How to find the document edges in various coloured backgrounds using opencv python? [Document Scanning in various backgrounds]

Question

I am currently have a document that needs to be smart scanned.

For that, I need to find proper contours of the document in any background so that I can do a warped perspective projection and detection with that image.

The main issue faced while doing this is that the document edge detects any kind of background.

I have tried to use the function HoughLineP and tried to find contours on the grayscale blurred image passed through canny edge detection until now.

            MORPH = 9
            CANNY = 84
            HOUGH = 25

            IM_HEIGHT, IM_WIDTH, _ = rescaled_image.shape

            # convert the image to grayscale and blur it slightly
            gray = cv2.cvtColor(rescaled_image, cv2.COLOR_BGR2GRAY)
            gray = cv2.GaussianBlur(gray, (7,7), 0)

            #dilate helps to remove potential holes between edge segments
            kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(MORPH,MORPH))
            dilated = cv2.dilate(gray, kernel)

            # find edges and mark them in the output map using the Canny algorithm
            edged = cv2.Canny(dilated, 0, CANNY)
            test_corners = self.get_corners(edged)

            approx_contours = []

    (_, cnts, hierarchy) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
            cnts = sorted(cnts, key=cv2.contourArea, reverse=True)[:5]

            # loop over the contours
            for c in cnts:
                # approximate the contour
                approx = cv2.approxPolyDP(c, 80, True)
                if self.is_valid_contour(approx, IM_WIDTH, IM_HEIGHT):
                    approx_contours.append(approx)
                    break

How to find a proper bounding box around the document via OpenCV code. Any help will be much appreciated. (The document is taken from the camera in any angle and any coloured background.)

Answer 1

Following code might help you to detect/segment the page in the image...

import cv2
import matplotlib.pyplot as plt
import numpy as np
image = cv2.imread('test_p.jpg')
image = cv2.imread('test_p.jpg')
print(image.shape)
ori = image.copy()
image = cv2.resize(image, (image.shape[1]//10,image.shape[0]//10))

Resized the image to make the operations more faster so that we can work on realtime..

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (11,11), 0)
edged = cv2.Canny(gray, 75, 200)
print("STEP 1: Edge Detection")
plt.imshow(edged)
plt.show()
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cnts = sorted(cnts[1], key = cv2.contourArea, reverse = True)[:5]

Here we will consider only first 5 contours from the sorted list based on area Here the size of the gaussian blur is bit sensitive, so chose it accordingly based on the image size. After the above operations image may look like..

for c in cnts:
    ### Approximating the contour
    #Calculates a contour perimeter or a curve length
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.01 * peri, True)
    # if our approximated contour has four points, then we
    # can assume that we have found our screen
    screenCnt = approx
    if len(approx) == 4:
        screenCnt = approx
        break
    # show the contour (outline) 
    print("STEP 2: Finding Boundary")
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
image_e = cv2.resize(image,(image.shape[1],image.shape[0]))
cv2.imwrite('image_edge.jpg',image_e)
plt.imshow(image_e)
plt.show()

Final Image may look like...

Rest of the things may be handled after getting the final image...

Code Reference :- Git Repository

I guess this answer would be helpful...