import cv2
import numpy as np
import pytesseract
from PIL import ImageGrab

def ExtractText(img):
	return pytesseract.image_to_string(img, config='-c tessedit_char_whitelist=WASD --psm 13')

def GetBoxStartPos(img):
	for x in range(0, 50):
		for y in range(0, 50):
			r, g, b = img[x, y]
			color = int(r) + int(g) + int(b)
			if color < pixel_color_threshold:
				return (x+8, y+7)
	return (8, 7)

def ProcessImage(ind):
	# USE BELOW TO GET FILE FROM DISK
	# img_ori = cv2.imread('images/bdo_letters{0}.jpg'.format(ind))
	# img_ori = img_ori[330:430, 750:1160]
	# USE BELOW FOR SCREENSHOT
	img_ori = ImageGrab.grab().crop(image_areas['letters']).convert('RGB') # crop image from screenshot
	img_ori = np.array(img_ori) # convert to cv2 image
	img_ori = img_ori[:, :, ::-1].copy()

	pos = GetBoxStartPos(img_ori) # get start pos of letters
	box = img_ori[pos[0]:pos[0]+y_len, pos[1]:pos[1]+x_len]

	# join letters to remove gaps between
	letters = []
	for i in range(0, 10):
		x_pos = i*l_len + i*gap_len
		letter = box[:, x_pos:x_pos+l_len]
		letters.append(letter)
	box_format = cv2.hconcat(letters)

	# box_format = cv2.cvtColor(box_format, cv2.COLOR_BGR2GRAY) # convert to grayscale
	# img1 = cv2.threshold(box_format,128,255,cv2.THRESH_BINARY_INV|cv2.THRESH_OTSU)[1]
	# img_gauss = cv2.GaussianBlur(box_format,(3,3),0) # gaussian filter (optional)
	# img2 = cv2.threshold(img_gauss,128,255,cv2.THRESH_BINARY_INV|cv2.THRESH_OTSU)[1]

	cv2.imwrite('img_original_{0}.jpg'.format(ind), box_format)
	# cv2.imwrite('img_processed2_{0}.jpg'.format(ind), img2)

	text = ExtractText(box_format)
	print(text)
	# text = ExtractText(img2)
	# print(text)

image_areas = {
	'bite': (720, 55, 890, 80),
	'letters': (750, 330, 1160, 430),
}

pixel_color_threshold = 200
x_len = 355
y_len = 22
l_len = 18
gap_len = 19
pytesseract.pytesseract.tesseract_cmd = 'C:/Program Files/Tesseract-OCR/tesseract'

# ProcessImage(8)
for i in range(1, 9):
	print('-- PROCESSING IMAGE {0} --'.format(i))
	ProcessImage(i)