I want a lightweight, browser-based application that automatically straightens converging verticals in real-estate photos. Accuracy of correction is the top priority; I need the result to look as if the building was photographed with a perfectly level camera. ShiftN (shiftn.de) comes close, but often leaves residual tilt or unnatural stretching, so the new tool must be noticeably more reliable. Core workflow • User drags one or many images onto the page. • The app detects vanishing points, computes the homography, and warps each photo so the main verticals are perfectly parallel. • A side-by-side before/after viewer lets the user verify the fix. • Corrected files are downloadable individually or in a zip when batch mode is used. Key requirements 1. Accuracy first: the algorithm should handle wide-angle shots, slight rolls, and images with minimal vertical cues. 2. Batch processing and an interactive before-after comparison are mandatory. 3. Front-end must run in any modern browser; a minimal back-end is fine as long as uploads are secure and temporary. 4. Code must be clean and well-documented so I can extend it later. I have no fixed stack preference, so feel free to propose OpenCV, TensorFlow, or any combination of JavaScript (e.g. WebAssembly-compiled OpenCV), Python (FastAPI, Flask), or other technologies you’re comfortable with. Please outline: • The libraries and frameworks you’ll rely on. • Your approach to vanishing-point detection and homography estimation (e.g., RANSAC line clustering, deep-learning refinement, etc.). • Expected development timeline with major milestones. • Fixed price or milestone-based quote. Generic copy-paste replies will be ignored; I’m looking for a concise explanation of your method and why it will produce more accurate results than ShiftN.