我正在尝试从图像中提取红色。我有代码应用阈值仅保留指定范围内的值:
img=cv2.imread('img.bmp')
img_hsv=cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
lower_red = np.array([0,50,50]) #example value
upper_red = np.array([10,255,255]) #example value
mask = cv2.inRange(img_hsv, lower_red, upper_red)
img_result = cv2.bitwise_and(img, img, mask=mask)
但是,正如我所检查的,红色的色相值可以在范围内,比如说从 0 到 10,以及在 170 到 180 的范围内。因此,我想保留这两个范围中任何一个的值。我尝试将阈值设置为 10 到 170 并使用
cv2.bitwise_not()
函数,但随后我也得到了所有白色。我认为最好的选择是为每个范围创建一个蒙版并使用它们,所以在继续之前我必须以某种方式将它们连接在一起。
有没有办法使用 OpenCV 连接两个掩模?或者还有其他方法可以实现我的目标吗?
编辑。我带来了不太优雅但有效的解决方案:
image_result = np.zeros((image_height,image_width,3),np.uint8)
for i in range(image_height): #those are set elsewhere
for j in range(image_width): #those are set elsewhere
if img_hsv[i][j][1]>=50 \
and img_hsv[i][j][2]>=50 \
and (img_hsv[i][j][0] <= 10 or img_hsv[i][j][0]>=170):
image_result[i][j]=img_hsv[i][j]
它几乎满足了我的需求,OpenCV 的功能可能也差不多,但如果有更好的方法来做到这一点(使用一些专用功能并编写更少的代码)请与我分享。 :)
我只需将蒙版添加在一起,然后使用
np.where
来蒙版原始图像。
img=cv2.imread("img.bmp")
img_hsv=cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# lower mask (0-10)
lower_red = np.array([0,50,50])
upper_red = np.array([10,255,255])
mask0 = cv2.inRange(img_hsv, lower_red, upper_red)
# upper mask (170-180)
lower_red = np.array([170,50,50])
upper_red = np.array([180,255,255])
mask1 = cv2.inRange(img_hsv, lower_red, upper_red)
# join my masks
mask = mask0+mask1
# set my output img to zero everywhere except my mask
output_img = img.copy()
output_img[np.where(mask==0)] = 0
# or your HSV image, which I *believe* is what you want
output_hsv = img_hsv.copy()
output_hsv[np.where(mask==0)] = 0
这应该比循环遍历图像的每个像素更快、更易读。
要检测红色,您可以使用 HSV 颜色阈值脚本来确定阈值下限/上限,然后使用
cv2.bitwise_and()
获取蒙版。使用这个输入图像,
我们得到这个结果和掩模
代码
import numpy as np
import cv2
image = cv2.imread('1.jpg')
result = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([155,25,0])
upper = np.array([179,255,255])
mask = cv2.inRange(image, lower, upper)
result = cv2.bitwise_and(result, result, mask=mask)
cv2.imshow('mask', mask)
cv2.imshow('result', result)
cv2.waitKey()
带有滑块的HSV颜色阈值脚本,记得更改图像文件路径
import cv2
import sys
import numpy as np
def nothing(x):
pass
# Load in image
image = cv2.imread('1.jpg')
# Create a window
cv2.namedWindow('image')
# create trackbars for color change
cv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv
cv2.createTrackbar('SMin','image',0,255,nothing)
cv2.createTrackbar('VMin','image',0,255,nothing)
cv2.createTrackbar('HMax','image',0,179,nothing)
cv2.createTrackbar('SMax','image',0,255,nothing)
cv2.createTrackbar('VMax','image',0,255,nothing)
# Set default value for MAX HSV trackbars.
cv2.setTrackbarPos('HMax', 'image', 179)
cv2.setTrackbarPos('SMax', 'image', 255)
cv2.setTrackbarPos('VMax', 'image', 255)
# Initialize to check if HSV min/max value changes
hMin = sMin = vMin = hMax = sMax = vMax = 0
phMin = psMin = pvMin = phMax = psMax = pvMax = 0
output = image
wait_time = 33
while(1):
# get current positions of all trackbars
hMin = cv2.getTrackbarPos('HMin','image')
sMin = cv2.getTrackbarPos('SMin','image')
vMin = cv2.getTrackbarPos('VMin','image')
hMax = cv2.getTrackbarPos('HMax','image')
sMax = cv2.getTrackbarPos('SMax','image')
vMax = cv2.getTrackbarPos('VMax','image')
# Set minimum and max HSV values to display
lower = np.array([hMin, sMin, vMin])
upper = np.array([hMax, sMax, vMax])
# Create HSV Image and threshold into a range.
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
mask = cv2.inRange(hsv, lower, upper)
output = cv2.bitwise_and(image,image, mask= mask)
# Print if there is a change in HSV value
if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):
print("(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)" % (hMin , sMin , vMin, hMax, sMax , vMax))
phMin = hMin
psMin = sMin
pvMin = vMin
phMax = hMax
psMax = sMax
pvMax = vMax
# Display output image
cv2.imshow('image',output)
# Wait longer to prevent freeze for videos.
if cv2.waitKey(wait_time) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
玩这个。
#blurring and smoothin
img1=cv2.imread('marathon.png',1)
hsv = cv2.cvtColor(img1,cv2.COLOR_BGR2HSV)
#lower red
lower_red = np.array([0,50,50])
upper_red = np.array([10,255,255])
#upper red
lower_red2 = np.array([170,50,50])
upper_red2 = np.array([180,255,255])
mask = cv2.inRange(hsv, lower_red, upper_red)
res = cv2.bitwise_and(img1,img1, mask= mask)
mask2 = cv2.inRange(hsv, lower_red2, upper_red2)
res2 = cv2.bitwise_and(img1,img1, mask= mask2)
img3 = res+res2
img4 = cv2.add(res,res2)
img5 = cv2.addWeighted(res,0.5,res2,0.5,0)
kernel = np.ones((15,15),np.float32)/225
smoothed = cv2.filter2D(res,-1,kernel)
smoothed2 = cv2.filter2D(img3,-1,kernel)
cv2.imshow('Original',img1)
cv2.imshow('Averaging',smoothed)
cv2.imshow('mask',mask)
cv2.imshow('res',res)
cv2.imshow('mask2',mask2)
cv2.imshow('res2',res2)
cv2.imshow('res3',img3)
cv2.imshow('res4',img4)
cv2.imshow('res5',img5)
cv2.imshow('smooth2',smoothed2)
cv2.waitKey(0)
cv2.destroyAllWindows()
在 HSV 色轮上,红色的“H”值为 0°(和 360°)。
您的问题表明您正在尝试获得 -20 到 +20 的“H”范围,实现此目的的一种方法是在转换为 HSV 时交换红/蓝通道。这样,红色变为240°(原来是蓝色的度数),我们可以简单地选择值220-260(实际上cv2中的110-130,因为H值减半)
要交换红/蓝通道,只需使用 cv2 常数
COLOR_RGB2HSV
而不是 COLOR_BGR2HSV
示例代码
img=cv2.imread('img.bmp')
img_hsv=cv2.cvtColor(img, cv2.COLOR_RGB2HSV) #### <-- note the change from BGR to RGB
lower_red = np.array([110,50,50]) #example value
upper_red = np.array([130,255,255]) #example value
mask = cv2.inRange(img_hsv, lower_red, upper_red)
img_result = cv2.bitwise_and(img, img, mask=mask)
这只需要调用一次
inRange
方法,并且不需要任何额外的逻辑。