OpenCV学习指南(五)轮廓提取

前言

  有很多算法被用来实现提取轮廓，比如:Canny，阈值分割，提取傅里叶变换的高频信息，还有别具一格的蚁群算法，当然比较常见的作法是使用阈值分割+边缘查找，在OpenCV里是threshold和findContours两个函数的组合使用，和Canny。
  轮廓提取的算法很多，而其目的都是为了找到图像中灰阶差比较大的位置。而所谓亚像素提取，则是使用了插值算法，以找出灰阶差最大的位置。

提取傅里叶变换的高频信息

##############
#图像中的轮廓提取
#时间：2019/1/3
#作者：cclplus
#仅供学习交流使用
#如有疑问或者需求，可以联系作者的邮箱
#如果你有什么好的建议或者指导，我将不胜感激


import cv2
import numpy as np
from matplotlib import pyplot as plt
import copy
img = cv2.imread('liuyifei.jpg',0)
f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)

rows,cols = img.shape
crow,ccol = int(rows/2) , int(cols/2)
for i in range(crow-30,crow+30):
    for j in range(ccol-30,ccol+30):
        fshift[i][j]=0.0
f_ishift = np.fft.ifftshift(fshift)
img_back = np.fft.ifft2(f_ishift)#进行高通滤波
# 取绝对值
img_back = np.abs(img_back)
plt.subplot(121),plt.imshow(img,cmap = 'gray')#因图像格式问题，暂已灰度输出
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
#先对灰度图像进行伽马变换，以提升暗部细节
rows,cols = img_back.shape
gamma=copy.deepcopy(img_back)
rows=img.shape[0]
cols=img.shape[1]
for i in range(rows):
    for j in range(cols):
        gamma[i][j]=5.0*pow(gamma[i][j],0.34)#0.34这个参数是我手动调出来的，根据不同的图片，可以选择不同的数值
#对灰度图像进行反转

for i in range(rows):
    for j in range(cols):
        gamma[i][j]=255-gamma[i][j]
plt.subplot(122),plt.imshow(gamma,cmap = 'gray')
plt.title('Result in HPF'), plt.xticks([]), plt.yticks([])
plt.show()

原图

输出结果

通过蚁群算法进行图片轮廓提取

相关代码我上传到了我的github上
https://github.com/YuruTu/Ant_colony

  效果不够理想，这也算得上蚁群算法的一大特点，对参数要求较高，需要调参。相关内容，笔者会持续更新

Canny边缘检测

  

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('liuyifei.jpg',0)
edges = cv2.Canny(img,100,200)

plt.subplot(121),plt.imshow(img,cmap='gray')
plt.title('original'),plt.xticks([]),plt.yticks([])
plt.subplot(122),plt.imshow(edges,cmap='gray')
plt.title('edge'),plt.xticks([]),plt.yticks([])

plt.show()

使用cuda加速提取轮廓

#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 
#include 

using namespace cv;
using namespace std;
// GPU constant memory to hold our kernels (extremely fast access time)
__constant__ float convolutionKernelStore[256];

/**
 * Convolution funcion para cuda.  Destino tendra el mismo width/height como la fuente,
 *
 * @param source      Source imagen host
 * @param width       Source imagen width
 * @param height      Source imagen height
 * @param paddingX    source imagen padding x
 * @param paddingY    source imagen padding y
 * @param kOffset     offset en kernel almacenamiento de memoria constante
 * @param kWidth      kernel width
 * @param kHeight     kernel height
 * @param destination Imagen de destino
 */
__global__ void convolve(unsigned char *source, int width, int height, int paddingX, int paddingY, unsigned int kOffset, int kWidth, int kHeight, unsigned char *destination)
{
	// Calculate our pixel's location
	int x = (blockIdx.x * blockDim.x) + threadIdx.x;
	int y = (blockIdx.y * blockDim.y) + threadIdx.y;

	float sum = 0.0;
	int   pWidth = kWidth / 2;
	int   pHeight = kHeight / 2;

	//Solo ejecuta validos pixeles
	if (x >= pWidth + paddingX && y >= pHeight + paddingY && x < (blockDim.x * gridDim.x) - pWidth - paddingX &&
		y < (blockDim.y * gridDim.y) - pHeight - paddingY)
	{
		for (int j = -pHeight; j <= pHeight; j++)
		{
			for (int i = -pWidth; i <= pWidth; i++)
			{
				// Sample the weight for this location
				int ki = (i + pWidth);
				int kj = (j + pHeight);
				float w = convolutionKernelStore[(kj * kWidth) + ki + kOffset];


				sum += w * float(source[((y + j) * width) + (x + i)]);
			}
		}
	}

	// Promedio sum
	destination[(y * width) + x] = (unsigned char)sum;
}

__global__ void pythagoras(unsigned char *a, unsigned char *b, unsigned char *c)
{
	int idx = (blockIdx.x * blockDim.x) + threadIdx.x;

	float af = float(a[idx]);
	float bf = float(b[idx]);

	c[idx] = (unsigned char)sqrtf(af*af + bf * bf);
}

// crea imagen buffer
unsigned char* createImageBuffer(unsigned int bytes, unsigned char **devicePtr)
{
	unsigned char *ptr = NULL;
	cudaSetDeviceFlags(cudaDeviceMapHost);
	cudaHostAlloc(&ptr, bytes, cudaHostAllocMapped);
	cudaHostGetDevicePointer(devicePtr, ptr, 0);
	return ptr;
}


int main(int argc, char** argv) {
	// Abre la camaraweb
	cv::VideoCapture camera(0);
	cv::Mat          frame;
	if (!camera.isOpened())
		return -1;

	// capture windows
	cv::namedWindow("Source");
	cv::namedWindow("Greyscale");
	cv::namedWindow("Blurred");
	cv::namedWindow("Sobel");

	// Funciones para obtener el tiempo de ejecucion 
	cudaEvent_t start, stop;
	cudaEventCreate(&start);
	cudaEventCreate(&stop);

	// Crea kernel gaussian(sum = 159)
	const float gaussianKernel5x5[25] =
	{
		2.f / 159.f,  4.f / 159.f,  5.f / 159.f,  4.f / 159.f, 2.f / 159.f,
		4.f / 159.f,  9.f / 159.f, 12.f / 159.f,  9.f / 159.f, 4.f / 159.f,
		5.f / 159.f, 12.f / 159.f, 15.f / 159.f, 12.f / 159.f, 5.f / 159.f,
		4.f / 159.f,  9.f / 159.f, 12.f / 159.f,  9.f / 159.f, 4.f / 159.f,
		2.f / 159.f,  4.f / 159.f,  5.f / 159.f,  4.f / 159.f, 2.f / 159.f,
	};
	cudaMemcpyToSymbol(convolutionKernelStore, gaussianKernel5x5, sizeof(gaussianKernel5x5), 0);
	const unsigned int gaussianKernel5x5Offset = 0;

	// Sobel gradient kernels
	const float sobelGradientX[9] =
	{
		-1.f, 0.f, 1.f,
		-2.f, 0.f, 2.f,
		-1.f, 0.f, 1.f,
	};
	const float sobelGradientY[9] =
	{
		1.f, 2.f, 1.f,
		0.f, 0.f, 0.f,
		-1.f, -2.f, -1.f,
	};
	cudaMemcpyToSymbol(convolutionKernelStore, sobelGradientX, sizeof(sobelGradientX), sizeof(gaussianKernel5x5));
	cudaMemcpyToSymbol(convolutionKernelStore, sobelGradientY, sizeof(sobelGradientY), sizeof(gaussianKernel5x5) + sizeof(sobelGradientX));
	const unsigned int sobelGradientXOffset = sizeof(gaussianKernel5x5) / sizeof(float);
	const unsigned int sobelGradientYOffset = sizeof(sobelGradientX) / sizeof(float) + sobelGradientXOffset;

	// Crea CPU/GPU imagenes compartidos
	camera >> frame;
	unsigned char *sourceDataDevice, *blurredDataDevice, *edgesDataDevice;
	cv::Mat source(frame.size(), CV_8U, createImageBuffer(frame.size().width * frame.size().height, &sourceDataDevice));
	cv::Mat blurred(frame.size(), CV_8U, createImageBuffer(frame.size().width * frame.size().height, &blurredDataDevice));
	cv::Mat edges(frame.size(), CV_8U, createImageBuffer(frame.size().width * frame.size().height, &edgesDataDevice));

	// Crea 2 imagenes temporales (sobel gradients)
	unsigned char *deviceGradientX, *deviceGradientY;
	cudaMalloc(&deviceGradientX, frame.size().width * frame.size().height);
	cudaMalloc(&deviceGradientY, frame.size().width * frame.size().height);

	// Loop while captura imagenes
	while (1)
	{
		// Captura la imagen en escala de grises
		camera >> frame;
		cvtColor(frame, source, COLOR_RGB2GRAY);
		_sleep(1);
		// Graba el tiempo que demora el proceso
		cudaEventRecord(start);
		{
			// convolution kernel  parametros
			dim3 cblocks(frame.size().width / 16, frame.size().height / 16);
			dim3 cthreads(16, 16);

			// pythagoran kernel parametros
			dim3 pblocks(frame.size().width * frame.size().height / 256);
			dim3 pthreads(256, 1);

			//  gaussian blur (first kernel in store @ 0)
			convolve <<> > (sourceDataDevice, frame.size().width, frame.size().height, 0, 0, gaussianKernel5x5Offset, 5, 5, blurredDataDevice);

			// sobel gradient convolutions (x&y padding is now 2 because there is a border of 2 around a 5x5 gaussian filtered image)
			convolve << > > (blurredDataDevice, frame.size().width, frame.size().height, 2, 2, sobelGradientXOffset, 3, 3, deviceGradientX);
			convolve << > > (blurredDataDevice, frame.size().width, frame.size().height, 2, 2, sobelGradientYOffset, 3, 3, deviceGradientY);
			pythagoras << > > (deviceGradientX, deviceGradientY, edgesDataDevice);

			cudaThreadSynchronize();
		}
		cudaEventRecord(stop);

		// Muestra tiempo de ejecucion
		float ms = 0.0f;
		cudaEventSynchronize(stop);
		cudaEventElapsedTime(&ms, start, stop);
		std::cout << "Elapsed GPU time: " << ms << " milliseconds" << std::endl;

		// Muestra resultados
		imshow("Source", frame);
		imshow("Greyscale", source);
		imshow("Blurred", blurred);
		imshow("Sobel", edges);

		// Spin
		if (cv::waitKey(1) == 27) break;
	}

	// Exit
	cudaFreeHost(source.data);
	cudaFreeHost(blurred.data);
	cudaFreeHost(edges.data);
	cudaFree(deviceGradientX);
	cudaFree(deviceGradientY);

	return 0;
}

  很多时候加上Cuda是有必要的，如果你要使用hough变换之类的时间复杂度比较高的代码，Gpu编程会给你带来多个数量级的加速。
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