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UNet_UAE_for_Lane_Detection/utils/utils_metrics.py

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2024-08-23 19:42:44 +08:00
import csv
import os
from os.path import join
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.nn.functional as F
from PIL import Image
def f_score(inputs, target, beta=1, smooth = 1e-5, threhold = 0.5):
n, c, h, w = inputs.size()
nt, ht, wt, ct = target.size()
if h != ht and w != wt:
inputs = F.interpolate(inputs, size=(ht, wt), mode="bilinear", align_corners=True)
temp_inputs = torch.softmax(inputs.transpose(1, 2).transpose(2, 3).contiguous().view(n, -1, c),-1)
temp_target = target.view(n, -1, ct)
#--------------------------------------------#
# 计算dice系数
#--------------------------------------------#
temp_inputs = torch.gt(temp_inputs, threhold).float()
tp = torch.sum(temp_target[...,:-1] * temp_inputs, axis=[0,1])
fp = torch.sum(temp_inputs , axis=[0,1]) - tp
fn = torch.sum(temp_target[...,:-1] , axis=[0,1]) - tp
score = ((1 + beta ** 2) * tp + smooth) / ((1 + beta ** 2) * tp + beta ** 2 * fn + fp + smooth)
score = torch.mean(score)
return 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
#------------------------------------------------#
label = np.array(Image.open(gt_imgs[ind]))
# 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"))