基于小波变换的图像融合python版（红外和可见光）
- 一、小波变换
- 二、图像融合
- 1、采用对比度拉伸，完成红外图像增强
- 2、采用Surf特征点匹配，完成图像配准
- 3、采用HSV通道小波变换，完成图像融合
- 4、总的代码
- 5、融合效果 上图
- 三、视频融合
- 四、总结
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
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16# 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
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44# 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
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65# 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
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173import 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
复制代码
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52video_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
以上就是灵巧绿茶最近收集整理的关于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，更多相关基于小波变换内容请搜索靠谱客的其他文章。
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