python+openCV (入门级)车道线检测 学习条记

• 媒介
  
• 一、openCV安装
  
• 二、实验使用cv2中库函数
  
• 
  
  
  
  • 1.读取图片
    
  • 2.图片表现
    
  • 3.延时/停息
    
  • 4.生存图片
    
  • 5.清晰全部窗口
    
    
  
• 三、Canny边沿检测
  
• 
  
  
  
  • 1.高斯滤波
    
  • 2.图片转换
    
  • 3.边沿检测
    
    
  
• 四、ROI and mask
  
• 五、霍夫变更
  
• 六、离群值过滤
  
• 七、最小二乘法拟合
  
• 八、车道线标注
  
• 九、视频读写
  
• 
  
  
  
  • 1.打开视频
    
  • 2.按帧读取视频
    
  • 3.视频编码格式设置
    
  • 4.写视频参数设置
    
    
  
• 十、其他
  
• 总结
  
• 附视频检测完备步伐
  

cv2.imread(const String & filename, int flags = IMREAD_COLOR)        
复制代码

cv2.imshow(window_name, img)        
复制代码

cv2.waitKey(int x)
复制代码

if cv2.waitKey(1) & 0xFF == ord(‘q’): break  
#ord()是将字符转换为ASCII码
复制代码

cv2.imwrite(newfile_name, img)
复制代码

cv2.destroyAllWindows()
复制代码

gaussian = cv2.GaussianBlur(color_img, (gaussian_ksize,gaussian_ksize), gaussian_sigmax)
复制代码

gray_img = cv2.cvtColor(input_image, flag)
复制代码

edge_img = cv2.Canny(gray_img,canny_threshold1,canny_threshold2)
复制代码

poly_pts = numpy.array([[[0,368],[300,210],[340,210],[640,368]]])
复制代码

mask = np.zeros_like(gray_img)
复制代码

cv2.fillPoly(mask, pts, color)
复制代码

img_mask = cv2.bitwise_and(gray_img, mask)
复制代码

lines = cv2.HoughLinesP(edge_img,  1, np.pi / 180, 15, minLineLength=40, maxLineGap=20)
复制代码

idx = np.argmax(diff)
复制代码

示例：
one_dim_array = np.array([1, 4, 5, 3, 7, 2, 6])
print(np.argmax(one_dim_array))
打印结果为        4
复制代码

lines.pop(idx)
复制代码

x_coords = np.ravel([[line[0][0], line[0][2]] for line in lines])
复制代码

poly = np.polyfit(x, y, deg)
复制代码

y_e = np.polyval(poly, x)
复制代码

cv2.line(img, tuple(line[0]), tuple(line[1]), color,thickness)
复制代码

capture = cv2.VideoCapture('video.mp4')
复制代码

ret, frame = capture.read()
复制代码

fourcc = cv2.VideoWriter_fourcc('X', 'V', 'I', 'D')
复制代码

outfile = cv2.VideoWriter(filename, fourcc, 25, (1280,368))
复制代码

ret, frame = capture.read() 
复制代码

output = np.concatenate((origin, frame), axis=1)
复制代码

error: OpenCV(4.5.5) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\smooth.dispatch.cpp:617: error: (-215:Assertion failed) !_src.empty() in function 'cv::GaussianBlur'
复制代码

> 卡住的问题有人说是openCV对一部分硬件的适配不好，我比较在赞成这种观点。 报错  cv2.error  error:
> (-215:Assertion failed) dst.data == (uchar*)dst_ptr in function
> 'cvShowImage'
> 
> 同样的代码，我发现在公司电脑1060上，cv2.imshow()就可以显示，但在自己2080ti上就总是报错如上。于是锁定在cv2版本问题，果然卸掉最新的4.0版本，换上3.4.4.19就好了。
> 
> 可能是4.0版本与2080ti的卡还不兼容。
> 来源：https://blog.caogenba.net/qq_39938666/article/details/88179966
复制代码

import cv2
import numpy as np
#高斯滤波+canny边缘检测
def get_edge_img(color_img, gaussian_ksize=5, gaussian_sigmax=1,
                 canny_threshold1=50, canny_threshold2=100):
    #param intoduction
    #color_img 输入图片    gaussian_ksize 高斯核大小
    #gaussian_sigmax X方向上的高斯核标准偏差
    gaussian = cv2.GaussianBlur(color_img, (gaussian_ksize, gaussian_ksize),
                                gaussian_sigmax)
    gray_img = cv2.cvtColor(gaussian,cv2.IMREAD_GRAYSCALE)
    edge_img = cv2.Canny(gray_img,canny_threshold1,canny_threshold2);
    return edge_img
def roi_mask(gray_img):
    poly_pts = np.array([[[0,368],[300,210],[340,210],[640,368]]])
    mask = np.zeros_like(gray_img)
    cv2.fillPoly(mask, pts=poly_pts, color=255)
    img_mask = cv2.bitwise_and(gray_img, mask)
    return img_mask
def get_lines(edge_img):
    
    #斜率计算
    def calculate_slope(line):
        x_1, y_1, x_2, y_2 = line[0]
        slope = (y_2-y_1)/(x_2-x_1)
        return slope
    #离群值过滤
    def reject_abnormal_lines(lines, threshold):
        slopes = [calculate_slope(line) for line in lines]
        while len(lines)>0:
            mean = np.mean(slopes)
            diff = [abs(s-mean) for s in slopes]
            idx = np.argmax(diff)
            if diff[idx]>threshold:
                slopes.pop(idx)
                lines.pop(idx)
            else:
                break
        return lines
    
    #最小二乘拟合
    def least_squares_fit(lines):
    
        x_coords = np.ravel([[line[0][0], line[0][2]] for line in lines])
        y_coords = np.ravel([[line[0][1], line[0][3]] for line in lines])
        # 2. 进行直线拟合.得到多项式系数
        poly = np.polyfit(x_coords, y_coords, deg=1)
        # 3. 根据多项式系数,计算两个直线上的点,用于唯一确定这条直线
        point_min = (np.min(x_coords), np.polyval(poly, np.min(x_coords)))
        point_max = (np.max(x_coords), np.polyval(poly, np.max(x_coords)))
        return np.array([point_min, point_max],dtype=np.int0)
    lines = cv2.HoughLinesP(edge_img, 1, np.pi/180, 15, minLineLength=40, maxLineGap=20)
    left_lines = [line for line in lines if calculate_slope(line)<0]
    right_lines = [line for line in lines if calculate_slope(line)>0]
    
    left_lines = reject_abnormal_lines(left_lines, threshold=0.2)
    right_lines = reject_abnormal_lines(right_lines, threshold=0.2)
    return least_squares_fit(left_lines),least_squares_fit(right_lines)
def draw_line(img, lines):
    left_line, right_line = lines
    cv2.line(img, tuple(left_line[0]), tuple(left_line[1]), color=(0,255,255),thickness=5)
    cv2.line(img, tuple(right_line[0]), tuple(right_line[1]), color=(0,255,255),thickness=5)
def show_lane(color_img):
    edge_img = get_edge_img(color_img)
    mask_gray_img = roi_mask(edge_img)
    lines = get_lines(mask_gray_img)
    draw_line(color_img, lines)
    return color_img
if __name__ == '__main__':
    capture = cv2.VideoCapture('video.mp4')
    fourcc = cv2.VideoWriter_fourcc('X', 'V', 'I', 'D')
    outfile = cv2.VideoWriter('output.avi', fourcc, 25, (1280,368))
    while True:
        ret, frame = capture.read() 
        origin = np.copy(frame)
        frame = show_lane(frame)
        output = np.concatenate((origin, frame), axis=1)
        outfile.write(output)
        cv2.imshow('video', output)
        cv2.waitKey(10)
复制代码