find_rect_and_transform.py 文件源码

def extract_rect(im):
    imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)

    ret,thresh = cv2.threshold(imgray, 127, 255, 0)

    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # finding contour with max area
    largest = None
    for cnt in contours:
        if largest == None or cv2.contourArea(cnt) > cv2.contourArea(largest):
            largest = cnt

    peri = cv2.arcLength(largest, True)
    appr = cv2.approxPolyDP(largest, 0.02 * peri, True)

    #cv2.drawContours(im, appr, -1, (0,255,0), 3)
    points_list = [[i[0][0], i[0][1]] for i in appr] 

    left  = sorted(points_list, key = lambda p: p[0])[0:2]
    right = sorted(points_list, key = lambda p: p[0])[2:4]

    print("l " + str(left))
    print("r " + str(right))

    lu = sorted(left, key = lambda p: p[1])[0]
    ld = sorted(left, key = lambda p: p[1])[1]

    ru = sorted(right, key = lambda p: p[1])[0]
    rd = sorted(right, key = lambda p: p[1])[1]

    print("lu " + str(lu))
    print("ld " + str(ld))
    print("ru " + str(ru))
    print("rd " + str(rd))

    lu_ = [ (lu[0] + ld[0])/2, (lu[1] + ru[1])/2 ]
    ld_ = [ (lu[0] + ld[0])/2, (ld[1] + rd[1])/2 ]
    ru_ = [ (ru[0] + rd[0])/2, (lu[1] + ru[1])/2 ]
    rd_ = [ (ru[0] + rd[0])/2, (ld[1] + rd[1])/2 ]

    print("lu_ " + str(lu_))
    print("ld_ " + str(ld_))
    print("ru_ " + str(ru_))
    print("rd_ " + str(rd_))

    src_pts = np.float32(np.array([lu, ru, rd, ld]))
    dst_pts = np.float32(np.array([lu_, ru_, rd_, ld_]))

    h,w,b = im.shape
    H, mask = cv2.findHomography(src_pts, dst_pts, cv2.RANSAC, 5.0)

    print("H" + str(H))

    imw =  cv2.warpPerspective(im, H, (w, h))

    return imw[lu_[1]:rd_[1], lu_[0]:rd_[0]] # cropping image