【opencv 官方教程】翻译3 图像处理下

Image Processing (imgproc module)

In this section you will learn about the image processing (manipulation) functions inside OpenCV.

边缘检测

Canny Edge Detector

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn a sophisticated alternative to detect edges.

理论

Canny Edge detector，也叫optimal detector，是由John F. Canny于1986年开发的。这种算法旨在满足下面三个标尺：

Low error rate: Meaning a good detection of only existent edges.
低错误率：仅仅在边缘检测出轮廓
Good localization: The distance between edge pixels detected and real edge pixels have to be minimized.
精准定位：检测出的边缘尽可能的与实际边缘重合
Minimal response: Only one detector response per edge.
最小回应：一个边缘只产生一个检测结果

步骤

过滤噪声：参见平滑处理

发现图像梯度大的部分：参见梯度；额外的，真正的梯度应有方向，这里是计算了方向的。

截取非最大部分：保存轮廓线边缘

Hysteresis（迟滞）：对于高于阈值上线的部分视为边界，低于阈值下线的被忽视，处于两个阈值之间的，且于高于阈值上线部分相连的，也被视为边界。

执行过程：

Create some needed variables 声明所需变量

Loads the source image 装载图片

Create a matrix of the same type and size of src (to be dst) 为输出图像开辟空间

Convert the image to grayscale (using the function cv::cvtColor 灰度转换

Create a window to display the results 创建显示窗口

Create a Trackbar for the user to enter the lower threshold for our Canny detector 创建Trackbar对象，输入阈值下线

Let's check the CannyThreshold function, step by step Canny算法实现

We fill a dst image with zeros (meaning the image is completely black) 初始化输出图像矩阵

Finally, we will use the function cv::Mat::copyTo to map only the areas of the image that are identified as edges 将处理结果拷贝到输出图像中

We display our result:显示结果

Hough变换

Hough Line Transform

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to detect lines
Hough Circle Transform

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to detect circles

这个我并不懂啊，据说是有很好的检测直线、圆的能力，但又被说性能不行。

重映射

Remapping

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to manipulate pixels locations

这节提出的理论：

假如有一个图像表达式：h(x,y)，表示在一定区间内，给定一个坐标（x,y)，则对应于该坐标的图像的值为h(x,y)，这是原图像在坐标上的映射。

现在把他重映射了，即有一个映射函数f，输入参数为原图像中的某点，能得到对应输出图像中的另外一点。

g(x,y)=f(h(x,y))
d

的

源码中的map_x map_y构成f（），即dst(x,y) = src(map_x(x,y),map_y(x,y))，然后通过remap函数实现重映射。

补充:我觉得吧，这里看似多此一举的绘制map_x、map_y，实际上是一种高效开发的必然。这样可以让操作和功能分离，保证了功能的自由拓展能力，而避免了重复的对操作（remap）过程的调试开发。

仿射变换

Affine Transformations

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to rotate, translate and scale our images

理论请自行查看，这是一种相对于重映射更为固定化的过程，因此具有更具描述性的操作。

本节提出两种变换手段：

cv::warpAffine cv::getRotationMatrix2D

像素均衡、直方图均衡化 Histogram Equalization

Histogram Equalization

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to improve the contrast in our images

理论：像素直方图是一个分布不太均匀的东西，这让我们感觉图像不够饱满。现在强行把他们之间的距离拉大，比如原先只有深红、猩红、血红这样色差不大的一些颜色，现在为了增加表现力，我把他们改为红黑、亮红、浅红这样差别比较大的颜色。

背景投影

Back Projection

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to use histograms to find similar objects in images

。。。貌似能抓住图像中的鲜艳部分，有挖出背景的功能。各位大神原谅我这弱鳥，实在是啃不动这些图形图像专业的东西。我尽量把更多直接明了的部分放进来，有需要用到这些功能的可以方便检索。

模式匹配（模板搜索）

Template Matching

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to match templates in an image

功能：找到src中与目标最为匹配的区域

这里提出了六种匹配方式：

method=CV_TM_SQDIFF

R(x,y)=∑x′,y′(T(x′,y′)−I(x+x′,y+y′))2
method=CV_TM_SQDIFF_NORMED

R(x,y)=∑x′,y′(T(x′,y′)−I(x+x′,y+y′))2∑x′,y′T(x′,y′)2⋅∑x′,y′I(x+x′,y+y′)2‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾√
method=CV_TM_CCORR

R(x,y)=∑x′,y′(T(x′,y′)⋅I(x+x′,y+y′))
method=CV_TM_CCORR_NORMED

R(x,y)=∑x′,y′(T(x′,y′)⋅I(x+x′,y+y′))∑x′,y′T(x′,y′)2⋅∑x′,y′I(x+x′,y+y′)2‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾√
method=CV_TM_CCOEFF

R(x,y)=∑x′,y′(T′(x′,y′)⋅I′(x+x′,y+y′))

where

T′(x′,y′)=T(x′,y′)−1/(w⋅h)⋅∑x″,y″T(x″,y″)I′(x+x′,y+y′)=I(x+x′,y+y′)−1/(w⋅h)⋅∑x″,y″I(x+x″,y+y″)
method=CV_TM_CCOEFF_NORMED

R(x,y)=∑x′,y′(T′(x′,y′)⋅I′(x+x′,y+y′))∑x′,y′T′(x′,y′)2⋅∑x′,y′I′(x+x′,y+y′)2‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾√

寻找轮廓

Finding contours in your image

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to find contours of objects in our image

Use the OpenCV function cv::findContours
Use the OpenCV function cv::drawContours

寻找和绘制轮廓

凸包

Convex Hull

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to get hull contours and draw them!

Original

创建边界区域、圆

Creating Bounding boxes and circles for contours

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to obtain bounding boxes and circles for our contours.

Creating Bounding rotated boxes and ellipses for contours

Compatibility: > OpenCV 2.0

Author: Ana Huamán

Where we learn how to obtain rotated bounding boxes and ellipses for our contours.

关键看这里

For every found contour we now apply approximation to polygons with accuracy +-3 and stating that the curve must me closed.

After that we find a bounding rect for every polygon and save it to boundRect.

At last we find a minimum enclosing circle for every polygon and save it to center and radius vectors.

for( size_t i = 0; i < contours.size(); i++ )

{

approxPolyDP( Mat(contours[i]), contours_poly[i], 3, true );

boundRect[ i] = boundingRect( Mat(contours_poly[i]) );

minEnclosingCircle( contours_poly[i], center[i], radius[i] );

}

We found everything we need, all we have to do is to draw.

效果：