OpecCV를 통해 차선 인식하기

 

위와 같이 사진을 사용하여 길 위의 차선을 인식할 것입니다.

 

 

 

사용함수!

def canny_edge(image) :
    gray_conversion = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blur_conversion = cv2.GaussianBlur(gray_conversion, (5,5), 0)
    canny_conversion = cv2.Canny(blur_conversion, 50, 150)
    return canny_conversion

def reg_of_interest(image) :
    image_height = image.shape[0]
    polygons = np.array( [[ (200, image_height) , (1100, image_height), (550, 250) ]] )
    image_mask = np.zeros_like(image)
    cv2.fillPoly(image_mask, polygons, 255)
    masking_image = cv2.bitwise_and(image, image_mask)
    return masking_image

def show_lines(image, lines) : 
    lines_image = np.zeros_like(image)
    if lines is not None :
        for i in range(len(lines)):
            for x1,y1,x2,y2 in lines[i]:
                cv2.line(lines_image,(x1,y1),(x2,y2),(255,0,0), 10 )
    return lines_image

def make_coordinates(image, line_parameters):
    slope, intercept = line_parameters
    y1 = image.shape[0]
    y2 = int(y1*(3/5))
    x1 = int((y1- intercept)/slope)
    x2 = int((y2 - intercept)/slope)
    return np.array([x1, y1, x2, y2])

def average_slope_intercept(image, lines):
    left_fit = []
    right_fit = []
    for line in lines:
        x1, y1, x2, y2 = line.reshape(4)
        parameter = np.polyfit((x1, x2), (y1, y2), 1)
        slope = parameter[0]
        intercept = parameter[1]
        if slope < 0:
            left_fit.append((slope, intercept))
        else:
            right_fit.append((slope, intercept))
    left_fit_average =np.average(left_fit, axis=0)
    right_fit_average = np.average(right_fit, axis =0)
    left_line =make_coordinates(image, left_fit_average)
    right_line = make_coordinates(image, right_fit_average)

    return np.array([[left_line, right_line]])

 

 

컴퓨터 비젼과 넘파이를 import합니다.

import cv2
import numpy as np

 

이미지를 가져옵니다.

image = cv2.imread('data3/test_image.jpg')
lanelines_image = image.copy()

 

 

차선 인식을 더 수월하기 위해 흑백으로 사진을 변환하고 이미지를 블러처리해줍니다. 

그리고 캐니를 사용하여 그림에 있는 테두리를 잡아줍니다.

 

canny_conversion = canny_edge(lanelines_image)

cv2.imshow('Canny', canny_conversion)

그리고 roi 함수를 사용해서 도로의 테두리만 표출하게 해줍니다.

roi_conversion = reg_of_interest(canny_conversion)

허프 변환을 통해 라인을 이어주고 선의 기울기 평균값을 적용하여 직선을 하나씩만 그려줍니다.

lines = cv2.HoughLinesP(roi_conversion, 1, np.pi/180, 100, minLineLength = 40, maxLineGap = 5)
averaged_lines = average_slope_intercept(lanelines_image, lines)
lines_image = show_lines(lanelines_image, averaged_lines)

이젠 원본이미지에 라인을 합칩니다.

combine_image = cv2.addWeighted(lanelines_image, 0.8, lines_image, 1, 1)

 

아래는 전체코드입니다~

import cv2
import numpy as np


def reg_of_interest(image) :
    image_height = image.shape[0]
    polygons = np.array( [[ (200, image_height) , (1100, image_height), (550, 250) ]] )
    image_mask = np.zeros_like(image)
    cv2.fillPoly(image_mask, polygons, 255)
    masking_image = cv2.bitwise_and(image, image_mask)
    return masking_image

# 6. 케니에지 처리하는 함수
def canny_edge(image) :
    gray_conversion = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blur_conversion = cv2.GaussianBlur(gray_conversion, (5,5), 0)
    canny_conversion = cv2.Canny(blur_conversion, 50, 150)
    return canny_conversion

def show_lines(image, lines) : 
    lines_image = np.zeros_like(image)
    if lines is not None :
        for i in range(len(lines)):
            for x1,y1,x2,y2 in lines[i]:
                cv2.line(lines_image,(x1,y1),(x2,y2),(255,0,0), 10 )
    return lines_image

# 여러 선을, 하나의 선으로 만들어 주는 함수.
# 방법은? 기울기와 y절편을 평균으로 해서 하나의 기울기와 y절편을 갖도록 만드는 방법.
def make_coordinates(image, line_parameters):
    slope, intercept = line_parameters
    y1 = image.shape[0]
    y2 = int(y1*(3/5))
    x1 = int((y1- intercept)/slope)
    x2 = int((y2 - intercept)/slope)
    return np.array([x1, y1, x2, y2])

def average_slope_intercept(image, lines):
    left_fit = []
    right_fit = []
    for line in lines:
        x1, y1, x2, y2 = line.reshape(4)
        parameter = np.polyfit((x1, x2), (y1, y2), 1)
        slope = parameter[0]
        intercept = parameter[1]
        if slope < 0:
            left_fit.append((slope, intercept))
        else:
            right_fit.append((slope, intercept))
    left_fit_average =np.average(left_fit, axis=0)
    right_fit_average = np.average(right_fit, axis =0)
    left_line =make_coordinates(image, left_fit_average)
    right_line = make_coordinates(image, right_fit_average)

    return np.array([[left_line, right_line]])

#이미지 가져오기
image = cv2.imread('data3/test_image.jpg')
lanelines_image = image.copy()

#흰 검으로 변환해서 라인 검출함.
canny_conversion = canny_edge(lanelines_image)
roi_conversion = reg_of_interest(canny_conversion)

# cv2.imshow('ori', canny_conversion)
# cv2.imshow('ori', roi_conversion)

#라인 이어주기
lines = cv2.HoughLinesP(roi_conversion, 1, np.pi/180, 100, minLineLength = 40, maxLineGap = 5)

averaged_lines = average_slope_intercept(lanelines_image, lines)

#선을 기울기 평균값으로 적용
lines_image = show_lines(lanelines_image, averaged_lines)

#원본 이미지에 라인 그리기
combine_image = cv2.addWeighted(lanelines_image, 0.8, lines_image, 1, 1)

cv2.imshow('ori', lanelines_image)
cv2.imshow("roi", lines_image)
cv2.imshow("combined", combine_image)