我尝试使用OpenCV中的cv2.drawContours函数显示填充轮廓。我从Canny检测得到的边缘图像中开发了一个轮廓列表，并在为层次定义启用RETR_EXTERNAL的情况下查找轮廓。然而，我遇到了一个问题，尽管在cv2.drawContours命令中使用-1标志来指示填充轮廓，仅显示轮廓本身（即边缘）。例如：

mask = np.zeros(rawimg.shape, np.uint8)
cv2.drawContours(mask, contours[246], -1, (0,255,255), -1)

由于我只检索外部轮廓，我不认为我看到的只是在每个边缘处找到的内部和外部轮廓之间的差异，所以我有点困惑，为什么它显示轮廓，但不像-1标志所建议的那样填充。

• EDIT：* 完整的代码包含在下面。问题是与行：cv2.drawContours（mask，cnt，2，（0，255，255），-1）虽然按照Dan建议的方式进行了格式化，但会生成以下图像：

x1c 0d1x. cnt是一个单一的轮廓线，因此这意味着它将引用轮廓线中的一个单一点。

cv2.drawContours(mask, cnt, -1, (0,255,255), -1)

轮廓像以前一样打印，但是轮廓仍然没有被填充，因为命令末尾的-1标志表明它应该被填充。
测试图像：

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')`