# -*- coding: utf-8 -*-
"""
Created on Thu Nov 22 14:16:46 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)

# Following method is much faster -> 0.00143s
# Still want to speed up further by lowering reliance on memory, which is quite heavy..
k = np.sum(img_thresh) / 255

# Taking indices for num of rows.
x_ind = np.arange(0,img_thresh.shape[1])
y_ind = np.arange(0,img_thresh.shape[0])
coords = np.zeros((img_thresh.shape[0], img_thresh.shape[1], 2))
coords_x = np.zeros((img_thresh.shape[0], img_thresh.shape[1]))
coords_y = np.zeros((img_thresh.shape[0], img_thresh.shape[1]))
coords_x[:,:] = x_ind


# Even this is extremely quick as it goes through rows in the numpy array.
for element in y_ind:
    coords_y[element,:] = element
    
# Now need to get the average x value and y value for centre of gravity
xb = int(np.sum(coords_x[img_thresh == 255])/k)
yb = int(np.sum(coords_y[img_thresh == 255])/k)

centre = (int(np.sum(coords_x[img_thresh == 255])/k), int(np.sum(coords_y[img_thresh == 255])/k))

#x,y,k,xb,yb = 0,0,0,0,0
#
## this is inherently slow...like very very slow...0.114s
#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))


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.
# First get equation of the circle:
# y = sart(r2 - (x-c)2 + c)

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