仅获取图像中的外部轮廓

我有这段代码，可以在图像中绘制轮廓，但我只需要外部轮廓：

import cv2
import numpy as np

camino= "C:/Users/Usuario/Documents/Deteccion de Objetos/123.jpg"
img = cv2.imread("C:/Users/Usuario/Documents/Deteccion de Objetos/123.jpg")

grises= cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

bordes= cv2.Canny(grises, 100, 250)

ctns = cv2.findContours(bordes, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
ctns = ctns[0] if len(ctns)==2 else ctns[1]
for c in ctns:
    cv2.drawContours(img,[c], -1,(0,0,255),2)

print ('Numero de contornos es ', len(ctns))
texto= 'Contornos encontrados ' + str(len(ctns))

cv2.putText(img, texto, (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.7,  
    (255, 0, 0), 1)


cv2.imshow('Bordes', bordes)
cv2.imshow('Imagen', img)
cv2.waitKey(0)
cv2.destroyAllWindows().

This is my original image:

This is the obtained image with the contours:

在本例中，我只需要为每个实体检测 10 个轮廓 1，但它检测到 450 个轮廓。

这是一种使用阈值+形态学操作+轮廓过滤的方法

首先我们转换为灰度，然后转换为二值图像的大津阈值（左），然后使用轮廓区域过滤去除虚线（右）

从这里开始，我们执行接近变形以删除文本，然后反转图像（左）。我们找到轮廓并将所有小于阈值的轮廓填充为黑色（右）

接下来，我们再次反转并使用大矩形内核执行变形打开以删除小边缘和尖峰

最后我们找到轮廓来得到我们的结果

import cv2

image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (5,5), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Remove dotted lines
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area < 5000:
        cv2.drawContours(thresh, [c], -1, (0,0,0), -1)

# Fill contours
close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
close = 255 - cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, close_kernel, iterations=6)
cnts = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area < 15000:
        cv2.drawContours(close, [c], -1, (0,0,0), -1)

# Smooth contours
close = 255 - close
open_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (20,20))
opening = cv2.morphologyEx(close, cv2.MORPH_OPEN, open_kernel, iterations=3)

# Find contours and draw result
cnts = cv2.findContours(opening, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(image, [c], -1, (36,255,12), 3)

cv2.imshow('thresh', thresh)
cv2.imshow('opening', opening)
cv2.imshow('image', image)
cv2.waitKey()