CN110796642A - Method for determining fruit quality degree and related product - Google Patents
Method for determining fruit quality degree and related product Download PDFInfo
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- CN110796642A CN110796642A CN201910955598.2A CN201910955598A CN110796642A CN 110796642 A CN110796642 A CN 110796642A CN 201910955598 A CN201910955598 A CN 201910955598A CN 110796642 A CN110796642 A CN 110796642A
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Abstract
The embodiment of the application provides a method for determining the quality degree of fruits and a related product, wherein the method comprises the following steps: acquiring a target image of a fruit, wherein the target image comprises a quality degree identification mark; acquiring the quality degree identification mark from the target image; according to quality degree discernment sign, determine the first quality degree of fruit, consequently, accuracy when can promote fruit quality degree and determine.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a method for determining fruit quality degree and a related product.
Background
With the development of the times, people pursue the quality of life more and more. Fruits are an indispensable part of people's lives, but in the existing lives, when people buy the fruits, the quality degree of some fruits is difficult to distinguish according to naked eyes, but when people buy the fruits, the quality degree of the fruits is usually distinguished based on a mode of naked eye observation, so that people can easily buy stale fruits when buying the fruits, and the accuracy of judging the quality degree of the fruits is low.
Disclosure of Invention
The embodiment of the application provides a method for determining the quality degree of fruits and a related product, which can improve the accuracy of determining the quality degree of the fruits.
A first aspect of an embodiment of the present application provides a method for determining a quality degree of a fruit, the method including:
acquiring a target image of a fruit, wherein the target image comprises a quality degree identification mark;
acquiring the quality degree identification mark from the target image;
and determining the first quality degree of the fruit according to the quality degree identification mark.
Optionally, the obtaining the quality degree identifier from the target image includes:
acquiring identification information of the quality degree identification mark from the target image;
determining a target area of the quality degree identification mark according to the identification information;
and extracting the image of the target area to obtain the quality degree identification mark.
Optionally, the quality degree identifier includes a color reference region and a quality degree identifier, and the determining the first quality degree of the fruit according to the quality degree identifier includes:
acquiring first shooting attribute information of the color reference area;
determining an attribute information offset according to the first shooting attribute information and preset shooting attribute information;
acquiring second shooting attribute information of the quality degree identification area;
determining target attribute information of the quality degree identification area according to the second shooting attribute information and the attribute information offset;
and determining a first quality degree of the fruit according to the target attribute information.
Optionally, the determining, according to the target attribute information, a first quality degree of the fruit includes:
acquiring a quality degree corresponding to each piece of sub-attribute information in the N pieces of sub-attribute information to obtain N sub-quality degrees;
and determining a first quality degree of the fruit according to the N sub-quality degrees.
Optionally, the method further includes:
acquiring a reference skin color and skin texture of the fruit according to the target image;
determining the target skin color of the fruit according to the attribute information offset and the reference skin color;
determining a second quality degree of the fruit according to the target skin color and the skin texture;
and determining the target quality degree of the fruit according to the first quality degree and the second quality degree.
A second aspect of embodiments of the present application provides a fruit quality degree determination apparatus, which includes a first acquisition unit, a second acquisition unit, and a determination unit, wherein,
the first acquisition unit is used for acquiring a target image of the fruit, and the target image comprises a quality degree identification mark;
the second obtaining unit is used for obtaining the quality degree identification mark from the target image;
the determining unit is used for determining the first quality degree of the fruit according to the quality degree identification mark.
Optionally, in the aspect of acquiring the quality degree identifier from the target image, the second acquiring unit is configured to:
acquiring identification information of the quality degree identification mark from the target image;
determining a target area of the quality degree identification mark according to the identification information;
and extracting the image of the target area to obtain the quality degree identification mark.
Optionally, the quality degree identifier includes a color reference region and a quality degree identifier region, and in the aspect of determining the first quality degree of the fruit according to the quality degree identifier, the determining unit is configured to:
acquiring first shooting attribute information of the color reference area;
determining an attribute information offset according to the first shooting attribute information and preset shooting attribute information;
acquiring second shooting attribute information of the quality degree identification area;
determining target attribute information of the quality degree identification area according to the second shooting attribute information and the attribute information offset;
and determining a first quality degree of the fruit according to the target attribute information.
Optionally, the target attribute information includes N pieces of sub-attribute information, where N is a positive integer, and in the aspect of determining the first quality degree of the fruit according to the target attribute information, the determining unit is configured to:
acquiring a quality degree corresponding to each piece of sub-attribute information in the N pieces of sub-attribute information to obtain N sub-quality degrees;
and determining a first quality degree of the fruit according to the N sub-quality degrees.
Optionally, the apparatus is further configured to:
acquiring a reference skin color and skin texture of the fruit according to the target image;
determining the target skin color of the fruit according to the attribute information offset and the reference skin color;
determining a second quality degree of the fruit according to the target skin color and the skin texture;
and determining the target quality degree of the fruit according to the first quality degree and the second quality degree.
A third aspect of the embodiments of the present application provides a terminal, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the step instructions in the first aspect of the embodiments of the present application.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program makes a computer perform part or all of the steps as described in the first aspect of embodiments of the present application.
A fifth aspect of embodiments of the present application provides a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps as described in the first aspect of embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has at least the following beneficial effects:
through the target image who obtains fruit, the target image includes quality degree identification mark, follows in the target image, acquire quality degree identification mark, according to quality degree identification mark determines the first quality degree of fruit, consequently, for among the current scheme, adopt artificial mode to differentiate fruit quality degree, can determine the quality degree of fruit according to quality degree identification mark in the image through carrying out the analysis to the image including fruit, can promote the accuracy when the quality degree of fruit is confirmed to a certain extent.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic diagram of a fruit quality level obtaining system according to an embodiment of the present disclosure;
fig. 2A is a schematic flow chart of a method for determining a quality level of a fruit according to an embodiment of the present disclosure;
fig. 2B is a schematic diagram of a quality level identifier according to an embodiment of the present disclosure;
FIG. 2C provides a schematic illustration of a texture region for an embodiment of the present application;
fig. 3 is a schematic flow chart of another method for determining the quality degree of fruit according to the embodiment of the present application;
fig. 4 is a schematic flow chart of another method for determining the quality degree of fruit according to the embodiment of the present application;
fig. 5 is a schematic flow chart of another method for determining the quality degree of fruit according to the embodiment of the present application;
fig. 6 is a schematic structural diagram of a terminal according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a fruit quality level determining apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The electronic device according to the embodiments of the present application may include various handheld devices, vehicle-mounted devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and various forms of User Equipment (UE), Mobile Stations (MS), terminal equipment (terminal), and so on. For convenience of description, the above-mentioned apparatuses are collectively referred to as electronic devices.
In order to better understand the method for determining the fruit quality degree provided by the embodiment of the present application, a brief description will be given below to a fruit quality degree obtaining system to which the method for determining the fruit quality degree is applied. Referring to fig. 1, fig. 1 is a schematic diagram of a fruit quality level obtaining system according to an embodiment of the present disclosure. As shown in fig. 1, fruit quality degree acquisition system includes electronic device, quality degree identification mark and fruit container, wherein, can set up quality degree identification mark in the fruit container, fruit surface etc. and fruit in the fruit container is placed and can be placed fruit in the region, and electronic device includes the camera, specifically can be: electronic device passes through the camera and acquires the target image of fruit, the target image includes quality degree discernment sign, when carrying out image acquisition, can acquire many images, regard the best image of image quality in these many images as the target image, electronic device acquires quality degree discernment sign from the target image, electronic device is according to quality degree discernment sign, determine the first quality degree of fruit, therefore, for in the current scheme, adopt artificial mode to differentiate fruit quality degree, can determine the quality degree of fruit through analyzing the image including fruit, quality degree discernment sign according to in the image determines the quality degree of fruit, accuracy and intelligence when can promote the quality degree of fruit to a certain extent and confirm.
The quality degree of the fruit can be understood as the freshness, rot degree and the like of the fruit, and the higher the quality degree is, the fresher the fruit is and the smaller the rot degree is, and the lower the quality degree is, the fresher the fruit is and the higher the rot degree is.
Referring to fig. 2A, fig. 2A is a schematic flow chart illustrating a method for determining a fruit quality level according to an embodiment of the present disclosure. As shown in fig. 2A, the method for determining the quality of fruit includes steps 201 and 203 as follows:
201. and acquiring a target image of the fruit, wherein the target image comprises a quality degree identification mark.
The quality level identification mark can be understood as a specific pattern or code which can be recognized by computer graphics technology and is used for positioning a reference image, and the corresponding identification program is preset with identification logic for the target mark.
Optionally, when the target image of the fruit is obtained, the target image can be obtained through a camera of the electronic device. In the image acquisition, a plurality of images may be acquired, and an image with the best image quality among the plurality of images may be used as the target image.
Optionally, a method for obtaining a target image of a fruit may include: acquiring images of fruits under a plurality of M wave bands to obtain M reference images; and fusing the M reference images to obtain a target image. The obtaining of the images of the fruit under the plurality of M bands may be understood as illuminating the fruit with the light of the M bands, and then taking a picture of the fruit under the illumination of the light of each band. The light beams in the M bands may be visible light or invisible light, and are only illustrated as distances herein, and are not particularly limited. The method for obtaining the target image by fusing the M reference images may be as follows: extracting each reference image in the M reference images for blocking to obtain M image blocks of each reference image; and obtaining the image block with the highest image quality of each image block under M wave bands to obtain M target image blocks, and combining the M image blocks to obtain a target image. The image quality may include the sharpness of the image, etc.
In this example, through obtaining the image of fruit under M wave bands, fuse this M image, obtain the method of target image, can be according to the difference of the absorption rate of the light of fruit to different wave bands to combine the image block that image quality is the highest, obtain the target image, thereby can promote the accuracy when the target image is obtained to a certain extent.
202. And acquiring a quality degree identification mark from the target image.
The method comprises the steps of locating a region of a quality degree identification mark from a target image, and then obtaining the quality degree identification mark by adopting an image extraction method.
203. And identifying the mark according to the quality degree to determine the first quality degree of the fruit.
And determining the first quality degree of the fruit according to the attribute information of the quality degree identification, wherein the attribute information comprises color, brightness and the like.
In one possible embodiment, a possible method for obtaining the quality degree identifier from the target image includes steps a1-A3 as follows:
a1, acquiring identification information of the quality degree identification mark from the target image;
a2, determining a target area of the quality degree identification mark according to the identification information;
and A3, extracting the image of the target area to obtain a quality degree identification mark.
The identification information of the quality degree identification mark may be an identification icon set at a preset position, and the identification icon is used to identify the position information of the quality degree identification mark. The logo icon may be made of a special material, such as a material having a high exposure characteristic or a material having a low exposure characteristic when a picture is taken. Wherein, the identification icon can be arranged at the center of the quality degree identification mark and the like.
Optionally, the fruit container may further include a positioning identifier, where the positioning identifier is used to perform fast image positioning during image processing, and an image of a specific area in the image may be directly extracted as the target area according to the positioning identifier.
A possible method for determining a target area of the quality degree identification mark according to the identification information may be: acquiring an image in a preset area of the identification information; determining the boundary of the fruit placement area according to the image of the preset area; and determining a target area according to the boundary of the fruit placement area and the identification information. The method specifically comprises the following steps: determining a reference area according to the boundary of the placement area of the fruit; and determining a target area according to the reference area and the shape of the quality degree identification mark. For example, if the reference area is rectangular and the identification mark is rectangular, the target area is a rectangular area having the size of the identification code area centered on the mark icon, the length of the rectangular area is equal to the length of the reference area, and the width of the rectangular area is equal to the width of the reference area.
The method for extracting the image map of the target region can be a local binary pattern, a local map structure and the like.
In this example, through the identification information of quality degree identification, can fix a position information of quality degree identification fast to can promote the efficiency of obtaining quality degree identification to a certain extent.
In one possible embodiment, the quality level identifier includes a color reference area and a quality level identification area, and a possible method for determining a first quality level of the fruit according to the quality level identifier includes steps B1-B5 as follows:
b1, acquiring first shooting attribute information of the color reference area;
b2, determining an attribute information offset according to the first shooting attribute information and preset shooting attribute information;
b3, acquiring second shooting attribute information of the quality degree identification area;
b4, determining target attribute information of the quality degree identification area according to the second shooting attribute information and the attribute information offset;
and B5, determining the first quality degree of the fruit according to the target attribute information.
The first shooting attribute information comprises color difference, brightness, resolution and RGB value of each pixel point.
As shown in fig. 2B, fig. 2B is a schematic diagram of a quality level identifier. The quality degree identification mark comprises a color reference area and a quality degree identification area. And comparing the first shooting attribute information with preset shooting attribute information, and taking the difference between the first shooting attribute information and the preset shooting attribute information as an attribute information offset. It is understood that the value of the first photographing attribute information is higher than the value of the preset photographing attribute information when the offset is positive, and the value of the first photographing attribute information is lower than the value of the preset photographing attribute information when the offset is negative.
And taking the sum of the second shooting attribute information and the attribute information offset as the target attribute information of the quality degree identification area. The target attribute information may include the color of the quality degree identification region, the brightness of the color, and the like. Different colors and brightness can represent different freshness of the fruit.
According to the target attribute information, the method for determining the first quality degree of the fruit can be as follows: and determining a first quality degree corresponding to the target attribute information according to a mapping relation between the preset attribute information and the quality degree. The mapping relation can be obtained through a quality degree determination model. A training method of a possible quality degree determination model comprises the following steps: and training the neural network model by a supervised or unsupervised method, and when the training data is converged, obtaining weight data of each layer of the neural network as the weight data of the quality degree determination model, thus obtaining the quality degree determination model. The sample data during training is the target information and quality level.
Optionally, the target image may be calibrated, and then the target attribute information may be acquired according to the calibrated image. The method for calibrating the target image may be to perform a calibration operation on the image according to the attribute information offset. The attribute information of the quality degree identification area obtained by calibration is the same as the target attribute information.
In this example, the target attribute information is determined by the attribute information offset, and the first quality degree is determined by the target attribute information, so that the accuracy in determining the first quality degree can be improved to a certain extent.
In a possible embodiment, the target attribute information includes N sub-attribute information, and a possible method for determining the first quality degree of the fruit according to the target attribute information includes steps C1-C2, as follows:
c1, acquiring the quality degree corresponding to each piece of the N pieces of sub-attribute information to obtain N sub-quality degrees;
and C2, determining the first quality degree of the fruit according to the N sub-quality degrees.
The quality degree identification region may include a plurality of sub-regions, each sub-region may correspond to N sub-attribute information, and the sub-attribute information may be color information of the sub-region, luminance information of the sub-region, resolution of the sub-region, RGB value of each pixel point in the sub-region, information in the two-dimensional code in the quality degree identification region, historical quality degree information of the fruit, and the like.
The corresponding sub-quality degree can be determined according to the mapping relation between different sub-attribute information and the quality degree. The average of the N sub-quality degrees may be used as the first quality degree, and certainly, the weight calculation may also be performed according to the weight corresponding to each sub-attribute information to obtain the first quality degree.
In this example, the first quality degree is determined according to the sub-quality degrees corresponding to the N pieces of sub-attribute information, and the first quality degree can be determined according to a plurality of factors, so that the accuracy in obtaining the quality degree can be improved to a certain extent.
In a possible embodiment, the first quality level may be further corrected according to the skin color and skin texture of the fruit to obtain a more accurate target quality level, and a possible method for obtaining the target quality level includes steps D1-D4 as follows:
d1, acquiring reference skin color and skin texture of the fruit according to the target image;
d2, determining the target skin color of the fruit according to the attribute information offset and the reference skin color;
d3, determining a second quality degree of the fruit according to the target skin color and skin texture;
d4, determining the target quality degree of the fruit according to the first quality degree and the second quality degree.
The method for obtaining the skin color of the fruit may adopt the method for obtaining the target attribute information, and is not described herein again. The method for obtaining the skin texture can adopt a depth-of-field distance measurement method, an ultrasonic distance measurement method, an infrared distance measurement method and other methods, and because the distances between different areas of the texture and the electronic device are different, the skin texture can be determined according to the distance information by adopting the method. The target quality level may characterize the final freshness of the fruit, with higher quality levels providing fresher fruit and lower quality levels providing fresher fruit.
Optionally, the method for determining the second quality degree of the fruit according to the skin color and the skin texture may be: determining a first reference quality degree according to a mapping relation between the skin color and the quality degree; determining a second reference quality degree according to the skin texture; and taking the average value of the first reference quality degree and the second reference quality degree as the second quality degree. The method for determining the second reference quality degree according to the skin texture may be: determining a texture mutation area according to the skin texture; acquiring color information of the texture mutation region and texture information of the texture mutation region; and determining a second quality degree according to the color information of the texture mutation region and the texture information of the texture mutation region. The texture mutation region can be understood as: in the area where the texture is broken with the texture of the adjacent area, for example, after the fruit is rotted, the texture of the rotted area may have different features from that of the normal area, for example, strawberry, the raised part of the texture of the normal area of strawberry may be plump, the raised part of the texture of the rotted area or the area about to be rotted may be smooth, or the rotted area may have a concave part. As shown in fig. 2C, a schematic texture diagram of the rotted region (abrupt region) and the normal region (normal texture) is shown.
The target quality level may be an average of the first quality level and the second quality level: if the first quality degree is lower than the second quality degree, obtaining weights corresponding to the first quality degree and the second quality degree, performing weight calculation according to the corresponding weights to obtain a target quality degree, and if the first quality degree is higher than or equal to the second quality degree, taking the average value of the first quality degree and the second quality degree as the target quality degree.
In this example, through the reference epidermis colour and the epidermis texture of fruit, determine the second quality degree, determine the target quality degree according to second quality degree and first quality degree to can confirm the target quality degree through first quality degree and second quality degree, accuracy when having promoted the target quality degree and acquireing.
In a possible embodiment, the quality degree of the fruit may be obtained directly according to the quality degree obtaining model, specifically, the target image is input into the quality degree obtaining model, and the quality degree obtaining model outputs the quality degree of the fruit.
Referring to fig. 3, fig. 3 is a schematic flow chart of another method for determining a quality level of fruit according to an embodiment of the present application. As shown in fig. 3, the method for determining the quality of fruit comprises steps 301-305 as follows:
301. acquiring a target image of the fruit, wherein the target image comprises a quality degree identification mark;
302. acquiring identification information of a quality degree identification mark from a target image;
303. determining a target area of the quality degree identification mark according to the identification information;
304. extracting an image of the target area to obtain a quality degree identification mark;
305. and identifying the mark according to the quality degree to determine the first quality degree of the fruit.
In this example, through the identification information of quality degree identification, can fix a position information of quality degree identification fast to can promote the efficiency of obtaining quality degree identification to a certain extent.
Referring to fig. 4, fig. 4 is a schematic flow chart of another method for determining a quality level of fruit according to an embodiment of the present disclosure. As shown in fig. 4, the method for determining the quality of fruit includes steps 401 and 407, which are as follows:
401. acquiring a target image of the fruit, wherein the target image comprises a quality degree identification mark;
402. acquiring a quality degree identification mark from a target image;
the quality degree identification mark comprises a color reference area and a quality degree identification area.
403. Acquiring first shooting attribute information of a color reference area;
404. determining an attribute information offset according to the first shooting attribute information and preset shooting attribute information;
405. acquiring second shooting attribute information of the quality degree identification area;
406. determining target attribute information of the quality degree identification area according to the second shooting attribute information and the attribute information offset;
407. and determining the first quality degree of the fruit according to the target attribute information.
In this example, the target attribute information is determined by the attribute information offset, and the first quality degree is determined by the target attribute information, so that the accuracy in determining the first quality degree can be improved to a certain extent.
Referring to fig. 5, fig. 5 is a schematic flow chart of another method for determining a quality level of fruit according to an embodiment of the present disclosure. As shown in fig. 5, the method for determining the quality of fruit includes steps 501 and 511, which are as follows:
501. acquiring a target image of the fruit, wherein the target image comprises a quality degree identification mark;
502. acquiring a quality degree identification mark from a target image;
the quality degree identification mark comprises a color reference area and a quality degree identification area.
503. Acquiring first shooting attribute information of a color reference area;
504. determining an attribute information offset according to the first shooting attribute information and preset shooting attribute information;
505. acquiring second shooting attribute information of the quality degree identification area;
506. determining target attribute information of the quality degree identification area according to the second shooting attribute information and the attribute information offset;
507. determining a first quality degree of the fruit according to the target attribute information;
508. acquiring reference skin color and skin texture of the fruit according to the target image;
509. determining the target skin color of the fruit according to the attribute information offset and the reference skin color;
510. determining a second quality degree of the fruit according to the target skin color and the skin texture;
511. and determining the target quality degree of the fruit according to the first quality degree and the second quality degree.
In this example, through the reference epidermis colour and the epidermis texture of fruit, determine the second quality degree, determine the target quality degree according to second quality degree and first quality degree to can confirm the target quality degree through first quality degree and second quality degree, accuracy when having promoted the target quality degree and acquireing.
In accordance with the foregoing embodiments, please refer to fig. 6, fig. 6 is a schematic structural diagram of a terminal according to an embodiment of the present application, and as shown in the drawing, the terminal includes a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions, and the program includes instructions for performing the following steps;
acquiring a target image of the fruit, wherein the target image comprises a quality degree identification mark;
acquiring a quality degree identification mark from a target image;
and identifying the mark according to the quality degree to determine the first quality degree of the fruit.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the terminal includes corresponding hardware structures and/or software modules for performing the respective functions in order to implement the above-described functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the terminal may be divided into the functional units according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
In accordance with the above, please refer to fig. 7, fig. 7 is a schematic structural diagram of a device for determining a fruit quality level according to an embodiment of the present application. As shown in fig. 7, the apparatus comprises a first acquisition unit 701, a second acquisition unit 702 and a determination unit 703, wherein,
the first obtaining unit 701 is configured to obtain a target image of a fruit, where the target image includes a quality degree identification;
the second obtaining unit 702 is configured to obtain the quality degree identifier from the target image;
the determining unit 703 is configured to determine the first quality degree of the fruit according to the quality degree identification.
Optionally, in the aspect of acquiring the quality degree identifier from the target image, the second acquiring unit 702 is configured to:
acquiring identification information of the quality degree identification mark from the target image;
determining a target area of the quality degree identification mark according to the identification information;
and extracting the image of the target area to obtain the quality degree identification mark.
Optionally, the quality degree identifier includes a color reference region and a quality degree identifier, and in the aspect of determining the first quality degree of the fruit according to the quality degree identifier, the determining unit 703 is configured to:
acquiring first shooting attribute information of the color reference area;
determining an attribute information offset according to the first shooting attribute information and preset shooting attribute information;
acquiring second shooting attribute information of the quality degree identification area;
determining target attribute information of the quality degree identification area according to the second shooting attribute information and the attribute information offset;
and determining a first quality degree of the fruit according to the target attribute information.
Optionally, the target attribute information includes N pieces of sub-attribute information, where N is a positive integer, and in the aspect of determining the first quality degree of the fruit according to the target attribute information, the determining unit 703 is configured to:
acquiring a quality degree corresponding to each piece of sub-attribute information in the N pieces of sub-attribute information to obtain N sub-quality degrees;
and determining a first quality degree of the fruit according to the N sub-quality degrees.
Optionally, the apparatus is further configured to:
acquiring a reference skin color and skin texture of the fruit according to the target image;
determining the target skin color of the fruit according to the attribute information offset and the reference skin color;
determining a second quality degree of the fruit according to the target skin color and the skin texture;
and determining the target quality degree of the fruit according to the first quality degree and the second quality degree.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the fruit quality degree determination methods as described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program, the computer program causing a computer to perform part or all of the steps of any one of the above-described method embodiments of determining a degree of fruit quality.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and the like.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash memory disks, read-only memory, random access memory, magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A method of determining a quality level of fruit, the method comprising:
acquiring a target image of a fruit, wherein the target image comprises a quality degree identification mark;
acquiring the quality degree identification mark from the target image;
and determining the first quality degree of the fruit according to the quality degree identification mark.
2. The method according to claim 1, wherein the obtaining the quality degree identifier from the target image comprises:
acquiring identification information of the quality degree identification mark from the target image;
determining a target area of the quality degree identification mark according to the identification information;
and extracting the image of the target area to obtain the quality degree identification mark.
3. The method according to claim 1 or 2, wherein the quality level identifier comprises a color reference region and a quality level identifier region, and wherein determining the first quality level of the fruit based on the quality level identifier comprises:
acquiring first shooting attribute information of the color reference area;
determining an attribute information offset according to the first shooting attribute information and preset shooting attribute information;
acquiring second shooting attribute information of the quality degree identification area;
determining target attribute information of the quality degree identification area according to the second shooting attribute information and the attribute information offset;
and determining a first quality degree of the fruit according to the target attribute information.
4. The method of claim 3, wherein the target attribute information includes N sub-attribute information, N being a positive integer, and wherein determining the first quality level of the fruit based on the target attribute information comprises:
acquiring a quality degree corresponding to each piece of sub-attribute information in the N pieces of sub-attribute information to obtain N sub-quality degrees;
and determining a first quality degree of the fruit according to the N sub-quality degrees.
5. The method of claim 3, further comprising:
acquiring a reference skin color and skin texture of the fruit according to the target image;
determining the target skin color of the fruit according to the attribute information offset and the reference skin color;
determining a second quality degree of the fruit according to the target skin color and the skin texture;
and determining the target quality degree of the fruit according to the first quality degree and the second quality degree.
6. A fruit quality degree determination apparatus, characterized in that the apparatus comprises a first acquisition unit, a second acquisition unit, and a determination unit, wherein,
the first acquisition unit is used for acquiring a target image of the fruit, and the target image comprises a quality degree identification mark;
the second obtaining unit is used for obtaining the quality degree identification mark from the target image;
the determining unit is used for determining the first quality degree of the fruit according to the quality degree identification mark.
7. The apparatus according to claim 6, wherein in said obtaining the quality degree identifier from the target image, the second obtaining unit is configured to:
acquiring identification information of the quality degree identification mark from the target image;
determining a target area of the quality degree identification mark according to the identification information;
and extracting the image of the target area to obtain the quality degree identification mark.
8. The apparatus according to claim 6 or 7, wherein the quality level identifier comprises a color reference area and a quality level identifier area, and wherein the determining unit is configured to, in the determining of the first quality level of the fruit based on the quality level identifier:
acquiring first shooting attribute information of the color reference area;
determining an attribute information offset according to the first shooting attribute information and preset shooting attribute information;
acquiring second shooting attribute information of the quality degree identification area;
determining target attribute information of the quality degree identification area according to the second shooting attribute information and the attribute information offset;
and determining a first quality degree of the fruit according to the target attribute information.
9. A terminal, comprising a processor, an input device, an output device, and a memory, the processor, the input device, the output device, and the memory being interconnected, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-5.
10. A computer-readable storage medium, characterized in that the computer storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to perform the method according to any of claims 1-5.
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Cited By (1)
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| CN111299186A (en) * | 2020-02-21 | 2020-06-19 | 杨伟 | Fruit grading method, device and equipment |
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Application publication date: 20200214 |