C++ CImg::InitPixels方法代码示例

CImg* CDimageView::Dwtstep(CImg* pImg, int Inv)
{
	// 初始化目标图像
	CImg* mImg = new CImg(*pImg);
	mImg->InitPixels(0);
	int width=pImg->GetWidthPixel();
	int height=pImg->GetHeight();
	int nMaxWLevel = Log2(width);
	int nMaxHLevel = Log2(height);
	int nMaxLevel;

	if (width == 1<<nMaxWLevel && height == 1<<nMaxHLevel)
		nMaxLevel = min(nMaxWLevel, nMaxHLevel);

	// 如果小波变换的存储内存还没有分配，则分配此内存,用double保存graylevel
	if(!Dwttemp){			
		Dwttemp = new double[width*height];

		// 将图象数据放入m_pDbImage中 
		for (int i=0; i<height; i++)
		{
			for (int j=0; j<width; j++)
				Dwttemp[i * width + j] = double(pImg->GetGray(j,i));
		}
	}

	if (!DWTStep_2D(Dwttemp, nMaxWLevel-Curdpth, nMaxHLevel-Curdpth,nMaxWLevel, nMaxHLevel, Inv))
		return FALSE;
	
	if (Inv)
		Curdpth --;
	// 否则加1
	else
		Curdpth ++;

	BYTE gray;
	//将double里gray数据转入24位bmp(gray,gray,gray)
	int lfw = width>>Curdpth, lfh = height>>Curdpth; //尺度区域
		for (int i=0; i<height; i++)
		{
			for (int j=0; j<width; j++){
				if(i<lfh && j<lfw){ //低频X低频，>0直接转
					gray = BYTE(Dwttemp[i * width + j]);
					mImg->SetPixel(j,i,RGB(gray,gray,gray));
				}
				else{  //小于零部分 +128显示
					gray = BYTE(Dwttemp[i * width + j]+128);
					mImg->SetPixel(j,i,RGB(gray,gray,gray));
				}
			}

		}
	return mImg;
}

CImg CImg::operator ! ()
{
	CImg grayRet = *this;
	grayRet.InitPixels(255); //结果图像置白

	int nHeight = GetHeight();
	int nWidth = GetWidthPixel();

	int i,j;

	for(i=0; i<nHeight; i++)
	{
		for(j=0; j<nWidth; j++)
		{
			int pixel = 255 - GetGray(j, i);
			grayRet.SetPixel(j, i, RGB(pixel, pixel, pixel));
		}
	}

	return grayRet;
}

CImg* CDimageView::Canny(CImg* pImg)
{
	// 各方向梯度值

	// 使用Prewitt模板计算各个方向上的梯度值
	CImg* imgGH=PrewittEdge(pImg,1);
	CImg* imgGV=PrewittEdge(pImg,2);
	CImg* imgGCW=PrewittEdge(pImg,3);
	CImg* imgGCCW=PrewittEdge(pImg,4);
	CImg* imgGratitude = new CImg(*pImg);
	imgGratitude->InitPixels(0);

	int width=pImg->GetWidthPixel();
	int height=pImg->GetHeight();
	// 最大梯度方向
	BYTE * pbDirection = new BYTE [height * width];

	memset(pbDirection, 0,height * width * sizeof(BYTE));

	// 寻找每点的最大梯度方向并写入对应的最大梯度值

	for (int i=0; i<height; i++)
	{
		for (int j=0; j<width; j++)
		{
			BYTE gray = 0;

			if (imgGH->GetGray(j, i) > gray)
			{
				gray = imgGH->GetGray(j, i);
				pbDirection[i * width + j] = 1;
				imgGratitude->SetPixel(j, i, RGB(gray, gray, gray));
			}

			if (imgGV->GetGray(j, i) > gray)
			{
				gray = imgGV->GetGray(j, i);
				pbDirection[i * width + j] = 2;
				imgGratitude->SetPixel(j, i, RGB(gray, gray, gray));
			}

			if (imgGCW->GetGray(j, i) > gray)
			{
				gray = imgGCW->GetGray(j, i);
				pbDirection[i * width + j] = 3;
				imgGratitude->SetPixel(j, i, RGB(gray, gray, gray));
			}

			if (imgGCCW->GetGray(j, i) > gray)
			{
				gray = imgGCCW->GetGray(j, i);
				pbDirection[i * width + j] = 4;
				imgGratitude->SetPixel(j, i, RGB(gray, gray, gray));
			}
		}
	}

	// 阈值化时重用前面的对象
	CImg *pImgThreL = imgGH, *pImgThreH = imgGV;

	// 检查阈值参数，如未给出阈值则计算以取得最佳阈值
	int bThreH;
	int bThreL;

	bThreH = 1.2 * imgGratitude->DetectThreshold(100);
	bThreL = 0.4 * bThreH;
		

	// 将最大梯度图像按高低值分别进行阈值化
	imgGratitude->Threshold(pImgThreL, bThreL);
	imgGratitude->Threshold(pImgThreH, bThreH);

	// 初始化目标图像
	CImg* mImg = new CImg(*pImg);
	mImg->InitPixels(0);
	
	// 根据低阈值图像在高阈值图像上进行边界修补
	for (int i=1; i<height-1; i++)
	{
		for (int j=1; j<width-1; j++)
		{
			if (pImgThreH->GetGray(j, i))
			{
				// 高阈值图像上发现点直接确定
				mImg->SetPixel(j, i, RGB(255, 255, 255));
				
				// 搜索梯度最大方向上的邻域
				switch ( pbDirection[i * width + j] ) {
					case 1:	// 水平方向
						if (pImgThreL->GetGray(j+1, i))
						{
							pImgThreH->SetPixel(j+1, i, RGB(255, 255, 255));
						}
						if (pImgThreL->GetGray(j-1, i))
						{
							pImgThreH->SetPixel(j-1, i, RGB(255, 255, 255));
						}
						break;

					case 2:	// 垂直方向
//.........这里部分代码省略.........