图像分割-手把手系列1：评价指标 - 好文

目标检测与语义分割交流群：

1群：371315462

2群：935621769

原文连接：https://blog.csdn.net/majinlei121/article/details/78965435

度量标准（准确度）(pixel accuracy, mean accuracy, mean IU, frequency weighted IU)

原文讲解的非常好，转载过来备份一下。

深度学习之语义分割中的度量标准（准确度）(pixel accuracy, mean accuracy, mean IU, frequency
weighted IU)

关键部分来了，如何变成代码呢，有以下两种方案：

（1）参考这个连接：https://github.com/martinkersner/py_img_seg_eval 【已验证，好用】

（2）使用下方原博文的代码【未验证】

下面是根据全卷积语义分割的准确度程序编写
import _init_paths import os import numpy as np from PIL import Image import
matplotlib.pyplot as plt from skimage import io from timer import Timer import
cv2 from datetime import datetime import caffe test_file = 'test.txt'
file_path_img = 'JPEGImages' file_path_label = 'SegmentationClass' save_path =
'output/results' test_prototxt = 'Models/test.prototxt' weight =
'Training/Seg_iter_10000.caffemodel' layer = 'conv_seg' save_dir = False # True
if save_dir: save_dir = save_path else: save_dir = False # load net net =
caffe.Net(test_prototxt, weight, caffe.TEST) # load test.txt test_img =
np.loadtxt(test_file, dtype=str) def fast_hist(a, b, n): k = (a >= 0) & (a < n)
return np.bincount(n * a[k].astype(int) + b[k], minlength=n**2).reshape(n, n) #
seg test print '>>>', datetime.now(), 'Begin seg tests' n_cl =
net.blobs[layer].channels hist = np.zeros((n_cl, n_cl)) # timers _t = {'im_seg'
: Timer()} # load image and label i = 0 for img_name in test_img:
_t['im_seg'].tic() img = Image.open(os.path.join(file_path_img, img_name +
'.jpg')) img = img.resize((512, 384), Image.ANTIALIAS) in_ = np.array(img,
dtype=np.float32) in_ = in_[:,:,::-1] # rgb to bgr in_ -= np.array([[[68.2117,
78.2288, 75.4916]]])#数据集平均值，根据需要修改 in_ = in_.transpose((2,0,1)) label =
Image.open(os.path.join(file_path_label, img_name + '.png')) label =
label.resize((512, 384), Image.ANTIALIAS)#图像大小（宽，高），根据需要修改 label =
np.array(label, dtype=np.uint8) # shape for input (data blob is N x C x H x W),
set data net.blobs['data'].reshape(1, *in_.shape) net.blobs['data'].data[...] =
in_ net.forward() _t['im_seg'].toc() print 'im_seg: {:d}/{:d} {:.3f}s' \
.format(i + 1, len(test_img), _t['im_seg'].average_time) i += 1 hist +=
fast_hist(label.flatten(), net.blobs[layer].data[0].argmax(0).flatten(), n_cl)
if save_dir: seg = net.blobs[layer].data[0].argmax(axis=0) result =
np.array(img, dtype=np.uint8) index = np.where(seg == 1) for i in
xrange(len(index[0])): result[index[0][i], index[1][i], 0] = 255
result[index[0][i], index[1][i], 1] = 0 result[index[0][i], index[1][i], 2] = 0
result = Image.fromarray(result.astype(np.uint8))
result.save(os.path.join(save_dir, img_name + '.jpg')) iter = len(test_img) #
overall accuracy acc = np.diag(hist).sum() / hist.sum() print '>>>',
datetime.now(), 'Iteration', iter, 'overall accuracy', acc # per-class accuracy
acc = np.diag(hist) / hist.sum(1) print '>>>', datetime.now(), 'Iteration',
iter, 'mean accuracy', np.nanmean(acc) # per-class IU iu = np.diag(hist) /
(hist.sum(1) + hist.sum(0) - np.diag(hist)) print '>>>', datetime.now(),
'Iteration', iter, 'mean IU', np.nanmean(iu) freq = hist.sum(1) / hist.sum()
print '>>>', datetime.now(), 'Iteration', iter, 'fwavacc', \ (freq[freq > 0] *
iu[freq > 0]).sum()

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