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{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMeaCDE0/A8gxHJC+1SUs4o"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","source":["#@markdown Build a dataset from ALL images in /content/ with EXIF metadata (using exiftool) as separate columns and WebM files, saving to Google Drive\n","\n","# Step 1: Install required libraries and exiftool\n","!pip install Pillow imageio[ffmpeg] datasets pandas\n","!apt-get update && apt-get install -y libimage-exiftool-perl\n","\n","# Step 2: Import required libraries\n","import os\n","import glob\n","import subprocess\n","from PIL import Image\n","import imageio.v3 as iio\n","import pandas as pd\n","from datasets import Dataset, Features, Image as HFImage, Value\n","from google.colab import drive\n","\n","# Step 3: Mount Google Drive\n","drive.mount('/content/drive')\n","output_dir = '/content/drive/My Drive/exif_dataset' #@param {type:'string'}\n","\n","# Step 4: Define function to extract metadata using exiftool\n","def get_exif_data(image_path):\n","    try:\n","        # Run exiftool to extract all metadata as JSON\n","        result = subprocess.run(\n","            ['exiftool', '-j', image_path],\n","            stdout=subprocess.PIPE,\n","            stderr=subprocess.PIPE,\n","            text=True,\n","            check=True\n","        )\n","        # Parse JSON output (exiftool -j returns a list of dictionaries)\n","        metadata = eval(result.stdout)[0]  # First item in the list\n","        return metadata\n","    except subprocess.CalledProcessError as e:\n","        print(f\"exiftool error for {image_path}: {e.stderr}\")\n","        return {\"Error\": f\"exiftool failed: {str(e)}\"}\n","    except Exception as e:\n","        return {\"Error\": f\"Failed to read metadata: {str(e)}\"}\n","\n","# Step 5: Define function to convert image to WebM\n","def convert_to_webm(image_path, output_path):\n","    try:\n","        img = iio.imread(image_path)\n","        iio.imwrite(output_path, img, codec='vp8', fps=1, quality=8)\n","        return True\n","    except Exception as e:\n","        print(f\"Error converting {image_path} to WebM: {str(e)}\")\n","        return False\n","\n","# Step 6: Collect ALL images from /content/\n","image_dir = \"/content/\"\n","image_extensions = [\"*.jpg\", \"*.jpeg\", \"*.png\"]\n","image_paths = []\n","for ext in image_extensions:\n","    image_paths.extend(glob.glob(os.path.join(image_dir, ext)))\n","\n","if not image_paths:\n","    print(\"No images found in /content/\")\n","else:\n","    # Step 7: Process all images to collect metadata keys and data\n","    images = []\n","    webm_paths = []\n","    metadata_list = []\n","    all_metadata_keys = set()\n","\n","    for img_path in image_paths:\n","        print(f\"\\nProcessing {img_path}:\")\n","\n","        # Load image\n","        try:\n","            img = Image.open(img_path).convert('RGB')\n","        except Exception as e:\n","            print(f\"Error loading image {img_path}: {str(e)}\")\n","            continue\n","\n","        # Extract metadata with exiftool\n","        metadata = get_exif_data(img_path)\n","        print(\"Metadata (via exiftool):\")\n","        for key, value in metadata.items():\n","            print(f\"  {key}: {value}\")\n","            all_metadata_keys.add(key)  # Collect unique metadata keys\n","\n","        # Convert to WebM\n","        webm_path = os.path.splitext(img_path)[0] + \".webm\"\n","        if convert_to_webm(img_path, webm_path):\n","            print(f\"  Saved WebM: {webm_path}\")\n","            images.append(img)\n","            webm_paths.append(webm_path)\n","            metadata_list.append(metadata)\n","        else:\n","            print(f\"  Skipped WebM conversion for {img_path}\")\n","            continue\n","\n","    # Step 8: Check if any images were processed\n","    if not images:\n","        print(\"No images were successfully processed.\")\n","    else:\n","        # Step 9: Prepare dataset dictionary with separate columns for each metadata key\n","        data_dict = {'image': images, 'webm_path': webm_paths}\n","\n","        # Initialize columns for each metadata key with None\n","        for key in all_metadata_keys:\n","            data_dict[key] = [None] * len(images)\n","\n","        # Populate metadata values\n","        for i, metadata in enumerate(metadata_list):\n","            for key, value in metadata.items():\n","                data_dict[key][i] = str(value)  # Convert values to strings\n","\n","        # Step 10: Define dataset features\n","        features = Features({\n","            'image': HFImage(),\n","            'webm_path': Value(\"string\"),\n","            **{key: Value(\"string\") for key in all_metadata_keys}  # Dynamic columns for metadata keys\n","        })\n","\n","        # Step 11: Create Hugging Face Dataset\n","        dataset = Dataset.from_dict(data_dict, features=features)\n","\n","        # Step 12: Verify the dataset\n","        print(\"\\nDataset Summary:\")\n","        print(dataset)\n","        if len(dataset) > 0:\n","            print(\"\\nExample of accessing first item:\")\n","            print(\"WebM Path:\", dataset['webm_path'][0])\n","            print(\"Image type:\", type(dataset['image'][0]))\n","            print(\"Image size:\", dataset['image'][0].size)\n","            print(\"Metadata columns (first item):\")\n","            for key in all_metadata_keys:\n","                if dataset[key][0] is not None:\n","                    print(f\"  {key}: {dataset[key][0]}\")\n","\n","        # Step 13: Save dataset to Google Drive\n","        try:\n","            os.makedirs(output_dir, exist_ok=True)\n","            dataset.save_to_disk(output_dir)\n","            print(f\"\\nDataset saved to {output_dir}\")\n","        except Exception as e:\n","            print(f\"Error saving dataset to Google Drive: {str(e)}\")"],"metadata":{"id":"qVr4anf9KMh7"},"execution_count":null,"outputs":[]}]}