圖像分割-手把手系列1：評價指標

目標檢測與語義分割交流羣：

1羣：371315462

2羣：935621769

 

原文連接：https://blog.csdn.net/majinlei121/article/details/78965435

度量標準（準確度）(pixel accuracy, mean accuracy, mean IU, frequency weighted IU)

原文講解的非常好，轉載過來備份一下。

深度學習之語義分割中的度量標準（準確度）(pixel accuracy, mean accuracy, mean IU, frequency weighted IU)

關鍵部分來了，如何變成代碼呢，有以下兩種方案：

（1）參考這個連接：https://github.com/martinkersner/py_img_seg_eval 【已驗證，好用】

（2）使用下方原博文的代碼【未驗證】

下面是根據全卷積語義分割的準確度程序編寫

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]]])#數據集平均值，根據需要修改
    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)#圖像大小（寬，高），根據需要修改
    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()