一、小波变换

小波分析是一个比较难的分支，用户采用小波变换，可以实现图像压缩，振动信号的分解与重构等，因此在实际工程上应用较广泛。小波分析与Fourier变换相比，小波变换是空间域和频率域的局部变换，因而能有效地从信号中提取信息。小波变换通过伸缩和平移等基本运算，实现对信号的多尺度分解与重构，从而很大程度上解决了Fourier变换带来的很多难题。
小波分析作一个新的数学分支，它是泛函分析、Fourier分析、数值分析的完美结晶；小波分析也是一种“时间—尺度”分析和多分辨分析的新技术，它在信号分析、语音合成、图像压缩与识别、大气与海洋波分析等方面的研究，都有广泛的应用。

二、图像融合

1、采用对比度拉伸，完成红外图像增强

# 裁剪线性RGB对比度拉伸：（去掉2%百分位以下的数，去掉98%百分位以上的数，上下百分位数一般相同，并设置输出上下限）
def truncated_linear_stretch(image, truncated_value=2, maxout=255, min_out=0):
def gray_process(gray, maxout=maxout, minout=min_out):
truncated_down = np.percentile(gray, truncated_value)
truncated_up = np.percentile(gray, 100 - truncated_value)
gray_new = ((maxout - minout) / (truncated_up - truncated_down)) * gray
gray_new[gray_new < minout] = minout
gray_new[gray_new > maxout] = maxout
return np.uint8(gray_new)
(b, g, r) = cv2.split(image)
b = gray_process(b)
g = gray_process(g)
r = gray_process(r)
result = cv2.merge((b, g, r))# 合并每一个通道
return result

2、采用Surf特征点匹配，完成图像配准

# RGB图片配准函数，采用白天的可见光与红外灰度图，计算两者Surf共同特征点，之间的仿射矩阵。
def Images_matching(img_base, img_target):
img_base=cv2.cvtColor(img_base, cv2.COLOR_BGR2GRAY)
img_target=cv2.cvtColor(img_target, cv2.COLOR_BGR2GRAY)
hessian = 400
# 初始化surf算子
surf = cv2.xfeatures2d.SURF_create(hessian)
# surf = cv2.xfeatures2d_SURF.create(hessian)
# surf = cv2.SIFT_create(hessian)
# 使用surf算子计算特征点和特征点周围的特征向量
kp1, des1 = surf.detectAndCompute(img_base, None)
# 1136
1136, 64
kp2, des2 = surf.detectAndCompute(img_target, None)
# 进行KNN特征匹配
FLANN_INDEX_KDTREE = 0
# 建立FLANN匹配器的参数
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
# 配置索引，密度树的数量为5
searchParams = dict(checks=50)
# 指定递归次数
flann = cv2.FlannBasedMatcher(indexParams, searchParams)
# 建立匹配器
matches = flann.knnMatch(des1, des2, k=2)
# 得出匹配的关键点
list: 1136
good = []
# 提取优秀的特征点
for m, n in matches:
if m.distance < 0.7 * n.distance:
# 如果第一个邻近距离比第二个邻近距离的0.7倍小，则保留
good.append(m)
# 134
src_pts = np.array([kp1[m.queryIdx].pt for m in good])
# 查询图像的特征描述子索引
# 134, 2
dst_pts = np.array([kp2[m.trainIdx].pt for m in good])
# 训练(模板)图像的特征描述子索引
H = cv2.findHomography(dst_pts, src_pts, cv2.RANSAC)
# 生成变换矩阵
H[0]: 3, 3
H[1]: 134, 1
return H[0]

3、采用HSV通道小波变换，完成图像融合

# YUV通道小波变换RGB融合，低频均值高频最大值
def Images_fusion(img_base,img_target):
# 可见光，配准后的红外光
# 按照不同的融合方式，计算通道融合
def fuseCoeff(cooef1, cooef2, method):
if (method == 'mean'):
cooef = (abs(cooef1) + abs(cooef2)) / 2
# abs 绝对值
# cooef=0.5*cooef1+1.5*cooef2
elif (method == 'min'):
cooef = np.minimum(cooef1, cooef2)
elif (method == 'max'):
cooef = np.maximum(abs(cooef1), abs(cooef2))
return cooef
# HSV的小波融合过程
LOW_METHOD = 'mean'
HIGH_METHOD = 'max'
YUVimg_base = cv2.cvtColor(img_base, cv2.COLOR_BGR2YUV)
# 可见光转换为HSV
# cv2.imwrite("D:/VS/vsprj/cuda/cudawavetest/2.jpg", YUVimg_base)
grayimg_target = cv2.cvtColor(img_target, cv2.COLOR_BGR2GRAY)
#红外灰度化
# cv2.imwrite("D:/VS/vsprj/cuda/cudawavetest/1.jpg",grayimg_target)
Yimg_base = YUVimg_base[:, :, 0]
# 1024，1024
wavelet = 'haar'
cooef_base = pywt.wavedec2(Yimg_base[:, :], wavelet, level=1)
# base灰度图的小波展开
512, 512
3
start = time.time()
cooef_target = pywt.wavedec2(grayimg_target[:, :], wavelet, level=1)
# target灰度图的小波展开
512, 512
3
end = time.time()-start
print("小波展开:{}".format(end))
fusedCooef = []
for i in range(len(cooef_base)):
if (i == 0):
fusedCooef.append(fuseCoeff(cooef_base[0], cooef_target[0], LOW_METHOD))
# 低频部分取均值
else:
# 高频部分取最大值
c1 = fuseCoeff(cooef_base[i][0], cooef_target[i][0], HIGH_METHOD)
c2 = fuseCoeff(cooef_base[i][1], cooef_target[i][1], HIGH_METHOD)
c3 = fuseCoeff(cooef_base[i][2], cooef_target[i][2], HIGH_METHOD)
fusedCooef.append((c1, c2, c3))
# 高频合并
tempfusedImage = pywt.waverec2(fusedCooef, wavelet)
# 小波逆变换
fusedImage = np.multiply(np.divide(tempfusedImage - np.min(tempfusedImage), (np.max(tempfusedImage) -
np.min(tempfusedImage))), 255)
# 逆变换后归一至（0，255）
start = time.time()
fusedImage = fusedImage.astype(np.uint8)
Yimg_new = fusedImage
fusedYUV = cv2.merge([Yimg_new, YUVimg_base[:, :, 1], YUVimg_base[:, :, 2]])
# 用小波变换替换V通道
end = time.time() - start
print("图像重建:{}".format(end))
fusedBGR = cv2.cvtColor(fusedYUV, cv2.COLOR_YUV2BGR)
# 融合后的HSV转为BGR
return fusedBGR

4、总的代码

import cv2
import numpy as np
import pywt #引入小波模块
import time
from PIL import Image
# 裁剪线性RGB对比度拉伸：（去掉2%百分位以下的数，去掉98%百分位以上的数，上下百分位数一般相同，并设置输出上下限）
def truncated_linear_stretch(image, truncated_value=2, maxout=255, min_out=0):
def gray_process(gray, maxout=maxout, minout=min_out):
truncated_down = np.percentile(gray, truncated_value)
truncated_up = np.percentile(gray, 100 - truncated_value)
gray_new = ((maxout - minout) / (truncated_up - truncated_down)) * gray
gray_new[gray_new < minout] = minout
gray_new[gray_new > maxout] = maxout
return np.uint8(gray_new)
(b, g, r) = cv2.split(image)
b = gray_process(b)
g = gray_process(g)
r = gray_process(r)
result = cv2.merge((b, g, r))# 合并每一个通道
return result
# RGB图片配准函数，采用白天的可见光与红外灰度图，计算两者Surf共同特征点，之间的仿射矩阵。
def Images_matching(img_base, img_target):
img_base=cv2.cvtColor(img_base, cv2.COLOR_BGR2GRAY)
img_target=cv2.cvtColor(img_target, cv2.COLOR_BGR2GRAY)
hessian = 400
# 初始化surf算子
surf = cv2.xfeatures2d.SURF_create(hessian)
# surf = cv2.xfeatures2d_SURF.create(hessian)
# surf = cv2.SIFT_create(hessian)
# 使用surf算子计算特征点和特征点周围的特征向量
kp1, des1 = surf.detectAndCompute(img_base, None)
# 1136
1136, 64
kp2, des2 = surf.detectAndCompute(img_target, None)
# 进行KNN特征匹配
FLANN_INDEX_KDTREE = 0
# 建立FLANN匹配器的参数
indexParams = dict(algorithm=FLANN_INDEX_KDTREE, trees=5)
# 配置索引，密度树的数量为5
searchParams = dict(checks=50)
# 指定递归次数
flann = cv2.FlannBasedMatcher(indexParams, searchParams)
# 建立匹配器
matches = flann.knnMatch(des1, des2, k=2)
# 得出匹配的关键点
list: 1136
good = []
# 提取优秀的特征点
for m, n in matches:
if m.distance < 0.7 * n.distance:
# 如果第一个邻近距离比第二个邻近距离的0.7倍小，则保留
good.append(m)
# 134
src_pts = np.array([kp1[m.queryIdx].pt for m in good])
# 查询图像的特征描述子索引
# 134, 2
dst_pts = np.array([kp2[m.trainIdx].pt for m in good])
# 训练(模板)图像的特征描述子索引
H = cv2.findHomography(dst_pts, src_pts, cv2.RANSAC)
# 生成变换矩阵
H[0]: 3, 3
H[1]: 134, 1
return H[0]
# YUV通道小波变换RGB融合，低频均值高频最大值
def Images_fusion(img_base,img_target):
# 可见光，配准后的红外光
# 按照不同的融合方式，计算通道融合
def fuseCoeff(cooef1, cooef2, method):
if (method == 'mean'):
cooef = (abs(cooef1) + abs(cooef2)) / 2
# abs 绝对值
# cooef=0.5*cooef1+1.5*cooef2
elif (method == 'min'):
cooef = np.minimum(cooef1, cooef2)
elif (method == 'max'):
cooef = np.maximum(abs(cooef1), abs(cooef2))
return cooef
# HSV的小波融合过程
LOW_METHOD = 'mean'
HIGH_METHOD = 'max'
YUVimg_base = cv2.cvtColor(img_base, cv2.COLOR_BGR2YUV)
# 可见光转换为HSV
# cv2.imwrite("D:/VS/vsprj/cuda/cudawavetest/2.jpg", YUVimg_base)
grayimg_target = cv2.cvtColor(img_target, cv2.COLOR_BGR2GRAY)
#红外灰度化
# cv2.imwrite("D:/VS/vsprj/cuda/cudawavetest/1.jpg",grayimg_target)
Yimg_base = YUVimg_base[:, :, 0]
# 1024，1024
wavelet = 'haar'
cooef_base = pywt.wavedec2(Yimg_base[:, :], wavelet, level=1)
# base灰度图的小波展开
512, 512
3
start = time.time()
cooef_target = pywt.wavedec2(grayimg_target[:, :], wavelet, level=1)
# target灰度图的小波展开
512, 512
3
end = time.time()-start
print("小波展开:{}".format(end))
fusedCooef = []
for i in range(len(cooef_base)):
if (i == 0):
fusedCooef.append(fuseCoeff(cooef_base[0], cooef_target[0], LOW_METHOD))
# 低频部分取均值
else:
# 高频部分取最大值
c1 = fuseCoeff(cooef_base[i][0], cooef_target[i][0], HIGH_METHOD)
c2 = fuseCoeff(cooef_base[i][1], cooef_target[i][1], HIGH_METHOD)
c3 = fuseCoeff(cooef_base[i][2], cooef_target[i][2], HIGH_METHOD)
fusedCooef.append((c1, c2, c3))
# 高频合并
tempfusedImage = pywt.waverec2(fusedCooef, wavelet)
# 小波逆变换
fusedImage = np.multiply(np.divide(tempfusedImage - np.min(tempfusedImage), (np.max(tempfusedImage) -
np.min(tempfusedImage))), 255)
# 逆变换后归一至（0，255）
start = time.time()
fusedImage = fusedImage.astype(np.uint8)
Yimg_new = fusedImage
fusedYUV = cv2.merge([Yimg_new, YUVimg_base[:, :, 1], YUVimg_base[:, :, 2]])
# 用小波变换替换V通道
end = time.time() - start
print("图像重建:{}".format(end))
fusedBGR = cv2.cvtColor(fusedYUV, cv2.COLOR_YUV2BGR)
# 融合后的HSV转为BGR
return fusedBGR
#
def main():
matchimg_di = cv2.imread('images/oripics/basic_day_infrared.jpg')
# 1080, 1920, 3
matchimg_dv = cv2.imread('images/oripics/basic_day_visual.jpg')
# matchimg_di = cv2.imread('left_2020_11_30-15_15_31.jpg')
# 1080, 1920, 3
# matchimg_dv = cv2.imread('right_2020_11_30-15_15_31.jpg')
# 1080, 1920, 3
orimg_nv=matchimg_dv
orimg_ni=matchimg_di
# orimg_nv = cv2.imread('images/oripics/night_visual_40m.jpg')
# orimg_ni = cv2.imread('images/oripics/night_infrared_40m.jpg')
# orimg_nv = cv2.imread('images/oripics/left_2020_11_30-20_06_54.jpg')
# orimg_ni = cv2.imread('images/oripics/right_2020_11_30-20_06_54.jpg')
# orimg_nv = cv2.imread('images/oripics/left_2020_11_30-20_08_09.jpg')
# orimg_ni = cv2.imread('images/oripics/right_2020_11_30-20_08_09.jpg')
orimg_nv = cv2.imread('images/oripics/left_video_2020_11_30-20_09_32_73.jpg')
# 1024, 1024, 3
orimg_ni = cv2.imread('images/oripics/right_video_2020_11_30-20_09_32_73.jpg')
# 1024, 1024, 3
#用白天图像进行配准
# enhance_matchimg_di = truncated_linear_stretch(matchimg_di)# 配准模板红外图像RGB增强
h, w = orimg_nv.shape[:2]
# 1024 1024
H = Images_matching(matchimg_dv, matchimg_di)
# (3, 3)
# enhance_orimg_ni=truncated_linear_stretch(orimg_ni) # 需融合红外图像RGB增强
matched_ni = cv2.warpPerspective(orimg_ni, H, (w, h))
cv2.imwrite("./1.jpg",matched_ni)# 红外图像按照仿射矩阵配准 1024, 1024, 3
start = time.time()
fusion = Images_fusion(orimg_nv, matched_ni)
cv2.imwrite("./2.jpg",fusion)
end = time.time()-start
print(end)
# enhance=truncated_linear_stretch(fusion)#融合图像RGB增强
# cv2.imshow('0', cv2.resize(orimg_nv, (600, 400)))
# cv2.imshow('1', cv2.resize(orimg_ni, (600, 400)))
cv2.imshow('2', cv2.resize(fusion, (1200, 800)))
cv2.waitKey(0)
# name_fusionfile='YUV_04.jpg'
# path_fusionfile='images/fusion/'+name_fusionfile
# cv2.imwrite(path_fusionfile, fusion)
if __name__ == '__main__':
main()

5、融合效果 上图

融合图片


融合后，效果一般，但是将就看吧，熟悉一下融合过程。

三、视频融合

一个可见光摄像头，一个红外摄像头，就可以实现实时夜间车外图像融合啦

video_path_infrared = "../videos/ir/video_2020_11_30-20_05_30.avi"
video_path_visible = "../videos/vi/video_2020_11_30-20_05_30.avi"
video_save_path = "../videos/out.avi"
video_fps = 25
# matchimg_di = cv2.imread('images/oripics/basic_day_infrared.jpg')
# 1080, 1920, 3
# matchimg_dv = cv2.imread('images/oripics/basic_day_visual.jpg')
matchimg_di = cv2.imread('left_2020_11_30-15_15_31.jpg')
# 1080, 1920, 3
matchimg_dv = cv2.imread('right_2020_11_30-15_15_31.jpg')
H = Images_matching(matchimg_dv, matchimg_di)
capture_in = cv2.VideoCapture(video_path_infrared)
capture_vi = cv2.VideoCapture(video_path_visible)
if video_save_path != "":
fourcc = cv2.VideoWriter_fourcc(*'XVID')
size = (int(capture_vi.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture_vi.get(cv2.CAP_PROP_FRAME_HEIGHT)))
out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size)
fps = 0.0
while (True):
t1 = time.time()
# 读取某一帧
ref1, frame1 = capture_in.read()
ref2, frame2 = capture_vi.read()
# 格式转变，BGRtoRGB
frame1 = cv2.cvtColor(frame1, cv2.COLOR_BGR2RGB)
frame2 = cv2.cvtColor(frame2, cv2.COLOR_BGR2RGB)
# 转变成Image
# frame1 = Image.fromarray(np.uint8(frame1))
# frame2 = Image.fromarray(np.uint8(frame2))
h, w = frame1.shape[:2]
matched_ni = cv2.warpPerspective(frame2, H, (w, h))
cv2.imwrite("./1.jpg", matched_ni)
# 进行检测
fusion = Images_fusion(frame1, matched_ni)
fusion = np.array(fusion)
end = time.time() - t1
# RGBtoBGR满足opencv显示格式
frame = cv2.cvtColor(fusion, cv2.COLOR_RGB2BGR)
fps = (fps + (1. / end )) / 2
print("fps= %.2f" % (fps))
frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow("video", frame)
c = cv2.waitKey(1) & 0xff
if video_save_path != "":
out.write(frame)
if c == 27:
capture.release()
break
capture.release()
out.release()
cv2.destroyAllWindows()

一定选对配准图片，不然会出现鬼影O

四、总结

这个方法试出来视频融合的时候如何加速，大家如果有什么好方法，多教教我！

最后

以上就是灵巧绿茶为你收集整理的基于小波变换的图像融合（附加视频融合）代码一、小波变换二、图像融合三、视频融合四、总结的全部内容，希望文章能够帮你解决基于小波变换的图像融合（附加视频融合）代码一、小波变换二、图像融合三、视频融合四、总结所遇到的程序开发问题。

如果觉得靠谱客网站的内容还不错，欢迎将靠谱客网站推荐给程序员好友。