GestureRecognitionfor2Signs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31664b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install os opencv-contrib-python keras tensorflow numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cf1fbbf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "72a2dbd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_dir(directory):\n",
    "    if not os.path.exists(directory):\n",
    "        os.makedirs(directory)\n",
    "        return None\n",
    "    else:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a59eb8b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# DataSet Building\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "i=0\n",
    "image_Count=0\n",
    "\n",
    "while i < 7:\n",
    "\n",
    "    ret, frame = cap.read()\n",
    "    frame = cv2.flip(frame,1)\n",
    "\n",
    "    #region of intereset\n",
    "    roi = frame[100:400, 320:600]\n",
    "    cv2.imshow('roi',roi)\n",
    "    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\n",
    "    roi = cv2.resize(roi , (28, 28) ,interpolation = cv2.INTER_AREA)\n",
    "\n",
    "    cv2.imshow('roi sacled and gray ',roi)\n",
    "    copy = frame.copy()\n",
    "    cv2.rectangle(copy,(320, 100) ,(620, 400) ,(255,0,0), 5)\n",
    "\n",
    "    if i == 0:\n",
    "        image_Count = 0\n",
    "        cv2.putText(copy, \" Hit Enter to Record when Ready \" ,(100,100) , cv2.FONT_HERSHEY_COMPLEX, 1,(0,255,0), 1)\n",
    "\n",
    "    if i == 1:\n",
    "        image_Count += 1\n",
    "        cv2.putText(copy, \" Recording 1st Gesture -Train \", (100, 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)\n",
    "        cv2.putText(copy,str(image_Count) , (400, 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)\n",
    "        gesture_one = './handgestures/train/0/'\n",
    "        make_dir(gesture_one)\n",
    "        cv2.imwrite(gesture_one + str(image_Count) + \".jpg\" ,roi)\n",
    "\n",
    "    if i == 2:\n",
    "        image_Count += 1\n",
    "        cv2.putText(copy, \" Recording 1st Gesture -Train \", (100, 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)\n",
    "        cv2.putText(copy, str(image_Count), (400, 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)\n",
    "        gesture_one = './handgestures/test/0/'\n",
    "        make_dir(gesture_one)\n",
    "        cv2.imwrite(gesture_one + str(image_Count) + \".jpg\", roi)\n",
    "\n",
    "    if i == 3:\n",
    "        cv2.putText(copy, \" Hit Enter to Record when Ready to recond 2nd gesture \" ,(100,100) , cv2.FONT_HERSHEY_COMPLEX, 1,(0,255,0), 1)\n",
    "\n",
    "    if i == 4:\n",
    "        image_Count += 1\n",
    "        cv2.putText(copy, \" Recording 2nd Gesture -Train \", (100, 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)\n",
    "        cv2.putText(copy,str(image_Count) , (400, 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)\n",
    "        gesture_two = './handgestures/train/1/'\n",
    "        make_dir(gesture_two)\n",
    "        cv2.imwrite(gesture_two + str(image_Count) + \".jpg\" ,roi)\n",
    "\n",
    "    if i == 5:\n",
    "        image_Count += 1\n",
    "        cv2.putText(copy, \" Recording 2nd Gesture -Train \", (100, 100), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)\n",
    "        cv2.putText(copy, str(image_Count), (400, 400), cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)\n",
    "        gesture_two = './handgestures/test/1/'\n",
    "        make_dir(gesture_two)\n",
    "        cv2.imwrite(gesture_two + str(image_Count) + \".jpg\", roi)\n",
    "\n",
    "    if i == 6:\n",
    "        cv2.putText(copy, \" Hit Enter to Exit \", (100, 100),cv2.FONT_HERSHEY_COMPLEX, 1, (0, 255, 0), 1)\n",
    "    cv2.imshow('frame', copy)\n",
    "\n",
    "    if cv2.waitKey(1) == 13 : #13 is Enter Key\n",
    "        image_Count = 0\n",
    "        i+=1\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1158bc8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "##DATA AUGMENTATION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9479f9dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, BatchNormalization\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.models import load_model\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "598b9e5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1370 images belonging to 2 classes.\n",
      "Found 478 images belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "num_classes = 2\n",
    "img_rows,img_cols = 28,28\n",
    "batch_size = 32\n",
    "\n",
    "train_data_dir ='./handgestures/train'\n",
    "validation_data_dir ='./handgestures/test'\n",
    "# data augmentation\n",
    "train_datagen = ImageDataGenerator(\n",
    "    rescale = 1./255,\n",
    "    rotation_range=30,\n",
    "    width_shift_range=0.3,\n",
    "    height_shift_range=0.3,\n",
    "    horizontal_flip=True,\n",
    "    fill_mode='nearest')\n",
    "\n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "    train_data_dir,\n",
    "    target_size =(img_rows,img_cols),\n",
    "    batch_size=batch_size,\n",
    "    color_mode='grayscale',\n",
    "    class_mode='binary')\n",
    "\n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "    validation_data_dir,\n",
    "    target_size =(img_rows,img_cols),\n",
    "    batch_size=batch_size,\n",
    "    color_mode='grayscale',\n",
    "    class_mode='binary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1d54e60e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 26, 26, 64)        640       \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 13, 13, 64)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 11, 11, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 5, 5, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 3, 3, 64)          36928     \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 1, 1, 64)         0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 64)                0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               8320      \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 128)               0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 129       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 82,945\n",
      "Trainable params: 82,945\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Model Creation\n",
    "model = Sequential()\n",
    "model.add(Conv2D(64, kernel_size=(3,3), activation= 'relu', input_shape=(28,28,1) ))\n",
    "model.add(MaxPooling2D(pool_size=(2,2) ))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3,3), activation= 'relu' ))\n",
    "model.add(MaxPooling2D(pool_size=(2,2) ))\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=(3,3), activation= 'relu' ))\n",
    "model.add(MaxPooling2D(pool_size=(2,2) ))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation= 'relu'))\n",
    "model.add(Dropout(0.20))\n",
    "#1<->num_Classes\n",
    "model.add(Dense(1, activation= 'sigmoid'))\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "db210c42",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "42/42 [==============================] - 5s 78ms/step - loss: 0.6910 - accuracy: 0.5448 - val_loss: 0.6715 - val_accuracy: 0.5982\n",
      "Epoch 2/10\n",
      "42/42 [==============================] - 3s 70ms/step - loss: 0.6289 - accuracy: 0.6592 - val_loss: 0.5635 - val_accuracy: 0.6004\n",
      "Epoch 3/10\n",
      "42/42 [==============================] - 3s 69ms/step - loss: 0.4752 - accuracy: 0.7825 - val_loss: 0.4780 - val_accuracy: 0.6071\n",
      "Epoch 4/10\n",
      "42/42 [==============================] - 3s 70ms/step - loss: 0.3287 - accuracy: 0.8797 - val_loss: 0.1627 - val_accuracy: 0.9933\n",
      "Epoch 5/10\n",
      "42/42 [==============================] - 4s 84ms/step - loss: 0.2629 - accuracy: 0.9058 - val_loss: 0.6540 - val_accuracy: 0.6272\n",
      "Epoch 6/10\n",
      "42/42 [==============================] - 3s 78ms/step - loss: 0.1973 - accuracy: 0.9380 - val_loss: 0.0179 - val_accuracy: 1.0000\n",
      "Epoch 7/10\n",
      "42/42 [==============================] - 3s 76ms/step - loss: 0.1546 - accuracy: 0.9537 - val_loss: 0.1033 - val_accuracy: 0.9732\n",
      "Epoch 8/10\n",
      "42/42 [==============================] - 3s 75ms/step - loss: 0.1903 - accuracy: 0.9439 - val_loss: 0.0069 - val_accuracy: 1.0000\n",
      "Epoch 9/10\n",
      "42/42 [==============================] - 3s 72ms/step - loss: 0.1148 - accuracy: 0.9701 - val_loss: 0.0048 - val_accuracy: 1.0000\n",
      "Epoch 10/10\n",
      "42/42 [==============================] - 3s 75ms/step - loss: 0.1271 - accuracy: 0.9567 - val_loss: 0.0121 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Training the model\n",
    "model.compile(loss ='binary_crossentropy',\n",
    "              optimizer='rmsprop',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "nb_train_samples = 1370\n",
    "nb_validation_samples = 478\n",
    "epochs = 10\n",
    "\n",
    "history = model.fit(\n",
    "    train_generator ,\n",
    "    steps_per_epoch= nb_train_samples // batch_size,\n",
    "    epochs=epochs,\n",
    "    validation_data= validation_generator,\n",
    "    validation_steps =nb_validation_samples // batch_size\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6bcfb9f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Saving the Model\n",
    "model.save(\"My_Gesture_CNN.h5\")\n",
    "\n",
    "classifier = load_model('My_Gesture_CNN.h5')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1142d10e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 36ms/step\n",
      "Result is : 1\n",
      "1/1 [==============================] - 0s 35ms/step\n",
      "Result is : 1\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Result is : 1\n"
     ]
    }
   ],
   "source": [
    "#Testing the Model\n",
    "\n",
    "cap = cv2.VideoCapture(0)\n",
    "\n",
    "while True:\n",
    "\n",
    "    ret, frame =cap.read()\n",
    "    frame = cv2.flip(frame,1)\n",
    "\n",
    "    #defining roi\n",
    "    # region of intereset\n",
    "    x = 100\n",
    "    y = 800\n",
    "    w = 320\n",
    "    z = 920\n",
    "    roi = frame[x:y, w:z]\n",
    "    cv2.imshow('roi', roi)\n",
    "    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\n",
    "    roi = cv2.resize(roi, (28, 28), interpolation=cv2.INTER_AREA)\n",
    "    cv2.imshow('roi scaled and gray ', roi)\n",
    "    copy = frame.copy()\n",
    "    cv2.rectangle(copy, (320, 100), (620, 400), (255, 0, 0), 5)\n",
    "    \n",
    "    roi = roi.reshape(1,28,28,1)\n",
    "    roi = roi/255\n",
    "    y_predict = (classifier.predict(roi) > 0.5).astype(\"int32\")\n",
    "    # y_predict = np.argmax(classifier.predict(roi), axis=1)\n",
    "    #classifier.predict_classes(roi,1, verbose = 0)\n",
    "    result = str(y_predict[0][0])\n",
    "    print(\"Result is : \"+result)\n",
    "    cv2.putText(copy,'Sign:'+str(result),(300,400),cv2.FONT_HERSHEY_COMPLEX, 2, (0, 255, 0), 2)\n",
    "    cv2.imshow('frame',copy)\n",
    "\n",
    "    if cv2.waitKey(1) == 13:  # 13 is Enter Key\n",
    "        break\n",
    "\n",
    "cap.release()\n",
    "cv2.destroyAllWindows()"
   ]
  }
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