def plot_confusion_matrix(y_true, y_pred, labels):
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
cmap = plt.cm.binary
cm = confusion_matrix(y_true, y_pred)
tick_marks = np.array(range(len(labels))) + 0.5
np.set_printoptions(precision=2)
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
plt.figure(figsize=(10, 8), dpi=120)
ind_array = np.arange(len(labels))
x, y = np.meshgrid(ind_array, ind_array)
intFlag = 0 # 标记在图片中对文字是整数型还是浮点型
for x_val, y_val in zip(x.flatten(), y.flatten()):
#
if (intFlag):
c = cm[y_val][x_val]
plt.text(x_val,
y_val,
"%d" % (c, ),
color='red',
fontsize=8,
va='center',
ha='center')
else:
c = cm_normalized[y_val][x_val]
if (c > 0.01):
#这里是绘制数字，可以对数字大小和颜色进行修改
plt.text(x_val,
y_val,
"%0.2f" % (c, ),
color='red',
fontsize=7,
va='center',
ha='center')
else:
plt.text(x_val,
y_val,
"%d" % (0, ),
color='red',
fontsize=7,
va='center',
ha='center')
if (intFlag):
plt.imshow(cm, interpolation='nearest', cmap=cmap)
else:
plt.imshow(cm_normalized, interpolation='nearest', cmap=cmap)
plt.gca().set_xticks(tick_marks, minor=True)
plt.gca().set_yticks(tick_marks, minor=True)
plt.gca().xaxis.set_ticks_position('none')
plt.gca().yaxis.set_ticks_position('none')
plt.grid(True, which='minor', linestyle='-')
plt.gcf().subplots_adjust(bottom=0.15)
plt.title('')
plt.colorbar()
xlocations = np.array(range(len(labels)))
plt.xticks(xlocations, labels, rotation=90)
plt.yticks(xlocations, labels)
plt.ylabel('Index of True Classes')
plt.xlabel('Index of Predict Classes')
#plt.savefig('confusion_matrix.png', dpi=300)
plt.show()
from Evaluator import Evaluator
ev = Evaluator(2, cm)
pa = ev.Pixel_Accuracy()
pac = ev.Pixel_Accuracy_Class()
miou = ev.Mean_Intersection_over_Union()
iou = ev.Class_IOU()
print('\n Pixel_Accuracy:', pa, '\n Pixel_Accuracy_Class:', pac,
'\n Mean_Intersection_over_Union:', miou, '\n Class_IOU:', iou)
