41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
import pandas as pd
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import matplotlib.pyplot as plt
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# Carrega CSV salvo pelo benchmark
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df = pd.read_csv("benchmark_results.csv")
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fig, ax1 = plt.subplots(figsize=(12, 6))
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df_sorted = df.sort_values("avg_duration_sec", ascending=True)
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color = 'tab:blue'
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ax1.set_xlabel('Modelo')
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ax1.set_ylabel('Duração [blue] (s)', color=color)
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ax1.bar(df_sorted["model"], df_sorted["avg_duration_sec"], color=color, alpha=0.6)
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ax1.tick_params(axis='y', labelcolor=color)
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plt.xticks(rotation=45)
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# Segundo eixo: acurácia
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ax2 = ax1.twinx()
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color = 'tab:green'
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ax2.set_ylabel('Acurácia média (similarity)', color=color)
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ax2.plot(df_sorted["model"], df_sorted["avg_similarity_score"], color=color, marker='o')
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ax2.tick_params(axis='y', labelcolor=color)
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plt.title("Média de Tempo e Acurácia por Modelo com 7 Imagens")
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plt.tight_layout()
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plt.grid(True)
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plt.show()
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'''
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curl -X POST http://localhost:5006/benchmark_face_match \
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-F "person_id=vitor" \
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-F "images[]=@/home/v/Desktop/reconhecimento/imgs/a.jpg" \
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-F "images[]=@/home/v/Desktop/reconhecimento/imgs/b.jpg" \
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-F "images[]=@/home/v/Desktop/reconhecimento/imgs/c.jpg" \
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-F "images[]=@/home/v/Desktop/reconhecimento/imgs/d.jpg" \
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-F "images[]=@/home/v/Desktop/reconhecimento/imgs/e.jpg" \
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-F "images[]=@/home/v/Desktop/reconhecimento/imgs/f.jpg" \
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-F "images[]=@/home/v/Desktop/reconhecimento/imgs/g.jpg"
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''' |