import cv2

import numpy as np

# Processing image based on upper and lower bounds passed to function

def color_isolate(hsv, lower, upper) :

return median_2

# Defines range of colour to track based on value passed by trackbar

def choose_colour(x):

return lower, upper, text_colour

cap = cv2.VideoCapture(0)

# Use trackbar to switch between tracking a red, blue or green object

cv2.namedWindow('img')

cv2.createTrackbar('RGB', 'img', 0, 2, choose_colour)

# i = 0

colour_dict = {0:"Red", 1:"Green", 2:"Blue"}

while(True):

ret, frame = cap.read()

img = cv2.flip(frame, 1)

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

# Obtain position of trackbar to pick which colour to track

a_pos = cv2.getTrackbarPos('RGB', 'img')

text = colour_dict[a_pos]

# Use position of trackbar to pick colour and then isolate objects of matching colour

lower, upper, text_colour = choose_colour(a_pos)

fnres = color_isolate(hsv, lower, upper)

# Extract contours from image after processing

_,contours,_ = cv2.findContours(fnres, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

try:

# Obtain contour of greatest size

c = max(contours, key = cv2.contourArea)

area = cv2.contourArea(c)

# Use contour only if area greater than 150 to avoid noise

if area >= 150:

rect = cv2.minAreaRect(c)

box = cv2.boxPoints(rect)

box = np.int0(box)

cv2.drawContours(img,[box],0,(0,0,255),2)

pass

except Exception:

# Display message if no object found in image

cv2.putText(img,"No contours in frame",(10,60),cv2.FONT_HERSHEY_SIMPLEX,0.9,(0,0,0),1,cv2.LINE_AA)

cv2.putText(img,text,(10,40),cv2.FONT_HERSHEY_SIMPLEX,1.2,text_colour,2,cv2.LINE_AA)

cv2.imshow("img", img)

# Save image if spacebar pressed

# Press Q to close the window

key = cv2.waitKey(1)

if key == 113:

break

elif key == 32:

cv2.imwrite("colourTrack%d.png"%i,img)

i += 1

cap.release()

cv2.destroyAllWindows()