face recognition by vgg16.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.applications import vgg16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pre trained weights : create my model\n",
    "model = vgg16.VGG16(weights='imagenet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.layers[0].input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.layers[0].__class__.__name__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.layers[1].trainable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in model.layers:\n",
    "    layer.trainable=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.layers[4].trainable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.layers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_model=model.output\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_model=Dense(1024, activation='relu')(top_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_model= Dense(512, activation='relu')(top_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_model= Dense(4, activation='softmax')(top_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newmodel=Model(inputs=model.input,outputs=top_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newmodel.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newmodel.layers[0].input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for training set\n",
    "cap = cv2.VideoCapture(0)\n",
    "count = 0\n",
    "\n",
    "# Collect 100 samples of your face from webcam input\n",
    "while True:\n",
    "\n",
    "        ret, frame = cap.read()\n",
    "    #if face_extractor(frame) is not None:\n",
    "        count =count+ 1\n",
    "        face = cv2.resize(frame, (224, 224))\n",
    "        #face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        # Save file in specified directory with unique name\n",
    "        file_name_path = 'face-recog//train//name//image' + str(count) + '.jpg'\n",
    "        cv2.imwrite(file_name_path, face)\n",
    "\n",
    "        # Put count on images and display live count\n",
    "        #cv2.putText(face, str(count), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)\n",
    "        cv2.imshow('Face ', face)\n",
    "        \n",
    "    #else:\n",
    "     #   print(\"Face not found\")\n",
    "        #pass\n",
    "\n",
    "        if cv2.waitKey(1) == 13 or count == 100: #13 is the Enter Key\n",
    "             break\n",
    "        \n",
    "        \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()      \n",
    "print(\"Collecting Samples Complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#for testing set\n",
    "cap = cv2.VideoCapture(0)\n",
    "count = 0\n",
    "\n",
    "# Collect 30 samples of your face from webcam input\n",
    "while True:\n",
    "\n",
    "        ret, frame = cap.read()\n",
    "    #if face_extractor(frame) is not None:\n",
    "        count =count+ 1\n",
    "        face = cv2.resize(frame, (224, 224))\n",
    "        #face = cv2.cvtColor(face, cv2.COLOR_BGR2GRAY)\n",
    "\n",
    "        # Save file in specified directory with unique name\n",
    "        file_name_path = 'face-recog//test//namr//image' + str(count) + '.jpg'\n",
    "        cv2.imwrite(file_name_path, face)\n",
    "\n",
    "        # Put count on images and display live count\n",
    "        #cv2.putText(face, str(count), (50, 50), cv2.FONT_HERSHEY_COMPLEX, 1, (0,255,0), 2)\n",
    "        cv2.imshow('Face ', face)\n",
    "        \n",
    "    #else:\n",
    "     #   print(\"Face not found\")\n",
    "        #pass\n",
    "\n",
    "        if cv2.waitKey(1) == 13 or count == 30: #13 is the Enter Key\n",
    "             break\n",
    "        \n",
    "cap.release()\n",
    "cv2.destroyAllWindows()      \n",
    "print(\"Collecting Samples Complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_data_dir = 'face-recog/train/'\n",
    "validation_data_dir = 'face-recog/train/'\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "      rescale=1./255,\n",
    "      rotation_range=20,\n",
    "      width_shift_range=0.2,\n",
    "      height_shift_range=0.2,\n",
    "      horizontal_flip=True,\n",
    "      fill_mode='nearest')\n",
    " \n",
    "validation_datagen = ImageDataGenerator(rescale=1./255)\n",
    " \n",
    "# Change the batchsize according to your system RAM\n",
    "train_batchsize = 16\n",
    "val_batchsize = 10\n",
    " \n",
    "train_generator = train_datagen.flow_from_directory(\n",
    "        train_data_dir,\n",
    "        target_size=(224, 224),\n",
    "        batch_size=train_batchsize,\n",
    "        class_mode='categorical')\n",
    " \n",
    "validation_generator = validation_datagen.flow_from_directory(\n",
    "        validation_data_dir,\n",
    "        target_size=(224, 224),\n",
    "        batch_size=val_batchsize,\n",
    "        class_mode='categorical',\n",
    "        shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newmodel.fit(\n",
    "        train_generator,\n",
    "        steps_per_epoch=200,\n",
    "        epochs=10,\n",
    "        validation_data=validation_generator,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "newmodel.save('detect.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.preprocessing import image\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image = image.load_img('img.jpg', \n",
    "               target_size=(224,224))\n",
    "test_image = image.img_to_array(test_image)\n",
    "import numpy as np\n",
    "test_image = np.expand_dims(test_image, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted=newmodel.predict(validation_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_generator.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results=newmodel.predict(test_image)"
   ]
  }
 ],
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