Python opencv：添加填充/放大轮廓

Question

我正在使用findContours（）函数获取图像的轮廓，然后成功提取所需的轮廓。

问题是ROI，在这种情况下，我想从图像中提取ID卡，但是提取的ROI是卡的内部，我看不到卡的边缘和角落。我想从图片中获得完整的卡片，添加一些“填充”可以解决该问题，但找不到解决方法：

我提取轮廓的部分：

for c in cnts:

  print('c')
  print(c)

  peri = cv2.arcLength(c, True)
  approx = cv2.approxPolyDP(c, 0.015 * peri, True)

  if len(approx) == 4:
      screenCnt = approx
      break

我如何获得完整的卡？

当前结果是：

我想要这样的东西：

完整代码：

# USAGE
# python find_screen.py --query queries/query_marowak.jpg

# import the necessary packages
from skimage import exposure
import numpy as np
import argparse
import imutils
import cv2
import sys

# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-q", "--query", required = True,
    help = "Path to the query image")
args = vars(ap.parse_args())

# load the query image, compute the ratio of the old height
# to the new height, clone it, and resize it
image = cv2.imread(args["query"])
ratio = image.shape[0] / 300.0
orig = image.copy()
image = imutils.resize(image, height = 300)

# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray, 11, 17, 17)
edged = cv2.Canny(gray, 30, 200)

cv2.imshow("gray", gray)
cv2.imshow("edged", edged)
cv2.waitKey(0)

# find contours in the edged image, keep only the largest
# ones, and initialize our screen contour
cnts = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:10]
screenCnt = None

# loop over our contours
for c in cnts:

    print('c')
    print(c)

    # approximate the contour
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.015 * peri, True)

    # if our approximated contour has four points, then
    # we can assume that we have found our screen
    if len(approx) == 4:
        screenCnt = approx

        break

# now that we have our screen contour, we need to determine
# the top-left, top-right, bottom-right, and bottom-left
# points so that we can later warp the image -- we'll start
# by reshaping our contour to be our finals and initializing
# our output rectangle in top-left, top-right, bottom-right,

# and bottom-left order
pts = screenCnt.reshape(4, 2)
rect = np.zeros((4, 2), dtype = "float32")

print('rect')
print(rect)

# the top-left point has the smallest sum whereas the
# bottom-right has the largest sum
s = pts.sum(axis = 1)
rect[0] = pts[np.argmin(s)]
rect[2] = pts[np.argmax(s)]

# compute the difference between the points -- the top-right
# will have the minumum difference and the bottom-left will
# have the maximum difference
diff = np.diff(pts, axis = 1)
rect[1] = pts[np.argmin(diff)] 
rect[3] = pts[np.argmax(diff)]

print('rect 2')
print(rect[0])
print(rect[1])
print(rect[2])
print(rect[3])

# multiply the rectangle by the original ratio
rect *= ratio

print('rect 2')
print(rect)

# now that we have our rectangle of points, let's compute
# the width of our new image
(tl, tr, br, bl) = rect

print(tl[0])

widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))

# ...and now for the height of our new image
heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))

# take the maximum of the width and height values to reach
# our final dimensions
maxWidth = max(int(widthA), int(widthB))
maxHeight = max(int(heightA), int(heightB))

print('maxWidth')
print(maxWidth)
print('maxHeight')
print(maxHeight)

# construct our destination points which will be used to
# map the screen to a top-down, "birds eye" view
dst = np.array([
    [0, 0],
    [maxWidth - 1, 0],
    [maxWidth - 1, maxHeight - 1],
    [0, maxHeight - 1]], dtype = "float32")

# calculate the perspective transform matrix and warp
# the perspective to grab the screen
M = cv2.getPerspectiveTransform(rect, dst)
warp = cv2.warpPerspective(orig, M, (maxWidth, maxHeight))

# convert the warped image to grayscale and then adjust
# the intensity of the pixels to have minimum and maximum
# values of 0 and 255, respectively
warp = cv2.cvtColor(warp, cv2.COLOR_BGR2GRAY)
warp = exposure.rescale_intensity(warp, out_range = (0, 255))

# show our images
cv2.imshow("image", image)
cv2.imshow("edge", edged)
cv2.imshow("warp", imutils.resize(warp, height = 300))
cv2.waitKey(0)

原始图片：