评价指标
IoUs, PA_Recall, Precision计算，来源于憨批的语义分割3——unet模型详解以及训练自己的unet模型（划分斑马线）_Bubbliiiing的博客-CSDN博客_憨批语义分割的github
import csv
import os
from os.path import join
import matplotlib.pyplot as plt
import numpy as np
from tensorflow.keras import backend
from PIL import Image
def Iou_score(smooth = 1e-5, threhold = 0.5):
def _Iou_score(y_true, y_pred):
# score calculation
y_pred = backend.greater(y_pred, threhold)
y_pred = backend.cast(y_pred, backend.floatx())
intersection = backend.sum(y_true[...,:-1] * y_pred, axis=[0,1,2])
union = backend.sum(y_true[...,:-1] + y_pred, axis=[0,1,2]) - intersection
score = (intersection + smooth) / (union + smooth)
return score
return _Iou_score
def f_score(beta=1, smooth = 1e-5, threhold = 0.5):
def _f_score(y_true, y_pred):
y_pred = backend.greater(y_pred, threhold)
y_pred = backend.cast(y_pred, backend.floatx())
tp = backend.sum(y_true[..., :-1] * y_pred, axis=[0,1,2])
fp = backend.sum(y_pred , axis=[0, 1, 2]) - tp
fn = backend.sum(y_true[..., :-1], axis=[0, 1, 2]) - tp
score = ((1 + beta ** 2) * tp + smooth) \
/ ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + smooth)
return score
return _f_score
# 设标签宽W，长H
def fast_hist(a, b, n):
#--------------------------------------------------------------------------------#
# a是转化成一维数组的标签，形状(H×W,)；b是转化成一维数组的预测结果，形状(H×W,)
#--------------------------------------------------------------------------------#
k = (a >= 0) & (a < n)
#--------------------------------------------------------------------------------#
# np.bincount计算了从0到n**2-1这n**2个数中每个数出现的次数，返回值形状(n, n)
# 返回中，写对角线上的为分类正确的像素点
#--------------------------------------------------------------------------------#
return np.bincount(n * a[k].astype(int) + b[k], minlength=n ** 2).reshape(n, n)
def per_class_iu(hist):
return np.diag(hist) / np.maximum((hist.sum(1) + hist.sum(0) - np.diag(hist)), 1)
def per_class_PA_Recall(hist):
return np.diag(hist) / np.maximum(hist.sum(1), 1)
def per_class_Precision(hist):
return np.diag(hist) / np.maximum(hist.sum(0), 1)
def per_Accuracy(hist):
return np.sum(np.diag(hist)) / np.maximum(np.sum(hist), 1)
def compute_mIoU(gt_dir, pred_dir, png_name_list, num_classes, name_classes=None):
print('Num classes', num_classes)
#-----------------------------------------#
# 创建一个全是0的矩阵，是一个混淆矩阵
#-----------------------------------------#
hist = np.zeros((num_classes, num_classes))
#------------------------------------------------#
# 获得验证集标签路径列表，方便直接读取
# 获得验证集图像分割结果路径列表，方便直接读取
#------------------------------------------------#
gt_imgs = [join(gt_dir, x+".png" ) for x in png_name_list]
pred_imgs = [join(pred_dir, x+".png") for x in png_name_list]
#------------------------------------------------#
# 读取每一个（图片-标签）对
#------------------------------------------------#
for ind in range(len(gt_imgs)):
#------------------------------------------------#
# 读取一张图像分割结果，转化成numpy数组
#------------------------------------------------#
pred = np.array(Image.open(pred_imgs[ind]))
#------------------------------------------------#
# 读取一张对应的标签，转化成numpy数组
#------------------------------------------------#
print(gt_imgs[ind])
label = np.array(Image.open(gt_imgs[ind]))
#---------------------------------------------------------#
#根据自己的标签修改
label[label==255]=1
label[label==200]=2
#---------------------------------------------------------#
# 如果图像分割结果与标签的大小不一样，这张图片就不计算
if len(label.flatten()) != len(pred.flatten()):
print(
'Skipping: len(gt) = {:d}, len(pred) = {:d}, {:s}, {:s}'.format(
len(label.flatten()), len(pred.flatten()), gt_imgs[ind],
pred_imgs[ind]))
continue
#------------------------------------------------#
# 对一张图片计算21×21的hist矩阵，并累加
#------------------------------------------------#
hist += fast_hist(label.flatten(), pred.flatten(), num_classes)
# 每计算10张就输出一下目前已计算的图片中所有类别平均的mIoU值
if name_classes is not None and ind > 0 and ind % 10 == 0:
print('{:d} / {:d}: mIou-{:0.2f}%; mPA-{:0.2f}%; Accuracy-{:0.2f}%'.format(
ind,
len(gt_imgs),
100 * np.nanmean(per_class_iu(hist)),
100 * np.nanmean(per_class_PA_Recall(hist)),
100 * per_Accuracy(hist)
)
)
#------------------------------------------------#
# 计算所有验证集图片的逐类别mIoU值
#------------------------------------------------#
IoUs = per_class_iu(hist)
PA_Recall = per_class_PA_Recall(hist)
Precision = per_class_Precision(hist)
#------------------------------------------------#
# 逐类别输出一下mIoU值
#------------------------------------------------#
if name_classes is not None:
for ind_class in range(num_classes):
print('===>' + name_classes[ind_class] + ':\tIou-' + str(round(IoUs[ind_class] * 100, 2)) \
+ '; Recall (equal to the PA)-' + str(round(PA_Recall[ind_class] * 100, 2))+ '; Precision-' + str(round(Precision[ind_class] * 100, 2)))
#-----------------------------------------------------------------#
# 在所有验证集图像上求所有类别平均的mIoU值，计算时忽略NaN值
#-----------------------------------------------------------------#
print('===> mIoU: ' + str(round(np.nanmean(IoUs) * 100, 2)) + '; mPA: ' + str(round(np.nanmean(PA_Recall) * 100, 2)) + '; Accuracy: ' + str(round(per_Accuracy(hist) * 100, 2)))
return np.array(hist, np.int), IoUs, PA_Recall, Precision
def adjust_axes(r, t, fig, axes):
bb = t.get_window_extent(renderer=r)
text_width_inches = bb.width / fig.dpi
current_fig_width = fig.get_figwidth()
new_fig_width = current_fig_width + text_width_inches
propotion = new_fig_width / current_fig_width
x_lim = axes.get_xlim()
axes.set_xlim([x_lim[0], x_lim[1] * propotion])
def draw_plot_func(values, name_classes, plot_title, x_label, output_path, tick_font_size = 12, plt_show = True):
fig = plt.gcf()
axes = plt.gca()
plt.barh(range(len(values)), values, color='royalblue')
plt.title(plot_title, fontsize=tick_font_size + 2)
plt.xlabel(x_label, fontsize=tick_font_size)
plt.yticks(range(len(values)), name_classes, fontsize=tick_font_size)
r = fig.canvas.get_renderer()
for i, val in enumerate(values):
str_val = " " + str(val)
if val < 1.0:
str_val = " {0:.2f}".format(val)
t = plt.text(val, i, str_val, color='royalblue', va='center', fontweight='bold')
if i == (len(values)-1):
adjust_axes(r, t, fig, axes)
fig.tight_layout()
fig.savefig(output_path)
if plt_show:
plt.show()
plt.close()
def show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes, tick_font_size = 12):
draw_plot_func(IoUs, name_classes, "mIoU = {0:.2f}%".format(np.nanmean(IoUs)*100), "Intersection over Union", \
os.path.join(miou_out_path, "mIoU.png"), tick_font_size = tick_font_size, plt_show = True)
print("Save mIoU out to " + os.path.join(miou_out_path, "mIoU.png"))
draw_plot_func(PA_Recall, name_classes, "mPA = {0:.2f}%".format(np.nanmean(PA_Recall)*100), "Pixel Accuracy", \
os.path.join(miou_out_path, "mPA.png"), tick_font_size = tick_font_size, plt_show = False)
print("Save mPA out to " + os.path.join(miou_out_path, "mPA.png"))
draw_plot_func(PA_Recall, name_classes, "mRecall = {0:.2f}%".format(np.nanmean(PA_Recall)*100), "Recall", \
os.path.join(miou_out_path, "Recall.png"), tick_font_size = tick_font_size, plt_show = False)
print("Save Recall out to " + os.path.join(miou_out_path, "Recall.png"))
draw_plot_func(Precision, name_classes, "mPrecision = {0:.2f}%".format(np.nanmean(Precision)*100), "Precision", \
os.path.join(miou_out_path, "Precision.png"), tick_font_size = tick_font_size, plt_show = False)
print("Save Precision out to " + os.path.join(miou_out_path, "Precision.png"))
with open(os.path.join(miou_out_path, "confusion_matrix.csv"), 'w', newline='') as f:
writer = csv.writer(f)
writer_list = []
writer_list.append([' '] + [str(c) for c in name_classes])
for i in range(len(hist)):
writer_list.append([name_classes[i]] + [str(x) for x in hist[i]])
writer.writerows(writer_list)
print("Save confusion_matrix out to " + os.path.join(miou_out_path, "confusion_matrix.csv"))
使用方法，可绘制柱状图
miou_out_path为存放图片地址，
name_classes为自己要分割的类，包含背景，大小为需要识别的目标数加一，目标像素外的像素都为属于背景。
hist, IoUs, PA_Recall, Precision = compute_mIoU(gt_dir, pred_dir, image_ids, num_classes, name_classes) # 执行计算mIoU的函数
print("Get miou done.")
show_results(miou_out_path, hist, IoUs, PA_Recall, Precision, name_classes)
浙公网安备 33010602011771号