苏州网站排名优化报价,电脑iis做网站,锦州做网站多少钱,科研实验室网站建设import cv2 as cv
import os
import numpy as npimport time# 遍历文件夹函数
def getFileList(dir, Filelist, extNone):获取文件夹及其子文件夹中文件列表输入 dir#xff1a;文件夹根目录输入 ext: 扩展名返回#xff1a; 文件路径列表None):获取文件夹及其子文件夹中文件列表输入 dir文件夹根目录输入 ext: 扩展名返回 文件路径列表newDir dirif os.path.isfile(dir):if ext is None:Filelist.append(dir)else:if ext in dir[-3:]:Filelist.append(dir)elif os.path.isdir(dir):for s in os.listdir(dir):newDir os.path.join(dir, s)getFileList(newDir, Filelist, ext)return Filelistdef mid(follow, mask, img):height follow.shape[0] # 输入图像高度width follow.shape[1] # 输入图像宽度half int(width / 2) # 输入图像中线# 从下往上扫描赛道,最下端取图片中线为分割线for y in range(height - 1, -1, -1):if y height - 1: # 刚开始从底部扫描时left 0right width - 1left_scale 0.5 # 初始赛道追踪范围right_scale 0.5 # 初始赛道追踪范围elif left 0 and right width - 1: # 下层没有扫描到赛道时left_scale 0.25 # 赛道追踪范围right_scale 0.25 # 赛道追踪范围elif left 0: # 仅左下层没有扫描到赛道时left_scale 0.25 # 赛道追踪范围right_scale 0.2 # 赛道追踪范围elif right width - 1: # 仅右下层没有扫描到赛道时left_scale 0.2 # 赛道追踪范围right_scale 0.25 # 赛道追踪范围else:left_scale 0.2 # 赛道追踪范围right_scale 0.2 # 赛道追踪范围# 根据下层左线位置和scale设置左线扫描范围left_range mask[y][max(0, left - int(left_scale * width)):min(left int(left_scale * width), width - 1)]# 根据下层右线位置和scale设置右线扫描范围right_range mask[y][max(0, right - int(right_scale * width)):min(right int(right_scale * width), width - 1)]# 左侧规定范围内未找到赛道if (left_range np.zeros_like(left_range)).all():left left # 取图片最左端为左线else:left int((max(0, left - int(left_scale * width)) np.average(np.where(left_range 255))) * 0.4 left * 0.6) # 取左侧规定范围内检测到赛道像素平均位置为左线# 右侧规定范围内未找到赛道if (right_range np.zeros_like(right_range)).all():right right # 取图片最右端为右线else:right int((max(0, right - int(right_scale * width)) np.average(np.where(right_range 255))) * 0.4 right * 0.6) # 取右侧规定范围内检测到赛道像素平均位置为右线mid int((left right) / 2) # 计算中点# follow[y, mid] 255 # 画出拟合中线,实际使用时为提高性能可省略# img[y, max(0, left - int(left_scale * width)):min(left int(left_scale * width), width - 1)] [0, 0, 255]# img[y, max(0, right - int(right_scale * width)):min(right int(right_scale * width), width - 1)] [0, 0, 255]if y int((360 / 480) * follow.shape[0]): # 设置指定提取中点的纵轴位置mid_output midcv.circle(follow, (mid_output, int((360 / 480) * follow.shape[0])), 5, 255, -1) # opencv为(x,y),画出指定提取中点error (half - mid_output) / width * 640 # 计算图片中点与指定提取中点的误差return follow, error, img # error为正数左转,为负数右转n -1
# 存放图片的文件夹路径
path ./d1
imglist getFileList(path, [])
for imgpath in imglist:n 1if n 0:continuestart_time time.time()img cv.imread(imgpath)img cv.resize(img, (640, 480))# HSV阈值分割img_hsv cv.cvtColor(img, cv.COLOR_BGR2HSV)mask cv.inRange(img_hsv, np.array([43, 60, 90]), np.array([62, 255, 255]))follow mask.copy()follow, error, img mid(follow, mask, img)print(n, ferror:{error})end_time time.time()print(time:, end_time - start_time, s)cv.imshow(img, img)cv.imshow(mask, mask)cv.imshow(follow, follow)cv.waitKey(0)cv.destroyAllWindows()
