警报和强迫自己起床的开源设备

描述

理念的起源

在过去的几年里，我很难在早上醒来。我尝试了许多解决方案，例如警报和强迫自己起床并开始行动，但都没有奏效。我决定想出一个解决方案来解决所有问题，如果我在闹钟响起 5 分钟后没有起床，这个设备会在我头上放一个枕头。

电子产品

我使用的最终电子设备非常简单。它由一个 Raspberry Pi 3 B、一个 Raspberry Pi 相机、3 条公对母跳线、一个微型伺服器和一个电池组组成。

 

 

 

1 / 4

 

我将伺服引脚连接到树莓派上的引脚 4、6 和 11。我还将相机插入 Raspberry Pi 上的带状电缆的小插槽。

代码

在详细介绍我的项目的代码和面部识别部分之前，我想在 Youtube 上向cytrontech 大喊大叫，因为它发布了这个视频，展示了如何使用 Opencv 进行基本的面部识别。

在我开始使用我的 Raspberry Pi 之前，我确保安装了最新版本的 Raspberry Pi OS 的新映像。然后我开始下载opencv以便开始处理图像。一旦我确认我已经下载了 opencv 并且完全是最新的，我就开始浏览 cytrontech 视频。

代码部分由四个文件组成，其中两个与原始视频中的相同。

import cv2
name = 'Suad' #replace with your name
cam = cv2.VideoCapture(0)
cv2.namedWindow("press space to take a photo", cv2.WINDOW_NORMAL)
cv2.resizeWindow("press space to take a photo", 500, 300)
img_counter = 0
while True:
ret, frame = cam.read()
if not ret:
print("failed to grab frame")
break
cv2.imshow("press space to take a photo", frame)
k = cv2.waitKey(1)
if k%256 == 27:
# ESC pressed
print("Escape hit, closing…")
break
elif k%256 == 32:
# SPACE pressed
img_name = "dataset/"+ name +"/image_{}.jpg".format(img_counter)
cv2.imwrite(img_name, frame)
print("{} written!".format(img_name))
img_counter += 1
cam.release()
cv2.destroyAllWindows()

这是第一个名为 face_shot.py 的文件。它用于拍摄您的脸部照片并收集数据以训练模型。

#! /usr/bin/python
# import the necessary packages
from imutils import paths
import face_recognition
#import argparse
import pickle
import cv2
import os
# our images are located in the dataset folder
print("[INFO] start processing faces…")
imagePaths = list(paths.list_images("dataset"))
# initialize the list of known encodings and known names
knownEncodings = []
knownNames = []
# loop over the image paths
for (i, imagePath) in enumerate(imagePaths):
# extract the person name from the image path
print("[INFO] processing image {}/{}".format(i + 1,
len(imagePaths)))
name = imagePath.split(os.path.sep)[–2]
# load the input image and convert it from RGB (OpenCV ordering)
# to dlib ordering (RGB)
image = cv2.imread(imagePath)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# detect the (x, y)-coordinates of the bounding boxes
# corresponding to each face in the input image
boxes = face_recognition.face_locations(rgb,
model="hog")
# compute the facial embedding for the face
encodings = face_recognition.face_encodings(rgb, boxes)
# loop over the encodings
for encoding in encodings:
# add each encoding + name to our set of known names and
# encodings
knownEncodings.append(encoding)
knownNames.append(name)
# dump the facial encodings + names to disk
print("[INFO] serializing encodings…")
data = {"encodings": knownEncodings, "names": knownNames}
f = open("encodings.pickle", "wb")
f.write(pickle.dumps(data))
f.close()

这是名为 train_model.py 的第二个文件。它用于根据您使用 face_shot.py 拍摄的图像来训练模型。

#! /usr/bin/python

# import the necessary packages
from datetime import datetime
import servo_move
from imutils.video import VideoStream
from imutils.video import FPS
import face_recognition
import imutils
import pickle
import time
import cv2

now = datetime.now()
da_time = datetime(2021, 4, 7, 12, 35, 00)
x = 0
#Initialize 'currentname' to trigger only when a new person is identified.
currentname = "unknown"
#Determine faces from encodings.pickle file model created from train_model.py
encodingsP = "encodings.pickle"
#use this xml file
#https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml
cascade = "haarcascade_frontalface_default.xml"

# load the known faces and embeddings along with OpenCV's Haar
# cascade for face detection
print("[INFO] loading encodings + face detector…")
data = pickle.loads(open(encodingsP, "rb").read())
detector = cv2.CascadeClassifier(cascade)

# initialize the video stream and allow the camera sensor to warm up
print("[INFO] starting video stream…")
vs = VideoStream(src=0).start()
#vs = VideoStream(usePiCamera=True).start()
time.sleep(2.0)

# start the FPS counter
fps = FPS().start()

# loop over frames from the video file stream
while True:
	# grab the frame from the threaded video stream and resize it
	# to 500px (to speedup processing)
	frame = vs.read()
	frame = imutils.resize(frame, width=500)
	
	# convert the input frame from (1) BGR to grayscale (for face
	# detection) and (2) from BGR to RGB (for face recognition)
	gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
	rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)

	# detect faces in the grayscale frame
	rects = detector.detectMultiScale(gray, scaleFactor=1.1, 
		minNeighbors=5, minSize=(30, 30),
		flags=cv2.CASCADE_SCALE_IMAGE)

	# OpenCV returns bounding box coordinates in (x, y, w, h) order
	# but we need them in (top, right, bottom, left) order, so we
	# need to do a bit of reordering
	boxes = [(y, x + w, y + h, x) for (x, y, w, h) in rects]

	# compute the facial embeddings for each face bounding box
	encodings = face_recognition.face_encodings(rgb, boxes)
	names = []

	# loop over the facial embeddings
	for encoding in encodings:
		# attempt to match each face in the input image to our known
		# encodings
		matches = face_recognition.compare_faces(data["encodings"],
			encoding)
		name = "Unknown" #if face is not recognized, then print Unknown

		# check to see if we have found a match
		if True in matches:
			# find the indexes of all matched faces then initialize a
			# dictionary to count the total number of times each face
			# was matched
			matchedIdxs = [i for (i, b) in enumerate(matches) if b]
			counts = {}

			# loop over the matched indexes and maintain a count for
			# each recognized face face
			for i in matchedIdxs:
				name = data["names"][i]
				counts[name] = counts.get(name, 0) + 1

			# determine the recognized face with the largest number
			# of votes (note: in the event of an unlikely tie Python
			# will select first entry in the dictionary)
			name = max(counts, key=counts.get)
			
			#If someone in your dataset is identified, print their name on the screen
			if currentname != name:
				currentname = name
				print(currentname)
		
		# update the list of names
		names.append(name)


	# loop over the recognized faces
	for ((top, right, bottom, left), name) in zip(boxes, names):
		# draw the predicted face name on the image – color is in BGR
		cv2.rectangle(frame, (left, top), (right, bottom),
			(0, 255, 0), 2)
		y = top - 15 if top - 15 > 15 else top + 15
		cv2.putText(frame, name, (left, y), cv2.FONT_HERSHEY_SIMPLEX,
			.8, (255, 0, 0), 2)


	# display the image to our screen
	cv2.imshow("Facial Recognition is Running", frame)
	key = cv2.waitKey(1) & 0xFF

	# quit when 'q' key is pressed
	if key == ord("q"):
		break

	# update the FPS counter
	fps.update()

	current_time = datetime.now()
	if (currentname == "will") and (current_time.time() > da_time.time()) and (x == 0):
		exec(open("servo_move.py").read())
		x = 1

# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))

# do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()

这是视频中名为 face_rec.py 的第三个也是最后一个文件。它是您想要实际启动面部识别软件时运行的文件。我只添加了几行代码，它们是：

from datetime import datetime
import servo_move

now = datetime.now()
da_time = datetime(2021, 4, 7, 12, 35, 00)
x = 0

	current_time = datetime.now()
	if (currentname == "will") and (current_time.time() > da_time.time()) and (x =              = 0):
		exec(open("servo_move.py").read())
		x = 1

这些代码行检查当前时间是否为上午 7:35，即闹钟响后 5 分钟。如果是并且我的脸在那里，那么它会执行一个名为servo_move.py 的文件。

import RPi.GPIO as GPIO
import time

GPIO.setmode(GPIO.BOARD)

GPIO.setup(11,GPIO.OUT)
servo1 = GPIO.PWM(11,50)
servo1.start(0)
servo1.ChangeDutyCycle(12)
time.sleep(2)
servo1.ChangeDutyCycle(2)
time.sleep(0.5)
servo1.ChangeDutyCycle(0)
servo1.stop()
GPIO.cleanup()

这是servo_move.py。它使伺服器移动 180 度然后向后移动。

制造

我必须制作的第一件作品是在我的电子部分展示的“电子板”。它只是一块木头，一切都依赖于它。

这是一个非常简单的设计，只是一些木头与我 3D 打印的一些铰链相连。我想说铰链不是我自己设计的，它们是guppyk在 thingiverse 上制作的。我使用的铰链和它们的许多变体可以在这里下载。

我会做什么不同

这个项目最终确实按预期工作，但这并不意味着我不会改变某些方面。如果我再做一次，我会把木头喷漆成黑色，这样胶带和零件就不会那么突出了。我也会制作一个更永久的电子板版本。