python opencv HSV range finder creating trackbars

Question

I want to find the HSV value of a LASER dot using opencv and python. I got the code http://opencv-srf.blogspot.com.au/2010/09/object-detection-using-color-seperation.html from here but it is in c++, installing visual studio and opencv takes time so i changed the code in python

import cv2
import numpy as np

def callback(x):
pass

cap = cv2.VideoCapture(0)
cv2.namedWindow('image')

ilowH = 0
ihighH = 179

ilowS = 0
ihighS = 255
ilowV = 0
ihighV = 255

# create trackbars for color change
cv2.createTrackbar('lowH','image',ilowH,179,callback)
cv2.createTrackbar('highH','image',ihighH,179,callback)

cv2.createTrackbar('lowS','image',ilowS,255,callback)
cv2.createTrackbar('highS','image',ihighS,255,callback)

cv2.createTrackbar('lowV','image',ilowV,255,callback)
cv2.createTrackbar('highV','image',ihighV,255,callback)



while(1):
 ret, frame = cap.read()
 hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
 cv2.imshow('hsv', hsv)
 lower_hsv = np.array([ilowH, ilowS, ilowV])
 higher_hsv = np.array([ihighH, ihighS, ihighV])
 mask = cv2.inRange(hsv, lower_hsv, higher_hsv)
 cv2.imshow('mask', mask)
 cv2.imshow('frame', frame)
 print ilowH, ilowS, ilowV
 if(cv2.waitKey(1) & 0xFF == ord('q')):
    break


cv2.destroyAllWindows()
cap.release()

but this code doesnot threshold anything. It seems like the trackbars i created doesnot change the value of ilowH ,ilowS, ilowV . I checked it by printing those values inside while loop. What could be the problem for not thresholding any of those values or is there better code in python to find HSV values of the LASER. Thank you, any help is appreciated.

Answer 1

You can grab the trackbar values with cv2.getTrackbarPos(). Also note that sometimes it puts trackbars out of order, which is annoying, but at least they're labeled.

However, I don't think that these trackbars will work very well for live video feed. There's a lot of freezing issues. You'll have to have a super low framerate (works for me with cv2.waitKey(500) if you're actually trying to display it). This is mostly due to the trackbars sucking, not the thresholding operation, which is not that slow.

You need to add your trackbars after you create the named window. Then, for your while loop, try:

while True:
    # grab the frame
    ret, frame = cap.read()

    # get trackbar positions
    ilowH = cv2.getTrackbarPos('lowH', 'image')
    ihighH = cv2.getTrackbarPos('highH', 'image')
    ilowS = cv2.getTrackbarPos('lowS', 'image')
    ihighS = cv2.getTrackbarPos('highS', 'image')
    ilowV = cv2.getTrackbarPos('lowV', 'image')
    ihighV = cv2.getTrackbarPos('highV', 'image')

    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    lower_hsv = np.array([ilowH, ilowS, ilowV])
    higher_hsv = np.array([ihighH, ihighS, ihighV])
    mask = cv2.inRange(hsv, lower_hsv, higher_hsv)

    frame = cv2.bitwise_and(frame, frame, mask=mask)

    # show thresholded image
    cv2.imshow('image', frame)
    k = cv2.waitKey(1000) & 0xFF # large wait time to remove freezing
    if k == 113 or k == 27:
        break

and finally end the file with a cv2.destroyAllWindows()

As an aside, the maximum H value for HSV is 180, not 179.

Shameless plug: I happened to just finish a project doing precisely this, but on images. You can grab it on GitHub here. There is an example; try running it and then modifying as you need. It will let you change the colorspace and threshold inside each different colorspace, and it will print the final thresholding values that you ended on. Additionally it will return the output image from the operation for you to use, too. Hopefully it is useful for you! Feel free to send any issues or suggestions through GitHub for the project.

Here is an example of it running:

And as output it gives you:

Colorspace: HSV 
Lower bound: [68.4, 0.0, 0.0] 
Upper bound: [180.0, 255.0, 255.0]

as well as the binary image. I am currently working on getting this into a web application as well, but that probably won't be finished for a few days.

Answer 2

Use this code to find range of masking of real-time video! this might save you time. Below is a whole code, Check it and run it to have a test.

import cv2
import numpy as np

camera = cv2.VideoCapture(0)

def nothing(x):
    pass

cv2.namedWindow('marking')

cv2.createTrackbar('H Lower','marking',0,179,nothing)
cv2.createTrackbar('H Higher','marking',179,179,nothing)
cv2.createTrackbar('S Lower','marking',0,255,nothing)
cv2.createTrackbar('S Higher','marking',255,255,nothing)
cv2.createTrackbar('V Lower','marking',0,255,nothing)
cv2.createTrackbar('V Higher','marking',255,255,nothing)


while(1):
    _,img = camera.read()
    img = cv2.flip(img,1)

    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    hL = cv2.getTrackbarPos('H Lower','marking')
    hH = cv2.getTrackbarPos('H Higher','marking')
    sL = cv2.getTrackbarPos('S Lower','marking')
    sH = cv2.getTrackbarPos('S Higher','marking')
    vL = cv2.getTrackbarPos('V Lower','marking')
    vH = cv2.getTrackbarPos('V Higher','marking')

    LowerRegion = np.array([hL,sL,vL],np.uint8)
    upperRegion = np.array([hH,sH,vH],np.uint8)

    redObject = cv2.inRange(hsv,LowerRegion,upperRegion)

    kernal = np.ones((1,1),"uint8")

    red = cv2.morphologyEx(redObject,cv2.MORPH_OPEN,kernal)
    red = cv2.dilate(red,kernal,iterations=1)

    res1=cv2.bitwise_and(img, img, mask = red)


    cv2.imshow("Masking ",res1)

    if cv2.waitKey(10) & 0xFF == ord('q'):
        camera.release()
        cv2.destroyAllWindows()
        break`

Thanks! Hugs..