#!/usr/bin/python

import io
import picamera
import cv2
import numpy as np
import datetime
import time
import os
from si_prefix import si_format
import Adafruit_CharLCD as LCD

# Set FPS (though RPi is probably to slow to meet it ;-)
FPS = 1.0
frameTimeDelta = datetime.timedelta(seconds=1.0/FPS)
dimTimeDelta = datetime.timedelta(seconds=10)

# Init LCD
lcd_rs = 27
lcd_en        = 22
lcd_d4        = 25
lcd_d5        = 24
lcd_d6        = 23
lcd_d7        = 18
lcd_backlight = 4

lcd_columns = 16
lcd_rows    = 2

lcd = LCD.Adafruit_CharLCD(lcd_rs, lcd_en, lcd_d4, lcd_d5, lcd_d6, lcd_d7,
                           lcd_columns, lcd_rows, lcd_backlight)

lcd.clear()
lcd.message("Init scanner.")

# Init camera
camera = picamera.PiCamera()
camera.resolution = (1280, 720)

lcd.clear()
lcd.message("Init scanner..")

# Init classifier, various options:
#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml (~22sec)
#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml (>25sec)
#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml (~21sec)
#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt_tree.xml (~14sec, seems to miss many faces)
classifier = cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml')
lcd.clear()
lcd.message("Init scanner...")

prevFaceCount = 0
totalUse = 0 #in face-seconds

# make sure log file exists
if not os.path.exists("scan_face.log")
	with open("scan_face.log","w") as f:
		f.write("{},{},{}".format(time.time(), 0,0))

# get last line of log file and update 'total use' using that.
with open("scan_face.log", "rb") as f:
    first = f.readline()        # Read the first line.
    f.seek(-2, os.SEEK_END)     # Jump to the second last byte.
    while f.read(1) != b"\n":   # Until EOL is found...
        f.seek(-2, os.SEEK_CUR) # ...jump back the read byte plus one more.
    last = f.readline()         # Read last line.
    bits = last.split(",")
    totalUse = bits[2]

log = open("scan_face.log", "a")

lastFaceTime = datetime.datetime.utcnow()

while True:
	print(time.time(),"GO")
	start = datetime.datetime.utcnow()
	stream = io.BytesIO()
	camera.capture(stream, format='jpeg')
	print(time.time(),"captured")
	buff = np.fromstring(stream.getvalue(), dtype=np.uint8)
	image = cv2.imdecode(buff,1)
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	print(time.time(),"grayed")
	faces = classifier.detectMultiScale(gray, 1.2, 5, minSize=(30,20))

	
	print(time.time(),"Found {} faces".format(len(faces)))
	
	end = datetime.datetime.utcnow()
	# take the frame as being representative of whole frame
	scanDuration = (end - start).total_seconds()
	totalUse += len(faces) * scanDuration
	
	lcd.clear()
	#~ lcd.message("viewers {:>8}\nview-min. {:>7.2f}".format(len(faces), totalUse/60))
	lcd.message("{:>7} viewers \n{:>7}view-sec".format(len(faces), si_format(totalUse,precision=1)))

	log.write("{},{},{}".format(time.time(), len(faces), int(totalUse)))
	log.flush()

	os.fsync(log.fileno()) 

	if len(faces) < 1 and end - lastFaceTime > dimTimeDelta:
		lcd.set_backlight(0)
	else:
		lcd.set_backlight(1)
	
	if len(faces) > 0:
		lastFaceTime = end
		
	#~ if end - start < frameTimeDelta:
		
		#~ waitTime = frameTimeDelta - (end-start)
		#~ print("wait {}".waitTime.total_seconds())
		#~ time.sleep(waitTime.total_seconds())