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crf.py
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import numpy as np
import pydensecrf.densecrf as dcrf
import cv2
try:
from cv2 import imread, imwrite
except ImportError:
# 如果没有安装OpenCV，就是用skimage
from skimage.io import imread, imsave
imwrite = imsave
from pydensecrf.utils import unary_from_labels, create_pairwise_bilateral, create_pairwise_gaussian
"""
original_image_path 原始图像路径
predicted_image_path 之前用自己的模型预测的图像路径
CRF_image_path 即将进行CRF后处理得到的结果图像保存路径
"""
def CRFs(original_image_path, predicted_image_path, CRF_image_path):
print("original_image_path: ", original_image_path)
img = imread(original_image_path)
img = cv2.resize(img, (128, 128), interpolation=cv2.INTER_CUBIC)
# 将predicted_image的RGB颜色转换为uint32颜色 0xbbggrr
anno_rgb = imread(predicted_image_path).astype(np.uint32)
anno_lbl = anno_rgb[:, :, 0] + (anno_rgb[:, :, 1] << 8) + (anno_rgb[:, :, 2] << 16)
# 将uint32颜色转换为1,2,...
colors, labels = np.unique(anno_lbl, return_inverse=True)
# 如果你的predicted_image里的黑色（0值）不是待分类类别，表示不确定区域，即将分为其他类别
# 那么就取消注释以下代码
# HAS_UNK = 0 in colors
# if HAS_UNK:
# colors = colors[1:]
# 创建从predicted_image到32位整数颜色的映射。
colorize = np.empty((len(colors), 3), np.uint8)
colorize[:, 0] = (colors & 0x0000FF)
colorize[:, 1] = (colors & 0x00FF00) >> 8
colorize[:, 2] = (colors & 0xFF0000) >> 16
# 计算predicted_image中的类数。
n_labels = len(set(labels.flat))
# n_labels = len(set(labels.flat)) - int(HAS_UNK) ##如果有不确定区域，用这一行代码替换上一行
###########################
### 设置CRF模型 ###
###########################
use_2d = False
use_2d = True
###########################################################
##不是很清楚什么情况用2D
##作者说“对于图像，使用此库的最简单方法是使用DenseCRF2D类”
##作者还说“DenseCRF类可用于通用（非二维）密集CRF”
##但是根据我的测试结果一般情况用DenseCRF比较对
#########################################################33
if use_2d:
# 使用densecrf2d类
d = dcrf.DenseCRF2D(img.shape[1], img.shape[0], n_labels)
# 得到一元势（负对数概率）
U = unary_from_labels(labels, n_labels, gt_prob=0.2, zero_unsure=None)
# U = unary_from_labels(labels, n_labels, gt_prob=0.2, zero_unsure=HAS_UNK)## 如果有不确定区域，用这一行代码替换上一行
d.setUnaryEnergy(U)
# 增加了与颜色无关的术语，只是位置-----会惩罚空间上孤立的小块分割,即强制执行空间上更一致的分割
d.addPairwiseGaussian(sxy=(3, 3), compat=3, kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
# 增加了颜色相关术语，即特征是(x,y,r,g,b)-----使用局部颜色特征来细化它们
d.addPairwiseBilateral(sxy=(80, 80), srgb=(13, 13, 13), rgbim=img, compat=10,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
'''
addPairwiseGaussian函数里的sxy为公式中的 $\theta_{\gamma}$,
addPairwiseBilateral函数里的sxy、srgb为$\theta_{\alpha}$ 和 $\theta_{\beta}$
'''
else:
# 使用densecrf类
d = dcrf.DenseCRF(img.shape[1] * img.shape[0], n_labels)
# 得到一元势（负对数概率）
U = unary_from_labels(labels, n_labels, gt_prob=0.5, zero_unsure=None)
# U = unary_from_labels(labels, n_labels, gt_prob=0.7, zero_unsure=HAS_UNK)## 如果有不确定区域，用这一行代码替换上一行
d.setUnaryEnergy(U)
# 这将创建与颜色无关的功能，然后将它们添加到CRF中
feats = create_pairwise_gaussian(sdims=(3, 3), shape=img.shape[:2])
d.addPairwiseEnergy(feats, compat=8, kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
# 这将创建与颜色相关的功能，然后将它们添加到CRF中
feats = create_pairwise_bilateral(sdims=(80, 80), schan=(13, 13, 13),
img=img, chdim=2)
d.addPairwiseEnergy(feats, compat=10,
kernel=dcrf.DIAG_KERNEL,
normalization=dcrf.NORMALIZE_SYMMETRIC)
####################################
### 做推理和计算 ###
####################################
# 进行5次推理
Q = d.inference(10)
# 找出每个像素最可能的类
MAP = np.argmax(Q, axis=0)
# 将predicted_image转换回相应的颜色并保存图像
MAP = colorize[MAP, :]
imwrite(CRF_image_path, MAP.reshape(img.shape))
print("CRF图像保存在", CRF_image_path, "!")
CRFs('sample_1_image.png', 'sample_1_image_label_pre.png', 'CRF_sample1_pre.png')