Shulou(Shulou.com)06/01 Report--

This article mainly introduces how Python uses OpenCV to zoom the image, which has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, the following let Xiaobian take you to understand.

OpenCV: picture zooming and image pyramid

The easiest way to scale an image is, of course, to call the resize function!

The resize function can accurately convert a source image into a target image of a specified size.

To reduce the image, it is generally recommended to use CV_INETR_AREA for interpolation; to enlarge the image, it is recommended to use CV_INTER_LINEAR.

Now let's talk about the first way to call, specify the size of the picture you want, that is, you fill in the length and height of the picture you want. # include # includeusing namespace std;using namespace cv; / / zooming in and out of images int main () {Mat img = imread ("lol5.jpg"); imshow ("original image", img); Mat dst = Mat::zeros (512,512, CV_8UC3); / / I want to convert to resize (img, dst, dst.size ()); imshow ("after resizing", dst); waitKey (0);}

Second, fill in the ratio you want to shrink or enlarge. # include # int main () {Mat img = imread ("lol5.jpg"); imshow ("original", img); Mat dst; resize (img, dst, Size (), 0.5 and 0.5); / / my length and width have become 0.5x imshow ("after resizing", dst); waitKey (0);}

Next, let's talk about the image pyramid.

To put it bluntly, the image pyramid is used for image scaling, doing the same thing as the resize function, so do we still need to learn it? I think it is necessary, er, because I will encounter this term in learning convolution neural network, so I can learn it all. I can't even get around him in graphics.

Tell me what an image pyramid is.

In fact, it is very easy to understand, as shown in the image above, we compare layers of images to pyramids. The higher the level, the smaller the size of the image and the lower the resolution.

Two types of pyramids:

Gaussian pyramid: for downsampling, the main image pyramid

For example, a small image is reconstructed into a large image.

The image pyramid has two high-frequency terms: up-sampling and down-sampling. Now let's talk about them.

Upsampling: the picture is enlarged (the so-called up is larger), using the PryUp function

Downsampling: the picture is zoomed out (the so-called down is smaller), using the PryDown function

Downsampling takes the steps:

Convolution the image with Gaussian kernel

Remove all even rows and columns

Downsampling is image compression, which will lose image information.

Upsampling steps:

Enlarge the image twice as much in each direction, and fill the new rows and columns with 0

Use the same kernel (multiplied by 4) to convolution the enlarged image to get an approximate value of the new pixels.

There is a serious problem in both upsampling and downsampling, that is, the image becomes blurred, because the loss of information occurs in the process of zooming. To solve this problem, we have to look at the pyramid of Laplace.

The usage of pryUp and pryDown in OpenCV is shown below.

# include # include using namespace std;using namespace cv;// image pyramid int main () {Mat img = imread ("lol8.jpg"); imshow ("original image", img); Mat dst,dst2; pyrUp (img, dst, Size (img.cols*2, img.rows*2)); / / double pyrDown (img, dst2, Size (img.cols* 0.5, img.rows* 0.5)) / / reduced to half of the original imshow ("size enlarged", dst); imshow ("size reduced", dst2); waitKey (0);}

Obviously, whether zooming in or out, the image has become blurred, which is his fatal flaw.

Thank you for reading this article carefully. I hope the article "Python how to use OpenCV to zoom images" shared by the editor will be helpful to everyone. At the same time, I also hope that you will support and pay attention to the industry information channel. More related knowledge is waiting for you to learn!

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.