You're asking about where to put the API key for your backend server, which is a critical security consideration!
These lines:
# In your server's environment (e.g., .env file, or directly in your deployment environment)
GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/google-cloud-keyfile.json
# OR if using a direct API key (less common for Video Intelligence, but possible for some APIs)
GOOGLE_VIDEO_INTELLIGENCE_API_KEY=YOUR_ACTUAL_API_KEY_HEREAre not code to be placed directly into a file. They are instructions on how to set up environment variables for your server-side application.
Here's how you typically implement them in a Node.js project:
.envfile (Recommended for Development)For local development, the most common and secure way to manage environment variables is using a .env file and the dotenv npm package.
File Name: .env
Location: Create this file in the root directory of your Node.js backend project. This means it should be at the same level as your package.json and your main server file (e.g., server.js or app.ts).
Content of your .env file:
# .env file in the root of your backend project
# Option 1: Path to your Google Cloud service account key file (recommended for production)
# Replace /path/to/your/google-cloud-keyfile.json with the actual path on your server
GOOGLE_APPLICATION_CREDENTIALS=/Users/youruser/projects/my-video-ai-project/google-cloud-key.json
# OR
# Option 2: Direct API Key (less common for Video Intelligence, but used by some APIs)
# Replace YOUR_ACTUAL_API_KEY_HERE with your real Google Video Intelligence API key
# GOOGLE_VIDEO_INTELLIGENCE_API_KEY=AIzaSyC...Important:
.env file to version control (Git)! Add .env to your .gitignore file..env file.dotenvPackageIf you haven't already, install the dotenv package in your Node.js backend project:
npm install dotenv
# or
yarn add dotenvIn your main server file (e.g., server.js or app.ts), you need to load these environment variables at the very beginning of your script.
File: server.js (or app.ts if using TypeScript)
Section: At the very top of the file, before any other imports or code that might try to access environment variables.
Line of Code:
// server.js
// Line 1: Import and configure dotenv to load variables from .env file
require('dotenv').config();
// Line 2 (or later): Now you can import other modules and use process.env
const { VideoIntelligenceServiceClient } = require('@google-cloud/video-intelligence');
const WebSocket = require('ws');
// The Google Cloud client library will automatically look for GOOGLE_APPLICATION_CREDENTIALS
// in process.env when it's initialized.
const videoClient = new VideoIntelligenceServiceClient();
// If you were using a direct API key (less common for Video Intelligence, but for other APIs):
// const myApiKey = process.env.GOOGLE_VIDEO_INTELLIGENCE_API_KEY;
// console.log('My API Key:', myApiKey); // For testing, but don't log in production!
// ... rest of your server code (WebSocket setup, etc.)Explanation:
require('dotenv').config(); is the line that reads your .env file and loads its key-value pairs into process.env, making them accessible throughout your Node.js application.VideoIntelligenceServiceClient) are designed to automatically pick up credentials from the GOOGLE_APPLICATION_CREDENTIALS environment variable, which should point to your service account key file.By following these steps, your API key will be securely managed on the server-side and never exposed in your client-side index.html file.
Okay, this is a comprehensive guide to building the real-time AI video incident detection system. This will involve setting up two separate parts:
index.html): This will run in your browser, access your local camera, send its video stream to your backend, and display the live video and incident log received from the backend.Before you write any code, you need to set up your Google Cloud Project to use the Video Intelligence API.
Create a Google Cloud Project:
Enable the Video Intelligence API:
Create a Service Account Key:
google-cloud-key.json and place it in a secure location on your server where your backend code will run (e.g., in your backend project directory, but outside of version control).This server will be the brain of your AI processing.
Step 1: Create Your Backend Project Directory
Create a new folder for your backend project.
mkdir video-ai-backend
cd video-ai-backendStep 2: Initialize Node.js Project and Install Dependencies
npm init -y
npm install ws @google-cloud/video-intelligence dotenvws: For creating the WebSocket server.@google-cloud/video-intelligence: The official Google Cloud client library for the Video Intelligence API.dotenv: To load environment variables from a .env file (for local development).Step 3: Create the .env File
Create a file named .env in the root of your video-ai-backend directory.
Content of .env:
# .env file in video-ai-backend/
# Path to your downloaded Google Cloud service account key file
# IMPORTANT: Replace this with the actual absolute path to your google-cloud-key.json file
# Example: /Users/youruser/video-ai-backend/google-cloud-key.json
GOOGLE_APPLICATION_CREDENTIALS=/path/to/your/google-cloud-key.jsonStep 4: Create the .gitignore File
Create a file named .gitignore in the root of your video-ai-backend directory. This prevents sensitive files and temporary directories from being committed to Git.
Content of .gitignore:
# .gitignore in video-ai-backend/
node_modules/
.env
*.logStep 5: Create the Server File (server.js)
Create a file named server.js in the root of your video-ai-backend directory.
Content of server.js:
// server.js
// 1. Load environment variables from .env file (MUST be at the very top)
require('dotenv').config();
// 2. Import necessary modules
const { VideoIntelligenceServiceClient } = require('@google-cloud/video-intelligence');
const WebSocket = require('ws');
// 3. Initialize Google Video Intelligence client
// It automatically uses GOOGLE_APPLICATION_CREDENTIALS from process.env
const videoClient = new VideoIntelligenceServiceClient();
// 4. Set up WebSocket Server
const WS_PORT = process.env.PORT || 8080; // Use environment port or default to 8080
const wss = new WebSocket.Server({ port: WS_PORT });
console.log(`Backend WebSocket server started on port ${WS_PORT}`);
wss.on('connection', ws => {
console.log('Client connected to WebSocket');
let streamingAnnotateCall = null; // Google AI streaming call for this client
let isAiStreamActive = false;
ws.on('message', async message => {
// Assuming messages are either control commands or video data
const messageStr = message.toString();
if (messageStr === 'START_AI_STREAM') {
if (isAiStreamActive) {
console.log('AI stream already active for this client.');
return;
}
console.log('Starting AI stream to Google Video Intelligence API...');
isAiStreamActive = true;
// Initialize the streaming annotation call
streamingAnnotateCall = videoClient.streamingAnnotateVideo();
// Handle responses from Google AI
streamingAnnotateCall.on('data', response => {
// This is where you get the AI detection results!
// console.log('Received AI detection response:', JSON.stringify(response, null, 2));
// Process AI results into an incident
const incident = processAiDetectionResults(response);
if (incident) {
// Send the incident back to the frontend via WebSocket
ws.send(JSON.stringify({ type: 'incident', data: incident }));
}
});
streamingAnnotateCall.on('error', err => {
console.error('Google Video Intelligence Streaming API error:', err);
ws.send(JSON.stringify({ type: 'error', message: 'AI processing error: ' + err.message }));
isAiStreamActive = false;
if (streamingAnnotateCall) streamingAnnotateCall.end();
});
streamingAnnotateCall.on('end', () => {
console.log('Google Video Intelligence Streaming API stream ended.');
isAiStreamActive = false;
});
// Send initial configuration to Google AI
streamingAnnotateCall.write({
videoContext: {
labelDetectionConfig: {
stationaryCamera: true,
labelDetectionMode: 'SHOT_AND_FRAME_MODE' // Detect labels per shot and per frame
},
// Add other features as needed, e.g.:
// objectTrackingConfig: {},
// explicitContentDetectionConfig: {},
// faceDetectionConfig: {},
}
});
} else if (messageStr === 'STOP_AI_STREAM') {
console.log('Stopping AI stream to Google Video Intelligence API...');
if (streamingAnnotateCall) {
streamingAnnotateCall.end(); // End the gRPC stream
streamingAnnotateCall = null;
}
isAiStreamActive = false;
} else if (isAiStreamActive) {
// Assume 'message' is a video data chunk (Blob from browser)
// IMPORTANT: In a real system, you'd need to handle these chunks carefully.
// Google Video Intelligence API expects raw video bytes.
// If your browser sends WebM chunks, you might need FFmpeg on the server
// to decode them into raw frames or a continuous stream before sending to Google.
// For this example, we'll just pass the raw buffer, assuming it's compatible
// or that the API can handle the format directly (which might not always be true for WebM chunks).
if (streamingAnnotateCall) {
streamingAnnotateCall.write({ inputContent: message });
}
}
});
ws.on('close', () => {
console.log('Client disconnected from WebSocket');
if (streamingAnnotateCall) {
streamingAnnotateCall.end(); // Clean up gRPC stream on client disconnect
streamingAnnotateCall = null;
}
isAiStreamActive = false;
});
});
// --- Placeholder for your incident processing logic (Server-Side) ---
// This function takes the raw AI response and determines if an incident occurred.
function processAiDetectionResults(aiResponse) {
// This is a very simplified example.
// In a real application, you'd analyze aiResponse.annotationResults
// (e.g., segmentLabelAnnotations, objectAnnotations, etc.)
// to identify specific events.
if (aiResponse.annotationResults && aiResponse.annotationResults.segmentLabelAnnotations) {
for (const annotation of aiResponse.annotationResults.segmentLabelAnnotations) {
// Example: Look for "person" and "falling" labels
const description = annotation.entity.description;
const confidence = annotation.segments[0]?.confidence || 0;
if (description.includes('falling') && confidence > 0.7) {
return {
id: 'inc_' + Date.now(),
timestamp: new Date().toISOString(),
type: 'Accident',
description: `High confidence fall detected: ${description} (Confidence: ${(confidence * 100).toFixed(0)}%)`,
status: 'ongoing',
confidence: confidence,
location: 'AI Detected Zone', // This would be more specific in a real system
severity: 'high'
};
}
if (description.includes('fire') && confidence > 0.6) {
return {
id: 'inc_' + Date.now(),
timestamp: new Date().toISOString(),
type: 'Fire Hazard',
description: `Potential fire detected: ${description} (Confidence: ${(confidence * 100).toFixed(0)}%)`,
status: 'ongoing',
confidence: confidence,
location: 'AI Detected Zone',
severity: 'critical'
};
}
// Add more detection rules here based on your needs
}
}
// If no specific incident is detected, you might still want to log raw detections
// or return null if no incident.
return null; // No incident detected from this specific AI response
}index.html)This file will now connect to your backend server via WebSocket and send your camera stream.
Step 1: Update index.html
Replace the entire content of your index.html file with the following:
Step 1: Start Your Backend Server
Navigate to your video-ai-backend directory:
cd video-ai-backendRun your server:
node server.jsYou should see output like: Backend WebSocket server started on port 8080. Keep this terminal window open.
Step 2: Open Your Frontend Webpage
index.html file.index.html to open it in your web browser.Step 3: Interact with the Dashboard
Start AI Stream: Click the "Start AI Stream" button.
processAiDetectionResults logic, it will send an incident back to your browser, and it will appear in the "Real-time Incident Log."server.js directly passes MediaRecorder WebM chunks to Google AI. While this might work for some simple cases, for robust, continuous streaming to Google Video Intelligence API, you will almost certainly need to integrate FFmpeg on your backend server. FFmpeg would decode the incoming WebM chunks into raw video frames or a continuous stream format that the Google API prefers for optimal performance and accuracy. This is a more advanced topic involving child processes or FFmpeg libraries in Node.js.