Fabric Shrinkage Measurement Software

@youssefEngineer

I will build the offline Windows application in Python using OpenCV and a lightweight GUI. Proposed approach: • Camera capture workflow for before-wash and after-wash images • Calibration step using a ruler or known reference distance to convert pixels to millimetres • Detection of the four fabric reference marks using a configurable combination of contour, blob, and template-based methods • Separate X/Y displacement calculations for length-wise and width-wise shrinkage • Clear result output such as “Length shrinkage: 3.2% | Width shrinkage: 2.7%” • Command-line trigger or minimal API for future PLC integration • Windows executable installer, complete commented Python source, rebuild instructions, and a user manual I would first create a calibration and detection test tool, then validate the algorithm against knitted and woven samples with varying background and yarn colours. The final validation will target the required 30 consecutive samples, ±0.5 mm tolerance, and under 5 seconds per image pair. Two points will help finalize the detection design: 1. What type, approximate resolution, and lens/FOV will the USB camera use? 2. Will the four reference marks have a consistent shape, colour, or marking method across all samples? I can begin with the calibration workflow and detection prototype, then package the validated measurement process as the Windows application.

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@worksoft

Hi, We can deliver this standalone fabric shrinkage measurement tool using Python, OpenCV, and PyQt/Tkinter. Here is our plan: 1) Calibration & Precision: Includes an interactive pixel-to-mm calibration step using a standard target to ensure sub-millimeter scale mapping, meeting your +/- 0.5 mm tolerance. 2) Mark Detection: Combines adaptive thresholding, color space conversion (HSV), and contour/blob filtering to reliably locate the 4 reference marks across varying knitted/woven textures and yarn colors. Shrinkage Calculation: Computes Euclidean distances between the 4 detected points in X and Y before and after washing Deliverables: 1) Offline Windows executable (.exe) with a clean, text-based UI showing length and width percentage changes 2) Command-line flags (--trigger) for seamless external PLC automation. 3) Fully commented Python source code, build scripts, and a brief calibration manual. Ready to start immediately upon confirming your reference mark specs. Regards, Sujata

@mayankinnovative

I understand you need a reliable, offline Windows vision application that captures fabric before/after washing, detects four reference marks automatically, and calculates length/width shrinkage to ±0.5 mm with results in under 5 seconds. I’ll build the measurement engine in Python with OpenCV, using camera calibration and perspective correction first, followed by robust mark detection using contour/blob/template techniques selected and tuned for knitted and woven fabrics with varying colours/backgrounds. Calibration will convert pixel measurements to accurate millimetres, with validation against a calibrated ruler. A lightweight PyQt desktop UI will provide camera setup, calibration, capture and instant text results, while keeping the measurement engine modular. I’ll also expose a CLI/API trigger for future PLC integration. I’ll validate detection across 30+ consecutive samples, tune edge cases, package a simple offline Windows installer, provide commented rebuildable source and a concise user manual. I’m confident I can deliver a practical, accurate and maintainable solution ready for QC use.

@KarthikTechCon

Hello! I am excited to submit my proposal for your Fabric Shrinkage Measurement Software project. With over 15 years of experience in Python, OpenCV, and GUI development, I am confident in delivering a robust solution tailored to your needs. Plan Outline: 1. Vision Workflow: I will implement a reliable detection algorithm using OpenCV to accurately identify the four reference marks on both pre- and post-wash images, ensuring a measurement accuracy of ±0.5 mm across various fabric types. 2. Desktop Application: A lightweight Windows application will be developed using PyQt for a simple installation and user-friendly interface. 3. Result Display: The application will provide a clear text summary of shrinkage percentages immediately after capturing the second image. 4. Automation Hooks: I will include command-line functionality or a minimal API for seamless integration with external PLCs. I will ensure repeatable mark detection, provide an executable installer, full commented source code, and a user manual for setup and calibration. I can start immediately upon agreement. Looking forward to collaborating on this project! Budget: ₹50,000 INR Timeline: 3 weeks Best, Karthik

@vishal1145

Your detection tolerance of ±0.5 mm will fail if you rely on basic blob detection without sub-pixel refinement and lens distortion correction. At typical industrial camera resolutions, pixel-level accuracy translates to 1-2 mm error, which means you need corner detection algorithms (like Harris or Shi-Tomasi) combined with camera calibration matrices to hit your spec. Quick questions - what is the working distance from lens to fabric, and are you using telecentric optics or standard perspective lenses? And what is the maximum fabric size you need to measure in a single frame? Here is the architectural approach: - OPENCV + CAMERA CALIBRATION: Build a checkerboard calibration routine that computes intrinsic/extrinsic parameters, then apply undistortion before mark detection to eliminate barrel distortion that causes measurement drift. - COMPUTER VISION PIPELINE: Use adaptive thresholding plus contour filtering by area/circularity to isolate marks, then apply cornerSubPix for sub-pixel localization, ensuring your ±0.5 mm tolerance holds across lighting variations and fabric textures. - DESKTOP APPLICATION: Package as a PyQt5 executable with PyInstaller, exposing a REST API on localhost:5000 so your PLC can POST trigger commands and receive JSON shrinkage results without GUI interaction. I've built similar vision systems for automotive QC lines where 0.3 mm precision was mandatory under 3-second cycle times. Let's schedule a 15-minute call to confirm your camera specs and mark geometry before I draft the detection logic.

@Shrutibimpl

Hello, I can build the desktop utility for calculating fabric shrinkage using Python and OpenCV. My approach involves using a lightweight graphical interface wrapper to capture images from a USB camera, applying contour and blob detection algorithms to accurately locate the four reference marks within a tolerance of plus or minus 0.5 millimeters, and computing the length-wise and width-wise percentage changes. The application will run completely offline on Windows, display a clear text summary of the results upon capturing the second image, and include a command-line flag to allow external triggering from a PLC. 1) What specific type or shape of reference marks will be placed on the fabric, such as printed dots or square markers? 2) Do you have a preferred Python GUI library for the offline Windows executable, such as PyQt or Tkinter? Thanks, Bharat

@sharmin283

With strong expertise in Python and a background spanning traditional web development to cutting-edge AI engineering, I am uniquely positioned to tackle your Fabric Shrinkage Measurement Software project. My AI skills will benefit your Vision workflow as I am well-versed in OpenCV and have experience with both yarn and background color variance scenarios. I bring proficiency in using Python libraries like contour, blob, and template techniques to meet impressive measurements standards. For the desktop application, whether you prefer PyQt or Tkinter, I can deliver a lightweight, user-friendly Windows executable that is highly desired for its simple installation process. Additionally, my experience working with APIs allows me to seamlessly integrate your proposed minimal API system or a command line flag to trigger automation hooks from an external PLC. Having delivered numerous end-to-end applications, I understand the importance of clear and concise result delivery. Therefore, your software will not just display "Length shrinkage: 3.2 % Width shrinkage: 2.7 %", but also leave room for future extensions. Ultimately, my main aim is exceeding expectations while honoring agreed timelines. From accurate detection of the reference marks with ≤0.5 mm error to delivering an executable installer alongside full commented Python sources; this project will be a perfect avenue to leverage my diverse yet scalable skill-set.

@bojans9

Hello, I'm a Senior Software Developer | AI & Cloud with 8+ years of experience working with Robotics, Mechanical Engineering, Technical Support, Troubleshooting. I understand you need a field technician in Spain to deploy, operate and support autonomous marine robots on commercial vessels, ensuring missions run safely and equipment stays ready. The real challenge is handling robots around ports and water while keeping mission prep consistent. A common issue: small setup mistakes delay deployment or cause incomplete mission reports. My approach: first, follow your training program and map each mission’s prep steps. Then, handle robot deployment, checks and live support with clear logs. Finally, produce mission reports with photos and performance notes so TeleOps teams can review quickly. I've worked with Robotics, Mechanical Engineering, Technical Support, Troubleshooting on hands‑on systems, so I watch for risks like missed pre‑mission checks or unclear fault notes. A few questions to better understand the scope: Q1 - Which ports or regions in Spain will be used? This helps plan travel and readiness. Q2 - How are mission reports structured today? I’d keep them consistent for smooth review. Q3 - What night‑operation schedule is typical? That shapes availability planning. Happy to go over the details here in chat and suggest the best approach. Looking forward to hearing from you.

@FreeLancers360

With a comprehensive background in Full Stack Development, Software Architecture, and significant expertise in Python utilization, I am well-poised to build and deliver an excellent fabric shrinkage measurement software that will meet your exact requirements. My Python skills extend to encompass libraries such as OpenCV which will be deployed optimally for this project. I understand the significance of vision workflows and have consistently leveraged tools like contouring, blob detection and template matching to deliver high precision ensures hyper-accurate data delivery, ±0.5 mm error tolerance in this case. My prowess is not limited to backend systems; it extends to creating intuitive frontend interfaces too. That is why the provided lightweight Windows executable will be expertly designed with PyQt or Tkinter, ensuring offline-friendly installation, and easy navigation for all users. The display of your desired text summary (Length shrinkage: 3.2 % Width shrinkage: 2.7 %) will be clean and concise as requested with a consideration of future extensibility. Additionally, I recognize how crucial extensibility and integration are in modern technology projects. Therefore, I guarantee effective automation hooks by exposing a command-line flag or minimal API allowing future PLC triggers. Deliverables would include the executable installer, full commented Python source code adhering to PEP coding guidelines, and a brief user procedures

@gsantharam7088

Hi, I can build this Windows-based textile shrinkage measurement system using Python, OpenCV, and a lightweight desktop GUI. I’ll design a calibrated vision pipeline to detect the four reference marks in before/after images, calculate precise X/Y distances, and report length-wise and width-wise shrinkage as percentages. My approach will include camera calibration, perspective/scale correction, robust mark detection using contour/blob/template techniques, and validation against a calibrated ruler. I’ll test the solution across knitted and woven fabrics with varying colours/backgrounds, targeting ≤0.5 mm measurement error and <5 seconds per image pair. I’ll deliver the Windows executable/installer, complete commented Python source, calibration workflow, simple operator interface, PLC-friendly CLI/API trigger, and concise user documentation. I can also structure the application modularly so additional measurements or automation features can be added later. I’m ready to discuss the camera setup and reference-mark design and start immediately.

@Hrolgar

AATCC 135 and ISO 5077 style shrinkage marks sit roughly 350mm apart, which means hitting +/-0.5mm isn't really about a better camera, it's about the calibration surviving a frame that never sits at exactly the same distance twice. A fixed pixel-per-mm ratio drifts the moment the rig gets bumped, so the mapping needs to be a homography solved fresh off the four marks each run, not a stored constant. For detection I'd run adaptive thresholding to isolate the marks regardless of whether the sample is a tight weave or a loose knit, then refine the centroid through contour or blob fitting rather than trusting the raw threshold edge. That's where the sub-pixel accuracy actually comes from. Length and width shrinkage fall out of comparing the pre and post wash centroid pairs against that calibrated mm mapping. Packaging as a single offline PyInstaller exe keeps it off any Python install on the shop floor, and I'd wire the CLI flags for the PLC trigger now even though it's not live yet, so nothing needs rebuilding when that integration lands later. M1: calibration pipeline, mark detection, homography mapping proven on sample images, ₹21,500, 3 days. M2: shrinkage calculation engine, accuracy validation against known samples, ₹28,500, 4 days. M3: CLI/API hook, PyInstaller packaging, exe handoff, ₹15,000, 3 days. M1 on its own settles whether the +/-0.5mm number holds on your actual fabric before the rest gets built, so that's where I'd start.

@jaroslavcaprata

Hi, I’d build this as a lightweight Python/OpenCV Windows application focused on repeatable measurement accuracy rather than overcomplicating it with ML. My approach would be: * Calibrate the camera once using a known reference to establish pixel-to-mm scale and lens correction. * Detect the four fabric marks using adaptive thresholding + contour/blob/template matching, with sub-pixel refinement where useful. * Correct perspective with homography so small camera/fabric alignment changes do not distort X/Y measurements. * Compare calibrated distances before and after washing, then calculate length-wise and width-wise shrinkage automatically. The UI can remain simple: capture “Before”, capture “After”, then immediately display both shrinkage percentages. I’ll also expose the measurement routine through a CLI/minimal API for future PLC integration. Deliverables will include the Windows executable, full commented Python source, calibration workflow, error handling, and a concise setup/user manual. I would validate the algorithm against repeated samples and tune detection for both knitted and woven fabrics with varying colours/backgrounds.

@Abror86

Hello Hope you are doing well. "Fabric shrinkage measurement with computer vision" I can develop a Windows application using Python and OpenCV to detect four reference marks and accurately calculate length-wise and width-wise shrinkage. I can build a calibrated vision pipeline with camera calibration, robust mark detection, sub-millimeter measurement, and validation across knitted and woven fabrics. I will provide the offline executable, complete commented source code, PLC-friendly trigger interface, calibration procedure, user manual, and performance testing. Could you share sample before-and-after fabric images and the camer Hello Hope you are doing well. "Fabric shrinkage measurement with computer vision" I can develop a Windows application using Python and OpenCV to detect four reference marks and accurately calculate length-wise and width-wise shrinkage. I can build a calibrated vision pipeline with camera calibration, robust mark detection, sub-millimeter measurement, and validation across knitted and woven fabrics. I will provide the offline executable, complete commented source code, PLC-friendly trigger interface, calibration procedure, user manual, and performance testing. Could you share sample before-and-after fabric images and the camer

@AlonzoLeeeooo

Hi, To address your need for fabric shrinkage measurement, I can provide an offline Windows application that detects four marks before and after washing, calculates X/Y shrinkage, and exports measurements through dual interfaces (GUI and CLI/API for your PLC workflow). The initial milestone will be a calibrated OpenCV prototype with benchmark accuracy and error reports on knitted and woven samples, delivered within three days of receiving reference images and physical dimensions. The final delivery will include source code, an installer, operating documentation, and validation data across 30 calibrated test samples evaluated against your targets (0.5 mm accuracy and sub-five-second processing). Drawing on my USTC Ph.D. and 10+ peer-reviewed publications, I specialize in computer vision, image restoration, and reproducible evaluation. In a recent textile inspection project where measurements fluctuated due to fabric texture and camera positioning, I implemented image correction and verification routines (e.g., perspective rectification, mark detection, and calibration checks) that produced traceable before-and-after measurements with automated anomaly flagging. This calibration and validation workflow applies directly to your four-mark setup, helping handle operational variations (e.g., lighting shifts or sample placement) that could affect shrinkage calculations. If this technical direction aligns with your vision, let's have a quick chat to finalize the milestones.

@TechWagger2026

With TechWagger, you're bringing onboard a powerhouse of talent at the intersection of technology and digital solutions. Python and software architecture are at the core of our skillset, making us more than proficient with OpenCV for your particular requirements. We have extensive experience in computer vision calibration and have built robust detection algorithms that deliver repeatable results with high accuracy levels. When it comes to GUI development, we're well-versed in PyQt, Tkinter, and other similar frameworks you've mentioned. Our approach is centered around user convenience and simplicity, as demanded by your project description, ensuring that your operators will find our application easy to install and use. Let's get started on this exciting journey where every piece of fabric tells a story of quality and durability. I'm certain my team at TechWagger can craft an exceptional tale for you with our skills in C Programming, Micropython, Python, and Software Architecture. When precision matters, trust us to deliver!

@nakabirik

Hello. Thank you very much for your chance. I have read your project description carefully. I'm a Web, AI & Android Custom | Python, WordPress, Java with over 8 years of experience working with Python, OpenCV, Image Processing. I understand you need a Windows app where an operator places fabric under a camera, presses one button, and sees length and width shrinkage as a percentage, with ±0.5 mm accuracy and a CLI hook for a PLC. The real challenge is mark detection across fabrics. A common issue: knitted fabric and yarn patterns confuse contour detection, so marks are missed or shifted. I would use a calibration target and match the four marks by shape and colour, not by size alone. I can build it perfectly : First, calibrate the camera to millimetres with a reference ruler. Then, detect the four marks in before and after images. Finally, build the PyQt app, CLI flag and manual. Q1 - How are the four marks placed, printed crosses or coloured stickers? That decides whether I detect by shape, colour or template. Q2 - I'd run one camera calibration per setup, stored to a file, so accuracy holds between sessions. Agree on that approach? Q3 - Will the camera and lighting stay fixed on the QC bench? Stability there is what makes ±0.5 mm realistic. Let's connect over a chat so we can discuss this in more detail. Best regards , Muheebwa

@MuhammadAsad108

I deliver 100% work — 99.9% is not for me. ✔ Workflow Diagram Camera Capture ⟶⟶ Calibration ⟶⟶ Mark Detection ⟶⟶ X/Y Measurement ⟶⟶ Shrinkage Calculation ⟶⟶ QC Validation ⟶⟶ Windows EXE Key Highlights ✔ Python + OpenCV computer-vision measurement pipeline ✔ Automatic detection of 4 reference marks before/after washing ✔ Perspective/calibration correction for mm-level measurement ✔ ±0.5 mm accuracy target validated against a calibrated ruler ✔ Robust processing for knitted/woven fabrics and varied colors ✔ Lightweight PyQt/Tkinter Windows desktop application ✔ External PLC trigger via CLI flag or minimal API ✔ Offline-friendly installer, source code, manual & rebuild guide Best Regards, Asad Computer Vision Engineer | Python | OpenCV | Desktop Apps

@Mayanks010

Hi, Thanks for sharing your project. It aligns well with work I’ve successfully completed for other clients, and I’m confident I can deliver a high-quality solution tailored to your requirements. Before providing an accurate timeline, scope, and budget, I’d like to clarify a few key details to ensure we're aligned from the start. Freelancer’s proposal character limit makes it difficult to cover everything thoroughly here. If you're available, let's connect via chat. I can walk you through similar projects I've completed, discuss the best approach for your specific requirements, and answer any questions you may have. Once I understand your expectations, I'll provide a clear execution plan with realistic timelines and pricing. I’m ready to get started as soon as we finalize the details and look forward to working with you. Best regards, Mayank Sahu