getting data completed

Backend/SF_Dulantha_Dificiency_identificationV1.ipynb

+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Y2jOdgL3j-o8"
+      },
+      "source": [
+        "# 🟨 Getting the Data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lDXyIngTMaXf",
+        "outputId": "eb920137-7143-4c2c-e43e-a0aa82d93b49"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "t-ZDw43fiy-1"
+      },
+      "outputs": [],
+      "source": [
+        "data_location = '/content/drive/MyDrive/RP_SmartFarmer/Dulantha Madhushan - Nutrient deficiency/Dataset/Dificiency Data.rar'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "widI8pNLhUci",
+        "outputId": "13c35bb6-dd84-4ffd-f585-402d31aeaa41"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install patool"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 105
+        },
+        "id": "U-MmiQnjF9gG",
+        "outputId": "3d634674-631e-4ed7-d8d7-708c1f9050f5"
+      },
+      "outputs": [],
+      "source": [
+        "import patoolib\n",
+        "patoolib.extract_archive(data_location, outdir=\"/content\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_YdAEKT6GSoX",
+        "outputId": "4fdd684c-a8b2-4fa3-ee3e-91cd0c9ec280"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "for dirpath , dirnames , filenames in os.walk(\"Dificiency Data\"):\n",
+        "  print(f\"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gLo3FV6OGVP0",
+        "outputId": "c9f815fd-004f-44d5-dba0-745f657c09fb"
+      },
+      "outputs": [],
+      "source": [
+        "train_dir = \"Dificiency Data/Trainig\"\n",
+        "test_dir = \"Dificiency Data/Testing\"\n",
+        "\n",
+        "#lets get the class names\n",
+        "import pathlib\n",
+        "import numpy as np\n",
+        "\n",
+        "data_dir = pathlib.Path(train_dir)\n",
+        "class_names = np.array(sorted([item.name for item in data_dir.glob('*')]))\n",
+        "print(class_names)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vVCpvSuKlGG1"
+      },
+      "outputs": [],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "import matplotlib.image as mpimg\n",
+        "import random\n",
+        "import os\n",
+        "\n",
+        "def view_random_image(target_dir , target_class):\n",
+        "  target_folder = target_dir+\"/\"+target_class\n",
+        "  random_image = random.sample(os.listdir(target_folder) ,1)\n",
+        "\n",
+        "  img = mpimg.imread(target_folder+\"/\"+random_image[0])\n",
+        "  plt.imshow(img)\n",
+        "  plt.title(target_class)\n",
+        "  plt.axis(\"off\")\n",
+        "\n",
+        "  print(f\"Image Shape:{img.shape}\") \n",
+        "\n",
+        "  return img"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tVx10O9CGXn2",
+        "outputId": "173a0a39-9cfa-4f76-cf91-9e67e7da54d0"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "img = view_random_image(target_dir=train_dir , \n",
+        "                        target_class = random.choice(class_names))"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [
+        "Y2jOdgL3j-o8",
+        "HvDEsB_fj7_7",
+        "RFJbEeyUlUgn",
+        "z_3CSlVYgcU7",
+        "gx3orjVQMLLt"
+      ],
+      "provenance": []
+    },
+    "gpuClass": "standard",
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}