matching_metrics.py

import numpy as np
import matplotlib.pyplot as plt
from scipy.ndimage import label, zoom

from stardist.matching import matching
from stardist.plot import render_label, render_label_pred 
np.random.seed(42)

def create_example():
    rand_gt = np.random.uniform(0,1,(8,8))
    rand_pred = rand_gt+.1*np.random.uniform(-1,1,(8,8))
    y_true, _ = label(zoom(rand_gt, (32,32))>.7)
    y_pred, _ = label(zoom(rand_pred, (32,32))>.7)
    return y_true, y_pred



# create some example data 
y_true, y_pred = create_example()

# Calculate the matching (with IoU threshold `thresh`) and all metrics
res = matching(y_true, y_pred, thresh=0.5)

print(res)

# plot the label masks
for i, (img,title) in enumerate(zip((render_label(y_true),
                                  render_label(y_pred),
                                  render_label_pred(y_true, y_pred)),
                                 ("True","Pred"," TP/FP/FN (green/red/blue)"))):
    plt.subplot(1,3,i+1)
    plt.imshow(img)
    plt.axis("off")
    plt.title(title, fontsize=8)