Automation and Efficiency of Architectural Landscape Simulation (Image Synthesis)
💬 Client Feedback
“The synthesis process, which used to be a tedious manual task, is now completed instantly, and the quality is impeccable. We are very much looking forward to expanding this into a fully automated business.”
Challenge: Inefficient Landscape Simulation via Manual Labor
In the creation of architectural landscape simulations (image synthesis), processes such as extracting buildings based on blueprints, assimilating them into the background, and fine-tuning angles were entirely done manually, requiring significant time and effort.
Solution: Building a Fully Automated Process using OpenCV
1. 【】: Implemented pixel-perfect shift synthesis processing utilizing Python's OpenCV.
2. 【】: Automated angle adjustment using perspective projection transformation, scripting the process to blend naturally with the background.
3. 【】: Formulated a roadmap for further "complete assimilation" and "zero-to-one generation" by introducing Photoshop API and professional image engine APIs (such as Replicate) as future prospects.
Result: Dramatic Reduction in Work Hours and Expansion to New Business
Through this technological breakthrough, we successfully and rapidly built an automation script for synthesis processing. This not only significantly reduced manual labor but also completed the foundation for expanding this very mechanism into a new business model. By multiplying the knowledge cultivated in real-world retail (reuse business) with digital technology, we are driving unique value creation.