sign_language.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model('C://Users/Admin/Downloads/model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 148, 148, 64)      1792      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 74, 74, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 72, 72, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 36, 36, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 34, 34, 128)       73856     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 17, 17, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 15, 15, 128)       147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 6272)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               3211776   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 24)                12312     \n",
      "=================================================================\n",
      "Total params: 3,484,248\n",
      "Trainable params: 3,484,248\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00\n",
      "  0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00\n",
      "  0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00\n",
      "  0.000000e+00 0.000000e+00 0.000000e+00 3.615493e-05 9.999639e-01\n",
      "  0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "\n",
    "img = image.load_img(\"C://Users/Admin/Pictures/Camera Roll/V.jpg\", target_size=(150, 150))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "images = np.vstack([x])\n",
    "classes = model.predict(images, batch_size=10)\n",
    "print(classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T\n",
      "G\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "None\n",
      "None\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "None\n",
      "P\n",
      "None\n",
      "Y\n",
      "Y\n",
      "Y\n",
      "Y\n",
      "C\n",
      "Y\n",
      "Y\n",
      "Y\n",
      "Y\n",
      "Y\n",
      "Y\n",
      "Y\n",
      "None\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "Y\n",
      "Y\n",
      "Y\n",
      "Y\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "None\n",
      "Y\n",
      "Y\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "Y\n",
      "None\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "P\n",
      "None\n",
      "C\n",
      "None\n",
      "None\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "None\n",
      "P\n",
      "None\n",
      "None\n",
      "C\n",
      "C\n",
      "None\n",
      "None\n",
      "P\n",
      "P\n",
      "None\n",
      "P\n",
      "C\n",
      "C\n",
      "None\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "None\n",
      "None\n",
      "None\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "None\n",
      "P\n",
      "None\n",
      "C\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "P\n",
      "None\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n",
      "C\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "from tensorflow.python.util import deprecation\n",
    "deprecation._PRINT_DEPRECATION_WARNINGS = False\n",
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "import cv2\n",
    "predict = ['A','B','C','D','E','F','G','H','I','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y']\n",
    "def loadmodel(filepath):\n",
    "\tmodel = tf.keras.models.load_model(filepath,custom_objects=None,compile=True)\n",
    "\treturn model\n",
    "\t\n",
    "\n",
    "def prediction(img,model):\n",
    "\t#img = image.load_img(path, target_size=(150, 150))\n",
    "\tx = image.img_to_array(img)\n",
    "\tx = np.expand_dims(x, axis=0)\n",
    "\n",
    "\timages = np.vstack([x])\n",
    "\tclasses = model.predict(images)\t\n",
    "\tfor i in range(0,24):\n",
    "\t\tif classes[0][i] == 1:\n",
    "\t\t\treturn predict[i]\n",
    "    \n",
    "\n",
    "model = loadmodel('C://Users/Admin/Downloads/model.h5')\n",
    "height = 150\n",
    "width = 150\n",
    "dim = (width, height)\n",
    "cap = cv2.VideoCapture(0)\n",
    "while True:\n",
    "    # Read frame from camera\n",
    "    ret, image_np = cap.read(dim)\n",
    "    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n",
    "    dimensions = (150,150)\n",
    "    image_np = cv2.resize(image_np,dimensions)\n",
    "    print(prediction(image_np,model))\n",
    "    #print(image_np_expanded)\n",
    "\n",
    "    # Display output\n",
    "    cv2.imshow('object detection', cv2.resize(image_np, (800, 480)))\n",
    "\n",
    "    if cv2.waitKey(25) & 0xFF == ord('q'):\n",
    "        cv2.destroyAllWindows()\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}