Stabilization of white paper inside image. It will reduce perspective distortion also. For all questions pls write me on email: loguntsov@gmail.com

stab.py

import cv, cv2, numpy as np
import sys, math as m
import glob, os

def get_new(old):
    new = np.ones(old.shape, np.uint8)
    cv2.bitwise_not(new,new)
    return new

def remove(contour, n):
    if len(contour) <= n:
        return contour

    c = np.array([])
    s = -1
    for i, val in enumerate(contour):
        c0 = np.copy(contour)
        c0 = np.delete(c0, i, axis = 0)
        s0 = m.fabs(cv2.contourArea(c0))
        if s0 > s:
            s = s0
            c = c0
            print c, s

    return remove(c,n)

def resize(file):
    cv2.namedWindow('result', cv2.WINDOW_NORMAL)
    cv2.resizeWindow('result', 2000, 1000)


    orig = cv2.imread(file)
    cv2.imshow('result', orig)

    # these constants are carefully picked
    MORPH = 10
    CANNY = 150
    HOUGH = 15
    HX = 1620
    HY = 1080


    gray = cv2.cvtColor(orig, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (5,5), 0)

    img = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY)[1]

    cv2.imshow('result', img)
    #cv2.waitKey(0)

    # this is to recognize white on white
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(MORPH,MORPH))
    dilated = cv2.dilate(img, kernel, iterations=1)
    cv2.imshow('result', dilated)

    edges = cv2.Canny(dilated, 0.4*CANNY, CANNY, apertureSize=3)
    cv2.imshow('result', edges)

    lines = cv2.HoughLinesP(edges, 1,  3.14/180, HOUGH)
    for line in lines[0]:
         cv2.line(edges, (line[0], line[1]), (line[2], line[3]),
                         (255,255,255), 2, 8)

    # finding contours
    contours, _ = cv2.findContours(edges.copy(), cv.CV_RETR_EXTERNAL,
                                   cv.CV_CHAIN_APPROX_TC89_KCOS)
    contours = filter(lambda cont: cv2.arcLength(cont, True) > 4000, contours)
    #contours = filter(lambda cont: m.fabs(cv2.contourArea(cont)) > 50000, contours)

    # simplify contours down to polygons
    rects = []
    new1 = get_new(img)
    # simplify contours down to polygons
    rects = []
    for cont in contours:
        rect = cv2.approxPolyDP(cont, 200, True).copy().reshape(-1, 2)
        rects.append(rect)

    c = cv2.convexHull(np.array([x for sublist in rects for x in sublist]))

    # that's basically it

    # show only contours
    new = get_new(img)


    for point in c :
        print( point )

    # new1 = cv2.preCornerDetect(new, 5)

    # cv2.GaussianBlur(new, (9,9), 0, new)
    # new = cv2.Canny(new, 0, CANNY, apertureSize=3)

    p = remove(c, 4)
    extBot = p[0][0]
    extRight = p[1][0]
    extLeft = p[2][0]
    extTop = p[3][0]

    cv2.circle(new, tuple(extLeft), 10, (0, 0, 0), -1)
    cv2.circle(new, tuple(extRight), 10, (0, 0, 0), -1)
    cv2.circle(new, tuple(extTop), 10, (0, 0, 0), -1)
    cv2.circle(new, tuple(extBot), 10, (0, 0, 0), -1)

    cv2.drawContours(orig, [p],-1,(255,255,255),1)
    cv2.drawContours(new, [p],-1,(0,255,0),1)

    pts1 = np.float32([extLeft,extTop,extRight, extBot])

    pts2 = np.float32([[0,0],[HX,0],[0,HY],[HX,HY]])

    M = cv2.getPerspectiveTransform(pts1,pts2)

    dst = cv2.warpPerspective(orig,M,(HX,HY))


    #cv2.waitKey(0)

    #cv2.waitKey(0)

    #cv2.waitKey(0)
    cv2.imshow('result', new)
    #cv2.waitKey(0)
    cv2.imshow('result', dst)
    #cv2.waitKey(0)

    cv2.destroyAllWindows()
    cv2.imwrite("result/" + os.path.basename(file), dst)



for file in glob.glob("./*.jpg"):
    resize(file)