使用OpenCV(Python)改善轮廓检测

问题描述

我正在尝试从照片中识别卡片.我设法在理想的照片上完成了我想做的事情，但是现在很难在光照稍有不同的情况下应用相同的过程，等等.因此，问题在于如何使以下轮廓检测更加健壮.

I am trying to identify cards from a photo. I managed to do what I wanted on ideal photos, but I am now having hard time applying the same procedure with slightly different lighting, etc. So the question is about making the following contour detection more robust.

我需要共享大部分代码，以使拍摄者能够制作出感兴趣的图像，但是我的问题仅与最后一块和图像有关.

I need to share a big part of my code for the takers to be able to make the images of interest, but my question relates only to the last block and image.

import numpy as np
import cv2
from matplotlib import pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
import math

img = cv2.imread('image.png')
img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
plt.imshow(img)

然后检测到卡:

# Prepocess
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray,(1,1),1000)
flag, thresh = cv2.threshold(blur, 120, 255, cv2.THRESH_BINARY)
# Find contours
contours, hierarchy = cv2.findContours(thresh,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea,reverse=True)
# Select long perimeters only
perimeters = [cv2.arcLength(contours[i],True) for i in range(len(contours))]
listindex=[i for i in range(15) if perimeters[i]>perimeters[0]/2]
numcards=len(listindex)
# Show image
imgcont = img.copy()
[cv2.drawContours(imgcont, [contours[i]], 0, (0,255,0), 5) for i in listindex]
plt.imshow(imgcont)

视角已纠正:

#plt.rcParams['figure.figsize'] = (3.0, 3.0)
warp = range(numcards)
for i in range(numcards):
    card = contours[i]
    peri = cv2.arcLength(card,True)
    approx = cv2.approxPolyDP(card,0.02*peri,True)
    rect = cv2.minAreaRect(contours[i])
    r = cv2.cv.BoxPoints(rect)

    h = np.array([ [0,0],[399,0],[399,399],[0,399] ],np.float32)
    approx = np.array([item for sublist in approx for item in sublist],np.float32)
    transform = cv2.getPerspectiveTransform(approx,h)
    warp[i] = cv2.warpPerspective(img,transform,(400,400))

# Show perspective correction
fig = plt.figure(1, (10,10))
grid = ImageGrid(fig, 111, # similar to subplot(111)
                nrows_ncols = (4, 4), # creates 2x2 grid of axes
                axes_pad=0.1, # pad between axes in inch.
                aspect=True, # do not force aspect='equal'
                )

for i in range(numcards):
    grid[i].imshow(warp[i]) # The AxesGrid object work as a list of axes.

那是我遇到的问题.我想检测形状的轮廓.我发现的最佳方法是在灰色图像上使用bilateralFilter和AdaptativeThreshold的组合:

That were I am having my problem. I want to detect the contour of the shapes. The best way I found is using a combination of bilateralFilter and AdaptativeThreshold on a gray image:

fig = plt.figure(1, (10,10))
grid = ImageGrid(fig, 111, # similar to subplot(111)
                nrows_ncols = (4, 4), # creates 2x2 grid of axes
                axes_pad=0.1, # pad between axes in inch.
                aspect=True, # do not force aspect='equal'
                )
for i in range(numcards):
    image2 = cv2.bilateralFilter(warp[i].copy(),10,100,100)
    grey = cv2.cvtColor(image2,cv2.COLOR_BGR2GRAY)
    grey2 = cv2.cv.AdaptiveThreshold(cv2.cv.fromarray(grey), cv2.cv.fromarray(grey), 255, cv2.cv.CV_ADAPTIVE_THRESH_MEAN_C, cv2.cv.CV_THRESH_BINARY, blockSize=31, param1=6)
    grid[i].imshow(grey,cmap=plt.cm.binary)

这与我想要的非常接近，但但是我该如何改善它以得到白色的闭合轮廓，以及其他所有黑色的轮廓?

This is very close to what I would like, but how can I improve it to get closed contours in white, and everything else in black?

推荐答案

为什么不仅仅使用Canny并在找到轮廓之后应用透视校正(因为它似乎会使边缘模糊)?例如，使用您在问题中提供的小图像(较大的结果可能会更好):

Why not just use Canny and apply perspective correction after finding the contours (because it seems to blur the edges)? For example, using the small image you provided in your question (the result could be better on a bigger one):

基于代码的某些部分:

import numpy as np
import cv2

import math

img = cv2.imread('image.bmp')

# Prepocess
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
flag, thresh = cv2.threshold(gray, 120, 255, cv2.THRESH_BINARY)

# Find contours
img2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)

# Select long perimeters only
perimeters = [cv2.arcLength(contours[i],True) for i in range(len(contours))]
listindex=[i for i in range(15) if perimeters[i]>perimeters[0]/2]
numcards=len(listindex)

card_number = -1 #just so happened that this is the worst case
stencil = np.zeros(img.shape).astype(img.dtype)
cv2.drawContours(stencil, [contours[listindex[card_number]]], 0, (255, 255, 255), cv2.FILLED)
res = cv2.bitwise_and(img, stencil)
cv2.imwrite("out.bmp", res)
canny = cv2.Canny(res, 100, 200)
cv2.imwrite("canny.bmp", canny)

首先，为简单起见，删除除单张卡以外的所有东西，然后应用Canny边缘检测器:

First, remove everything except a single card for simplicity, then apply Canny edge detector:

然后，您可以扩张/腐蚀，纠正透视图，移除最大的轮廓等.

Then you can dilate/erode, correct perspective, remove the largest contour etc.

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