python - opencv无法提取图像中的最大轮廓

Question

考虑这张图片：

我只想提取代表图像中最大轮廓的数字，但opencv总是显示原始图像和小于数字的小轮廓。所以当我运行这个功能

def contouTreat(image):
  gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  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")
  cv2.drawContours(image, cnts, -1, (0, 255, 0), 3)
  #cv2_imshow(image)
  ROI_number = 0
  arr=[]
  v=True 
  for c in cnts:
   area = cv2.contourArea(c)
   if area != image.shape[1]*image.shape[0]:
     x,y,w,h = cv2.boundingRect(c)
     #if minc != x:
     
     x,y,w,h = cv2.boundingRect(c)
     #if area < 800 and area > 200:
     #if area < 1620   and h>58 and w <50:
     #if  h>(70*image.shape[1])/100 and w>(60*image.shape[0])/100 :
     if v:
         ROI = image[y:y+h, x:x+w] 
         print(h)
         print(w)
     cv2_imshow(ROI)                 
 return None     
image=cv2.imread("/content/téléchargement (2).png")   
contouTreat(image)

我得到了这个结果：

标签： pythonpython-3.xopencvcomputer-visionopencv-contour

Answer 1

您正在同一图像上绘制轮廓，因此您将获得具有两个绘制轮廓的更大 ROI。

建议的解决方案：
在填充了零的临时图像上绘制每个轮廓，并从临时图像中裁剪 ROI。

• 创建用零填充的临时图像：

   tmp_im = np.zeros_like(image)
  

• 绘制一个填充有白色的轮廓，并将其用作蒙版：

   cv2.drawContours(tmp_im, [c], 0, (255, 255, 255), cv2.FILLED)  # Draw white contour on black image
   tmp_im = cv2.bitwise_and(image, tmp_im)  # Apply bitwise with `image` - required in case there are black regions inside the contour.
  

• 在轮廓周围画绿线（可能不需要）：

   cv2.drawContours(tmp_im, [c], -1, (0, 255, 0), 3)  # Draw green line around the contour
  

• 裁剪投资回报率：

   ROI = tmp_im[y:y + h, x:x + w]
  

---

完整的代码示例：

import numpy as np
import cv2
from imutils import contours

def contouTreat(image):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    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")
    cv2.drawContours(image, cnts, -1, (0, 255, 0), 3)
    #cv2_imshow(image)
    ROI_number = 0
    arr = []
    v = True 
    for c in cnts:
        area = cv2.contourArea(c)
        if area != image.shape[1] * image.shape[0]:
            x,y,w,h = cv2.boundingRect(c)
               
            if v:
                tmp_im = np.zeros_like(image)
                cv2.drawContours(tmp_im, [c], 0, (255, 255, 255), cv2.FILLED)  # Draw white contour on black image
                tmp_im = cv2.bitwise_and(image, tmp_im)  # Apply bitwise with `image` - required in case there are black regions inside the contour.
                cv2.drawContours(tmp_im, [c], -1, (0, 255, 0), 3)  # Draw green line around the contour

                ROI = tmp_im[y:y + h, x:x + w]

                print(h)
                print(w)

            cv2.imshow('ROI' + str(ROI_number), ROI)
            ROI_number += 1

    return None


image = cv2.imread("telechargement.png")

contouTreat(image)

cv2.waitKey()
cv2.destroyAllWindows()

---

结果：

投资回报率0：

投资回报率1：