KO | EN

GitLite

Minimalist Open Source Repository Search Engine

gitlite — search

#typescript #ai-agents #cli #claude-code #codex #developer-tools #ai #dsh-plugin #ai-agent #mcp #deepseek-harness #windows

deep-floorplan-recognition

★ 30 Open GitHub ↗

State-of-the-art Floor Plan Recognition System. This innovative software utilizes advanced algorithms to accurately interpret and analyze architectural floor plans.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Floor Plan Recognition using a Multi-task Network

## Requirements

- Please install OpenCV
- Please install Python 2.7
- Please install tensorflow-gpu

Our code has been tested by using tensorflow-gpu==1.10.1 & OpenCV==3.1.0. We used Nvidia Titan Xp GPU with CUDA 9.0 installed.

## Python packages

- [numpy]
- [scipy]
- [Pillow]
- [matplotlib]

## Data

We share all our annotations and train-test split file [here](https://mycuhk-my.sharepoint.com/:f:/g/personal/1155052510_link_cuhk_edu_hk/EgyJhisy04hNnxKncWl5zksBf9zDKDpMJ7c0V-q53_pxuA?e=P0BjZd). Or download the annotation using the link in file "dataset/download_links.txt". The additional round plan is included in the annotations.

Our annotations are saved as png format. The name with suffixes "\_wall.png", "\_close.png" and "\_room.png" are denoted "wall", "door & window" and "room types" label, respectively. We used these labels to train our multi-task network.

The name with suffixes "\_close_wall.png" is the combination of "wall", "door & window" label. We don't use this label in our paper, but maybe useful for other tasks.

The name with suffixes "\_multi.png" is the combination of all the labels. We used this kind of label to retrain the general segmentation network.

We also provide our training data on R3D dataset in "tfrecord" format, which can improve the loading speed during training.

To create the "tfrecord" training set, please refer to the example code in "utils/create_tfrecord.py"

All the raw floor plan image please refer to the following two links:

- R2V: <https://github.com/art-programmer/FloorplanTransformation.git>
- R3D: <http://www.cs.toronto.edu/~fidler/projects/rent3D.html>

## Usage

To use our demo code, please first download the pretrained model, find the link in "pretrained/download_links.txt" file, unzip and put it into "pretrained" folder, then run

```bash
python demo.py --im_path=./demo/45719584.jpg 
```

To train the network, simply run

```bash
python main.py --pharse=Train
```

Run the following command to generate network outputs, all results are saved as png format.

```bash
python main.py --pharse=Test
```

To compute the evaluation metrics, please first inference the results, then simply run

```bash
python scores.py --dataset=R3D
```

To use our post-processing method, please first inference the results, then simply run

```bash
python postprocess.py
```

or

```bash
python postprocess.py --result_dir=./[result_folder_path]
```

## Contact
If you want to contact me, you can reach me at email@maikpaixao.com.br

## License
This project uses the following license: MIT License.

deep-floorplan-recognition

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README