![]() ![]() We also need to keep in mind the interpolation method of our resizing function. As we explore machine learning/deep learning techniques, we’ll find that our internal algorithms often ignore the aspect ratio of an image but more on that once we understand the fundamentals of computer vision. Exceptions most certainly do apply, though. In general, you’ll want to preserve the images’ aspect ratio when resizing - especially if these images are presented as output to the user. They have been resized by ignoring the ratio of the width to the height of the image. And on the right, we have two images that have been distorted by not preserving the aspect ratio. rotatingImg = cv2.Figure 1: Ignoring the aspect ratio of an image can lead to resized images that look distorted, squished, and crunched. Next is to apply the rotation settings that we have defined on the image we read earlier and display the image. Rotate = cv2.getRotationMatrix2D(center,170,1) First we have to determine the center point of rotation which we can determine from the width and height of the image, then determine the degree of rotation of the image and the dimensions of the image output. Image Rotating OpenCVĬhanging the rotation isn’t that difficult either. croppedImg = imgįrom the command above, the crop results from our initial image will appear following the coordinates we specified earlier. First, we determine the initial x coordinate and final x, then determine the initial y coordinate and end y coordinates of the image that has been said to be read earlier. It is not always possible to express the needed information with words and.Ĭropping application to OpenCV is very easy we need to determine where the coordinates of the image to be cropped. In Word documents, you may be introducing various terms, thoughts, or data. How to Insert a Line in Microsoft Word Documents shape can also be applied to see if the image is grayscale or color image. Please note that if we read the image in grayscale form, the output will only produce rows and columns. The command will output (680, 850, 2) where 680 is the width, and 850 is the height in pixel size, while 2 is the image channel (RGB), or it means that the image has 680 rows and 850 columns. Shape ) to display the dimensions of our source image. Henceforth, we will use the image above in this paper. Let’s first try reading our image source and displaying it with the functions previously described. As explained earlier in this article, we will learn how to apply resizing, cropping, and rotating techniques to images. Now we can go back to the original topic of basic image manipulation in OpenCV and Python. import cv2įor details on OpenCV Core Image Operations, please read the OpenCV documentation. To write / save images in OpenCV using a function cv2.imwrite()where the first parameter is the name of the new file that we will save and the second parameter is the source of the image itself. import cv2Ĭv2.imshow('Displaying Images', img) Writing / Saving Images Displaying an Imageĭisplaying an image in OpenCV using a function cv2.imshow()where the first parameter is the window name to display the image and the second parameter is the image itself. destroyAllWindows ( ) is to close other windows that are currently open. Whiskey ( 0 ) is to keep the window displaying the image. To read images in OpenCV, use a function cv2.imread()where the first parameter is the image file name complete with its extension. ![]()
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