Leg shape data collection method and trousers manufacturing method and system thereof

Abstract

The invention discloses a leg shape data collection method and a trousers manufacturing method and system thereof, and belongs to the field of garment production, a handheld X-ray scanner can penetrate through the garment to penetrate through the garment, then the leg shape in the garment is imaged, and then the length of each place is measured to obtain the leg shape data. A leg section shape can be formed by intersecting and overlapping a plurality of lengths, at the moment, the section shapes of all the parts are arranged and connected to form a corresponding leg shape model, trousers are manufactured through the model, finally, a customizing person tries on the trousers through air bag simulation to make a final draft, and at the moment, the optimal customized trousers are obtained.

Description

Technical Field

[0001] This invention discloses a method for collecting leg shape data and a method and system for manufacturing trousers, belonging to the field of clothing production. Background Technology

[0002] With economic development, the production of trousers has transitioned from the original general-purpose design to customized design.

[0003] However, since customized design requires measuring leg data, traditional measurement methods involve squeezing the pants inward, resulting in inaccurate data. Furthermore, because the legs are quite sensitive, people may unconsciously tense up during measurement, causing muscle tension and deviations in the measurement data, ultimately leading to unsuitable customized pants.

[0004] A new solution is proposed to address the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a leg shape data collection method and a method and system for manufacturing pants, in order to solve the above-mentioned problems.

[0006] This invention achieves the above objective through the following technical solution: a leg shape data collection method, comprising, S1 determines the baseline. The first baseline is the line connecting the heel, popliteal fossa, Yinmen acupoint, Chengfu acupoint, Zhibian acupoint, and Baomang acupoint. The second baseline is the line connecting Kunlun acupoint, Fuyang acupoint, Yangjiao acupoint, Yanglingquan acupoint, Zhongdu acupoint, Juliao acupoint, and Daimai acupoint. S2 establishes a basic database and measures the cross-sectional shape of the model doll along the waist, hips, upper thigh, middle thigh, femur, knee, shinbone, middle calf, and ankle by manually measuring the model doll. S3 establishes the basic model of the leg. The model is positioned vertically using the first and second reference lines. At this time, the cross-sectional shape measured in S2 generates two points at the corresponding first and second reference lines. Based on these two points, the model is placed on the corresponding points of the first and second reference lines according to the horizontal height. After all the measurement data in S2 has been placed, the edges of the adjacent shape data are connected to generate the corresponding leg model. S4 establishes data projection, scans the model doll from multiple angles using a human body scanner, and picks up the cross-sectional shape data of the measurement position in S2. Finally, the shape of the corresponding area in S2 that was scanned is bound to the leg base model in S3. S5 establishes a data port, binds the first data of manual measurement in S2 with the second data scan of the human body scanner, and converts the manual measurement data in S2 into data directly input by the human body scanner. S6 data detection involves scanning multiple testers with a body scanner and then manually measuring data at various locations of the testers to ensure that the scanned data matches the manually measured data. If an error occurs, the process returns to S2 to rebuild the database.

[0007] Preferably, the baseline in S1 can also be determined by the line connecting the tibia, knee, and femur.

[0008] Preferably, when S2 is performing knee and hip position data, it is necessary to add data for half squat and full squat.

[0009] Preferably, the human body scanner used in S4 is a handheld X-ray scanner.

[0010] Preferably, the method also includes S7 establishing an infrared temperature sensing area database and marking temperature difference areas.

[0011] A method for making trousers, comprising: S8 creates a pants model, which is based on the model created in S3. The model is divided into three types: tight, fitted, and loose. Tight is when the distance from the pants to the leg is between 0.5cm and 2cm, fitted is between 2cm and 4cm, and loose is more than 4cm. The S9 airbag simulation pumps air into the airbag based on the pants shape data, causing the internal diameter of the airbag to expand to match the cross-sectional size of the target pants shape. The airbag is then suspended on the wearer to create a realistic pant shape simulation, and final fine-tuning is performed. S10 is finalized and production begins. Once the customer confirms the data in S9, the data will be sent to the manufacturer for direct production.

[0012] Preferably, the pant style selection in S8 also includes the selection of material, and after the material selection, the corresponding material is attached to the surface of the airbag in S9 to simulate the real touch.

[0013] Preferably, the airbag in S9 has a material bonded to the inner thigh portion of its surface to prevent it from inflating.

[0014] A control system for collecting leg shape data includes: The scanning module is a handheld scanning device with the ability to penetrate clothing to obtain the contours of the skin surface; The central processing module summarizes the information scanned by the scanning module and stitches them together to form a 3D model. The display screen images the model processed by the central processing module. The fine-tuning input port, a touchscreen port for manual data input, complements the data from the scanning module. The air pump receives data from the central processing module and then performs the pumping action. An airbag is used to receive air pumped out by an air pump and to change its inner diameter by expanding. Infrared thermal sensors are used to collect information on areas prone to sweating in order to recommend the appropriate fit.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: by using the characteristic of handheld X-ray scanners that can penetrate clothing, the scanner passes through the clothing and then images the leg shape inside the clothing. By measuring the length of each part, the cross-sectional shape of the leg can be formed by the intersection and overlap of several lengths. Then, the cross-sectional shapes of each part are arranged and connected to generate the corresponding leg model. The pants are made using the model. Finally, the custom pants are made by having the customer try them on using an airbag simulation to make the final design. At this point, the best custom pants are obtained. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of a leg shape data collection method according to the present invention; Figure 2 This is a structural schematic diagram of a method for manufacturing trousers according to the present invention; Figure 3 This is a schematic diagram of the system for making trousers using a leg shape data collection method according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. In this description, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Example 1:

[0018] A method for collecting leg shape data includes: S1 establishes the baseline by using the line connecting the heel, popliteal fossa, Yinmen acupoint, Chengfu acupoint, Zhibian acupoint, and Baomang acupoint as the first baseline, and the line connecting Kunlun acupoint, Fuyang acupoint, Yangjiao acupoint, Yanglingquan acupoint, Zhongdu acupoint, Juliao acupoint, and Daimai acupoint as the second baseline. The baseline can also be determined by the line connecting the tibia, knee, and femur. Multiple baselines can be established as needed, and different baselines can be used according to different body types. Here, in the human acupoint diagram, two relatively straight lines connecting the ankle and waist are used as baselines. These are only used to locate points for the cross-section of subsequent data and can be adjusted according to actual needs. They are not lines with any special meaning. S2 establishes a basic database by manually measuring the cross-sectional shape of the model dummy along the waist, hips, upper thigh, middle thigh, femur tip, knee, shinbone tip, middle shin, and ankle. More sets of data measurement points can be set here according to the actual situation. When measuring the knee and hip positions, data for half-squatting and full-squatting positions need to be added. S3 establishes the basic model of the leg. The model is positioned vertically by using the first and second reference lines. At this time, the cross-sectional shape measured in S2 generates two points at the corresponding first and second reference lines. Then, based on these two points, the model is placed on the corresponding points of the first and second reference lines according to the horizontal height. After all the measurement data in S2 has been placed, the edges of the adjacent shape data are connected to generate the corresponding leg model. S4 establishes data projection, scans the model doll from multiple angles using a human body scanner, and picks up the cross-sectional shape data of the measurement position in S2. Finally, the shape of the corresponding area in S2 that was scanned is bound to the leg base model in S3. S5 establishes a data port, binds the first data of manual measurement in S2 with the second data scan of the human body scanner, and converts the manual measurement data in S2 into data directly input by the human body scanner. S6 data detection involves scanning multiple testers with a body scanner and then manually measuring data at each position of the testers to ensure that the scanned data is consistent with the manually measured data. If an error occurs, the process returns to S2 to rebuild the database. At least 50 sets of data are required for verification. The human body scanner used in S4 is a handheld X-ray scanner. The handheld X-ray scanner here must be a device that complies with GB 9706.1-2020 "Medical electrical equipment - Part 1: General requirements for basic safety and basic performance", with a scanning resolution greater than 500 dpi, a penetration thickness of less than 5 mm, and a single scan time of less than 0.5 s.

[0019] This process uses a handheld X-ray scanner to irradiate the leg from multiple angles. Taking advantage of the fact that X-rays can penetrate clothing, the width of the image area is displayed on the image. Then, by imaging from multiple angles, the planar images are stitched together to form a columnar structure, thus constructing a complete leg model and obtaining leg data.

[0020] Body type is classified by weight to height ratio: ≤18.5 for lean, 18.5-24.9 for normal, and ≥25 for obese. The baseline for lean is determined by connecting the bones, the baseline for obese is determined by connecting acupoints, and both are acceptable for normal. Example 2:

[0021] A method for making trousers, comprising: S8 creates a pants model, which is based on the model created in S3. The model is divided into three types: tight, fitted, and loose. Tight is when the distance from the pants to the leg is between 0.5cm and 2cm, fitted is between 2cm and 4cm, and loose is more than 4cm. The S9 airbag simulation pumps air into the airbag based on the pants shape data, causing the internal diameter of the airbag to expand to match the cross-sectional size of the target pants shape. The airbag is then suspended on the wearer to create a realistic pant shape simulation, and final fine-tuning is performed. S10 is finalized and production begins. Once the customer confirms the data in S9, the data will be sent to the manufacturer for direct production. The S8's pant style selection also includes material selection, and after material selection, the corresponding material will be glued to the airbag surface in the S9 to simulate a real touch. The airbag in the S9 has a material bonded to the inner thigh surface to prevent it from inflating.

[0022] The above manufacturing method allows customers to simulate the inner diameter of the trouser leg using suspended airbags after selecting the pattern. This ensures that the customer experiences the same sense of space when wearing the trousers as when they were customizing them. The selected material is then pasted onto the surface of the airbags to mimic the tactile feel, further enhancing the overall feel of the finished product. Additionally, thermal imaging is used to create openings in areas with high temperatures to increase breathability, while a sweat-wicking layer is added to the inner fabric in areas with low temperatures. The temperature range is based on the customer's average body temperature; areas above or below the average are considered high-temperature areas, and areas below the average are considered low-temperature areas.

[0023] The number of airbags is the same as the number of cross-sections measured. Example 3:

[0024] A control system for collecting leg shape data includes: The scanning module is a handheld scanning device with the ability to penetrate clothing to obtain the contours of the skin surface; The central processing module summarizes the information scanned by the scanning module and stitches them together to form a 3D model. The display screen images the model processed by the central processing module. The fine-tuning input port, a touchscreen port for manual data input, complements the data from the scanning module. The air pump receives data from the central processing module and then performs the pumping action. An airbag is used to receive air pumped out by an air pump and to change its inner diameter by expanding.

[0025] The aforementioned system allows the central processing module to utilize a ResNet-50-based deep learning algorithm while the scanning module performs scanning. The training dataset consists of 100,000 sets of 3D human leg shape data, with a model simulation similarity of ≥98%. This large dataset of human leg shapes enables more detailed simulation of the central processing module's model. The simulated leg shape is then displayed on the screen. The operator inquires about the customer's desired tightness via a touchscreen and inputs the corresponding data. At this point, different airbags are connected to the air pump. Once the airbags are inflated, the selected fabric is applied, and the system is then manually connected and delivered to the customer for a fitting to adjust the fit. The system allows for further data adjustments based on the customer's preferences, and the air pump is used to control the inflation level of the airbags until the desired fit is achieved.

[0026] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0027] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A method for collecting leg shape data, characterized in that, include: S1 determines the baseline. The first baseline is the line connecting the heel, popliteal fossa, Yinmen acupoint, Chengfu acupoint, Zhibian acupoint, and Baomang acupoint. The second baseline is the line connecting Kunlun acupoint, Fuyang acupoint, Yangjiao acupoint, Yanglingquan acupoint, Zhongdu acupoint, Juliao acupoint, and Daimai acupoint. S2 establishes a basic database and measures the cross-sectional shape of the model doll along the waist, hips, upper thigh, middle thigh, femur, knee, shinbone, middle calf, and ankle by manually measuring the model doll. S3 establishes the basic model of the leg. The model is positioned vertically by using the first and second reference lines. At this time, the cross-sectional shape measured in S2 generates two points at the corresponding first and second reference lines. Then, based on these two points, the model is placed on the corresponding points of the first and second reference lines according to the horizontal height. After all the measurement data in S2 has been placed, the edges of the adjacent shape data are connected to generate the corresponding leg model. S4 establishes data projection, scans the model doll from multiple angles using a human body scanner, and picks up the cross-sectional shape data of the measurement position in S2. Finally, the shape of the corresponding area in S2 that was scanned is bound to the leg base model in S3. S5 establishes a data port, binds the first data of manual measurement in S2 with the second data scan of the human body scanner, and converts the manual measurement data in S2 into data directly input by the human body scanner. S6 data detection involves scanning multiple testers with a body scanner and then manually measuring data at various locations of the testers to ensure that the scanned data matches the manually measured data. If an error occurs, the process returns to S2 to rebuild the database.

2. The leg shape data collection method according to claim 1, characterized in that: The baseline in S1 can also be determined by connecting the tibia, knee, and femur.

3. The leg shape data collection method according to claim 1, characterized in that: When performing knee and hip position data in S2, data for half squat and full squat should be added.

4. The leg shape data collection method according to claim 1, characterized in that: The human body scanner used in S4 is a handheld X-ray scanner.

5. The leg shape data collection method according to claim 4, characterized in that: It also includes S7 establishing an infrared temperature sensing area database and marking temperature difference areas.

6. A method for manufacturing trousers, using data from a leg shape data collection method as described in any one of claims 1-5, characterized in that, include: S8 creates a pants model, which is based on the model created in S3. The model is divided into three types: tight, fitted, and loose. Tight is when the distance from the pants to the leg is between 0.5cm and 2cm, fitted is between 2cm and 4cm, and loose is more than 4cm. The S9 airbag simulation pumps air into the airbag based on the pants shape data, causing the internal diameter of the airbag to expand to match the cross-sectional size of the target pants shape. The airbag is then suspended on the wearer to create a realistic pant shape simulation, and final fine-tuning is performed. S10 is finalized and production begins. Once the customer confirms the trouser size data for S9, the data will be sent to the manufacturer for direct production.

7. A method for manufacturing trousers according to claim 6, characterized in that: The S8's pant style selection also includes material selection, and after material selection, the corresponding material will be glued to the airbag surface in the S9 to simulate a real touch.

8. A method for manufacturing trousers according to claim 6, characterized in that: The airbag in the S9 has a material bonded to the inner thigh surface to prevent it from inflating.

9. A control system for manufacturing trousers using a leg shape data collection method, controlling the trouser manufacturing method as described in any one of claims 6-8, characterized in that, include: The scanning module is a handheld scanning device with the ability to penetrate clothing to obtain the contours of the skin surface; The central processing module summarizes the information scanned by the scanning module and stitches them together to form a 3D model. The display screen images the model processed by the central processing module. The fine-tuning input port, a touchscreen port for manual data input, complements the data from the scanning module. The air pump receives data from the central processing module and then performs the pumping action. An airbag is used to receive air pumped out by an air pump and to change its inner diameter by expanding. Infrared thermal sensors are used to collect information on areas prone to sweating in order to recommend the appropriate fit.

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