【python】通过深度图生成雾图（HAZERD）

目录

完整代码matlab：

python版本：

使用时记得下载相关数据集：

效果：

官方：

下一步：

完整代码matlab：

参见：https://blog.csdn.net/Crystal_remember/article/details/119529529

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
import os
from PIL import Image
import scipy.ioIMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG','.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
#图片转矩阵
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)# 判断文件夹中是否有以上类型图片，没有则返回0
def is_image_file(filename):#如果不都为空、0、false，则any()返回truereturn any(filename.endswith(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 = []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(不包括子目录)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 imagesdef 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 bythe paper:HAZERD: an outdoor scene dataset and benchmark for single image dehazingIEEE Internation Conference on Image Processing, Sep 2017The paper and additional information on the project are available at:https://labsites.rochester.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 adepth value of 0 correspond to objects that are very far and for thesimulation of haze these are placed a distance of 2 times the visualrange.Authors:Yanfu Zhang: yzh185@ur.rochester.eduLi Ding: l.ding@rochester.eduGaurav Sharma: gaurav.sharma@rochester.eduLast update: May 2017python version update : Aug 2021Authors :Haoying Sun : 1913434222@qq.comparse inputs and set default valuesSet default parameter values. Some of these are used only if they are notpassed in:param img_name: the directory and name of a haze-free RGB image, the nameshould 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] = airlightA = airlightif save_dir != '':if not os.path.exists(save_dir):os.makedirs(save_dir)print('Simulating hazy image for:{}'.format(img_name))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 RGBI = 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 areasd = np.where(d==0,2*VR,d)if pert_perlin:d = d * ((perlin_noise(np.zeros(d.shape)) - 0.5) + 1)#convert depth map to transmissionbeta = beta_param / VRbeta = np.ones(d.shape) * betatransmission = np.exp((-beta*d))transmission_3 = np.array([transmission,transmission,transmission])#Obtain simulated linear RGB hazy image.Eq. 3 in the HazeRD paperIc = transmission_3 * I+ (1 - transmission_3) * Aelse:Ic = pil_to_np(depth_or_trans_name) * I + (1 - pil_to_np(depth_or_trans_name)) * A# convert linear RGB to sRGBI2 = lrgb2srgb(Ic)haze_img = np_to_pil(I2)return haze_imgdef 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.1Iex = Ipexp(-βex * rp)   EQ.2O = 1 - exp(-βex * rp)   EQ.3Iex：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 asimplified implementation of the paper:an image sunthesizerKen Perlin, SIGGRAPH, Jul. 1985The bicubic interpolation is used, compared to the original version.Reference:HAZERD: an outdoor scene dataset and benchmark for single image dehazingIEEE International Conference on Image Processing, Sep 2017The paper and additional information on the project are available at:https://labsites.rochester.edu/gsharma/research/computer-vision/hazerd/If you use this code, please cite our paper.Input:im: depth mapvarargin{1}: decay termOutput:im: result of transmission with perlin noise addedAuthors:Yanfu Zhang: yzh185@ur.rochester.eduLi Ding: l.ding@rochester.eduGaurav Sharma: gaurav.sharma@rochester.eduLast update: May 2017:return:"""(h, w, c) = im.shapei = 1l_bound = min(h,w)while i <= l_bound:#d = imresize(randn(i, i)*decay, im.shape, 'bicubic')d = cv2.resize(np.random.randn(i,i)*decay, im.shape, interpolation=cv2.INTER_CUBIC)im = im+di = i*2 im = (im - np.min(im)) / (np.max(im) - np.min(im))return imdef srgb2lrgb(I0):gamma = ((I0 + 0.055) / 1.055)**2.4scale = I0 / 12.92return np.where (I0 > 0.04045, gamma, scale)def lrgb2srgb(I1):gamma =  1.055*I1**(1/2.4)-0.055scale = I1 * 12.92return 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.shapeA = 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) + sizetd = math.exp(-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
# 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)