我想使用网络摄像头和python opencv跟踪白色。我已经有了跟踪蓝色的代码。
_, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of blue color in HSV
lower_blue = np.array([110,100,100])
upper_blue = np.array([130,255,255])
#How to define this range for white color
# Threshold the HSV image to get only blue colors
mask = cv2.inRange(hsv, lower_blue, upper_blue)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('frame',frame)
cv2.imshow('mask',mask)
cv2.imshow('res',res)
值我应该给出下限和上限 来跟踪白色 !! ?? 我尝试改变价值观,但我得到了其他颜色,但白色没有运气!!!
是HSV值或BGR值指定为下限和上限???
PS:我必须将最后一个结果作为二进制图像进行进一步处理!!
请帮帮我!!!
答案 0 :(得分:32)
让我们来看看HSV色彩空间:
你需要白色,靠近中心而且相当高。从
开始sensitivity = 15
lower_white = np.array([0,0,255-sensitivity])
upper_white = np.array([255,sensitivity,255])
然后根据您的需要调整阈值。
您也可以考虑使用HSL 色彩空间,即色调,饱和度,亮度。然后你只需要看亮度来检测白色,并且识别其他颜色会很容易。 HSV和HSL都保持相似的颜色。此外,HSL可能会更准确地检测白色 - 这就是原因:
答案 1 :(得分:12)
我为追踪白色而写了这个:
import cv2
import numpy as np
cap = cv2.VideoCapture(0)
while(1):
_, frame = cap.read()
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# define range of white color in HSV
# change it according to your need !
lower_white = np.array([0,0,0], dtype=np.uint8)
upper_white = np.array([0,0,255], dtype=np.uint8)
# Threshold the HSV image to get only white colors
mask = cv2.inRange(hsv, lower_white, upper_white)
# Bitwise-AND mask and original image
res = cv2.bitwise_and(frame,frame, mask= mask)
cv2.imshow('frame',frame)
cv2.imshow('mask',mask)
cv2.imshow('res',res)
k = cv2.waitKey(5) & 0xFF
if k == 27:
break
cv2.destroyAllWindows()
我试图跟踪手机的白屏并得到了这个:
您可以尝试更改HSV值 您也可以尝试HSL色彩空间,如Legat所说,它会更准确
答案 2 :(得分:3)
这是一个HSV颜色阈值脚本,用于使用滑块确定上下限
结果
使用此示例图片
具有这些较低/较高的阈值
lower_white = np.array([0,0,168])
upper_white = np.array([172,111,255])
我们得到孤立的白色像素(左)和二进制蒙版(右)
这是脚本,记住要更改输入图像的路径
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()