是否存在类似于OpenCV findContours的功能，该功能可检测曲线并用样条线替换点? [英] Is there a function similar to OpenCV findContours that detects curves and replaces points with a spline?

问题描述

我正在尝试拍摄以下图像，描绘出白色形状，然后将生成的路径导出为pdf.我的问题是，findContours似乎只能沿着形状的边缘找到点.是否存在类似于findContours的解决方案，该解决方案可以检测形状中的曲线，并在有曲线的地方用样条线替换其点?如果我使用scipy.interpolate，它将忽略直线并将整个轮廓变成一个大的弯曲形状，这也不是一件好事.我需要可以同时做这两种事情的东西.

I am trying to take the below image, trace the white shape, and export the resulting path to pdf. The problem I have is that findContours seeming only finds points along the edge of the shape. Is there a solution out there, similar to findContours, that detects curves in a shape and replaces its points with a spline wherever there is a curve? If I use scipy.interpolate it ignores straight lines and turns the entire contour into one big curved shape, which is no good either. I need something that does both things.

import numpy as np
import cv2
from scipy.interpolate import splprep, splev
from pyx import *
import matplotlib.pyplot as plt

#read in image file                                                             
original = cv2.imread('test.jpg')

#blur the image to smooth edges                                                 
im = cv2.medianBlur(original,5)

#threshold the image                                                            
imgray = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
ret,thresh = cv2.threshold(imgray,170,255,cv2.THRESH_BINARY)                                                             

#findContours                                                                   
im2, contours, hierarchy = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_\
APPROX_SIMPLE)

#drawContours
cv2.drawContours(original, [approx], -1, (0,255,0), 3)                          
cv2.imshow("Imageee", original)                                                 
cv2.waitKey(0)

推荐答案

除了使用带有标志cv2.CHAIN_APPROX_SIMPLE的cv2.findContours逼近轮廓外，我们可以手动进行.

Except using cv2.findContours with flag cv2.CHAIN_APPROX_SIMPLE to approx the contours, we can do it manually.

1. 使用带有标志cv2.CHAIN_APPROX_NONE的cv2.findContours查找轮廓.
2. 使用cv2.arcLength计算轮廓长度.
3. 使用cv2.approxPoolyDP和epsilon = eps * arclen手动逼近轮廓.

1. use cv2.findContours with flag cv2.CHAIN_APPROX_NONE to find contours.
2. use cv2.arcLength to calculate the contour length.
3. use cv2.approxPoolyDP to approx the contour manually with epsilon = eps * arclen.

---

这是eps=0.005时的结果之一:

---

Here is one of the results when eps=0.005:

更多结果:

#!/usr/bin/python3
# 2018.01.04 13:01:24 CST
# 2018.01.04 14:42:58 CST

import cv2
import numpy as np
import os
img = cv2.imread("test.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret,threshed = cv2.threshold(gray,170,255,cv2.THRESH_BINARY)

# find contours without approx
cnts = cv2.findContours(threshed,cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)[-2]

# get the max-area contour
cnt = sorted(cnts, key=cv2.contourArea)[-1]

# calc arclentgh
arclen = cv2.arcLength(cnt, True)

# do approx
eps = 0.0005
epsilon = arclen * eps
approx = cv2.approxPolyDP(cnt, epsilon, True)

# draw the result
canvas = img.copy()
for pt in approx:
    cv2.circle(canvas, (pt[0][0], pt[0][1]), 7, (0,255,0), -1)

cv2.drawContours(canvas, [approx], -1, (0,0,255), 2, cv2.LINE_AA)

# save
cv2.imwrite("result.png", canvas)

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