120 lines
3.3 KiB
Python
Executable File
120 lines
3.3 KiB
Python
Executable File
#!/usr/bin/python
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import io
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import picamera
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import cv2
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import numpy as np
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import datetime
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import time
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import os
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from si_prefix import si_format
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import Adafruit_CharLCD as LCD
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# Set FPS (though RPi is probably to slow to meet it ;-)
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FPS = 1.0
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frameTimeDelta = datetime.timedelta(seconds=1.0/FPS)
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dimTimeDelta = datetime.timedelta(seconds=10)
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# Init LCD
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lcd_rs = 27
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lcd_en = 22
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lcd_d4 = 25
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lcd_d5 = 24
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lcd_d6 = 23
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lcd_d7 = 18
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lcd_backlight = 4
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lcd_columns = 16
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lcd_rows = 2
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lcd = LCD.Adafruit_CharLCD(lcd_rs, lcd_en, lcd_d4, lcd_d5, lcd_d6, lcd_d7,
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lcd_columns, lcd_rows, lcd_backlight)
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lcd.clear()
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lcd.message("Init scanner.")
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# Init camera
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camera = picamera.PiCamera()
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camera.resolution = (1280, 720)
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lcd.clear()
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lcd.message("Init scanner..")
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# Init classifier, various options:
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#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml (~22sec)
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#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml (>25sec)
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#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml (~21sec)
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#~ /usr/share/opencv/haarcascades/haarcascade_frontalface_alt_tree.xml (~14sec, seems to miss many faces)
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classifier = cv2.CascadeClassifier('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml')
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lcd.clear()
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lcd.message("Init scanner...")
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prevFaceCount = 0
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totalUse = 0 #in face-seconds
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# make sure log file exists
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if not os.path.exists("scan_face.log")
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with open("scan_face.log","w") as f:
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f.write("{},{},{}".format(time.time(), 0,0))
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# get last line of log file and update 'total use' using that.
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with open("scan_face.log", "rb") as f:
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first = f.readline() # Read the first line.
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f.seek(-2, os.SEEK_END) # Jump to the second last byte.
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while f.read(1) != b"\n": # Until EOL is found...
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f.seek(-2, os.SEEK_CUR) # ...jump back the read byte plus one more.
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last = f.readline() # Read last line.
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bits = last.split(",")
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totalUse = bits[2]
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log = open("scan_face.log", "a")
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lastFaceTime = datetime.datetime.utcnow()
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while True:
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print(time.time(),"GO")
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start = datetime.datetime.utcnow()
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stream = io.BytesIO()
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camera.capture(stream, format='jpeg')
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print(time.time(),"captured")
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buff = np.fromstring(stream.getvalue(), dtype=np.uint8)
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image = cv2.imdecode(buff,1)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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print(time.time(),"grayed")
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faces = classifier.detectMultiScale(gray, 1.2, 5, minSize=(30,20))
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print(time.time(),"Found {} faces".format(len(faces)))
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end = datetime.datetime.utcnow()
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# take the frame as being representative of whole frame
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scanDuration = (end - start).total_seconds()
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totalUse += len(faces) * scanDuration
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lcd.clear()
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#~ lcd.message("viewers {:>8}\nview-min. {:>7.2f}".format(len(faces), totalUse/60))
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lcd.message("{:>7} viewers \n{:>7}view-sec".format(len(faces), si_format(totalUse,precision=1)))
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log.write("{},{},{}".format(time.time(), len(faces), int(totalUse)))
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log.flush()
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os.fsync(log.fileno())
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if len(faces) < 1 and end - lastFaceTime > dimTimeDelta:
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lcd.set_backlight(0)
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else:
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lcd.set_backlight(1)
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if len(faces) > 0:
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lastFaceTime = end
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#~ if end - start < frameTimeDelta:
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#~ waitTime = frameTimeDelta - (end-start)
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#~ print("wait {}".waitTime.total_seconds())
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#~ time.sleep(waitTime.total_seconds())
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