try:
    from PIL import Image
except ImportError:
    import Image
from cv2 import cv2
import pytesseract
import argparse
import numpy as np
from imutils.object_detection import non_max_suppression

pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
faceCascade = cv2.CascadeClassifier(
    'haarcascade/haarcascade_frontalface_default.xml')


def decode_predictions(scores, geometry):
	# grab the number of rows and columns from the scores volume, then
	# initialize our set of bounding box rectangles and corresponding
	# confidence scores
	(numRows, numCols) = scores.shape[2:4]
	rects = []
	confidences = []

	# loop over the number of rows
	for y in range(0, numRows):
		# extract the scores (probabilities), followed by the
		# geometrical data used to derive potential bounding box
		# coordinates that surround text
		scoresData = scores[0, 0, y]
		xData0 = geometry[0, 0, y]
		xData1 = geometry[0, 1, y]
		xData2 = geometry[0, 2, y]
		xData3 = geometry[0, 3, y]
		anglesData = geometry[0, 4, y]

		# loop over the number of columns
		for x in range(0, numCols):
			# if our score does not have sufficient probability,
			# ignore it
			if scoresData[x] < 0.5:
				continue

			# compute the offset factor as our resulting feature
			# maps will be 4x smaller than the input image
			(offsetX, offsetY) = (x * 4.0, y * 4.0)

			# extract the rotation angle for the prediction and
			# then compute the sin and cosine
			angle = anglesData[x]
			cos = np.cos(angle)
			sin = np.sin(angle)

			# use the geometry volume to derive the width and height
			# of the bounding box
			h = xData0[x] + xData2[x]
			w = xData1[x] + xData3[x]

			# compute both the starting and ending (x, y)-coordinates
			# for the text prediction bounding box
			endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
			endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
			startX = int(endX - w)
			startY = int(endY - h)

			# add the bounding box coordinates and probability score
			# to our respective lists
			rects.append((startX, startY, endX, endY))
			confidences.append(scoresData[x])

	# return a tuple of the bounding boxes and associated confidences
	return (rects, confidences)


def findNumber(url):
	image = cv2.imread("./"+url)
	orig = image.copy()
	(origH, origW) = image.shape[:2]
	(newW, newH) = (320,320)
	rW = origW / float(newW)
	rH = origH / float(newH)
	image = cv2.resize(image, (newW, newH))
	(H, W) = image.shape[:2]
	layerNames = [
		"feature_fusion/Conv_7/Sigmoid",
		"feature_fusion/concat_3"]
	net = cv2.dnn.readNet("../EAST/frozen_east_text_detection.pb")
	blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),
								(123.68, 116.78, 103.94), swapRB=True, crop=False)
	net.setInput(blob)
	(scores, geometry) = net.forward(layerNames)
	(rects, confidences) = decode_predictions(scores, geometry)
	boxes = non_max_suppression(np.array(rects), probs=confidences)
	results = []
	for (startX, startY, endX, endY) in boxes:
		
		startX = int(startX * rW)
		startY = int(startY * rH)
		endX = int(endX * rW)
		endY = int(endY * rH)
		
		dX = int((endX - startX) * 0.0)
		dY = int((endY - startY) * 0.0)
		
		startX = max(0, startX - dX)
		startY = max(0, startY - dY)
		endX = min(origW, endX + (dX * 2))
		endY = min(origH, endY + (dY * 2))
		
		roi = orig[startY:endY, startX:endX]
		
		config = ("-l eng --oem 1 --psm 7")
		text = pytesseract.image_to_string(roi, config=config)
		
		results.append(((startX, startY, endX, endY), text))
		
	results = sorted(results, key=lambda r: r[0][1])
	wyniki = []
	for ((startX, startY, endX, endY), text) in results:
		if( text.isdigit() ):
			wyniki.append(text)
		# print("OCR TEXT")
		# print("========")
		# print("{}\n".format(text))
		
		# text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
		# output = orig.copy()
		# cv2.rectangle(output, (startX, startY), (endX, endY),
				# (0, 0, 255), 2)
		# cv2.putText(output, text, (startX, startY - 20),
				# cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)
				
		# cv2.imshow("Text Detection", output)
		# cv2.waitKey(0)

	return wyniki