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Sabrina Pereira
Sabrina Pereira

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Plot FiftyOne visualisations with Seaborn

#computervision #python #datascience

Why

I wanted more customisation options to draw figures for my thesis paper. It took me a while to figure this out, so I thought I'd share.

How

I'm assuming you already have a FiftyOne dataset with computed embeddings and visualization. If not, you'll need to create a dataset and compute the embeddings and visualization before proceeding.

I already have everything saved, so I load my dataset and the compute_visualization results before plotting:

import fiftyone as fo # load dataset dataset = fo.load_dataset("dataset_name") # load computed visualisation results = dataset.load_brain_results("vis_name")
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I have a sample field called "vehicle_type" that I want to use as the hue in my seaborn plot. To obtain this information for each sample, I wrote a simple function:

def get_vehicle_type(sample_id): return dataset[sample_id]["vehicle_type"]
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Next, I convert results.points into a pandas DataFrame and fetch the "vehicle_type" information from the FiftyOne dataset.

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt # turn results.points into dataframe df_viz = pd.DataFrame(results.points, columns=["x", "y"]) # get sample ids for each sample df_viz["sample_id"] = results.sample_ids # use sample id to get the sample field info I need df_viz["vehicle_type"] = df_viz["sample_id"].apply(get_vehicle_type)
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Finally, I plot the results using seaborn:

sns.scatterplot(data=df_viz, x='x', y='y', hue='vehicle_type', palette='mako_r', alpha=.9, s=1, edgecolor='none') plt.title('Image Uniqueness') plt.axis('off') plt.show()
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Seaborn allows for greater control over the appearance of the plot. Since I don't need the plot to be interactive, this is the perfect solution for creating uniform plots for my paper.

Final result

A scatter plot showing different clusters of similar images

Extra: compute embeddings and visualisation 

import fiftyone.zoo as foz # compute embeddings model = foz.load_zoo_model("mobilenet-v2-imagenet-torch") embeddings = dataset.compute_embeddings(model) # pickle embeddings for later use, this the computation takes a while with open('embeddings.pkl', 'wb') as file: pickle.dump(embeddings, file) # Compute visualization results = fob.compute_visualization( dataset, embeddings=embeddings, seed=42, brain_key="vis_name" )
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