问题描述
我有赛车游戏中的公路照片.
I have road photos from the racing game.
我想检测黄色和白色车道.
如果我使用RGB空间，
I want to detect yellow and white lanes.
If I use RGB space,
def select_rgb_white_yellow(image):
# white color mask
lower = np.uint8([123, 116, 116])
upper = np.uint8([186, 172, 160])
white_mask = cv2.inRange(image, lower, upper)
# yellow color mask
lower = np.uint8([134, 121, 100])
upper = np.uint8([206, 155, 87])
yellow_mask = cv2.inRange(image, lower, upper)
# combine the mask
mask = cv2.bitwise_or(white_mask, yellow_mask)
masked = cv2.bitwise_and(image, image, mask = mask)
return masked
我只有白道.
因此，使用此代码对HLS空间进行了一些调整.
I only get white lanes.
So tweaked little bit, using HLS space, by using this code.
def convert_hls(image):
return cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
然后，再次提取黄色和白色车道，
Then, extracting the yellow and white lane again,
def select_white_yellow(image):
converted = convert_hls(image)
# white color mask
lower = np.uint8([0, 0, 0])
upper = np.uint8([0, 0, 255])
white_mask = cv2.inRange(converted, lower, upper)
# yellow color mask
lower = np.uint8([ 10, 0, 100])
upper = np.uint8([ 40, 255, 255])
yellow_mask = cv2.inRange(converted, lower, upper)
# combine the mask
mask = cv2.bitwise_or(white_mask, yellow_mask)
return cv2.bitwise_and(image, image, mask = mask)
然后它将无法再检测到白色车道.是否有检测白色和黄色车道的好方法?
Then it cannot detect white lane anymore. Is there good way to detect both white and yellow lane?
这是我发现的所有RGB颜色代码
Here are all RGB colour code that I found
Yellow
c2974A (194, 149, 74)
a07444 (160, 116, 68)
b38e55 (179, 142, 85)
867964 (134, 121, 100)
ce9b57 (206, 155, 87)
ce9853 (206, 152, 83)
white
b4a59d (180, 165, 157)
b9a99a (185, 169, 154)
baaca0 (186, 172, 160)
867e79 (134, 126, 121)
7b7474 (123, 116, 116)
827d7c (130, 125, 124)
推荐答案
扩展注释:这是一个两阶段方法的实现.花一些时间看一下中间的图像/遮罩，以完全了解发生的一切.
To extend the comment: this is an implementation of a 2 stage approach. Take some time to look at the intermediate images/masks to fully understand everything that happens.
裁剪到感兴趣的区域
您可以自动执行此操作，但是我作了一些欺骗并手动完成了操作.修剪过的天空区域几乎不会有路面，这是一个简单的解决方案，足以满足我的需求.同样，我也切掉了右侧的HUD框，因为它们具有与道路相似的灰色并且受到干扰.在它们上方绘制黑匣子更为整齐，因此将它们排除在处理之外.
Cropping to region of interest
You can automate this, but I cheated a little and did it manually. The cropped sky area will rarely ever have road surface, this is an easy solution that suffices (for now) I think. Similarly, I also cut of the HUD boxes on the right hand side, as they have similar gray colors as the road and were interfering. It's more neat to draw black boxes over them, so they are excluded from processing.
隔离道路
将裁剪后的图像转换为HSV，仅选择灰色值.经过一些噪声消除后，我使用findContours绘制了凸包，从而改进了蒙版.如果性能是一个问题，则可以通过调整 close mask 步骤来跳过它.
选择行
使用遮罩可以创建仅路面的图像.您可以使用它进行分色，而不必担心选择周围环境.我的结果并不完美，但是我认为您使用的图像版本较大，可以提供更好的结果.
Selecting lines
Using the mask you can create an image of only the road surface. You can use this for color separation without having to worry about also selecting surroundings. My result is not perfect, but I assume you have larger versions of the images which will give better results.
结果:
代码:
import cv2
import numpy as np
# load image
image = cv2.imread('pw12b.jpg')
# crop image
h,w = image.shape[:2]
image = image[200:h-20,20:550]
# create hsv
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# set lower and upper color limits
low_val = (0,0,0)
high_val = (179,45,96)
# Threshold the HSV image
mask = cv2.inRange(hsv, low_val,high_val)
# remove noise
mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel=np.ones((8,8),dtype=np.uint8))
# close mask
mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel=np.ones((20,20),dtype=np.uint8))
# improve mask by drawing the convexhull
ret, contours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
hull = cv2.convexHull(cnt)
cv2.drawContours(mask,[hull],0,(255), -1)
# erode mask a bit to migitate mask bleed of convexhull
mask = cv2.morphologyEx(mask, cv2.MORPH_ERODE, kernel=np.ones((5,5),dtype=np.uint8))
# remove this line, used to show intermediate result of masked road
road = cv2.bitwise_and(image, image,mask=mask)
# apply mask to hsv image
road_hsv = cv2.bitwise_and(hsv, hsv,mask=mask)
# set lower and upper color limits
low_val = (0,0,102)
high_val = (179,255,255)
# Threshold the HSV image
mask2 = cv2.inRange(road_hsv, low_val,high_val)
# apply mask to original image
result = cv2.bitwise_and(image, image,mask=mask2)
#show image
cv2.imshow("Result", result)
cv2.imshow("Road", road)
cv2.imshow("Mask", mask)
cv2.imshow("Image", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
这篇关于OpenCV-车道检测，黄色和白色车道的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！