如何仅从图像中提取外部轮廓(OpenCV) [英] How to extract only outer contours from an image (OpenCV)

问题描述

我正在尝试使用简单的 OpenCV 等高线方法从下面的图像中提取数字，但是我在等高线上重叠了边界框

cv2.RETR_EXTERNAL 应该仅返回层次结构中的外部轮廓，但是从下面的输出中可以看出，它不起作用

代码:

从matplotlib中的

 导入pyplot作为plt导入cv2img = cv2.imread('image.png'，0)_，轮廓，_ = cv2.findContours(img，cv2.RETR_EXTERNAL，cv2.CHAIN_APPROX_SIMPLE)imgRGB = cv2.cvtColor(img.copy()，cv2.COLOR_GRAY2RGB)对于轮廓中的c:x，y，w，h = cv2.boundingRect(c)cv2.rectangle(imgRGB，(x，y)，(x + w，y + h)，(0,255,0)，2)无花果= plt.figure()斧= fig.add_subplot(111)ax.imshow(imgRGB，cmap ='gray') 

要求:

  opencv-python == 3.4.5.20matplotlib == 3.1.2 

解决方案

在查找轮廓之前，您需要先模糊然后应用阈值.您需要执行此操作，因为如果直接在灰度图像上找到轮廓，则会有细小颗粒被拾取为轮廓.这是一个简单的过程:

• 加载图像，灰度，高斯模糊，大津的阈值
• 找到轮廓并使用

  提取/保存的投资回报率

  代码

    import cv2从imutils导入轮廓图片= cv2.imread('1.png')原始= image.copy()灰色= cv2.cvtColor(图像，cv2.COLOR_BGR2GRAY)模糊= cv2.GaussianBlur(灰色，(3,3)，0)阈值= cv2.threshold(灰色，0，255，cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]cnts = cv2.findContours(阈值，cv2.RETR_EXTERNAL，cv2.CHAIN_APPROX_SIMPLE)如果len(cnts)== 2，则cnts = cnts [0]否则，cnts [1](cnts，_)= outlines.sort_contours(cnts，method ="left-to-right")num = 0对于C中的cnts:x，y，w，h = cv2.boundingRect(c)cv2.rectangle(image，(x，y)，(x + w，y + h)，(36,255,12)，1)投资报酬率=原始[y:y + h，x:x + w]cv2.imwrite('ROI _ {}.png'.format(num)，ROI)num + = 1cv2.imshow('image'，image)cv2.waitKey() 

  I am trying to extract digits from the below image using simple OpenCV contours approach, but I am getting overlapping bounding boxes over contours

  cv2.RETR_EXTERNAL should return only outer contours in the hierarchy but it’s not working as can be seen from the below output

  Code:

  from matplotlib import pyplot as plt
  import cv2
  
  img = cv2.imread('image.png', 0)
  
  _, contours, _ = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
  
  imgRGB = cv2.cvtColor(img.copy(), cv2.COLOR_GRAY2RGB)
  
  for c in contours:
      x,y,w,h = cv2.boundingRect(c)
      cv2.rectangle(imgRGB, (x, y), (x+w, y+h), (0,255,0), 2)
  
  fig = plt.figure()
  ax = fig.add_subplot(111)
  ax.imshow(imgRGB, cmap='gray')
  

  Requirements:

  opencv-python==3.4.5.20
  matplotlib==3.1.2
  

  解决方案

  You need to blur then apply a threshold before finding contours. You need to do this because if you find contours directly on the grayscale image, there are tiny particles which are picked up as contours. Here's a simple process:

  • Load image, grayscale, Gaussian blur, Otsu's threshold
  • Find contours and sort using imutils.contours.sort_contours() with the left-to-right parameter
  • Obtain bounding box then extract ROI using Numpy slicing

  ---

  Here's the detected bounding boxes highlighted in green

  Extracted/saved ROIs

  Code

  import cv2
  from imutils import contours
  
  image = cv2.imread('1.png')
  original = image.copy()
  gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
  blur = cv2.GaussianBlur(gray, (3,3), 0)
  thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
  
  cnts = cv2.findContours(thresh, 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")
  num = 0
  for c in cnts:
      x,y,w,h = cv2.boundingRect(c)
      cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 1)
      ROI = original[y:y+h, x:x+w]
      cv2.imwrite('ROI_{}.png'.format(num), ROI)
      num += 1
  
  cv2.imshow('image', image)
  cv2.waitKey()
  

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