Initial Commit

Readme.md

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+# A Watched Pot Never Boils
+
+This repo uses a Raspberry Pi and a 433Mhz remote controlled socket to enforce the old idiom, "A Watched Pot Never Boils". 
+
+This code is unmaintained, and was written at 4am after seeing a video by Tom Scott that contained this very concept. 
+
+A writeup of how this was done can be found on my blog, [here]()

WatchPotNeverBoils.ino

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+/*
+Send an n to turn on, a f to turn off
+*/
+
+
+
+#include <RCSwitch.h>
+
+RCSwitch mySwitch = RCSwitch();
+int inbyte = 0;   // for incoming serial data
+
+
+void setup() {
+
+  Serial.begin(9600);
+  
+  // Transmitter is connected to Arduino Pin #10  
+  mySwitch.enableTransmit(10);
+  
+  // Optional set protocol (default is 1, will work for most outlets)
+  // mySwitch.setProtocol(2);
+
+  // Optional set pulse length.
+   mySwitch.setPulseLength(242);
+  
+  // Optional set number of transmission repetitions.
+  // mySwitch.setRepeatTransmit(15);
+  
+}
+
+void loop() {
+
+  /* Same switch as above, but using binary code */
+  if (Serial.available() > 0) {
+    inbyte = Serial.read();
+    switch (inbyte) {
+      case 102:
+        mySwitch.send("000011110000000000000000");
+      case 110:
+        mySwitch.send("000011110000000011000000");
+    }
+  }
+
+}

cv.py

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+import io
+import picamera
+import cv2
+import numpy
+import serial
+
+#Load a cascade file for detecting faces
+face_cascade = cv2.CascadeClassifier('/home/pi/opencv-3.3.0/data/haarcascades/haarcascade_frontalface_default.xml')
+
+ser = serial.Serial('/dev/ttyACM0')
+
+while True:
+    #Create a memory stream so photos doesn't need to be saved in a file
+    stream = io.BytesIO()
+
+    #Get the picture (low resolution, so it should be quite fast)
+    #Here you can also specify other parameters (e.g.:rotate the image)
+    with picamera.PiCamera() as camera:
+        camera.resolution = (320, 240)
+        camera.capture(stream, format='jpeg')
+
+    #Convert the picture into a numpy array
+    buff = numpy.fromstring(stream.getvalue(), dtype=numpy.uint8)
+
+    #Now creates an OpenCV image
+    image = cv2.imdecode(buff, 1)
+
+    #Convert to grayscale
+    gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+
+    #Look for faces in the image using the loaded cascade file
+    faces = face_cascade.detectMultiScale(gray, 1.1, 5)
+    print("Found "+str(len(faces))+" face(s)")
+
+    if len(faces) > 0:
+        ser.write(b'f')
+    else:
+        ser.write(b'n')
+