如何检测图像中的单独图形？ [英] How to detect separate figures in an image?

问题描述

我有一个类似于以下的图像。我想将两个数字 7 和 4 分开，因为我想要一个边框

如何我可以用OpenCV做到吗？我不知道如何执行此操作，并在考虑是否可以通过使用Sobel运算符进行某些操作。

  s = cv2.Sobel（img，cv2.CV_64F，1,0 ，ksize = 5）
  

，但不知道如何从此处继续。

解决方案

细分并检测图像中的图形，主要思想如下：

1. 使用
   
   
   

   检测到的轮廓

   
   
   
   
   
   

   裁剪并保存的投资回报率

   
   
   
   
   
   

   输出

    检测到的轮廓：2 
     

   代码

    将numpy导入为np 
    import cv2 
    from imutils导入轮廓
    
   ＃加载图像，灰度，高斯模糊，Canny边缘检测
    image = cv2.imread（ 1.png）
    original = image.copy（）
    grey = cv2。 cvtColor（图像，cv2.COLOR_BGR2GRAY）
   模糊= cv2.GaussianBlur（灰色，（3,3），0）
    canny = cv2.Canny（模糊，120，255，1）
    
   ＃查找轮廓
    outline_list = [] 
    ROI_number = 0 
    cnts = cv2.findContours（canny，cv2.RETR_EXTERNAL，cv2.CHAIN_APPROX_SIMPLE）
    cnts = cnts [ 0]如果len（cnts）== 2 else cnts [1] 
    cnts，_ = outlines.sort_contours（cnts，method = left-to-right）
    for c in cnts：
   ＃获取每个轮廓的边界矩形
   x，y，w，h = cv2.boundingRect（c）
    
   ＃查找轮廓的ROI 
    roi = image [y： y + h，x：x + w] 
    
   ＃绘制边界框矩形，使用Numpy切片
   裁剪cv2.rectangle（image，（x，y），（x + w，y + h），（0,255,0），3）
    ROI =原始[y：y + h， x：x + w] 
    cv2.imwrite（'ROI _ {}。png'.format（ROI_number），ROI）
    outline_list.append（c）
    ROI_number + = 1 
    
    print（'检测到轮廓：{}'。format（len（contour_list）））
    cv2.imshow（ image，image）
    cv2.imshow（ canny，canny ）
    cv2.waitKey（）
     

   
   

   I have an image similar to the following. I want to separate two numbers 7 and 4 as shown in the image, in that I want to have a bounding box for each of these two objects.

   How could I do this with OpenCV? I have no idea, how could I do this and was thinking if there is some way by using Sobel operator. The only thing that I tired was getting the Sobel.

   s = cv2.Sobel(img, cv2.CV_64F,1,0,ksize=5)
   

   but have no idea on how to proceed from here.

   解决方案

   To segment and detect figures in an image, the main idea is as follows:

   1. Convert image into grayscale using cv2.cvtColor
   2. Blur image with cv2.GaussianBlur
   3. Find edges with cv2.Canny
   4. Find contours with cv2.findContours and sort from left-to-right using imutils.contours.sort_contours() to ensure that when we iterate through contours, they are in the correct order
   5. Iterate through each contour
      • Obtain bounding rectangle using cv2.boundingRect
      • Find ROI of each contour with Numpy slicing
      • Draw bounding box rectangle using cv2.rectangle

   ---

   Canny Edge Detection

   Detected Contours

   Cropped and saved ROIs

   Output

   Contours Detected: 2
   

   Code

   import numpy as np
   import cv2
   from imutils import contours
   
   # Load image, grayscale, Gaussian blur, Canny edge detection
   image = cv2.imread("1.png")
   original = image.copy()
   gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
   blurred = cv2.GaussianBlur(gray, (3,3), 0)
   canny = cv2.Canny(blurred, 120, 255, 1)
   
   # Find contours
   contour_list = []
   ROI_number = 0
   cnts = cv2.findContours(canny, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
   cnts = cnts[0] if len(cnts) == 2 else cnts[1]
   cnts, _ = contours.sort_contours(cnts, method="left-to-right")
   for c in cnts:
       # Obtain bounding rectangle for each contour
       x,y,w,h = cv2.boundingRect(c)
   
       # Find ROI of the contour
       roi = image[y:y+h, x:x+w]
   
       # Draw bounding box rectangle, crop using Numpy slicing
       cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),3)
       ROI = original[y:y+h, x:x+w]
       cv2.imwrite('ROI_{}.png'.format(ROI_number), ROI)
       contour_list.append(c)
       ROI_number += 1
   
   print('Contours Detected: {}'.format(len(contour_list)))
   cv2.imshow("image", image) 
   cv2.imshow("canny", canny)
   cv2.waitKey()
   

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