仅获取图像中的外部轮廓

时间:2019-11-18 17:39:20

标签: python image opencv image-processing contour

我有这段代码,可以在图像中绘制轮廓,但是我只需要外部轮廓即可。

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().

这是我的原始图片: original image

这是获得的轮廓图像: the obtained image with the contours

在这种情况下,我只需要为每个实体检测10个轮廓1,但它可以检测450个轮廓。

2 个答案:

答案 0 :(得分:1)

这是一种使用阈值处理+形态学操作+轮廓过滤的方法

首先,我们将其转换为灰度,然后将Otsu的阈值转换为二进制图像(左),然后使用轮廓区域过滤去除虚线(右)

从这里开始,执行morph close操作以删除文本,然后反转图像(左)。我们找到轮廓并填充所有小于黑色阈值的轮廓(右)

接下来,我们再次反转并使用大矩形核执行变形打开操作,以去除小的边缘和尖峰

最后我们找到轮廓以得到结果

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()

答案 1 :(得分:0)

您可以尝试结合一些变形运算符进行泛洪填充。