputting the target image. In this way, since the third image is divided into several pixel cells and processed separately, the workload of image processing can be reduced. Therefore, the method and the device for filtering the target image can improve the filtering precision of the target image and reduce the workload of image processing.
Based on the same technical concept, embodiments of the present application further provide an image refinement processing apparatus, an electronic device, a computer storage medium, and the like, which can be specifically referred to in the following embodiments.
Referring to fig. 2, fig. 2 is a schematic structural diagram of an image refinement processing apparatus according to an embodiment of the present disclosure. As shown in fig. 2, the apparatus may include:
the preprocessing module 10 is configured to perform size adjustment and rasterization processing on each target image in an image library to obtain a first image;
the integration module 20 is configured to screen out layers meeting preset characteristic conditions from the layers of the first image, and integrate the layers to obtain a second image;
the processing module 30 is configured to perform graying, color space standardization, and color gradation processing on the second image to obtain a third image;
the comparison module 40 is configured to divide the third image into a plurality of pixel lattices, and compare a combination vector obtained by combining the grayscale data of the color levels in the plurality of pixel lattices with a feature vector of the feature image;
and an output module 50, configured to output the target image if the combined vector is the same as the feature vector.
In a possible embodiment, the pre-processing module 10 comprises:
the zooming unit is used for zooming each target image in the image library into a preset size range according to the preset size range;
and the conversion unit is used for converting the format of the scaled target image from a vector image to a raster image to obtain a first image.
In one possible embodiment, the integration module 20 includes:
an aspect ratio comparison unit, configured to, for each layer of the first image, compare a first aspect ratio of an image feature in the layer with a second aspect ratio of an image feature in the feature image, and obtain a confidence level;
the deleting unit is used for deleting the layer if the confidence coefficient exceeds a target confidence coefficient threshold value;
and the integration unit is used for integrating the layers with the confidence degrees not exceeding the target confidence degree threshold value to obtain a second image.
In a possible embodiment, the integration module 20 further comprises:
and the training unit is used for training the confidence coefficient threshold based on a non-maximum suppression algorithm to obtain a target confidence coefficient threshold.
In one possible embodiment, the processing module 30 comprises:
the graying unit is used for performing graying processing on the second image to obtain a grayscale image;
the normalization unit is used for carrying out color space normalization processing on the gray-scale map so as to remove pixels with the brightness lower than first brightness and pixels with the brightness higher than second brightness in the gray-scale map to obtain a standard map;
and the color grading unit is used for performing color grading processing on the standard image to obtain a third image.
In one possible embodiment, the comparison module 40 comprises:
a dividing unit configured to divide the third image into a plurality of square pixel cells;
the acquisition unit is used for acquiring the color gradation data in each square pixel grid;
the combination unit is used for combining the color gradation data in the square pixel grids to obtain a combination vector;
and the vector comparison unit is used for comparing the combined vector with the characteristic vector of the characteristic image.
In a possible embodiment, the combination unit is specifically configured to:
counting a gradient histogram of each square pixel grid based on the color gradation data of each square pixel grid to form a feature descriptor of each square pixel grid;
forming pixel blocks by each square pixel grid with preset number;
connecting the feature descriptors of a preset number of square pixel grids in each pixel block in series to obtain the feature descriptors of each pixel block;
and connecting the feature descriptors of all the pixel blocks in series to obtain a combined vector.
In one possible implementation, the apparatus further comprises an image library management module comprising:
the storage unit is used for storing a plurality of characteristic images containing characteristics as an image library;
and the updating unit is used for updating the image library based on an updating operation.
In one possible implementation, the apparatus further includes a cached image management module, the cached image management module including:
the temporary storage unit is used for temporarily storing the image in the image refinement processing process into a cache image library;
and the image deleting unit is used for deleting the temporarily stored images in the cache image library according to a preset period.
An embodiment of the present application discloses an electronic device, as shown in fig. 3, including: a processor 301, a memory 302 and a bus 303, wherein the memory 302 stores machine-readable instructions executable by the processor 301, when the electronic device is operated, the processor 301 communicates with the memory 302 via the bus 303, and the processor 301 executes the machine-readable instructions to perform the following steps:
aiming at each target image in an image library, carrying out size adjustment and rasterization processing on the target image to obtain a first image;
screening layers meeting preset characteristic conditions from all layers of the first image, and integrating the layers to obtain a second image;
carrying out graying, color space standardization and color gradation processing on the second image to obtain a third image;
dividing the third image into a plurality of pixel grids, and comparing a combination vector obtained by combining the color gradation data in the pixel grids with a characteristic vector of a characteristic image;
and if the combined vector is the same as the feature vector, outputting the target image.
In a possible implementation, the step of resizing and rasterizing, by the processor 301, for each target image in the image library, the target image to obtain a first image includes:
for each target image in an image library, zooming the target image into a preset size range according to the preset size range;
and converting the format of the scaled target image from a vector image to a raster image to obtain a first image.
In a possible implementation manner, the step of the processor 301 screening out, from layers of the first image, a layer that meets a preset characteristic condition, and integrating a plurality of layers to obtain a second image includes:
for each layer of the first image, comparing a first aspect ratio of the image features in the layer with a second aspect ratio of the image features in the feature image to obtain a confidence coefficient;
if the confidence coefficient exceeds a target confidence coefficient threshold value, deleting the layer;
and integrating the layers with the confidence degrees not exceeding the target confidence degree threshold value to obtain a second image.
In one possible implementation, the processor 301 is further configured to perform:
and training the confidence level threshold based on a non-maximum suppression algorithm to obtain a target confidence level threshold.
In a possible implementation, the step of performing graying, color space normalization and color scaling on the second image by the processor 301 to obtain a third image includes:
carrying out graying processing on the second image to obtain a grayscale image;
carrying out color space standardization processing on the gray scale map to remove pixels with brightness lower than first brightness and pixels with brightness higher than second brightness in the gray scale map to obtain a standard map;
and carrying out color gradation processing on the standard image to obtain a third image.
In a possible implementation manner, the step of comparing, by the processor 301, a combination vector obtained by dividing the third image into a plurality of pixel cells and combining the grayscale data of the plurality of pixel cells with the feature vector of the feature image includes:
dividing the third image into a number of square pixel cells;
acquiring color level gray data in each square pixel grid;
combining the color gradation data in the square pixel grids to obtain a combined vector;
and comparing the combination vector with the feature vector of the feature image.
In one possible implementation, the step of combining the grayscale data of the color levels in the square pixel cells by the processor 301 to obtain a combination vector includes:
counting a gradient histogram of each square pixel grid based on the color gradation data of each square pixel grid to form a feature descriptor of each square pixel grid;
forming pixel blocks by each square pixel grid with preset number;
connecting the feature descriptors of a preset number of square pixel grids in each pixel block in series to obtain the feature descriptors of each pixel block;
and connecting the feature descriptors of all the pixel blocks in series to obtain a combined vector.
In one possible implementation, the processor 301 is further configured to perform:
storing a plurality of characteristic images containing characteristics as an image library;
updating the image library based on an update operation.
In one possible implementation, the processor 301 is further configured to perform:
temporarily storing the image in the image refinement processing process into a cache image library;
and deleting the temporarily stored images in the cache image library according to a preset period.
The computer program product of the image refinement processing method provided in the embodiment of the present application includes a computer-readable storage medium storing a nonvolatile program code executable by a processor, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, and is not described herein again.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to corresponding processes in the method embodiments, and are not described in detail in this application. In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and there may be other divisions in actual implementation, and for example, a plurality of modules or components may be combined or 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 of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 application 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 functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including 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 application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (12)
1. An image refinement processing method is characterized by comprising the following steps:
aiming at each target image in an image library, carrying out size adjustment and rasterization processing on the target image to obtain a first image;
screening layers meeting preset characteristic conditions from all layers of the first image, and integrating the layers to obtain a second image;
carrying out graying, color space standardization and color gradation processing on the second image to obtain a third image;
dividing the third image into a plurality of pixel grids, and comparing a combination vector obtained by combining the color gradation data in the pixel grids with a characteristic vector of a characteristic image;
and if the combined vector is the same as the feature vector, outputting the target image.
2. The method of claim 1, wherein the step of resizing and rasterizing each target image in the image library to obtain a first image comprises:
for each target image in an image library, zooming the target image into a preset size range according to the preset size range;
and converting the format of the scaled target image from a vector image to a raster image to obtain a first image.
3. The method according to claim 1, wherein the step of screening out layers satisfying a preset characteristic condition from the layers of the first image and integrating the layers to obtain a second image comprises:
for each layer of the first image, comparing a first aspect ratio of the image features in the layer with a second aspect ratio of the image features in the feature image to obtain a confidence coefficient;
if the confidence coefficient exceeds a target confidence coefficient threshold value, deleting the layer;
and integrating the layers with the confidence degrees not exceeding the target confidence degree threshold value to obtain a second image.
4. The method of claim 3, further comprising:
and training the confidence level threshold based on a non-maximum suppression algorithm to obtain a target confidence level threshold.
5. The method of claim 1, wherein the step of graying, color space normalizing and color-scaling the second image to obtain a third image comprises:
carrying out graying processing on the second image to obtain a grayscale image;
carrying out color space standardization processing on the gray scale map to remove pixels with brightness lower than first brightness and pixels with brightness higher than second brightness in the gray scale map to obtain a standard map;
and carrying out color gradation processing on the standard image to obtain a third image.
6. The method according to claim 1, wherein the step of dividing the third image into a plurality of pixel cells and comparing a combination vector obtained by combining the gradation data of the color levels in the plurality of pixel cells with the feature vector of the feature image comprises:
dividing the third image into a number of square pixel cells;
acquiring color level gray data in each square pixel grid;
combining the color gradation data in the square pixel grids to obtain a combined vector;
and comparing the combination vector with the feature vector of the feature image.
7. The method of claim 6, wherein the step of combining the gray scale data of the color levels in the square pixel cells to obtain a combined vector comprises:
counting a gradient histogram of each square pixel grid based on the color gradation data of each square pixel grid to form a feature descriptor of each square pixel grid;
forming pixel blocks by each square pixel grid with preset number;
connecting the feature descriptors of a preset number of square pixel grids in each pixel block in series to obtain the feature descriptors of each pixel block;
and connecting the feature descriptors of all the pixel blocks in series to obtain a combined vector.
8. The method of claim 1, further comprising:
storing a plurality of characteristic images containing characteristics as an image library;
updating the image library based on an update operation.
9. The method of claim 1, further comprising:
temporarily storing the image in the image refinement processing process into a cache image library;
and deleting the temporarily stored images in the cache image library according to a preset period.
10. An image refinement processing apparatus, comprising:
the preprocessing module is used for carrying out size adjustment and rasterization processing on each target image in the image library to obtain a first image;
the integration module is used for screening out layers meeting preset characteristic conditions from all layers of the first image and integrating the layers to obtain a second image;
the processing module is used for carrying out graying, color space standardization and color gradation processing on the second image to obtain a third image;
the comparison module is used for dividing the third image into a plurality of pixel grids and comparing a combination vector obtained by combining the color gradation data in the pixel grids with the characteristic vector of the characteristic image;
and the output module is used for outputting the target image if the combined vector is the same as the characteristic vector.
11. An electronic device, comprising: a processor, a storage medium and a bus, the storage medium storing machine-readable instructions executable by the processor, the processor and the storage medium communicating via the bus when the electronic device is operating, the processor executing the machine-readable instructions to perform the steps of the method according to any one of claims 1 to 9.
12. A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, is adapted to carry out the steps of the method according to any one of claims 1 to 9.
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