Add example images and rewrite the inroduction contents

yihong1120 · Oct 5, 2024 · e696c17 · e696c17
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diff --git a/README-zh-tw.md b/README-zh-tw.md
@@ -14,8 +14,8 @@
 <br>
 
 <div align="center">
-   <a href="https://www.python.org/downloads/release/python-3124/">
-      <img src="https://img.shields.io/badge/python-3.12.4-blue?logo=python" alt="Python 3.12.4">
+   <a href="https://www.python.org/downloads/release/python-3127/">
+      <img src="https://img.shields.io/badge/python-3.12.7-blue?logo=python" alt="Python 3.12.7">
    </a>
    <a href="https://github.com/ultralytics/ultralytics">
       <img src="https://img.shields.io/badge/YOLOv8-ultralytics-blue?logo=yolo" alt="YOLOv8">
@@ -30,7 +30,7 @@
       <img src="https://img.shields.io/badge/pre--commit-3.8.0-blue?logo=pre-commit" alt="Pre-commit 3.8.0">
    </a>
    <a href="https://docs.pytest.org/en/latest/">
-      <img src="https://img.shields.io/badge/pytest-8.3.2-blue?logo=pytest" alt="pytest 8.3.2">
+      <img src="https://img.shields.io/badge/pytest-8.3.3-blue?logo=pytest" alt="pytest 8.3.3">
    </a>
    <a href="https://codecov.io/github/yihong1120/Construction-Hazard-Detection" >
       <img src="https://codecov.io/github/yihong1120/Construction-Hazard-Detection/graph/badge.svg?token=E0M66BUS8D"/>
@@ -39,13 +39,28 @@
 
 <br>
 
-"建築工地危險檢測系統" 是一款旨在提升建築工地安全的人工智慧工具。利用 YOLOv8 模型進行物體偵測，此系統能夠辨識潛在的危險，例如未戴安全帽的工人、未穿安全背心的工人、靠近機具的工人以及靠近車輛的工人。系統採用後處理演算法來提升偵測的準確性。設計用於實時環境部署，能夠即時分析並發出警報以應對偵測到的危險。
+"建築工地危險檢測系統" 是一款以人工智慧驅動的工具，旨在提升工地的安全性。該系統利用 YOLOv8 模型進行物件偵測，能夠識別以下潛在危險：
 
-一個新開發的功能允許系統使用安全錐畫出多邊形，以定義管制區並計算這些區域內的人數。如果有人進入這些管制區域，系統將發送通知。
+- 未佩戴安全帽的工人
+- 未穿著安全背心的工人
+- 靠近機械或車輛的工人
+- 進入限制區域的工人
 
-此外，系統可以通過網頁介面即時整合 AI 辨識結果，或使用 LINE、Messenger、微信和 Telegram 推播通知來發送訊息和現場即時影像，以便及時發出警報和提醒。
+後處理算法進一步提高了偵測的準確性。該系統專為即時部署而設計，能夠對檢測到的危險進行即時分析並發出警報。
 
-###### TODO: 支援WhatsApp通知。
+此外，該系統可通過網頁介面即時整合 AI 辨識結果，並通過 LINE、Messenger、微信和 Telegram 等即時通訊應用程式發送通知及現場實時影像，及時提醒和通知相關人員。系統還支援多種語言，允許用戶接收通知並以其首選語言與介面互動。支援的語言包括：
+
+- 🇹🇼 繁體中文（台灣）
+- 🇨🇳 簡體中文（中國大陸）
+- 🇫🇷 法文
+- 🇬🇧 英文
+- 🇹🇭 泰文
+- 🇻🇳 越南文
+- 🇮🇩 印尼文
+
+多語言支援使該系統在全球範圍內的使用者都能方便使用，提升了不同地區的可用性。
+
+> **待辦事項**：新增對 WhatsApp 通知的支援。
 
 <br>
 <br>
@@ -58,31 +73,58 @@
 
 ## 內容
 
+- [危險偵測範例](#hazard-detection-examples)
 - [操作說明](#操作說明)
 - [附加信息](#附加信息)
 - [數據集信息](#數據集信息)
 - [貢獻](#貢獻)
 - [開發路線圖](#開發路線圖)
 - [授權](#授權)
 
+## 危險偵測範例
+
+以下是系統實時危險偵測的範例：
+
+<div style="display: flex; justify-content: space-between; flex-wrap: wrap;">
+  <!-- 範例 1: 未佩戴安全帽或安全背心的工人 -->
+  <div style="text-align: center; flex-basis: 33%;">
+    <img src="./assets/images/demo/person_did_not_wear_safety_vest.png" alt="未佩戴安全帽或安全背心的工人" style="width: 300px; height: 200px; object-fit: cover;">
+    <p>未佩戴安全帽或安全背心的工人</p>
+  </div>
+
+  <!-- 範例 2: 工人接近機具或車輛 -->
+  <div style="text-align: center; flex-basis: 33%;">
+    <img src="./assets/images/demo/person_near_machinery.jpg" alt="工人接近機具或車輛" style="width: 300px; height: 200px; object-fit: cover;">
+    <p>工人接近機具或車輛</p>
+  </div>
+
+  <!-- 範例 3: 工人在限制區域內 -->
+  <div style="text-align: center; flex-basis: 33%;">
+    <img src="./assets/images/demo/persons_in_restricted_zones.jpg" alt="工人在限制區域內" style="width: 300px; height: 200px; object-fit: cover;">
+    <p>工人在限制區域內</p>
+  </div>
+</div>
+
 ## 操作說明
 
 在運行應用程式之前，您需要配置系統，指定視頻流的詳細信息和其他參數，這些信息需要在 YAML 配置文件中進行設置。示例配置文件 `configuration.yaml` 應該看起來像這樣：
 
 ```yaml
 # 這是一個視頻配置列表
 - video_url: "rtsp://example1.com/stream"  # 視頻的 URL
-   image_name: "cam1"  # 圖像的名稱
-   label: "label1"  # 視頻的標籤
-   model_key: "yolov8n"  # 視頻使用的模型鍵
-   line_token: "token1"  # 用於通知的 Line Token
-   run_local: True  # 本地運行物件檢測
+  image_name: "cam1"  # 圖像的名稱
+  label: "label1"  # 視頻的標籤
+  model_key: "yolov8n"  # 視頻使用的模型鍵
+  line_token: "token1"  # 用於通知的 Line Token
+  run_local: True  # 本地運行物件檢測
+  language: "en" # 將語言設置成英文
 - video_url: "rtsp://example2.com/stream"
-   image_name: "cam2"
-   label: "label2"
-   model_key: "yolov8n"
-   line_token: "token2"
-   run_local: True
+  image_name: "cam2"
+  label: "label2"
+  model_key: "yolov8n"
+  line_token: "token2"
+  run_local: True
+  language: "zh-TW"
 ```
 
 數組中的每個對象代表一個視頻流配置，包含以下字段：
@@ -98,6 +140,7 @@
 - `model_key`: 用於機器學習模型的鍵標識符。
 - `line_token`: 用於發送通知的 LINE 訊息 API Token，有關如何取得TOKEN的資訊，請參閱 [Line Notify教學](docs/zh/line_notify_guide_zh.md)。
 - `run_local`: 布爾值，指示是否在本地運行物件檢測。
+- `language`: 通知和系統訊息的語言。
 
 <br>
 

diff --git a/README.md b/README.md
@@ -14,8 +14,8 @@
 <br>
 
 <div align="center">
-   <a href="https://www.python.org/downloads/release/python-3124/">
-      <img src="https://img.shields.io/badge/python-3.12.4-blue?logo=python" alt="Python 3.12.4">
+   <a href="https://www.python.org/downloads/release/python-3127/">
+      <img src="https://img.shields.io/badge/python-3.12.7-blue?logo=python" alt="Python 3.12.7">
    </a>
    <a href="https://github.com/ultralytics/ultralytics">
       <img src="https://img.shields.io/badge/YOLOv8-ultralytics-blue?logo=yolo" alt="YOLOv8">
@@ -30,7 +30,7 @@
       <img src="https://img.shields.io/badge/pre--commit-3.8.0-blue?logo=pre-commit" alt="Pre-commit 3.8.0">
    </a>
    <a href="https://docs.pytest.org/en/latest/">
-      <img src="https://img.shields.io/badge/pytest-8.3.2-blue?logo=pytest" alt="pytest 8.3.2">
+      <img src="https://img.shields.io/badge/pytest-8.3.3-blue?logo=pytest" alt="pytest 8.3.3">
    </a>
    <a href="https://codecov.io/github/yihong1120/Construction-Hazard-Detection" >
       <img src="https://codecov.io/github/yihong1120/Construction-Hazard-Detection/graph/badge.svg?token=E0M66BUS8D" alt="Codecov">
@@ -42,13 +42,28 @@
 
 <br>
 
-"Construction-Hazard-Detection" is an AI-driven tool aimed at enhancing safety on construction sites. Utilising the YOLOv8 model for object detection, this system identifies potential hazards such as workers without helmets, workers without safety vests, workers in close proximity to machinery, and workers near vehicles. Post-processing algorithms are employed to enhance the accuracy of the detections. The system is designed for deployment in real-time environments, providing immediate analysis and alerts for detected hazards.
+"Construction-Hazard-Detection" is an AI-powered tool designed to enhance safety on construction sites. By leveraging the YOLOv8 model for object detection, it identifies potential hazards such as:
 
-A newly developed feature allows the system to use safety cones to draw polygons, defining controlled zones and calculating the number of people within these zones. Notifications can be sent if people enter these controlled areas.
+- Workers without helmets
+- Workers without safety vests
+- Workers near machinery or vehicles
+- Workers in restricted areas
 
-Additionally, the system can integrate AI recognition results in real-time through a web interface or use LINE, Messenger, WeChat, and Telegram notifications to send messages and real-time on-site images for prompt alerts and reminders.
+Post-processing algorithms further improve detection accuracy. The system is built for real-time deployment, offering instant analysis and alerts for identified hazards.
 
-###### TODO: Add supports for WhatsApp notification.
+Additionally, the system integrates AI recognition results in real-time via a web interface. It can send notifications and real-time on-site images through messaging apps like LINE, Messenger, WeChat, and Telegram for prompt alerts and reminders. The system also supports multiple languages, enabling users to receive notifications and interact with the interface in their preferred language. Supported languages include:
+
+- 🇹🇼 Traditional Chinese (Taiwan)
+- 🇨🇳 Simplified Chinese (Mainland China)
+- 🇫🇷 French
+- 🇬🇧 English
+- 🇹🇭 Thai
+- 🇻🇳 Vietnamese
+- 🇮🇩 Indonesian
+
+This multi-language support makes the system accessible to a global audience, improving usability across different regions.
+
+> **TODO**: Add support for WhatsApp notifications, and switch from YOLOv8 to YOLO11
 
 <br>
 <br>
@@ -61,31 +76,58 @@ Additionally, the system can integrate AI recognition results in real-time throu
 
 ## Contents
 
+- [Hazard Detection Examples](#hazard-detection-examples)
 - [Usage](#usage)
 - [Additional Information](#additional-information)
 - [Dataset Information](#dataset-information)
 - [Contributing](#contributing)
 - [Development Roadmap](#development-roadmap)
 - [License](#license)
 
+## Hazard Detection Examples
+
+Below are examples of real-time hazard detection by the system:
+
+<div style="display: flex; justify-content: space-between; flex-wrap: wrap;">
+  <!-- Example 1: Workers without Helmets or Safety Vests -->
+  <div style="text-align: center; flex-basis: 33%;">
+    <img src="./assets/images/demo/person_did_not_wear_safety_vest.png" alt="Workers without helmets or safety vests" style="width: 300px; height: 200px; object-fit: cover;">
+    <p>Workers without Helmets or Safety Vests</p>
+  </div>
+
+  <!-- Example 2: Workers near Machinery or Vehicles -->
+  <div style="text-align: center; flex-basis: 33%;">
+    <img src="./assets/images/demo/person_near_machinery.jpg" alt="Workers near machinery or vehicles" style="width: 300px; height: 200px; object-fit: cover;">
+    <p>Workers near Machinery or Vehicles</p>
+  </div>
+
+  <!-- Example 3: Workers in Restricted Areas -->
+  <div style="text-align: center; flex-basis: 33%;">
+    <img src="./assets/images/demo/persons_in_restricted_zones.jpg" alt="Workers in restricted areas" style="width: 300px; height: 200px; object-fit: cover;">
+    <p>Workers in Restricted Areas</p>
+  </div>
+</div>
+
 ## Usage
 
 Before running the application, you need to configure the system by specifying the details of the video streams and other parameters in a YAML configuration file. An example configuration file `configuration.yaml` should look like this:
 
 ```yaml
 # This is a list of video configurations
 - video_url: "rtsp://example1.com/stream"  # URL of the video
-   image_name: "cam1"  # Name of the image
-   label: "label1"  # Label of the video
-   model_key: "yolov8n"  # Model key for the video
-   line_token: "token1"  # Line token for notification
-   run_local: True  # Run object detection locally
+  image_name: "cam1"  # Name of the image
+  label: "label1"  # Label of the video
+  model_key: "yolov8n"  # Model key for the video
+  line_token: "token1"  # Line token for notification
+  run_local: True  # Run object detection locally
+  language: "en" # Set language to English
 - video_url: "rtsp://example2.com/stream"
-   image_name: "cam2"
-   label: "label2"
-   model_key: "yolov8n"
-   line_token: "token2"
-   run_local: True
+  image_name: "cam2"
+  label: "label2"
+  model_key: "yolov8n"
+  line_token: "token2"
+  run_local: True
+  language: "zh-TW"
 ```
 
 Each object in the array represents a video stream configuration with the following fields:
@@ -101,6 +143,7 @@ Each object in the array represents a video stream configuration with the follow
 - `model_key`: The key identifier for the machine learning model to use.
 - `line_token`: The LINE messaging API token for sending notifications.  For information on how to obtain a LINE token, please refer to [line_notify_guide_en](docs/en/line_notify_guide_en.md).
 - `run_local`: Boolean value indicating whether to run object detection locally.
+- `language`: The language for notifications and system messages.
 
 <br>