Enable LCD

scan_faces.py

@@ -5,19 +5,41 @@ import numpy as np
 import datetime
 import time
 
+import Adafruit_CharLCD as LCD
 
+# Init camera
 camera = picamera.PiCamera()
 camera.resolution = (1280, 720)
 
+# 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')
 
+# Set FPS (though RPi is probably to slow to meet it ;-)
 FPS = 1.0
 frameTimeDelta = datetime.timedelta(seconds=1.0/FPS)
 
+# 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)
+
+prevFaceCount = 0
+totalUse = 0
+
 while True:
 	print(time.time(),"GO")
 	start = datetime.datetime.utcnow()
@@ -30,9 +52,21 @@ while True:
 	print(time.time(),"grayed")
 	faces = classifier.detectMultiScale(gray, 1.2, 5, minSize=(30,20))
 	
+	# TODO: time passed since last * prevFaceCount = usage
+	
 	print(time.time(),"Found {} faces".format(len(faces)))
 	
+	lcd.clear()
+	lcd.message("Current {:>8}\nTotal {:>10}".format(len(faces), totalUse))
+	if len(faces) < 1 and prevFaceCount < 1:
+		lcd.set_backlight(0)
+	else:
+		lcd.set_backlight(1)
+	
 	end = datetime.datetime.utcnow()
+	prevFaceCount = len(faces)
+	totalUse += len(faces)
+	
 	if end - start < frameTimeDelta:
 		
 		waitTime = frameTimeDelta - (end-start)