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shape-detection

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🟣 Object detection of abstract shapes with neural networks

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- bw-multiple-rectangles-dist.png
- bw-multiple-rectangles-iou.png
- bw-multiple-rectangles-mse.png
- bw-single-rectangle_no-prediction.png
- bw-single-rectangle_prediction.png
- bw-three-rectangles-100k-images.png
- bw-three-rectangles-50k-images.png
- bw-two-rectangles-or-triangles.png
- bw-two-rectangles_flips.png
- bw-two-rectangles_no-flipping.png
- bw-two-rectangles_with-flipping.png
- color-multiple-shapes_three-colors_bboxes.png
- color-multiple-shapes_three-colors_classification.png
- color-multiple-shapes_two-colors_bboxes.png
- color-multiple-shapes_two-colors_classification.png
- IOU.jpg
.gitignore
color-multiple-shapes.ipynb
LICENSE
multiple-rectangles-or-triangles.ipynb
multiple-rectangles.ipynb
README.md
single-rectangle.ipynb
two-rectangles-or-triangles.ipynb
two-rectangles.ipynb

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// repository documentation

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**Read my [Medium post](https://towardsdatascience.com/object-detection-with-neural-networks-a4e2c46b4491) on this project**

*Note: Some people reached out and told me that they can't replicate some of the results in this repo. I couldn't find the reason, potentially one of the dependencies changed since I did this project a few years ago. Unfortunately I don't have any notes on which exact setup I used back then. If you find anything, please reach out to me :)*

This is an example of object detection with neural networks (implemented with [keras](https://keras.io/)). The training images contain abstract geometric shapes and can be easily bootstraped.

The code is split up into several Jupyter notebooks. They increase in complexity:

* Detection of rectangles in numpy arrays: [single-rectangle](single-rectangle.ipynb), [two-rectangles](two-rectangles.ipynb), [multiple-rectangles](multiple-rectangles.ipynb)
* Additional classification between rectangles and triangles: [two-rectangles-or-triangles](two-rectangles-or-triangles.ipynb), [multiple-rectangles-or-triangles](multiple-rectangles-or-triangles.ipynb)
* Application to more complex images (using convnets): [color-multiple-shapes](color-multiple-shapes.ipynb)

![](https://raw.githubusercontent.com/jrieke/shape-detection/master/plots/bw-single-rectangle_prediction.png)

**Requirements**: Python 2.7, keras (v1.0.5), theano or tensorflow, numpy, matplotlib, jupyter, pycairo (only for color-multiple-shapes.ipynb)