Spaces:
Build error
Build error
File size: 2,935 Bytes
d8997c5 f48e15f d8997c5 1d64201 1666228 1d64201 1666228 1d64201 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | ---
title: 2D Floorplan Vectorizer
emoji: 🖌️
colorFrom: blue
colorTo: green
sdk: docker
app_file: Dockerfile
pinned: false
---
# 2D Floorplan Vectorizer
A Streamlit web app that allows you to upload 2D floorplan images and automatically vectorize them into COCO-style annotations using a trained Mask R-CNN model.
---
## How to Run the App
1. **Clone the repository:**
```bash
git clone <this-repo-link>
cd inovonics-ui-vectorizer
```
2. **Install the required Python packages:**
```bash
pip install -r requirements.txt
```
3. **Download the pretrained model:**
- Download `model_final.pth` from [Google Drive here](https://drive.google.com/file/d/1yr64AOgaYZPTcQzG6cxG6lWBENHR9qjW/view?usp=sharing).
- Place it inside:
```plaintext
inovonics-ui-vectorizer/rcnn_model/output/model_final.pth
```
4. **Run the app:**
```bash
streamlit run app.py
```
5. Open your browser at [http://localhost:8501](http://localhost:8501) to start using the app!
---
## Project Structure
```plaintext
inovonics-ui-vectorizer/
├── app.py # Streamlit frontend app
├── public/
│ └── logo.png # App logo
├── rcnn_model/
│ ├── extraction/ # Extract information from uploaded png image
│ │ └── annotation_builder.py
│ │ └── floorplan_sampler.py
│ │ └── from_labelme_runner.py
│ │ └── svg_to_json.py
│ ├── output/ # Empty folder while cloning. Place the pth file here
│ ├── preprocessing/ # Preprocess the image before sending to model
│ │ └── cleaning_images.py
│ │ └── cleaning_single_image.py
│ │ └── splitting_dataset.py
│ │ └── svg_to_yolo.py
│ ├── results/ # Empty folder while cloning. The resulting image and JSON will be stored here
│ ├── sample/ # Sample images for the model
│ ├── scripts/ # Model training, evaluation and inference. Streamlit runs the rcnn_run.py file from the frontend
│ │ └── rcnn_config.py
│ │ └── rcnn_eval.py
│ │ └── rcnn_full_tuner.py
│ │ └── rcnn_run.py
│ │ └── rcnn_train.py
│ ├── uploads/ # Temporary folder for streamlit to store the user uploaded image
│ ├── utils/ # Utility functions during model train and preprocessing
│ │ └── coco_to_inovonics_json.py
│ │ └── floorplan_vectorizer_utils.py
│ │ └── inovonics_ann_builder.py
├── README.md # (this file)
├── requirements.txt # Python dependencies
└── .gitignore # Files to ignore during Git commits
|