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It extracts the images, randomly shuffles them, and splits them into 70% training and 30% testing sets. All images are resized to 224x224 pixels, roatated and saved into separate folders (`Train` and `Test`) within the same Google Drive directory. Simultaneously, the GPS coordinates (Latitude and Longitude) from images in the training and testing datasets are extracted and stored together with the image names in the same folder, under the files named \"test_coordinates.csv\" and \"train_coordinates.csv.\" This process is repeated for each color dataset, ensuring the images are organized and prepared for machine learning tasks." ], "metadata": { "id": "5EqwysV94EzQ" } }, { "cell_type": "markdown", "source": [ "# Imports" ], "metadata": { "id": "upmhmty23pLd" } }, { "cell_type": "code", "source": [ "!pip install exifread" ], "metadata": { "id": "b1r1pYqsJfFe", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "1440771b-f0a1-429e-cf16-2d6cf3e70511" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: exifread in /usr/local/lib/python3.10/dist-packages (3.0.0)\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "import exifread, csv\n", "import zipfile\n", "import random\n", "from PIL import Image\n", "from pathlib import Path\n", "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "id": "dkP8I3tt2ErX", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "47a71eff-fb0f-4e9c-b7b1-0318fc9b1d49" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Helper Funcitons" ], "metadata": { "id": "RV3gMTlQ3m07" } }, { "cell_type": "code", "source": [ "# for extracting gps coodinates, as specified in the helper notebook\n", "def get_exif_data(image_path):\n", " with open(image_path, 'rb') as image_file:\n", " tags = exifread.process_file(image_file)\n", " return tags\n", "\n", "def export_exif_to_json(exif_data, output_file):\n", " # Convert tags to a serializable format\n", " exif_data_serializable = {str(tag): str(value) for tag, value in exif_data.items()}\n", " with open(output_file, 'w') as json_file:\n", " json.dump(exif_data_serializable, json_file, indent=4)\n", "\n", "# Function to convert GPS coordinates in degrees, minutes, and seconds to decimal degrees\n", "def convert_to_decimal_degrees(value):\n", " d, m, s = value.values\n", " return d.num / d.den + (m.num / m.den) / 60 + (s.num / s.den) / 3600\n", "\n", "# extract files from zip\n", "def extract_zip(zip_file, extract_to):\n", " with zipfile.ZipFile(zip_file, 'r') as z:\n", " z.extractall(extract_to)\n", "\n", "# resize images to 224 by 224\n", "def resize_image(input_path, output_path, size=(224, 224)):\n", " with Image.open(input_path) as img:\n", " try:\n", " exif = img._getexif()\n", " if exif:\n", " orientation = exif.get(274)\n", " if orientation == 3:\n", " img = img.rotate(180, expand=True)\n", " elif orientation == 6:\n", " img = img.rotate(270, expand=True)\n", " elif orientation == 8:\n", " img = img.rotate(90, expand=True)\n", " except (AttributeError, KeyError, IndexError):\n", " pass\n", " # Resize the imag\n", " img_resized = img.resize(size)\n", " img_resized.save(output_path)\n", "\n", "# process color data\n", "def process_color(folder_path, output_folder, color, train_csv, test_csv):\n", " color_abbreviation = {\"Green\": \"g\", \"Orange\": \"o\", \"Blue\": \"b\"}[color]\n", "\n", " # shuffle color\n", " image_files = list(Path(folder_path).rglob(\"*.JPG\"))\n", " random.shuffle(image_files)\n", "\n", " # train and test split\n", " split_index = int(0.7 * len(image_files))\n", " train_files = image_files[:split_index]\n", " test_files = image_files[split_index:]\n", "\n", " # train and test output directories\n", " train_output_dir = Path(output_folder) / \"Train\"\n", " test_output_dir = Path(output_folder) / \"Test\"\n", "\n", " # processing\n", " for split, files, output_dir, csv_file in [\n", " (\"train\", train_files, train_output_dir, train_csv),\n", " (\"test\", test_files, test_output_dir, test_csv),\n", " ]:\n", " with open(csv_file, mode='a', newline='') as csv_file:\n", " fieldnames = [\"file_name\", \"Latitude\", \"Longitude\"]\n", " writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n", " if os.stat(csv_file.name).st_size == 0: # Write header if file is empty\n", " writer.writeheader()\n", "\n", " for i, file in enumerate(files):\n", " exif_data = get_exif_data(file)\n", " if exif_data:\n", " gps_latitude = exif_data.get(\"GPS GPSLatitude\")\n", " gps_latitude_ref = exif_data.get(\"GPS GPSLatitudeRef\")\n", " gps_longitude = exif_data.get(\"GPS GPSLongitude\")\n", " gps_longitude_ref = exif_data.get(\"GPS GPSLongitudeRef\")\n", "\n", " if gps_latitude and gps_longitude:\n", " # Convert latitude and longitude to decimal degrees\n", " latitude = convert_to_decimal_degrees(gps_latitude)\n", " longitude = convert_to_decimal_degrees(gps_longitude)\n", "\n", " # Adjust for N/S and E/W references\n", " if gps_latitude_ref and gps_latitude_ref.values[0] == \"S\":\n", " latitude = -latitude\n", " if gps_longitude_ref and gps_longitude_ref.values[0] == \"W\":\n", " longitude = -longitude\n", "\n", " # Write GPS data to CSV\n", " renamed_file = f\"{file.stem}_{color_abbreviation}{file.suffix}\"\n", " writer.writerow({\"file_name\": renamed_file, \"Latitude\": latitude, \"Longitude\": longitude})\n", "\n", " # Resize and save image to the appropriate folder\n", " output_file = output_dir / f\"{file.stem}_{color_abbreviation}{file.suffix}\"\n", " resize_image(file, output_file)\n", "\n", " print(f\"Processed {color}: {len(train_files)} train, {len(test_files)} test images.\")" ], "metadata": { "id": "lTkgO5xk3aV1" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "blue = '/content/drive/MyDrive/CIS 5190 Project Folder/Photos/Blue.zip'\n", "blue_path_first = \"/content/drive/MyDrive/CIS 5190 Project Folder/Photos/Blue_extracted\"\n", "extract_zip(blue, blue_path_first)" ], "metadata": { "id": "mkuVnzJTWDAV" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Creating the Dataset and Extracting GPS coordinates" ], "metadata": { "id": "8sEEzaI84KTc" } }, { "cell_type": "markdown", "source": [ "Processed Green: 795 train, 341 test images.\n", "\n", "Processed Orange: 2128 train, 912 test images.\n", "\n", "Processed Blue: 1546 train, 663 test images." ], "metadata": { "id": "AFUqYuiAWs1C" } }, { "cell_type": "code", "source": [ "google_drive_folder = \"/content/drive/MyDrive/CIS 5190 Project Folder/Photos\"\n", "green_path = \"/content/drive/MyDrive/CIS 5190 Project Folder/Photos/Green_extracted/Green\"\n", "orange_path = \"/content/drive/MyDrive/CIS 5190 Project Folder/Photos/Orange_extracted/Orange\"\n", "blue_path = \"/content/drive/MyDrive/CIS 5190 Project Folder/Photos/Blue_extracted/Blue\"\n", "\n", "# output CSVs\n", "train_csv = os.path.join(google_drive_folder, \"train_coordinates.csv\")\n", "test_csv = os.path.join(google_drive_folder, \"test_coordinates.csv\")\n", "open(train_csv, \"w\").close()\n", "open(test_csv, \"w\").close()\n", "\n", "# Process colors\n", "colors = [\"Blue\",\"Green\", \"Orange\"]\n", "for color in colors:\n", " if color == \"Green\":\n", " file_path = green_path\n", " elif color == \"Orange\":\n", " file_path = orange_path\n", " elif color == \"Blue\":\n", " file_path = blue_path\n", " process_color(file_path, google_drive_folder, color, train_csv, test_csv)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "55PtJv7SJq3a", "outputId": "90370af8-65bf-4180-977f-de897578e756" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Processed Blue: 1546 train, 663 test images.\n" ] } ] }, { "cell_type": "markdown", "source": [ "# Save Datasets from Drive to Hugging Face 2" ], "metadata": { "id": "ImdR6IkH667S" } }, { "cell_type": "code", "source": [ "!pip install huggingface_hub" ], "metadata": { "id": "HyZaOFtFBnh9" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from datasets import Dataset\n", "from PIL import Image\n", "import pandas as pd\n", "import os\n", "\n", "# Define a function to load and embed images as PIL objects\n", "def load_image_as_pil(row, base_path):\n", " image_path = os.path.join(base_path, row[\"file_name\"]) # Combine base path and file name\n", " image = Image.open(image_path).convert(\"RGB\") # Ensure image is in RGB format\n", " return {\"image\": image}\n", "\n", "# Paths to train and test CSVs and image directories\n", "train_csv_path = \"/content/drive/MyDrive/CIS 5190 Project Folder/Photos/train/train.csv\"\n", "test_csv_path = \"/content/drive/MyDrive/CIS 5190 Project Folder/Photos/test/test.csv\"\n", "train_image_base_path = \"/content/drive/MyDrive/CIS 5190 Project Folder/Photos/train/images/\"\n", "test_image_base_path = \"/content/drive/MyDrive/CIS 5190 Project Folder/Photos/test/images/\"\n", "\n", "# Process train dataset\n", "train_df = pd.read_csv(train_csv_path)\n", "train_dataset = Dataset.from_pandas(train_df)\n", "\n", "# Map function to load images for the train dataset\n", "train_dataset = train_dataset.map(lambda row: load_image_as_pil(row, train_image_base_path))\n", "\n", "# Remove the file_name column (optional)\n", "train_dataset = train_dataset.remove_columns(\"file_name\")\n", "\n", "# Save the train dataset\n", "train_dataset.save_to_disk(\"train_dataset_with_images\")\n", "\n", "# Process test dataset\n", "test_df = pd.read_csv(test_csv_path)\n", "test_dataset = Dataset.from_pandas(test_df)\n", "\n", "# Map function to load images for the test dataset\n", "test_dataset = test_dataset.map(lambda row: load_image_as_pil(row, test_image_base_path))\n", "\n", "# Remove the file_name column (optional)\n", "test_dataset = test_dataset.remove_columns(\"file_name\")\n", "\n", "# Save the test dataset\n", "test_dataset.save_to_disk(\"test_dataset_with_images\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 145, "referenced_widgets": [ "13091fc4919a47ed915e9543e07c9b7c", "35a9e799e8c54627ab758e7d5a77805d", "e30951342ea049dfbc29cc15ea389cc9", "0a8abcc463c542ef9f6a0e6159976947", "cb8a6dc8774648f395aac558a3b26e8b", "da2f65073cd44f45be004f742afa5afa", "85c29bd6c2704c8a933288cc0b8ec733", "d2959ddd456540f192145827386a39d0", "4d4b8d116df5452d9e8d9e73aa63ce63", "0f446b764b694f8a8767d6c984a1c7a4", "6dcbf020f53f42118cdec8aff98260fa", "b855030b6e6b45c7af1a2994e01c1ae6", "f2f0478683104ed38f3e7ce91ea7d49a", "cd754146510a4606b8be83ce00127eb0", "548acbd9827247ebb5e213844ade777e", "4ef0a5f1cf9b496290e2e45961505550", "35b43e33ab304662999d565d70fb7675", "e4bca80ce4b2493c86b319d22245c8b0", "09c066acd78b4fae844534dbeefa3126", "5649c9fa12db49a8a71e93091c3c90b1", "d3715870caeb482ead09d679a1be685a", "f99dad5363264ad9a0cc69d8bfa61e4f", "0a03f9279a8c435091dd80af5182ac0c", "1c03709abe24404995ec155f2262e1ad", "26f115045df84af294156d152fc7230a", "2a9af9cb5cf44612ba887f57efe197f0", "091163eddcb3420eaba4b5eaa0dc655e", "593839bd6eb249328d4418569619696f", "397731f566454ac698a74ffdc95c9489", "d78db99abd0d42049b4e815d11c6074c", "9501cce2defe48ed90da0fb0249fb1fb", "61bf3c4df8214d55a533d51cdbe9ca15", "38cfbf03898d4c0fbec266f8fd16f669", "76252d8797aa4890a143635fdb473886", "69f4a518e2954b9baf05a944aca4c91a", "b2c56287bc124d8b80e2d1f89fe154d5", "3cf97ec90dcb488baa8a4823916ce252", "f2263ef2da3948b1935f35dbe1c80e5e", "e8d104ca6e4b43fdbf69547f6fe4c814", "e5d537054e2a438ca6b270903f7cc01d", "50527a70e8f5459b9da6655c97514f18", "b22b095ac1f946b9a77529d989278f79", "6ee4a380799b477f9823b540c0ddc05b", "b02d5762a1bd4a8e965aa2284d7c0342" ] }, "id": "d-wVEUa07qRW", "outputId": "98d4bb7a-764e-4498-8179-9ead4faa2dcd" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Map: 0%| | 0/4469 [00:00