notebooks/Item Quantity Classifier.ipynb

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    "# BASALT 2022 Inventory Item Classifier\n",
    "\n",
    "## Background\n",
    "I am working on potential solutions for https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition. The goal of this classifier is to extract a bit of information about the inverntory. In this case, I am attempting to fill a dictionary of all the items quanities in the inventory. When I play minecraft, an understanding of what is in my inventory as I play is very important. My model would have no way of knowing what is in its inventory without having it open otherwise. \n",
    "\n",
    "## Data Mining\n",
    "In order to get the data to train this model on, I used a list of items that are in the 1.16 minecraft version. With that, I looped through the list and started a new gym environment that spawned in the game with each item at each quanitity. After I had images of all the inventories from this loop, I broke it into pieces of each inventory square. From there, I saved that image file as the name of the item and the quantity. After I had that, I went right into the code below.\n",
    "\n",
    "## Model Code\n",
    "\n",
    "### Imports"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a9543786",
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   "source": [
    "import os\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from detecto import core, utils, visualize\n",
    "import matplotlib.pyplot as plt\n",
    "import cv2\n",
    "import gdown"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaff7634",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "767c5575",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_dataset_quantity(img_folder):\n",
    "    \n",
    "    img_data_array=[]\n",
    "    class_name=[]\n",
    "    for file in os.listdir(img_folder):    \n",
    "        if \".jpg\" in file:\n",
    "            image_path= os.path.join(img_folder, file)\n",
    "            image= np.array(Image.open(image_path))\n",
    "            image = image.astype('float32')\n",
    "            image /= 255  \n",
    "            img_data_array.append(image)\n",
    "            if file.split(\"-\")[0] == \"empty\":\n",
    "                class_name.append(1)\n",
    "            else:\n",
    "                class_name.append(int(file.split(\"-\")[1].replace('.jpg', \"\")))\n",
    "    return img_data_array , class_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e8cc7dd7",
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   "source": [
    "def load_quantity_train_data():\n",
    "    trainPath = \"../assets/datasets/Item Classifier Data/train/\"\n",
    "\n",
    "    xTrainData, yTrainData = create_dataset_quantity(trainPath)\n",
    "\n",
    "    xTrainData = np.array(xTrainData, np.float32)\n",
    "    y_train = np.array(list(map(int,yTrainData)), np.int64)\n",
    "\n",
    "    n_samples_train = len(xTrainData)\n",
    "    x_train = xTrainData.reshape((n_samples_train, -1))\n",
    "\n",
    "    return x_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "48067439",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_quantity_test_data():\n",
    "    testPath = \"../assets/datasets/Item Classifier Data/test/\"\n",
    "\n",
    "    xTestData, yTestData = create_dataset_quantity(testPath)\n",
    "\n",
    "    xTestData = np.array(xTestData, np.float32)\n",
    "    y_test = np.array(list(map(int,yTestData)), np.int64)\n",
    "\n",
    "    n_samples_test = len(xTestData)\n",
    "    x_test = xTestData.reshape((n_samples_test, -1))\n",
    "\n",
    "    return x_test, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcfd1c09",
   "metadata": {},
   "source": [
    "### Build Model\n",
    "Keep in mind, I tested this on 4-5 different machine learning models outside of this notebook. For this problem, SkLearn's Gaussian Naive Bayes model worked best and that is what I used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "95f15ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = GaussianNB()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e5cb6b1",
   "metadata": {},
   "source": [
    "### Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f8a200a8",
   "metadata": {},
   "outputs": [
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       "GaussianNB()"
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     "execution_count": 6,
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   ],
   "source": [
    "x_train, y_train = load_quantity_train_data()\n",
    "model.fit(x_train, y_train)"
   ]
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   "cell_type": "markdown",
   "id": "f1d21296",
   "metadata": {},
   "source": [
    "### Test Model"
   ]
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     "text": [
      "\n",
      "Accuracy of Quantity Classifier:  1.0\n"
     ]
    }
   ],
   "source": [
    "x_test, y_test = load_quantity_test_data()\n",
    "\n",
    "predicted = []\n",
    "for pred in model.predict(x_test):\n",
    "    predicted.append(pred)\n",
    "            \n",
    "            \n",
    "numCorrect = 0\n",
    "for i in range(0, len(y_test)):\n",
    "    if not y_test[i] == predicted[i]:\n",
    "        print(y_test[i], predicted[i])\n",
    "    else:\n",
    "        numCorrect += 1\n",
    "    \n",
    "print(\"\\nAccuracy of Quantity Classifier: \", numCorrect/len(y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4eb21e",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "This model was very effective at solving the item classification problem. Pretty tough to beat 100% accuracy. Obviously this will be hurt by cursor occlusion, so, I plan to address that as it becomes a problem."
   ]
  }
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