使用OpenCV(Python)改进轮廓检测

Question

我正在尝试从照片中识别卡片.我设法做了我想要的理想照片,但我现在很难应用相同的程序,稍微不同的照明等.所以问题是关于使以下轮廓检测更健壮.

我需要分享我的代码的大部分内容,以便能够制作感兴趣的图像,但我的问题只涉及最后一个块和图像.

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)

然后检测到卡片:

# 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)

观点得到纠正:

#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.

那是我遇到了问题.我想检测形状的轮廓.我发现最好的办法是使用组合bilateralFilter和AdaptativeThreshold灰色图像:

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) 

这非常接近我想要的,但我怎样才能改进它以获得白色的闭合轮廓,以及其他所有的黑色轮廓？

Answer 1

除了右下角的图像之外，以下步骤通常应该有效：

1. 膨胀和侵蚀二元掩模以桥接轮廓片段之间的任何一或两个像素间隙。
2. 使用最大抑制将形状边界上的厚二元蒙版变成薄边缘。
3. 正如流程中之前使用的那样，使用 cvFindcontours 来识别闭合轮廓。可以测试该方法识别的每个轮廓是否闭合。
4. 作为此类问题的通用解决方案，我建议您尝试我的算法来找到给定点周围的闭合轮廓。通过固定检查主动分段