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1. #CREATE TEXT SCANNER SWIFT FULL WIKIPEDIA DESCRIPTION#
2. #CREATE TEXT SCANNER SWIFT UPGRADE IMUTILSNEXT UP#

UITextField.textColor: UIColor // get or set the color of the text on the field. UITextField.attributedText: NSAttributedString // get or set the attributed text the field displays. UITextField.text: String // get or set the text the field displays.

OpenCV based source code to scan documents from images exactly like mobile apps such as Adobe Scan, Microsoft Lens, CamScanner. Here is the syntax for the Java Scanner. After you import the Java Scanner class, you can start to use it to collect user input.

Create Text Scanner Swift Full Wikipedia Description

Implementing QR Code Reader Programmatically. The full Wikipedia description is here. Java.Whenever you need to perform a 4 point perspective transform, you should be using this module.The other key feature of QR Codes is that instead of requiring a chunky hand-held scanner to scan them, many modern cell phones can scan them.

Create Text Scanner Swift Upgrade ImutilsNext Up

To install imutils , simply: $ pip install -upgrade imutilsNext up, let’s import the threshold_local function from scikit-image. You can read more about imutils in my this post. # import the necessary packagesFrom pyimagesearch.transform import four_point_transformFrom skimage.filters import threshold_local# construct the argument parser and parse the argumentsAp.add_argument("-i", "-image", required = True,Help = "Path to the image to be scanned")Lines 2-7 handle importing the necessary Python packages that we’ll need.We’ll start by importing our four_point_transform function which I discussed last week.We’ll also be using the imutils module, which contains convenience functions for resizing, rotating, and cropping images. However, we can create a Command line Tool in Xcode and use the readLine() function to take input from users.Open up your favorite Python IDE, (I like Sublime Text 2), create a new file, name it scan.py , and let’s get started. In Swift, we cannot directly take input from the Xcode playground. And you guessed it, we’ll be using it to build our very own document scanner.Swift Basic Input.

On the left we have the original image and on the right we have the edges detected in the image.On the left you can see my receipt from Whole Foods. Let’s take a look: # load the image and compute the ratio of the old height# to the new height, clone it, and resize itImage = imutils.resize(image, height = 500)# convert the image to grayscale, blur it, and find edgesGray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)# show the original image and the edge detected imageFirst, we load our image off disk on Line 17.In order to speedup image processing, as well as make our edge detection step more accurate, we resize our scanned image to have a height of 500 pixels on Lines 17-20.We also take special care to keep track of the ratio of the original height of the image to the new height ( Line 18) — this will allow us to perform the scan on the original image rather than the resized image.From there, we convert the image from RGB to grayscale on Line 24, perform Gaussian blurring to remove high frequency noise (aiding in contour detection in Step 2), and perform Canny edge detection on Line 26.The output of Step 1 is then shown on Lines 30 and 31.Take a look below at the example document: Figure 1: The first step of building a document scanning app. Step 1: Edge DetectionThe first step to building our document scanner app using OpenCV is to perform edge detection. We’ll need only a single switch image, -image , which is the path to the image that contains the document we want to scan.Now that we have the path to our image, we can move on to Step 1: Edge Detection. Lastly, we’ll use NumPy for numerical processing, argparse for parsing command line arguments, and cv2 for our OpenCV bindings.Lines 10-13 handle parsing our command line arguments. This post has been updated to make use of threshold_local.

Certainly this is not a “scan” of any means. Furthermore, there is also my desk in the image. It is definitely not a 90-degree, top-down view of the page.

And it’s also safe to assume (or at least should be) that the piece of paper has four edges.And that’s exactly what the code below does: # find the contours in the edged image, keeping only the# largest ones, and initialize the screen contourCnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)Cnts = sorted(cnts, key = cv2.contourArea, reverse = True)Approx = cv2.approxPolyDP(c, 0.02 * peri, True)# if our approximated contour has four points, then we# can assume that we have found our screen# show the contour (outline) of the piece of paperCv2.drawContours(image, , -1, (0, 255, 0), 2)We start off by finding the contours in our edged image on Line 37. Step 2: Finding ContoursContour detection doesn’t have to be hard.In fact, when building a document scanner, you actually have a serious advantage…Take a second to consider what we’re actually building.A document scanner simply scans in a piece of paper.A piece of paper is assumed to be a rectangle.Therefore, we can create a simple heuristic to help us build our document scanner.The heuristic goes something like this: we’ll assume that the largest contour in the image with exactly four points is our piece of paper to be scanned.This is also a reasonably safe assumption — the scanner app simply assumes that the document you want to scan is the main focus of our image. We can clearly see the outline of the receipt.Let’s move on to Step 2.

Step 3: Apply a Perspective Transform & ThresholdThe last step in building a mobile document scanner is to take the four points representing the outline of the document and apply a perspective transform to obtain a top-down, “birds eye view” of the image.Let’s take a look: # apply the four point transform to obtain a top-downWarped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio)# convert the warped image to grayscale, then threshold it# to give it that 'black and white' paper effectWarped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)T = threshold_local(warped, 11, offset = 10, method = "gaussian")Warped = (warped > T).astype("uint8") * 255Print("STEP 3: Apply perspective transform")Cv2.imshow("Original", imutils.resize(orig, height = 650))Cv2.imshow("Scanned", imutils.resize(warped, height = 650))Line 62 performs the warping transformation. The scanner app will assume that (1) the document to be scanned is the main focus of the image and (2) the document is rectangular, and thus will have four distinct edges.From there, Lines 55 and 56 display the contours of the document we went to scan.And now let’s take a look at our example image: Figure 2: The second step of building a document scanning app is to utilize the edges in the image to find the contours of the piece of paper.As you can see, we have successfully utilized the edge detected image to find the contour (outline) of the document, illustrated by the green rectangle surrounding my receipt.Lastly, let’s move on to Step 3, which will be a snap using my four_point_transform function. This allows us to only examine the largest of the contours, discarding the rest.We then start looping over the contours on Line 42 and approximate the number of points on Line 44 and 45.If the approximated contour has four points ( Line 49), we assume that we have found the document in the image.And again, this is a fairly safe assumption.

More ExamplesThe receipt example was all well and good. And on the right, we have the scanned image!Notice how the perspective of the scanned image has changed — we have a top-down, 90-degree view of the image.And thanks to our adaptive thresholding, we also have a nice, clean black and white feel to the document as well.We have successfully built our document scanner!All in less than 5 minutes and under 75 lines of code (most of which are comments anyway). The original image is on the left and the scanned image on the right.On the left we have the original image we loaded off disk. Python + OpenCV document scanning resultsAnd speaking of output, take a look at our example document by running the script: $ python scan.py -image images/receipt.jpgFigure 3: Applying step 3 of our document scanner, perspective transform. Again, you can read more about this function in last week’s post.We’ll pass two arguments into four_point_transform : the first is our original image we loaded off disk ( not the resized one), and the second argument is the contour representing the document, multiplied by the resized ratio.So, you may be wondering, why are we multiplying by the resized ratio?We multiply by the resized ratio because we performed edge detection and found contours on the resized image of height=500 pixels.However, we want to perform the scan on the original image, not the resized image, thus we multiply the contour points by the resized ratio.To obtain the black and white feel to the image, we then take the warped image, convert it to grayscale and apply adaptive thresholding on Lines 66-68.Finally, we display our output on Lines 72-74.