Opencv: Choosing Hsv Thresholds For Color Filtering

In order to filter a color out of an image, it's necessary to set boundaries as to which color needs to be detected. I have a feeling this is mostly a trial-and-error process. Are

Solution 1:

You can use a HSV color thresholder script to isolate the desired color range

import cv2
import sys
import numpy as np

defnothing(x):
    pass# 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

img = cv2.imread('1.png')
output = img
waitTime = 33while(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(img, cv2.COLOR_BGR2HSV)
    mask = cv2.inRange(hsv, lower, upper)
    output = cv2.bitwise_and(img,img, mask= mask)

    # Print if there is a change in HSV valueif( (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(waitTime) & 0xFF == ord('q'):
        break

cv2.destroyAllWindows()

Solution 2:

Another option is to use online image color picker. You can upload your image and will get some values like HSV: 97.5° 5.1% 61.57% in your case. Note, you need to convert them to OpenCV scales of H, S and V.

H, hue in OpenCV varies from 0 to 180, but in the outer world it is usually measured in degrees from 0 to 360, so to get the H of your color h = 97.5° / 2 = 48.7

S and V are measured from 0 ( = 0% in outer world) to 255 ( = 100% in outer world), so

s = 255 * 5.1% = 13v = 255 * 61.57% = 157

So, the target HSV color is (49, 13, 157). I suggest using ±10 for the range. Or being even more strict. I think it might be OK for your case to select only the pixels of the central graph, without any labels, and then apply morphological operation close, if needed.