This is an example showing the use of Mask RCNN in a real application.
We train the model to detect balloons only, and then we use the generated
masks to keep balloons in color while changing the rest of the image to
grayscale.
## Installation
From the [Releases page](https://github.com/matterport/Mask_RCNN/releases) page:
1. Download `mask_rcnn_balloon.h5`. Save it in the root directory of the repo (the `mask_rcnn` directory).
2. Download `balloon_dataset.p3`. Expand it such that it's in the path `mask_rcnn/datasets/balloon/`.
## Apply color splash using the provided weights
Apply splash effect on an image:
```bash
python3 balloon.py splash --weights=/path/to/mask_rcnn/mask_rcnn_balloon.h5 --image=<file name or URL>
```
Apply splash effect on a video. Requires OpenCV 3.2+:
```bash
python3 balloon.py splash --weights=/path/to/mask_rcnn/mask_rcnn_balloon.h5 --video=<file name or URL>
```
## Run Jupyter notebooks
Open the `inspect_balloon_data.ipynb` or `inspect_balloon_model.ipynb` Jupter notebooks. You can use these notebooks to explore the dataset and run through the detection pipelie step by step.
## Train the Balloon model
Train a new model starting from pre-trained COCO weights