{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: annotatedimage_component"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import pathlib\n", "from PIL import Image\n", "import numpy as np\n", "import urllib.request\n", "\n", "\n", "source_dir = pathlib.Path(__file__).parent\n", "\n", "urllib.request.urlretrieve(\n", "  'https://gradio-builds.s3.amazonaws.com/demo-files/base.png',\n", "   str(source_dir / \"base.png\")\n", ")\n", "urllib.request.urlretrieve(\n", "    \"https://gradio-builds.s3.amazonaws.com/demo-files/buildings.png\",\n", "    str(source_dir / \"buildings.png\")\n", ")\n", "\n", "base_image = Image.open(str(source_dir / \"base.png\"))\n", "building_image = Image.open(str(source_dir / \"buildings.png\"))\n", "\n", "# Create segmentation mask\n", "building_image = np.asarray(building_image)[:, :, -1] > 0\n", "\n", "with gr.Blocks() as demo:\n", "    gr.AnnotatedImage(\n", "        value=(base_image, [(building_image, \"buildings\")]),\n", "        height=500,\n", "    )\n", "\n", "demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}