CMM536_data_loading_tutorial.ipynb

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      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/carlosfmorenog/CMM536_Data_Loading_Tutorial/blob/main/CMM536_data_loading_tutorial.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# clone the github repo containing the images\n",
        "!git clone https://github.com/carlosfmorenog/CMM536_Data_Loading_Tutorial"
      ],
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        "id": "3FaEM3-OXF7F",
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        "colab": {
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'CMM536_Data_Loading_Tutorial'...\n",
            "remote: Enumerating objects: 44, done.\u001b[K\n",
            "remote: Counting objects: 100% (44/44), done.\u001b[K\n",
            "remote: Compressing objects: 100% (42/42), done.\u001b[K\n",
            "remote: Total 44 (delta 10), reused 8 (delta 1), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (44/44), 768.36 KiB | 8.44 MiB/s, done.\n",
            "Resolving deltas: 100% (10/10), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KvGYoy1fW9tf"
      },
      "source": [
        "# Version 1: Loading the images and converting both the 2D (flattened) and 3D lists into numpy arrays"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the 3D image numpy array  (6, 100, 100)\n",
            "Sample of the 2D flattened numpy array\n",
            "[[185 190 197 ... 146 146 146]\n",
            " [  8   9  11 ...  12   6 132]\n",
            " [190 190 191 ...  38  37  38]\n",
            " [109 119 112 ...  12  14  34]\n",
            " [101 101 101 ... 178 176 182]\n",
            " [ 59  65  66 ... 127 132 111]]\n",
            "List of class labels\n",
            "['nomask', 'nomask', 'mask', 'mask', 'mask', 'mask']\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "resize_width = 100\n",
        "resize_height = 100\n",
        "path = '/content/CMM536_Data_Loading_Tutorial/data'\n",
        "images = [] # List to append the images in 2D, thus resulting on a 3D array.\n",
        "imagesflattened = [] # List to append the images in 1D (flattened), thus resulting on a 2D array.\n",
        "target = [] # List to append the target (vector of labels)\n",
        "\n",
        "for root, dirs, files in os.walk(path):\n",
        "     for file in files:\n",
        "        with open(os.path.join(root, file), \"r\") as auto:\n",
        "            try:\n",
        "                img = cv2.imread(root+'/'+file, 0)\n",
        "                img = cv2.resize(img, (resize_width, resize_height))\n",
        "                images.append(img)\n",
        "                # Append the flattened image to the pixel repo\n",
        "                imagesflattened.append(img.flatten())\n",
        "                # Append the folder where the image is to the target list\n",
        "                target.append(root.replace(path,'').replace('\\\\','').replace('/',''))\n",
        "            except Exception as e:\n",
        "                print(\"Invalid file \"+file+\" skipped.\")\n",
        "\n",
        "# Convert the lists into numpy arrays\n",
        "images = np.array(images)\n",
        "imagesflattened_numpyarray = np.array(imagesflattened)\n",
        "print(\"Shape of the 3D image numpy array \", images.shape)\n",
        "print(\"Sample of the 2D flattened numpy array\")\n",
        "print(imagesflattened_numpyarray)\n",
        "print(\"List of class labels\")\n",
        "print(target)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p_XZJQO2W9t2"
      },
      "source": [
        "# Version 2: Loading the images and converting the 2D (flattened) list into a Pandas Dataframe\n",
        "\n",
        "The 3D list is still converted into a numpy array since you cannot convert a 3D list into a 2D table!"
      ]
    },
    {
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      "outputs": [
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          "text": [
            "Shape of the 3D image numpy array  (6, 25, 25)\n",
            "Sample of the 2D flattened Pandas DataFrame\n"
          ]
        },
        {
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
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            ],
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              "type": "dataframe",
              "variable_name": "imagesflattened_pandas"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import cv2\n",
        "from pandas import DataFrame\n",
        "\n",
        "# I used a smaller resize factor to render the Pandas DataFrame better\n",
        "resize_width = 25\n",
        "resize_height = 25\n",
        "path = '/content/CMM536_Data_Loading_Tutorial/data'\n",
        "images = [] # List to append the images in 2D, thus resulting on a 3D array.\n",
        "imagesflattened = [] # List to append the images in 1D (flattened), thus resulting on a 2D Pandas DataFrane.\n",
        "target = [] # List to append the target (vector of labels)\n",
        "\n",
        "for root, dirs, files in os.walk(path):\n",
        "     for file in files:\n",
        "        with open(os.path.join(root, file), \"r\") as auto:\n",
        "            try:\n",
        "                img = cv2.imread(root+'/'+file, 0)\n",
        "                img = cv2.resize(img, (resize_width, resize_height))\n",
        "                images.append(img)\n",
        "                # Append the flattened image to the pixel repo\n",
        "                imagesflattened.append(img.flatten())\n",
        "                # Append the folder where the image is to the target list\n",
        "                target.append(root.replace(path,'').replace('\\\\','').replace('/',''))\n",
        "            except Exception as e:\n",
        "                print(\"Invalid file \"+file+\" skipped.\")\n",
        "\n",
        "# convert the 3D list into numpy array\n",
        "images = np.array(images)\n",
        "print(\"Shape of the 3D image numpy array \", images.shape)\n",
        "# Convert the 2D list into pandas dataframe\n",
        "imagesflattened_pandas = DataFrame(imagesflattened)\n",
        "# add the class as the last column of the dataframe\n",
        "imagesflattened_pandas['class']=DataFrame(target)\n",
        "print(\"Sample of the 2D flattened Pandas DataFrame\")\n",
        "imagesflattened_pandas"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "9Uj6T3_jW9uF"
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        "The values in the data structures are different because in the `numpy array` the images are resized to $100 \\times 100$ while the `pandas dataframe` contains $25 \\times 25$ flattened images (plus the **class/label/target** in the last column)."
      ]
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