CN113806613B - Training image set generation method, training image set generation device, computer equipment and storage medium - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, and discloses a training image set generation method, device, equipment and medium, wherein the method comprises the following steps: acquiring an image set to be generated and the generation quantity; crawling historical labels similar to the target labels and the target descriptions by using a text similarity technology, and determining the category to be migrated; acquiring a corresponding model to be migrated, identifying each image to be generated, obtaining a target area corresponding to each image to be generated, and recording the target area as an image to be processed; performing image enhancement processing on each image to be processed based on factor enhancement proportion to generate a training image; and associating each training image; all the images to be processed and the training images are determined as a training image set. Therefore, the invention realizes the automatic generation of the training image set based on a small amount of image sets to be generated with zero annotation, and improves the accuracy and efficiency of the generation of the training image set. The invention is suitable for the field of artificial intelligence and can further promote the construction of smart cities.
Description
Technical Field
The present invention relates to the field of data acquisition technologies for data processing, and in particular, to a training image set generating method, a training image set generating device, a computer device, and a storage medium.
Background
The artificial intelligence technology is a comprehensive subject, and relates to the technology with wide fields, namely the technology with a hardware level and the technology with a software level. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions. Among them, computer Vision (CV) is a science of how to "look" a machine, and generally includes technologies such as image processing, image recognition, image semantic understanding, image retrieval, and optical character recognition (OCR, optical Character Recognition).
As artificial intelligence technology is mature, image recognition technology is increasingly widely applied to daily life. In order to obtain an image recognition model with higher recognition accuracy, a large number of marked samples are needed to train the image recognition model, and in the prior art, manual input and other manual marking modes are generally adopted to realize the training of the sample, so that labor cost is consumed, the marking efficiency of the sample is greatly reduced, and great difficulty is brought to model training.
Disclosure of Invention
The invention provides a training image set generation method, a device, computer equipment and a storage medium, which can automatically generate a training image set based on a small number of zero-labeling image sets to be generated, reduce the manual labeling time and the workload of collecting training images, improve the labeling efficiency, save the input cost and improve the accuracy and the efficiency of generating the training image set.
A training image set generation method, comprising:
acquiring an image set to be generated and the generation quantity; wherein the image set to be generated comprises a plurality of images to be generated; one target label is associated with one image to be generated; one of the object tags corresponds to one object description; the generation number is the number of training images generated for training through the image set to be generated;
a text similarity technology is applied, history labels similar to the target labels and the target descriptions are crawled in a history label library, and the category to be migrated is determined according to all the crawled history labels;
obtaining a model to be migrated corresponding to the category to be migrated from an identification model library, identifying each image to be generated through the model to be migrated to obtain a target area corresponding to each image to be generated, and recording the image to be generated marked with the target area as an image to be processed;
Performing image enhancement processing on each image to be processed based on factor enhancement proportion to generate training images with the generated number;
associating each training image with the target label associated with the image to be generated corresponding to each training image;
and determining all the images to be processed and all the associated training images as a training image set.
A training image set generation apparatus comprising:
the acquisition module is used for acquiring the image set to be generated and the generation quantity; wherein the image set to be generated comprises a plurality of images to be generated; one target label is associated with one image to be generated; one of the object tags corresponds to one object description; the generation number is the number of training images generated for training through the image set to be generated;
the crawling module is used for crawling history labels similar to the target labels and the target descriptions in a history label library by using a text similarity technology, and determining the category to be migrated according to all the crawled history labels;
the migration module is used for acquiring a model to be migrated corresponding to the category to be migrated from the identification model library, identifying each image to be generated through the model to be migrated to obtain a target area corresponding to each image to be generated, and recording the image to be generated marked with the target area as an image to be processed;
The generating module is used for carrying out image enhancement processing on each image to be processed based on factor enhancement proportion, and generating the training images with the generated quantity;
the association module is used for associating each training image with the target label associated with the image to be generated corresponding to each training image;
and the determining module is used for determining all the images to be processed and all the associated training images as a training image set.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the training image set generation method described above when the computer program is executed.
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the training image set generation method described above.
The invention provides a training image set generation method, a training image set generation device, computer equipment and a storage medium, wherein the training image set generation method comprises the steps of obtaining an image set to be generated and the generation quantity; a text similarity technology is applied, history labels similar to the target labels and the target descriptions are crawled in a history label library, and the category to be migrated is determined according to all the crawled history labels; obtaining a model to be migrated corresponding to the category to be migrated from an identification model library, identifying each image to be generated through the model to be migrated to obtain a target area corresponding to each image to be generated, and recording the image to be generated marked with the target area as an image to be processed; performing image enhancement processing on each image to be processed based on factor enhancement proportion to generate training images with the generated number; associating each training image with the target label associated with the image to be generated corresponding to each training image; all the images to be processed and all the associated training images are determined to be training image sets, so that the training image sets can be automatically generated based on a small number of zero-marked image sets to be generated through text similarity technology, crawling technology and migration learning technology and by applying image enhancement processing, the manual marking time and the workload of collecting training images are reduced, the marking efficiency is improved, the input cost is saved, the accuracy and the efficiency of generating the training image sets are improved, and high-quality training images are provided for subsequent training.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic view of an application environment of a training image set generating method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a training image set generation method in an embodiment of the invention;
FIG. 3 is a flowchart of step S20 of a training image set generation method according to an embodiment of the present invention;
FIG. 4 is a flowchart of step S30 of a training image set generation method according to an embodiment of the present invention;
FIG. 5 is a flowchart of step S40 of a training image set generation method according to an embodiment of the present invention;
FIG. 6 is a flowchart of step S404 of a training image set generation method in an embodiment of the present invention;
FIG. 7 is a schematic block diagram of a training image set generating apparatus in an embodiment of the present invention;
FIG. 8 is a schematic diagram of a computer device in accordance with an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The training image set generating method provided by the invention can be applied to an application environment as shown in fig. 1, wherein a client (computer equipment or terminal) communicates with a server through a network. Among them, clients (computer devices or terminals) include, but are not limited to, personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (ContentDelivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
In one embodiment, as shown in fig. 2, a training image set generating method is provided, and the technical scheme mainly includes the following steps S10-S60:
s10, acquiring an image set to be generated and the generation quantity; wherein the image set to be generated comprises a plurality of images to be generated; one target label is associated with one image to be generated; one of the object tags corresponds to one object description; the generated number is the number of training images generated for training by the image set to be generated.
As will be appreciated, the image set to be generated is a set of all the images to be generated, the images to be generated are historically collected images related to the target labels, the target labels are labels given by classifying the contents of the images to be generated, such as labels of which the target labels are a flip or non-flip, a face or non-face, a certificate or non-certificate, and the like, and the target is described as text contents describing relevant key features of the category of the target labels, for example: for the target label to be an identity document, the target is described as a rectangular document containing a face head portrait and 18-bit characters, and the number of generation is the number of training images generated for training through the image set to be generated.
S20, crawling historical labels similar to the target labels and the target description in a historical label library by using a text similarity technology, and determining the category to be migrated according to all the crawled historical labels.
Understandably, the crawling process is: firstly, crawling out related descriptions of the target labels by using a web crawler technology, summarizing the crawled out related descriptions to extract keywords and occurrence times of each keyword, taking the keywords as network descriptions, secondly, carrying out keyword aggregation on the network descriptions and the target descriptions, namely, giving keywords consistent with the network descriptions to the target descriptions, weighting the target descriptions, namely, giving important words in the target descriptions an object for key comparison, and finally, comparing the history descriptions under each history label with the weighted target descriptions in the history label library by using the text similarity technology, and determining the history label with the highest similarity value after comparison as the history label similar to the target label and the target description, for example: the target label is an identity card, the target description is a rectangular certificate containing a face head portrait and 18-bit characters, the face head portrait, the 18-bit characters and the rectangle are weighted, a text similarity technology is applied, the history label is crawled in the history label library to be an image of a rectangular photo frame containing a landscape of a person, the similarity value of the image and the weighted target description is highest, and the photo frame is determined to be a history label similar to the identity card.
The method comprises the steps of storing all identifiable history labels in a history label library, wherein the history labels are of types which can be identified through models which are trained corresponding to the history labels, one history label corresponds to one history description, the history description is a description of related characteristics of the history labels, a text similarity technology is a technology for comparing similarity degrees between two texts by using a text similarity algorithm, the text similarity algorithm is a technology for performing word embedding (word embedding) conversion processing on the two texts, performing equal weight conversion on the processed two texts according to keywords with weights in the word embedding conversion processing process, and performing similarity calculation on the processed two texts to obtain similarity values between the two texts.
In an embodiment, as shown in fig. 3, in step S20, that is, the text similarity technique is applied, a history tag similar to the target tag and the target description is crawled in a history tag library, and a category to be migrated is determined according to all crawled history tags, which includes:
S201, crawling the network description matched with the target label by using a web crawler technology.
It is to be understood that the web crawler technology is a technology for automatically capturing a program or script of web information according to a certain rule to obtain required information, crawling descriptions related to the target annotation category from the internet, summarizing and refining all the crawled descriptions, extracting keywords by using a TF-IDF algorithm, and determining the occurrence times of each keyword of the extracted keywords as a network description.
Wherein the TF-IDF algorithm is a weighted technique for information retrieval (information retrieval) and text mining (text mining), and the TF-IDF algorithm is a statistical method for evaluating the importance of a word in a text, the importance of a word or word increasing in proportion to the number of times it appears in the document, but decreasing in inverse proportion to the frequency of its occurrence in the corpus.
S202, keyword weighting is carried out on the target description according to the network description, and focusing description is obtained.
Understandably, words conforming to keywords in the network description are found in the target description, and the found words are weighted according to the occurrence times of the keywords, so that the focusing description is obtained.
S203, comparing the history description under each history label with the focusing description in the history label library by using a text similarity technology to obtain a similarity value corresponding to each history description.
The text similarity technology is a technology for comparing the similarity degree between two texts by using a text similarity algorithm, the text similarity algorithm is a technology for performing word embedding (word parts) conversion processing on the two texts, performing equal weight conversion according to a keyword with weight in the word embedding conversion processing, performing similarity calculation on the two texts after processing to obtain a similarity value between the two texts, the word embedding (word parts) conversion processing is also called word2vec, namely, converting a word into a vector (vector) to represent, and assigning equal weight to the vector converted by the keyword with weight in the conversion process, then comparing the historical description under each historical tag with the focus description by using the text similarity algorithm in the history tag library, wherein the output similarity value is obtained by adding equal weight to the vector corresponding to the keyword with weight, namely, the similarity value corresponding to the keyword with weight has higher overall influence on the similarity value of the vector with weight.
S204, determining the history label corresponding to the history description corresponding to the maximum similarity value as the category to be migrated.
Understandably, the history label corresponding to the maximum similarity value is recorded as the category to be migrated, where the category to be migrated indicates the category to be migrated.
The invention realizes that the network description matched with the target label is crawled by applying the web crawler technology; according to the network description, keyword weighting is carried out on the target description, and focusing description is obtained; comparing the history description under each history label with the focusing description in the history label library by using a text similarity technology to obtain a similarity value corresponding to each history description; the history label corresponding to the history description corresponding to the maximum similarity value is determined to be the category to be migrated, so that the category to be migrated is automatically identified from the target classification identification library by applying the web crawler technology, the keyword weighting technology and the text similarity technology, the cost of manual identification is reduced, the category to be migrated can be quickly and accurately found, and the identification accuracy and reliability are improved.
S30, obtaining a model to be migrated corresponding to the category to be migrated from an identification model library, identifying each image to be generated through the model to be migrated to obtain a target area corresponding to each image to be generated, and recording the image to be generated marked with the target area as an image to be processed.
As can be appreciated, the recognition model library stores all the various to-be-migrated models corresponding to the history labels, the to-be-migrated models are trained image recognition models for recognizing the history labels corresponding to the to-be-migrated models, for example, the to-be-migrated models can be deep learning models, or target detection models, the network structure of the to-be-migrated models can be selected according to requirements, for example, the network structure of the to-be-migrated models can be a fast R-CNN model, an SSD and a YOLO model, etc., further, the to-be-migrated models are set as target detection models based on the fact that the network structure is the fast R-CNN model, because an Anchor (sliding basic frame) used for extracting features in the fast R-CNN model has the characteristic of translational invariance, the to-be-generated images are subjected to target recognition through the to-be-migrated models, the process of extracting migration features corresponding to the history labels in the to-be-generated images, the extracted migration features can be positioned into a region corresponding to the migration features, namely, the target region corresponding to the target recognition features can be accurately recognized into the target region through the extracted features, and the target region can be accurately determined in the region to be generated to be compared with the target region.
In an embodiment, as shown in fig. 4, in step S30, that is, the identifying each image to be generated by the model to be migrated to obtain the target area corresponding to each image to be generated includes:
s301, extracting migration features of the images to be generated through the models to be migrated, and carrying out region identification according to the extracted migration features to obtain identification regions of the images to be generated.
Understandably, the migration features corresponding to the history tag are extracted from the image to be generated, and the area conforming to the migration features can be located through the extracted migration features, that is, the area corresponding to the target tag is identified through the features of the similar history tag, and the area corresponding to the identifier is determined as the identification area corresponding to the image to be generated.
S302, performing target fine adjustment on the identification areas of the images to be generated to obtain target areas corresponding to the images to be generated.
Understandably, the target fine tuning process is to perform edge segmentation on an image in a preset range adjacent to an edge of the identification area output by the to-be-migrated model corresponding to the to-be-generated image, identify an edge line, and perform an edge shrinking adjustment process on the identification area along the edge line, so as to obtain the target area corresponding to the identification area.
The method and the device realize migration feature extraction of the images to be generated through the models to be migrated, and region identification is carried out according to the extracted migration features to obtain identification regions of the images to be generated; and performing target fine adjustment on the identification areas of the images to be generated to obtain target areas corresponding to the images to be generated, so that the areas to be identified in the images to be generated can be roughly identified in the model to be migrated, the final target areas to be identified can be accurately defined through the target fine adjustment, and the accuracy and reliability of positioning of the target areas are improved.
In an embodiment, in step S302, the performing target fine adjustment on the identification area of each image to be generated to obtain a target area corresponding to each image to be generated includes:
and carrying out edge segmentation in a preset range adjacent to the identification area of the image to be generated, and identifying edge lines.
Understandably, the edge dividing is a process of identifying the edge in the identification area, identifying the pixel points with the color difference value of the pixel points between the adjacent pixel points being larger than the preset color difference value, dividing the identified pixel points into closed lines, namely dividing the line which can be enclosed into the closed areas, and dividing the edge line, wherein the edge line is the line which can be enclosed into a closed area by the pixel points with the color difference value being larger than the preset color difference value.
And carrying out edge reduction on the identification area according to the edge line to obtain the target area corresponding to the image to be generated.
Understandably, the edge of the identification area is reduced according to the area surrounded by the edge line, the edge is reduced to a process that the identification area is reduced to be overlapped with the edge line and can cover the area surrounded by the edge line, and finally, the reduced area is determined as the target area, and one image to be generated corresponds to one target area.
The invention realizes that the edge line is identified by carrying out edge segmentation in the preset range adjacent to the identification area of the image to be generated; according to the edge line, the identification area is subjected to edge reduction to obtain the target area corresponding to the image to be generated, so that automatic fine adjustment of the identification area is realized, the true areas which want to be accurate are closed, and the accuracy of target area identification is improved.
And S40, performing image enhancement processing on each image to be processed based on factor enhancement proportion, and generating the training images with the generated number.
The factor enhancement ratio is a preset ratio of factors of each image enhancement, the factor enhancement ratio is set on the basis of the total number of the generated image sets, the factor enhancement ratio is an enlarged ratio, namely, the ratio of the number of the generated image sets is enlarged according to the total number of the generated image sets, and the factors of the image enhancement comprise rotation factors, blurring factors, contrast factors, brightness factors and balancing factors; the rotation factors correspond to a rotation angle image enhancement algorithm; the fuzzy factors correspond to a fuzzy image enhancement algorithm; the contrast factors correspond to a contrast image enhancement algorithm; the brightness factor corresponds to a brightness image enhancement algorithm; the balance factors correspond to a color balance image enhancement algorithm; the image enhancement processing based on the factor enhancement proportion is a method for processing each image to be processed according to image enhancement algorithms corresponding to different image enhancement factors, and the factor enhancement proportion is continuously adjusted according to the generated performance index value of the test image in the processing process, and the image is continuously closed to a high performance index value, so that a training image is generated.
Firstly, setting a rotation angle parameter according to the adjusted factor enhancement proportion, and carrying out image rotation processing on each image to be processed according to the rotation angle image enhancement algorithm and the set rotation angle parameter to generate a plurality of rotation test images corresponding to each image to be processed; setting a blur degree parameter, carrying out image blur processing on each image to be processed according to a blur image enhancement algorithm and the set blur degree parameter, and generating a plurality of blur test images corresponding to each image to be processed; setting contrast parameters, carrying out color contrast processing on each image to be processed according to a contrast image enhancement algorithm and the contrast parameters after setting, and generating a plurality of contrast test images corresponding to each image to be processed; setting an ambiguity parameter, and carrying out brightness enhancement processing on each image to be processed according to a brightness image enhancement algorithm and the set brightness parameter to generate a plurality of brightness test images corresponding to each image to be processed; setting color balance parameters, carrying out color balance treatment on each image to be treated according to a brightness color balance image enhancement algorithm and the set color balance parameters, and generating a plurality of balance test images corresponding to each image to be treated; finally, recording all the rotation test image, the blurring test image, the contrast test image, the brightness test image and the balance test image as the test images.
Inputting each test image into the model to be migrated, and identifying the test image by the model to be migrated, so as to determine a performance index value, for example: the performance index value is the accuracy rate, and the occupation ratio of the factors corresponding to the performance index value and the image enhancement factors is adjusted according to the accuracy rate of each test image, namely the factor enhancement ratio is adjusted, so that the test images with high performance index values can be optimally output, and the training images are images required by subsequent training.
In an embodiment, as shown in fig. 5, in the step S40, that is, performing the image enhancement processing based on the factor enhancement ratio on each of the to-be-processed images, generating the generated number of training images includes:
s401, determining the ratio of the generated number to the number of the images to be generated in the image set to be generated as a target duty ratio; the number of the generated images is larger than the number of the images to be generated in the image set to be generated.
Understandably, the number of generated images is divided by the number of all the images to be generated, and rounding is performed to obtain the target duty ratio, wherein the rounding may be rounding up or rounding down, and the number of generated images is greater than the number of images to be generated in the set of images to be generated.
S402, performing multi-equal division processing on all the images to be processed based on the target duty ratio to obtain an image sequence group comprising a plurality of unit image sets which are sequentially ordered.
The method comprises the steps of carrying out sorting on all to-be-processed images corresponding to all to-be-generated images in an image set to be generated, carrying out multi-halving processing on all to-be-processed images after sorting according to the number of target duty ratios, dividing a plurality of unit image sets, wherein one of the unit image sets corresponds to one of the unit image sets in a halving mode, determining the to-be-processed images which remain less than one of the halving mode as one unit image set, taking the serial number of the first to-be-processed image in the unit image set as the serial number of the unit image set in the halving processing, and sequentially sorting all the unit image sets according to the serial numbers, so that all the unit image sets after sequential sorting are determined as the image sequence group.
S403, selecting the first unit image set in the image sequence group.
S404, performing image enhancement processing on the selected unit image set according to the target duty ratio and the preset factor enhancement ratio, generating a plurality of test images corresponding to the unit image set, and determining a performance index value corresponding to the unit image set.
Understandably, the factor enhancement ratio is initially a preset ratio value among a rotation factor dimension, a blur factor dimension, a contrast factor dimension, a brightness factor dimension and a balance factor dimension, and the factor enhancement ratio is adjusted according to an effect of a test image output after image enhancement processing is performed on each unit image set, until after all the unit image sets are subjected to image enhancement processing, the factor enhancement ratio is stopped being adjusted, and the image enhancement processing includes a processing procedure of an image enhancement algorithm corresponding to each factor, for example: the factors comprise rotation factors, blurring factors, contrast factors, brightness factors and balance factors, wherein the rotation factors correspond to a rotation angle image enhancement algorithm, the blurring factors correspond to an image blurring algorithm, the contrast factors correspond to a contrast image enhancement algorithm, the brightness factors correspond to a brightness image enhancement algorithm, the balance factors correspond to a color balance image enhancement algorithm, and the rotation angle image enhancement algorithm is used for carrying out image rotation processing on the image to be generated in the unit image set; performing image blurring processing on the image to be generated in the unit image set by using an image blurring algorithm; performing color contrast processing on the image to be generated in the unit image set by using a contrast image enhancement algorithm; performing brightness enhancement processing on the image to be generated in the unit image set by using a brightness image enhancement algorithm; and performing color balance processing on the to-be-generated images in the unit image set by using a color balance image enhancement algorithm, so as to obtain a plurality of test images corresponding to the to-be-generated images and enhancement areas corresponding to the test images, wherein the enhancement areas are areas which are obtained by synchronously moving or not changing target areas of the to-be-generated images along with the algorithm in the image enhancement processing, the obtained test images are input into the to-be-migrated model, the test images are identified through the to-be-migrated model, an identification result is obtained, a performance index value corresponding to the unit image set is determined according to the coincidence rate between the identification result corresponding to each test image and the enhancement areas, and the performance index value reflects the effect of the generated test image.
In an embodiment, as shown in fig. 6, in step S404, that is, performing image enhancement processing on the selected unit image set according to the target duty ratio and the preset factor enhancement ratio, generating a plurality of test images corresponding to the unit image set, and determining a performance index value corresponding to the unit image set includes:
s4041, determining the number of each factor generation corresponding to one image to be processed according to the target duty ratio and the preset factor enhancement ratio.
Understandably, the total number is divided according to the factor enhancement ratio by taking the number of the target duty ratio as the total number, so as to obtain the number of each factor generation required to be generated for the image to be generated, namely, the number generated for a rotation factor dimension, a blur factor dimension, a contrast factor dimension, a brightness factor dimension and a balance factor dimension, wherein the number of factor generation comprises the number of rotation factor generation, the number of blur factor generation, the number of contrast factor generation, the number of brightness factor generation and the number of balance factor generation, the rotation factor dimension corresponds to the number of rotation factor generation, the number of blur factor dimension corresponds to the number of contrast factor generation, the number of brightness factor dimension corresponds to the number of brightness factor generation, and the balance factor dimension corresponds to the number of balance factor generation.
S4042, performing image enhancement processing on each image to be processed in the unit image set according to the number of generated factors to obtain a test image corresponding to each image to be processed in the unit image set and an enhancement region corresponding to the test image.
Understandably, according to the number of the generated factors, image rotation processing, image blurring processing, color contrast processing, brightness enhancement processing and color balance processing are correspondingly performed on each image to be processed in the unit image set, so as to generate a test image corresponding to each image to be processed and an enhancement region corresponding to each image to be processed in the unit image set.
The rotation factor generation quantity corresponds to a rotation angle image enhancement algorithm; the generation quantity of the fuzzy factors corresponds to a fuzzy image enhancement algorithm; the number of the contrast factors corresponds to the contrast image enhancement algorithm; the brightness factor generation quantity corresponds to a brightness image enhancement algorithm; the number of balance factor generation corresponds to the color balance image enhancement algorithm.
In an embodiment, in step S4042, the performing image enhancement processing on each of the to-be-processed images in the unit image set according to the generated number of each factor to obtain a test image corresponding to each of the to-be-processed images in the unit image set and an enhancement region corresponding to each of the to-be-processed images, includes:
And setting a rotation angle parameter, a ambiguity parameter, a contrast parameter, a brightness parameter and a color balance parameter according to the generation quantity of each factor.
It is understood that the rotation angle parameter is set according to the rotation factor generation number in the factor generation number, the rotation angle parameter is a step length or an angle value of the rotation angle of the image to be processed, the rotation angle parameter can be obtained by dividing the rotation angle by 360 degrees as a total measurement range according to the rotation factor generation number, the rotation angle parameter of each rotation is obtained, the rotation angle parameter of each rotation is determined, the blur degree parameter is set according to the blur factor generation number in the factor generation number, the blur degree parameter is a parameter of radial blur of adjacent pixel points in the step length of each pixel point in the image to be processed, the step length can be set according to the requirement, for example, an increment of 2 is set, the blur degree parameter of each blur step length is set according to the blur factor generation number, the contrast parameter is set according to the contrast factor generation number, the contrast parameter is a step length of each pixel point to be processed is set, the contrast parameter is generated according to the increment of 0.2, the contrast parameter is a contrast parameter is generated according to the contrast factor to be processed, the contrast parameter is generated brightness parameter is generated according to the increment of each increment of 2, the contrast parameter is processed according to the contrast factor generation number in each increment of the factor generation number, and setting the color balance parameters according to the balance factor generation quantity in the factor generation quantity, wherein the color balance parameters are parameters of step sizes for correcting the color shift of the image to be processed.
And respectively carrying out image rotation processing, image blurring processing, color contrast processing, brightness enhancement processing and color balance processing on each image to be processed according to the rotation angle parameter, the ambiguity parameter, the contrast parameter, the brightness parameter and the color balance parameter, and generating a plurality of test images corresponding to each image to be processed and enhancement areas corresponding to the test images.
Understandably, according to the number of the rotation factor generation, setting a rotation angle parameter, and according to the rotation angle image enhancement algorithm and the set rotation angle parameter, performing image rotation processing on each image to be processed, and generating a plurality of rotation test images corresponding to each image to be processed and enhancement areas corresponding to the rotation test images; one image to be processed corresponds to the rotation test images with the same number as the rotation factors; setting a blur degree parameter according to the generated quantity of the blur factors, and carrying out image blur processing on each image to be processed according to a blur image enhancement algorithm and the set blur degree parameter to generate a plurality of blur test images corresponding to each image to be processed and enhancement areas corresponding to each image to be processed; one image to be processed corresponds to the fuzzy test images with the same quantity as the fuzzy factors; setting contrast parameters according to the number of the contrast factor generation, and carrying out color contrast processing on each image to be processed according to a contrast image enhancement algorithm and the contrast parameters after setting to generate a plurality of contrast test images corresponding to each image to be processed and enhancement areas corresponding to the contrast test images; one image to be processed corresponds to the contrast test images with the same number as the contrast factors; setting brightness parameters according to the brightness factor generation quantity, carrying out brightness enhancement processing on each image to be processed according to a brightness image enhancement algorithm and the brightness parameters after setting, and generating a plurality of brightness test images corresponding to each image to be processed and enhancement areas corresponding to each brightness test image to be processed; one of the images to be processed corresponds to the brightness test images with the same number as the brightness factor; setting color balance parameters according to the generated quantity of the balance factors, carrying out color balance processing on each image to be processed according to a brightness color balance image enhancement algorithm and the set color balance parameters, and generating a plurality of balance test images corresponding to each image to be processed and enhancement areas corresponding to the balance test images; one image to be processed corresponds to the balance test images with the same number as the balance factors; recording all of the rotation test image, the blur test image, the contrast test image, the brightness test image, and the balance test image as the test images.
The image rotation processing is a processing process of rotating the rotation angle parameter by taking an image center point as a center, the image blurring processing is a processing process of blurring an image according to the blurring degree parameter, the color contrast processing is a processing process of making a difference value between pixel points in the image to be processed more obvious according to the contrast parameter, the brightness enhancement processing is a processing process of increasing a brightness value of the pixel points in the image to be processed according to the brightness parameter, and the color balance processing is a process of balancing the pixel points in the image to be processed according to the color balance parameter.
The invention realizes that the rotation angle parameter, the ambiguity parameter, the contrast parameter, the brightness parameter and the color balance parameter are respectively set by generating the quantity according to each factor; according to the rotation angle parameter, the ambiguity parameter, the contrast parameter, the brightness parameter and the color balance parameter, respectively performing image rotation processing, image blurring processing, color contrast processing, brightness enhancement processing and color balance processing on each image to be processed, and generating a plurality of test images corresponding to each image to be processed and enhancement areas corresponding to the test images.
S4043, inputting each test image obtained by image enhancement processing into the model to be migrated, and identifying the test image through the model to be migrated to obtain an identification result.
Understandably, inputting the test image into the model to be migrated, extracting implicit features of the test image through the model to be migrated, and identifying regions according to the extracted implicit features, thereby identifying regions with the implicit features as the identification result, wherein the implicit features are migration features corresponding to the migrated model to be migrated.
S4044, determining a performance index value corresponding to the unit image set according to the identification result and the enhancement region corresponding to each test image.
Understandably, the region part in the identification result corresponding to one test image and the region part of the enhancement region are subjected to overlap ratio calculation, so that the accuracy is judged, and finally, the performance index value corresponding to the unit image set and related to each factor is determined.
The method and the device realize that the generation quantity of each factor corresponding to one image to be processed is determined according to the target duty ratio and the preset factor enhancement ratio; performing image enhancement processing on each image to be processed in the unit image set according to the number of generated factors to obtain a test image corresponding to each image to be processed in the unit image set and an enhancement area corresponding to the test image; inputting each test image obtained by image enhancement processing into the model to be migrated, and identifying the test image through the model to be migrated to obtain an identification result; according to the identification result and the enhancement area corresponding to each test image, the performance index value corresponding to the unit image set is determined, so that the identification result of the test image is automatically identified, the performance index value is automatically judged, an adjustment basis is provided for the subsequent factor enhancement proportion, manual identification and adjustment are not needed, the cost is saved, the quality of the subsequent training image set generation is improved, and the unit image set can be closed to high-quality images.
S405, detecting whether there is a unit image set other than the unit image set of which order is first in the image series group.
S406, if the existence of the unit image sets except the first unit image set is detected, deleting the first unit image set from the image sequence group, adjusting the factor enhancement ratio based on the performance index value, and returning to the step of selecting the first unit image set in the image sequence group until the image sequence group is empty, and recording all the test images as the training images.
It is understood that if a unit image set other than the unit image set ranked first is detected in the image sequence group, the unit image set ranked first is removed from the image sequence group, and according to the performance index, the values corresponding to the factors in the factor enhancement ratio are adjusted, that is, the ratio value between the factors is adjusted, the factor corresponding to the accuracy higher than the accuracy mean is increased, the factor corresponding to the accuracy lower than the accuracy mean is decreased, and after the factor enhancement ratio is adjusted, the step of selecting the unit image set ranked first in the image sequence group is performed, and the factor enhancement ratio is continuously adjusted until no unit image set is detected in the image sequence group, that is, the image sequence group is empty, so that all the test images are recorded as the training images.
Thus, the invention realizes that the factor enhancement ratio can be continuously adjusted through the image enhancement processing based on the factor enhancement ratio, thereby generating a high-quality training image.
In an embodiment, after the step S405, that is, after the step of detecting whether the unit image sets except the unit image set of the first order exist in the image sequence group, the method further includes:
if no unit image set except the first unit image set is detected, the unit image sets are processed by image enhancement, and the factor enhancement ratio is not required to be adjusted.
And S50, associating each training image with the target label associated with the image to be generated corresponding to each training image.
Understandably, an association relationship between the training image and the target label associated with the image to be generated corresponding to the image to be processed corresponding to the training image is established.
S60, determining all the images to be processed and all the associated training images as a training image set.
Understandably, all the images to be processed and all the associated training images are recorded as a training image set, and then the training image set is used as a sample of a subsequent training neural network, so that the time and cost for collecting the sample are saved, and the accuracy of the neural network learning is improved.
The invention realizes the generation of the image set and the generation quantity by acquiring the image set to be generated; a text similarity technology is applied, history labels similar to the target labels and the target descriptions are crawled in a history label library, and the category to be migrated is determined according to all the crawled history labels; obtaining a model to be migrated corresponding to the category to be migrated from an identification model library, identifying each image to be generated through the model to be migrated to obtain a target area corresponding to each image to be generated, and recording the image to be generated marked with the target area as an image to be processed; performing image enhancement processing on each image to be processed based on factor enhancement proportion to generate training images with the generated number; associating each training image with the target label associated with the image to be generated corresponding to each training image; all the images to be processed and all the associated training images are determined to be training image sets, so that the training image sets can be automatically generated based on a small number of zero-marked image sets to be generated through text similarity technology, crawling technology and migration learning technology and by applying image enhancement processing, the manual marking time and the workload of collecting training images are reduced, the marking efficiency is improved, the input cost is saved, the accuracy and the efficiency of generating the training image sets are improved, and high-quality training images are provided for subsequent training.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
In an embodiment, a training image set generating device is provided, and the training image set generating device corresponds to the training image set generating method in the embodiment one by one. As shown in fig. 7, the training image set generating device includes an acquisition module 11, a crawling module 12, a migration module 13, a generation module 14, an association module 15, and a determination module 16. The functional modules are described in detail as follows:
an obtaining module 11, configured to obtain a set of images to be generated and a generation number; wherein the image set to be generated comprises a plurality of images to be generated; one target label is associated with one image to be generated; one of the object tags corresponds to one object description; the generation number is the number of training images generated for training through the image set to be generated;
the crawling module 12 is configured to crawl historical tags similar to the target tag and the target description in a historical tag library by using a text similarity technology, and determine a category to be migrated according to all the crawled historical tags;
The migration module 13 is configured to obtain a model to be migrated corresponding to the category to be migrated from an identification model library, identify each image to be generated by using the model to be migrated, obtain a target area corresponding to each image to be generated, and record the image to be generated marked with the target area as a processed image;
a generating module 14, configured to perform image enhancement processing based on factor enhancement ratios on each of the images to be processed, and generate the generated number of training images;
an association module 15, configured to associate each training image with the target label associated with the image to be generated corresponding to each training image;
a determining module 16, configured to determine all the images to be processed and all the associated training images as a training image set.
For specific limitations of the training image set generating device, reference may be made to the above limitation of the training image set generating method, and no further description is given here. The respective modules in the training image set generating device described above may be implemented in whole or in part by software, hardware, or a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a client or a server, and the internal structure of which may be as shown in fig. 8. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a readable storage medium, an internal memory. The readable storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the readable storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a training image set generation method.
In one embodiment, a computer device is provided that includes a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the training image set generation method of the above embodiments when executing the computer program.
In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the training image set generation method of the above embodiment.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (8)
1. A training image set generation method, comprising:
acquiring an image set to be generated and the generation quantity; wherein the image set to be generated comprises a plurality of images to be generated; one target label is associated with one image to be generated; one of the object tags corresponds to one object description; the generation number is the number of training images generated for training through the image set to be generated;
A text similarity technology is applied, history labels similar to the target labels and the target descriptions are crawled in a history label library, and the category to be migrated is determined according to all the crawled history labels;
obtaining a model to be migrated corresponding to the category to be migrated from an identification model library, identifying each image to be generated through the model to be migrated to obtain a target area corresponding to each image to be generated, and recording the image to be generated marked with the target area as an image to be processed;
performing image enhancement processing on each image to be processed based on factor enhancement proportion to generate training images with the generated number;
associating each training image with the target label associated with the image to be generated corresponding to each training image;
determining all the images to be processed and all the associated training images as a training image set;
the text similarity technology is applied, the history labels similar to the target labels and the target descriptions are crawled in a history label library, and the category to be migrated is determined according to all crawled history labels, which comprises the following steps:
Crawling a network description matched with the target tag by using a web crawler technology;
according to the network description, keyword weighting is carried out on the target description, and focusing description is obtained;
comparing the history description under each history label with the focusing description in the history label library by using a text similarity technology to obtain a similarity value corresponding to each history description;
determining the history label corresponding to the history description corresponding to the maximum similarity value as the category to be migrated;
performing image enhancement processing on each image to be processed based on factor enhancement proportion, and generating the generated number of training images, wherein the method comprises the following steps:
determining the ratio of the generated number to the number of the images to be generated in the image set to be generated as a target duty ratio; the generation number is larger than the number of the images to be generated in the image set to be generated;
performing multi-equal division processing on all the images to be processed based on the target duty ratio to obtain an image sequence group comprising a plurality of unit image sets which are sequentially ordered;
selecting the first unit image set in the image sequence group;
Performing image enhancement processing on the selected unit image set according to the target duty ratio and the preset factor enhancement ratio, generating a plurality of test images corresponding to the unit image set, and determining a performance index value corresponding to the unit image set;
detecting whether a unit image set except the unit image set of which the order is first exists in the image sequence group;
if the unit image sets except the first unit image set are detected to exist, deleting the first unit image set from the image sequence group, adjusting the factor enhancement proportion based on the performance index value, returning to the step of selecting the first unit image set in the image sequence group until the image sequence group is empty, and recording all the test images as the training images.
2. The training image set generating method according to claim 1, wherein the identifying each of the images to be generated by the model to be migrated to obtain the target area corresponding to each of the images to be generated includes:
extracting migration characteristics of each image to be generated through the model to be migrated, and carrying out region identification according to the extracted migration characteristics to obtain identification regions of each image to be generated;
And performing target fine adjustment on the identification areas of the images to be generated to obtain target areas corresponding to the images to be generated.
3. The training image set generating method according to claim 2, wherein the performing target fine adjustment on the identification area of each of the images to be generated to obtain a target area corresponding to each of the images to be generated includes:
edge segmentation is carried out in a preset range adjacent to the identification area of the image to be generated, and edge lines are identified;
and carrying out edge reduction on the identification area according to the edge line to obtain the target area corresponding to the image to be generated.
4. The training image set generating method according to claim 1, wherein the performing image enhancement processing on the selected unit image set according to the target duty ratio and a preset factor enhancement ratio to generate a plurality of test images corresponding to the unit image set, and determining a performance index value corresponding to the unit image set includes:
determining the number of each factor generation corresponding to one image to be processed according to the target duty ratio and the preset factor enhancement ratio;
performing image enhancement processing on each image to be processed in the unit image set according to the number of generated factors to obtain a test image corresponding to each image to be processed in the unit image set and an enhancement area corresponding to the test image;
Inputting each test image obtained by image enhancement processing into the model to be migrated, and identifying the test image through the model to be migrated to obtain an identification result;
and determining a performance index value corresponding to the unit image set according to the identification result and the enhancement region corresponding to each test image.
5. The training image set generating method as claimed in claim 4, wherein said performing image enhancement processing on each of said to-be-processed images in said unit image set according to the number of each factor generation to obtain a test image corresponding to each of said to-be-processed images in said unit image set and an enhancement region corresponding to each of said to-be-processed images, comprises:
according to the generation quantity of each factor, respectively setting a rotation angle parameter, a ambiguity parameter, a contrast parameter, a brightness parameter and a color balance parameter;
and respectively carrying out image rotation processing, image blurring processing, color contrast processing, brightness enhancement processing and color balance processing on each image to be processed according to the rotation angle parameter, the ambiguity parameter, the contrast parameter, the brightness parameter and the color balance parameter, and generating a plurality of test images corresponding to each image to be processed and enhancement areas corresponding to the test images.
6. A training image set generating apparatus that implements the training image set generating method according to any one of claims 1 to 5, characterized in that the training image set generating apparatus includes:
the acquisition module is used for acquiring the image set to be generated and the generation quantity; wherein the image set to be generated comprises a plurality of images to be generated; one target label is associated with one image to be generated; one of the object tags corresponds to one object description; the generation number is the number of training images generated for training through the image set to be generated;
the crawling module is used for crawling history labels similar to the target labels and the target descriptions in a history label library by using a text similarity technology, and determining the category to be migrated according to all the crawled history labels;
the migration module is used for acquiring a model to be migrated corresponding to the category to be migrated from the identification model library, identifying each image to be generated through the model to be migrated to obtain a target area corresponding to each image to be generated, and recording the image to be generated marked with the target area as an image to be processed;
The generating module is used for carrying out image enhancement processing on each image to be processed based on factor enhancement proportion, and generating the training images with the generated quantity;
the association module is used for associating each training image with the target label associated with the image to be generated corresponding to each training image;
and the determining module is used for determining all the images to be processed and all the associated training images as a training image set.
7. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the training image set generation method according to any of claims 1 to 5 when executing the computer program.
8. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the training image set generation method of any one of claims 1 to 5.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10013436B1 (en) * | 2014-06-17 | 2018-07-03 | Google Llc | Image annotation based on label consensus |
| CN111582410A (en) * | 2020-07-16 | 2020-08-25 | 平安国际智慧城市科技股份有限公司 | Image recognition model training method and device, computer equipment and storage medium |
| CN111881900A (en) * | 2020-07-01 | 2020-11-03 | 腾讯科技（深圳）有限公司 | Corpus generation, translation model training and translation method, apparatus, device and medium |
| WO2021017261A1 (en) * | 2019-08-01 | 2021-02-04 | 平安科技（深圳）有限公司 | Recognition model training method and apparatus, image recognition method and apparatus, and device and medium |
| CN112765387A (en) * | 2020-12-31 | 2021-05-07 | 中国工商银行股份有限公司 | Image retrieval method, image retrieval device and electronic equipment |
| CN112926654A (en) * | 2021-02-25 | 2021-06-08 | 平安银行股份有限公司 | Pre-labeling model training and certificate pre-labeling method, device, equipment and medium |
-
2021
- 2021-09-29 CN CN202111153778.2A patent/CN113806613B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10013436B1 (en) * | 2014-06-17 | 2018-07-03 | Google Llc | Image annotation based on label consensus |
| WO2021017261A1 (en) * | 2019-08-01 | 2021-02-04 | 平安科技（深圳）有限公司 | Recognition model training method and apparatus, image recognition method and apparatus, and device and medium |
| CN111881900A (en) * | 2020-07-01 | 2020-11-03 | 腾讯科技（深圳）有限公司 | Corpus generation, translation model training and translation method, apparatus, device and medium |
| CN111582410A (en) * | 2020-07-16 | 2020-08-25 | 平安国际智慧城市科技股份有限公司 | Image recognition model training method and device, computer equipment and storage medium |
| CN112765387A (en) * | 2020-12-31 | 2021-05-07 | 中国工商银行股份有限公司 | Image retrieval method, image retrieval device and electronic equipment |
| CN112926654A (en) * | 2021-02-25 | 2021-06-08 | 平安银行股份有限公司 | Pre-labeling model training and certificate pre-labeling method, device, equipment and medium |
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