### Start Training

Source: https://github.com/art-programmer/floorplantransformation/blob/master/pytorch/README.md

Initiates the training process. Set --restore=1 to resume from a checkpoint.

```bash
python train.py --restore=0
```

--------------------------------

### Install Dependencies

Source: https://github.com/art-programmer/floorplantransformation/blob/master/pytorch/README.md

Installs the necessary project dependencies from the requirements file.

```bash
pip install -r requirements.txt
```

--------------------------------

### Run Annotator

Source: https://github.com/art-programmer/floorplantransformation/blob/master/annatator/README.md

Execute the main Lua script to start the annotator. Ensure the loadFilenames function is updated with the correct image and save paths.

```bash
qlua main.lua
```

--------------------------------

### Train Network from Pretrained Model

Source: https://github.com/art-programmer/floorplantransformation/blob/master/README.md

Use this command to train the network starting from a pretrained pose estimation model. Ensure you provide the correct path to the downloaded model.

```bash
th main.lua -loadPoseEstimationModel  "path to the downloaded pose estimation model"
```

--------------------------------

### Resume Training from Saved Model

Source: https://github.com/art-programmer/floorplantransformation/blob/master/README.md

Load a previously trained model and resume the training process. Specify the path to the downloaded pretrained model.

```bash
th main.lua -loadModel  "path to the downloaded pretrained model"
```

--------------------------------

### Run Testing

Source: https://github.com/art-programmer/floorplantransformation/blob/master/pytorch/README.md

Executes the testing task for the project.

```bash
python train.py --task=test
```

--------------------------------

### Evaluate Performance on Benchmark

Source: https://github.com/art-programmer/floorplantransformation/blob/master/README.md

Assess the performance of the model using the benchmark dataset. Provide the model path and the directory where results should be saved.

```bash
th evaluate.lua -loadModel "model path" -resultPath "path to save results"
```

--------------------------------

### Run Python 3D Model Viewer

Source: https://github.com/art-programmer/floorplantransformation/blob/master/README.md

Launch the Python-based viewer for 3D models using Panda3D. Ensure you are in the 'rendering/' directory.

```bash
python viewer.py
```

--------------------------------

### Run Annotator with Debugging

Source: https://github.com/art-programmer/floorplantransformation/blob/master/annatator/README.md

Run the annotator with verbose debugging output enabled. This is useful for troubleshooting and understanding the annotator's internal processes.

```bash
qlua -lenv main.lua
```

--------------------------------

### Compile and Run C++ 3D Model Generator

Source: https://github.com/art-programmer/floorplantransformation/blob/master/README.md

Compile and execute the C++ command-line tool for generating 3D models from vectorization data. Navigate to the C++ code directory, build the project, and run the executable with the data file.

```bash
cd popup/code/
cmake .
make
./popup_cli ../data/floorplan_1.txt
```

--------------------------------

### Make Prediction on Floorplan Image

Source: https://github.com/art-programmer/floorplantransformation/blob/master/README.md

Generate vectorization results, rendering images, and data for 3D models from a floorplan image. Specify the model path, input image path, and desired output filename.

```bash
th predict.lua -loadModel "model path" -floorplanFilename "path to the floorplan image" -outputFilename "output filename"
```

--------------------------------

### Run Python 3D Model Renderer

Source: https://github.com/art-programmer/floorplantransformation/blob/master/README.md

Execute the Python script to render a single view of a 3D model. Camera pose and model specifications can be adjusted within the script.

```bash
python rendering.py
```

--------------------------------

### C++ 3D Model Generation Data Format

Source: https://github.com/art-programmer/floorplantransformation/blob/master/README.md

This CSV format is used for input data files to the C++ 3D model generator. It includes dimensions, wall descriptions, and opening descriptions.

```csv
width height
the number of walls
(Wall descriptions)
x_1, y_1, x_2, y_2, room type on the left, room type on the right
...
(Opening descriptions)
x_1, y_1, x_2, y_2, 'door', dummy, dummy
(Icon descriptions)
x_1, y_1, x_2, y_2, icon type, dummy, dummy
```

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