标签:absolute 填充 pack 最大 sed env alpha 中间 计算
https://zhuanlan.zhihu.com/p/60190848
2-1 安装 Anaconda3 环境
2-2 在Anaconda3 环境种安装开发IDE spyder
刚开始找不到spyder,但是我安装完vs code之后就出现了选择安装spyder的图标
2-3 安装opencv和contrib扩展库
2-4安装matplotlib库
https://blog.csdn.net/weixin_42116878/article/details/80525811
、
找不到 matplotlib问题
解决:
先查看matplotlib的安装路径
pip show matplotlib
然后将路径添加
import sys
sys.path.append(" F:/dongdong/0tool/python/anaconda3/lib/site-packages")
其中括号内的为上一步实际查出的安装路径。
3 工程代码
搞一个纯英文路径
使用spyder打开
获取图片的时候路径地址,目前相对路径和绝对路径有问题,容易报错读不到图
结果图
单张图
视频流
放开代码注释
#!/usr/bin/env python2 # -*- coding: utf-8 -*- #import sys #sys.path.append("F:/dongdong/0tool/python/Anaconda3/Lib/site-packages") import numpy as np import cv2 as cv import matplotlib.pyplot as plt # 灰度图转换 def grayscale(image): return cv.cvtColor(image, cv.COLOR_RGB2GRAY) # Canny边缘检测 def canny(image, low_threshold, high_threshold): return cv.Canny(image, low_threshold, high_threshold) # 高斯滤波 def gaussian_blur(image, kernel_size): return cv.GaussianBlur(image, (kernel_size, kernel_size), 0) # 生成感兴趣区域即Mask掩模 def region_of_interest(image, vertices): mask = np.zeros_like(image) # 生成图像大小一致的zeros矩 # 填充顶点vertices中间区域 if len(image.shape) > 2: channel_count = image.shape[2] ignore_mask_color = (255,) * channel_count else: ignore_mask_color = 255 # 填充函数 cv.fillPoly(mask, vertices, ignore_mask_color) masked_image = cv.bitwise_and(image, mask) return masked_image # 原图像与车道线图像按照a:b比例融合 def weighted_img(img, initial_img, a=0.8, b=1., c=0.): return cv.addWeighted(initial_img, a, img, b, c) # def reset_global_vars(): # # global SET_LFLAG # global SET_RFLAG # global LAST_LSLOPE # global LAST_RSLOPE # global LAST_LEFT # global LAST_RIGHT # # SET_RFLAG = 0 # SET_LFLAG = 0 # LAST_LSLOPE = 0 # LAST_RSLOPE = 0 # LAST_RIGHT = [0, 0, 0] # LAST_LEFT = [0, 0, 0] def draw_lines(image, lines, color=[255,0,0], thickness=2): right_y_set = [] right_x_set = [] right_slope_set = [] left_y_set = [] left_x_set = [] left_slope_set = [] slope_min = .35 # 斜率低阈值 slope_max = .85 # 斜率高阈值 middle_x = image.shape[1] / 2 # 图像中线x坐标 max_y = image.shape[0] # 最大y坐标 for line in lines: for x1, y1, x2, y2 in line: fit = np.polyfit((x1, x2), (y1, y2), 1) # 拟合成直线 slope = fit[0] # 斜率 if slope_min < np.absolute(slope) <= slope_max: # 将斜率大于0且线段X坐标在图像中线右边的点存为右边车道线 if slope > 0 and x1 > middle_x and x2 > middle_x: right_y_set.append(y1) right_y_set.append(y2) right_x_set.append(x1) right_x_set.append(x2) right_slope_set.append(slope) # 将斜率小于0且线段X坐标在图像中线左边的点存为左边车道线 elif slope < 0 and x1 < middle_x and x2 < middle_x: left_y_set.append(y1) left_y_set.append(y2) left_x_set.append(x1) left_x_set.append(x2) left_slope_set.append(slope) # 绘制左车道线 if left_y_set: lindex = left_y_set.index(min(left_y_set)) # 最高点 left_x_top = left_x_set[lindex] left_y_top = left_y_set[lindex] lslope = np.median(left_slope_set) # 计算平均值 # 根据斜率计算车道线与图片下方交点作为起点 left_x_bottom = int(left_x_top + (max_y - left_y_top) / lslope) # 绘制线段 cv.line(image, (left_x_bottom, max_y), (left_x_top, left_y_top), color, thickness) # 绘制右车道线 if right_y_set: rindex = right_y_set.index(min(right_y_set)) # 最高点 right_x_top = right_x_set[rindex] right_y_top = right_y_set[rindex] rslope = np.median(right_slope_set) # 根据斜率计算车道线与图片下方交点作为起点 right_x_bottom = int(right_x_top + (max_y - right_y_top) / rslope) # 绘制线段 cv.line(image, (right_x_top, right_y_top), (right_x_bottom, max_y), color, thickness) def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap): # rho:线段以像素为单位的距离精度 # theta : 像素以弧度为单位的角度精度(np.pi/180较为合适) # threshold : 霍夫平面累加的阈值 # minLineLength : 线段最小长度(像素级) # maxLineGap : 最大允许断裂长度 lines = cv.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap) return lines def process_image(image): rho = 1 # 霍夫像素单位 theta = np.pi / 180 # 霍夫角度移动步长 hof_threshold = 20 # 霍夫平面累加阈值threshold min_line_len = 30 # 线段最小长度 max_line_gap = 60 # 最大允许断裂长度 kernel_size = 5 # 高斯滤波器大小size canny_low_threshold = 75 # canny边缘检测低阈值 canny_high_threshold = canny_low_threshold * 3 # canny边缘检测高阈值 alpha = 0.8 # 原图像权重 beta = 1. # 车道线图像权重 lambda_ = 0. imshape = image.shape # 获取图像大小 # 灰度图转换 gray = grayscale(image) # 高斯滤波 blur_gray = gaussian_blur(gray, kernel_size) # Canny边缘检测 edge_image = canny(blur_gray, canny_low_threshold, canny_high_threshold) # 生成Mask掩模 vertices = np.array([[(0, imshape[0]), (9 * imshape[1] / 20, 11 * imshape[0] / 18), (11 * imshape[1] / 20, 11 * imshape[0] / 18), (imshape[1], imshape[0])]], dtype=np.int32) masked_edges = region_of_interest(edge_image, vertices) # 基于霍夫变换的直线检测 lines = hough_lines(masked_edges, rho, theta, hof_threshold, min_line_len, max_line_gap) line_image = np.zeros_like(image) # 绘制车道线线段 draw_lines(line_image, lines, thickness=10) # 图像融合 lines_edges = weighted_img(image, line_image, alpha, beta, lambda_) return lines_edges if __name__ == ‘__main__‘: # cap = cv.VideoCapture("./test_videos/solidYellowLeft.mp4") # while(cap.isOpened()): # _, frame = cap.read() # processed = process_image(frame) # cv.imshow("image", processed) # cv.waitKey(1) image = cv.imread(‘1.png‘) line_image = process_image(image) cv.imshow(‘image‘,line_image) cv.waitKey(0)
标签:absolute 填充 pack 最大 sed env alpha 中间 计算
原文地址:https://www.cnblogs.com/kekeoutlook/p/11144952.html