We can have different values for the second and the third argument.Īfter we find the contours, we are sorting them, by the area they cover in a reversed order, which means, we will get the biggest contour first, which we are doing by pointing to the first element in the list ( ).Īfter we have the biggest contour, we are drawing the contour by calling the function drawContours which has arguments like the image that we want to draw on, the contour, the color (in BGR format (it’s RGB format, but it is different in O penCV)), and the width of the line.Īfter we have done this, the result is the image below. The first one is the image, the second is the second is contour retrieval mode and the third is the contour approximation method. In findContours function we have three arguments. With the code above, we are finding the contours of the image. I = sorted (cnts, key =cv2.contourArea, reverse = True )Ĭv2.drawContours(img1,- 1 ,( 0, 255, 0 ), 3 ) Image 7: Morphology, erosion and dilation on imageĬv2.findContours(py(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE) On the image bellow, we can see how our images look. With the code above we are transforming the images from images with three channels of color to images with one channel of color, or simply we are making them black and white images. Gray5 = cv2.cvtColor(img5,cv2.COLOR_BGR2GRAY) Gray4 = cv2.cvtColor(img4,cv2.COLOR_BGR2GRAY) Gra圓 = cv2.cvtColor(img3,cv2.COLOR_BGR2GRAY) Gray2 = cv2.cvtColor(img2,cv2.COLOR_BGR2GRAY) Gray1 = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY) With the code above, we are importing the OpenCV library, and we are reading five pictures that contain barcode. We are going to use the O penCV library that contains different tools and algorithms that can help you do image processing. In the next lines of code, we are going to explain you, how can you do barcode detection in a image. By using some simple techniques so you can understand it in a short time, and then you can try it yourselves. In this post we are going to show you, how can you do a barcode detection in an image using OpenCV. Well, most of us know that stuff is not just movie scenes, and that by using the right image processing techniques you can recognize whatever objects you want in an image. In action movies, we often see scenes where some of the actors is detected moving around in the city by doing facial recognition using video surveillance from public cameras. One image is worth a thousand words, it is what people tend to say often.
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