Tracking yellow color object with OpenCV python

Question

How can I track a yellow object in python using opencv? And, if possible, how can I get the position of the object?

I've tried using the following method but i can't figure out how to lower and upper ranges work.

import numpy as np
import cv2


cap = cv2.VideoCapture(0)
while True:
    screen =  np.array(ImageGrab.grab())
    ret, img = cap.read()
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

    #Help
    lower = np.array([])
    upper = np.array([])

    mask = cv2.inRange(hsv, lower, upper)

    cv2.imshow('screen', mask)



    if cv2.waitKey(25) & 0xFF == ord('q'):
        cv2.destroyAllWindows()
        break

It should find yellow objects and possibly get their position.

Answer 1

You can convert the image to HSV then use color thresholding. Using this example image

With lower/upper ranges of

lower = np.array([22, 93, 0])
upper = np.array([45, 255, 255])

We can isolate yellow

To get the position of the object (I'm assuming you want a bounding box), you can find contours on the resulting mask

import numpy as np
import cv2

image = cv2.imread('yellow.jpg')
original = image.copy()
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
lower = np.array([22, 93, 0], dtype="uint8")
upper = np.array([45, 255, 255], dtype="uint8")
mask = cv2.inRange(image, lower, upper)

cnts = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    cv2.rectangle(original, (x, y), (x + w, y + h), (36,255,12), 2)

cv2.imshow('mask', mask)
cv2.imshow('original', original)
cv2.waitKey()

You can use this script to find the color threshold ranges

import cv2
import sys
import numpy as np

def nothing(x):
    pass

useCamera=False

# Check if filename is passed
if (len(sys.argv) <= 1) :
    print("'Usage: python hsvThresholder.py <ImageFilePath>' to ignore camera and use a local image.")
    useCamera = True

# 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 to display
if useCamera:
    cap = cv2.VideoCapture(0)
    # Wait longer to prevent freeze for videos.
    waitTime = 330
else:
    img = cv2.imread(sys.argv[1])
    output = img
    waitTime = 33

while(1):

    if useCamera:
        # Capture frame-by-frame
        ret, img = cap.read()
        output = img

    # 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 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(waitTime) & 0xFF == ord('q'):
        break

# Release resources
if useCamera:
    cap.release()
cv2.destroyAllWindows()

Answer 2

The lower and upper value is always depends on the range you want to choose. There is no hard and fast rule that it should be in this range for a particular color. because it might vary even because of the lighting conditions.

With respect to your tracking, I would recommend you to follow this tutorial.

https://www.pyimagesearch.com/2015/09/14/ball-tracking-with-opencv/

It gives explanation about masking, segmentation, tracking.