HDR多帧曝光融合Python代码实现

HDR算法Python实现：Github。先看结果：
使用三脚架用自己手机分别设置-3、0、1 EV拍摄的输入图像：

输出结果（左：0ev，右：HDR结果）：

main.py:

from image_io import load_img, save_img
import os
from exposure_fusion import exposure_fusioninput_path = 'data/case5'   # 输入的不同曝光的图片
save_img_name = ''.join(['hdr_output_', os.path.split(input_path)[-1], '.png'])   # output图片文件名
nlev = 9    # 金字塔层数，不能设置过小，否则引起分层input_path_list = []
for i in os.listdir(input_path):input_path_list.append(os.path.join(input_path, i))I = load_img(input_path_list)
R = exposure_fusion(I, [1, 1, 1, nlev])save_img(R * 255, save_img_name)

image_io.py:

import cv2
import numpy as np
import mathdef load_img(imgs_path_list):imgs_arr = []for img_path in imgs_path_list:img_arr = cv2.imread(img_path)imgs_arr.append(img_arr/255)return imgs_arrdef save_img(img_arr, save_path):cv2.imwrite(save_path, img_arr)def offset_process(imgs_arr, nlev):# 图像扩边，防止金字塔采样时出现奇数宽高size = np.shape(imgs_arr[0])h = size[0]w = size[1]last_h = h/(2**(nlev-1))last_w = w/(2**(nlev-1))offset = [0, 0, 0, 0]new_h = math.ceil(last_h) * (2**(nlev-1))new_w = math.ceil(last_w) * (2**(nlev-1))total_add_h = new_h - htotal_add_w = new_w - wahead_add_h = total_add_h // 2ahead_add_w = total_add_w // 2behind_add_h = total_add_h - ahead_add_hbehind_add_w = total_add_w - ahead_add_wfor idx in range(len(imgs_arr)):imgs_arr[idx] = cv2.copyMakeBorder(imgs_arr[idx], ahead_add_h, behind_add_h, ahead_add_w, behind_add_w, cv2.BORDER_REFLECT)offset[0] = ahead_add_hoffset[1] = behind_add_hoffset[2] = ahead_add_woffset[3] = behind_add_wreturn imgs_arr, offset

exposure_fusion.py:

import numpy as np
from gaussian_pyramid import gaussian_pyramid
from laplacian_pyramid import laplacian_pyramid
from image_io import offset_process
from reconstruct_laplacian_pyramid import reconstruct_laplacian_pyramid
from cv2_process import rgb2gray, imfilter
import mathdef exposure_fusion(imgs_arr, param):contrast_parm = param[0]sat_parm = param[1]wexp_parm = param[2]nlev = param[3]size = np.shape(imgs_arr)r = size[1]c = size[2]N = size[0]max_lev = math.floor(math.log2(min(r, c)))if nlev > max_lev:nlev = max_lev# 图像四周扩边处理，让金字塔每层采样都为偶数imgs_arr, offset = offset_process(imgs_arr, nlev)size = np.shape(imgs_arr)r = size[1]c = size[2]W = np.ones([N, r, c])if contrast_parm == 1:W = (W * contrast(imgs_arr, size)) ** contrast_parmif sat_parm == 1:W = (W * saturation(imgs_arr, size)) ** sat_parmif wexp_parm == 1:W = (W * well_exposedness(imgs_arr, size)) ** wexp_parmW = W + 1e-12yuan_W = WW = np.sum(W, axis=0)temp_W = np.expand_dims(W, axis=0)W = np.concatenate([temp_W for i in range(N)], axis=0)W = yuan_W / Wpyr = gaussian_pyramid(np.zeros([r, c, 3]), nlev)for i in range(N):pyrW=  gaussian_pyramid(W[i, :, :], nlev)pyrI = laplacian_pyramid(imgs_arr[i], nlev)for l in range(nlev):W_temp = np.expand_dims(pyrW[l], axis=-1)w = np.concatenate([W_temp, W_temp, W_temp], axis=-1)pyr[l] = pyr[l] + w * pyrI[l]R = reconstruct_laplacian_pyramid(pyr)# 将扩充的四周边缘像素舍弃R = R[offset[0]:r-offset[1], offset[2]:c-offset[3], :]return Rdef contrast(I, size):h = np.array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])N = size[0]C = np.zeros([N, size[1], size[2]])for i in range(N):temp = 255 * I[i] / (I[i].max() - I[i].min())mono = rgb2gray(temp.astype('float32'))C[i, :, :] = np.abs(imfilter(mono, h))return Cdef saturation(I, size):N = size[0]C = np.zeros([N, size[1], size[2]])for i in range(N):B = I[i][:, :, 0]G = I[i][:, :, 1]R = I[i][:, :, 2]mu = (B+R+G) / 3C[i, :, :] = np.sqrt(((R-mu)**2 + (G-mu)**2 + (B-mu)**2)/3)return Cdef well_exposedness(I, size):N = size[0]C = np.zeros([N, size[1], size[2]])sig = 0.2for i in range(N):B = np.exp(-0.5*I[i][:, :, 0] - 0.5)**2/sig**2G = np.exp(-0.5*I[i][:, :, 1] - 0.5)**2/sig**2R = np.exp(-0.5*I[i][:, :, 2] - 0.5)**2/sig**2C[i, :, :] = B * G * Rreturn C

gaussian_pyramid.py:

from cv2_process import downsampledef gaussian_pyramid(I, nlev):pyr = [I]for l in range(1, nlev):I = downsample(I)pyr.append(I)return pyr

laplacian_pyramid.py

from cv2_process import downsample, upsampledef laplacian_pyramid(I, nlev):pyr = []J = Ifor l in range(nlev-1):I = downsample(J)pyr.append(J - upsample(I))J = Ipyr.append(J)return pyr

reconstruct_laplacian_pyramid.py

from cv2_process import upsampledef reconstruct_laplacian_pyramid(pyr):nlev = len(pyr)R = pyr[nlev-1]for l in range(nlev-2, -1, -1):R = pyr[l] + upsample(R)return R

cv2_process.py:

import cv2def downsample(I):return cv2.resize(I, dsize=(0, 0), fx=0.5, fy=0.5)def upsample(I):return cv2.resize(I, dsize=(0, 0), fx=2, fy=2)def rgb2gray(I):img_gray = cv2.cvtColor(I, cv2.COLOR_BGR2GRAY)return img_gray / 255def imfilter(I, h):return cv2.filter2D(I, -1, h)def contrast_clahe(I):b, g, r = cv2.split(I)clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8, 8))b = clahe.apply(b)g = clahe.apply(g)r = clahe.apply(r)image = cv2.merge([b, g, r])return image

其他结果1：
输入：

输出：

其他结果2：
输入：

输出：

参考：
[1] Mertens T, Kautz J, Van Reeth F. Exposure fusion[C]//15th Pacific Conference on Computer Graphics and Applications (PG’07). IEEE, 2007: 382-390.
[2] https://github.com/Mericam/exposure-fusion

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