Generally, images have three colors channels Red, Green, and Blue (RGB) which produces the colors in the pixel. Before diving into this, first let’s learn how colors in images are represented. Something’s strange about this figure above. image_path = 'dog2.JPG' def plt_display(image, title): fig = plt.figure() a = fig.add_subplot(1, 1, 1) imgplot = plt.imshow(image) a.set_title(title) image = cv2.imread(image_path) plt_display(image, 'cv2-BGR') %tensorflow_version 1.x import cv2 import matplotlib.pyplot as plt import numpy as np from skimage import io import tensorflow as tf We’ll import a few essential libraries in python. We’ll start with opening a simple JPEG image of a dog with two frameworks Tensorflow 1.x and OpenCV. Please note that I am an employee of Intel and all information and opinions shown in the blog are my own and don’t represent those of my employer. Also, thanks to Tejas Pandey for the help in achieving consistency between OpenCV and Tensorflow. It will also show a way to make them work consistently. This blog will discuss one such use case in detail where OpenCV and Tensorflow show the differences in reading and resizing a JPEG image. It can take up multiple days to figure out what went wrong and could delay the project extensively. The blog “How Tensorflow’s tf.image.resize stole 60 days of my life” ( ) is a perfect example of this type of situation. Therefore, CV solutions developed in one framework may not work as expected in the other framework. In today’s rapid development of frameworks, every framework has its own way of handling images, each with its own specifications. For this, one needs to use a framework to open those images to do some processing on them. Modern Computer Vision (CV) is currently a hot field of research which involves largely working with images. rotatingImg = cv2.A dive into the differences in JPEG image read and resizing with OpenCV, Tensorflow and Pillow and also on how to make them consistent. 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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