# -*- coding: utf-8 -*-
"""
Created on Thu Nov 22 10:51:21 2018

@author: pivatom
"""

import numpy as np
import cv2

img = cv2.imread('H:\car\GestureRecognition\IMG_0818.png', 1)

# Downscale the image
img = cv2.resize(img, None, fx=0.1, fy=0.1, interpolation = cv2.INTER_AREA)

img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

img_gray[img_gray[:,:] > 90] = 255
img_gray[img_gray[:,:] < 90] = 0

# Threshold to binary.
ret,img_thresh = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)

x,y,k,xb,yb = 0,0,0,0,0

# this is inherently slow...
for pix in img_thresh:
    for j in pix:
        if j == 255:
            k += 1
            xb += x
            yb += y
        x += 1
    y += 1
    x = 0
    
centre = (int(xb/k), int(yb/k))
print(centre)

cv2.rectangle(img_thresh, centre, (centre[0] + 20, centre[1] + 20), (0,0,255), 3)
cv2.circle(img_thresh, centre, 140, (0,0,0), 3)

# Now need to trace around the circle to figure out where the fingers are.

cv2.imshow("Binary-cot-out", img_thresh)
cv2.waitKey(0)
cv2.destroyAllWindows()



#img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

#lower_skin = np.array([2, 102, 153])
#upper_skin = np.array([7.5, 153, 255])
#
## Only need mask, as we can just use this to calculate the 
#mask = cv2.inRange(img_hsv, lower_skin, upper_skin)
#
#cv2.imshow("Mask", mask)
#cv2.waitKey(0)
#cv2.destroyAllWindows()