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https://github.com/sefakcmn00/documentation-scanner-opencv
It is the documentation scanner project of our Computer Vision work with Opencv. In this project, documents shown to the computer camera were scanned using Opencv artificial intelligence libraries. Below I tried to explain the details of the project step by step.
https://github.com/sefakcmn00/documentation-scanner-opencv
computervision documantation opencv opencv-python python3 scanner
Last synced: about 1 month ago
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It is the documentation scanner project of our Computer Vision work with Opencv. In this project, documents shown to the computer camera were scanned using Opencv artificial intelligence libraries. Below I tried to explain the details of the project step by step.
- Host: GitHub
- URL: https://github.com/sefakcmn00/documentation-scanner-opencv
- Owner: sefakcmn00
- Created: 2022-08-03T14:48:54.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-08-07T13:31:21.000Z (over 2 years ago)
- Last Synced: 2023-03-07T13:22:47.999Z (almost 2 years ago)
- Topics: computervision, documantation, opencv, opencv-python, python3, scanner
- Language: Python
- Homepage:
- Size: 487 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Documentation-Scanner-Opencv
-----------------------------------------------
It is the documentation scanner project of our Computer Vision work with Opencv. In this project, documents shown to the computer camera were scanned using Opencv artificial intelligence libraries. Below I tried to explain the details of the project step by step.
## Documantation-Sacnner-Opencv
First, we will download the libraries we will use.
```Python
import cv2
import numpy as np
import utlis
```
### Camera Options
```Python
webCamFeed = True
cap = cv2.VideoCapture(1)
cap.set(10, 160)
heightImg = 640
widthImg = 480
```
We add the necessary lines of code to start our webcam in the operations here.
If you do not want to operate with the webcam, you can also scan any document image into the program. (pathImage = "image name".
We have defined #camera screen widths above.
```Python
utlis.initializeTrackbars()
count = 0
```
```Python
while True:
if webCamFeed:
success, img = cap.read()
else:
img = cv2.imread(pathImage)
img = cv2.resize(img, (widthImg, heightImg)) #GÖRÜNTÜYÜ YENİDEN BOYUTLANDIRMA AMACIYLA EKLEDİĞİMİZ KOD.
imgBlank = np.zeros((heightImg, widthImg, 3), np.uint8) # GEREKİRSE HATA AYIKLAMANIN TEST EDİLMESİ İÇİN BOŞ BİR GÖRÜNTÜ OLUŞTURMAK AMACIYLA EKLEDİĞİMİZ KOD.
imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # GÖRÜNTÜYÜ GRİ ÖLÇEKLE DÖNÜŞTÜRMEK AMACIYLA GİRİLEN KOD.
imgBlur = cv2.GaussianBlur(imgGray, (5, 5), 1) # GAUSS BLUR EKLEMEK AMACIYLA GİRİLEN KOD.
thres = utlis.valTrackbars() # EŞİKLER İÇİN İZLEME ÇUBUĞU DEĞERLERİNİ ALMAK AMACIYLA GİRİLEN KOD.
imgThreshold = cv2.Canny(imgBlur, thres[0], thres[1]) # CANNY BLUR UYGULAYIN
kernel = np.ones((5, 5))
imgDial = cv2.dilate(imgThreshold, kernel, iterations=2) # GENİŞLETME UYGULAMASI.
imgThreshold = cv2.erode(imgDial, kernel, iterations=1) # EROZYON UYGULAMASI.
```
```Python
#################################### CONTOUR İŞLEMLERİ #################################################################
## TÜM CONTOUR'LARI BULMAK AMACIYLA AŞAĞIDAKİ KOD SATIRLARINI EKLİYORUZ.
###
#CONTOUR Nedir? Konturlar, aynı renk veya yoğunluğa sahip tüm sürekli noktaları (sınır boyunca) birleştiren bir eğri olarak basitçe açıklanabilir.
#Konturlar, şekil analizi ve nesne algılama ve tanıma için kullanışlı bir araçtır.
###
imgContours = img.copy() # GÖRÜNTÜ AMAÇLI KOPYALAMA
imgBigContour = img.copy() # GÖRÜNTÜ AMAÇLI KOPYALAMA
contours, hierarchy = cv2.findContours(imgThreshold, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE) # TÜM KONTURLARI BUL
cv2.drawContours(imgContours, contours, -1, (0, 255, 0), 10) # TESPİT EDİLEN TÜM KONTURLARI ÇİZİM
```
```Python
# EN BÜYÜK COUNTOUR'U BULMA
biggest, maxArea = utlis.biggestContour(contours)
if biggest.size != 0:
biggest = utlis.reorder(biggest)
cv2.drawContours(imgBigContour, biggest, -1, (0, 255, 0), 20) # EN BÜYÜK KONTUR
imgBigContour = utlis.drawRectangle(imgBigContour, biggest, 2)
pts1 = np.float32(biggest) # ÇÖZGÜ İÇİN NOKTALARI HAZIRLA
pts2 = np.float32([[0, 0], [widthImg, 0], [0, heightImg], [widthImg, heightImg]]) # ÇÖZGÜ İÇİN NOKTALARI HAZIRLA
matrix = cv2.getPerspectiveTransform(pts1, pts2)
imgWarpColored = cv2.warpPerspective(img, matrix, (widthImg, heightImg)) #warp perspektif yani görünütü çarpıtma işlemleri
# HER TARAFTAN 20 PİKSEL FORMUNU KALDIR
imgWarpColored = imgWarpColored[20:imgWarpColored.shape[0] - 20, 20:imgWarpColored.shape[1] - 20]
imgWarpColored = cv2.resize(imgWarpColored, (widthImg, heightImg))
# UYARLANABİLİR EŞİK UYGULAMA
imgWarpGray = cv2.cvtColor(imgWarpColored, cv2.COLOR_BGR2GRAY)
imgAdaptiveThre = cv2.adaptiveThreshold(imgWarpGray, 255, 1, 1, 7, 2)
imgAdaptiveThre = cv2.bitwise_not(imgAdaptiveThre)
imgAdaptiveThre = cv2.medianBlur(imgAdaptiveThre, 3)
# Görüntüleme için Görüntü Dizisi
imageArray = ([img, imgGray, imgThreshold, imgContours],
[imgBigContour, imgWarpColored, imgWarpGray, imgAdaptiveThre])
else:
imageArray = ([img, imgGray, imgThreshold, imgContours],
[imgBlank, imgBlank, imgBlank, imgBlank])
# EKRAN ETİKETLERİ
lables = [["Orijinal", "Gri", "Esik", "Contours"],
["En buyuk Contour", "Warp Perspektif", "Warp Gri", "Uyarlanabilir Esik"]]
stackedImage = utlis.stackImages(imageArray, 0.75, lables)
cv2.imshow("Program", stackedImage)
```
# Program Output
