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miou.py
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miou.py
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from os.path import join
import numpy as np
from PIL import Image
# 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(hist):
return np.diag(hist) / np.maximum(hist.sum(1), 1)
def compute_mIoU(gt_dir, pred_dir, png_name_list, num_classes, name_classes):
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 ind > 0 and ind % 10 == 0:
print('{:d} / {:d}: mIou-{:0.2f}; mPA-{:0.2f}'.format(ind, len(gt_imgs),
100 * np.nanmean(per_class_iu(hist)),
100 * np.nanmean(per_class_PA(hist))))
#------------------------------------------------#
# 计算所有验证集图片的逐类别mIoU值
#------------------------------------------------#
mIoUs = per_class_iu(hist)
mPA = per_class_PA(hist)
#------------------------------------------------#
# 逐类别输出一下mIoU值
#------------------------------------------------#
for ind_class in range(num_classes):
print('===>' + name_classes[ind_class] + ':\tmIou-' + str(round(mIoUs[ind_class] * 100, 2)) + '; mPA-' + str(round(mPA[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(mIoUs) * 100, 2)) + '; mPA: ' + str(round(np.nanmean(mPA) * 100, 2)))
return mIoUs
if __name__ == "__main__":
gt_dir = "VOCdevkit/VOC2007/SegmentationClass"
pred_dir = "miou_pr_dir"
png_name_list = open("VOCdevkit/VOC2007/ImageSets/Segmentation/val.txt",'r').read().splitlines()
#------------------------------#
# 分类个数+1
# 2+1
#------------------------------#
num_classes = 21
#--------------------------------------------#
# 区分的种类，和json_to_dataset里面的一样
#--------------------------------------------#
name_classes = ["background","aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
compute_mIoU(gt_dir, pred_dir, png_name_list, num_classes, name_classes) # 执行计算mIoU的函数