[英]Tracking white color using python opencv
我想使用網絡攝像頭和 python opencv 跟蹤白色。 我已經有了跟蹤藍色的代碼。
_, 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)
我應該給出什么值作為跟蹤白色的下限和上限???? 我嘗試更改值,我得到了其他 colors 但白色沒有運氣!!!
是指定為下限和上限的 HSV 值或 BGR 值???
PS:我必須將最后的結果作為二進制圖像進行進一步處理!
請幫我 !!!
讓我們看一下 HSV 顏色空間:
你需要白色,它靠近中心並且相當高。 從...開始
sensitivity = 15
lower_white = np.array([0,0,255-sensitivity])
upper_white = np.array([255,sensitivity,255])
然后根據您的需要調整閾值。
您也可以考慮使用 HSL顏色空間,它代表 Hue、Saturation、 Lightness 。 然后,您只需要查看亮度即可檢測白色並識別其他顏色會很容易。 HSV 和 HSL 都保持相似的顏色接近。 此外,HSL 可能會更准確地檢測白色 - 這就是原因:
我寫這個是為了跟蹤白色:
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()
我試圖跟蹤我手機的白屏並得到了這個:
您可以嘗試更改 HSV 值您也可以嘗試使用 Legat 所說的 HSL 顏色空間,它會更准確
這是一個 HSV 顏色閾值腳本,用於使用滑塊確定下限和上限
結果
使用此示例圖像
有了這些下限/上限閾值
lower_white = np.array([0,0,168])
upper_white = np.array([172,111,255])
我們得到孤立的白色像素(左)和二進制掩碼(右)
這是腳本,記得更改輸入圖像路徑
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()
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