Notes

import cv2

import cv2 as cv

import numpy as np

 

kernel = np.ones((5,5),np.uint8)

 

# Take input from webcam

cap = cv2.VideoCapture(0)

 

# Reduce the size of video to 320x240 so rpi can process faster

cap.set(3,320)

cap.set(4,240)

 

def nothing(x):

    pass

# Creating a windows for later use

cv2.namedWindow('HueComp')

cv2.namedWindow('SatComp')

cv2.namedWindow('ValComp')

cv2.namedWindow('closing')

cv2.namedWindow('tracking')

 

 

# Creating track bar for min and max for hue, saturation and value

# You can adjust the defaults as you like

cv2.createTrackbar('hmin', 'HueComp',12,179,nothing)

cv2.createTrackbar('hmax', 'HueComp',37,179,nothing)

 

cv2.createTrackbar('smin', 'SatComp',96,255,nothing)

cv2.createTrackbar('smax', 'SatComp',255,255,nothing)

 

cv2.createTrackbar('vmin', 'ValComp',186,255,nothing)

cv2.createTrackbar('vmax', 'ValComp',255,255,nothing)

 

# My experimental values

# hmn = 12

# hmx = 37

# smn = 145

# smx = 255

# vmn = 186

# vmx = 255

 

 

while(1):

 

    buzz = 0

    _, frame = cap.read()

 

    #converting to HSV

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

    hue,sat,val = cv2.split(hsv)

 

    # get info from track bar and appy to result

    hmn = cv2.getTrackbarPos('hmin','HueComp')

    hmx = cv2.getTrackbarPos('hmax','HueComp')

   

 

    smn = cv2.getTrackbarPos('smin','SatComp')

    smx = cv2.getTrackbarPos('smax','SatComp')

 

 

    vmn = cv2.getTrackbarPos('vmin','ValComp')

    vmx = cv2.getTrackbarPos('vmax','ValComp')

 

    # Apply thresholding

    hthresh = cv2.inRange(np.array(hue),np.array(hmn),np.array(hmx))

    sthresh = cv2.inRange(np.array(sat),np.array(smn),np.array(smx))

    vthresh = cv2.inRange(np.array(val),np.array(vmn),np.array(vmx))

 

    # AND h s and v

    tracking =cv2.bitwise_and(hthresh,cv2.bitwise_and(sthresh,vthresh))

 

    # Some morpholigical filtering

    dilation = cv2.dilate(tracking,kernel,iterations = 1)

    closing = cv2.morphologyEx(dilation, cv2.MORPH_CLOSE, kernel)

    closing = cv2.GaussianBlur(closing,(5,5),0)

 

    # Detect circles using HoughCircles

    circles =cv2.HoughCircles(closing,cv2.HOUGH_GRADIENT,2,120,param1=120,param2=50,minRadius=10,maxRadius=0)

    # circles = np.uint16(np.around(circles))

   

 

    #Draw Circles

    if circles is not None:

            for i in circles[0,:]:

                # If the ball is far, draw it in green

                if int(round(i[2])) < 30:

                    cv2.circle(frame,(int(round(i[0])),int(round(i[1]))),int(round(i[2])),(0,255,0),5)

                    cv2.circle(frame,(int(round(i[0])),int(round(i[1]))),2,(0,255,0),10)

                # else draw it in red

                elif int(round(i[2])) > 35:

                    cv2.circle(frame,(int(round(i[0])),int(round(i[1]))),int(round(i[2])),(0,0,255),5)

                    cv2.circle(frame,(int(round(i[0])),int(round(i[1]))),2,(0,0,