从曲率上下阈值检测点/特征 - 曲线四边形形状检测算法

Question

是否有可能从一组沿具有粗曲率的线的点创建一个多边形，以便在两个曲率值之间选择这些点？

我正在尝试使用 python 的 opencv 包 (cv2) 从给定图像中检索近似的曲线四边形。

例如： 给定边缘检测后的图像，如下所示：

在使用 cv2.findContours 找到轮廓后，如下所示：

(旁注：如果这实际上会给出一个方形而不是绕线，那就太好了 - 还需要一种算法来关闭该图像右侧形状中的间隙。膨胀/erosion 可能会起作用，但可能会删除某些可能希望保留的功能。)

之后，我们可以像这样在轮廓上使用 polyDPApprox：

但是，这与曲率无关 - 它只是通过使用与线条的最大偏差来进行近似。如果我们想省略一些精细的细节（想法是这些可能来自错误）并保留曲率较小的点（宽的形状） - 我们可以使用函数来提供这样的东西吗？：

(红色填充只是表示形状将被封闭成曲线四边形。)

相关问题： Is it possible in OpenCV to plot local curvature as a heat-map representing an object's "pointiness"?

这是用于分析输入图像以防万一有人需要的函数：

# 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，转灰度，大津阈值
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. 绘制轮廓。找到蒙版的外部轮廓并在图像上绘制

---

输入图片->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()