【python】通过深度图⽣成雾图（HAZERD）⽬录
完整代码matlab：
python版本：
# -*- coding: gbk -*-
import os
import glob
import cv2, math
import numpy as np
from PIL import Image
import random
import numpy as np
import h5py
原油在线含水分析仪import os
from PIL import Image
import scipy.io
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
def pil_to_np(img_PIL):
'''Converts image in PIL format to np.array.
From W x H x C [0...255] to C x W x H [0..1]
'''
ar = np.array(img_PIL)
if len(ar.shape) == 3:
ar = ar.transpose(2, 0, 1)
else:
ar = ar[None, ...]
return ar.astype(np.float32) / 255.
#矩阵转图⽚
def np_to_pil(img_np):
'''Converts image in np.array format to PIL image.
From C x W x H [0..1] to W x H x C [0...255]
'''
ar = np.clip(img_np * 255, 0, 255).astype(np.uint8)
if img_np.shape[0] == 1:
ar = ar[0]
else:
ar = ar.transpose(1, 2, 0)
return Image.fromarray(ar)
def is_image_file(filename):
#如果不都为空、0、false，则any()返回true
return dswith(extension) for extension in IMG_EXTENSIONS)
#返回⽂件夹内⽂件绝对路径组成的列表
def make_dataset(dir):
images = []
images = []
assert os.path.isdir(dir), '%s is not a valid directory' % dir
# os.walk(top[, topdown=True[, onerror=None[, followlinks=False]]]) 通过在⽬录树中游⾛输出在⽬录中的⽂件名，top返回三项（root,dirs,files），分别代表：
# 当前正在遍历的这个⽂件夹的本⾝的地址； list类型，内容是该⽂件夹中所有的⽬录的名字(不包括⼦⽬录)； list类型，内容是该⽂件夹中所有的⽂件(不包括⼦⽬ for root, _, fnames in sorted(os.walk(dir)): for fname in fnames:
#print(fname)
#拼接出图⽚的地址，并加⼊到images列表
path = os.path.join(root, fname)
images.append(path)
return images
def hazy_simu(img_name,depth_or_trans_name,save_dir,pert_perlin = 0,airlight=0.76,is_imdepth=1,visual_range = [0.05, 0.1, 0.2, 0.5, 1]):
"""
This is the function for haze simulation with the parameters given by
the paper:
HAZERD: an outdoor scene dataset and benchmark for single image dehazing
IEEE Internation Conference on Image Processing, Sep 2017
The paper and additional information on the project are available at:
hester.edu/gsharma/research/computer-vision/hazerd/
If you use this code, please cite our paper.
IMPORTANT NOTE: The code uses the convention that pixel locations with a
depth value of 0 correspond to objects that are very far and for the
simulation of haze these are placed a distance of 2 times the visual
range.
Authors:
Yanfu Zhang: hester.edu
Li Ding: l.ding@rochester.edu
Gaurav Sharma: gaurav.sharma@rochester.edu
Last update: May 2017
python version update : Aug 2021
Authors :
插接式母线槽Haoying Sun : 1913434222@qq
parse inputs and set default values
Set default parameter values. Some of these are used only if they are not
passed in
:param img_name: the directory and name of a haze-free RGB image, the name
should be in the format of ..._RGB.jpg
:param depth_name: the corresponding directory and name of the depth map, in
.mat file, the name should be in the format of ..._depth.mat
:
param save_dir: the directory to save the simulated images
:param pert_perlin: 1 for adding perlin noise, default 0
:param airlight: 3*1 matrix in the range [0,1]
:param visual_range: a vector of any size
:return: image name of hazy image
"""
visual_range = [0.05, 0.1, 0.2, 0.5, 1] # visual range in km #可⾃⾏调整，或者使⽤range函数设置区间，此时需要修改beta_param，尚未研究
beta_param = 3.912 #Default beta parameter corresponding to visual range of 1000m
# A = np.zeros((1, 1, 3))
# A[0, 0, 0] = airlight
# A[0, 0, 1] = airlight
# A[0, 0, 2] = airlight
A = airlight
if save_dir != '':
if not ists(save_dir):
os.makedirs(save_dir)
print('Simulating hazy image for:{}'.format(img_name))
VR = random.choice(visual_range)
VR = random.choice(visual_range)
print('Viusal value: {} km'.format(VR) )
im1 = cv2.imread(img_name)
img_pil = pil_to_np(im1)
#convert sRGB to linear RGB
I = srgb2lrgb(img_pil)
if is_imdepth:
depths = get_depth(depth_or_trans_name)
d = depths/1000 # convert meter to kilometer
#Set regions where depth value is set to 0 to indicate no valid depth to
#a distance of two times the visual range. These regions typically
#correspond to sky areas
d = np.where(d==0,2*VR,d)
if pert_perlin:
d = d * ((perlin_s(d.shape)) - 0.5) + 1)
#convert depth map to transmission
beta = beta_param / VR
beta = np.ones(d.shape) * beta
transmission = np.exp((-beta*d))
transmission_3 = np.array([transmission,transmission,transmission])
#Obtain simulated linear RGB hazy image.Eq. 3 in the HazeRD paper
Ic = transmission_3 * I+ (1 - transmission_3) * A
else:
Ic = pil_to_np(depth_or_trans_name) * I + (1 - pil_to_np(depth_or_trans_name)) * A
# convert linear RGB to sRGB
I2 = lrgb2srgb(Ic)
haze_img = np_to_pil(I2)
return haze_img
def perlin_noise(im, decay=2):
"""
————————————————————————————————————————————————————————————————————————⾮均匀雾（柏林噪声）通常⽤柏林噪声来模拟⼤⽓湍流引起的⾮均匀雾，论⽂ Towards Simulating Foggy and Hazy Images and Evaluating Their Authenticity
⽣成三个不同频率参数(f=130、60、10）的三维柏林噪声，并通过以下⽅法组合:
noise = 1/3 Sum_i(noise_i/2^(i-1)),i从1到3
求得的noise与消光系数相乘，带⼊到Eq.2，模拟⾮均匀雾。
I = Iex + Op*Ial EQ.1
Iex = Ipexp(-βex * rp) EQ.2
O = 1 - exp(-βex * rp) EQ.3
Iex：⽬标反射光经过⼤⽓中微粒衰减；Ial：环境光经过微粒散射，形成背景光； Op：模糊度；βex：消光系数
———————————————————————————————————————————————————————————————————————— This is the function for adding perlin noise to the depth map. It is a
simplified implementation of the paper:
an image sunthesizer
蝇蛆蛋白Ken Perlin, SIGGRAPH, Jul. 1985
The bicubic interpolation is used, compared to the original version.
Reference:
HAZERD: an outdoor scene dataset and benchmark for single image dehazing
IEEE International Conference on Image Processing, Sep 2017
The paper and additional information on the project are available at:低压有源滤波
hester.edu/gsharma/research/computer-vision/hazerd/
If you use this code, please cite our paper.
Input:
im: depth map
varargin{1}: decay term
Output:
Output:
im: result of transmission with perlin noise added
Authors:
Yanfu Zhang: hester.edu
Li Ding: l.ding@rochester.edu
Gaurav Sharma: gaurav.sharma@rochester.edu
Last update: May 2017
:return:
"""
(h, w, c) = im.shape
i = 1
l_bound = min(h,w)
while i <= l_bound:
细胞核染#d = imresize(randn(i, i)*decay, im.shape, 'bicubic')
d = size(np.random.randn(i,i)*decay, im.shape, interpolation=cv2.INTER_CUBIC) im = im+d
i = i*2
im = (im - np.min(im)) / (np.max(im) - np.min(im))
return im
def srgb2lrgb(I0):
gamma = ((I0 + 0.055) / 1.055)**2.4
scale = I0 / 12.92
return np.where (I0 > 0.04045, gamma, scale)
def lrgb2srgb(I1):
gamma = 1.055*I1**(1/2.4)-0.055
scale = I1 * 12.92
return np.where (I1 > 0.0031308, gamma, scale)
#获取深度矩阵
def get_depth(depth_or_trans_name):
#depth_or_trans_name为mat类型⽂件或者img类型⽂件地址
data = scipy.io.loadmat(depth_or_trans_name)
depths = data['imDepth'] #深度变量
#print(data.keys()) #打印mat⽂件中所有变量
depths = np.array(depths)
return depths
def AddHaze1(img):
img = cv2.imread(img)
img = np.array(img)
img_f = img
(row, col, chs) = img.shape
A = 0.5 # 亮度
beta = 0.08 # 雾的浓度
size = math.sqrt(max(row, col)) # 雾化尺⼨
center = (row // 2, col // 2) # 雾化中⼼
for j in range(row):
for l in range(col):
d = -0.04 * math.sqrt((j - center[0]) ** 2 + (l - center[1]) ** 2) + size
td = p(-beta * d)
img_f[j][l][:] = img_f[j][l][:] * td + A * (1 - td)
return Image.fromarray(img_f.astype(np.uint8))
#请修改以下路径
img_dir = 'I:\HazeRD\data\img'
depth_dir = 'I:\HazeRD\data\depth'
save_dir = 'I:\HazeRD\data\depth\simu' #存放⽣成雾图结果
img_list = make_dataset(img_dir)
depth_list = make_dataset(depth_dir)
# # ⽣成雾图结果可视化
# # ⽣成雾图结果可视化
旗杆底座
# a = hazy_simu(img_list[0],depth_list[0],save_dir)
# Image.Image.show(a)
# cv2.waitKey(1000)
# #原始图⽚可视化
# img_name= img_list[0]
# im1 = cv2.imread(img_name)
# im1 = Image.fromarray(im1.astype(np.uint8))
# Image.Image.show(im1)
# cv2.waitKey(1000)
# #深度图可视化
# depth_name = depth_list[0]
# depths = get_depth(depth_name)
# im1 = Image.fromarray(depths.astype(np.uint8))
# Image.Image.show(im1)
# cv2.waitKey(1000)
下载地址见底部，注意输⼊数据格式⼀致，请下载原始数据！⽬录安排如下：
效果：
原始图：
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