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