首页 > 解决方案 > 将锯齿状边缘近似为线

问题描述

我正在尝试为墨迹上的角找到准确的位置,如下所示:

我的想法是将线条拟合到边缘,然后找到它们相交的位置。截至目前,我已经尝试使用具有各种 epsilon 值的 cv2.approxPolyDP() 来逼近边缘,但这看起来不像是要走的路。我的 cv.approxPolyDP 代码给出以下结果:

理想情况下,这就是我想要制作的(绘制在油漆上):

是否有针对此类问题的 CV 功能?我已经考虑在阈值步骤之前使用高斯模糊,尽管该方法似乎对于角点查找不是非常准确。此外,我希望这对旋转图像具有鲁棒性,因此如果没有其他考虑,对垂直和水平线的过滤不一定会起作用。

代码*:

import numpy as np
from PIL import ImageGrab
import cv2


def process_image4(original_image):  # Douglas-peucker approximation
    # Convert to black and white threshold map
    gray = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
    gray = cv2.GaussianBlur(gray, (5, 5), 0)
    (thresh, bw) = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

    # Convert bw image back to colored so that red, green and blue contour lines are visible, draw contours
    modified_image = cv2.cvtColor(bw, cv2.COLOR_GRAY2BGR)
    contours, hierarchy = cv2.findContours(bw, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    cv2.drawContours(modified_image, contours, -1, (255, 0, 0), 3)

    # Contour approximation
    try:  # Just to be sure it doesn't crash while testing!
        for cnt in contours:
            epsilon = 0.005 * cv2.arcLength(cnt, True)
            approx = cv2.approxPolyDP(cnt, epsilon, True)
            # cv2.drawContours(modified_image, [approx], -1, (0, 0, 255), 3)
    except:
        pass
    return modified_image


def screen_record():
    while(True):
        screen = np.array(ImageGrab.grab(bbox=(100, 240, 750, 600)))
        image = process_image4(screen)
        cv2.imshow('window', image)
        if cv2.waitKey(25) & 0xFF == ord('q'):
            cv2.destroyAllWindows()
            break

screen_record()

标签: pythonimageopencvimage-processingcomputer-vision

解决方案


这是使用阈值+形态学操作的潜在解决方案:

  1. 获取二值图像。我们加载图像,用cv2.bilateralFilter()灰度,然后是 Otsu 的阈值进行模糊

  2. 形态学运算。我们执行一系列形态学打开和关闭以平滑图像并去除噪声

  3. 找到扭曲的近似掩码。我们找到对象的边界矩形坐标,cv2.arcLength()然后cv2.approxPolyDP()将其绘制到蒙版上

  4. 寻找角落。我们使用已经实现的 Shi-Tomasi Corner Detector 来cv2.goodFeaturesToTrack()进行角点检测。看一下这个关于每个参数的解释


这是每个步骤的可视化:

二值图像->形态学运算->近似掩码->检测到的角点

以下是角坐标:

(103, 550)
(1241, 536)

这是其他图像的结果

(558, 949)
(558, 347)

最后对于旋转后的图像

(201, 99)
(619, 168)

代码

import cv2
import numpy as np

# Load image, bilaterial blur, and Otsu's threshold
image = cv2.imread('1.png')
mask = np.zeros(image.shape, dtype=np.uint8)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.bilateralFilter(gray,9,75,75)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Perform morpholgical operations
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10,10))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1)
close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, kernel, iterations=1)

# Find distorted rectangle contour and draw onto a mask
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
rect = cv2.minAreaRect(cnts[0])
box = cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(image,[box],0,(36,255,12),4)
cv2.fillPoly(mask, [box], (255,255,255))

# Find corners
mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
corners = cv2.goodFeaturesToTrack(mask,4,.8,100)
offset = 25
for corner in corners:
    x,y = corner.ravel()
    cv2.circle(image,(x,y),5,(36,255,12),-1)
    x, y = int(x), int(y)
    cv2.rectangle(image, (x - offset, y - offset), (x + offset, y + offset), (36,255,12), 3)
    print("({}, {})".format(x,y))
    
cv2.imshow('image', image)
cv2.imshow('thresh', thresh)
cv2.imshow('close', close)
cv2.imshow('mask', mask)
cv2.waitKey()

注意:扭曲边界框的想法来自先前的答案如何从模糊图像中找到扭曲矩形的准确角位置


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