使用Python和OpenCV查找红色

Question

我试图从图像中提取红色.我有代码应用阈值只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,正如我检查的那样,红色可以在范围内具有Hue值,比如从0到10,以及在170到180的范围内.因此,我想从这两个范围中的任何一个中保留值.我尝试将阈值从10设置为170并使用cv2.bitwise_not函数,但随后我也获得了所有白色.我认为最好的选择是为每个范围创建一个掩码并使用它们,所以我不得不在继续之前将它们连接在一起.

有没有办法使用OpenCV加入两个面具？或者还有其他方法可以实现我的目标吗？

编辑.我来的不是很优雅,但工作解决方案:

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,而OpenCV的功能可能几乎完全相同,但如果有更好的方法(使用一些专用函数和编写更少的代码),请与我分享.:)

Answer 1

我只是将面具添加到一起,并用于np.where掩盖原始图像.

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

这比循环遍历图像的每个像素要快得多,也更易读.

Answer 2

要检测红色，您可以使用 HSV 颜色阈值脚本来确定下限/上限阈值，然后cv2.bitwise_and()获取遮罩。使用这个输入图像，

我们得到这个结果并屏蔽

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()

带滑块的 HSV 颜色阈值脚本，记得更改图片文件路径

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()