从曲率的上下阈值检测点/特征 - 曲线四边形检测算法

Question

是否可以沿着具有粗糙曲率的线从一组点创建多边形,以便在两个曲率值之间选择点？

我试图使用python的opencv包(cv2)从给定的图像中检索近似的曲线四边形形状.

例如:给出边缘检测后的图像,例如:

并在使用cv2.findContours找到等高线之后:

(旁注:如果这实际上会给出方形形状而不是围绕直线会很好 - 这个图像在其右侧的形状中的间隙关闭的算法也是必需的.扩张/侵蚀可能会起作用但是会可能会摆脱可能需要保留的某些功能.)

之后,我们可以在轮廓上使用polyDPApprox,如下所示:

然而,这不是曲率依赖的 - 它只是通过使用线的最大偏差来近似.如果我们想省略一些精细的细节(这个想法可能是因为错误)并保持曲率较小的点(宽的形状) - 我们可以使用函数来提供这样的东西吗？:

(红色填充表示形状将被封闭为曲线四边形.)

相关问题: 在OpenCV中是否可以将局部曲率绘制为表示对象"点"的热图？

以下是用于分析输入图像的函数,以防任何人想要它:

# input binary image
def find_feature_points(img):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    cv2.namedWindow('img', WINDOW_NORMAL)
    cv2.imshow("img", gray)
    cv2.waitKey(0)

    contours, hierarchy = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # Draw contours to image
    print contours
    copy = img.copy()
    # img - Image.
    # pts - Array of polygonal curves.
    # npts - Array of polygon vertex counters.
    # ncontours - Number of curves.
    # isClosed - Flag indicating whether the drawn polylines are closed or not. If they are closed, the function draws a line from the last vertex of each curve to its first vertex.
    # color - Polyline color.
    # thickness - Thickness of the polyline edges.
    # lineType - Type of the line segments. See the line() description.
    # shift - Number of fractional bits in the vertex coordinates.
    cv2.polylines(img=copy, pts=contours, isClosed=1,  color=(0,0,255), thickness=3)

    cv2.namedWindow('contour', WINDOW_NORMAL)
    cv2.imshow("contour", copy)
    cv2.waitKey(0)

    # Find approximation to contours
    approx_conts = []
    for c in contours:
        curve = c
        epsilon = 200
        closed = True
        approx_conts.append(cv2.approxPolyDP(curve, epsilon, closed))

    # draw them
    cv2.drawContours(img, approx_conts, -1, (0, 255, 0), 3)
    cv2.namedWindow('approx', WINDOW_NORMAL)
    cv2.imshow("approx", img)
    cv2.waitKey(0)
    return 

Answer 1

这是一个可能的解决方案。这个想法是：

1. 获取二值图像。加载图像，转换为灰度，以及Otsu的阈值
2. 找到凸包。确定对象的周围周长并将其绘制到蒙版上
3. 执行形态学操作。使用 morph close 填充小孔以连接轮廓
4. 画出轮廓。找到掩模的外部轮廓并绘制到图像上

输入图像->结果

代码

import cv2
import numpy as np

# Load image, convert to grayscale, threshold
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Find convex hull and draw onto a mask
mask = np.zeros(image.shape, dtype=np.uint8)
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cnt = cnts[0]
hull = cv2.convexHull(cnt,returnPoints = False)
defects = cv2.convexityDefects(cnt,hull)
for i in range(defects.shape[0]):
    s,e,f,d = defects[i,0]
    start = tuple(cnt[s][0])
    end = tuple(cnt[e][0])
    far = tuple(cnt[f][0])
    cv2.line(mask,start,end,[255,255,255],3)

# Morph close to fill small holes
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (3,3))
close = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=2)

# Draw outline around input image
close = cv2.cvtColor(close, cv2.COLOR_BGR2GRAY)
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
cv2.drawContours(image,cnts,0,(36,255,12),1)

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