Document Scanner OpenCV Python With Source Code

Document Scanner OpenCV Python With Source Code

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Document Scanner OpenCV Python With Source Code

The Document Scanner OpenCV Python was developed using Python OpenCV, the scanner takes a poorly scanned image, finds the corners of the document, applies the perspective transformation to get a top-down view of the document, sharpens the image, and applies an adaptive color threshold to clean up the image.

What is OpenCV?

OpenCV is short for Open Source Computer Vision. Intuitively by the name, it is an open-source Computer Vision and Machine Learning library. This library is capable of processing real-time image and video while also boasting analytical capabilities. It supports the Deep Learning frameworks.

In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.

By the way if you are new to python programming and you don’t know what would be the the Python IDE to use, I have here a list of Best Python IDE for Windows, Linux, Mac OS that will suit for you. I also have here How to Download and Install Latest Version of Python on Windows.

To start executing Document Scanner OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm in your computer.

Document Scanner OpenCV Python With Source Code : Steps on how to run the project

Time needed: 5 minutes

These are the steps on how to run Document Scanner OpenCV Python With Source Code

  • Step 1: Download the given source code below.

    First, download the given source code below and unzip the source code.
    Document Scanner OpenCV download source code

  • Step 2: Import the project to your PyCharm IDE.

    Next, import the source code you’ve download to your PyCharm IDE.
    Document Scanner OpenCV open project

  • Step 3: Run the project.

    last, run the project with the command “py main.py”
    Document Scanner OpenCV run project

Installed Libraries

import numpy as np
import cv2
import imutils

Complete Source Code

import numpy as np
import cv2
import imutils

args_image ="docu.jpg"

image = cv2.imread(args_image)
image=cv2.resize(image,(500,500))
orig = image.copy()
cv2.imshow("Original Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
grayImageBlur = cv2.blur(grayImage,(2,2))
edgedImage = cv2.Canny(grayImageBlur, 100, 300, 3)

cv2.imshow("gray", grayImage)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imshow("grayBlur", grayImageBlur)
cv2.waitKey(0)
cv2.destroyAllWindows()
cv2.imshow("Edge Detected Image", edgedImage)
cv2.waitKey(0)
cv2.destroyAllWindows()

allContours = cv2.findContours(edgedImage.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
allContours = imutils.grab_contours(allContours)
allContours = sorted(allContours, key=cv2.contourArea, reverse=True)[:1]

perimeter = cv2.arcLength(allContours[0], True)
ROIdimensions = cv2.approxPolyDP(allContours[0], 0.02*perimeter, True)
cv2.drawContours(image, [ROIdimensions], -1, (0,255,0), 2)
cv2.imshow("Contour Outline", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
ROIdimensions = ROIdimensions.reshape(4,2)
rect = np.zeros((4,2), dtype="float32")
s = np.sum(ROIdimensions, axis=1)
rect[0] = ROIdimensions[np.argmin(s)]
rect[2] = ROIdimensions[np.argmax(s)]
diff = np.diff(ROIdimensions, axis=1)
rect[1] = ROIdimensions[np.argmin(diff)]
rect[3] = ROIdimensions[np.argmax(diff)]

(tl, tr, br, bl) = rect

widthA = np.sqrt((tl[0] -tr[0])**2 + (tl[1] - tr[1])**2 )
widthB = np.sqrt((bl[0] - br[0])**2 + (bl[1] - br[1])**2 )
maxWidth = max(int(widthA), int(widthB))

heightA = np.sqrt((tl[0] - bl[0])**2 + (tl[1] - bl[1])**2 )
heightB = np.sqrt((tr[0] - br[0])**2 + (tr[1] - br[1])**2 )
maxHeight = max(int(heightA), int(heightB))
dst = np.array([
    [0,0],
    [maxWidth-1, 0],
    [maxWidth-1, maxHeight-1],
    [0, maxHeight-1]], dtype="float32")

transformMatrix = cv2.getPerspectiveTransform(rect, dst)
scan = cv2.warpPerspective(orig, transformMatrix, (maxWidth, maxHeight))
cv2.imshow("Scaned",scan)
cv2.waitKey(0)
cv2.destroyAllWindows()
scanGray = cv2.cvtColor(scan, cv2.COLOR_BGR2GRAY)
cv2.imshow("scanGray", scanGray)
cv2.waitKey(0)
cv2.destroyAllWindows()
from skimage.filters import threshold_local
T = threshold_local(scanGray, 9, offset=8, method="gaussian")
scanBW = (scanGray > T).astype("uint8") * 255
cv2.imshow("scanned", scanBW)
cv2.waitKey(0)
cv2.destroyAllWindows()

Download the Source Code below

Anyway, if you want to level up your programming knowledge, especially Python OpenCV, try this new article I’ve made for you Best OpenCV Projects With Source Code For Beginners .

DOWNLOAD

Summary

This Project With Source Code takes a poorly scanned image, finds the corners of the document, applies the perspective transformation to get a top-down view of the document, sharpens the image, and applies an adaptive color threshold to clean up the image. and this Python project was developed and designed using Python OpenCV, and this Python OpenCV Project also includes a downloadable Python Project With Source Code for free.

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