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Enable LCD

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Artwork1 2017-12-15 19:29:58 +01:00
parent 3439e34f4f
commit de19898f38
1 changed files with 34 additions and 0 deletions
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34
scan_faces.py
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@ -5,19 +5,41 @@ import numpy as np
import datetime import datetime
import time import time
import Adafruit_CharLCD as LCD
# Init camera
camera = picamera.PiCamera() camera = picamera.PiCamera()
camera.resolution = (1280, 720) camera.resolution = (1280, 720)
# Init classifier, various options:
#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml (~22sec) #~ /usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml (~22sec)
#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml (>25sec) #~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml (>25sec)
#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml (~21sec) #~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml (~21sec)
#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt_tree.xml (~14sec, seems to miss many faces) #~ /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') 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 FPS = 1.0
frameTimeDelta = datetime.timedelta(seconds=1.0/FPS) 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: while True:
print(time.time(),"GO") print(time.time(),"GO")
start = datetime.datetime.utcnow() start = datetime.datetime.utcnow()
@ -30,9 +52,21 @@ while True:
print(time.time(),"grayed") print(time.time(),"grayed")
faces = classifier.detectMultiScale(gray, 1.2, 5, minSize=(30,20)) 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))) 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() end = datetime.datetime.utcnow()
prevFaceCount = len(faces)
totalUse += len(faces)
if end - start < frameTimeDelta: if end - start < frameTimeDelta:
waitTime = frameTimeDelta - (end-start) waitTime = frameTimeDelta - (end-start)