在图像中找到矩形，最好与滑雪图像

Question

我有一个Image从相机（我的VW T5范的内部）获得。我想知道如何准确地重现形状。我使用lensfun和gimp纠正镜头变形。我想通过利用矩形特征来量化剩余的扭曲。我可以使用match_template检测矩形，但我不知道如何检测矩形。

最后我想标记它们并测量宽度和长度。为了更好地显示中心，我试图找到这些中心，但这并不像我想象的那么简单，因为一个简单的阈值可以多次计算相同的点。我如何找到所有检测到的矩形的中心？

这可以通过skimage完成吗？或者我应该使用opencv？

import numpy as np 
import matplotlib.pyplot as plt 
from scipy import ndimage as ndi 
from scipy.ndimage.filters import maximum_filter 
from scipy.ndimage import center_of_mass 
from skimage.color import rgb2grey 
from skimage.feature import match_template 
from skimage import feature, io 
from skimage import img_as_float 
%matplotlib inline 

image = io.imread('../lensfun_cut.JPG') 
imgray = rgb2grey(image) 

    template = imgray[1365:1390,445:470] 
    result = match_template(imgray, template) 

    # 
    hit = np.where(result>0.90) 
    fig, ax = plt.subplots(ncols=1,figsize=(8,8)) 
    ax.imshow(result,cmap=plt.cm.spectral) 

    # loop over the detected regions and draw a circe around them 
    for i in range(len(hit[0])): 
     rect = plt.Circle((hit[1][i],hit[0][i]), 20,linewidth=1, edgecolor='r', facecolor='none') 
     ax.add_patch(rect) 

Answer 1

我OpenCV的解决方案快到手了，对不起。

• 我建议你使用一个自适应阈值从底色正常区段矩形具有不同的照明
• drawcontours可以绘制轮廓
• 我给你的轮廓特征清单，并定义一个链接，这可能会帮助您选择合适的矩形
• 我正在使用一些轮廓特征来选择适当的斑点，您可能需要对此进行微调。

结果看起来是这样：

这里是代码：

import cv2 
import numpy as np 
from matplotlib import pyplot as plt 

img = cv2.imread('lensfun_cut.jpg',0) 

# Adaptive threshold to detect rectangles independent from background illumination 
th3 = cv2.adaptiveThreshold(img,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,31,5) 

# Detect contours 
_, contours, hierarchy = cv2.findContours(th3.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) 

# Draw contours 
h, w = th3.shape[:2] 
vis = np.zeros((h, w, 3), np.uint8) 
cv2.drawContours(vis, contours, -1, (128,255,255), 3) 

# Print Features of each contour and select some contours 
contours2=[] 
for i, cnt in enumerate(contours): 
    cnt=contours[i] 
    M = cv2.moments(cnt) 

    if M['m00'] != 0: 
     # for definition of features cf http://docs.opencv.org/3.1.0/d1/d32/tutorial_py_contour_properties.html#gsc.tab=0 
     cx = int(M['m10']/M['m00']) 
     cy = int(M['m01']/M['m00']) 
     area = cv2.contourArea(cnt) 
     x,y,w,h = cv2.boundingRect(cnt) 
     aspect_ratio = float(w)/h 
     rect_area = w*h 
     extent = float(area)/rect_area   

     print i, cx, cy, area, aspect_ratio, rect_area, extent 

     if area < 80 and area > 10: 
      contours2.append(cnt) 

#Draw selected contours 
h, w = th3.shape[:2] 
vis2 = np.zeros((h, w, 3), np.uint8) 
cv2.drawContours(vis2, contours2, -1, (128,255,255), 3) 

titles = ['Original Image', 'Adaptive Gaussian Thresholding', 'Contours', 'Selected Contours'] 
images = [img, th3, vis, vis2] 
for i in xrange(4): 
    plt.subplot(2,2,i+1),plt.imshow(images[i],'gray') 
    plt.title(titles[i]) 
    plt.xticks([]),plt.yticks([]) 
plt.show()