CN115830351B - Image processing method, apparatus and storage medium - Google Patents
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Abstract
The invention provides an image processing method, an image processing device and a storage medium, which belong to the technical field of image processing and specifically comprise the following steps: acquiring an image set to be processed and extracting the image to obtain an image to be verified; performing similarity evaluation on the image to be verified and other images of the image set to obtain similar images, taking the similar images and the image to be verified as the image set to be verified when the number of the similar images is larger than a first number threshold, and screening selectable images in the image set to be verified through image noise; evaluating the human face similarity of the target human face reference image and the optional image, taking the image smaller than a threshold value as an alternative suspected unqualified image, and taking the rest as a secondary screening image; and obtaining an image quality score based on the image noise and the human face similarity of the secondary screening image, and taking the image quality score smaller than a threshold value as an alternative suspected unqualified image, and taking the rest as a recommended image, thereby further improving the screening efficiency of the high-quality image.
Description
Technical Field
The present invention relates to the field of image processing technologies, and in particular, to an image processing method, apparatus, and storage medium.
Background
When image processing is carried out, particularly when screening of batched character photos is carried out, a plurality of similar photos exist, particularly when the number of the photos is large, the screening of low-quality photos becomes a very difficult work, and in order to realize automatic screening of the photos, a face image test set is established in an authorized patent and authority bulletin No. CN107609493B, a method and a device for optimizing a face image quality evaluation model; identifying the similarity between the face picture to be detected and a sample face picture in a preset face database, and obtaining the identification result of each face picture to be detected according to the similarity and the picture identity information; determining the quality fraction of each face picture to be detected according to the identification result; the face picture to be detected and the corresponding quality score are used as training data to carry out neural network training, and an optimized face picture quality evaluation model and parameters are obtained, but the following technical problems exist:
1. the quality of the image is evaluated and the low-quality image is identified without considering the image noise of the combined image, and when the image is screened, particularly when the number of the images is large, if the first screening cannot be performed by combining the images with large image noise at the same time, the final processing efficiency is obviously reduced.
2. If the number of images to be identified is smaller than a predetermined threshold value, that is, if the number of images to be identified is smaller than the predetermined threshold value, the number of images to be identified is smaller, and if the number of images to be identified is not determined, the final processing efficiency is also affected.
In view of the above technical problems, the present invention provides an image processing method, apparatus, and storage medium.
Disclosure of Invention
In order to achieve the purpose of the invention, the invention adopts the following technical scheme:
according to an aspect of the present invention, there is provided an image processing method.
An image processing method, comprising:
s11, acquiring a batch of image sets to be processed, and sequentially extracting images based on the image sets to obtain images to be verified;
s12, extracting image features based on the image to be verified, carrying out similarity evaluation based on the image features and the image features of other images of the image set to obtain similar images with similarity larger than a first threshold value with the image to be verified, judging whether the number of the similar images is larger than the first number threshold value, if so, entering a step S13, otherwise, removing the similar images and the image to be verified, and returning to the step S11 based on the removed image set as a new image set;
s13, constructing an image set to be verified based on the similar images and the image set to be verified, identifying image noise based on the image set to be verified, taking an image with the image noise larger than a first noise threshold value as an alternative suspected unqualified image, and taking the rest images in the image set to be verified as optional images;
s14, acquiring a target face reference image of an image set, adopting a face similarity evaluation model based on a machine learning algorithm to obtain the face similarity of the target face reference image and the optional image, taking the optional image with the face similarity smaller than a similarity threshold value as an optional suspected unqualified image, and taking the rest of the optional images as secondary screening images;
and S15, obtaining an image quality score based on the image noise and the human face similarity of the secondary screening image, taking the secondary screening image with the image quality score smaller than a first quality threshold as an alternative suspected unqualified image, and taking the rest as a recommended image.
Through the evaluation of the similar images, the technical problem that fewer candidate photos of the similar images or candidate photos without the similar images need excessive screening is avoided, so that the number of screening of candidate suspected unqualified images is further reduced, and the screening efficiency is improved.
The identification and screening of the candidate suspected unqualified images are realized based on the image noise, the human face similarity and the image quality score, so that the number of images needing to be screened is further reduced, the quality evaluation of the images from multiple angles is realized, the screening of the images with high quality is realized, and the accuracy and the effectiveness of the candidate photos are further improved.
In another aspect, in an embodiment of the present application, there is provided a computer device, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor runs the computer program with an image processing method as described above.
In another aspect, the present invention provides a computer storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform an image processing method as described above.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
The above and other features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.
Fig. 1 is a flowchart of an image processing method according to embodiment 1;
FIG. 2 is a flowchart showing specific steps of determining similar images having a similarity to an image to be authenticated greater than a first threshold according to embodiment 1;
fig. 3 is a flowchart of specific steps of face similarity determination according to embodiment 1;
FIG. 4 is a flowchart of specific steps for image quality score determination according to example 1;
fig. 5 is a frame diagram of a computer storage medium in embodiment 3.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus detailed descriptions thereof will be omitted.
The terms "a," "an," "the," and "said" are used to indicate the presence of one or more elements/components/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. in addition to the listed elements/components/etc.
Summary of the prior art problems:
in some fixed occasions, for example, during wedding photo shooting, artistic photo shooting and the like, a large number of identical negative films are often formed in the same posture, and conventionally, the large number of identical negative films are screened in a manual mode, so that time and effort are consumed, and images with higher quality can be missed, and therefore, a need for solving the problem is needed.
Example 1
In order to solve the above-mentioned problems, according to an aspect of the present invention, as shown in fig. 1, there is provided an image processing method, comprising:
s11, acquiring a batch of image sets to be processed, and sequentially extracting images based on the image sets to obtain images to be verified;
specifically, before the extraction of the image to be verified, the number of images of the image set is determined, and if and only if the number of images of the image set is greater than a second number threshold, the extraction of the image to be verified is performed.
For example, if the number of images in the image set to be verified is less than or equal to 3, the extraction of the image set to be verified is not needed at this time, and the amount of data to be processed is obviously low.
S12, extracting image features based on the image to be verified, carrying out similarity evaluation based on the image features and the image features of other images of the image set to obtain similar images with similarity larger than a first threshold value with the image to be verified, judging whether the number of the similar images is larger than the first number threshold value, if so, entering a step S13, otherwise, removing the similar images and the image to be verified, and returning to the step S11 based on the removed image set as a new image set;
specifically, as shown in fig. 2, the specific steps of determining the similar image with the similarity to the image to be verified being greater than the first threshold value are as follows:
s21, obtaining peak signal-to-noise ratios of the image to be verified and other images of the image set, taking other images with the peak signal-to-noise ratios larger than a first signal-to-noise ratio threshold as alternative similar images, and extracting features of a color histogram vector and a color moment vector based on the image to be verified to obtain feature vectors of the image to be verified;
specifically, peak signal-to-noise ratio (PSNR), which is commonly abbreviated as "PSNR," is an engineering term that represents the ratio of the maximum possible power of a signal to the destructive noise power that affects its accuracy of representation. Because many signals have a very wide dynamic range, peak signal-to-noise ratios are often expressed in logarithmic decibels units. The peak signal-to-noise ratio is often used as a measure of the quality of the signal reconstruction in the field of image compression etc., which is often defined simply by means of the Mean Square Error (MSE).
Specifically, the calculation formula of the peak signal-to-noise ratio is:
specifically, the specific steps of color histogram vector extraction are as follows:
1) Adjusting the size of an image, and normalizing an H component histogram obtained under an HSV (Hue-Saturation-Value) space;
2) The value range of the H component in OpenCV is [0, 180], and the H component is divided into 60 areas, wherein each area contains 3 degree levels;
3) Frequency of occurrence within 3 metric orders for each zoneSuperposition summation, extracting 60-dimensional vector v representing image characteristics by frequency composition of pixels in 60 areas 1 。
Specifically, the specific steps of extracting the color moment vector are as follows:
the color moment is a global feature for representing the color information of the image, and the color feature is mainly concentrated in the low-order moment, so that the color distribution of the image can be effectively represented by generally selecting the first-order moment, the second-order moment and the third-order moment.
The first, second and third order color moments of the image are formulated as follows:
wherein: p is p ij Is the pixel value of the j pixel point of the ith color channel component of the three-channel image, and N is the number of pixels.
S22, extracting the characteristics of a color histogram vector and a color moment vector based on the alternative similar image to obtain the characteristic vector of the alternative similar image, performing characteristic point matching based on Euclidean distance between the characteristic vector of the image to be verified and the characteristic vector of the alternative similar image, and obtaining the basic similarity between the image to be verified and the alternative similar image based on the characteristic point;
s23, calculating the structural similarity of the image to be verified and the alternative similar image, obtaining the similarity of the alternative similar image based on the peak signal-to-noise ratio, the basic similarity and the structural similarity of the alternative similar image, and taking the alternative similar image with the similarity larger than a first threshold value as the similar image with the similarity larger than the first threshold value.
Specifically, the first threshold is determined according to the number of images of the image set, wherein the larger the number of images of the image set is, the larger the first threshold is.
Specifically, the calculation formula of the similarity is as follows:
wherein min is a minimum function, Z is the peak signal-to-noise ratio of the alternative similar image, and S1 and S2 are respectively the basic similarity and the structural similarity.
For a specific example, when the similarity of the candidate similar image is 0.69, the first threshold is 0.6, and the candidate similar image is taken as the similar image.
Specifically, the similar images and the images to be verified are eliminated, and the image set after the elimination is used as a new image set, so that the elimination of the images with less similar quantity of the images to be verified is realized.
S13, constructing an image set to be verified based on the similar images and the image set to be verified, identifying image noise based on the image set to be verified, taking an image with the image noise larger than a first noise threshold value as an alternative suspected unqualified image, and taking the rest images in the image set to be verified as optional images;
specifically, for example, the image noise is identified by filtering the image set to be verified in a gaussian filtering mode to obtain a filtered image set, and obtaining the image noise of the image set to be verified based on a difference value between the filtered image set and the image set to be verified.
For example, the key codes for judging the image noise are as follows:
the key code implementation of the algorithm flow is as follows:
- (void)filter
{
[MBProgressHUD showHUDAddedTo:self.view animated:YES];
callback after completion of the/judgment
NSOperation *completeOperation = [NSBlockOperation blockOperationWithBlock:^
{
dispatch_async(dispatch_get_main_queue(), ^{
Method for determining similar images
NSMutableArray *photoPool = [self.photoAssets mutableCopy];
[self comparePhotos:photoPool complete:^{
[MBProgressHUD hideHUDForView:self.view animated:YES];
[self.collectionView reloadData];
}];
});
}];
Determination of/(blur, exposure, etc
for (PhotoModel *model in self.photoAssets) {
AnalyzeOperation *op = [[AnalyzeOperation alloc] initWithModel:model];
[self.queue addOperation:op];
[completeOperation addDependency:op]; }
[self.queue addOperation:completeOperation];
}
Specifically, besides image noise, the ambiguity of the similar images can be identified, and the images with the ambiguity greater than a certain threshold value are used as candidate suspected unqualified images.
Specifically, the objective evaluation may be classified into: full reference image blur assessment (Full Reference Image Blur Assessment, FR-IBA), partial reference image blur assessment (Reduced Reference Image Blur Assessment, RR-IBA), no reference image blur assessment (No Reference Image Blur Assessment, NR-IBA). The objective ambiguity evaluation method can also refer to the objective image quality evaluation method, but as only one index of ambiguity is concerned, the algorithm design is more targeted, and the emphasis should be placed on the extraction of the fuzzy characteristic parameters.
The ambiguity-assessment algorithm can be in any of several categories: (1) Pixel-based techniques, including analyzing statistical characteristics of pixel gray values and correlations between pixels; (2) Based on the transform domain technique, this uses the principle that the more the high frequency components are in the transform domain, the clearer the image, and the less the high frequency components are, the more blurred the image; (3) The image gradient-based technology utilizes the gradient of the image edge to measure the image blurring degree, and the larger the gradient is, the clearer the image is.
The calculation formula of the ambiguity is as follows:
where f (x, y) $ is the original image, d (x, y) $ is the point spread function (Point Spread Function, PSF),
is convolution and n (x, y) is additive noise.
S14, acquiring a target face reference image of an image set, adopting a face similarity evaluation model based on a machine learning algorithm to obtain the face similarity of the target face reference image and the optional image, taking the optional image with the face similarity smaller than a similarity threshold value as an optional suspected unqualified image, and taking the rest of the optional images as secondary screening images;
specifically, as shown in fig. 3, the specific steps of face similarity determination are as follows:
s31, based on the selectable images, detecting an image area in the selectable images with confidence degrees larger than a first confidence degree threshold by adopting a DNN neural network-based face detection model to serve as a face image;
s32, based on the face image and the target face reference image, LBP feature extraction is carried out to respectively obtain LBP features of the face image and the target face reference image;
s33, uniformly dividing the face image and the target face reference image into opposite sub-areas, counting histograms of the face image and the target face reference image in the sub-areas according to LBP values, taking the histograms as distinguishing features, and determining the face similarity of the face image and the target face reference image by adopting a cosine similarity method.
Specifically, the target face reference image of the image set is determined according to the historical face image of the target person of the image set.
And S15, obtaining an image quality score based on the image noise and the human face similarity of the secondary screening image, taking the secondary screening image with the image quality score smaller than a first quality threshold as an alternative suspected unqualified image, and taking the rest as a recommended image.
Specifically, as shown in fig. 4, the specific steps of determining the image quality score are:
s41, constructing an input set based on image noise and human face similarity of the secondary screening image;
s42, transmitting the input set to an image quality evaluation model based on an SSA-PNN neural network algorithm to obtain an evaluation result;
for a specific example, the image quality evaluation model is constructed by the following specific steps:
step 1: SSA algorithm parameters are initialized. The specific parameters include initial sparrow number n, initial sparrow position, the ratio of discoverer to joiner, upper limit value of iteration times, upper and lower boundary values, population warning value R2, safety value ST, etc.
Step 2: and calculating the fitness value of each virtual sparrow.
Step 3: and sequentially updating the positions of the discoverer, the joiner and the alerter in the virtual sparrow group.
Step 4: and (3) iterating the loop until the set loop times are initialized, and if the loop times are not full, turning to the step (2).
Step 5: substituting the smoothing factor parameters obtained by SSA optimization into the PNN, and starting network training on the training set data by the PNN network, wherein the training is completed, the PNN judges the input data and obtains the result of the image quality score of the model.
Specifically, the value range of the image quality score is between 0 and 1, wherein the larger the image quality score is, the higher the image quality of the image is.
Specifically, the location update formula of the finder is:
specifically, adding cauchy variation disturbance to the population after updating the positions of optimal individuals of the population, calculating the fitness value of the sparrow after disturbance, comparing the fitness value with the cauchy variation disturbance, reserving the sparrow population with low fitness value, and then recording the positions of the optimal individuals of the current population;
specifically, the cauchy variation is obtained from a cauchy distribution of continuous probability, and is mainly characterized in that the peak value at zero is smaller, and the peak value is slowly reduced to zero value, so that the variation range is more uniform.
Wherein: x is the original position of the individual,
for individual positions after Cauchy variation, u is a random number between 0 and 1.
S43, determining an image quality score of the secondary screening image based on the evaluation result.
Specifically, the candidate suspected unqualified images and the recommended images are respectively stored by adopting different folders.
In a specific example, the candidate suspected defective image is placed in a candidate suspected defective image folder, and the recommended image is placed in a recommended image folder.
For example, as shown in table 1, a total of 97 films are generated in the actual shooting process, and 10 different photos are respectively corresponding, wherein the number of generated recommended images is 29, and the number of unqualified images is 68, so that the image screening efficiency is greatly improved, and the specific results of performing image screening and outputting the recommended images by adopting the method are as follows:
table 1 recommended image generation results
| Photo type | Number of similar images | Number of failed images | Recommended image quantity |
| Photo 1 | 12 | 8 | 4 |
| Photograph 2 | 15 | 9 | 6 |
| Photograph 3 | 9 | 7 | 2 |
| Photograph 4 | 11 | 8 | 3 |
| Photograph 5 | 15 | 10 | 5 |
| Photograph 6 | 18 | 12 | 3 |
| Photograph 7 | 17 | 11 | 6 |
Through the evaluation of the similar images, the technical problem that fewer candidate photos of the similar images or candidate photos without the similar images need excessive screening is avoided, so that the number of screening of candidate suspected unqualified images is further reduced, and the screening efficiency is improved.
The identification and screening of the candidate suspected unqualified images are realized based on the image noise, the human face similarity and the image quality score, so that the number of images needing to be screened is further reduced, the quality evaluation of the images from multiple angles is realized, the screening of the images with high quality is realized, and the accuracy and the effectiveness of the candidate photos are further improved.
Example 2
In an embodiment of the present application, a computer device is provided, including: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor runs the computer program with an image processing method as described above.
Example 3
As shown in fig. 5, the present invention provides a computer storage medium having a computer program stored thereon, which when executed in a computer causes the computer to execute an image processing method as described above.
In the several embodiments provided in this application, it should be understood that the disclosed systems and methods may be implemented in other ways as well. The system embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present invention as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present invention. The technical scope of the present invention is not limited to the description, but must be determined according to the scope of claims.
Claims (12)
1. An image processing method, comprising:
s11, acquiring a batch of image sets to be processed, and sequentially extracting images based on the image sets to obtain images to be verified;
s12, extracting image features based on the image to be verified, carrying out similarity evaluation based on the image features and the image features of other images of the image set to obtain similar images with similarity larger than a first threshold value with the image to be verified, judging whether the number of the similar images is larger than the first number threshold value, if so, entering a step S13, otherwise, removing the similar images and the image to be verified, and returning to the step S11 based on the removed image set as a new image set;
s13, constructing an image set to be verified based on the similar images and the image set to be verified, identifying image noise based on the image set to be verified, taking an image with the image noise larger than a first noise threshold value as an alternative suspected unqualified image, and taking the rest images in the image set to be verified as optional images;
s14, acquiring a target face reference image of an image set, adopting a face similarity evaluation model based on a machine learning algorithm to obtain the face similarity of the target face reference image and the optional image, taking the optional image with the face similarity smaller than a similarity threshold value as an optional suspected unqualified image, and taking the rest of the optional images as secondary screening images;
and S15, obtaining an image quality score based on the image noise and the human face similarity of the secondary screening image, taking the secondary screening image with the image quality score smaller than a first quality threshold as an alternative suspected unqualified image, and taking the rest as a recommended image.
2. An image processing method according to claim 1, characterized in that the number of images of the image set is determined before the extraction of the image to be verified is performed, and the extraction of the image to be verified is performed if and only if the number of images of the image set is greater than a second number threshold.
3. An image processing method according to claim 1, wherein the specific step of determining similar images having a similarity to the image to be verified greater than a first threshold value is:
acquiring peak signal-to-noise ratios of the image to be verified and other images of the image set, taking other images with the peak signal-to-noise ratios larger than a first signal-to-noise ratio threshold as alternative similar images, and extracting features of a color histogram vector and a color moment vector based on the image to be verified to obtain feature vectors of the image to be verified;
extracting the characteristics of a color histogram vector and a color moment vector based on the candidate similar image to obtain a characteristic vector of the candidate similar image, performing characteristic point matching based on Euclidean distance between the characteristic vector of the image to be verified and the characteristic vector of the candidate similar image, and obtaining basic similarity between the image to be verified and the candidate similar image based on the characteristic point;
calculating the structural similarity of the image to be verified and the alternative similar image, obtaining the similarity of the alternative similar image based on the peak signal-to-noise ratio, the basic similarity and the structural similarity of the alternative similar image, and taking the alternative similar image with the similarity larger than a first threshold value as the similar image with the similarity larger than the first threshold value.
4. A method of image processing according to claim 3, wherein the color moment vector is represented using third order moments.
5. An image processing method according to claim 1, wherein the first threshold is determined based on the number of images of the image set, wherein the greater the number of images of the image set, the greater the first threshold.
6. The image processing method as claimed in claim 4, wherein the similarity is calculated by the formula:
7. The image processing method as claimed in claim 1, wherein the specific steps of face similarity determination are:
based on the selectable images, detecting an image area in the selectable images with confidence coefficient larger than a first confidence coefficient threshold value by adopting a DNN neural network-based face detection model to serve as a face image;
performing LBP feature extraction based on the face image and the target face reference image to respectively obtain LBP features of the face image and the target face reference image;
and uniformly dividing the face image and the target face reference image into opposite sub-areas, counting histograms of the face image and the target face reference image in the sub-areas according to LBP values, taking the histograms as distinguishing features, and determining the face similarity of the face image and the target face reference image by adopting a cosine similarity method.
8. The image processing method of claim 1, wherein the target face reference image of the image set is determined based on historical face images of target persons of the image set.
9. An image processing method according to claim 1, wherein the specific step of determining the image quality score is:
constructing an input set based on image noise and human face similarity of the secondary screening image;
transmitting the input set to an image quality evaluation model based on an SSA-PNN neural network algorithm to obtain an evaluation result;
and determining an image quality score of the secondary screening image based on the evaluation result.
10. The image processing method according to claim 1, wherein the candidate suspected defective image and the recommended image are stored in different folders, respectively.
11. A computer device, comprising: a communicatively coupled memory and processor, and a computer program stored on the memory and capable of running on the processor, characterized by: the processor, when running the computer program, performs an image processing method according to any one of claims 1-10.
12. A computer storage medium having stored thereon a computer program which, when executed in a computer, causes the computer to perform an image processing method as claimed in any of claims 1-10.
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