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Add supported types of streaming in config section
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yihong1120 committed Jul 11, 2024
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31 changes: 19 additions & 12 deletions README-zh-tw.md
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## 配置

在運行應用程序之前,您需要通過在 YAML 配置文件中指定視頻流和其他參數的詳細信息來配置系統。一個示例配置文件 `configuration.yaml` 應該如下所示
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
# 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 objection detection locally
# 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
- video_url: "rtsp://example2.com/stream"
image_name: "cam2"
label: "label2"
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數組中的每個對象代表一個視頻流配置,包含以下字段:
- `video_url`: 直播視頻流的 URL。
- `api_url`: 機器學習模型服務器 API 端點的 URL。
- `model_key`: 要使用的機器學習模型的鍵標識符。
- `line_token`: 用於發送通知的 LINE 消息 API 令牌。
- `video_url`: 現場視頻流的 URL。這可以包括:
- 監控流
- RTSP
- 副流
- YouTube 視頻或直播
- Discord
- `image_name`: 分配給圖像或攝影機的名稱。
- `label`: 分配給視頻流的標籤。
- `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。
- `run_local`: 布爾值,指示是否在本地運行物件檢測。

## 使用 Docker

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10 changes: 9 additions & 1 deletion README.md
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Each object in the array represents a video stream configuration with the following fields:
- `video_url`: The URL of the live video stream.
- `video_url`: The URL of the live video stream. This can include:
- Surveillance streams
- RTSP streams
- Secondary streams
- YouTube videos or live streams
- Discord streams
- `image_name`: The name assigned to the image or camera.
- `label`: The label assigned to the video stream.
- `model_key`: The key identifier for the machine learning model to use.
- `line_token`: The LINE messaging API token for sending notifications.
- `run_local`: Boolean value indicating whether to run object detection locally.

## Usage for Docker

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