我嘗試檢測下圖中的黃線，但陰影遮住了黃色道路。我們有什么方法可以處理嗎？編碼如下：import cv2import numpy as npimage = cv2.imread('Road.jpg')hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)low_yellow = np.array([18, 94, 140])up_yellow = np.array([48, 255, 255])mask = cv2.inRange(hsv, low_yellow, up_yellow)edges = cv2.Canny(mask, 75, 150)lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 50, maxLineGap=250)for line in lines:? x1, y1, x2, y2 = line[0]? cv2.line(image, (x1, y1), (x2, y2), (0, 255, 0), 5)? # cv2.imshow('image', img)? cv2.imwrite("result.jpg", edges)
1 回答
幕布斯6054654
TA貢獻1876條經驗 獲得超7個贊
這是轉換為實驗室和自動閾值的代碼您必須使用適當的方法檢測線條。一種高級解決方案是訓練數據集進行分割（神經網絡 Ex：Unet）
import cv2
import numpy as np
img = cv2.imread('YourImagePath.jpg')
cv2.imshow("Original", img)
k = cv2.waitKey(0)
# Convert To lab
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
# display b channel
cv2.imshow("Lab", lab[:, :, 2])
k = cv2.waitKey(0)
# auto threshold using Otsu
ret , mask = cv2.threshold(lab[:, :, 2] , 0 , 255 , cv2.THRESH_BINARY+
cv2.THRESH_OTSU)
#display Binary
cv2.imshow("Binary", mask)
k = cv2.waitKey(0)
cv2.destroyAllWindows()
輸出：
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