CN108389230A - Refrigerator capacity automatic testing method, system, equipment and storage medium - Google Patents
Refrigerator capacity automatic testing method, system, equipment and storage medium Download PDFInfo
- Publication number
- CN108389230A CN108389230A CN201810188121.1A CN201810188121A CN108389230A CN 108389230 A CN108389230 A CN 108389230A CN 201810188121 A CN201810188121 A CN 201810188121A CN 108389230 A CN108389230 A CN 108389230A
- Authority
- CN
- China
- Prior art keywords
- refrigerator
- region
- commodity
- storage rack
- image
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20021—Dividing image into blocks, subimages or windows
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Computational Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Multimedia (AREA)
- General Engineering & Computer Science (AREA)
- Computing Systems (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Geometry (AREA)
- Cold Air Circulating Systems And Constructional Details In Refrigerators (AREA)
Abstract
The present invention provides a kind of refrigerator capacity automatic testing method, system, equipment and storage mediums, including：Acquire several images, the refrigerator region in described image is identified on the image, refrigerator in described image includes that at least one transparent window for being used to check commodity and at least one put the magazine of the commodity, and the magazine is arranged on the inside of the transparent window；The magazine is identified on the refrigerator region；And then identify the storage position region in each corresponding commodity region of commodity and commodity to be stored in the magazine, according to the memory space for storing position region described in the areal calculation in a commodity region；The memory space in cumulative each storage position region and the memory space in each commodity region generate the memory capacity of the refrigerator.The present invention can realize automatically generating for refrigerator memory capacity, carry out the acquisition of image without artificial, and calculating is identified to image, save manpower, improve the computational efficiency of refrigerator capacity.
Description
Technical field
The present invention relates to artificial intelligence, and in particular, to a kind of refrigerator capacity automatic testing method, system, equipment and deposits
Storage media.
Background technology
Businessman often leases some refrigerators in public places such as market, subway, railway stations and carries out selling for some commodity.Such as
Some laughable companies lease refrigerator and carry out selling for bottled or canned client in public places.But the quantity of these refrigerators compared with
More, being distributed extremely wide and these refrigerators generally has the manager of public place to safeguard.Therefore, businessman is difficult to arrange people
Power goes to check the deployment scenarios of commodity in the arrangement of commodity in the capacity of each refrigerator, each refrigerator and refrigerator.
With the development of artificial intelligence technology, the especially appearance of deep learning method, image recognition is made to start to have in fact
With the value of change.Therefore, can be in terms of artificial intelligence, above-mentioned technical problem proposes a solution.
Invention content
For the defects in the prior art, the object of the present invention is to provide a kind of refrigerator capacity automatic testing method, system,
Equipment and storage medium.
According to refrigerator capacity automatic testing method provided by the invention, include the following steps：
Step S1：Several images are acquired, identify the refrigerator region in described image, described image on the image
On refrigerator include at least one transparent window for checking commodity and at least one put the commodity magazine, the storing
Frame is arranged on the inside of the transparent window；
Step S2：The magazine is identified on the refrigerator region；
Step S3：And then identify the storage position in each commodity corresponding commodity region and commodity to be stored in the magazine
Region, according to the memory space for storing position region described in the areal calculation in a commodity region；
Step S4：The memory space in cumulative each storage position region and the memory space in each commodity region generate institute
State the memory capacity of refrigerator.
Preferably, each described to deposit when the magazine includes multiple storage racks being arranged in order in the vertical direction
Put frame include it is multiple in the horizontal direction on the storage position that is arranged in order, further include following steps：
Step M1：Go out each storage rack in the refrigerator region recognition；
Step M2：The corresponding commodity region of each commodity on each storage rack and commodity to be stored are identified successively
Position region is stored, and then according on the areal calculation in the commodity region storage rack on each storage rack
The memory space in the storage position region；
Step M3:The storage of the memory space and each commodity region in the storage position region on cumulative each storage rack
Space generates the memory capacity of each storage rack, and then the memory capacity of cumulative each storage rack generates the refrigerator
Memory capacity.
Preferably, when on a storage rack without storage commodity, according to the upper storage on the basis of the storage rack
The memory capacity of the memory capacity of frame and next storage rack and average value determine the memory capacity of the storage rack.
Preferably, the step S1 includes the following steps：
Step S101：Multiple training images for refrigerator identification are obtained, refrigerator image is established using the training image
Identification model；
Step S102：Described image is obtained, if extracting the subgraph of dried object in described image；
Step S103：The subgraph is matched with described image identification model, if in the subgraph of the dried object
It identifies the subgraph of refrigerator, and then determines refrigerator region.
Preferably, the step S1 includes the following steps：
Step S101：Collected described image is subjected to denoising to filter desultory point；
Step S102：Described image after filtering desultory point is subjected to grey level histogram stretching, to protrude refrigerator region；
Step S103：Described image is subjected to binary conversion treatment and generates binary image, to obtain refrigerator region and refrigerator
The location information in region；
Step S104：And then feature extraction is carried out to the binary image, it is screened by area, length and curvature parameters
Go out refrigerator region；
Step S105：The refrigerator region finally is determined by stencil matching to the refrigerator region of extraction.
Preferably, the step M1 includes the following steps：
Step M101：Multigroup training image for storage rack identification is obtained, every group of training image, which includes multiple, has one
The storage rack of same settings quantity establishes storage rack identification model using multigroup training image；
Step M102：Described image is obtained, the refrigerator region of described image is matched with the storage rack identification model, it is real
The identification in storage rack region in existing described image.
Preferably, the step M1 includes the following steps：
Step M101：Described image is clustered with the numerical value of the storage rack of multiple settings, generates multiple be separated from each other
Set of pixels；
Step M102：Calculate the central point of each set of pixels in the set of pixels at a distance from each pixel, and
The central point of each set of pixels is at a distance from each pixel of the adjacent set of pixels；
Step M103：In the numerical value of the storage rack of the multiple setting, the target value of a storage rack is selected, it is described to deposit
Put frame target value each of make the central point of the set of pixels in the set of pixels at a distance from each pixel it is minimum and every
The central point of a set of pixels is maximum at a distance from each pixel of the adjacent set of pixels；
Step M104：Each storage rack described in clustering recognition is carried out by the target value of the storage rack.
According to refrigerator capacity automatic checkout system provided by the invention, for realizing the refrigerator capacity side of detection automatically
Method, including：
Image capture module identifies the refrigerator area in described image on the image for acquiring several images
Domain, the refrigerator in described image include that at least one transparent window for checking commodity and at least one put the storing of the commodity
Frame, the magazine are arranged on the inside of the transparent window；
Magazine identification module, for identifying the magazine on the image；
Memory space computing module, for identification corresponding commodity region of each commodity and quotient to be stored in the magazine
The storage position region of product, according to the memory space for storing position region described in the areal calculation in a commodity region；
Memory capacity generation module, the memory space for the cumulative each storage position region and each commodity region
Memory space generates the memory capacity of the refrigerator.
According to refrigerator capacity automatic checkout equipment provided by the invention, including：
Processor；
Memory, wherein being stored with the executable instruction of the processor；
Wherein, the processor is configured to detect automatically to execute the refrigerator capacity via the execution executable instruction
The step of method.
According to computer readable storage medium provided by the invention, for storing program, described program is performed realization
The step of refrigerator capacity automatic testing method.
Compared with prior art, the present invention has following advantageous effect：
The present invention can be by collected a variety of images, the refrigerator on automatic identification image, and then identify in magazine
Commodity and commodity to be stored storage position region, to realize automatically generating for refrigerator memory capacity, without manually carrying out figure
The acquisition of picture, and calculating is identified to image, manpower is saved, the computational efficiency of refrigerator capacity is improved.
Description of the drawings
Upon reading the detailed description of non-limiting embodiments with reference to the following drawings, other feature of the invention,
Objects and advantages will become more apparent upon：
Fig. 1 is the step flow chart of refrigerator capacity automatic testing method in the present invention；
Fig. 2 is in the present invention when there are the step flows of refrigerator capacity automatic testing method when storage rack in magazine
Figure；
Fig. 3 is the step flow chart for identifying refrigerator in the present invention in first embodiment；
Fig. 4 is the step flow chart for identifying refrigerator in the present invention in second embodiment；
Fig. 5 is the step flow chart for identifying storage rack in the present invention in first embodiment；
Fig. 6 is the step flow chart for identifying storage rack in the present invention in second embodiment；
Fig. 7 is the module diagram of refrigerator capacity automatic checkout system in the present invention；
Fig. 8 is the structural schematic diagram of refrigerator capacity automatic checkout equipment in the present invention；And
Fig. 9 is the structural schematic diagram of this present invention Computer readable storage medium storing program for executing.
Specific implementation mode
With reference to specific embodiment, the present invention is described in detail.Following embodiment will be helpful to the technology of this field
Personnel further understand the present invention, but the invention is not limited in any way.It should be pointed out that the ordinary skill of this field
For personnel, without departing from the inventive concept of the premise, various modifications and improvements can be made.These belong to the present invention
Protection domain.
In the present embodiment, Fig. 1 is the step flow chart of refrigerator capacity automatic testing method in the present invention.As shown in Figure 1,
The present invention provides a kind of refrigerator capacity automatic testing method, includes the following steps：
Step S1：Several images are acquired, identify the refrigerator region in described image, described image on the image
On refrigerator include at least one transparent window for checking commodity and at least one put the commodity magazine, the storing
Frame is arranged on the inside of the transparent window；
Step S2：The magazine is identified on the image；
Step S3：And then identify the storage position in each commodity corresponding commodity region and commodity to be stored in the magazine
Region, according to the memory space for storing position region described in the areal calculation in a commodity region；
Step S4：The memory space in cumulative each storage position region and the memory space in each commodity region generate institute
State the memory capacity of refrigerator.
In the present embodiment, described image passes through the bats such as mobile phone, camera, the robot for loading camera or unmanned plane
It takes the photograph.
Fig. 2 is in the present invention when there are the step flows of refrigerator capacity automatic testing method when storage rack in magazine
Figure, as shown in Fig. 2, when the magazine includes multiple storage racks being arranged in order in the vertical direction, each storage
Frame include it is multiple in the horizontal direction on the storage position that is arranged in order, further include following steps：
Step M1：Go out each storage rack in the refrigerator region recognition；
Step M2：The corresponding commodity region of each commodity on each storage rack and commodity to be stored are identified successively
Position region is stored, and then according on the areal calculation in the commodity region storage rack on each storage rack
The memory space in the storage position region；
In the present embodiment, the quantity of storage commodity may be used to indicate in the memory space.As when memory space
When numerical value is 3, the space that three commodity can be stored on the storage rack is indicated.
Step M3:The storage of the memory space and each commodity region in the storage position region on cumulative each storage rack
Space generates the memory capacity of each storage rack, and then the memory capacity of cumulative each storage rack generates the refrigerator
Memory capacity.
In variation, when on a storage rack without storage commodity, according to upper one on the basis of the storage rack
The memory capacity of the memory capacity of storage rack and next storage rack and average value determine the memory capacity of the storage rack.It is deforming
Example in, on refrigerator on a storage rack store commodity small volume, the commodity amount that can be stored is more, thus memory capacity compared with
Greatly, as being 8, the volume of storage commodity is larger on the lower storage rack of refrigerator, and the commodity amount that can be stored is less, therefore stores
Capacity is smaller, is such as 6, then the capacity that can calculate the storage rack is 7.
Fig. 3 is the step flow chart for identifying refrigerator in the present invention in first embodiment；As shown in figure 3, the step S1 packets
Include following steps：
Step S101：Multiple training images for refrigerator identification are obtained, refrigerator image is established using the training image
Identification model；
Step S102：Described image is obtained, if extracting the subgraph of dried object in described image；
Step S103：The subgraph is matched with described image identification model, if the subgraph in the dried object is known
Do not go out the subgraph of refrigerator, and then determines refrigerator region.
Fig. 4 is the step flow chart for identifying refrigerator in the present invention in second embodiment；As shown in figure 4, the step S1 packets
Include following steps：
Step S101：Collected described image is subjected to denoising to filter desultory point；
Step S102：Described image after filtering desultory point is subjected to grey level histogram stretching, to protrude refrigerator region；
Step S103：Described image is subjected to binary conversion treatment and generates binary image, to obtain refrigerator region and refrigerator
The location information in region；
Step S104：And then feature extraction is carried out to the binary image, it is screened by area, length and curvature parameters
Go out refrigerator region；
Step S105：The refrigerator region finally is determined by stencil matching to the refrigerator region of extraction.
In variation, refrigerator can also be identified by the way of machine learning, specifically, the step S1 includes following step
Suddenly：
Step S101：Multiple training images for refrigerator identification are obtained, refrigerator image is established using the training image
Identification model；
Step S102：Described image is obtained, described image is matched with described image identification model, is realized in described image
The identification in refrigerator region.
In this embodiment, the training image for being used for refrigerator identification is 10000.
In variation, refrigerator can also be identified by the way of computer vision, specifically comprises the following steps S101：It obtains
Multiple are taken to establish refrigerator image recognition model using the training image for the training image of refrigerator identification；
Step S102：Described image is obtained, by all pixels of described image according to from left to right, from top to bottom suitable
Sequence is divided into several m pixels to be multiplied by the subgraph of n-pixel；
Step S103：The subgraph is matched with described image identification model, realizes the identification of refrigerator in described image.
The subgraph can certainly be carried out to the identification that template matches realize subgraph, and then realize the identification of refrigerator in image.
In variation, Markov chain Monte-Carlo algorithm can also be used to identify refrigerator, specifically, the step S1
Include the following steps：
Step S101：The outer profile for all commodity being arranged on magazine or storage rack in identification described image；
Step S102：The outer profile of all commodity restrained so that all commodity after restraining each time
Outer profile area is less than or equal to the outer profile area of all commodity before convergence；
Step S103：After setting number convergence, the position of all commodity is determined；
Step S104：According to refrigerator region described in the location determination of all commodity.
In this embodiment, because the storage area of commodity is to determine in each refrigerator, quotient in refrigerator is determined to work as
When the position of product, so as to be inferred to the refrigerator region according to the design parameter of refrigerator.When to the outer of all commodity
Markov chain Monte-Carlo algorithm is used when profile is restrained.
Fig. 5 is the step flow chart for identifying storage rack in the present invention in first embodiment；As shown in figure 5, the step M1
Include the following steps：
Step M101：Multigroup training image for storage rack identification is obtained, every group of training image, which includes multiple, has one
The storage rack of same settings quantity establishes storage rack identification model using multigroup training image；The storage rack identifies mould
Type is linear regression model (LRM).
Step M102：Described image is obtained, the refrigerator region of described image is matched with the storage rack identification model, it is real
The identification in storage rack region in existing described image.
Fig. 6 is the step flow chart for identifying storage rack in the present invention in second embodiment；As shown in fig. 6, the step M1
Include the following steps：
Step M101：Described image is clustered with the numerical value of the storage rack of multiple settings, generates multiple be separated from each other
Set of pixels；
Step M102：Calculate the central point of each set of pixels in the set of pixels at a distance from each pixel, and
The central point of each set of pixels is at a distance from each pixel of the adjacent set of pixels；
Step M103：In the numerical value of the storage rack of the multiple setting, the target value of a storage rack is selected, it is described to deposit
Put frame target value each of make the central point of the set of pixels in the set of pixels at a distance from each pixel it is minimum and every
The central point of a set of pixels is maximum at a distance from each pixel of the adjacent set of pixels；
Step M104：Each storage rack described in clustering recognition is carried out by the target value of the storage rack.It is described
Cluster is that clustering algorithm in the prior art is realized.
When using refrigerator capacity automatic testing method provided by the invention, several described images are to pick up from a city
The refrigerator image of all supermarkets and market, refrigerator image are the product for storing a businessman user, the bottled or tank of such as laughable company
Dress is laughable.The businessman user becomes export structure that can be through the invention, it is thus understood that the memory capacity of a refrigerator in each supermarket,
In each supermarket in the memory capacity of all refrigerators and each city in all supermarkets refrigerator total capacity.In the present invention also
The storage position region that can identify the corresponding commodity region of each commodity and commodity to be stored on each storage rack successively, into
And according to the storage position area on the areal calculation in the commodity region storage rack on each storage rack
The memory space in domain, so as to calculate the storage position region of commodity to be stored and the corresponding quotient of each commodity on each storage rack
The ratio in product region, and then can judge whether commodity are in great demand on the storage rack, place depositing for same commodity as worked as in a supermarket
It puts in frame, the storage position region of commodity to be stored and the ratio in the corresponding commodity region of each commodity are more than a setting value, are such as
1, it can be determined that the commodity are in great demand, and commercial user can increase the supply to the commodity, to improve sale effect.When a supermarket
In the middle storage rack for placing same commodity, within the time limit of setting, such as three days, the storage position region of commodity to be stored and each quotient
The ratio in the corresponding commodity region of product is less than a setting value, is such as 0, it can be determined that the holdup in the sale of commodities, commercial user can
Reduce the supply to the commodity.
Fig. 7 is the module diagram of refrigerator capacity automatic checkout system in the present invention.As shown in fig. 7, the implementation of the present invention
A kind of refrigerator capacity automatic checkout system is also provided in example, for realizing above-mentioned refrigerator capacity automatic testing method, the ice
Tankage automatic checkout system 100 includes：
Image capture module 101 identifies the refrigerator in described image on the image for acquiring several images
Region, the refrigerator in described image include that at least one transparent window for checking commodity and at least one put the storage of the commodity
Rack, the magazine are arranged on the inside of the transparent window；
Magazine identification module 102, for identifying the magazine on the image；
Memory space computing module 103 the corresponding commodity region of each commodity and waits depositing in the magazine for identification
The storage position region for putting commodity, according to the memory space for storing position region described in the areal calculation in a commodity region；
Memory capacity generation module 104, the memory space for cumulative each storage position region and each commodity area
The memory space in domain generates the memory capacity of the refrigerator.
A kind of refrigerator capacity automatic checkout equipment, including processor are also provided in the embodiment of the present invention.Memory, wherein depositing
Contain the executable instruction of processor.Wherein, processor is configured to be performed refrigerator capacity certainly via execution executable instruction
The step of dynamic detection method.
As above, in the embodiment by collected a variety of images, the refrigerator on automatic identification image, and then identify storing
The storage position region of commodity and commodity to be stored on frame, to realize automatically generating for refrigerator memory capacity, without manually into
The acquisition of row image, and calculating is identified to image, manpower is saved, the computational efficiency of refrigerator capacity is improved.
Person of ordinary skill in the field it is understood that various aspects of the invention can be implemented as system, method or
Program product.Therefore, various aspects of the invention can be embodied in the following forms, i.e.,：It is complete hardware embodiment, complete
The embodiment combined in terms of full Software Implementation (including firmware, microcode etc.) or hardware and software, can unite here
Referred to as " circuit ", " module " or " platform ".
Fig. 8 is the structural schematic diagram of the refrigerator capacity automatic checkout equipment of the present invention.It is described according to this referring to Fig. 8
The electronic equipment 600 of this embodiment of invention.The electronic equipment 600 that Fig. 8 is shown is only an example, should not be to this hair
The function and use scope of bright embodiment bring any restrictions.
As shown in figure 8, electronic equipment 600 is showed in the form of universal computing device.The component of electronic equipment 600 can wrap
It includes but is not limited to：At least one processing unit 610, at least one storage unit 620, (including the storage of connection different platform component
Unit 620 and processing unit 610) bus 630, display unit 640 etc..
Wherein, storage unit has program stored therein code, and program code can be executed by processing unit 610 so that processing is single
Member 610 execute described in this specification above-mentioned electronic prescription circulation processing method part according to the various exemplary implementations of the present invention
The step of mode.For example, processing unit 610 can execute step as shown in fig. 1.
Storage unit 620 may include the readable medium of volatile memory cell form, such as Random Access Storage Unit
(RAM) 6201 and/or cache memory unit 6202, it can further include read-only memory unit (ROM) 6203.
Storage unit 620 can also include program/utility with one group of (at least one) program module 6205
6204, such program module 6205 includes but not limited to：Operating system, one or more application program, other program moulds
Block and program data may include the realization of network environment in each or certain combination in these examples.
Bus 630 can be to indicate one or more in a few class bus structures, including storage unit bus or storage
Cell controller, peripheral bus, graphics acceleration port, processing unit use the arbitrary bus structures in a variety of bus structures
Local bus.
Electronic equipment 600 can also be with one or more external equipments 700 (such as keyboard, sensing equipment, bluetooth equipment
Deng) communication, can also be enabled a user to one or more equipment interact with the electronic equipment 600 communicate, and/or with make
Any equipment that the electronic equipment 600 can be communicated with one or more of the other computing device (such as router, modulation /demodulation
Device etc.) communication.This communication can be carried out by input/output (I/O) interface 650.Also, electronic equipment 600 can be with
By network adapter 660 and one or more network (such as LAN (LAN), wide area network (WAN) and/or public network,
Such as internet) communication.Network adapter 660 can be communicated by bus 630 with other modules of electronic equipment 600.It should
Understand, although being not shown in Fig. 8, other hardware and/or software module can be used in conjunction with electronic equipment 600, including unlimited
In：Microcode, device driver, redundant processing unit, external disk drive array, RAID system, tape drive and number
According to backup storage platform etc..
A kind of computer readable storage medium is also provided in the embodiment of the present invention, for storing program, program is performed
The step of refrigerator capacity automatic testing method of realization.In some possible embodiments, various aspects of the invention may be used also
In the form of being embodied as a kind of program product comprising program code, when program product is run on the terminal device, program generation
Code is each according to the present invention described in this specification above-mentioned electronic prescription circulation processing method part for making terminal device execute
The step of kind illustrative embodiments.
As it appears from the above, the program of the computer readable storage medium of the embodiment is when being executed, the present invention can be by adopting
The a variety of images collected, the refrigerator on automatic identification image, and then identify the storage of the commodity and commodity to be stored in magazine
Position region carries out the acquisition of image, and image is identified to realize automatically generating for refrigerator memory capacity without artificial
It calculates, saves manpower, improve the computational efficiency of refrigerator capacity.
Fig. 9 is the structural schematic diagram of the computer readable storage medium of the present invention.Refering to what is shown in Fig. 9, describing according to this
The program product 800 for realizing the above method of the embodiment of invention, may be used the read-only storage of portable compact disc
Device (CD-ROM) and include program code, and can be run on terminal device, such as PC.However, the journey of the present invention
Sequence product is without being limited thereto, in this document, readable storage medium storing program for executing can be any include or storage program tangible medium, the journey
Sequence can be commanded the either device use or in connection of execution system, device.
The arbitrary combination of one or more readable mediums may be used in program product.Readable medium can be that readable signal is situated between
Matter or readable storage medium storing program for executing.Readable storage medium storing program for executing for example can be but be not limited to electricity, magnetic, optical, electromagnetic, infrared ray or partly lead
System, device or the device of body, or the arbitrary above combination.More specific example (the non exhaustive row of readable storage medium storing program for executing
Table) include：Electrical connection, portable disc, hard disk, random access memory (RAM), read-only storage with one or more conducting wires
Device (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disc read only memory (CD-
ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.
Computer readable storage medium may include in a base band or as the data-signal that a carrier wave part is propagated,
In carry readable program code.The data-signal of this propagation may be used diversified forms, including but not limited to electromagnetic signal,
Optical signal or above-mentioned any appropriate combination.Readable storage medium storing program for executing can also be any readable Jie other than readable storage medium storing program for executing
Matter, which can send, propagate either transmission for used by instruction execution system, device or device or and its
The program of combined use.The program code for including on readable storage medium storing program for executing can transmit with any suitable medium, including but not
It is limited to wireless, wired, optical cable, RF etc. or above-mentioned any appropriate combination.
It can be write with any combination of one or more programming languages for executing the program that operates of the present invention
Code, programming language include object oriented program language-Java, C++ etc., further include conventional process
Formula programming language-such as " C " language or similar programming language.Program code can be calculated fully in user
It executes in equipment, partly execute on a user device, being executed, partly in user calculating equipment as an independent software package
Upper part executes or is executed in remote computing device or server completely on a remote computing.It is being related to remotely counting
In the situation for calculating equipment, remote computing device can pass through the network of any kind, including LAN (LAN) or wide area network
(WAN), it is connected to user calculating equipment, or, it may be connected to external computing device (such as utilize ISP
To be connected by internet).
The present invention can be by collected a variety of images, the refrigerator on automatic identification image, and then identify in magazine
Commodity and commodity to be stored storage position region, to realize automatically generating for refrigerator memory capacity, without manually carrying out figure
The acquisition of picture, and calculating is identified to image, manpower is saved, the computational efficiency of refrigerator capacity is improved.
Specific embodiments of the present invention are described above.It is to be appreciated that the invention is not limited in above-mentioned
Particular implementation, those skilled in the art can make various deformations or amendments within the scope of the claims, this not shadow
Ring the substantive content of the present invention.
Claims (10)
1. a kind of refrigerator capacity automatic testing method, which is characterized in that include the following steps：
Step S1：Several images are acquired, identify the refrigerator region in described image on the image, in described image
Refrigerator includes that at least one transparent window for being used to check commodity and at least one put the magazine of the commodity, and the storing is set up
It sets on the inside of the transparent window；
Step S2：The magazine is identified on the refrigerator region；
Step S3：And then identify the storage position area in each corresponding commodity region of commodity and commodity to be stored in the magazine
Domain, according to the memory space for storing position region described in the areal calculation in a commodity region；
Step S4：The memory space in cumulative each storage position region and the memory space in each commodity region generate the ice
The memory capacity of case.
2. refrigerator capacity automatic testing method according to claim 1, which is characterized in that when the magazine includes multiple
When the storage rack being arranged in order in the vertical direction, each storage rack include it is multiple in the horizontal direction on be arranged in order deposit
Position is put, further includes following steps：
Step M1：Go out each storage rack in the refrigerator region recognition；
Step M2：The storage in each commodity corresponding the commodity region and commodity to be stored on each storage rack is identified successively
Position region, and then according to described on the areal calculation in the commodity region storage rack on each storage rack
Store the memory space in position region；
Step M3:The memory space of the memory space and each commodity region in the storage position region on cumulative each storage rack
The memory capacity of each storage rack is generated, and then the memory capacity of cumulative each storage rack generates depositing for the refrigerator
Store up capacity.
3. refrigerator capacity automatic testing method according to claim 2, which is characterized in that do not have when on a storage rack
When storing commodity, according on the basis of the storage rack the memory capacity of a upper storage rack and the memory capacity of next storage rack and
Average value determine the memory capacity of the storage rack.
4. refrigerator capacity automatic testing method according to claim 1, which is characterized in that the step S1 includes following step
Suddenly：
Step S101：Multiple training images for refrigerator identification are obtained, refrigerator image recognition is established using the training image
Model；
Step S102：Described image is obtained, if extracting the subgraph of dried object in described image；
Step S103：The subgraph is matched with described image identification model, if being identified in the subgraph of the dried object
Go out the subgraph of refrigerator, and then determines refrigerator region.
5. refrigerator capacity automatic testing method according to claim 1, which is characterized in that the step S1 includes following step
Suddenly：
Step S101：Collected described image is subjected to denoising to filter desultory point；
Step S102：Described image after filtering desultory point is subjected to grey level histogram stretching, to protrude refrigerator region；
Step S103：Described image is subjected to binary conversion treatment and generates binary image, to obtain refrigerator region and refrigerator region
Location information；
Step S104：And then feature extraction is carried out to the binary image, filter out ice by area, length and curvature parameters
Case region；
Step S105：The refrigerator region finally is determined by stencil matching to the refrigerator region of extraction.
6. refrigerator capacity automatic testing method according to claim 2, which is characterized in that the step M1 includes following step
Suddenly：
Step M101：Multigroup training image for storage rack identification is obtained, every group of training image includes that multiple are identical with one
The storage rack for setting quantity, storage rack identification model is established using multigroup training image；
Step M102：Described image is obtained, the refrigerator region of described image is matched with the storage rack identification model, realizes institute
State the identification in storage rack region in image.
7. refrigerator capacity automatic testing method according to claim 2, which is characterized in that the step M1 includes following step
Suddenly：
Step M101：Described image is clustered with the numerical value of the storage rack of multiple settings, generates multiple pictures being separated from each other
Element collection；
Step M102：Calculate the central point of each set of pixels in the set of pixels at a distance from each pixel, and each
The central point of set of pixels is at a distance from each pixel of the adjacent set of pixels；
Step M103：In the numerical value of the storage rack of the multiple setting, the target value of a storage rack, the storage rack are selected
Target value each of make the central point of the set of pixels and minimum and each picture at a distance from each pixel in the set of pixels
The central point of element collection is maximum at a distance from each pixel of the adjacent set of pixels；
Step M104：Each storage rack described in clustering recognition is carried out by the target value of the storage rack.
8. a kind of refrigerator capacity automatic checkout system, automatic for realizing the refrigerator capacity described in any one of claim 1 to 7
Detection method, which is characterized in that including：
Image capture module identifies the refrigerator region in described image, institute on the image for acquiring several images
State the refrigerator on image include at least one transparent window for checking commodity and at least one put the commodity magazine, institute
Magazine is stated to be arranged on the inside of the transparent window；
Magazine identification module, for identifying the magazine on the image；
Memory space computing module, for identification corresponding commodity region of each commodity and commodity to be stored in the magazine
Position region is stored, according to the memory space for storing position region described in the areal calculation in a commodity region；
Memory capacity generation module, the storage of memory space and each commodity region for cumulative each storage position region
Space generates the memory capacity of the refrigerator.
9. a kind of refrigerator capacity automatic checkout equipment, which is characterized in that including：
Processor；
Memory, wherein being stored with the executable instruction of the processor；
Wherein, the processor is configured to come any one of perform claim requirement 1 to 7 institute via the execution executable instruction
The step of stating refrigerator capacity automatic testing method.
10. a kind of computer readable storage medium, for storing program, which is characterized in that described program is performed realization power
Profit requires the step of any one of 1 to 7 refrigerator capacity automatic testing method.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810188121.1A CN108389230A (en) | 2018-03-07 | 2018-03-07 | Refrigerator capacity automatic testing method, system, equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810188121.1A CN108389230A (en) | 2018-03-07 | 2018-03-07 | Refrigerator capacity automatic testing method, system, equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108389230A true CN108389230A (en) | 2018-08-10 |
Family
ID=63066848
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810188121.1A Pending CN108389230A (en) | 2018-03-07 | 2018-03-07 | Refrigerator capacity automatic testing method, system, equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108389230A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109712324A (en) * | 2018-12-28 | 2019-05-03 | 广东便捷神科技股份有限公司 | A kind of automatic vending machine image-recognizing method, good selling method and vending equipment |
| CN109784172A (en) * | 2018-12-14 | 2019-05-21 | 上海扩博智能技术有限公司 | Ground heap commodity occupied area evaluation method, system, equipment and medium in market |
| CN110781780A (en) * | 2019-10-11 | 2020-02-11 | 浙江大华技术股份有限公司 | Vacancy detection method and related device |
| CN111310729A (en) * | 2020-03-16 | 2020-06-19 | 南京掌控网络科技有限公司 | Method for acquiring refrigerator purity based on image recognition technology and asset management system |
| CN112113558A (en) * | 2020-08-27 | 2020-12-22 | 上海扩博智能技术有限公司 | Unmanned aerial vehicle yaw angle error measuring and calculating method, system, equipment and storage medium |
| WO2023125469A1 (en) * | 2021-12-28 | 2023-07-06 | 海尔智家股份有限公司 | Spare-capacity detection apparatus |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101809601A (en) * | 2007-08-31 | 2010-08-18 | 埃森哲环球服务有限公司 | Planogram extraction based on image processing |
| US8706293B2 (en) * | 2011-11-29 | 2014-04-22 | Cereson Co., Ltd. | Vending machine with automated detection of product position |
| CN105701519A (en) * | 2014-12-10 | 2016-06-22 | 株式会社理光 | Realogram scene analysis of images: superpixel scene analysis |
| CN107463945A (en) * | 2017-07-12 | 2017-12-12 | 浙江大学 | A kind of type of merchandize recognition methods based on depth matching network |
| CN107578052A (en) * | 2017-09-15 | 2018-01-12 | 北京京东尚科信息技术有限公司 | Kinds of goods processing method and system |
-
2018
- 2018-03-07 CN CN201810188121.1A patent/CN108389230A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101809601A (en) * | 2007-08-31 | 2010-08-18 | 埃森哲环球服务有限公司 | Planogram extraction based on image processing |
| US8706293B2 (en) * | 2011-11-29 | 2014-04-22 | Cereson Co., Ltd. | Vending machine with automated detection of product position |
| CN105701519A (en) * | 2014-12-10 | 2016-06-22 | 株式会社理光 | Realogram scene analysis of images: superpixel scene analysis |
| CN107463945A (en) * | 2017-07-12 | 2017-12-12 | 浙江大学 | A kind of type of merchandize recognition methods based on depth matching network |
| CN107578052A (en) * | 2017-09-15 | 2018-01-12 | 北京京东尚科信息技术有限公司 | Kinds of goods processing method and system |
Non-Patent Citations (2)
| Title |
|---|
| 于丽颖: "图像识别方法技术分析与应用", 《第24届全国煤矿自动化与信息化学术会议暨第6届中国煤矿信息化与自动化高层论坛论文集》 * |
| 翁和王: "关于人工智能中的图像识别技术的研究", 《信息通信》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109784172A (en) * | 2018-12-14 | 2019-05-21 | 上海扩博智能技术有限公司 | Ground heap commodity occupied area evaluation method, system, equipment and medium in market |
| CN109712324A (en) * | 2018-12-28 | 2019-05-03 | 广东便捷神科技股份有限公司 | A kind of automatic vending machine image-recognizing method, good selling method and vending equipment |
| CN109712324B (en) * | 2018-12-28 | 2020-12-18 | 广东便捷神科技股份有限公司 | Vending machine image identification method, vending method and vending equipment |
| CN110781780A (en) * | 2019-10-11 | 2020-02-11 | 浙江大华技术股份有限公司 | Vacancy detection method and related device |
| CN111310729A (en) * | 2020-03-16 | 2020-06-19 | 南京掌控网络科技有限公司 | Method for acquiring refrigerator purity based on image recognition technology and asset management system |
| CN112113558A (en) * | 2020-08-27 | 2020-12-22 | 上海扩博智能技术有限公司 | Unmanned aerial vehicle yaw angle error measuring and calculating method, system, equipment and storage medium |
| WO2023125469A1 (en) * | 2021-12-28 | 2023-07-06 | 海尔智家股份有限公司 | Spare-capacity detection apparatus |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108389230A (en) | Refrigerator capacity automatic testing method, system, equipment and storage medium | |
| CN108734162B (en) | Method, system, equipment and storage medium for identifying target in commodity image | |
| CN108416403B (en) | Method, system, equipment and storage medium for automatically associating commodity with label | |
| CN108647553B (en) | Method, system, device and storage medium for rapidly expanding images for model training | |
| CN107992820B (en) | Self-help goods selling method for container based on binocular vision | |
| CN109726759B (en) | Unmanned vending method, device, system, electronic equipment and computer readable medium | |
| CN109886169B (en) | Article identification method, device, equipment and storage medium applied to unmanned container | |
| CN109784172A (en) | Ground heap commodity occupied area evaluation method, system, equipment and medium in market | |
| CN109902636A (en) | Commodity identification model training method, system, equipment and storage medium | |
| CN109190919A (en) | It is sold critical sales index generation method, system, equipment and storage medium | |
| CN109242060A (en) | New restocking product fast searching method, system, equipment and storage medium | |
| CN110516628A (en) | Shelf vacant locations merchandise news acquisition methods, system, equipment and storage medium | |
| CN111881894A (en) | Method, system, equipment and storage medium for collecting goods selling information of container | |
| CN109978833A (en) | Picture quality automatic testing method, system, equipment and storage medium | |
| US20190034787A1 (en) | Relative position encoding based networks for action recognition | |
| CN112307864A (en) | Method and device for determining target object and man-machine interaction system | |
| CN111223099B (en) | Contraband detection method, computing equipment and storage medium | |
| CN113378958A (en) | Automatic labeling method, device, equipment, storage medium and computer program product | |
| CN113706227A (en) | Goods shelf commodity recommendation method and device | |
| CN116415794A (en) | AR (augmented reality) glasses-based warehouse-in and warehouse-out method and intelligent warehouse system | |
| CN109948736A (en) | Commodity identification model active training method, system, equipment and storage medium | |
| CN111126264A (en) | Image processing method, device, equipment and storage medium | |
| CN113674049A (en) | Commodity shelf position identification method and system based on picture search and storage medium | |
| CN111428743A (en) | Commodity identification method, commodity processing device and electronic equipment | |
| CN111353577B (en) | Multi-task-based cascade combination model optimization method and device and terminal equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180810 |
|
| RJ01 | Rejection of invention patent application after publication |