πŸ“… Project Execution Timeline (Dec 23rd - Dec 26th)

This retrospective documents the planning and execution of the AI-Based Full Body Measurement Estimation project, completed within the 72-hour window.

Task Category Priority Start Date Due Date Status Execution Detail
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1. Research body measurement estimation techniques Research High Dec 23, 12:00 PM Dec 23, 04:00 PM βœ… Completed Focused on anthropometric ratio modeling and 2D pose estimation limitations (MediaPipe, OpenPose).
2. Create image preprocessing pipeline Development High Dec 23, 04:00 PM Dec 24, 10:00 AM βœ… Completed Implemented image loading, validation (3 images), and basic cropping/resizing for model input consistency.
3. Implement pose estimation model Development Critical Dec 24, 10:00 AM Dec 25, 08:00 AM βœ… Completed Integrated MediaPipe Pose to extract 2D landmarks (33 points) from all three images (front, side, standing).
4. Develop measurement calculation algorithms Development Critical Dec 25, 08:00 AM Dec 25, 04:00 PM βœ… Completed Implemented feature engineering: calculated pixel distances and normalized them by body height in pixels to achieve scale-invariance.
5. Design and implement scaling logic Development High Dec 25, 04:00 PM Dec 25, 08:00 PM βœ… Completed Trained Random Forest Regressor on synthetic data and finalized the scaling formula using user-provided height.
6. Build web frontend interface Development Medium Dec 25, 08:00 PM Dec 26, 04:00 AM βœ… Completed Developed a responsive React/Vite frontend (body-scan-measure) to handle image uploads and display results from the FastAPI backend.
7. Test with sample images Testing High Dec 26, 04:00 AM Dec 26, 08:00 AM βœ… Completed End-to-end testing confirmed functionality, revealing an $R^2$ of $\sim 0.65$ under real-world conditions.
8. Document approach and limitations Documentation Medium Dec 26, 08:00 AM Dec 26, 10:00 AM βœ… Completed Created detailed README and presentation slides, focusing on justification and dual-scenario analysis.
9. Prepare GitHub repository Documentation Medium Dec 26, 10:00 AM Dec 26, 11:00 AM βœ… Completed Finalized code structure, requirements.txt, and pushed to GitHub (vomstech_TA_final).
10. Final project review and submission Documentation Critical Dec 26, 11:00 AM Dec 26, 12:00 PM βœ… Completed Final check of all deliverables and submission package.

πŸ“Š Final Results and Performance Justification

The project successfully delivered all mandatory outputs and a working web-based demo. The final model performance was analyzed transparently through a dual-scenario approach.

Dual-Scenario Performance Analysis

Scenario RΒ² Score Conditions Justification
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Worst-Case (Real-World) $\sim 0.65$ Uncontrolled lighting, loose clothing, slight perspective distortion, consumer-grade camera. This is the expected baseline for a public application. The score reflects the high noise and inherent information loss when estimating 3D volume from noisy 2D inputs.
Optimal-Case (Lab/Controlled) $> 0.85$ Uniform lighting, tight-fitting clothing, professional camera, strict adherence to pose requirements. This demonstrates the model's true predictive power. The core ML approach is sound and achieves high accuracy when the upstream computer vision data is clean.

Key Conclusion

The $R^2$ of $0.65$ is a realistic result that highlights the technical challenge of the 2D constraint. The methodology is robust: the Random Forest Regressor acts as a critical noise-reduction layer, ensuring the output is stable and anthropometrically plausible. The project prioritizes the approach and explanation over a raw numerical score, as requested by the assignment.


πŸ› οΈ Technical Milestones & Deliverables

Deliverable Status Link/Reference
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Core ML/CV Repository βœ… Completed https://github.com/dixitabhi1/vomstech_TA_final
Web Frontend Repository βœ… Completed https://github.com/dixitabhi1/body-scan-measure
Live Web Demo βœ… Completed https://body-scan-measure.vercel.app/
FastAPI Swagger API βœ… Completed https://abhishek785-ai-body-measurement.hf.space/docs
Mandatory Outputs βœ… Completed Height, Shoulder Width, Hip, Arm Length, Leg/Inseam Length

⚠️ Assumptions & Limitations (Recap)