Question

我正在使用Raspberry Pi Zero，Python 3脚本和OpenCV。我正在尝试创建单个像素连续路径，以在图像中描绘“硬”表面与“软”表面：我要结束的图像是“纯白色背景上的红色线条（在JPG下方）”（叠加在原始图像上只是为了显示其位置）。我希望能够在所需路径的大约10个像素内获得相对平滑的结果：

到目前为止，我已经相信我应该可以使用cv.watershed作为实现此目标的主要功能，但是到目前为止效果还不是很好。为了简化我的OpenCV学习曲线，我修改了watershed.py示例。
在使用分水岭之前，我先进行了150像素半径的高斯模糊处理，这使我获得了迄今为止最好的效果。然后，我放置两个分水岭标记，在图像的每一侧放置一个，并尝试使分水岭定义我想要的边框。

以下是使用分水岭的Python脚本（很抱歉，使用了不正确的代码段，但代码示例插入无效）：

#!/usr/bin/env python

'''
Watershed segmentation
=========
This program demonstrates the watershed segmentation algorithm
in OpenCV: watershed().
Usage
-----
watershed.py [image filename]
Keys
----
  1-7   - switch marker color
  SPACE - update segmentation
  r     - reset
  a     - toggle autoupdate
  ESC   - exit
'''


# Python 2/3 compatibility
from __future__ import print_function

import numpy as np
import cv2 as cv

class Sketcher:
    def __init__(self, windowname, dests, colors_func):
        self.prev_pt = None
        self.windowname = windowname
        self.dests = dests
        self.colors_func = colors_func
        self.dirty = False
        self.show()
        cv.setMouseCallback(self.windowname, self.on_mouse)

    def show(self):
        cv.imshow(self.windowname, self.dests[0])

    def on_mouse(self, event, x, y, flags, param):
        pt = (x, y)
        if event == cv.EVENT_LBUTTONDOWN:
            self.prev_pt = pt
        elif event == cv.EVENT_LBUTTONUP:
            self.prev_pt = None

        if self.prev_pt and flags & cv.EVENT_FLAG_LBUTTON:
            for dst, color in zip(self.dests, self.colors_func()):
                cv.line(dst, self.prev_pt, pt, color, 5)
            self.dirty = True
            self.prev_pt = pt
            self.show()


class App:
    def __init__(self, fn):
        self.img = cv.imread(fn)
        if self.img is None:
            raise Exception('Failed to load image file: %s' % fn)

        h, w = self.img.shape[:2]
        self.markers = np.zeros((h, w), np.int32)
        self.markers_vis = self.img.copy()
        self.cur_marker = 1
        self.colors = np.int32( list(np.ndindex(2, 2, 2)) ) * 255

        self.auto_update = True
        self.sketch = Sketcher('img', [self.markers_vis, self.markers], self.get_colors)

    def get_colors(self):
        return list(map(int, self.colors[self.cur_marker])), self.cur_marker

    def watershed(self):
        m = self.markers.copy()
        cv.watershed(self.img, m)
        overlay = self.colors[np.maximum(m, 0)]
        oim = cv.imread('1.JPG')
        vis = cv.addWeighted(oim, 0.5, overlay, 0.5, 0.0, dtype=cv.CV_8UC3)
        cv.imshow('watershed', vis)

    def run(self):
        while cv.getWindowProperty('img', 0) != -1 or cv.getWindowProperty('watershed', 0) != -1:
            ch = cv.waitKey(50)
            if ch == 27:
                break
            if ch >= ord('1') and ch <= ord('7'):
                self.cur_marker = ch - ord('0')
                print('marker: ', self.cur_marker)
            if ch == ord(' ') or (self.sketch.dirty and self.auto_update):
                self.watershed()
                self.sketch.dirty = False
            if ch in [ord('a'), ord('A')]:
                self.auto_update = not self.auto_update
                print('auto_update if', ['off', 'on'][self.auto_update])
            if ch in [ord('r'), ord('R')]:
                self.markers[:] = 0
                self.markers_vis[:] = self.img
                self.sketch.show()
        cv.destroyAllWindows()


if __name__ == '__main__':
    import sys
    try:
        fn = sys.argv[1]
    except:
        fn = '1-gb150.JPG'
    print(__doc__)
    App(fn).run()

这是到目前为止我所见过的最好的分水岭：