Upload Project1_dataset.ipynb

Project1_dataset.ipynb

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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9QLlZv6DlPC1"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "%cd /content/drive/MyDrive/sta_663/soybean/"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import pandas as pd\n",
+        "\n",
+        "# Function to read ids from a file and return them as a list with leading zeros\n",
+        "def read_ids(file_path):\n",
+        "    with open(file_path, 'r') as file:\n",
+        "        # Read the IDs, ensuring they are 6 digits long with leading zeros\n",
+        "        return [line.zfill(6) for line in file.read().splitlines()]\n",
+        "\n",
+        "# Function to read ids from a file and assign a set type\n",
+        "def assign_set_type(file_path, set_type):\n",
+        "    # Read the file content\n",
+        "    with open(file_path, 'r') as file:\n",
+        "        ids = file.read().splitlines()\n",
+        "    # Update the 'sets' column based on the ids in the file\n",
+        "    df.loc[df['unique_id'].isin(ids), 'sets'] = set_type\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "prkF3wVLld_k"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Read unique_ids from all.txt\n",
+        "all_file_path = '/content/drive/MyDrive/sta_663/soybean/ImageSets/Segmentation/all.txt'\n",
+        "unique_ids = read_ids(all_file_path)\n",
+        "\n",
+        "# Initialize the DataFrame with unique_ids and default 'train' set\n",
+        "df = pd.DataFrame(unique_ids, columns=['unique_id'])\n",
+        "df['sets'] = 'train'\n",
+        "\n",
+        "# Assign 'test' to the sets column for IDs from test.txt\n",
+        "test_file_path = '/content/drive/MyDrive/sta_663/soybean/ImageSets/Segmentation/test.txt'\n",
+        "assign_set_type(test_file_path, 'test')\n",
+        "\n",
+        "# Assign 'valid' to the sets column for IDs from val.txt\n",
+        "val_file_path = '/content/drive/MyDrive/sta_663/soybean/ImageSets/Segmentation/val.txt'\n",
+        "assign_set_type(val_file_path, 'valid')\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "nsFdyvBzlgB_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "file_path = '/content/drive/MyDrive/sta_663/soybean/dataset.csv'\n",
+        "df.to_csv(file_path, index=False)"
+      ],
+      "metadata": {
+        "id": "KjRGHJivliym"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Download the dataset.csv file and put into the same directory as the downloaded zip file"
+      ],
+      "metadata": {
+        "id": "qyyjofnUmXsh"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import os\n",
+        "import pandas as pd\n",
+        "import shutil"
+      ],
+      "metadata": {
+        "id": "hm7ZaB5ImAeA"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Replace with the path to your CSV file\n",
+        "csv_file_path = 'D:\\STA 663\\project_1\\dataset.csv'\n",
+        "# Replace with the directory where your images are currently stored\n",
+        "images_directory = 'D:\\STA 663\\project_1\\soybean\\JPEGImages'\n",
+        "# Replace with the directory where you want to create test/train/validate directories\n",
+        "output_base_directory = 'D:\\STA 663\\project_1'\n",
+        "\n",
+        "# Read the dataset\n",
+        "df = pd.read_csv(csv_file_path)\n",
+        "df['unique_id'] = df['unique_id'].astype(str).str.zfill(6)"
+      ],
+      "metadata": {
+        "id": "iTKsnTUdmI3N"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create directories for the sets if they don't exist\n",
+        "for set_type in ['test', 'train', 'valid']:\n",
+        "    set_directory = os.path.join(output_base_directory, set_type)\n",
+        "    if not os.path.exists(set_directory):\n",
+        "        os.makedirs(set_directory)\n",
+        "\n",
+        "# Function to move and rename files\n",
+        "def move_and_rename_files(row):\n",
+        "    file_name = f\"{row['unique_id']}.jpg\"  # Assuming the images are .jpg\n",
+        "    original_path = os.path.join(images_directory, file_name)\n",
+        "    if os.path.isfile(original_path):\n",
+        "        set_type = row['sets']\n",
+        "        new_name = f\"{row['unique_id']}_original.jpg\"\n",
+        "        new_path = os.path.join(output_base_directory, set_type, new_name)\n",
+        "        # Move and rename the file\n",
+        "        shutil.copy(original_path, new_path)  # Use shutil.copy if you want to keep the originals\n",
+        "\n",
+        "# Apply the function to each row in the dataframe\n",
+        "df.apply(move_and_rename_files, axis=1)"
+      ],
+      "metadata": {
+        "id": "2dvMgZcOmLt2"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "### Do the same thing for segmentation class"
+      ],
+      "metadata": {
+        "id": "aZb9yoXumrrp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Replace with the path to your CSV file\n",
+        "csv_file_path = 'D:\\STA 663\\project_1\\dataset.csv'\n",
+        "# Replace with the directory where your images are currently stored\n",
+        "images_directory = 'D:\\STA 663\\project_1\\soybean\\SegmentationClass'\n",
+        "# Replace with the directory where you want to create test/train/validate directories\n",
+        "output_base_directory = 'D:\\STA 663\\project_1'"
+      ],
+      "metadata": {
+        "id": "Ud79pkDMmyA_"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Function to move and rename files\n",
+        "def move_and_rename_files(row):\n",
+        "    file_name = f\"{row['unique_id']}.png\"  # Assuming the images are .jpg\n",
+        "    original_path = os.path.join(images_directory, file_name)\n",
+        "    if os.path.isfile(original_path):\n",
+        "        set_type = row['sets']\n",
+        "        new_name = f\"{row['unique_id']}_segmentation.jpg\"\n",
+        "        new_path = os.path.join(output_base_directory, set_type, new_name)\n",
+        "        # Move and rename the file\n",
+        "        shutil.copy(original_path, new_path)  # Use shutil.copy if you want to keep the originals\n",
+        "\n",
+        "# Apply the function to each row in the dataframe\n",
+        "df.apply(move_and_rename_files, axis=1)"
+      ],
+      "metadata": {
+        "id": "UoJLs5-Dm2u5"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}