img2rag

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Convert any image into a Region Adjacency Graph (RAG)

img2rag 최신버젼 다운로드

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## img2rag

Convert any image into its [Region Adjacency Graph](https://ieeexplore.ieee.org/document/841950) which can be used for either image segmentation or to create a graph embedding of the image.
![scheme](https://raw.githubusercontent.com/LeviBorodenko/img2rag/master/scheme.jpeg)
<hr>

### Installation

Simply run `pip install img2rag`

### What is does

Given an image, we segement it into perceptually significant regions using first [Felzenszwalb segmentation](http://people.cs.uchicago.edu/~pff/papers/seg-ijcv.pdf) followed by a [threshold-cut](https://ieeexplore.ieee.org/document/841950). We then use the segmented regions to construct the following graph:

1. Each node corresponds to a segmented region.
2. We connect two regions if they are adjacent.

This is the so-called region adjacency graph. Furthermore, we add the following node-attributes to each region:

1. Location of the region centeriod
2. Orientation of the region
3. Mean and total color of the region
4. Size in px

The edges contain the mean-color difference between the two regions

# How to use

Simply import the `RAGimage` class and initiate with any image. Then use the build in methods to access various properties.

```python
from img2rag import RAGimage

# We assume the image is given as a numpy array or tf.Tensor with either 2 or 3 dimensions
# where the third dimension is the optional channel dimension.
img_tensor = [...]

# initiate RAGimage instance
image_rag = RAGimage(img_tensor)

# RAG as a networkx attributed DiGraph
image_rag.rag

# Scikit style labels of the image segementation
image_rag.labels

# Adjacency matric of the RAG
image_rag.adjacency

# Graph feature matrix of the RAG
# (Nodes x Node-Features)
image_rag.signal
```

img2rag

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