hattori8 / DeepFloorplan

Introduction

https://github.com/zlzeng/DeepFloorplan を、python3.6で動くように変更したリポジトリです

Requirements

Code has been tested by using tensorflow-gpu==1.15.1 & OpenCV==3.3.1. I used Nvidia Geforce GTX1060 with CUDA 10.0 installed.

conda env create -f=requirements.yml

Data

We share all our annotations and train-test split file here. 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

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

To train the network, simply run

python main.py --phase=Train

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

python main.py --phase=Test

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

python scores.py --dataset=R3D

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

python postprocess.py

or

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