Tennis.ai.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import pandas as pd\n",
    "import cv2     # for capturing videos\n",
    "import math   # for mathematical operations\n",
    "import matplotlib.pyplot as plt    # for plotting the images\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from keras.preprocessing import image   # for preprocessing the images\n",
    "import numpy as np    # for mathematical operations\n",
    "from keras.utils import np_utils\n",
    "from skimage.transform import resize   # for resizing images\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Frame</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>frame0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>frame1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>frame2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>frame3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>frame4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Frame  State\n",
       "0  frame0      0\n",
       "1  frame1      0\n",
       "2  frame2      0\n",
       "3  frame3      0\n",
       "4  frame4      0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = []\n",
    "data = pd.read_csv(os.path.join(\"static\", \"images\", \"20161001_120007\", \"results.csv\"))     # reading the csv file\n",
    "data.head() # printing first five rows of the file\n",
    "for i in range(7200):\n",
    "    if i % 12 != 0:\n",
    "        data.values[i].\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The Label tool should save all frames in the static/images/<video name> folder\n",
    "#Labels.txt should be the same folder\n",
    "# run the fill.py to saturate labels\n",
    "#User Keras image preprocessing (https://keras.io/preprocessing/image/) to scale and batch images for the model\n",
    "# Plan to use a 3 second clip, sampled at 5 frames/sec (15 frames/batch) to train the model\n",
    "# LSTM and Conv2D "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using sample from here: https://github.com/keras-team/keras/blob/master/examples/conv_lstm.py\n",
    "dimensions = (1080, 1920, 3)\n",
    "# send in a 3 second clip to determine if the point starts = 3* (60fps/12) = 15\n",
    "seq = Sequential()\n",
    "#batch, steps, width, height, features\n",
    "\n",
    "seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),\n",
    "                   input_shape=(None, dimensions[0], dimensions[1], dimensions[2]),\n",
    "                   padding='same', return_sequences=True))\n",
    "seq.add(BatchNormalization())\n",
    "\n",
    "seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),\n",
    "                   padding='same', return_sequences=True))\n",
    "seq.add(BatchNormalization())\n",
    "\n",
    "seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),\n",
    "                   padding='same', return_sequences=True))\n",
    "seq.add(BatchNormalization())\n",
    "\n",
    "seq.add(ConvLSTM2D(filters=40, kernel_size=(3, 3),\n",
    "                   padding='same', return_sequences=True))\n",
    "seq.add(BatchNormalization())\n",
    "\n",
    "seq.add(Conv3D(filters=1, kernel_size=(3, 3, 3),\n",
    "               activation='relu',\n",
    "               padding='same', data_format='channels_last'))\n",
    "seq.compile(loss='binary_crossentropy', optimizer='adadelta')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq.fit(x, y, batch_size=15,\n",
    "\n",
    "        epochs=300, validation_split=0.1)"
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}