55 lines
1.9 KiB
Markdown
55 lines
1.9 KiB
Markdown
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# Color Splash Example
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This is an example showing the use of Mask RCNN in a real application.
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We train the model to detect balloons only, and then we use the generated
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masks to keep balloons in color while changing the rest of the image to
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grayscale.
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[This blog post](https://engineering.matterport.com/splash-of-color-instance-segmentation-with-mask-r-cnn-and-tensorflow-7c761e238b46) describes this sample in more detail.
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## Installation
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From the [Releases page](https://github.com/matterport/Mask_RCNN/releases) page:
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1. Download `mask_rcnn_balloon.h5`. Save it in the root directory of the repo (the `mask_rcnn` directory).
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2. Download `balloon_dataset.zip`. Expand it such that it's in the path `mask_rcnn/datasets/balloon/`.
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## Apply color splash using the provided weights
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Apply splash effect on an image:
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```bash
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python3 balloon.py splash --weights=/path/to/mask_rcnn/mask_rcnn_balloon.h5 --image=<file name or URL>
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```
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Apply splash effect on a video. Requires OpenCV 3.2+:
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```bash
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python3 balloon.py splash --weights=/path/to/mask_rcnn/mask_rcnn_balloon.h5 --video=<file name or URL>
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```
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## Run Jupyter notebooks
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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.
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## Train the Balloon model
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Train a new model starting from pre-trained COCO weights
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```
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python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=coco
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```
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Resume training a model that you had trained earlier
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```
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python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=last
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```
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Train a new model starting from ImageNet weights
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```
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python3 balloon.py train --dataset=/path/to/balloon/dataset --weights=imagenet
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```
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The code in `balloon.py` is set to train for 3K steps (30 epochs of 100 steps each), and using a batch size of 2.
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Update the schedule to fit your needs.
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