CN112163582A - Weighing method and device based on kitchen multi-sensor integrated scale and intelligent terminal - Google Patents
Weighing method and device based on kitchen multi-sensor integrated scale and intelligent terminal Download PDFInfo
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- 238000005303 weighing Methods 0.000 title claims abstract description 73
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Abstract
The application relates to the technical field of electronic scales, in particular to a weighing method and device based on a kitchen multi-sensor integrated scale and an intelligent terminal, and the method comprises the steps of acquiring image information of food materials on the kitchen multi-sensor integrated scale; determining the type information of the food materials weighed by the kitchen multi-sensor and the partition information occupied by each food material based on the image information; each subarea is correspondingly associated with a weight sensor; acquiring weight information of each subarea; and determining the weight information of each food material on the kitchen multi-sensor integrated scale according to the partition information occupied by each food material and the weight information of each partition. The kitchen multi-sensor integrated weighing system further comprises a memory and a processor, wherein the memory is stored with a computer program which can be loaded and executed by the processor, and when the computer program is executed, the weighing method based on the kitchen multi-sensor integrated weighing system is implemented. This application has the effect that a plurality of regions of weighing of being convenient for weigh multiple edible material simultaneously.
Description
Technical Field
The application relates to the technical field of electronic scales, in particular to a weighing method and device based on a kitchen multi-sensor integrated scale and an intelligent terminal.
Background
With the progress of the times, modern people pay more and more attention to the quality of diet. At present, people often search various high-quality recipes through a network and make high-quality recipes according to the recipes so as to improve the diet quality. When the required food materials are weighed according to the recipe, a kitchen scale is required. Kitchen scales are a tool used to accurately measure the weight of food material used during cooking.
In the correlation technique, the pan of kitchen balance is divided into four weighing areas, and every weighing area below corresponds and is equipped with a pressure sensor for divide the regional weighing to eat the material, and kitchen balance is equipped with the display screen, is used for showing four weighing areas and weighs the weight of eating the material, thereby makes kitchen balance can weigh four kinds of food materials simultaneously and show the weight of four kinds of food materials.
For the related art, the inventor thinks that when a cook uses the kitchen scale to weigh various food materials and one of the food materials needs to occupy two or more weighing areas, the information displayed by the display screen by the cook cannot directly know the total weight of the same food material occupying the two or more weighing areas, which causes the defect that the plurality of weighing areas of the kitchen scale are inconvenient to weigh various food materials simultaneously.
Disclosure of Invention
In order to weigh various food materials simultaneously in a plurality of weighing areas, the application provides a weighing method and device based on an integrated kitchen multi-sensor scale and an intelligent terminal.
In a first aspect, the application provides a weighing method based on a kitchen multi-sensor integrated scale, which adopts the following technical scheme:
the weighing method based on the kitchen multi-sensor integrated scale comprises the following steps:
acquiring image information of food materials placed on a kitchen multi-sensor integrated scale;
determining the type information of the food materials weighed by the kitchen multi-sensor and the partition information occupied by each food material based on the image information; each subarea is correspondingly associated with a weight sensor;
acquiring weight information of each subarea;
and determining the weight information of each food material on the kitchen multi-sensor integrated scale according to the partition information occupied by each food material and the weight information of each partition.
By adopting the technical scheme, when multiple food materials are weighed, the food material type information and the partition information occupied by each food material can be determined by analyzing the image information, each partition is a weighing area, the weight information of each food material can be determined by the partition information of each food material and the weight information of each partition, namely the total weight of each food material, so that the multiple food materials can be weighed by the weighing areas conveniently.
Optionally, the determining, based on the image information, food material type information weighed by the kitchen multi-sensor integrated scale includes:
performing image processing on the image information;
and classifying the food materials in the image information and generating food material type information.
By adopting the technical scheme, the food material type information can be generated more accurately according to the image information.
Optionally, the performing image processing on the image information includes:
carrying out graying processing on the image information to generate a grayscale image;
carrying out binarization processing on the gray level image to generate a binarization image, wherein each white area in the binarization image corresponds to one food material;
identifying the outline of each food material according to the binary image, and generating food material outline information;
and acquiring the average color of each food material in the image information to generate average color information.
By adopting the technical scheme, the food materials in the image information are correspondingly converted into the white areas, so that the outline of the food materials can be conveniently identified, and the characteristics for matching are provided for food material classification by the food material outline information and the average color information.
Optionally, the classifying the food materials in the image information and generating the food material type information include:
comparing the food material profile information and the average color information of each food material with preset profile information and preset color information preset in a database;
and classifying each food material according to the comparison result, and generating food material type information.
By adopting the technical scheme, more accurate food material type information can be generated.
Optionally, the determining, based on the image information, partition information occupied by each food material on the kitchen multi-sensor integrated scale includes:
carrying out edge detection on the image information to generate a detection image;
fusing a preset image and a detection image to generate a comparison image, wherein the preset image is an image when the kitchen multi-sensor integrated scale is empty, and the preset image comprises all subareas on the kitchen multi-sensor integrated scale;
analyzing and comparing the conditions of all food material occupying partitions in the image to obtain total occupying area information of the food materials;
and dividing the total occupied area information of the food materials based on the food material type information to obtain the partition information occupied by each food material.
By adopting the technical scheme, the accuracy of analyzing all the food material occupying zones is improved by comparing the generation of the images, so that the accuracy of the zone information occupied by each food material is improved.
Optionally, the performing edge detection on the image information to generate a detection image includes:
carrying out gray processing and Gaussian smoothing on the image information to generate a prepared image;
calculating the gradient strength and direction of each pixel point in the prepared image;
performing non-maximum suppression;
carrying out double-threshold detection;
a lag boundary tracking process is performed to generate a detection image.
By adopting the technical scheme, the prepared image generated after Gaussian smoothing filtering is smoother, noise interference is effectively removed, the accuracy of edge detection is favorably improved, stray response caused by the edge detection can be eliminated after non-maximum value suppression is carried out, real and potential edges can be determined by carrying out double-threshold detection, and isolated weak edges can be suppressed by carrying out hysteresis boundary tracking processing.
Optionally, the determining, according to the partition information occupied by each food material and the weight information of each partition, the weight information of each food material integrally weighed by the multiple kitchen sensors includes:
reading partition information occupied by each food material;
matching the partition information of each food material with the weight information of each partition, and determining a plurality of weight information corresponding to each partition information;
and accumulating a plurality of weight information corresponding to the same partition information to determine the weight information of each food material.
By adopting the technical scheme, the weight information corresponding to the partition information of each food material can be matched quickly, so that the weight information of each food material can be obtained conveniently.
In a second aspect, the application provides a weighing device based on the kitchen multi-sensor integrated scale, which adopts the following technical scheme:
weighing device based on integrative title of kitchen multisensor includes:
the camera module is used for acquiring image information of food materials placed on the kitchen multi-sensor integrated scale;
the classification module is used for determining the food material type information weighed by the kitchen multi-sensor and the partition information occupied by each food material based on the image information; each subarea is correspondingly associated with a weight sensor;
the acquisition module is used for acquiring the weight information of each subarea;
and the confirming module is used for determining the weight information of each food material on the kitchen multi-sensor integrated scale according to the partition information occupied by each food material and the weight information of each partition.
Through adopting above-mentioned technical scheme, when weighing multiple edible material, camera module can acquire to eat and arrange the image information on the integrative title of kitchen multisensor in, classification module can confirm through analysis image information and eat material kind information and the shared subregion information of each kind of edible material, and every subregion is the region of weighing, and the confirmation module can confirm the weight information of each kind of edible material through the subregion information of each kind of edible material and the weight information of each subregion, promptly the total weight of each kind of edible material to it weighs multiple edible material simultaneously to be convenient for a plurality of regions of weighing.
In a third aspect, the present application provides an intelligent terminal, which adopts the following technical scheme:
an intelligent terminal comprising a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and execute the kitchen multi-sensor scale-based weighing method according to any one of claims 1 to 7.
By adopting the technical scheme, the intelligent terminal can weigh food materials by using the weighing method based on the kitchen multi-sensor integrated scale through the computer program, so that a plurality of weighing areas can weigh various food materials conveniently.
In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium storing a computer program that can be loaded by a processor and that executes the kitchen multi-sensor scale-based weighing method according to any one of claims 1 to 7.
By adopting the technical scheme, the computer-readable storage medium can weigh the food materials by using the weighing method based on the kitchen multi-sensor integrated scale through the computer program, so that a plurality of weighing areas can weigh various food materials at the same time.
In summary, the invention includes at least one of the following beneficial technical effects:
1. when weighing multiple food materials, the food material type information and the partition information occupied by each food material can be determined by analyzing the image information, each partition is a weighing area, the weight information of each food material, namely the total weight of each food material, can be determined by the partition information of each food material and the weight information of each partition, and therefore multiple food materials can be weighed by the weighing areas conveniently.
2. The food materials in the image information are correspondingly transformed into white areas, so that the outline of the food materials is convenient to identify, and the outline information and the average color information of the food materials provide characteristics for matching for food material classification. The prepared image generated after Gaussian smooth filtering is smoother, noise interference is effectively removed, the accuracy of edge detection is improved, stray response caused by edge detection can be eliminated after non-maximum value suppression is carried out, real and potential edges can be determined by carrying out double-threshold detection, and isolated weak edges can be suppressed by carrying out lag boundary tracking processing.
3. The generation of the comparison image improves the accuracy of analyzing all food material occupied partitions, so that the accuracy of partition information occupied by each food material is improved. The method and the device are beneficial to quickly matching a plurality of weight information corresponding to the partition information of each food material, so that the weight information of each food material can be obtained conveniently.
Drawings
Fig. 1 is a flowchart of a weighing method based on kitchen multi-sensor integrated weighing in the embodiment.
Fig. 2 is another flowchart of the weighing method based on kitchen multi-sensor integrated weighing in the embodiment.
Fig. 3 is a block diagram of a weighing apparatus based on a kitchen multi-sensor integrated scale according to the present embodiment.
Fig. 4 is a block diagram of the structure of an intelligent terminal in this embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to fig. 1-4 and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The weighing method based on the kitchen multi-sensor integrated scale can be executed by the kitchen multi-sensor integrated scale, in the embodiment of the application, the kitchen multi-sensor integrated scale is provided with a camera, a display screen and a built-in computer, the camera can be a digital camera, the camera can directly shoot an image of food materials placed on the kitchen multi-sensor integrated scale, and then the image is transmitted to the built-in computer through a serial port, a parallel port or a USB interface. The display screen is electrically connected with the built-in computer.
The kitchen multi-sensor integrated scale is also provided with a plurality of gravity sensors, the position of food materials placed on the kitchen multi-sensor integrated scale is called as a detection area, the surface of the detection area is divided into a plurality of subareas through a subarea grid, the gravity sensors are installed under the subareas, so that the gravity sensors correspond to the subareas one to one, and each subarea is a weighing area. The gravity sensor can be a cantilever type displacement device made of an elastic sensitive element, and an energy storage spring made of the elastic sensitive element is used for driving an electric contact, so that the conversion from gravity change to an electric signal is realized.
The weighing method based on the kitchen multi-sensor integrated scale can be achieved through the camera, the built-in computer and the gravity sensors are matched with one another, the weighed food materials can be vegetables or fruits and the like, if a user places multiple food materials in a detection area at the same time, a certain distance needs to exist between the adjacent food materials, so that the weighing errors caused by the mutual influence of the adjacent food materials are avoided, the distance is also used for enabling the two adjacent food materials not to press one gravity sensor at the same time, and the multiple food materials can be weighed in multiple weighing areas at the same time.
It will be clear to those skilled in the art that a recipe will typically give target weights for a plurality of food materials, and that when a cook weighs a plurality of food materials according to the recipe, the goal of the cook is to adjust the weight of the plurality of food materials to their respective target weights. Therefore, in the adjusting process, a cook needs to take out the corresponding food material from the detection area or add the corresponding food material to the detection area according to the weight of each food material, and the weighing method based on the kitchen multi-sensor integrated scale is convenient for the cook to complete the adjustment of the weight of each food material.
Based on the above principle and the above application scenario, it should be noted that the present application provides a weighing method based on a kitchen multi-sensor integrated scale, that is, the method is described from the perspective of a processor, and the weighing method based on the kitchen multi-sensor integrated scale can be implemented on a smart device by programming as a computer program, which includes but is not limited to a computer, a network host, a smart terminal, and the like.
The embodiment of the application discloses weighing method based on kitchen multisensor is integrative to be called, includes:
referring to fig. 1, in step S100, image information of a food material placed on a kitchen multi-sensor integrated scale is acquired.
Specifically, a camera on the kitchen multi-sensor integrated scale is controlled to shoot the food materials in the detection area. The camera is fixed in directly over the detection area, and the camera lens is towards the detection area downwards to be favorable to better acquireing the image information that the edible material was arranged in the detection area, the image when also being convenient for acquire the detection area vacancy, the image information is the image when the edible material that the camera was shot was arranged in the detection area. The food materials placed in the detection area may be the same kind of food materials, or may be different kinds of food materials, for example: 3 apples, 1 apple and 1 radish, and 3 apples and 2 radishes. However, the total amount of the food materials needs to be controlled within the accommodating range of the detection area, and a certain distance needs to exist between two adjacent food materials.
When a cook places a food material in a detection area, the position of the food material may experience a short fluctuation. For example, a pear is placed in the detection area, and the pear rolls on the detection area under the action of gravity, and then stops and keeps a static state. What the camera needs to gather is the image information after the food material enters the static state to improve the accuracy of follow-up operation, reduce and weigh the error.
When the food materials do not enter the static state, the camera can be controlled to be in the opening state, but shooting is not carried out, and after all the food materials in the detection area enter the static state, the camera is controlled to carry out shooting, so that image information is obtained.
Step S110, determining the food material type information weighed by the kitchen multi-sensor and the partition information occupied by each food material based on the image information; one weight sensor is associated with each partition.
The partition information occupied by each food material refers to the condition that each food material occupies a partition in the detection area, namely, a plurality of partitions correspondingly occupied by each food material. The food material type information is the type of food material in the image information, and examples thereof include pear, apple, radish, and melon.
Specifically, determining the food material type information weighed by the kitchen multi-sensor integration comprises the following steps: and performing image processing on the image information so as to highlight the food materials in the image information, then classifying the food materials in the image information, and generating food material type information.
Wherein the image processing comprises: carrying out graying processing on the image information to generate a grayscale image; carrying out binarization processing on the gray level image to generate a binarization image, wherein each white area in the binarization image corresponds to one food material; identifying the outline of each food material according to the binary image, and generating food material outline information; acquiring the average color of each food material in the image information to generate average color information; comparing the food material profile information and the average color information of each food material with preset profile information and preset color information preset in a database; and classifying each food material according to the comparison result, and generating food material type information.
If the image information contains an apple, a pear and a radish, original pixels of the three food materials in the binary image are all converted into white, namely the three food materials are converted into three corresponding white areas, the outline of each white area corresponds to the corresponding food material, and the outline of each white area is identified to obtain corresponding food material outline information. The food materials in the image information are correspondingly converted into white areas, so that the outline of the food materials can be conveniently identified, and the outline information and the average color information of the food materials provide characteristic points for matching in the subsequent classification of the food materials.
The preset contour information in the database refers to contour information of various food materials which are uploaded to the database in advance, and the preset color information in the database refers to average color information of various food materials which are uploaded to the database in advance. And comparing the profile information and the average color information of each called food material with all preset profile information and preset color information in the database to obtain the type of each called food material.
For example, a food material is placed on the detection area, profile information and average color information of the food material are compared with all preset profile information and preset color information in the database, and the comparison result shows that the profile information and the average color information of the food material are respectively matched with the preset profile information and the preset color information of the pears in the database, so that the variety of the food material belongs to the pears.
Specifically, the determining the partition information occupied by each food material on the kitchen multi-sensor integrated scale includes: carrying out edge detection on the image information to generate a detection image; fusing a preset image and a detection image to generate a comparison image, wherein the preset image is an image when a detection area is vacant, and the preset image comprises all subareas on the kitchen multi-sensor integrated scale; analyzing and comparing the conditions of all food material occupying partitions in the image to obtain total occupying area information of the food materials; and dividing the total occupied area information of the food materials based on the food material type information to obtain the partition information occupied by each food material.
The subarea refers to a weighing area on the surface of the detection area, and the gravity sensors are in one-to-one correspondence with the weighing area, namely the subareas are in one-to-one correspondence with the gravity sensors.
The image information is subjected to edge detection, and the method adopts the Canny edge detection based on MATLAB, and comprises the following steps: carrying out gray processing and Gaussian smoothing on the image information to generate a preparation image, and calculating the gradient strength and direction of each pixel point in the preparation image; performing non-maxima suppression so that the pixels can more accurately represent actual edges in the image; performing dual threshold detection to remove spurious responses, filtering edge pixels with weak gradient values, and retaining edge pixels with high gradient values, which can be achieved by selecting high and low thresholds, i.e., if the gradient values of edge pixels are higher than the high thresholds, they are labeled as strong edge pixels, if the gradient values of edge pixels are less than the high thresholds and greater than the low thresholds, they are labeled as weak edge pixels, and if the gradient values of edge pixels are less than the low thresholds, they are suppressed; and performing hysteresis boundary tracking processing, and by checking the weak edge pixels and 8 neighborhood pixels thereof, as long as one of the weak edge pixels is a strong edge pixel, the weak edge point can be reserved as a real edge, and finally, a detection image is generated.
The graphs and all the subareas of all the food materials in the image are compared, the outline of the food material graph is clear, the edge of the food material graph is clear, the lines of the subareas are clear, the edge of the subareas is clear, and therefore the situation that all the food materials occupy the subareas is convenient to determine. The generation of the comparison image improves the accuracy of determining all the food material occupying partitions, so that the accuracy of the total occupied partitions of the food materials is improved.
Analyzing and comparing edges of food material graphs of food materials in the images to obtain total occupied area information of the food materials, and determining a plurality of partitions through which the outlines of the food material graphs pass. For example, if three food materials are placed on the detection area, which are the first food material, the second food material and the third food material, respectively, the edge of the food material graph of the first food material passes through 2 partitions, the edge of the food material graph of the second food material passes through 3 partitions, and the edge of the food material graph of the third food material passes through 5 partitions, the total occupied area is 2 partition grids occupied by the first food material, 3 partition grids occupied by the second food material, and 5 partition grids occupied by the third food material.
The total occupied area information of the food materials is divided based on the food material type information to obtain the partition information occupied by each food material, and the total occupied area information of the food materials is divided according to the partition occupied by each food material to obtain the partition information of each food material. Therefore, the method is beneficial to quickly determining the condition that each food material occupies the subarea, and is convenient to quickly determine the subarea information of each food material.
For example, three food materials, namely a first food material, a second food material and a third food material, are placed on the detection area, and the total area occupation information includes 2 area meshes occupied by the first food material, 3 area meshes occupied by the second food material and 5 area meshes occupied by the third food material. After analyzing the food material type information and comparing the images, obtaining that the first food material belongs to the pear, the second food material and the third food material both belong to the radish, and the partition information of each corresponding food material is as follows: the partition information of the pears is 2 partition grids occupied by the first food material, and the partition information of the radishes is 3 partition grids occupied by the second food material and 5 partition grids occupied by the third food material.
Step S120, acquiring weight information of each partition.
Specifically, the weight information of all gravity sensors in the kitchen multi-sensor integrated scale is read, and the weight information refers to the measured value of the gravity sensors.
After the food materials are placed in a detection area of the kitchen multi-sensor integrated scale, the weight of the food materials can fluctuate momentarily. For example, when a pear is placed in the examination area, the initial measurement is 0.235kg, and the measurement is 0.1kg in the resting state. In order to obtain a more accurate measurement value, it is necessary to obtain the measurement value of the gravity sensor after the food material enters a static state.
Step S130, determining the weight information of each food material on the kitchen multi-sensor integrated scale according to the partition information occupied by each food material and the weight information of each partition.
Specifically, determining the weight information of each food material on the kitchen multi-sensor integrated scale according to the partition information occupied by each food material and the weight information of each partition, includes: reading partition information occupied by each food material; matching the partition information of each food material with the weight information of each partition, and determining a plurality of weight information corresponding to each partition information; and accumulating a plurality of weight information corresponding to the same partition information to determine the weight information of each food material.
The matching mode for matching the partition information of each food material with the weight information of each partition is as follows: the way the tags match. The method comprises the steps that label information is added to each subarea in a detection area in advance, the subarea information of each food material carries the label information of the corresponding subarea, and a plurality of weight information corresponding to each subarea information can be determined by matching the label information carried in the subarea information with the label information carried in the weight information of each subarea.
And accumulating a plurality of weight information corresponding to the same partition information to obtain the weight information of each food material, namely the total weight of each food material. For example, it is determined that the partition information of the pear corresponds to 2 partitions, and the weight information of the 2 partitions is 0.03kg and 0.07kg respectively, the weight information of the pear is 0.1kg, that is, the total weight of the pear is 0.1 kg; the radish partition information includes 8 partitions, and the weight information of the 8 partitions is 0.05kg, 0.06kg, 0.05kg, 0.03kg, 0.04kg and 0.05kg, respectively, so that the radish weight information is 0.38kg, that is, the total weight of the radish is 0.38 kg.
Referring to fig. 2, the method according to the embodiment of the present application further includes step S140 of displaying weight information of each food material.
Specifically, step S140 is performed after step S130, and the display screen is controlled to display the weight information of each food material.
For example, a total weight of 0.1kg for pears and 0.38kg for radishes would indicate "pear, 0.1 kg" and "radish, 0.38 kg".
The method according to the embodiment of the application further includes step S150 of accumulating the weight information of all kinds of food materials and displaying the accumulated result.
Specifically, step S150 is performed after step S130, and the step of accumulating the weight information of all food materials and displaying the accumulation result includes: the generated weight information of all kinds of food materials is accumulated, namely the weight information of each kind of food material is accumulated, so that the total weight of various food materials on the detection area of the kitchen multi-sensor integrated scale can be calculated, and then the total weight of all kinds of food materials is displayed through the display screen.
For example, it was confirmed that three kinds of food materials, each weighing 0.1kg of pear, 0.12kg of apple and 0.25kg of radish, were placed on the detection area, and the result was 0.47 kg.
To sum up, when the process realizes weighing multiple food materials, the food material type information and the partition information occupied by each food material can be determined by analyzing the image information, each partition is a weighing area, the weight information of each food material, namely the total weight of each food material, can be determined by the partition information of each food material and the weight information of each partition, and therefore multiple food materials can be weighed simultaneously in multiple weighing areas. And finally, the weight information of all kinds of food materials can be accumulated, and the total weight of the various food materials is displayed through the display screen, so that a chef can know the total weight of the various food materials conveniently.
Referring to fig. 3, the embodiment of the application further discloses a weighing apparatus based on the kitchen multi-sensor integrated scale, including: the food material detection system comprises a camera module 200, a classification module 210, an acquisition module 220 and a confirmation module 230, wherein the camera module 200 is installed on a kitchen multi-sensor integrated scale and is positioned right above a detection area of the kitchen multi-sensor integrated scale, and when the camera module 200 works normally, the camera module 200 acquires image information of food materials placed on the kitchen multi-sensor integrated scale. The classification module 210 reads the image information from the camera module 200, and determines the food material type information and the partition information of each food material on the kitchen multi-sensor integrated scale based on the image information. The acquiring module 220 acquires the weight information of each partition so as to cooperate with the confirming module 230, and the confirming module 230 reads the weight information of each partition from the acquiring module 220 and the partition information occupied by each food material from the classifying module 210, and then determines the weight information of each food material integrally weighed by the kitchen multi-sensor according to the partition information occupied by each food material and the weight information of each partition.
To sum up, when the above-mentioned process has realized weighing multiple food materials, the camera module 200 can acquire the image information of the food materials placed on the kitchen multi-sensor integrated scale, the classification module 210 can determine the food material type information and the partition information occupied by each food material by analyzing the image information, and each partition is a weighing area, the determination module 230 can determine the weight information of each food material by the partition information of each food material and the weight information of each partition, that is, the total weight of each food material, thereby facilitating the weighing of multiple food materials in multiple weighing areas.
Referring to fig. 4, an intelligent terminal 300 includes a memory 320 and a processor 310, where the memory 310 stores a computer program that can be loaded and executed by the processor 310, and when the computer program is executed by the processor 310, the following steps are performed: acquiring image information of food materials placed on a kitchen multi-sensor integrated scale; determining the type information of the food materials weighed by the kitchen multi-sensor and the partition information occupied by each food material based on the image information; each subarea is correspondingly associated with a weight sensor; acquiring weight information of each subarea; and determining the weight information of each food material on the kitchen multi-sensor integrated scale according to the partition information occupied by each food material and the weight information of each partition.
Optionally, in the embodiment of the present application, when the computer program is executed by the processor 310, the following steps are performed: performing image processing on the image information; and classifying the food materials in the image information and generating food material type information.
Optionally, in the embodiment of the present application, when the computer program is executed by the processor 310, the following steps are performed: carrying out graying processing on the image information to generate a grayscale image; carrying out binarization processing on the gray level image to generate a binarization image, wherein each white area in the binarization image corresponds to one food material; identifying the outline of each food material according to the binary image, and generating food material outline information; and acquiring the average color of each food material in the image information to generate average color information.
Optionally, in the embodiment of the present application, when the computer program is executed by the processor 310, the following steps are performed: comparing the food material profile information and the average color information of each food material with preset profile information and preset color information preset in a database; and classifying each food material according to the comparison result, and generating food material type information.
Optionally, in the embodiment of the present application, when the computer program is executed by the processor 310, the following steps are performed: carrying out edge detection on the image information to generate a detection image; fusing a preset image and a detection image to generate a comparison image, wherein the preset image is an image when the kitchen multi-sensor integrated scale is empty, and the preset image comprises all subareas on the kitchen multi-sensor integrated scale; analyzing and comparing the conditions of all food material occupying partitions in the image to obtain total occupying area information of the food materials; and dividing the total occupied area information of the food materials based on the food material type information to obtain the partition information occupied by each food material.
Optionally, in the embodiment of the present application, when the computer program is executed by the processor 310, the following steps are performed: carrying out gray processing and Gaussian smoothing on the image information to generate a prepared image; calculating the gradient strength and direction of each pixel point in the prepared image; performing non-maximum suppression; carrying out double-threshold detection; a lag boundary tracking process is performed to generate a detection image.
Optionally, in the embodiment of the present application, when the computer program is executed by the processor 310, the following steps are performed: reading partition information occupied by each food material; matching the partition information of each food material with the weight information of each partition, and determining a plurality of weight information corresponding to each partition information; and accumulating a plurality of weight information corresponding to the same partition information to determine the weight information of each food material.
In summary, the above process realizes that the kitchen multi-sensor integrated scale 300 can weigh food materials by using the weighing method based on the kitchen multi-sensor integrated scale through the computer program, so that a plurality of weighing areas can weigh a plurality of food materials at the same time.
The embodiment of the application also discloses a computer readable storage medium, which stores a computer program capable of being loaded and executed by a processor, and when the computer program is executed by the processor, the steps of any one of the weighing methods based on the kitchen multi-sensor integrated scale are realized, and the same effect can be achieved.
The computer-readable storage medium includes, for example: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The foregoing is a preferred embodiment of the present application and is not intended to limit the scope of the application in any way, and any features disclosed in this specification (including the abstract and drawings) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
Claims (10)
1. Weighing method based on kitchen multisensor is weighed as an organic whole, its characterized in that includes:
acquiring image information of food materials placed on a kitchen multi-sensor integrated scale;
determining the type information of the food materials weighed by the kitchen multi-sensor and the partition information occupied by each food material based on the image information; each subarea is correspondingly associated with a weight sensor;
acquiring weight information of each subarea;
and determining the weight information of each food material on the kitchen multi-sensor integrated scale according to the partition information occupied by each food material and the weight information of each partition.
2. The weighing method based on the kitchen multi-sensor integrated scale according to claim 1, wherein the determining of the food material type information on the kitchen multi-sensor integrated scale based on the image information comprises:
performing image processing on the image information;
and classifying the food materials in the image information and generating food material type information.
3. The kitchen multi-sensor integrated scale-based weighing method according to claim 2, wherein the image processing of the image information comprises:
carrying out graying processing on the image information to generate a grayscale image;
carrying out binarization processing on the gray level image to generate a binarization image, wherein each white area in the binarization image corresponds to one food material;
identifying the outline of each food material according to the binary image, and generating food material outline information;
and acquiring the average color of each food material in the image information to generate average color information.
4. The kitchen multi-sensor integrated scale-based weighing method according to claim 3, wherein the classifying of the food materials in the image information and the generating of the food material type information comprise:
comparing the food material profile information and the average color information of each food material with preset profile information and preset color information preset in a database;
and classifying each food material according to the comparison result, and generating food material type information.
5. The weighing method based on the kitchen multi-sensor integrated scale according to claim 1, wherein the determining the partition information of each food material on the kitchen multi-sensor integrated scale based on the image information comprises:
carrying out edge detection on the image information to generate a detection image;
fusing a preset image and a detection image to generate a comparison image, wherein the preset image is an image when the kitchen multi-sensor integrated scale is empty, and the preset image comprises all subareas on the kitchen multi-sensor integrated scale;
analyzing and comparing the conditions of all food material occupying partitions in the image to obtain total occupying area information of the food materials;
and dividing the total occupied area information of the food materials based on the food material type information to obtain the partition information occupied by each food material.
6. The weighing method based on the kitchen multi-sensor integrated scale according to claim 5, wherein the edge detection of the image information to generate a detection image comprises:
carrying out gray processing and Gaussian smoothing on the image information to generate a prepared image;
calculating the gradient strength and direction of each pixel point in the prepared image;
performing non-maximum suppression;
carrying out double-threshold detection;
a lag boundary tracking process is performed to generate a detection image.
7. The weighing method based on the kitchen multi-sensor integrated scale as claimed in claim 1, wherein the determining of the weight information of each food material on the kitchen multi-sensor integrated scale according to the partition information occupied by each food material and the weight information of each partition comprises:
reading partition information occupied by each food material;
matching the partition information of each food material with the weight information of each partition, and determining a plurality of weight information corresponding to each partition information;
and accumulating a plurality of weight information corresponding to the same partition information to determine the weight information of each food material.
8. Weighing device based on integrative title of kitchen multisensor, its characterized in that includes:
the camera module is used for acquiring image information of food materials placed on the kitchen multi-sensor integrated scale;
the classification module is used for determining the food material type information weighed by the kitchen multi-sensor and the partition information occupied by each food material based on the image information; each subarea is correspondingly associated with a weight sensor;
the acquisition module is used for acquiring the weight information of each subarea;
and the confirming module is used for determining the weight information of each food material on the kitchen multi-sensor integrated scale according to the partition information occupied by each food material and the weight information of each partition.
9. An intelligent terminal, characterized by comprising a memory and a processor, wherein the memory is stored with a computer program which can be loaded by the processor and execute the weighing method based on the kitchen multi-sensor integrated scale according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored which can be loaded by a processor and which executes the weighing method based on kitchen multi-sensor scale according to any of claims 1 to 7.
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