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--- |
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license: cc-by-4.0 |
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--- |
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# VISEM-Tracking-graphs - HuggingFace Repository |
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This HuggingFace repository contains the pre-generated graphs for the sperm video dataset called VISEM-Tracking (https://huggingface.co/papers/2212.02842) . The graphs represent spatial and temporal relationships between sperm in a video. Spatial edges connect sperms within the same frame, while temporal edges connect sperms across different frames. |
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The graphs have been generated with varying spatial threshold values: 0.1, 0.2, 0.3, 0.4, and 0.5. Each spatial threshold determines the maximum distance between two nodes for them to be connected in the graph. The repository contains separate directories for each spatial threshold. |
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The source code used to generate graphs can be found here: https://github.com/vlbthambawita/visem-tracking-graphs |
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## Repository Structure |
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The repository is structured as follows: |
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- `spatial_threshold_0.1` |
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- `spatial_threshold_0.2` |
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- `spatial_threshold_0.3` |
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- `spatial_threshold_0.4` |
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- `spatial_threshold_0.5` |
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Inside each `spatial_threshold_X` directory, you will find: |
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- `frame_graphs`: A directory containing individual frame graphs as GraphML files. |
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- `video_graph.graphml`: A GraphML file containing the complete video graph. |
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## Usage |
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To use the graphs in this repository, you need to: |
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1. Download the desired graph files (frame graphs or video graph) for the spatial threshold of your choice. |
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2. Load the graphs using a graph library such as NetworkX in Python: |
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```python |
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import networkx as nx |
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# Load a frame graph |
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frame_graph = nx.read_graphml('path/to/frame_graph_X.graphml') |
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# Load the video graph |
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video_graph = nx.read_graphml('path/to/video_graph.graphml') |
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``` |
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TO USE THIS DATA, you need to cite the paper: |
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https://www.nature.com/articles/s41597-023-02173-4 |
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