Weather api added
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index.html
224
index.html
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>YOLOv8 Object Detection</title>
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<style>
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canvas {
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display:block;
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border: 1px solid black;
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margin-top:10px;
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}
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</style>
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</head>
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<body>
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<input id="uploadInput" type="file"/>
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<canvas></canvas>
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<script>
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/**
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* "Upload" button onClick handler: uploads selected
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* image file to backend, receives an array of
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* detected objects and draws them on top of image
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*/
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const input = document.getElementById("uploadInput");
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input.addEventListener("change",async(event) => {
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const file = event.target.files[0];
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const data = new FormData();
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data.append("image_file",file,"image_file");
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const response = await fetch("/detect",{
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method:"post",
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body:data
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});
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const boxes = await response.json();
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draw_image_and_boxes(file,boxes);
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})
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/**
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* Function draws the image from provided file
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* and bounding boxes of detected objects on
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* top of the image
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* @param file Uploaded file object
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* @param boxes Array of bounding boxes in format
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[[x1,y1,x2,y2,object_type,probability],...]
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*/
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function draw_image_and_boxes(file,boxes) {
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const img = new Image()
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img.src = URL.createObjectURL(file);
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img.onload = () => {
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const canvas = document.querySelector("canvas");
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canvas.width = img.width;
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canvas.height = img.height;
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const ctx = canvas.getContext("2d");
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ctx.drawImage(img,0,0);
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ctx.strokeStyle = "#00FF00";
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ctx.lineWidth = 5;
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ctx.font = "20px serif";
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boxes.forEach(([x1,y1,x2,y2,object_type, prob]) => {
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const label = `${object_type} ${prob.toFixed(2)}`;
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ctx.strokeRect(x1,y1,x2-x1,y2-y1);
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ctx.fillStyle = "#00ff00";
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const width = ctx.measureText(label).width;
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ctx.fillRect(x1,y1,width+10,25);
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ctx.fillStyle = "#000000";
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ctx.fillText(label,x1,y1+18);
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});
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}
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}
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</script>
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</body>
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<title>TomAIto</title>
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<style>
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canvas {
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border: 1px solid black;
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display: flex;
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flex-direction: column;
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max-width: 300px; /* Ustaw maksymalną szerokość formularza */
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margin: auto; /* Centruj formularz na stronie */
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margin-top: 10px;
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}
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#weatherInfo {
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display: flex;
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flex-direction: column;
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max-width: 300px; /* Ustaw maksymalną szerokość formularza */
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margin: auto; /* Centruj formularz na stronie */
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margin-top: 10px;
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}
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form {
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display: flex;
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flex-direction: column;
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max-width: 300px; /* Ustaw maksymalną szerokość formularza */
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margin: auto; /* Centruj formularz na stronie */
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}
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label {
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margin-top: 10px;
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}
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button {
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margin-top: 10px;
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}
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</style>
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</head>
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<body>
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<form id="uploadForm">
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<label for="cropType">Crop Type:</label>
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<select id="cropType" name="cropType">
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<option value="external">External</option>
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<option value="internal">Internal</option>
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<option value="greenhouse">Greenhouse</option>
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</select>
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<label for="location">Location:</label>
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<input type="text" id="location" name="location" required/>
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<label for="variety">Variety:</label>
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<input type="text" id="variety" name="variety" required/>
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<label for="uploadInput">Choose an image:</label>
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<input id="uploadInput" type="file" required/>
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<button type="Sprawdz">Sprawdz</button>
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</form>
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<canvas></canvas>
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<div id="weatherInfo"></div>
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<script>
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const form = document.getElementById("uploadForm");
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form.addEventListener("submit", async (event) => {
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event.preventDefault();
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const fileInput = document.getElementById("uploadInput");
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const cropTypeInput = document.getElementById("cropType");
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const locationInput = document.getElementById("location");
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const varietyInput = document.getElementById("variety");
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const file = fileInput.files[0];
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const data = new FormData();
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data.append("image_file", file);
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data.append("cropType", cropTypeInput.value);
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data.append("location", locationInput.value);
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data.append("variety", varietyInput.value);
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const response = await fetch("/detect", {
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method: "post",
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body: data
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});
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const boxes = await response.json();
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draw_image_and_boxes(file, boxes);
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const location = locationInput.value;
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getWeather(location);
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});
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function draw_image_and_boxes(file, boxes) {
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const img = new Image()
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img.src = URL.createObjectURL(file);
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img.onload = () => {
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const canvas = document.querySelector("canvas");
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canvas.width = img.width;
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canvas.height = img.height;
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const ctx = canvas.getContext("2d");
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ctx.drawImage(img, 0, 0);
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ctx.strokeStyle = "#00FF00";
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ctx.lineWidth = 5;
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ctx.font = "20px serif";
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boxes.forEach(([x1, y1, x2, y2, object_type, prob]) => {
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const label = `${object_type} ${prob.toFixed(2)}`;
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ctx.strokeRect(x1, y1, x2 - x1, y2 - y1);
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ctx.fillStyle = "#00ff00";
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const width = ctx.measureText(label).width;
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ctx.fillRect(x1, y1, width + 10, 25);
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ctx.fillStyle = "#000000";
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ctx.fillText(label, x1, y1 + 18);
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});
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}
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}
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async function getWeather(location) {
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const apiKey = "1fc6a52c96331d035a828cc0c1606241";
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const apiUrl = `https://api.openweathermap.org/data/2.5/weather?q=${location}&appid=${apiKey}`;
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try {
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const weatherResponse = await fetch(apiUrl);
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const weatherData = await weatherResponse.json();
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if (weatherData.main && weatherData.main.temp) {
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const temperatureKelvin = weatherData.main.temp;
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const temperatureCelsius = temperatureKelvin - 273.15;
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const tempRound = Math.round(temperatureCelsius);
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const weatherCondition = weatherData.weather[0].main;
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const isRaining = weatherCondition === 'Rain';
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let additionalInfo = `${isRaining ? 'Rain is expected in your town today, so you dont have to water your tomatoes' :
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'No rainfall is expected in the city today, so you need to water your tomatoes!'} Temperature in ${location} is ${tempRound} °C.`;
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if (tempRound < 15) {
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additionalInfo += ' So you need to cover your tomatoes!';
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}
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document.getElementById("weatherInfo").innerText = additionalInfo;
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} else {
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document.getElementById("weatherInfo").innerText = "Unable to fetch weather data.";
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}
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} catch (error) {
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console.error("Error fetching weather data:", error);
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document.getElementById("weatherInfo").innerText = "Error fetching weather data.";
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}
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}
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</script>
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</body>
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</html>
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#from ultralytics import YOLO
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from flask import request, Flask, jsonify
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from waitress import serve
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from PIL import Image
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import onnxruntime as ort
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import numpy as np
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#my changes
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import os
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script_dir = os.path.dirname(os.path.abspath(__file__))
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# Change the working directory to the script's directory
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os.chdir(script_dir)
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yolo_classes = ["b_fully_ripened",
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"b_half_ripened",
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"b_green",
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"l_fully_ripened",
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"l_half_ripened",
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"l_green"
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]
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#app start
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app = Flask(__name__)
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@app.route("/")
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def root():
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"""
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Site main page handler function.
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:return: Content of index.html file
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"""
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with open("index.html") as file:
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return file.read()
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@app.route("/detect", methods=["POST"])
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def detect():
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"""
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Handler of /detect POST endpoint
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Receives uploaded file with a name "image_file", passes it
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through YOLOv8 object detection network and returns and array
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of bounding boxes.
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:return: a JSON array of objects bounding boxes in format [[x1,y1,x2,y2,object_type,probability],..]
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"""
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buf = request.files["image_file"]
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boxes, orientation = detect_objects_on_image(buf.stream)
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#print(boxes)
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#print(orientation)
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return jsonify(boxes)
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def detect_objects_on_image(buf):
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input, img_width, img_height = prepare_input(buf)
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output = run_model(input)
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orientation = get_orientation(buf)
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processed_output = process_output(output, img_width, img_height, orientation)
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return processed_output, orientation
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def prepare_input(buf):
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img = Image.open(buf)
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img_width, img_height = img.size
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img = img.resize((640, 640))
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img = img.convert("RGB")
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input = np.array(img)
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input = input.transpose(2, 0, 1)
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input = input.reshape(1, 3, 640, 640) / 255.0
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return input.astype(np.float32), img_width, img_height
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def run_model(input):
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model = ort.InferenceSession("best.onnx", providers=['CPUExecutionProvider'])
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outputs = model.run(["output0"], {"images":input})
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return outputs[0]
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def process_output(output, img_width, img_height, orientation):
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output = output[0].astype(float)
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output = output.transpose()
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boxes = []
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for row in output:
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prob = row[4:].max()
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if prob < 0.5:
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continue
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class_id = row[4:].argmax()
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label = yolo_classes[class_id]
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xc, yc, w, h = row[:4]
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x1 = (xc - w/2) / 640 * img_width
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y1 = (yc - h/2) / 640 * img_height
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x2 = (xc + w/2) / 640 * img_width
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y2 = (yc + h/2) / 640 * img_height
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boxes.append([x1, y1, x2, y2, label, prob])
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# Adjust boxes based on orientation
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adjusted_boxes = adjust_boxes_for_orientation(boxes, orientation, img_width, img_height)
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# Sort and apply non-max suppression as before
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adjusted_boxes.sort(key=lambda x: x[5], reverse=True)
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result = []
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while len(adjusted_boxes) > 0:
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result.append(adjusted_boxes[0])
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adjusted_boxes = [box for box in adjusted_boxes if iou(box, adjusted_boxes[0]) < 0.7]
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return result
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def iou(box1,box2):
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return intersection(box1,box2)/union(box1,box2)
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def union(box1,box2):
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box1_x1,box1_y1,box1_x2,box1_y2 = box1[:4]
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box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
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box1_area = (box1_x2-box1_x1)*(box1_y2-box1_y1)
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box2_area = (box2_x2-box2_x1)*(box2_y2-box2_y1)
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return box1_area + box2_area - intersection(box1,box2)
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def intersection(box1,box2):
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box1_x1,box1_y1,box1_x2,box1_y2 = box1[:4]
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box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
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x1 = max(box1_x1,box2_x1)
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y1 = max(box1_y1,box2_y1)
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x2 = min(box1_x2,box2_x2)
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y2 = min(box1_y2,box2_y2)
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return (x2-x1)*(y2-y1)
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def get_orientation(image_path):
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with Image.open(image_path) as img:
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if hasattr(img, '_getexif'):
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exif_data = img._getexif()
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if exif_data is not None:
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return exif_data.get(274, 1) # Default to normal orientation
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return 1 # Default orientation if no EXIF data
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def adjust_boxes_for_orientation(boxes, orientation, img_width, img_height):
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adjusted_boxes = []
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for box in boxes:
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x1, y1, x2, y2, label, prob = box
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# Apply transformations based on orientation
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if orientation == 3: # 180 degrees
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x1, y1, x2, y2 = img_width - x2, img_height - y2, img_width - x1, img_height - y1
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elif orientation == 6: # 270 degrees (or -90 degrees)
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x1, y1, x2, y2 = img_height - y2, x1, img_height - y1, x2
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elif orientation == 8: # 90 degrees
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x1, y1, x2, y2 = y1, img_width - x2, y2, img_width - x1
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adjusted_boxes.append([x1, y1, x2, y2, label, prob])
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return adjusted_boxes
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""" def detect_objects_on_image(buf):
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"""
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""""
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Function receives an image,
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passes it through YOLOv8 neural network
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and returns an array of detected objects
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and their bounding boxes
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:param buf: Input image file stream
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:return: Array of bounding boxes in format [[x1,y1,x2,y2,object_type,probability],..]
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"""
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"""""
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model = YOLO("best.pt")
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results = model.predict(Image.open(buf))
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result = results[0]
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output = []
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for box in result.boxes:
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x1, y1, x2, y2 = [
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round(x) for x in box.xyxy[0].tolist()
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]
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class_id = box.cls[0].item()
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prob = round(box.conf[0].item(), 2)
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output.append([
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x1, y1, x2, y2, result.names[class_id], prob
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])
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return output
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"""
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serve(app, host='0.0.0.0', port=8080)
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#from ultralytics import YOLO
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from flask import request, Flask, jsonify
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from waitress import serve
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from PIL import Image
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import onnxruntime as ort
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import numpy as np
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import requests
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#my changes
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import os
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script_dir = os.path.dirname(os.path.abspath(__file__))
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# Change the working directory to the script's directory
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os.chdir(script_dir)
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yolo_classes = ["b_fully_ripened",
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"b_half_ripened",
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"b_green",
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"l_fully_ripened",
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"l_half_ripened",
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"l_green"
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]
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#app start
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app = Flask(__name__)
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@app.route("/")
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def root():
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"""
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Site main page handler function.
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:return: Content of index.html file
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"""
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with open("index.html") as file:
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return file.read()
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@app.route("/detect", methods=["POST"])
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def detect():
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buf = request.files["image_file"]
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crop_type = request.form.get("cropType")
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location = request.form.get("location")
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variety = request.form.get("variety")
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boxes, orientation = detect_objects_on_image(buf.stream)
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# Do something with crop_type, location, and variety here (e.g., store in database)
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return jsonify(boxes)
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def detect_objects_on_image(buf):
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input, img_width, img_height = prepare_input(buf)
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output = run_model(input)
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orientation = get_orientation(buf)
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processed_output = process_output(output, img_width, img_height, orientation)
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return processed_output, orientation
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def prepare_input(buf):
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img = Image.open(buf)
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img_width, img_height = img.size
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img = img.resize((640, 640))
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img = img.convert("RGB")
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input = np.array(img)
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input = input.transpose(2, 0, 1)
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input = input.reshape(1, 3, 640, 640) / 255.0
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return input.astype(np.float32), img_width, img_height
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def run_model(input):
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model = ort.InferenceSession("best.onnx", providers=['CPUExecutionProvider'])
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outputs = model.run(["output0"], {"images":input})
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return outputs[0]
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def process_output(output, img_width, img_height, orientation):
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output = output[0].astype(float)
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output = output.transpose()
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boxes = []
|
||||
for row in output:
|
||||
prob = row[4:].max()
|
||||
if prob < 0.5:
|
||||
continue
|
||||
|
||||
class_id = row[4:].argmax()
|
||||
label = yolo_classes[class_id]
|
||||
xc, yc, w, h = row[:4]
|
||||
x1 = (xc - w/2) / 640 * img_width
|
||||
y1 = (yc - h/2) / 640 * img_height
|
||||
x2 = (xc + w/2) / 640 * img_width
|
||||
y2 = (yc + h/2) / 640 * img_height
|
||||
|
||||
boxes.append([x1, y1, x2, y2, label, prob])
|
||||
|
||||
# Adjust boxes based on orientation
|
||||
adjusted_boxes = adjust_boxes_for_orientation(boxes, orientation, img_width, img_height)
|
||||
|
||||
# Sort and apply non-max suppression as before
|
||||
adjusted_boxes.sort(key=lambda x: x[5], reverse=True)
|
||||
result = []
|
||||
while len(adjusted_boxes) > 0:
|
||||
result.append(adjusted_boxes[0])
|
||||
adjusted_boxes = [box for box in adjusted_boxes if iou(box, adjusted_boxes[0]) < 0.7]
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def iou(box1,box2):
|
||||
return intersection(box1,box2)/union(box1,box2)
|
||||
|
||||
def union(box1,box2):
|
||||
box1_x1,box1_y1,box1_x2,box1_y2 = box1[:4]
|
||||
box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
|
||||
box1_area = (box1_x2-box1_x1)*(box1_y2-box1_y1)
|
||||
box2_area = (box2_x2-box2_x1)*(box2_y2-box2_y1)
|
||||
return box1_area + box2_area - intersection(box1,box2)
|
||||
|
||||
def intersection(box1,box2):
|
||||
box1_x1,box1_y1,box1_x2,box1_y2 = box1[:4]
|
||||
box2_x1,box2_y1,box2_x2,box2_y2 = box2[:4]
|
||||
x1 = max(box1_x1,box2_x1)
|
||||
y1 = max(box1_y1,box2_y1)
|
||||
x2 = min(box1_x2,box2_x2)
|
||||
y2 = min(box1_y2,box2_y2)
|
||||
return (x2-x1)*(y2-y1)
|
||||
|
||||
def get_orientation(image_path):
|
||||
with Image.open(image_path) as img:
|
||||
if hasattr(img, '_getexif'):
|
||||
exif_data = img._getexif()
|
||||
if exif_data is not None:
|
||||
return exif_data.get(274, 1) # Default to normal orientation
|
||||
return 1 # Default orientation if no EXIF data
|
||||
|
||||
def adjust_boxes_for_orientation(boxes, orientation, img_width, img_height):
|
||||
adjusted_boxes = []
|
||||
for box in boxes:
|
||||
x1, y1, x2, y2, label, prob = box
|
||||
|
||||
# Apply transformations based on orientation
|
||||
if orientation == 3: # 180 degrees
|
||||
x1, y1, x2, y2 = img_width - x2, img_height - y2, img_width - x1, img_height - y1
|
||||
elif orientation == 6: # 270 degrees (or -90 degrees)
|
||||
x1, y1, x2, y2 = img_height - y2, x1, img_height - y1, x2
|
||||
elif orientation == 8: # 90 degrees
|
||||
x1, y1, x2, y2 = y1, img_width - x2, y2, img_width - x1
|
||||
|
||||
adjusted_boxes.append([x1, y1, x2, y2, label, prob])
|
||||
|
||||
return adjusted_boxes
|
||||
|
||||
|
||||
""" def detect_objects_on_image(buf):
|
||||
"""
|
||||
""""
|
||||
Function receives an image,
|
||||
passes it through YOLOv8 neural network
|
||||
and returns an array of detected objects
|
||||
and their bounding boxes
|
||||
:param buf: Input image file stream
|
||||
:return: Array of bounding boxes in format [[x1,y1,x2,y2,object_type,probability],..]
|
||||
"""
|
||||
"""""
|
||||
model = YOLO("best.pt")
|
||||
results = model.predict(Image.open(buf))
|
||||
result = results[0]
|
||||
output = []
|
||||
for box in result.boxes:
|
||||
x1, y1, x2, y2 = [
|
||||
round(x) for x in box.xyxy[0].tolist()
|
||||
]
|
||||
class_id = box.cls[0].item()
|
||||
prob = round(box.conf[0].item(), 2)
|
||||
output.append([
|
||||
x1, y1, x2, y2, result.names[class_id], prob
|
||||
])
|
||||
return output
|
||||
|
||||
"""
|
||||
serve(app, host='0.0.0.0', port=8080)
|
||||
|
Loading…
Reference in New Issue
Block a user