python+opencv實現車道線檢測

python+opencv車道線檢測(簡易實現),供大傢參考,具體內容如下

技術棧:python+opencv

實現思路:

1、canny邊緣檢測獲取圖中的邊緣信息;
2、霍夫變換尋找圖中直線;
3、繪制梯形感興趣區域獲得車前范圍;
4、得到並繪制車道線;

效果展示:

代碼實現:

import cv2
import numpy as np


def canny():
 gray = cv2.cvtColor(lane_image, cv2.COLOR_RGB2GRAY)
 #高斯濾波
 blur = cv2.GaussianBlur(gray, (5, 5), 0)
 #邊緣檢測
 canny_img = cv2.Canny(blur, 50, 150)
 return canny_img


def region_of_interest(r_image):
 h = r_image.shape[0]
 w = r_image.shape[1]
 # 這個區域不穩定,需要根據圖片更換
 poly = np.array([
 [(100, h), (500, h), (290, 180), (250, 180)]
 ])
 mask = np.zeros_like(r_image)
 # 繪制掩膜圖像
 cv2.fillPoly(mask, poly, 255)
 # 獲得ROI區域
 masked_image = cv2.bitwise_and(r_image, mask)
 return masked_image


if __name__ == '__main__':
 image = cv2.imread('test.jpg')
 lane_image = np.copy(image)
 canny = canny()
 cropped_image = region_of_interest(canny)
 cv2.imshow("result", cropped_image)
 cv2.waitKey(0)

霍夫變換加線性擬合改良:

效果圖:

代碼實現:

主要增加瞭根據斜率作線性擬合過濾無用點後連線的操作;

import cv2
import numpy as np


def canny():
 gray = cv2.cvtColor(lane_image, cv2.COLOR_RGB2GRAY)
 blur = cv2.GaussianBlur(gray, (5, 5), 0)

 canny_img = cv2.Canny(blur, 50, 150)
 return canny_img


def region_of_interest(r_image):
 h = r_image.shape[0]
 w = r_image.shape[1]

 poly = np.array([
 [(100, h), (500, h), (280, 180), (250, 180)]
 ])
 mask = np.zeros_like(r_image)
 cv2.fillPoly(mask, poly, 255)
 masked_image = cv2.bitwise_and(r_image, mask)
 return masked_image


def get_lines(img_lines):
 if img_lines is not None:
 for line in lines:
 for x1, y1, x2, y2 in line:
 # 分左右車道
 k = (y2 - y1) / (x2 - x1)
 if k < 0:
  lefts.append(line)
 else:
  rights.append(line)


def choose_lines(after_lines, slo_th): # 過濾斜率差別較大的點
 slope = [(y2 - y1) / (x2 - x1) for line in after_lines for x1, x2, y1, y2 in line] # 獲得斜率數組
 while len(after_lines) > 0:
 mean = np.mean(slope) # 計算平均斜率
 diff = [abs(s - mean) for s in slope] # 每條線斜率與平均斜率的差距
 idx = np.argmax(diff) # 找到最大斜率的索引
 if diff[idx] > slo_th: # 大於預設的閾值選取
 slope.pop(idx)
 after_lines.pop(idx)
 else:
 break

 return after_lines


def clac_edgepoints(points, y_min, y_max):
 x = [p[0] for p in points]
 y = [p[1] for p in points]

 k = np.polyfit(y, x, 1) # 曲線擬合的函數,找到xy的擬合關系斜率
 func = np.poly1d(k) # 斜率代入可以得到一個y=kx的函數

 x_min = int(func(y_min)) # y_min = 325其實是近似找瞭一個
 x_max = int(func(y_max))

 return [(x_min, y_min), (x_max, y_max)]


if __name__ == '__main__':
 image = cv2.imread('F:\\A_javaPro\\test.jpg')
 lane_image = np.copy(image)
 canny_img = canny()
 cropped_image = region_of_interest(canny_img)
 lefts = []
 rights = []
 lines = cv2.HoughLinesP(cropped_image, 1, np.pi / 180, 15, np.array([]), minLineLength=40, maxLineGap=20)
 get_lines(lines) # 分別得到左右車道線的圖片

 good_leftlines = choose_lines(lefts, 0.1) # 處理後的點
 good_rightlines = choose_lines(rights, 0.1)

 leftpoints = [(x1, y1) for left in good_leftlines for x1, y1, x2, y2 in left]
 leftpoints = leftpoints + [(x2, y2) for left in good_leftlines for x1, y1, x2, y2 in left]

 rightpoints = [(x1, y1) for right in good_rightlines for x1, y1, x2, y2 in right]
 rightpoints = rightpoints + [(x2, y2) for right in good_rightlines for x1, y1, x2, y2 in right]

 lefttop = clac_edgepoints(leftpoints, 180, image.shape[0]) # 要畫左右車道線的端點
 righttop = clac_edgepoints(rightpoints, 180, image.shape[0])

 src = np.zeros_like(image)

 cv2.line(src, lefttop[0], lefttop[1], (255, 255, 0), 7)
 cv2.line(src, righttop[0], righttop[1], (255, 255, 0), 7)

 cv2.imshow('line Image', src)
 src_2 = cv2.addWeighted(image, 0.8, src, 1, 0)
 cv2.imshow('Finally Image', src_2)

 cv2.waitKey(0)

待改進:

代碼實用性差,幾乎不能用於實際,但是可以作為初學者的練手項目;
斑馬線檢測思路:獲取車前感興趣區域,判斷白色像素點比例即可實現;
行人檢測思路:opencv有內置行人檢測函數,基於內置的訓練好的數據集;

以上就是本文的全部內容,希望對大傢的學習有所幫助,也希望大傢多多支持WalkonNet。