opencv对遥感图像基本操作

参考：http://blog.csdn.net/wc781708249/article/details/78485415

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1、python版

说明：
数据类型：原类型16bit 4波段 (CV_16UC4)，转成32float(CV_32FC4)处理，处理完成后保存成CV_16UC4

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1.1绘图与写文本

# 画图dst=cv2.line(img,(0,0),(2000,2000),(255,0,0),5,cv2.LINE_AA) # 画线dst = cv2.rectangle(dst,(384,0),(510,128),(0,255,0),3) # 画矩形dst = cv2.circle(dst,(447,63), 63, (0,0,255), -1) # -1 表示填充dst = cv2.ellipse(dst,(256,256),(100,50),0,0,180,255,-1)  # 这里的255 相当于(255,0,0)# 画多边形pts = np.array([[10,5],[20,30],[70,20],[50,10]], np.int32)pts = pts.reshape((-1,1,2))dst = cv2.polylines(dst,[pts],True,(0,255,255),3)# 写入文字font = cv2.FONT_HERSHEY_SIMPLEX # 字体cv2.putText(dst,'OpenCV',(10,256), font, 4,(255,255,255),2,cv2.LINE_AA)# ----------------------------------------------------------

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1.2 分割和合并图像通道

# 分割和合并图像通道b,g,r = cv2.split(img)img = cv2.merge((r,g,b)) # BGR转成RGB 针对3个波段的# orb=img[:,:,0]g=img[:,:,1]r=img[:,:,2]img2 = cv2.merge((r,g,b))# 多于3个波段n_data=cv2.split(img)dst=cv2.merge((n_data[0],n_data[1],n_data[2])) # 这里只合并前3个波段

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1.3 制作边框图像

# 制作边框图像dst=cv2.copyMakeBorder(img,100,100,100,100,cv2.BORDER_CONSTANT,value=(255,0,0))replicate = cv2.copyMakeBorder(img,10,10,10,10,cv2.BORDER_REPLICATE)reflect = cv2.copyMakeBorder(img,10,10,10,10,cv2.BORDER_REFLECT)reflect101 = cv2.copyMakeBorder(img,10,10,10,10,cv2.BORDER_REFLECT_101)wrap = cv2.copyMakeBorder(img,10,10,10,10,cv2.BORDER_WRAP)constant= cv2.copyMakeBorder(img,10,10,10,10,cv2.BORDER_CONSTANT,value=(255,0,0))

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1.4 访问

# 访问和修改像素值px = img[100,100] # 访问像素值img[100,100] = [255,255,255] # 修改像素值img.item(10,10,2) # 访问像素值img.itemset((10,10,2),100) # 修改像素值# 访问图像属性img.shape # [h,w,c]img.size # h*w*cimg.dtype # float32type(img) # numpy array# 图像ROIball = img[280:340, 330:390]img[273:333, 100:160] = ball# 类型转换img.astype(np.float32)# 计算耗时e1 = cv2.getTickCount()# your code executione2 = cv2.getTickCount()time = (e2 - e1)/ cv2.getTickFrequency()

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1.5 图像算术运算

# 图像加法dst=cv2.add(img,img)# 图像混合dst = cv2.addWeighted(img,0.7,img,0.3,0)# 按位操作cv2.bitwise_not()cv2.bitwise_and()cv2.bitwise_or()cv2.bitwise_xor()

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1.6 阈值处理

# 简单的阈值ret,dst = cv2.threshold(img,np.mean(img)*1.5,np.max(img),cv2.THRESH_BINARY)cv2.THRESH_BINARYcv2.THRESH_BINARY_INVcv2.THRESH_TRUNCcv2.THRESH_TRUNCcv2.THRESH_TOZERO_INV# 自适应阈值dst = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_MEAN_C,\            cv2.THRESH_BINARY,11,2)# Otsu's thresholding after Gaussian filteringblur = cv2.GaussianBlur(img,(5,5),0)ret3,th3 = cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)

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1.7图像的几何变换

# 缩放dst = cv2.resize(img,None,fx=2, fy=2, interpolation = cv2.INTER_CUBIC) # h,w放大2倍height, width = img.shape[:2]dst = cv2.resize(img,(2*width, 2*height), interpolation = cv2.INTER_CUBIC) # h,w放大2倍# 平移rows,cols = img.shape[:2]M = np.float32([[1,0,100],[0,1,50]])dst = cv2.warpAffine(img,M,(cols,rows))# 旋转M = cv2.getRotationMatrix2D((cols/2,rows/2),90,1) # 逆时针旋转90度dst = cv2.warpAffine(img,M,(cols,rows))# 仿射变换pts1 = np.float32([[50,50],[200,50],[50,200]])pts2 = np.float32([[10,100],[200,50],[100,250]])M = cv2.getAffineTransform(pts1,pts2)dst = cv2.warpAffine(img,M,(cols,rows))# 透视转换pts1 = np.float32([[56,65],[368,52],[28,387],[389,390]])pts2 = np.float32([[0,0],[300,0],[0,300],[300,300]])M = cv2.getPerspectiveTransform(pts1,pts2)dst = cv2.warpPerspective(img,M,(300,300))# 重新映射map_x=np.zeros(img.shape[:2],np.float32)map_y=np.zeros(img.shape[:2],np.float32)for j in range(img.shape[0]):    for i in range(img.shape[1]):        # 将图片缩小一半，并将其显示在中间：        if i>img.shape[1]*0.25 and i<img.shape[1]*0.75 and j>img.shape[0]*0.25 and j<img.shape[0]*0.75:            map_x[j,i]=2*(i-img.shape[1]*0.25)+0.5            map_y[j,i]=2*(j-img.shape[1]*0.25)+0.5        else:            map_x[j,i]=0            map_y[j,i]=0        # 上下翻转图像        map_x[j,i]=i        map_y[j,i]=img.shape[0]-j        # # 左右对调        map_x[j,i]=img.shape[1]-i        map_y[j,i]=j        # # 结合上下翻转和左右对调        map_x[j,i] = img.shape[1] - i        map_y[j,i] = img.shape[0] - jdst=cv2.remap(img, map_x, map_y, cv2.INTER_LINEAR,None, cv2.BORDER_CONSTANT, (0, 0, 0))

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1.8 图像平滑

# 二维卷积kernel = np.ones((5,5),np.float32)/25dst = cv2.filter2D(img,-1,kernel)# 均值滤波dst=cv2.blur(img,(5,5))# 高斯滤波dst=cv2.GaussianBlur(img,(5,5),0)# 中值滤波dst=cv2.medianBlur(img,5)# 双边过滤dst=cv2.bilateralFilter(img,9,75,75)

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1.9 形态操作

# 腐蚀kernel = np.ones((5,5),np.uint8)dst = cv2.erode(img,kernel,iterations = 1)# 膨胀kernel = np.ones((7,7),np.uint8)  # 内核erosion = cv2.erode(img,kernel,iterations = 1)  # 腐蚀去除噪声dst = cv2.dilate(erosion,kernel,iterations = 1) # 膨胀恢复原形状

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2、C++版

说明：
数据类型：原类型16bit 4波段 (CV_16UC4)（不转成32float(CV_32FC4)处理 ，转float32处理完保存图像会出现各种问题），直接使用原格式处理，处理完成后保存成CV_16UC4

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2.1绘图与写文本

//////////////////////////////////////////////////////    /*画图*/    //Mat dst;//输出Mat    line(img, Point(0, 200), Point(1000, 1000), Scalar(0, 0, 0), 2, LINE_8);//画线    circle(img, Point(200, 200), 30, Scalar(0, 0, 255), FILLED, LINE_8);    ellipse(img,Point(100, 100),Size(50, 50),90,0,360,Scalar(255, 0, 0),2,LINE_AA);    rectangle(img,Point(0, 7 * w / 8),Point(w, w),Scalar(0, 255, 255),FILLED,LINE_8);    fillPoly(img,ppt,npt,1,Scalar(255, 255, 255), LINE_8);    putText(img, "Testing text rendering", org, rng.uniform(0, 8),        rng.uniform(0, 100)*0.05 + 0.1, randomColor(rng), rng.uniform(1, 10), LINE_8);    /////////////////////////////////////////////////////

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2.2 分割和合并图像通道

    std::vector<cv::Mat> imgMat(St.nbands);    std::vector<cv::Mat> tempMat(St.nbands);    cv::split(img, imgMat); //分离通道    tempMat.at(0) = (imgMat.at(2));//BGR-->RGB    tempMat.at(1) = (imgMat.at(1));    tempMat.at(2) = (imgMat.at(0));    Mat img2;    cv::merge(tempMat, img2);// 合并通道    imgMat.clear();    tempMat.clear();

附加类型转换

 img.convertTo(dst, CV_32FC4); //转成32F类型

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2.3 制作边框图像

Mat dst;    int top, bottom, left, right;    top = (int)(0.05*img.rows); bottom = (int)(0.05*img.rows);    left = (int)(0.05*img.cols); right = (int)(0.05*img.cols);    copyMakeBorder(img, dst, 50, 50, 50, 50, BORDER_CONSTANT, Scalar(255, 0, 0));

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2.4 访问

    //img类型为Mat    //ROI    Mat D(img, Rect(10, 10, 100, 100)); // using a rectangle    Mat E = img(Range::all(), Range(1, 3)); // using row and column boundaries    //复制    Mat F = img.clone();    Mat G;    img.copyTo(G);    img.channels(); //通道数    img.rows;//行数 ysize    img.cols;//列数 xsize    img.data;//获取img中所有像素值    CV_Assert(img.depth() == CV_8U);//判断数据类型    //访问像素    img.at<Vec3b>(y, x);    img.at<Vec3b>(Point(x, y));    uchar* p = img.ptr();    p[10];    //修改像素值    img.at<uchar>(y, x) = 128;    Vec3f intensity = img.at<Vec3f>(y, x);    float blue = intensity.val[0];    float green = intensity.val[1];    float red = intensity.val[2];    //计算耗时    double t = (double)getTickCount();    // do something ...    t = ((double)getTickCount() - t) / getTickFrequency();    cout << "Times passed in seconds: " << t << endl;    //类型数据转换    img.convertTo(img,CV_32FC4);

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2.5 图像算术运算

beta = (1.0 - alpha);addWeighted(src1, alpha, src2, beta, 0.0, dst);

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2.6 阈值处理