opencv resize()函数和imread()，基本图像操作

C++: void resize(InputArray src, OutputArray dst, Size dsize, double fx=0, double fy=0, int interpolation=INTER_LINEAR )

//0.5 0.5的比例缩放；
cv::resize(img, dst,cv::Size(0,0),(0.5),(0.5),1);
//将图像尺寸变为512,512
cv::resize(img, dst,cv::Size(512,512));

Python: cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) → dst

#按比例缩放
dst=cv2.resize(src,(0,0),fx=1/2,fy=1/2,interpolation=cv2.INTER_LINEAR)
#将源图像的大小变为512*512
dst=cv2.resize(src,(512,512))

interpolation –

interpolation method:

• INTER_NEAREST - a nearest-neighbor interpolation
• INTER_LINEAR - a bilinear interpolation (used by default)
• INTER_AREA - resampling using pixel area relation. It may be a preferred method for image decimation, as it gives moire’-free results. But when the image is zoomed, it is similar to the INTER_NEAREST method.
• INTER_CUBIC - a bicubic interpolation over 4x4 pixel neighborhood
• INTER_LANCZOS4 - a Lanczos interpolation over 8x8 pixel neighborhood

当压缩一幅图像时，选择INTER_AREA算法实现效果较好；当放大一幅图像时，常常选择INTER_CUBIC，但是速度较慢，INTER_LINEAR响应较快，效果也相对较好。

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C++: Mat imread(const string& filename, int flags=1 )

Python: cv2.imread(filename[, flags]) → retval

flags –

Flags specifying the color type of a loaded image:

• CV_LOAD_IMAGE_ANYDEPTH = 2 - If set, return 16-bit/32-bit image when the input has the corresponding depth, otherwise convert it to 8-bit.
• CV_LOAD_IMAGE_ANYCOLOR   =4
• CV_LOAD_IMAGE_COLOR = 1 - If set, always convert image to the color one
• CV_LOAD_IMAGE_GRAYSCALE = 0 - If set, always convert image to the grayscale one
• CV_LOAD_IMAGE_UNCHANGED  =-1
• >0 Return a 3-channel color image.
• =0 Return a grayscale image.
• <0 Return the loaded image as is (with alpha channel).

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at()函数遍历

uchar、Vec3b表示图像元素的类型.

void traverse_at(Mat img)
{
	int row = img.rows;
	int col = img.cols;
	for (int i = 0; i < row; i++)
	{
		for (int j = 0; j < col; j++)
		{
			img.at(i, j)[0] = 255 - img.at(i, j)[0];//B通道
			img.at(i, j)[1] = 255 - img.at(i, j)[1];//G通道
			img.at(i, j)[2] = 255 - img.at(i, j)[2];//R通道
		}
	}
	imshow("reverse", img);
	waitKey(0);
	destroyAllWindows();
}

step：行数*通道数，

指针方式遍历：

void traverse_pt(Mat img)
{
	int row = img.rows;
	int step = img.step;
	uchar* pImg = img.data;
	for (int i = 0; i < row; i++)
	{
		for (int j = 0; j < step; j++)
			pImg[j] = 255 - pImg[j];
		pImg += img.step;
	}
	imshow("reverse——pt", img);
	waitKey(0);
	destroyAllWindows();
}