![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:2000\/1*77qCS1xRajFADWdT50c2dg.jpeg\")
<\/figure>What is OpenCV-Python?<\/h1>\n
OpenCV is an open source computer vision<\/a> and machine learning library. It has 2500+ optimized algorithms\u2014a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. It has many interfaces, including Python, Java, C++, and Matlab.<\/p>\n Here, we\u2019re gonna tackle the Python interface.<\/p>\n Warning 1:<\/strong> On reading images this way via openCV, it isn\u2019t in RGB<\/em><\/strong> colorspace\u2014it\u2019s in BGR<\/em><\/strong>. Sometime this won\u2019t be an issue with you, you\u2019ll only have trouble if you want to add something colored to your image. and go on with your code with Warning 2: <\/strong>To close the window that\u2019s displaying the image, press any button.If you use the close button it may cause freezes<\/strong> (happens to me when I\u2019m on a Jupyter notebook).<\/p>\n For simplicity, throughout this tutorial I\u2019ll be using this method to view images:<\/p><\/blockquote>\n where This resizing function maintains the dimension-ratio of the original image.<\/p>\n More image scaling functions <\/em>here<\/em><\/a>.<\/em><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n Another example:<\/strong><\/p>\n This will turn all shades smaller than 150 to 10 and all greater to 200.<\/p>\n More thresholding functions <\/em>here<\/em><\/a>.<\/em><\/p>\n<\/div>\n<\/div>\n More blurring functions <\/em>here<\/em><\/a>.<\/em><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\nTable of Contents<\/h2>\n
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Installation<\/h1>\n
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Importing an Image & Viewing it<\/h1>\n
import cv2\nimage = cv2.imread(\".\/Path\/To\/Image.extension\")\ncv2.imshow(\"Image\", image)\ncv2.waitKey(0)\ncv2.destroyAllWindows()<\/span><\/pre>\n
\n<\/strong>There are two solutions:<\/p>\n\n
rgb_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)<\/span><\/pre>\nrgb_image<\/code> instead of image<\/code>.<\/em><\/p>\nimport cv2\ndef viewImage(image, name_of_window):\n cv2.namedWindow(name_of_window, cv2.WINDOW_NORMAL)\n cv2.imshow(name_of_window, image)\n cv2.waitKey(0)\n cv2.destroyAllWindows()<\/span><\/pre>\n<\/div>\n<\/div>\n![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:1000\/1*G0I556YQDMfy-sNRZ1qKXw.jpeg\")
<\/figure>Cropping<\/h1>\n
![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:700\/1*JOqAtM92uNHBxJ9uRAyKtg.jpeg\")
<\/figure>![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:700\/1*UuTiqdGQFfvA78djIPwVYA.jpeg\")
<\/figure>import cv2\ncropped = image[10:500, 500:2000]\nviewImage(cropped, \"Doggo after cropping.\")<\/span><\/pre>\nimage[10:500, 500:2000]<\/code> is image[y:y+h, x:x+w]<\/code><\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\nResizing<\/h1>\n
![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:700\/1*AJjT80DpN93YUWTUkOBOJQ.jpeg\")
<\/figure>![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:409\/1*YJzoBwWOtJhkksl3Mpv61w.jpeg\")
<\/figure>import cv2\nscale_percent = 20 # percent of original size\nwidth = int(img.shape[1] * scale_percent \/ 100)\nheight = int(img.shape[0] * scale_percent \/ 100)\ndim = (width, height)<\/span>resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)<\/span>viewImage(resized, \"After resizing with 20%\")<\/span><\/pre>\nRotating<\/h1>\n<\/div>\n<\/div>\n
![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:4928\/1*SVkI8I5XHs18gfS3jrL4Cw.jpeg\")
<\/figure>![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:4928\/1*j2pCsTfv_bF1ZAys8IQTLA.jpeg\")
<\/figure>import cv2\n(h, w, d) = image.shape\ncenter = (w \/\/ 2, h \/\/ 2)\nM = cv2.getRotationMatrix2D(center, 180, 1.0)\nrotated = cv2.warpAffine(image, M, (w, h))\nviewImage(rotated, \"Doggo after rotation by 190 degrees\")<\/span><\/pre>\nimage.shape<\/code> outputs the height, width, and channels. M<\/code> is the rotation matrix\u2014this rotates the image 180 degrees around its center.
\n-ve<\/code> angle rotates the image clockwise & +ve<\/code> angle rotates the image counterclockwise.<\/p>\n<\/div>\n<\/div>\n<\/div>\n\n\n\nGrayscaling and Thresholding (Black & White effect)<\/h1>\n
![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:700\/1*TRn-JK8tlKBCUSgkooKVpg.jpeg\")
<\/figure>import cv2\ngray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nret, threshold_image = cv2.threshold(im, 127, 255, 0)\nviewImage(gray_image, \"Gray-scale doggo\")\nviewImage(threshold_image, \"Black & White doggo\")<\/span><\/pre>\ngray_image<\/code> is the grayscale one-channeled version of the image.
\n<\/em>This threshold<\/code> function will turn all shades darker (smaller) than 127 to 0 and all brighter (greater) to 255.<\/p>\nret, threshold = cv2.threshold(im, 150, 200, 10)<\/span><\/pre>\n![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:2000\/1*lVHmCqfGqT0Fx5DIJnaRzQ.jpeg\")
<\/figure>![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:2000\/1*4p1SghdBZucSg5R2dZvn1Q.jpeg\")
<\/figure>Blurring\/Smoothing<\/h1>\n<\/div>\n<\/div>\n
![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:1920\/1*Rny9ak-Taj58otF4ao9uFA.jpeg\")
<\/figure>![\"\"](\"https:\/\/miro.medium.com\/v2\/resize:fit:1920\/1*WhEeegXBhcAllIRDN4TTvA.jpeg\")
<\/figure>import cv2\nblurred = cv2.GaussianBlur(image, (51, 51), 0)\nviewImage(blurred, \"Blurred doggo\")<\/span><\/pre>\nThe GaussianBlur function takes 3 parameters:<\/h2>\n
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Drawing a Rectangle\/Bounding Box on an Image<\/h1>\n<\/div>\n<\/div>\n