CODEWITHSUNDEEP
opencvimage-processingedge-detection
Sashank
Sashank

Reputation: 600

How to detect document from a picture in opencv?

I am trying to design an app similar to camscanner. For that, I have to take an image and then find the document in that. I started off with the code described here - http://opencvpython.blogspot.in/2012/06/sudoku-solver-part-2.html

I found the contours and the rectangular contour with max area should be the required document. For every contour, I am finding an approximate closed PolyDP. Of all the polyDP of size 4, the one with max area should be the required document. However, this method is not working.

The input image for the process is this input Image

I tried to print the contour with max area and this resulted in this (Contour inside letter 'C') enter image description here

Code:

img = cv2.imread('bounce.jpeg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray,(5,5),0) 
thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)
_, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

def biggestRectangle(contours):
    biggest = None
    max_area = 0
    indexReturn = -1
    for index in range(len(contours)):
            i = contours[index]
            area = cv2.contourArea(i)
            if area > 100:
                    peri = cv2.arcLength(i,True)
                    approx = cv2.approxPolyDP(i,0.1*peri,True)
                    if area > max_area: #and len(approx)==4:
                            biggest = approx
                            max_area = area
                            indexReturn = index
    return indexReturn

indexReturn = biggestRectangle(contours)
cv2.imwrite('hola.png',cv2.drawContours(img, contours, indexReturn, (0,255,0)))

What is going wrong in this? Is there any other method by which I can capture the document in this picture?

Upvotes: 2

Views: 8897

Answers (2)

monic
monic

Reputation: 126

Try this : output image

import cv2
import numpy as np

img = cv2.imread('bounce.jpg')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
invGamma = 1.0 / 0.3
table = np.array([((i / 255.0) ** invGamma) * 255
for i in np.arange(0, 256)]).astype("uint8")

# apply gamma correction using the lookup table
gray = cv2.LUT(gray, table)

ret,thresh1 = cv2.threshold(gray,80,255,cv2.THRESH_BINARY)

#thresh = cv2.adaptiveThreshold(gray,255,1,1,11,2)
_, contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

def biggestRectangle(contours):
    biggest = None
    max_area = 0
    indexReturn = -1
    for index in range(len(contours)):
            i = contours[index]
            area = cv2.contourArea(i)
            if area > 100:
                peri = cv2.arcLength(i,True)
                approx = cv2.approxPolyDP(i,0.1*peri,True)
                if area > max_area: #and len(approx)==4:
                        biggest = approx
                        max_area = area
                        indexReturn = index
    return indexReturn

indexReturn = biggestRectangle(contours)
hull = cv2.convexHull(contours[indexReturn])
cv2.imwrite('hola.png',cv2.drawContours(img, [hull], 0, (0,255,0),3))
#cv2.imwrite('hola.png',thresh1)

Upvotes: 11

Bennik2000
Bennik2000

Reputation: 1152

I would do it like this:

  1. Do preprocessing like blur / canny

  2. Extract all lines from the image using the hough line transform (open cv doc).

  3. Use the 4 strongest lines

  4. Try to construct the contour of the document using the four lines

Right now I do not have an OpenCV installed so I cannot try this approach but maybe it leads you in the right directon.

Upvotes: 3

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