修正粗糙边缘

Question

从一个高度(约130英尺)点击的另一个图像中提取的图像.现在,当提取这个较小的图像时,它包含一个实际上具有非常规则和平滑形状的对象,具有非常粗糙的边缘.现在我想检测不.角落,物体有(不使用轮廓).但由于这些粗糙的边缘,没有.检测到的角落大大增加.

以下是示例图片:

如何使边缘笔直？

Answer 1

我认为你正在寻找的是一个简单的边缘平滑算法.我为你实现了一个.它不会将彩色标志保存在外形内 - 如果这也很重要 - 因为你没有在问题中提到过 - 你必须自己想出那个部分.结果:

我已经实现了跟踪条,因此您可以使用平滑的值,但它适合您.按"c"确认您选择的值.

import cv2
import numpy as np


def empty_function(*arg):
    pass


def SmootherEdgesTrackbar(img, win_name):
    trackbar_name = win_name + "Trackbar"

    cv2.namedWindow(win_name, cv2.WINDOW_NORMAL)
    cv2.resizeWindow(win_name, 1000, 500)
    cv2.createTrackbar("first_blur", win_name, 3, 255, empty_function)
    cv2.createTrackbar("second_blur", win_name, 3, 255, empty_function)
    cv2.createTrackbar("threshold", win_name, 0, 255, empty_function)

    while True:
        first_blur_pos = cv2.getTrackbarPos("first_blur", win_name)
        second_blur_pos = cv2.getTrackbarPos("second_blur", win_name)
        thresh_pos = cv2.getTrackbarPos("threshold", win_name)
        if first_blur_pos < 3:
            first_blur_pos = 3
        if second_blur_pos < 3:
            second_blur_pos = 3
        img_res = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        img_res = smoother_edges(img_res, (first_blur_pos * 2 + 1, first_blur_pos * 2 + 1),
                                 (second_blur_pos * 2 + 1, second_blur_pos * 2 + 1))
        _, img_res = cv2.threshold(img_res, thresh_pos, 255, 0)
        cv2.imshow(win_name, img_res)

        key = cv2.waitKey(1) & 0xFF
        if key == ord("c"):
            break

    cv2.destroyAllWindows()
    return img_res


def unsharp_mask(img, blur_size, imgWeight, gaussianWeight):
    gaussian = cv2.GaussianBlur(img, blur_size, 0)
    return cv2.addWeighted(img, imgWeight, gaussian, gaussianWeight, 0)


def smoother_edges(img, first_blur_size, second_blur_size=(5, 5),
                   imgWeight=1.5, gaussianWeight=-0.5):
    # blur the image before unsharp masking
    img = cv2.GaussianBlur(img, first_blur_size, 0)
    # perform unsharp masking
    return unsharp_mask(img, second_blur_size, imgWeight, gaussianWeight)


# read the image
img = cv2.imread("sample.jpg")
# smoothen edges
img = SmootherEdgesTrackbar(img, "Smoother Edges Trackbar")
# show and save image
cv2.imshow("img", img)
cv2.imwrite("result.png", img)
cv2.waitKey(0)

编辑: 找出适合您的值后,只需删除轨迹栏功能并执行固定值的步骤.算法如下:

convert to gray
blur
unsharp mask
threshold 

在smoother_edges()函数中组合了2个中间步骤.