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visualize_confusion_matrix.py
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import numpy as np
import matplotlib.pyplot as plt
import os
# def show_confMat(confusion_mat, classes_name, set_name, out_dir,ax):
# """
# 可视化混淆矩阵，保存png格式
# :param confusion_mat: nd-array
# :param classes_name: list,各类别名称
# :param set_name: str, eg: 'valid', 'train'
# :param out_dir: str, png输出的文件夹
# :return:
# """
# # 归一化
# confusion_mat_N = confusion_mat.copy()
# # confusion_mat_N = confusion_mat_N.astype('float') / confusion_mat_N.sum(axis=1)[:, np.newaxis]
# # 获取颜色
# cmap = plt.cm.get_cmap(
# 'BuPu'
# ) # 更多颜色: http://matplotlib.org/examples/color/colormaps_reference.html
# im=ax.imshow(confusion_mat_N, cmap=cmap)
# # 设置文字
# xlocations = np.array(range(len(classes_name)))
# ax.xaxis.set_ticks_position('top')
# plt.xticks(xlocations, classes_name, rotation=60,)
# plt.yticks(xlocations, classes_name)
# # 保存
# plt.savefig(os.path.join(out_dir, 'Confusion_Matrix_' + set_name + '.png'))
def norm(mat):
confusion_mat_N = mat.copy()
confusion_mat_N = confusion_mat_N.astype('float') / confusion_mat_N.sum(axis=1)[:, np.newaxis]
return confusion_mat_N
data1=np.array([[21075636, 447976, 849189, 183406, 92624, 22837],
[ 799558, 20719389, 371593, 38434, 15779, 4655],
[ 649032, 203086, 14621448, 1887284, 4500, 5435],
[ 149387, 45380, 1501524, 16811874, 1009, 2924],
[ 106488, 16553, 5764, 1426, 664722, 480],
[ 183227, 148438, 5197, 910, 19850, 334929]])
data2=np.array([[21237527, 422226, 774217, 171876, 57830, 7992],
[ 548330, 21131585, 212216, 44021, 9822, 3434],
[ 669343, 190216, 14381328, 2127453, 2389, 56],
[ 164992, 27690, 1271714, 17045566, 533, 1603],
[ 91922, 16639, 3882, 1876, 680915, 199],
[ 243447, 130210, 8483, 1004, 7799, 301608]])
data3=np.array([[21213732, 455588, 748926, 178289, 65023, 10110],
[ 497497, 21232005, 164006, 43077, 11223, 1600],
[ 730607, 227176, 14492509, 1912634, 3506, 4353],
[ 172472, 37015, 1327723, 16971992, 861, 2035],
[ 78748, 11804, 2126, 1464, 700958, 333],
[ 200536, 187836, 3441, 592, 10572, 289574]])
data=[data1,data2,data3]
# # show_confMat(data1, ['imp surf', 'building', 'low_veg', 'tree', 'car', 'clutter'], "mit-b0", "./",1)
# # show_confMat(data2, ['imp surf', 'building', 'low_veg', 'tree', 'car', 'clutter'], "mit-b4", "./",2)
# fig, ax =plt.subplots(2,2, frameon = False)
# ax = ax.flatten()
# im=show_confMat(data2-data1, ['imp surf', 'building', 'low_veg', 'tree', 'car', 'clutter'], "mit-b4-fuse", "./",ax[0])
# im=show_confMat(data3-data1, ['imp surf', 'building', 'low_veg', 'tree', 'car', 'clutter'], "mit-b4-fuse", "./",ax[1])
# fig.colorbar(im, ax=[ax[0],ax[1]], fraction=0.03, pad=0.05)
# plt.show()
fig, ax = plt.subplots(1, 3)
ax = ax.flatten()
cmap = plt.cm.get_cmap('BuPu')
classes_name=['imp surf', 'building', 'low_veg', 'tree', 'car', 'clutter']
xlocations = np.array(range(len(classes_name)))
# for i in range(2):
# # im = ax[i].imshow(norm(data[i]),cmap=cmap)
# im = ax[i].imshow(data[i+1]-data[0],cmap=cmap)
# ax[i].xaxis.set_ticks_position('top')
# ax[i].set_xticks(xlocations)
# ax[i].set_xticklabels(classes_name, rotation=60)
# ax[i].set_yticks(xlocations)
# ax[i].set_yticklabels(classes_name)
for i in range(3):
# im = ax[i].imshow(norm(data[i]),cmap=cmap)
im = ax[i].imshow(norm(data[i]),cmap=cmap)
majorFormatter = plt.FormatStrFormatter('%1.1f')
for j in range(data[i].shape[0]):
for k in range(data[i].shape[0]):
ax[i].annotate('%.3f'%norm(data[i])[k, j],xy=(j, k), horizontalalignment='center', verticalalignment='center')
ax[i].xaxis.set_ticks_position('top')
ax[i].set_xticks(xlocations)
ax[i].set_xticklabels(classes_name, rotation=60)
ax[i].set_yticks(xlocations)
ax[i].set_yticklabels(classes_name)
fig.colorbar(im, ax=[ax[0], ax[1],ax[2]], fraction=0.015, pad=0.05)
# plt.imshow(data1, cmap=cmap)
# ax=plt.gca()
# ax.xaxis.set_ticks_position('top')
# plt.xticks(xlocations, classes_name, rotation=60,)
# plt.yticks(xlocations, classes_name)
# plt.colorbar()
plt.savefig('tjn.png', bbox_inches='tight')
plt.show()