Download E-books Practical Python and OpenCV: An Introductory, Example Driven Guide to Image Processing and Computer Vision PDF

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By Adrian Rosebrock

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Are you curious about machine imaginative and prescient and snapshot processing, yet don't understand the place to begin? My new e-book is your assured fast begin consultant to studying the basics of laptop imaginative and prescient and picture processing utilizing Python and OpenCV.

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1 snapshot modifications 6. 1. three Resizing to this point we’ve coated picture variations: translation and rotation. Now, we will discover how you can resize a picture. We’ll additionally outline one final approach for our imutils. py dossier, a comfort approach to aid us resize photographs comfortably. maybe, no longer unusually, we are going to be utilizing the cv2. resize functionality to resize our photos. yet we have to take note of the point ratio of the picture once we are utilizing this functionality. yet ahead of we get too deep into the main points, let’s leap correct into an instance: directory 6. 7: resize. py 1 2 three four import import import import numpy as np argparse imutils cv2 five 6 7 eight nine ap = argparse. ArgumentParser() ap. add_argument("-i", "--image", required = actual, support = "Path to the image") args = vars(ap. parse_args()) 10 eleven 12 photograph = cv2. imread(args["image"]) cv2. imshow("Original", picture) thirteen 14 15 r = one hundred fifty. zero / picture. shape[1] dim = (150, int(image. shape[0] * r)) sixteen 17 18 resized = cv2. resize(image, dim, interpolation = cv2. INTER_AREA) cv2. imshow("Resized (Width)", resized) traces 1-12 may still begin to think relatively redundant at this element. we're uploading our applications, constructing our argument parser, and eventually loading our snapshot and showing forty eight 6. 1 picture ameliorations it. the particular fascinating code doesn’t begin till traces 14 and 15. whilst resizing a picture, we have to take into account the point ratio of the picture. The element ratio is the proportional courting of the width and the peak of the picture. If we don’t remember of the point ratio, our resizing will go back effects that don’t glance right. Computing the element ratio is dealt with online 14. during this line of code we outline our new photo width to be a hundred and fifty pixels. so that it will compute the ratio of the recent top to the previous peak, we easily outline our ratio r to be the recent width (150 pixels) divided by way of the previous width, which we entry utilizing snapshot. shape[1]. Now that we've got our ratio, we will be able to compute the recent dimensions of the picture on-line 15. back, the width of the hot snapshot could be one hundred fifty pixels. the peak is then computed via multiplying the previous peak by means of our ratio and changing it to an integer. the particular resizing of the picture happens online 17. the 1st argument is the picture we want to resize and the second one is our computed dimensions for the hot photograph. final parameter is our interpolation process, that is the set of rules operating behind the curtain to address how the particular snapshot is resized. as a rule, i locate that utilizing cv2. INTER_AREA obtains the easiest effects whilst resizing; even though, different applicable offerings comprise cv2. INTER_LINEAR, cv2. INTER_CUBIC, and cv2. INTER_NEAREST. forty nine 6. 1 picture variations eventually, we express our resized photo on-line 18 within the instance we simply explored, we purely resized the picture via specifying the width. yet what if we needed to resize the picture by means of specifying the peak? All that calls for is a transformation to computing the point ratio: directory 6. eight: resize. py 19 20 r = 50. zero / snapshot. shape[0] dim = (int(image. shape[1] * r), 50) 21 22 23 24 resized = cv2.

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