173_Aqualoop Codes

BACKEND/app.py

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+import mediapipe as mp
+from mediapipe.tasks import python
+from mediapipe.tasks.python import vision
+import requests
+import cv2
+import numpy as np
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import base64
+import io
+from PIL import Image
+import os
+
+app = Flask(__name__)
+CORS(app, origins=['https://frontend-recyclopedia.vercel.app', 'https://recyclopedia-xi.vercel.app', 'http://localhost:5173', 'http://localhost:3000', 'http://localhost:8080'])
+
+model_url = "https://storage.googleapis.com/mediapipe-models/object_detector/efficientdet_lite0/float16/latest/efficientdet_lite0.tflite"
+model_path = "efficientdet_lite0.tflite"
+
+try:
+    with open(model_path, "rb") as model_file:
+        pass
+except FileNotFoundError:
+    model_response = requests.get(model_url)
+    with open(model_path, "wb") as model_file:
+        model_file.write(model_response.content)
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
+
+BaseOptions = mp.tasks.BaseOptions
+ObjectDetector = mp.tasks.vision.ObjectDetector
+ObjectDetectorOptions = mp.tasks.vision.ObjectDetectorOptions
+VisionRunningMode = mp.tasks.vision.RunningMode
+
+VISUALIZATION_MARGIN = 10
+TEXT_ROW_SIZE = 10
+TEXT_FONT_SIZE = 1
+TEXT_FONT_THICKNESS = 1
+GREEN = (0, 255, 0)
+RED = (255, 0, 0)
+BLUE = (0, 0, 255)
+
+Recyclable = {
+    "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl",
+    "book", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
+    "microwave", "oven", "toaster", "refrigerator", "clock"
+}
+
+Non_Recyclable = {
+    "car", "motorcycle", "airplane", "bus", "train", "truck", "boat",
+    "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
+    "backpack", "umbrella", "handbag", "tie", "suitcase",
+    "frisbee", "skis", "snowboard", "baseball bat", "baseball glove",
+    "skateboard", "surfboard", "tennis racket",
+    "chair", "couch", "bed", "dining table", "toilet", "potted plant",
+    "scissors", "teddy bear", "hair drier", "toothbrush"
+}
+
+Organic = {
+    "banana", "apple", "sandwich", "orange", "broccoli", "carrot",
+    "hot dog", "pizza", "donut", "cake",
+    "bird", "cat", "dog", "horse", "sheep", "cow", "elephant",
+    "bear", "zebra", "giraffe"
+}
+
+def visualize(input_image, detection_result):
+    for detection in detection_result.detections:
+        bounding_box = detection.bounding_box
+        box_start = bounding_box.origin_x, bounding_box.origin_y
+        box_end = bounding_box.origin_x + bounding_box.width, bounding_box.origin_y + bounding_box.height
+
+        detected_object = detection.categories[0]
+        object_name = detected_object.category_name
+        confidence_score = round(detected_object.score, 2)
+
+        box_color = RED if object_name in Non_Recyclable else BLUE if object_name in Recyclable else GREEN if object_name in Organic else (255, 255, 255)
+
+        cv2.rectangle(input_image, box_start, box_end, box_color, 3)
+
+        label_text = f"{object_name} ({confidence_score})"
+        text_position = (bounding_box.origin_x + VISUALIZATION_MARGIN, bounding_box.origin_y - VISUALIZATION_MARGIN)
+        cv2.putText(input_image, label_text, text_position, cv2.FONT_HERSHEY_PLAIN, TEXT_FONT_SIZE, box_color, TEXT_FONT_THICKNESS)
+
+    return input_image
+
+def get_object_classification(object_name):
+    if object_name in Recyclable:
+        return "Recyclable"
+    elif object_name in Non_Recyclable:
+        return "Non-Recyclable"
+    elif object_name in Organic:
+        return "Organic"
+    return "Unknown"
+
+@app.route('/detect', methods=['POST'])
+def detect_objects():
+    try:
+        received_image_data = request.json.get('image')
+        if not received_image_data:
+            return jsonify({'error': 'No image data provided'}), 400
+
+        image_bytes = base64.b64decode(received_image_data.split(',')[1])
+        pil_image = Image.open(io.BytesIO(image_bytes))
+
+        opencv_image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
+        mediapipe_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=cv2.cvtColor(opencv_image, cv2.COLOR_BGR2RGB))
+
+        detector_options = ObjectDetectorOptions(
+            base_options=BaseOptions(model_asset_path=model_path),
+            max_results=5,
+            running_mode=VisionRunningMode.IMAGE,
+            score_threshold=0.5)
+
+        with ObjectDetector.create_from_options(detector_options) as object_detector:
+            detection_results = object_detector.detect(mediapipe_image)
+
+            if not detection_results.detections:
+                return jsonify({
+                    'detections': [],
+                    'annotated_image': received_image_data
+                })
+
+            processed_detections = []
+            for detection in detection_results.detections:
+                detected_category = detection.categories[0]
+                detected_box = detection.bounding_box
+                processed_detections.append({
+                    'category': detected_category.category_name,
+                    'classification': get_object_classification(detected_category.category_name),
+                    'score': round(float(detected_category.score), 2),
+                    'bbox': {
+                        'x': detected_box.origin_x,
+                        'y': detected_box.origin_y,
+                        'width': detected_box.width,
+                        'height': detected_box.height
+                    }
+                })
+
+            image_for_visualization = np.copy(mediapipe_image.numpy_view())
+            annotated_result = visualize(image_for_visualization, detection_results)
+
+            _, encoded_buffer = cv2.imencode('.jpg', cv2.cvtColor(annotated_result, cv2.COLOR_BGR2RGB))
+            base64_image = base64.b64encode(encoded_buffer).decode('utf-8')
+
+            return jsonify({
+                'detections': processed_detections,
+                'annotated_image': f'data:image/jpeg;base64,{base64_image}'
+            })
+
+    except Exception as error:
+        print(f"Error in detection: {str(error)}")
+        return jsonify({'error': str(error)}), 500
+
+if __name__ == '__main__':
+    app.run(debug=True, port=5002)

BACKEND/gunicorn.conf.py

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+bind = "0.0.0.0:10000"
+workers = 2
+timeout = 120

BACKEND/requirements.txt

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+flask==3.0.2
+flask-cors==4.0.0
+mediapipe==0.10.5
+opencv-python==4.8.1.78
+pillow==10.2.0
+requests==2.31.0
+numpy==1.24.3 
+gunicorn==21.2.0

FRONTEND/README.md

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+# Welcome to your Lovable project
+
+## Project info
+
+**URL**: https://lovable.dev/projects/5fd8d356-14c6-4a8d-b53c-c2fb7cf89fa0
+
+## How can I edit this code?
+
+There are several ways of editing your application.
+
+**Use Lovable**
+
+Simply visit the [Lovable Project](https://lovable.dev/projects/5fd8d356-14c6-4a8d-b53c-c2fb7cf89fa0) and start prompting.
+
+Changes made via Lovable will be committed automatically to this repo.
+
+**Use your preferred IDE**
+
+If you want to work locally using your own IDE, you can clone this repo and push changes. Pushed changes will also be reflected in Lovable.
+
+The only requirement is having Node.js & npm installed - [install with nvm](https://github.com/nvm-sh/nvm#installing-and-updating)
+
+Follow these steps:
+
+```sh
+# Step 1: Clone the repository using the project's Git URL.
+git clone <YOUR_GIT_URL>
+
+# Step 2: Navigate to the project directory.
+cd <YOUR_PROJECT_NAME>
+
+# Step 3: Install the necessary dependencies.
+npm i
+
+# Step 4: Start the development server with auto-reloading and an instant preview.
+npm run dev
+```
+
+**Edit a file directly in GitHub**
+
+- Navigate to the desired file(s).
+- Click the "Edit" button (pencil icon) at the top right of the file view.
+- Make your changes and commit the changes.
+
+**Use GitHub Codespaces**
+
+- Navigate to the main page of your repository.
+- Click on the "Code" button (green button) near the top right.
+- Select the "Codespaces" tab.
+- Click on "New codespace" to launch a new Codespace environment.
+- Edit files directly within the Codespace and commit and push your changes once you're done.
+
+## What technologies are used for this project?
+
+This project is built with .
+
+- Vite
+- TypeScript
+- React
+- shadcn-ui
+- Tailwind CSS
+
+## How can I deploy this project?
+
+Simply open [Lovable](https://lovable.dev/projects/5fd8d356-14c6-4a8d-b53c-c2fb7cf89fa0) and click on Share -> Publish.
+
+## I want to use a custom domain - is that possible?
+
+We don't support custom domains (yet). If you want to deploy your project under your own domain then we recommend using Netlify. Visit our docs for more details: [Custom domains](https://docs.lovable.dev/tips-tricks/custom-domain/)

FRONTEND/components.json

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+{
+  "$schema": "https://ui.shadcn.com/schema.json",
+  "style": "default",
+  "rsc": false,
+  "tsx": true,
+  "tailwind": {
+    "config": "tailwind.config.ts",
+    "css": "src/index.css",
+    "baseColor": "slate",
+    "cssVariables": true,
+    "prefix": ""
+  },
+  "aliases": {
+    "components": "@/components",
+    "utils": "@/lib/utils",
+    "ui": "@/components/ui",
+    "lib": "@/lib",
+    "hooks": "@/hooks"
+  }
+}

FRONTEND/eslint.config.js

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+import js from "@eslint/js";
+import globals from "globals";
+import reactHooks from "eslint-plugin-react-hooks";
+import reactRefresh from "eslint-plugin-react-refresh";
+import tseslint from "typescript-eslint";
+
+export default tseslint.config(
+  { ignores: ["dist"] },
+  {
+    extends: [js.configs.recommended, ...tseslint.configs.recommended],
+    files: ["**/*.{ts,tsx}"],
+    languageOptions: {
+      ecmaVersion: 2020,
+      globals: globals.browser,
+    },
+    plugins: {
+      "react-hooks": reactHooks,
+      "react-refresh": reactRefresh,
+    },
+    rules: {
+      ...reactHooks.configs.recommended.rules,
+      "react-refresh/only-export-components": [
+        "warn",
+        { allowConstantExport: true },
+      ],
+      "@typescript-eslint/no-unused-vars": "off",
+    },
+  }
+);

FRONTEND/index.html

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+<!DOCTYPE html>
+<html lang="en">
+  <head>
+    <meta charset="UTF-8" />
+    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+    <title>Waste Segregation</title>
+  </head>
+
+  <body>
+    <div id="root"></div>
+    <!-- IMPORTANT: DO NOT REMOVE THIS SCRIPT TAG OR THIS VERY COMMENT! -->
+    <script src="https://cdn.gpteng.co/gptengineer.js" type="module"></script>
+    <script type="module" src="/src/main.tsx"></script>
+
+  </body>
+</html>

FRONTEND/netlify.toml

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+[build]
+  command = "npm run build"
+  publish = "dist"
+
+[[redirects]]
+  from = "/*"
+  to = "/index.html"
+  status = 200