30 lines
1 KiB
Python
30 lines
1 KiB
Python
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from sklearn.metrics import precision_recall_curve, precision_score, recall_score
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from matplotlib import pyplot as plt
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import pandas as pd
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import numpy as np
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from pathlib import Path
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from tqdm import tqdm
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import seaborn as sb
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import cv2 as cv
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def plot_precision_recall():
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data = pd.read_csv("/home/haosheng/dataset/camera/test/test_result.csv")
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plt.figure(figsize=(8,6))
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sb.set_style("white")
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for f in [50, 200, 500, 1000]:
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data_plot = data.query(f"f == {f}")
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sb.lineplot(x="p", y="recall",
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data=data_plot,
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label=f"Pixel threshold: {f}",
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linewidth=2.5,
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ci=None)
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plt.xlim([0.145,1.05])
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plt.ylim([0,1.05])
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plt.axvline(x=0.583333, ymin=0, ymax=0.6, linestyle='-.', color='gray')
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plt.axhline(y=0.624400, xmin=0, xmax=0.48, linestyle='-.', color='gray')
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plt.plot(0.583333, 0.624400,'ro')
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plt.xlabel("Precision")
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plt.ylabel("Recall")
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plt.legend()
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plt.savefig("figures/precision_recall.png")
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