How do I find corners of a paper when there are printed corners/lines on paper itself?

Question

I'm using openCV in Python to find the corners of a sheet of paper to unwarp it.

img = cv2.imread(images[i])

        corners = cv2.goodFeaturesToTrack(cv2.cvtColor(img,cv2.COLOR_BGR2GRAY),4,.01,1000,useHarrisDetector=True,k=.04)
        corners = np.float32(corners)
        print(corners)
        ratio = 1.6
        cardH = math.sqrt((corners[2][0][0] - corners[1][0][0]) * (corners[2][0][0] - corners[1][0][0]) + (corners[2][0][1] - corners[1][0][1]) * (
                    corners[2][0][1] - corners[1][0][1]))
        cardW = ratio * cardH;
        pts2 = np.float32(
            [[corners[0][0][0], corners[0][0][1]], [corners[0][0][0] + cardW, corners[0][0][1]], [corners[0][0][0] + cardW, corners[0][0][1] + cardH],
             [corners[0][0][0], corners[0][0][1] + cardH]])

        M = cv2.getPerspectiveTransform(corners, pts2)

        offsetSize = 500
        transformed = np.zeros((int(cardW + offsetSize), int(cardH + offsetSize)), dtype=np.uint8);
        dst = cv2.warpPerspective(img, M, transformed.shape)

Before: https://i.sstatic.net/NdUqG.jpg

After: https://i.sstatic.net/DkEh6.jpg

As you can see with these images, they're detecting edges inside the paper itself, rather than the corner of the paper. Should I consider using a different algorithm entirely? I'm quite lost.

I've tried increasing the minimum euclidean distance to 1000, but that really didn't do anything.

Please note, this no one's real information, this is a fake dataset found on Kaggle.

The kaggle dataset can be found https://www.kaggle.com/mcvishnu1/fake-w2-us-tax-form-dataset

Answer 1

Here is one way to do that in Python/OpenCV.

Note that the found corners are listed counter-clockwise from the top-most corner.

• Read the input
• Convert to gray
• Gaussian blur
• Otsu threshold
• Morphology open/close to clean up the threshold
• Get largest contour
• Approximate a polygon from the contour
• Get the corners
• Draw the polygon on the input
• Compute side lengths
• Compute output corresponding corners
• Get perspective transformation matrix from corresponding corner points
• Warp the input image according to the matrix
• Save the results

Input:

import cv2
import numpy as np

# read image
img = cv2.imread("efile.jpg")

# convert img to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# blur image
blur = cv2.GaussianBlur(gray, (3,3), 0)

# do otsu threshold on gray image
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

# apply morphology
kernel = np.ones((7,7), np.uint8)
morph = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
morph = cv2.morphologyEx(morph, cv2.MORPH_OPEN, kernel)

# get largest contour
contours = cv2.findContours(morph, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
area_thresh = 0
for c in contours:
    area = cv2.contourArea(c)
    if area > area_thresh:
        area_thresh = area
        big_contour = c

# draw white filled largest contour on black just as a check to see it got the correct region
page = np.zeros_like(img)
cv2.drawContours(page, [big_contour], 0, (255,255,255), -1)

# get perimeter and approximate a polygon
peri = cv2.arcLength(big_contour, True)
corners = cv2.approxPolyDP(big_contour, 0.04 * peri, True)

# draw polygon on input image from detected corners
polygon = img.copy()
cv2.polylines(polygon, [corners], True, (0,0,255), 1, cv2.LINE_AA)
# Alternate: cv2.drawContours(page,[corners],0,(0,0,255),1)

# print the number of found corners and the corner coordinates
# They seem to be listed counter-clockwise from the top most corner
print(len(corners))
print(corners)

# for simplicity get average of top/bottom side widths and average of left/right side heights
# note: probably better to get average of horizontal lengths and of vertical lengths
width = 0.5*( (corners[0][0][0] - corners[1][0][0]) + (corners[3][0][0] - corners[2][0][0]) )
height = 0.5*( (corners[2][0][1] - corners[1][0][1]) + (corners[3][0][1] - corners[0][0][1]) )
width = np.int0(width)
height = np.int0(height)

# reformat input corners to x,y list
icorners = []
for corner in corners:
    pt = [ corner[0][0],corner[0][1] ]
    icorners.append(pt)
icorners = np.float32(icorners)

# get corresponding output corners from width and height
ocorners = [ [width,0], [0,0], [0,height], [width,height] ]
ocorners = np.float32(ocorners)

# get perspective tranformation matrix
M = cv2.getPerspectiveTransform(icorners, ocorners)

# do perspective 
warped = cv2.warpPerspective(img, M, (width, height))

# write results
cv2.imwrite("efile_thresh.jpg", thresh)
cv2.imwrite("efile_morph.jpg", morph)
cv2.imwrite("efile_polygon.jpg", polygon)
cv2.imwrite("efile_warped.jpg", warped)

# display it
cv2.imshow("efile_thresh", thresh)
cv2.imshow("efile_morph", morph)
cv2.imshow("efile_page", page)
cv2.imshow("efile_polygon", polygon)
cv2.imshow("efile_warped", warped)
cv2.waitKey(0)

Thresholded image:

Morphology cleaned image:

Polygon drawn on input:

Extracted Corners (counterclockwise from top right corner)

4

[[[693  67]]
 [[ 23  85]]
 [[ 62 924]]
 [[698 918]]]

Warped Result: