n8n Photo Report Automation

Business Case

Site photos require organization, analysis, and reporting. This workflow automates photo collection, AI analysis, and report generation.

Workflow Overview

[Photo Upload] → [AI Analysis] → [Categorization] → [Report Generation] → [Distribution]

n8n Workflow Configuration

1. Photo Input Triggers

{ "nodes": [ { "name": "Photo Webhook", "type": "n8n-nodes-base.webhook", "parameters": { "httpMethod": "POST", "path": "photo-upload", "options": { "binaryData": true } } }, { "name": "Watch Dropbox Folder", "type": "n8n-nodes-base.dropbox", "parameters": { "operation": "listFolder", "path": "/SitePhotos/{{$today}}" } } ] }

2. AI Image Analysis

{ "name": "Analyze with Claude Vision", "type": "n8n-nodes-base.httpRequest", "parameters": { "method": "POST", "url": "https://api.anthropic.com/v1/messages", "headers": { "x-api-key": "={{$env.ANTHROPIC_API_KEY}}", "anthropic-version": "2023-06-01" }, "body": { "model": "claude-3-5-sonnet-20241022", "max_tokens": 1024, "messages": [{ "role": "user", "content": [ { "type": "image", "source": { "type": "base64", "media_type": "image/jpeg", "data": "={{$binary.data.toString('base64')}}" } }, { "type": "text", "text": "Analyze this construction site photo. Identify: 1) Work activity visible, 2) Approximate completion status, 3) Any safety concerns, 4) Weather conditions. Return JSON format." } ] }] } } }

3. Categorize and Store

{ "nodes": [ { "name": "Parse AI Response", "type": "n8n-nodes-base.code", "parameters": { "jsCode": "const response = JSON.parse($json.content[0].text);\n\nreturn [{\n json: {\n filename: $('Photo Webhook').first().json.filename,\n timestamp: new Date().toISOString(),\n activity: response.work_activity,\n completion: response.completion_status,\n safety_issues: response.safety_concerns,\n weather: response.weather,\n category: response.work_activity.includes('concrete') ? 'CONCRETE' :\n response.work_activity.includes('steel') ? 'STEEL' :\n response.work_activity.includes('mep') ? 'MEP' : 'GENERAL'\n }\n}];" } }, { "name": "Store in Airtable", "type": "n8n-nodes-base.airtable", "parameters": { "operation": "create", "table": "Site Photos", "fields": { "Filename": "={{$json.filename}}", "Date": "={{$json.timestamp}}", "Activity": "={{$json.activity}}", "Category": "={{$json.category}}", "Completion": "={{$json.completion}}", "Safety Issues": "={{$json.safety_issues}}" } } } ] }

4. Generate Photo Report

{ "nodes": [ { "name": "Schedule Report", "type": "n8n-nodes-base.scheduleTrigger", "parameters": { "rule": {"interval": [{"field": "cronExpression", "expression": "0 18 * * 1-5"}]} } }, { "name": "Get Today Photos", "type": "n8n-nodes-base.airtable", "parameters": { "operation": "list", "table": "Site Photos", "filterByFormula": "IS_SAME({Date}, TODAY(), 'day')" } }, { "name": "Generate Report", "type": "n8n-nodes-base.code", "parameters": { "jsCode": "const photos = $input.all();\n\nconst byCategory = {};\nlet safetyIssues = [];\n\nphotos.forEach(p => {\n const cat = p.json.fields.Category;\n if (!byCategory[cat]) byCategory[cat] = [];\n byCategory[cat].push(p.json.fields);\n \n if (p.json.fields['Safety Issues'] && p.json.fields['Safety Issues'] !== 'None') {\n safetyIssues.push({\n photo: p.json.fields.Filename,\n issue: p.json.fields['Safety Issues']\n });\n }\n});\n\nreturn [{\n json: {\n date: new Date().toISOString().split('T')[0],\n total_photos: photos.length,\n by_category: byCategory,\n safety_issues: safetyIssues,\n safety_count: safetyIssues.length\n }\n}];" } } ] }

5. Distribution

{ "name": "Send Report Email", "type": "n8n-nodes-base.emailSend", "parameters": { "toEmail": "={{$env.PHOTO_REPORT_RECIPIENTS}}", "subject": "Site Photo Report - {{$json.date}} ({{$json.total_photos}} photos)", "html": "<h2>Daily Photo Report</h2><p>Total Photos: {{$json.total_photos}}</p><h3>Safety Issues: {{$json.safety_count}}</h3>{{#if $json.safety_issues.length}}<ul>{{#each $json.safety_issues}}<li>{{photo}}: {{issue}}</li>{{/each}}</ul>{{/if}}" } }

Python Helper

import requests import base64

def upload_photo_to_workflow(image_path: str, webhook_url: str, metadata: dict): """Upload photo to n8n workflow.""" with open(image_path, 'rb') as f: image_data = base64.b64encode(f.read()).decode()

payload = {
    'filename': image_path.split('/')[-1],
    'image_data': image_data,
    'project_id': metadata.get('project_id'),
    'location': metadata.get('location'),
    'captured_by': metadata.get('captured_by')
}

response = requests.post(webhook_url, json=payload)
return response.json()

def batch_upload_photos(photo_paths: list, webhook_url: str, project_id: str): """Batch upload multiple photos.""" results = [] for path in photo_paths: result = upload_photo_to_workflow(path, webhook_url, {'project_id': project_id}) results.append(result) return results

Quick Start

• Import workflow to n8n

• Configure API keys:

• ANTHROPIC_API_KEY for Claude Vision

• Airtable credentials

• Email configuration

• Create Airtable base with "Site Photos" table

• Test with sample photo upload

Resources

• n8n Documentation: https://docs.n8n.io

• Claude Vision API: https://docs.anthropic.com

• DDC Book: Chapter 4.2 - Workflow Automation