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正如标题所述，我正在尝试进行图像分割，以期进行“车道”检测。这是我要测试的示例图像。

这是我的第一次编码尝试，基本上是在网上找到的。

from matplotlib import pyplot as plt
import os
import cv2
def image_seg_watershed():
    img = cv2.imread(os.path.join(img_file,img_file_list[0]))
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    ret, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
    plt.subplot(121), plt.imshow(thresh)
    plt.show()

这是输出。

有点接近，但不是我想要的。任何提示或有用的建议？

4

1 回答 1

2

一种潜在的方法是使用cv2.inRange(). 假设所需的线条是白色的，我们可以隔离此颜色范围内的像素。这是主要思想

  • 将图像转换为 HSV 格式，因为它更容易表示颜色
  • 使用下/上阈值执行颜色分割
  • 使用轮廓区域过滤以去除小颗粒

我们将图像转换为 HSV 格式，因为它比 RBG 或 BGR 格式更容易表示颜色。然后我们创建一个下/上阈值来检测白色像素并使用cv2.inRange()

import numpy as np
import cv2

image = cv2.imread('1.jpg')
result = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([0,0,200])
upper = np.array([179, 77, 255])
mask = cv2.inRange(image, lower, upper)
result = cv2.bitwise_and(result,result, mask=mask)

请注意，有小颗粒的噪音，所以下一步是去除其中的一些。我们可以在这里采取几种方法。一种是使用形态学操作来腐蚀/膨胀图像。另一种方法是寻找轮廓并使用轮廓区域过滤以忽略小颗粒。我将使用后一种方法。我们使用最小阈值区域来过滤掉粒子并使用黑色填充它们cv2.drawContours()。这是结果

cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

for c in cnts:
    area = cv2.contourArea(c)
    if area < 1:
        cv2.drawContours(result, [c], -1, (0,0,0), -1)

完整代码

import numpy as np
import cv2

image = cv2.imread('1.jpg')
result = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([0,0,200])
upper = np.array([179, 77, 255])
mask = cv2.inRange(image, lower, upper)
result = cv2.bitwise_and(result,result, mask=mask)

cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

for c in cnts:
    area = cv2.contourArea(c)
    if area < 1:
        cv2.drawContours(result, [c], -1, (0,0,0), -1)

cv2.imshow('mask', mask)
cv2.imshow('result', result)
cv2.waitKey()

您可以使用颜色阈值脚本来查找 HSV 的下/上边界

import cv2
import sys
import numpy as np

def nothing(x):
    pass

# Create a window
cv2.namedWindow('image')

# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin','image',0,255,nothing)
cv2.createTrackbar('VMin','image',0,255,nothing)
cv2.createTrackbar('HMax','image',0,179,nothing)
cv2.createTrackbar('SMax','image',0,255,nothing)
cv2.createTrackbar('VMax','image',0,255,nothing)

# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)

# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0

img = cv2.imread('1.jpg')
output = img
waitTime = 33

while(1):

    # get current positions of all trackbars
    hMin = cv2.getTrackbarPos('HMin','image')
    sMin = cv2.getTrackbarPos('SMin','image')
    vMin = cv2.getTrackbarPos('VMin','image')

    hMax = cv2.getTrackbarPos('HMax','image')
    sMax = cv2.getTrackbarPos('SMax','image')
    vMax = cv2.getTrackbarPos('VMax','image')

    # Set minimum and max HSV values to display
    lower = np.array([hMin, sMin, vMin])
    upper = np.array([hMax, sMax, vMax])

    # Create HSV Image and threshold into a range.
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower, upper)
    output = cv2.bitwise_and(img,img, mask= mask)

    # Print if there is a change in HSV value
    if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
        print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
        phMin = hMin
        psMin = sMin
        pvMin = vMin
        phMax = hMax
        psMax = sMax
        pvMax = vMax

    # Display output image
    cv2.imshow('image',output)

    # Wait longer to prevent freeze for videos.
    if cv2.waitKey(waitTime) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()
于 2019-08-28T20:25:38.997 回答