CPR - 1 - Importing data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Description:\n",
    "#Chart Pattern Image Recognition - Identify chart patterns by Convolutional Neural Networks \n",
    "#Model Type: Convolutional Neural Network\n",
    "#06-10-2022\n",
    "#Version - V1.0 Beta\n",
    "\n",
    "\n",
    "#------------------------------\n",
    "#Version Updates:\n",
    "#Import DATADIR\n",
    "#Resize the Images\n",
    "#Create the training dataset\n",
    "#Shuffle the data so the AI learns correctly \n",
    "#Pack into the variables right before we feed it to our neural network \n",
    "#Save the data so we dont have to be importing all the time \n",
    "\n",
    "#------------------------------\n",
    "#Future implementations \n",
    "#Add more data to the dataset\n",
    "#Add Automated Download dataset from google images \n",
    "#Add more CATEGORIES such as Flags, Pennant, Cup and handle and so on. \n",
    "#Add Auto Test Image from any Chart  \n",
    "\n",
    "### Author - Luiz Gabriel Bongiolo\n",
    "\n",
    "### References \n",
    "# Harrison Kinsley @Sentdex - Deep Learning with Python, TensorFlow, and Keras tutorial - https://pythonprogramming.net/introduction-deep-learning-python-tensorflow-keras/\n",
    "# Shaan Shah - Identifying Candlestick Patterns using Deep Learning - https://towardsdatascience.com/identifying-candlestick-patterns-using-deep-learning-b7d706726874\n",
    "# https://www.tensorflow.org/api_docs/python/tf/all_symbols\n",
    "# https://keras.io/api/layers/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt \n",
    "import os\n",
    "import cv2 \n",
    "import random \n",
    "\n",
    "# Remove warnings\n",
    "import sys\n",
    "\n",
    "if not sys.warnoptions:\n",
    "    import warnings\n",
    "    warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "DATADIR = \"./Chart Pattern Image Recognition/Chartpatterns/\"\n",
    "CATEGORIES = [\"Bearish\", \"Bullish\"]\n",
    "\n",
    "for category in CATEGORIES:\n",
    "    path = os.path.join(DATADIR, category)\n",
    "    for img in os.listdir(path):\n",
    "        img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE) #Remember to change grayscale if you need this\n",
    "        plt.imshow(img_array, cmap=\"gray\")\n",
    "        plt.show()\n",
    "        break\n",
    "    break \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[255 255 255 ... 255 255 255]\n",
      " [255 255 255 ... 255 255 255]\n",
      " [255 255 255 ... 255 255 255]\n",
      " ...\n",
      " [255 255 255 ... 255 255 255]\n",
      " [255 255 255 ... 255 255 255]\n",
      " [255 255 255 ... 255 255 255]]\n",
      "(938, 1698)\n"
     ]
    }
   ],
   "source": [
    "print(img_array)\n",
    "print(img_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Resizing the images \n",
    "\n",
    "IMG_SIZE = 200\n",
    "\n",
    "new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
    "plt.imshow(new_array, cmap=\"gray\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create the training dataset\n",
    "\n",
    "training_data = []\n",
    "\n",
    "def create_training_data():\n",
    "    for category in CATEGORIES:\n",
    "        path = os.path.join(DATADIR, category)                                   #path to cats or dogs dir\n",
    "        class_num = CATEGORIES.index(category)                                   # Classification of the dogs and cat to 0 and 1\n",
    "        for img in os.listdir(path):\n",
    "            try:\n",
    "                img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE) #Remember to change grayscale if you need this\n",
    "                new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n",
    "                training_data.append([new_array, class_num])\n",
    "            except Exception as e:\n",
    "                pass\n",
    "\n",
    "\n",
    "create_training_data()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    }
   ],
   "source": [
    "print(len(training_data)) #We should balance the dataset, it is good to have 50% each or 50 50 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Shuffle the data so the AI learns correctly \n",
    "\n",
    "random.shuffle(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "1\n",
      "0\n",
      "1\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "#Check labels \n",
    "\n",
    "for sample in training_data[:10]:\n",
    "    print(sample[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pack into the variables right before we feed it to our neural network \n",
    "\n",
    "X = []\n",
    "y = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "for features, label in training_data:\n",
    "    X.append(features)\n",
    "    y.append(label)\n",
    "\n",
    "\n",
    "X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)    #needs to be an array / -1 is for gray scale / we need to change 1 to 3, because we would have 3 values on the color scale \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save the data so we dont have to be importing all the time \n",
    "\n",
    "import pickle\n",
    "\n",
    "pickle_out = open(\"X.pickle\", \"wb\")\n",
    "pickle.dump(X, pickle_out)\n",
    "pickle_out.close()\n",
    "\n",
    "pickle_out = open(\"y.pickle\", \"wb\")\n",
    "pickle.dump(y, pickle_out)\n",
    "pickle_out.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle_in = open(\"X.pickle\", \"rb\")\n",
    "\n",
    "X = pickle.load(pickle_in)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X[1]\n",
    "#y[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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