Test paper color difference identification method and device, electronic equipment and storage medium

Abstract

The embodiment of the application provides a test paper color difference identification method and device, electronic equipment and a storage medium, and belongs to the technical field of test paper reading and analysis. The method comprises the following steps: acquiring a plurality of continuous original test paper images; performing target detection on the original test paper images to obtain the original test paper images in which target regions are framed; obtaining test paper color difference change information with a time sequence according to color information of the target regions and acquisition time of the original test paper images; and identifying test paper color differences of the original test paper images according to the test paper color difference change information. According to the color information of the target regions and the acquisition time of the original test paper images, the embodiment of the application can obtain the test paper color difference change information with the time sequence, and can quickly, accurately and objectively identify test paper with color changes according to time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of test paper reading and analysis, and particularly relates to a test paper color difference identification method and device, an electronic device and a storage medium. BACKGROUND

[0002] Test papers are often widely observed by naked eyes for comparison because their colors can be easily qualitatively distinguished. However, the quantitative detection of test papers on target detection objects is often performed by using standard color comparison cards, but there is a large subjective judgment and error in the test results because the test is also observed by naked eyes.

[0003] With the iteration of computer vision technology in recent years, its application is becoming more and more extensive. The above detection process includes the identification of color assisted by computer vision technology, that is, the color of an object in an image and a video is measured by means of computer vision technology.

[0004] In the related art, after color sampling, static and single-point sampling is often used to reduce the interference of sampling picture artifacts on the color difference identification accuracy, but it cannot achieve objective, accurate and fast detection result response for test papers with color changes over time.

[0005] Therefore, how to objectively, accurately and quickly respond to the detection results of test papers with color changes over time has become a technical problem to be solved. SUMMARY

[0006] The main purpose of the embodiments of the present application is to provide a test paper color difference identification method and device, an electronic device and a storage medium, which aims to improve the objective, accurate and fast detection results of test papers with color changes over time.

[0007] To achieve the above purpose, a first aspect of the embodiments of the present application provides a test paper color difference identification method, which comprises:

[0008] obtaining a plurality of continuous original test paper images;

[0009] performing target detection on the original test paper images to obtain the original test paper images with a target region framed out;

[0010] obtaining test paper color difference change information with time sequence according to the color information of the target region and the acquisition time of the original test paper images;

[0011] identifying the test paper color difference of the original test paper images according to the test paper color difference change information.

[0012] In some embodiments, after the test paper color difference of the original test paper images is identified according to the test paper color difference change information, the method comprises:

[0013] obtaining a preset reference database;

[0014] obtaining the concentration of the target detection object according to the offset degree of the test paper color difference change information with time sequence and the reference interval of the reference database.

[0015] In some embodiments, the target detection on the original test paper image to obtain the original test paper image framing the target region comprises:

[0016] target detection on the original test paper image according to a preset target detection algorithm to determine the target region and the non-target region of the original test paper image.

[0017] In some embodiments, after the step of target detection on the original test paper image to obtain the original test paper image framing the target region, the method comprises:

[0018] dimension reduction processing of the first initial color information of the target region according to a preset algorithm to obtain first candidate color information, and dimension reduction processing of the second initial color information of the non-target region according to the preset algorithm to obtain second candidate color information, wherein the first candidate color information and the second candidate color information are single-dimensional color information.

[0019] In some embodiments, after the step of dimension reduction processing of the first initial color information of the target region according to a preset algorithm to obtain first candidate color information, and dimension reduction processing of the second initial color information of the non-target region according to the preset algorithm to obtain second candidate color information, the method comprises:

[0020] mean operation processing of the first candidate color information of the target region and the second candidate color information of the non-target region to obtain a first standard original test paper image.

[0021] In some embodiments, after the step of dimension reduction processing of the first initial color information of the target region according to a preset algorithm to obtain first candidate color information, and dimension reduction processing of the second initial color information of the non-target region according to the preset algorithm to obtain second candidate color information, the method comprises:

[0022] mean difference operation processing of the first candidate color information of the target region and the second candidate color information of the non-target region to obtain a second standard original test paper image.

[0023] In some embodiments, before the step of target detection on the original test paper image to obtain the original test paper image framing the target region, the method comprises:

[0024] acquire a preset size parameter;

[0025] perform size change on the original test paper image according to the size parameter, so that the multiple frames of continuous original test paper images are in the same size.

[0026] To achieve the above object, a second aspect of the embodiment of the present application provides a test paper color difference recognition device, which comprises:

[0027] an image acquisition module, configured to acquire multiple frames of continuous original test paper images;

[0028] a target detection module, configured to perform target detection on the original test paper images to obtain original test paper images with a target region framed out;

[0029] a color difference analysis module, configured to obtain test paper color difference change information with time sequence according to color information of the target region and acquisition time of the original test paper images;

[0030] a color difference recognition module, configured to recognize test paper color difference of the original test paper images according to the test paper color difference change information.

[0031] To achieve the above object, a third aspect of the embodiment of the present application provides an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor implements the method of the first aspect when executing the computer program.

[0032] To achieve the above object, a fourth aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0033] The test paper color difference recognition method and device, electronic device and storage medium provided by the present application can acquire multiple frames of continuous original test paper images, frame out a target region of the original test paper images, and obtain test paper color difference change information with time sequence according to color information of the target region and acquisition time of the original test paper images. The test paper with color change according to time can be quickly, accurately and objectively recognized. BRIEF DESCRIPTION OF DRAWINGS

[0034] Figure 1 is a flowchart of the test paper color difference recognition method provided by the embodiment of the present application;

[0035] Figure 2 is a schematic diagram of the original test paper image acquired in the target detection and concentration analysis scene of the embodiment of the present application;

[0036] Figure 3is another identification diagram for framing out a target region in the embodiment of the present application;

[0037] Figure 4 is another flow chart of the test paper color difference identification method provided by the embodiment of the present application;

[0038] Figure 5 is another identification diagram for framing out a target region in the embodiment of the present application;

[0039] Figure 6 is another identification diagram for framing out a target region in the embodiment of the present application;

[0040] Figure 7 is another flow chart of the test paper color difference identification method provided by the embodiment of the present application;

[0041] Figure 8 is a ROI diagram for framing out a target region in the embodiment of the present application;

[0042] Figure 9 is another flow chart of the test paper color difference identification method provided by the embodiment of the present application;

[0043] Figure 10 is another flow chart of the test paper color difference identification method provided by the embodiment of the present application;

[0044] Figure 11 is another flow chart of the test paper color difference identification method provided by the embodiment of the present application;

[0045] Figure 12 is another flow chart of the test paper color difference identification method provided by the embodiment of the present application;

[0046] Figure 13 is another flow chart of the test paper color difference identification method provided by the embodiment of the present application;

[0047] Figure 14 is a structural schematic diagram of the test paper color difference identification device provided by the embodiment of the present application;

[0048] Figure 15 is a hardware structural schematic diagram of the electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0050] It should be noted that although the functional modules are divided in the device schematic diagram, and the logical sequence is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the sequence in the flowchart. The terms "first", "second", and the like in the specification and claims and the above-described drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the specification is for the purpose of describing the embodiments of the present application only and is not intended to be limiting of the present application.

[0052] Test papers are often widely observed by the naked eye by people due to their color easy to qualitatively distinguish. The quantitative detection of test papers on target detection objects is often carried out by standard color cards, but due to the same naked eye observation by testers, there is a large subjective judgment and error in the test results.

[0053] With the iteration of computer vision technology in recent years, its application is becoming more and more widely, and the above detection process includes the identification of color assisted by computer vision technology, that is, the color of the object in the image and video is measured by means of computer vision technology.

[0054] In the related art, after color sampling, static and single-point sampling is often used to reduce the interference of sampling picture artifacts on color difference identification accuracy, but it cannot achieve objective, accurate and fast detection result response for test papers with color changes according to time.

[0055] Therefore, how to objectively, accurately and quickly respond to the detection results of test papers with color changes according to time has become a technical problem to be solved.

[0056] Based on this, the embodiments of the present application provide a test paper color difference identification method and device, electronic equipment and storage medium, which aims to quickly, accurately and objectively identify test papers with color changes according to time.

[0057] The test paper color difference identification method and device, electronic equipment and storage medium provided by the embodiments of the present application are specifically described by the following embodiments, and first, the test paper color difference identification method in the embodiments of the present application is described.

[0058] The test paper color difference identification method provided by the embodiments of the present application relates to the technical field of test paper reading and analysis. The test paper color difference identification method provided by the embodiments of the present application can be applied to a terminal, can be applied to a server end, and can also be software running in the terminal or the server end. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, or the like; the server end can be configured as a separate physical server, can be configured as a server cluster or a distributed system formed by multiple physical servers, can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, and big data and artificial intelligence platforms; and the software can be an application that implements the test paper color difference identification method, but is not limited to the above forms.

[0059] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, in which tasks are performed by remote processing devices connected by a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0060] Please refer to Figure 1 , Figure 1 The method in the optional flowchart of the test paper color difference identification method provided by the embodiments of the present application can include but is not limited to steps S101 to S104. Figure 1

[0061] Step S101, obtaining a plurality of frames of continuous original test paper images;

[0062] Step S102, performing target detection on the original test paper images to obtain original test paper images with a target region framed out;

[0063] Step S103, obtaining test paper color difference change information with a time sequence according to color information of the target region and an acquisition time of the original test paper images;

[0064] Step S104, identifying test paper color difference of the original test paper images according to the test paper color difference change information. ​

[0065] Specifically, the original test paper refers to a test paper that has been detected by a target detection object. After the original test paper detects the target object, a reaction region appears on the test paper, which is the target region.

[0066] When each frame of the original test paper image is obtained, the acquisition time of the image can also be obtained. The target detection object can be detected according to the corresponding algorithm for each frame of the original test paper image, so that the target region on each frame of the original test paper can be framed. The color of the target region after the target detection object reacts is uniform, and the color difference between the original test paper is large. Because the color of the target region changes with time after the target region reacts with the target detection object, the processed original test paper image of the color change of the target region in multiple frames, combined with the acquisition time of the original test paper image, can obtain the test paper color difference change information with time sequence. Finally, according to the test paper color difference change information with time sequence, the test paper with color change according to time can be identified.

[0067] Please refer to Figures 2-3 In one example, in the detection of the water gel to the hydrogen sulfide gas solubility in the solution, after the water gel reacts with the hydrogen sulfide gas in the solution, the test paper is put into the reaction bottle, the video of the color change of the test paper is shot by the camera equipment, and the original test paper image of the required time point in the video is intercepted, so as to obtain the original test paper image. The original test paper image of the moment when the test paper is just put into the reaction bottle is obtained, as shown in Figure 2 The acquisition time of the original test paper image is 0 min, and the time is taken as the starting point, and the original test paper image is obtained at 1 min, 2 min, 4 min and 5 min. Then, each frame of the original test paper image is processed, as shown in Figure 3 The target region is framed by using the related algorithm, and the time sequence of the test paper color difference change information of the test paper is obtained according to the color change of the target region and the acquisition time of the original test paper image of the target region, that is, the color of the target region of the test paper at 0 min, 1 min, 2 min, 4 min and 5 min is obtained, so as to form the color change information of the target region with time sequence. Finally, according to the test paper change information, the color change of the test paper after detecting the target detection object can be identified.

[0068] It can be understood that, after the steps S101 to S104 shown in the embodiments of the present application, the original test paper images of the continuous multiple frames are acquired, and the time sequence test paper color difference change information can be obtained in combination with the acquisition time of the corresponding original test paper images. The identification method of the present application relies on computer vision, and the identification method of the present application can be used to quickly, accurately and objectively identify the test paper with color change according to time. In subsequent specific application scenarios, through the quantitative detection by the identification method of the present application, the concentration of the target detection object can be more accurately detected.

[0069] In step S101 of some embodiments, the original test paper images can be acquired by intercepting some frames of original test paper images of a video. The original test paper images can also be obtained by directly photographing by a camera at fixed time points, which is not limited thereto.

[0070] Referring to Figure 4 In some embodiments, step S104 can include but is not limited to steps S105 to S106:

[0071] Step S105, acquiring a preset reference database;

[0072] Step S106, obtaining the concentration of the target detection object according to the offset degree of the time sequence test paper color difference change information and the reference interval of the reference database.

[0073] Specifically, the reference database refers to a database including multiple calibration information about standard samples. For example, a certain calibration information is that, after calibration of a standard sample with a known concentration, the standard curve of the color value-time or the transmittance-time of the standard sample is a certain style. On the standard curve, a curve of a certain reference interval can be found as a reference object of the test paper change information. The offset degree refers to the slope of the curve. According to the offset degree of the curve, the concentration of the target detection object can be calculated. The test paper change information can be prepared into a curve with time as the horizontal coordinate and color value or transmittance as the vertical coordinate, or into a curve with color value or transmittance as the horizontal coordinate and time as the vertical coordinate. The horizontal coordinate of the standard curve is time, and the vertical coordinate is color value or transmittance, or the horizontal coordinate is color value or transmittance, and the vertical coordinate is time.

[0074] Referring to Figures 5-6In one example, the color change information of the test paper obtained from the multiple continuous original test paper images can be prepared into a curve with the ordinate as the color value and the abscissa as the time, which can be referred to as the measured curve in the subsequent process. Then, the preset reference database is obtained, and the reference interval with the highest approximation degree to the measured curve is found in the standard curve in the reference database. The slope of the curve in the reference interval is calculated, and the concentration of the hydrogen sulfide gas can be calculated according to the slope. Please refer to Figure 5 In one test paper, the higher the concentration is, the faster the target region fades, such as from blue to colorless. Please refer to Figure 6 In another test paper, the higher the concentration is, the faster the color of the target region changes, such as from blue to yellow.

[0075] It can be understood that the detection accuracy of the test paper is limited, and even if a computer is used, it is difficult to identify the corresponding difference when the concentrations of the detected objects are similar. However, the color change amplitude and the slope can be amplified by prolonging the penetration time or distance, which can obviously more accurately quantitatively determine the concentration of the detected object. This can effectively make up for the deficiency of the test paper detection and provide a simple and inexpensive method for accurate quantitative detection.

[0076] Please refer to Figure 7 In some embodiments, step S102 can include but is not limited to step S201:

[0077] S201: performing target detection on the original test paper image according to a preset target detection algorithm to determine the target region and the non-target region of the original test paper image.

[0078] Please refer to Figure 8 Specifically, the target detection algorithm includes the OpenCV detection boundary algorithm and the ROI mask method. The color of the non-target region includes the color of the surrounding defects, the intermediate defects, the ambient light, and the test paper itself in the original test paper image. In one example, the target region framed by the ROI mask is as shown in Figure 8 .

[0079] It can be understood that after the target region is framed, there will be other regions that affect the extraction of the color of the target region, that is, the non-target region. Therefore, the relevant regions can be marked as the non-target region as needed. For example, the rough edges of the target region or the intermediate color defects can be marked as the non-target region. For another example, the exposed place in the original test paper image can be marked as the non-target region. In the subsequent step, the color of the non-target region can be excluded when the color of the target region is extracted.

[0080] Please refer to Figure 9In some embodiments, step S201 can be followed by, but is not limited to, step S107:

[0081] S107: performing dimension reduction processing on the first initial color information of the target region according to a preset algorithm to obtain first candidate color information, and performing dimension reduction processing on the second initial color information of the non-target region according to the preset algorithm to obtain second candidate color information, wherein the first candidate color information and the second candidate color information are both single-dimensional color information.

[0082] Specifically, the preset algorithm refers to a Gamma correction algorithm and its improved method. The first initial color information refers to the color in the target region that has not been processed by the preset algorithm. The first candidate color refers to the color in the target region that has been processed by the preset algorithm. The second initial color information refers to the color in the non-target region that has not been processed by the preset algorithm. The second candidate color refers to the color in the non-target region that has been processed by the preset algorithm.

[0083] After obtaining the original test paper image, the pixel color of the image is identified, and the color value of the image pixel is determined using a color difference recognition extractor. The color value can be represented by an R (Red), G (Green), and B (Blue) color value, a hexadecimal color code, or an H (Hue), S (Saturation), and L (Lightness) color value. Then, the colors of the target region and the non-target region are processed by dimension reduction according to a preset algorithm.

[0084] In one example, the RBG mode color value is selected for dimension reduction processing. After determining the original test paper image using a color difference recognition extractor, the colors of the target region and the non-target region are reduced to single-dimensional colors by a Gamma correction algorithm. The specific formula for dimension reduction processing is as follows:

[0085]

[0086] wherein R is the red color value of the identified original test paper image, B is the blue color value of the identified original test paper image, G is the green color value of the identified original test paper image, and Value is the single-dimensional color value.

[0087] It can be understood that after reducing the colors of the target region and the non-target region to single-dimensional colors, the color difference change process can be displayed with single-dimensional color information, the data amount of color information is simplified, and the subsequent preparation into a curve is facilitated, and comparison with the standard curve in the reference database is facilitated.

[0088] Please refer to Figure 10In some embodiments, step S107 can be followed by, but is not limited to, step S108:

[0089] S108: performing mean operation on the first candidate color information of the target region and the second candidate color information of the non-target region to obtain a first standard original test paper image.

[0090] Specifically, the first standard original test paper image refers to the original test paper image after mean operation.

[0091] After reducing the color of the target region and the color of the non-target region to a single dimension color, the single dimension color can also be extracted for mean operation to obtain the average color of the single dimension color, and then the average color is used to fill the image.

[0092] It can be understood that after the target region is framed, the non-target region can still interfere with the extraction and recording of the color of the target region. For example, the edges of the target region or the defects in the middle are prone to uneven color and unobvious change. For another example, the color of the test paper itself and the ambient light can also interfere with the extraction of the accurate color of the target region. Therefore, after the mean operation of the color of the target region and the color of the non-target region, the interference of the color of the non-target region on the recording and extraction of the color of the target region can be reduced.

[0093] It should be noted that in step S108 of some embodiments, the mean operation can be performed multiple times.

[0094] Please refer to Figure 11 In some embodiments, step S107 can be followed by, but is not limited to, step S109:

[0095] S109: performing mean difference operation on the first candidate color information of the target region and the second candidate color information of the non-target region to obtain a second standard original test paper image.

[0096] Specifically, the second standard original test paper image refers to the original test paper image after mean difference operation.

[0097] After reducing the color of the target region and the color of the non-target region to a single dimension color, the single dimension color can also be extracted for mean difference operation to remove the influence of ambient light. The mean difference operation refers to the mean value of the single dimension color of each image segment of each sampling row of the current frame original test paper is subtracted from the mean value of the single dimension color of the same image segment of each sampling row of the previous frame original test paper.

[0098] In one example, in the photographed test paper change video, when the plurality of frames of continuous original test paper images are intercepted, and the color of the target region and the color of the non-target region of each frame of original test paper image are reduced to a single dimension of color, the mean value of the single dimension color of each image segment of each sampling row of the current frame of original test paper is subtracted from the mean value of the single dimension color of the same image segment of each sampling row of the previous frame of original test paper.

[0099] It can be understood that in some cases, during the process of photographing the test paper change and extracting the color of the target region of the original test paper picture, unexpected interference factors may occur, for example, power failure, switching on and off the light, etc. The mean value difference with the plurality of frames of continuous original test paper images can eliminate the unexpected interference factors as much as possible, so that the color of the target region can be extracted more accurately.

[0100] Referring to Figure 12 In some embodiments, step S102 can include but is not limited to step S110 before step S102:

[0101] S110: Obtain a preset size parameter;

[0102] S111: Change the size of the original test paper image according to the size parameter, so that the plurality of frames of continuous original test paper images are in the same size.

[0103] Specifically, after obtaining the plurality of frames of continuous original test paper images, the size of the original test paper images can be unified.

[0104] In one example, the size parameter after image processing is set first, and in the photographed test paper change video, after the plurality of frames of continuous original test paper images are intercepted, the intercepted original test paper images are modified according to the preset size parameter, and the original test paper images with unified size are obtained.

[0105] It can be understood that the test paper color difference recognition method of the present application relies on the technical machine vision method, that is, when processing the original test paper image, the image needs to be input into the image processing model, such as the CNN model. The dimension of the input vector of the image processing model needs to be fixed, otherwise it will cause the dynamic change of the network. Therefore, after unifying the image size, the dimension of the input vector can be fixed.

[0106] Referring to Figure 13 In some embodiments, step S103 can include but is not limited to step S112 before step S103:

[0107] S112: Calibrate the test paper color difference change information with time sequence as a reference.

[0108] Specifically, the test paper color difference change information with time sequence can be prepared into a chroma value-time standard curve or a transmittance-time curve. When prepared into a curve, the test paper color difference change information can be calibrated first, that is, the starting point of the curve is expressed, and the starting point of the curve is constructed.

[0109] It can be understood that the starting point of the curve is constructed first, so as to be compared with the reference interval of the standard curve in the database of the calibration information of the standard sample subsequently.

[0110] Please refer to Figure 14 The embodiment of the present application also provides a test paper color difference identification device, which can implement the test paper color difference method. The device comprises:

[0111] an image acquisition module, configured to acquire a plurality of continuous original test paper images;

[0112] a target detection module, configured to perform target detection on the original test paper images to obtain target test paper images in which target regions are framed;

[0113] a color difference analysis module, configured to obtain test paper color difference change information with time sequence according to color information of the target regions and acquisition time of the original test paper images;

[0114] a color difference identification module, configured to identify test paper color difference of the original test paper images according to the test paper color difference change information.

[0115] The specific implementation of the test paper color difference identification device is basically the same as the specific embodiment of the test paper color difference identification method, and will not be repeated here.

[0116] The embodiment of the present application also provides an electronic device, which comprises a memory and a processor. The memory stores a computer program, and the processor implements the test paper color difference method when executing the computer program. Please refer to Figure 15 , Figure 15 which shows the hardware structure of the electronic device of another embodiment. The electronic device comprises:

[0117] The processor 901 can be implemented in the form of a general-purpose CPU (Central Processing Unit), a microprocessor, an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits, and is used to execute related programs to implement the technical solutions provided by the embodiment of the present application.

[0118] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM), etc. The memory 902 can store an operating system and other application programs. When the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the related program codes are stored in the memory 902 and are called and executed by the processor 901 to implement the test paper color difference recognition method of the embodiments of the present application.

[0119] The input / output interface 903 is configured to realize information input and output.

[0120] The communication interface 904 is configured to realize the communication interaction between the device and other devices. The communication can be realized by a wired manner (for example, a USB, a network cable, etc.) or a wireless manner (for example, a mobile network, WIFI, Bluetooth, etc.).

[0121] The bus 905 is configured to transmit information between various components (for example, the processor 901, the memory 902, the input / output interface 903, and the communication interface 904) of the device.

[0122] The processor 901, the memory 902, the input / output interface 903, and the communication interface 904 are connected to each other through the bus 905 to realize the communication connection between the device.

[0123] The embodiments of the present application also provide a computer readable storage medium, which stores a computer program. The computer program is executed by a processor to implement the test paper color difference recognition method.

[0124] The memory is a non-transitory computer readable storage medium, which can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory can include a high-speed random access memory and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory remotely arranged relative to the processor. These remote memories can be connected to the processor through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0125] The test paper color difference identification method, test paper color difference identification device, electronic equipment and storage medium provided by the embodiments of the present application can obtain a plurality of continuous original test paper images, frame the target region of the original test paper image, and obtain test paper color difference change information with time sequence according to the color information of the target region and the acquisition time of the original test paper image. The test paper with color change according to time can be quickly, accurately and objectively identified. In subsequent specific application scenarios, when quantitative detection is performed, the test paper color difference change information with time sequence can prepare a measured curve of color value-time or a measured curve of light transmittance-time. After comparing the measured curve with the standard curve in the reference database, if a curve with high similarity is found in a certain reference interval of the standard curve, the concentration of the target detection object can be calculated through the offset degree of the curve. The color difference change amplitude and the slope can be amplified by prolonging the penetration time or distance, and the concentration of the target detection object can be more accurately detected.

[0126] The embodiments described in the embodiments of the present application are used to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems.

[0127] Those skilled in the art can understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and can include more or fewer steps than the figures shown, or combine certain steps or different steps.

[0128] The device embodiments described above are only schematic, and the units described as separate components can or can not be physically separated, that is, can be located in one place, or can be distributed on multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0129] Those skilled in the art can understand that all or some of the steps in the above disclosed method, the function modules / units in the system and the device can be implemented as software, firmware, hardware and their appropriate combinations.

[0130] The terms "first", "second", "third", "fourth", and the like in the description and in the claims of this application, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the use of the terms so termed is interchangeable under appropriate circumstances such that the embodiments of the application described herein are, for example, capable of orderly or chronological mundane operation, reverse order operation, based on circuitry availability, based on stated preference or the like, and that "default" or other orderings are thus permissible. Further, the terms "comprise", "comprising", "include", "including", and the like, are specifically intended to be open-ended. That is, references to individual steps and the like do not suhstantially exclude the presence of two or more of a given step or its integral presence in the process, method, system, article, or apparatus having been made with a wider scope. The use of notation such as "first", "second", "third", etc. does not generally limit the areas, but is used to connect like elements or to distinguish one claim from another. These terms can be used interchangeably when appropriate. Terms concerning the relative position of elements can be interpreted such that their use adheres to their normal meaning, but they can also be interpreted to mean the opposite according to specific claims.

[0131] It should be understood that, in the application, "at least one" refers to one or more, and "multiple" refers to two or more. "And / or" is used to describe the relationship between associated objects, which means that there can be three relationships, for example, "A and / or B" can represent three cases: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the front and rear associated objects. "At least one of the following" or similar expressions means any combination of these items, including any combination of single or multiple items. For example, at least one of a, b or c can mean a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0132] In several embodiments provided by the application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the above-mentioned units is only a logical functional division, and actual implementation can have another division manner, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed each other can be indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0133] The units described above as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the application.

[0134] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0135] If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes multiple instructions used to cause a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods in the embodiments of the present application. The foregoing storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various other media that can store programs.

[0136] The preferred embodiments of the embodiments of the present application are described above with reference to the accompanying drawings, and are not intended to limit the scope of the embodiments of the present application. Any modifications, equivalent replacements and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the embodiments of the present application.

Claims

1. A method for identifying color difference in test strips, characterized in that, include: Acquire multiple consecutive frames of raw test strip images; The original test strip image is subjected to target detection according to a preset target detection algorithm to determine the target area and non-target area of ​​the original test strip image; wherein, the target area is the area on the test strip where the reaction occurs, and the non-target area includes the rough edges or color defects in the middle of the target area; The first initial color information of the target region is dimensionality reduced according to a preset algorithm to obtain the first candidate color information, and the second initial color information of the non-target region is dimensionality reduced according to the preset algorithm to obtain the second candidate color information, wherein the first candidate color information and the second candidate color information are both single-dimensional color information. The first candidate color information of the target area and the second candidate color information of the non-target area are averaged to obtain the first standard original test strip image; Based on the color information of the target area and the acquisition time of the original test strip image, test strip color difference change information with a time series is obtained; Based on the color difference change information of the test strip, the color difference of the original test strip image is identified.

2. The test strip color difference identification method according to claim 1, characterized in that, After identifying the color difference of the original test strip image based on the color difference change information of the test strip, the method includes: Obtain the preset reference database; The concentration of the target analyte is obtained based on the degree of offset between the time-series color difference change information of the test strip and the reference interval of the reference database.

3. The test strip color difference identification method according to claim 1, characterized in that, After the steps of performing dimensionality reduction processing on the first initial color information of the target region according to a preset algorithm to obtain first candidate color information, and performing dimensionality reduction processing on the second initial color information of the non-target region according to the preset algorithm to obtain second candidate color information, the method further includes: The mean difference between the first candidate color information of the target area and the second candidate color information of the non-target area is processed to obtain the second standard original test strip image.

4. The test strip color difference identification method according to claim 1, characterized in that, Before the step of performing target detection on the original test strip image according to a preset target detection algorithm to determine the target region and non-target region of the original test strip image, the following steps are included: Get the preset size parameters; The original test strip image is resized according to the size parameters so that multiple consecutive frames of the original test strip image are at the same size.

5. A color difference recognition device for test strips, characterized in that, The device includes: The image acquisition module is used to acquire multiple consecutive frames of raw test strip images; The target detection module is used to perform target detection on the original test strip image according to a preset target detection algorithm to determine the target area and non-target area of ​​the original test strip image; wherein, the target area is the area on the test strip where a reaction occurs, and the non-target area includes the rough edges or color defects in the middle of the target area; The color difference analysis module is used to obtain time-series color difference change information of the test strip based on the color information of the target area and the acquisition time of the original test strip image; A color difference recognition module is used to identify the color difference of the original test strip image based on the color difference change information of the test strip; Wherein, after performing target detection on the original test strip image according to the preset target detection algorithm to determine the target area and non-target area of ​​the original test strip image, the test strip color difference recognition device is further configured to: perform dimensionality reduction processing on the first initial color information of the target area according to the preset algorithm to obtain the first candidate color information, and perform dimensionality reduction processing on the second initial color information of the non-target area according to the preset algorithm to obtain the second candidate color information, wherein the first candidate color information and the second candidate color information are both single-dimensional color information; and perform mean operation processing on the first candidate color information of the target area and the second candidate color information of the non-target area to obtain the first standard original test strip image.

6. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the test paper color difference recognition method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the test paper color difference recognition method according to any one of claims 1 to 4.

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