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{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: image_segmentation\n", "### Simple image segmentation using gradio's AnnotatedImage component.\n", " "]}, {"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 numpy as np\n", "import random\n", "\n", "with gr.Blocks() as demo:\n", " section_labels = [\n", " \"apple\",\n", " \"banana\",\n", " \"carrot\",\n", " \"donut\",\n", " \"eggplant\",\n", " \"fish\",\n", " \"grapes\",\n", " \"hamburger\",\n", " \"ice cream\",\n", " \"juice\",\n", " ]\n", "\n", " with gr.Row():\n", " num_boxes = gr.Slider(0, 5, 2, step=1, label=\"Number of boxes\")\n", " num_segments = gr.Slider(0, 5, 1, step=1, label=\"Number of segments\")\n", "\n", " with gr.Row():\n", " img_input = gr.Image()\n", " img_output = gr.AnnotatedImage(\n", " color_map={\"banana\": \"#a89a00\", \"carrot\": \"#ffae00\"}\n", " )\n", "\n", " section_btn = gr.Button(\"Identify Sections\")\n", " selected_section = gr.Textbox(label=\"Selected Section\")\n", "\n", " def section(img, num_boxes, num_segments):\n", " sections = []\n", " for a in range(num_boxes):\n", " x = random.randint(0, img.shape[1])\n", " y = random.randint(0, img.shape[0])\n", " w = random.randint(0, img.shape[1] - x)\n", " h = random.randint(0, img.shape[0] - y)\n", " sections.append(((x, y, x + w, y + h), section_labels[a]))\n", " for b in range(num_segments):\n", " x = random.randint(0, img.shape[1])\n", " y = random.randint(0, img.shape[0])\n", " r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))\n", " mask = np.zeros(img.shape[:2])\n", " for i in range(img.shape[0]):\n", " for j in range(img.shape[1]):\n", " dist_square = (i - y) ** 2 + (j - x) ** 2\n", " if dist_square < r**2:\n", " mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4\n", " sections.append((mask, section_labels[b + num_boxes]))\n", " return (img, sections)\n", "\n", " section_btn.click(section, [img_input, num_boxes, num_segments], img_output)\n", "\n", " def select_section(evt: gr.SelectData):\n", " return section_labels[evt.index]\n", "\n", " img_output.select(select_section, None, selected_section)\n", "\n", "if __name__ == \"__main__\":\n", " demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5} |