# detect orange  -adjustable upper and lower Hue threshholds, and lower Saturation and lower Value threshholds
import numpy as np
import cv2

def nothing(x):
    pass

# create video capture 
cap = cv2.VideoCapture(0)
cv2.namedWindow('Th')
cv2.createTrackbar('HL', 'Th',8,180,nothing)
cv2.createTrackbar('HH', 'Th',15,180,nothing)
cv2.createTrackbar('SL', 'Th',100,255,nothing)
cv2.createTrackbar('VL', 'Th',100,255,nothing)


# main loop
while(1):
    # get trackbar variables
    hl = cv2.getTrackbarPos('HL', 'Th')
    hh = cv2.getTrackbarPos('HH', 'Th')
    sl = cv2.getTrackbarPos('SL', 'Th')
    vl = cv2.getTrackbarPos('VL', 'Th')

    # read the current video frame
    _, frame = cap.read()

    # resize captured video frame to smaller size for faster processing
    frame = cv2.resize(frame, (300, 200), interpolation = cv2.INTER_LINEAR)

    # smooth it
    frame = cv2.blur(frame, (3, 3))

    # convert to hsv and find range of colors
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
    thresh = cv2.inRange(hsv, np.array((hl, sl, vl)), np.array((hh, 255, 255)))
    thresh2 = thresh.copy()

    # find contours in the threshold image
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
 
    # finding contour with maximum area and store it as best_cnt
    max_area = 0
    for cnt in contours:
        area = cv2.contourArea(cnt)
        if area > max_area:
            max_area = area
            best_cnt = cnt
        
    # find centroid of best_cnt and draw a red box there
    if max_area > 0:
        M = cv2.moments(best_cnt)
        cx, cy = int(M['m10']/M['m00']), int(M['m01']/M['m00'])
        cz = int(max_area) 
        cv2.rectangle(frame, (cx - 5, cy - 5), (cx + 5, cy + 5), (0, 0, 255), 2)

    # Show it, if key pressed is 'Esc', exit the loop
    cv2.imshow('Fr', frame)
    cv2.imshow('Th', thresh2)
    if cv2.waitKey(1) == 27:
        break

# Clean up everything before leaving
cap.release()
cv2.destroyAllWindows()