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import cv2 as cv
import os
import numpy as np
import time
# 遍历文件夹函数
def getFileList(dir, Filelist, ext=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 = dir
if 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 Filelist
def 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 = 0
right = width - 1
left_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 = mid
cv.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 += 1
if n < 0:
continue
start_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, f"error:{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()
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