race1-billtest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from picamera.array import PiYUVArray, PiRGBArray\n",
    "from picamera import PiCamera\n",
    "\n",
    "from scipy.signal import find_peaks, butter, filtfilt\n",
    "\n",
    "import time\n",
    "import matplotlib.pyplot as plt\n",
    "import skimage as ski\n",
    "import numpy as np\n",
    "\n",
    "# Camera resolution\n",
    "res = (640, 480)\n",
    "\n",
    "CAMERA_CENTER = res[0] // 2\n",
    "       \n",
    "from pwm import PWM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup servo and motor PWM signal to control the steering angle and the speed.\n",
    "\n",
    "The values specified period and duty cycle ar in nanoseconds.\n",
    "\n",
    "Servo and motor controlled follow the following protocol:\n",
    "  1. Control signal is read 50 times per second by each device (servo and speed controller). Thus period is 20 ms => 20000000 ns\n",
    "  2. To send the minimum value (full left for servo and break for motor) the duty cycle should be 1 ms => 1000000 ns\n",
    "  3. Maximum value corresponds to a duty cycle of 2 ms => 2000000 ns\n",
    "  \n",
    "However, these devices are not perfect and you can set a duty cycle outside this range and still get a response. I would recoment to play with it and find are the limits in your case. Pay attention that servo is limited by the mechanical construction how much it can turn the wheels. Do not set these limits to a value which servo cannot execute as it will try to do that and eventually burn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable servo\n",
    "SERVO_MIDDLE = 1500000\n",
    "\n",
    "servo = PWM(1)\n",
    "servo.period = 20000000\n",
    "servo.duty_cycle = SERVO_MIDDLE\n",
    "servo.enable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Enable servo\n",
    "MOTOR_BRAKE = 1000000\n",
    "\n",
    "motor = PWM(0)\n",
    "motor.period = 20000000\n",
    "motor.duty_cycle = MOTOR_BRAKE\n",
    "motor.enable = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "motor.duty_cycle = MOTOR_BRAKE "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The self driving code\n",
    "\n",
    "Quick explanation on how this algorithm works:\n",
    "  1. Initialize the camera, use a frame rate of 10 to 20 fps\n",
    "  2. Get the coefficients of the butterworth filter of order 3 and cut-off frequency of 0.1 (change cut of frequency if the peak functions is not working)\n",
    "  3. For each frame captured from the camera:\n",
    "    1. Get the blak and white part of the image\n",
    "    2. Select line 195 to use for lane detection (this number depends on your camera angle)\n",
    "    3. Filter the line and find the peaks that are greater than 160. White parts of the image have higher values than the black. The threshold of 160 depends on the ambient light, lower if the room is darker\n",
    "    4. Find the peaks left and right of the camera center, these peaks should corresponds to the white lines delimiting the track,\n",
    "    5. Compute the center point between these lines. This is where we want our car\n",
    "    6. Compute the error by substracting the center point from camera middle and multiply by 5000. 5000 is our P gain.\n",
    "    7. Adjust the duty cycle of the servo such that the"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(peaks) =  2\n",
      "305 [ 56 105]\n",
      "Turning Left\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n",
      "len(peaks) =  3\n"
     ]
    },
    {
     "ename": "PiCameraValueError",
     "evalue": "Incorrect buffer length for resolution 640x480",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/picamera/camera.py\u001b[0m in \u001b[0;36mcapture_continuous\u001b[0;34m(self, output, format, use_video_port, resize, splitter_port, burst, bayer, **options)\u001b[0m\n\u001b[1;32m   1701\u001b[0m                         \u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1702\u001b[0;31m                         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCAPTURE_TIMEOUT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1703\u001b[0m                             raise PiCameraRuntimeError(\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/picamera/encoders.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    392\u001b[0m         \"\"\"\n\u001b[0;32m--> 393\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    394\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    551\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m                 \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    299\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 300\u001b[0;31m                     \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    301\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mPiCameraValueError\u001b[0m                        Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-c515b03c0f9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     31\u001b[0m \u001b[0;31m# start car\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m \u001b[0mmotor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mduty_cycle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMOTOR_BRAKE\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m120000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mstream\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     34\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfirst_frame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m         \u001b[0mfirst_frame\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/picamera/camera.py\u001b[0m in \u001b[0;36mcapture_continuous\u001b[0;34m(self, output, format, use_video_port, resize, splitter_port, burst, bayer, **options)\u001b[0m\n\u001b[1;32m   1708\u001b[0m                     \u001b[0mcamera_port\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmmal\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMMAL_PARAMETER_CAMERA_BURST_CAPTURE\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1709\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1710\u001b[0;31m             \u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1711\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_encoders_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1712\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0muse_video_port\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/picamera/encoders.py\u001b[0m in \u001b[0;36mclose\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    429\u001b[0m         \u001b[0mattributes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    430\u001b[0m         \"\"\"\n\u001b[0;32m--> 431\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    432\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    433\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/picamera/encoders.py\u001b[0m in \u001b[0;36mstop\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    417\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_port\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mevent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 419\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_close_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/picamera/encoders.py\u001b[0m in \u001b[0;36m_close_output\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    347\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    348\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 349\u001b[0;31m                 \u001b[0mmo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose_stream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopened\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    350\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    351\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/picamera/mmalobj.py\u001b[0m in \u001b[0;36mclose_stream\u001b[0;34m(stream, opened)\u001b[0m\n\u001b[1;32m    369\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    370\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 371\u001b[0;31m             \u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    372\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    373\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/picamera/array.py\u001b[0m in \u001b[0;36mflush\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    294\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m         \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPiYUVArray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflush\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbytes_to_yuv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetvalue\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcamera\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresolution\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    297\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_rgb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.local/lib/python3.7/site-packages/picamera/array.py\u001b[0m in \u001b[0;36mbytes_to_yuv\u001b[0;34m(data, resolution)\u001b[0m\n\u001b[1;32m     91\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0my_len\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0muv_len\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     92\u001b[0m         raise PiCameraValueError(\n\u001b[0;32m---> 93\u001b[0;31m             'Incorrect buffer length for resolution %dx%d' % (width, height))\n\u001b[0m\u001b[1;32m     94\u001b[0m     \u001b[0;31m# Separate out the Y, U, and V values from the array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     95\u001b[0m     \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrombuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muint8\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mPiCameraValueError\u001b[0m: Incorrect buffer length for resolution 640x480"
     ]
    }
   ],
   "source": [
    "# Run a track detection algorithm on a single horizontal line.\n",
    "# Uses YUV420 image format as the Y component corresponds to image intensity (gray image)\n",
    "# and thus there is no need to convert from RGB to BW\n",
    "\n",
    "RUN_TIMER = 20 # seconds\n",
    "history=[]\n",
    "\n",
    "camera = PiCamera()\n",
    "        \n",
    "# Check the link below for the combinations between mode and resolution\n",
    "# https://picamera.readthedocs.io/en/release-1.13/fov.html#sensor-modes\n",
    "camera.sensor_mode = 7\n",
    "camera.resolution = res\n",
    "camera.framerate = 10\n",
    "\n",
    "# Initialize the buffer and start capturing\n",
    "rawCapture = PiYUVArray(camera, size=res)\n",
    "stream = camera.capture_continuous(rawCapture, format=\"yuv\", use_video_port=True)\n",
    "\n",
    "# Measure the time needed to process 300 images to estimate the FPS\n",
    "t = time.time()\n",
    "\n",
    "# To filter the noise in the image we use a 3rd order Butterworth filter\n",
    "\n",
    "# Wn = 0.02, the cut-off frequency, acceptable values are from 0 to 1\n",
    "b, a = butter(3, 0.1)\n",
    "\n",
    "line_pos    = CAMERA_CENTER\n",
    "first_frame = True\n",
    "\n",
    "# start car\n",
    "motor.duty_cycle = MOTOR_BRAKE + 120000\n",
    "for f in stream:\n",
    "    if first_frame:\n",
    "        first_frame = False\n",
    "        # Reset the buffer for the next image\n",
    "        rawCapture.truncate(0)\n",
    "        continue\n",
    "    \n",
    "    # Stop after RUN_TIMER seconds\n",
    "    if (time.time() - t) > RUN_TIMER:\n",
    "        break\n",
    "        \n",
    "    # Get the intensity component of the image (a trick to get black and white images)\n",
    "    I = f.array[:, :, 0]\n",
    "    \n",
    "    # Reset the buffer for the next image\n",
    "    rawCapture.truncate(0)\n",
    "    \n",
    "    # Select a horizontal line in the middle of the image\n",
    "    L = I[195, :]\n",
    "\n",
    "    # Smooth the transitions so we can detect the peaks \n",
    "    Lf = filtfilt(b, a, L)\n",
    "    history.append(Lf)\n",
    "\n",
    "    # Find peaks which are higher than 0.5\n",
    "    p = find_peaks(Lf, height=160)\n",
    "    \n",
    "    peaks = p[0]\n",
    "    \n",
    "    print(\"len(peaks) = \", len(peaks))\n",
    "    if(len(peaks) > 2):\n",
    "        motor.duty_cycle = MOTOR_BRAKE\n",
    "        continue\n",
    "        \n",
    "    line_left   = None\n",
    "    line_right  = None\n",
    "    peaks_left  = peaks[peaks < CAMERA_CENTER]\n",
    "    peaks_right = peaks[peaks > CAMERA_CENTER]\n",
    "    \n",
    "    track_width = 400\n",
    "\n",
    "    if peaks_left.size:\n",
    "        line_left = peaks_left.max()\n",
    "\n",
    "    if peaks_right.size:\n",
    "        line_right = peaks_right.min()\n",
    "        \n",
    "    if line_left and line_right:\n",
    "        line_pos    = (line_left + line_right ) // 2\n",
    "        track_width = line_right - line_left\n",
    "        motor.duty_cycle = MOTOR_BRAKE + 150000\n",
    "    elif line_left and not line_right:\n",
    "        track_width = 400\n",
    "        line_pos    = line_left + int(track_width / 2)\n",
    "        motor.duty_cycle = MOTOR_BRAKE + 120000\n",
    "    elif not line_left and line_right:\n",
    "        track_width = 400\n",
    "        line_pos    = line_right - int(track_width / 2)\n",
    "        motor.duty_cycle = MOTOR_BRAKE + 120000\n",
    "    else:\n",
    "        print(\"no line\")\n",
    "        \n",
    "    print(line_pos, peaks)\n",
    "\n",
    "        \n",
    "    DUTY_CYCLE = SERVO_MIDDLE + 5000 * (CAMERA_CENTER - line_pos)\n",
    "    if DUTY_CYCLE > 2000000:\n",
    "        DUTY_CYCLE = 2000000\n",
    "    if DUTY_CYCLE < 1000000:\n",
    "        DUTY_CYCLE = 1000000\n",
    "        \n",
    "    servo.duty_cycle =  DUTY_CYCLE\n",
    "    \n",
    "    if DUTY_CYCLE > SERVO_MIDDLE:\n",
    "        print(\"Turning Left\")\n",
    "    elif DUTY_CYCLE < SERVO_MIDDLE:\n",
    "        print(\"Turning Right\")\n",
    "    else:\n",
    "        print(\"Straight\")\n",
    "        \n",
    "#Initialize lines position\n",
    "#Check which lines are closer them in the next frame\n",
    "\n",
    "        \n",
    "        #print(line_pos)\n",
    "        \n",
    "motor.duty_cycle = MOTOR_BRAKE    \n",
    "    \n",
    "\n",
    "# Release resources\n",
    "stream.close()\n",
    "rawCapture.close()\n",
    "camera.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Release resources\n",
    "stream.close()\n",
    "rawCapture.close()\n",
    "camera.close()\n",
    "motor.duty_cycle = MOTOR_BRAKE   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 56 103 605]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(I)\n",
    "print(peaks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "peaks = p[0]\n",
    "\n",
    "line_left   = None\n",
    "line_right  = None\n",
    "peaks_left  = peaks[peaks < CAMERA_CENTER]\n",
    "peaks_right = peaks[peaks > CAMERA_CENTER]\n",
    "\n",
    "if peaks_left.size:\n",
    "    line_left = peaks_left.max()\n",
    "    \n",
    "if peaks_right.size:\n",
    "    line_right = peaks_right.min()\n",
    "    \n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "L = I[200, :]\n",
    "b, a = butter(3, 0.1)\n",
    "Lf = filtfilt(b, a, L)\n",
    "\n",
    "plt.plot(L)\n",
    "plt.plot(Lf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imshow(np.vstack(history))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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