{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "81598ea8-8e97-4ad7-a45f-bd928d0ef416", "metadata": {}, "outputs": [], "source": [ "import gradio as gr\n", "from ultralytics import YOLO\n", "import cv2\n", "import os\n", "\n", "def predict_image(image_input):\n", " image = cv2.imread(image_input)\n", " # load model\n", " model = YOLO(\"best.pt\")\n", " #run predict\n", " outputs = model.predict(source=image_input)\n", " results = output[0].cpu().numpy()\n", " for i, det in enumerate(results.boxes.xyxy):\n", " cv2.rectangle(image, (int(det[0]), int(det[1]), int(det[2]), int(det[3]),\n", " color=(0, 0, 255), thickness=2, lineType=cv2.Line_AA)\n", " return cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n", "\n", "inputs_image = [gr.components.Image(type=\"filepath\", label=\"Input Image\")]\n", "outputs_image = [gr.components.Image(type=\"numpy\", label=\"Output Image\")]\n", "\n", "interface_image = gr.Interface(fn = predict_image, inputs=inputs_image, outputs=outputs_image, \n", " title=\"Fire & Smoke Detector\", cache_examples=False)\n", "\n", "interface_image.launch(Debug=True)\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }