图像分割-手把手系列1：评价指标

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()