title: cv2.drawContours will not draw filled contour
body: <p>I'm trying to display a filled contour using the <code>cv2.drawContours</code> function in OpenCV.  I've developed a list of contours from an edge image derived from a <code>Canny</code> detection, and am finding the contours with <code>RETR_EXTERNAL</code> enabled for the hierarchy definition. However I'm running into an issue that despite using the <code>-1</code> flag in the <code>cv2.drawContours</code> command to indicate a filled contour, only the contour itself(i.e. edge) is displayed.  For example:</p>
  
  <pre><code>mask = np.zeros(rawimg.shape, np.uint8)
  cv2.drawContours(mask, contours[246], -1, (0,255,255), -1)
  </code></pre>
  
  <p>results in just the outline of contour 246 being displayed.  As I am only retrieving the external contours I don't think I am seeing just the difference between the internal and external contours found at each edge, so I'm a bit confused as to why it is displaying the contour, but not filling as the <code>-1</code> flag would suggest it should.</p>
  
  <hr>
  
  <p><em>EDIT:</em>
  The full code is included below.  The issue is with the line:
      cv2.drawContours(mask, cnt, 2, (0,255,255), -1)
  While this is formatted in the manner suggested by Dan, it results in the following image:</p>
  
  <p><a href="https://i.stack.imgur.com/nl8pa.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/nl8pa.jpg" alt="image linked"></a>.  cnt is a single contour, so this makes sense that it would be referring to a single point in the contour.  When the line is changed to:</p>
  
  <pre><code>cv2.drawContours(mask, cnt, -1, (0,255,255), -1)
  </code></pre>
  
  <p>the contour prints as before, however the contour is still not filled as the -1 flag at the end of the command would suggest it should be.</p>
  
  <p>The test image:</p>
  
  <p><a href="https://i.stack.imgur.com/H0L3L.jpg" rel="noreferrer"><img src="https://i.stack.imgur.com/H0L3L.jpg" alt="is uploaded here"></a></p>
  
  <pre><code>import os
  import cv2
  import numpy as np
  from matplotlib import pyplot as plt
  import copy as cp
  
  path = 'C:\\Users\\...deleted...\\Desktop\\testimage6.jpg'
  
  
  
  #Determine largest contour in the image
  def maxContour(contours):
      cnt_list = np.zeros(len(contours))
      for i in range(0,len(contours)):
          cnt_list[i] = cv2.contourArea(contours[i])
  
      max_value = np.amax(cnt_list)
      max_index = np.argmax(cnt_list)
      cnt = contours[max_index]
  
      return cnt, max_index
  
  
  if os.path.isfile(path):
      # Import the raw image to a working location and save to an isolated     variable
      # Import the raw image to a working location and save to an isolated     variable
      img = cv2.imread(path)
      rawimg = cv2.imread(path)
      saveimg = cv2.imread(path)
      imgray = cv2.cvtColor(saveimg, cv2.COLOR_BGR2GRAY)
      saveimgray = cp.copy(imgray)
  
      f1 = plt.figure(1)
      f1.set_size_inches(8,10)
      plt.title('Original Image')
      plt.xticks([]), plt.yticks([])
      plt.imshow(rawimg, cmap='gray')
      plt.savefig('output1.jpg', dpi=300)
      cv2.imshow('Raw Image',rawimg)
      cv2.waitKey(0)
      cv2.destroyWindow('Raw Image')
  
      # Impose an opening function as a filter
      kernel = np.ones((3,3),np.uint8)    
      opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
  
      f2 = plt.figure(2)
      f1.set_size_inches(8,10)
      plt.title('Opened Image')
      plt.xticks([]), plt.yticks([])
      plt.imshow(opening, cmap='gray')
      plt.savefig('output2.jpg', dpi=300)
      cv2.imshow('Opened Image', opening)
      cv2.waitKey(0)
      cv2.destroyWindow('Opened Image')
  
  
      #Extract the edges from the filtered image
      edges = cv2.Canny(opening,10,100)
      cv2.imshow('Edges', edges)
      cv2.waitKey(0)
      cv2.destroyWindow('Edges')
      f3=plt.figure(3)
      f3.set_size_inches(16,8)
      plt.title('Edge Image')
      plt.xticks([]), plt.yticks([])
      plt.imshow(edges, cmap='gray')
      plt.savefig('output3.jpg', dpi=300)
  
  
      #Detect contours in the edge image
      image, contours, hierarchy = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
      cv2.drawContours(img, contours, -1, (0,255,255), 2)
      cv2.imshow('Contours Image', img)
      cv2.waitKey(0)
      cv2.destroyWindow('Contours Image')
      f4=plt.figure(4)
      f4.set_size_inches(16,8)
      plt.title('Contour Image')
      plt.xticks([]), plt.yticks([])
      plt.imshow(img)
      plt.savefig('output2.jpg', dpi=300)
  
      #Find maximum area contour    
      cnt, max_index = maxContour(contours)
      print(max_index)
  
  
      # Calculate contour-based statistics
      # TBD
  
  
      #Test of removing max contour
      #grayimg = cv2.cvtColor(rawimg, cv2.COLOR_BGR2GRAY)
      mask = np.zeros(rawimg.shape, np.uint8)
      cv2.drawContours(mask, cnt, 2, (0,255,255), -1) 
      #ret, mask = cv2.threshold(grayimg, 10, 255, cv2.THRESH_BINARY)
      mask_inv = cv2.bitwise_not(mask)
      cv2.imshow('Mask Image', mask)
      cv2.waitKey(0)
      cv2.destroyWindow('Mask Image')
      cv2.imshow('Mask Image', mask_inv)
      cv2.waitKey(0)
      cv2.destroyWindow('Mask Image')
  
      #Fit ellipse to contour and calculate ellipse statistics
      (x,y), (w,h), angle = cv2.fitEllipse(cnt)    
      rect = cv2.minAreaRect(cnt)
      box = cv2.boxPoints(rect)
      box = np.int0(box)
      x = np.int0(x)
      y = np.int0(y)
      w = np.int0(0.5*w)
      h = np.int0(0.5*h)
  
  
      #output2 = cv2.ellipse(img, center, dim, angle, 0, 360, (255,0,0), 12)
      output2 = cv2.ellipse(img, (x,y), (w,h), angle, 0, 360, (255,0,0), 2)
      output3 = cv2.drawContours(output2, [box], 0, (0,255,0), 2)
      cv2.imshow('Ellipse Image',output2)
      cv2.waitKey(0)
      cv2.destroyWindow('Ellipse Image')
  
  
  
  else:
      print('file does not exist')`
  </code></pre>