图象滤波器　Coherence-enhance shock filter 实现

一：源码

https://github.com/Itseez/opencv/blob/master/samples/python2/coherence.py

def coherence_filter(img, sigma = 11, str_sigma = 11, blend = 0.5, iter_n = 4):h, w = img.shape[:2]for i in xrange(iter_n):print i,gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)eigen = cv2.cornerEigenValsAndVecs(gray, str_sigma, 3)eigen = eigen.reshape(h, w, 3, 2)  # [[e1, e2], v1, v2]x, y = eigen[:,:,1,0], eigen[:,:,1,1]gxx = cv2.Sobel(gray, cv2.CV_32F, 2, 0, ksize=sigma)gxy = cv2.Sobel(gray, cv2.CV_32F, 1, 1, ksize=sigma)gyy = cv2.Sobel(gray, cv2.CV_32F, 0, 2, ksize=sigma)gvv = x*x*gxx + 2*x*y*gxy + y*y*gyym = gvv < 0ero = cv2.erode(img, None)dil = cv2.dilate(img, None)img1 = eroimg1[m] = dil[m]img = np.uint8(img*(1.0 - blend) + img1*blend)print 'done'return img

二：

opencv mat.reshape　c是行优先，　matlab 列优先

三：

http://www.rosoo.net/a/201004/9157.html#CornerEigenValsAndVecs

#!/usr/bin/env python'''
Texture flow direction estimation.Sample shows how cv2.cornerEigenValsAndVecs function can be used
to estimate image texture flow direction.Usage:texture_flow.py [<image>]
'''import numpy as np
import cv2if __name__ == '__main__':import systry:fn = sys.argv[1]except:fn = 'data/starry_night.jpg'img = cv2.imread(fn)if img is None:print 'Failed to load image file:', fnsys.exit(1)gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)h, w = img.shape[:2]eigen = cv2.cornerEigenValsAndVecs(gray, 15, 3)eigen = eigen.reshape(h, w, 3, 2)  # [[e1, e2], v1, v2]flow = eigen[:,:,2]vis = img.copy()vis[:] = (192 + np.uint32(vis)) / 2d = 12points =  np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)for x, y in points:vx, vy = np.int32(flow[y, x]*d)cv2.line(vis, (x-vx, y-vy), (x+vx, y+vy), (0, 0, 0), 1, cv2.LINE_AA)cv2.imshow('input', img)cv2.imshow('flow', vis)cv2.waitKey()

int main (int argc, char** argv)
{cv::TickMeter tm;tm.start();cv::Mat img = cv::imread(argv[1]);cv::Mat gray = cv::Mat();cv::cvtColor(img, gray, CV_BGR2GRAY);// to preserve the original imagecv::Mat flow = gray.clone();int width = img.cols;int height = img.rows;int graySize = width * height;// "brighten" the flow image // C++ version of:// vis[:] = (192 + np.uint32(vis)) / 2for (unsigned int i=0; i<graySize; ++i){flow.data[i] = (uchar)((192 + (int)flow.data[i]) / 2);}cv::Mat eigen = cv::Mat(height, width, CV_32FC(6));cv::cornerEigenValsAndVecs(gray, eigen, 15, 3);// this is the equivalent to all the numpy's reshaping etc. to // generate the flow arrays// simply use channel 4 and 5 as the actual flow array in C++std::vector<cv::Mat> channels;cv::split(eigen, channels);int d = 12;cv::Scalar col(0, 0, 0);// C++ version of:// points =  np.dstack( np.mgrid[d/2:w:d, d/2:h:d] ).reshape(-1, 2)// including the actual line drawing partfor (unsigned int y=(d/2); y<flow.rows; y+=d){for (unsigned int x=(d/2); x<flow.cols; x+=d){if (x < flow.cols && y < flow.rows){cv::Point p(x, y);float dx = channels[4].at<float>(p) * (d/2);float dy = channels[5].at<float>(p) * (d/2);cv::Point p0(p.x - dx, p.y - dy);cv::Point p1(p.x + dx, p.y + dy);cv::line(flow, p0, p1, col, 1);}}}tm.stop();std::cout<<"Flow image generated in "<<tm.getTimeMilli()<<" ms."<<std::endl;cv::imshow("FLOW", flow);cv::waitKey();return 0;
}

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