如标题所示，我正在尝试进行图像分割，以期进行“车道”检测。这是我要测试的样本图像。
这是我从本质上在网上找到的东西的第一次编码尝试。
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。
有点亲密，但不是我想要的。有任何提示或有用的建议吗？
最佳答案
一种潜在的方法是使用 cv2.inRange() 进行颜色分割。假设所需的线条为白色，我们可以隔离此颜色范围内的像素。这是主要思想
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。然后我们创建一个上下阈值以检测白色像素，并使用
cv2.inRange()创建一个蒙版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，噪声很小，因此下一步是将其去除。我们可以在这里采取几种方法。一种是使用morphological operations腐 eclipse /扩大图像。另一种方法是找到轮廓并使用轮廓区域进行过滤以忽略小颗粒。我将使用后一种方法。我们使用最小阈值区域过滤掉粒子，并使用
cv2.drawContours()将其填充为黑色。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的上下边界
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()