Prediction_Laptop_Gestures_Automation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "source": [
    "import warnings\r\n",
    "warnings.filterwarnings('ignore')\r\n",
    "warnings.warn('DelftStack')\r\n",
    "warnings.warn('Do not show this message')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Imports**"
   ],
   "metadata": {
    "id": "E1-eS4iXKL6s"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "from ctypes import cast, POINTER\r\n",
    "from comtypes import CLSCTX_ALL\r\n",
    "from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume\r\n",
    "import math\r\n",
    "import pyautogui\r\n",
    "from playsound import playsound\r\n",
    "import os\r\n",
    "import sounddevice as sd\r\n",
    "import soundfile as sf\r\n",
    "import vlc\r\n",
    "import time\r\n",
    "import os\r\n",
    "from tkinter import *\r\n",
    "import subprocess\r\n",
    "import sys\r\n",
    "import tensorflow as tf\r\n",
    "import cv2\r\n",
    "import numpy as np"
   ],
   "outputs": [],
   "metadata": {
    "id": "UEKc5Vy6J-jN"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Volume UP**"
   ],
   "metadata": {
    "id": "X4-FvMpAKzT8"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "source": [
    "def increaseVolume():\r\n",
    "    devices = AudioUtilities.GetSpeakers()\r\n",
    "    interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None)\r\n",
    "    volume = cast(interface, POINTER(IAudioEndpointVolume))\r\n",
    "\r\n",
    "    currentVolumeDb = volume.GetMasterVolumeLevel()\r\n",
    "    volume.SetMasterVolumeLevel(currentVolumeDb + 6.0, None)\r\n"
   ],
   "outputs": [],
   "metadata": {
    "id": "CO698YNGKzT9"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Volume DOWN**"
   ],
   "metadata": {
    "id": "TVA8NgT0Kzeq"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "def decreaseVolume():\r\n",
    "    devices = AudioUtilities.GetSpeakers()\r\n",
    "    interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None)\r\n",
    "    volume = cast(interface, POINTER(IAudioEndpointVolume))\r\n",
    "\r\n",
    "    currentVolumeDb = volume.GetMasterVolumeLevel()\r\n",
    "    volume.SetMasterVolumeLevel(currentVolumeDb - 6.0, None)\r\n"
   ],
   "outputs": [],
   "metadata": {
    "id": "-H5Yy_mwKzeq"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Take an SS**"
   ],
   "metadata": {
    "id": "pDjaYoML30Ww"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "def takeSS():\r\n",
    "    myScreenshot = pyautogui.screenshot()\r\n",
    "    myScreenshot.save(r'saved_ss.png')\r\n"
   ],
   "outputs": [],
   "metadata": {
    "id": "KJpNzAyP27lU"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Play a song**"
   ],
   "metadata": {
    "id": "Rh7O1-kw5TuD"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "def playMusic():\r\n",
    "\r\n",
    "    os.add_dll_directory(r'C:/Program Files (x86)/VideoLAN')\r\n",
    "\r\n",
    "    p = vlc.MediaPlayer(\r\n",
    "        \"F:\\\\vision arcadia\\\\tsnsns\\\\Hand_Gestures_Automation-master\\\\Hand_Gestures_Automation-master\\\\Scam-1992-Ringtone.mp3\"\r\n",
    "    )\r\n",
    "    p.play()\r\n",
    "    time.sleep(30)\r\n",
    "    p.stop()"
   ],
   "outputs": [],
   "metadata": {
    "id": "zwXkPqOD5ajw"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**HELP**"
   ],
   "metadata": {
    "id": "h5QG-WSe3_O3"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "def showHelpMenu():\r\n",
    "    path = 'HELP.pdf'\r\n",
    "    os.system(path)\r\n"
   ],
   "outputs": [],
   "metadata": {
    "id": "bUXEd7mC3Fru"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Record a sound**"
   ],
   "metadata": {
    "id": "DsVrnWmh76by"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "def Voice_rec():\r\n",
    "    fs = 48000\r\n",
    "    myrecording = sd.rec(int(10 * fs), samplerate=fs, channels=2)\r\n",
    "    sd.wait()\r\n",
    "    return sf.write('Voice_Recording.flac', myrecording, fs)\r\n",
    "\r\n",
    "\r\n",
    "def recordSound():\r\n",
    "    master = Tk()\r\n",
    "    Label(master, text=\" Voice Recoder : \").grid(row=0, sticky=W, rowspan=5)\r\n",
    "    b = Button(master, text=\"Start\", command=Voice_rec)\r\n",
    "    b.grid(row=0, column=2, columnspan=2, rowspan=2, padx=5, pady=5)\r\n",
    "    mainloop()\r\n"
   ],
   "outputs": [],
   "metadata": {
    "id": "ErvKdO_73UMY"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Launch File Explorer**"
   ],
   "metadata": {
    "id": "eojsRDpz-Yo5"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "def launchFileExplorer():\r\n",
    "    path = r'C:/Program Files (x86)/IronPython 2.7/Lib'\r\n",
    "    sys.path.append(path)\r\n",
    "    subprocess.Popen('explorer \"C:/\"')\r\n"
   ],
   "outputs": [],
   "metadata": {
    "id": "QFv_DDLa7-Yi"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "**Function Call**"
   ],
   "metadata": {
    "id": "xJ8eui0dMxn2"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "source": [
    "import cv2\r\n",
    "import imutils \r\n",
    "import numpy as np\r\n",
    "from sklearn.metrics import pairwise\r\n",
    "\r\n",
    "bg = None\r\n",
    "\r\n",
    "\r\n",
    "# Function - To find the running average over the background\r\n",
    "def run_avg(image, accumWeight):\r\n",
    "    global bg\r\n",
    "    if bg is None:\r\n",
    "        bg = image.copy().astype(\"float\")\r\n",
    "        return\r\n",
    "    cv2.accumulateWeighted(image, bg, accumWeight)\r\n",
    "\r\n",
    "\r\n",
    "# Function - To segment the region of hand in the image\r\n",
    "def segment(image, threshold=30):\r\n",
    "    global bg\r\n",
    "    diff = cv2.absdiff(bg.astype(\"uint8\"), image)\r\n",
    "\r\n",
    "    thresholded = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)[1]\r\n",
    "\r\n",
    "    cnts = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL,\r\n",
    "                            cv2.CHAIN_APPROX_SIMPLE)[0]\r\n",
    "\r\n",
    "    if len(cnts) == 0:\r\n",
    "        return\r\n",
    "    else:\r\n",
    "        segmented = max(cnts, key=cv2.contourArea)\r\n",
    "        return (thresholded, segmented)\r\n",
    "\r\n",
    "\r\n",
    "# Function - To count the number of fingers in the segmented hand region\r\n",
    "\r\n",
    "# Main function\r\n",
    "if __name__ == \"__main__\":\r\n",
    "    accumWeight = 0.5\r\n",
    "\r\n",
    "    camera = cv2.VideoCapture(0, cv2.CAP_DSHOW)\r\n",
    "\r\n",
    "    top, right, bottom, left = 10, 350, 210, 550\r\n",
    "\r\n",
    "    num_frames = 0\r\n",
    "\r\n",
    "    imageNumber = 0\r\n",
    "    calibrated = False\r\n",
    "\r\n",
    "    while (True):\r\n",
    "        (grabbed, frame) = camera.read()\r\n",
    "\r\n",
    "        frame = imutils.resize(frame, width=700)\r\n",
    "\r\n",
    "        frame = cv2.flip(frame, 1)\r\n",
    "\r\n",
    "        clone = frame.copy()\r\n",
    "\r\n",
    "        (height, width) = frame.shape[:2]\r\n",
    "\r\n",
    "        roi = frame[top:bottom, right:left]\r\n",
    "\r\n",
    "        gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\r\n",
    "        gray = cv2.GaussianBlur(gray, (7, 7), 0)\r\n",
    "\r\n",
    "        if num_frames < 30:\r\n",
    "            run_avg(gray, accumWeight)\r\n",
    "            if num_frames == 1:\r\n",
    "                print(\">>> Please wait! calibrating...\")\r\n",
    "            elif num_frames == 29:\r\n",
    "                print(\">>> Calibration successfull...\")\r\n",
    "        else:\r\n",
    "            hand = segment(gray)\r\n",
    "\r\n",
    "            if hand is not None:\r\n",
    "                (thresholded, segmented) = hand\r\n",
    "                # show the thresholded image\r\n",
    "                cv2.imshow(\"Thesholded\", thresholded)\r\n",
    "                keypress = cv2.waitKey(1) & 0xFF\r\n",
    "                if keypress%256 == 32:\r\n",
    "                    # Set the directory CORRECTLY\r\n",
    "                    directory = \"opencv_frame.jpg\"\r\n",
    "                    cv2.imwrite(directory, thresholded)\r\n",
    "                    print(directory)\r\n",
    "                    break\r\n",
    "\r\n",
    "        # draw the segmented hand\r\n",
    "        cv2.rectangle(clone, (left, top), (right, bottom), (0, 255, 0), 2)\r\n",
    "\r\n",
    "        num_frames += 1\r\n",
    "\r\n",
    "        cv2.imshow(\"Video Feed\", clone)\r\n",
    "\r\n",
    "# free up memory\r\n",
    "camera.release()\r\n",
    "cv2.destroyAllWindows()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      ">>> Please wait! calibrating...\n",
      ">>> Calibration successfull...\n",
      "opencv_frame.jpg\n"
     ]
    }
   ],
   "metadata": {
    "id": "KqwRMxoGSKLO"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "source": [
    "export_dir = \"Model_Hand_Gestures\"\r\n",
    "new_model = tf.keras.models.load_model(export_dir)\r\n",
    "\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "class_map = {\r\n",
    "    1: 'C',\r\n",
    "    2: 'Fist',\r\n",
    "    3: 'Index',\r\n",
    "    4: 'L',\r\n",
    "    5: 'OK',\r\n",
    "    6: 'Palm',\r\n",
    "    7: 'Thumb'\r\n",
    "}\r\n",
    "\r\n",
    "\r\n",
    "def predictClass(img_path: str) -> int:\r\n",
    "    img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)\r\n",
    "    img = cv2.resize(img, (100, 100))\r\n",
    "    plt.imshow(img, cmap='gray')\r\n",
    "    plt.show()\r\n",
    "    img = img[np.newaxis, :, :, np.newaxis]\r\n",
    "    predicted_class = list(new_model.predict(img)[0])\r\n",
    "    # print(predicted_class)\r\n",
    "    return predicted_class.index(max(predicted_class)) + 1\r\n",
    "\r\n",
    "\r\n",
    "def automateTask(class_no):\r\n",
    "    if class_no == 6:\r\n",
    "        launchFileExplorer()\r\n",
    "    elif class_no == 4:\r\n",
    "        showHelpMenu()\r\n",
    "    elif class_no == 2:\r\n",
    "        takeSS()\r\n",
    "    elif class_no == 7:\r\n",
    "        recordSound()\r\n",
    "    elif class_no == 3:\r\n",
    "        increaseVolume()\r\n",
    "    elif class_no == 5:\r\n",
    "        playMusic()\r\n",
    "    else:\r\n",
    "        decreaseVolume()\r\n",
    "\r\n",
    "\r\n",
    "class_predicted = predictClass(\"opencv_frame.jpg\")\r\n",
    "print(\"Predicted Class: \", class_predicted)\r\n",
    "print(\"Gesture Predicted: \", class_map[class_predicted])\r\n",
    "automateTask(class_predicted)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Predicted Class:  7\n",
      "Gesture Predicted:  Thumb\n"
     ]
    }
   ],
   "metadata": {
    "id": "SMf5IvdNtdFH"
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.9.5",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.5 64-bit"
  },
  "interpreter": {
   "hash": "c569cd2e17f62341e0f08a54f9a867c3c0d3a6f67454072d7de41a8b5dff8343"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}