Aherobo Ovie Victor
158 lines
4.3 KiB
Markdown
158 lines
4.3 KiB
Markdown
# Smart Farm Photo Keyword Tagging AI - Usage Guide
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## 🚀 Quick Start
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### 1. Installation
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```bash
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# Install dependencies
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python3 -m pip install -r requirements.txt
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```
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### 2. Prepare Your Photos
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- Place agricultural photos in `data/raw/` directory
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- Supported formats: JPG, JPEG, PNG, TIFF, BMP
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- Any image size (system will handle resizing)
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### 3. Run the System
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```bash
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# Basic usage - process all images in data/raw/
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python3 src/main.py
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# Specify custom directories
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python3 src/main.py --input /path/to/your/photos --output /path/to/results
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```
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### 4. View Results
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- Results saved as CSV in `outputs/` directory
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- Filename format: `agricultural_keywords_YYYYMMDD_HHMMSS.csv`
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## 📊 Output Format
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The system generates a CSV file with these columns:
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| Column | Description | Example |
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|--------|-------------|---------|
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| `filename` | Original image filename | `farmer_cornfield.jpg` |
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| `human_keywords` | Manual keywords (for comparison) | `farmer, corn, agriculture` |
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| `ai_keywords` | AI-generated keywords | `farmer, corn, field, agriculture, male` |
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| `ai_title` | Descriptive title for stock photos | `Farmer working in cornfield` |
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| `location` | GPS location if available | `Iowa` or `GPS Location Available` |
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## 🔧 Advanced Usage
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### Batch Processing
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The system is designed for batch processing:
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- Handles 500+ images efficiently
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- Processes images sequentially to manage memory
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- Progress tracking during processing
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### Custom Input Directories
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```bash
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# Process photos from custom directory
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python3 src/main.py --input /Users/yourname/farm_photos --output /Users/yourname/results
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```
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### Using the Jupyter Notebook
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```bash
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# Start Jupyter
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jupyter notebook
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# Open notebooks/agricultural_keyword_analysis.ipynb
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# Run all cells for interactive analysis
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```
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## 📈 Performance
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### Expected Processing Times:
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- **Setup**: ~30 seconds (model loading)
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- **Per Image**: ~2-5 seconds
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- **Batch of 100**: ~5-10 minutes
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- **Batch of 500**: ~20-40 minutes
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### System Requirements:
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- **RAM**: 4GB minimum, 8GB recommended
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- **Storage**: 2GB for model files
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- **CPU**: Any modern processor (GPU optional)
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## 🎯 Keyword Quality
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### What the AI Recognizes Well:
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- ✅ People (farmers, workers)
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- ✅ Animals (cows, pigs, chickens)
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- ✅ Equipment (tractors, tools)
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- ✅ Crops (corn, wheat, vegetables)
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- ✅ Settings (fields, barns, farms)
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### Current Limitations:
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- ⚠️ May not distinguish farmer vs rancher perfectly
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- ⚠️ Gender identification needs improvement
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- ⚠️ Location extraction limited without GPS data
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- ⚠️ Some agriculture-specific terms may be generic
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## 🛠️ Troubleshooting
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### Common Issues:
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**"No images found"**
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- Check that images are in `data/raw/` directory
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- Verify file extensions are supported
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- System will create sample data if no images found
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**"Model loading error"**
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- Ensure internet connection for first-time model download
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- Check available disk space (2GB needed)
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- Restart if download was interrupted
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**"Out of memory"**
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- Process smaller batches
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- Close other applications
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- Consider using a machine with more RAM
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### Getting Help:
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1. Check the error message in terminal
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2. Verify all dependencies are installed
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3. Ensure input directory contains valid image files
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## 📝 Example Workflow
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```bash
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# 1. Prepare your photos
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mkdir -p data/raw
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cp /path/to/your/farm/photos/* data/raw/
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# 2. Run processing
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python3 src/main.py
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# 3. Check results
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ls outputs/
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cat outputs/agricultural_keywords_*.csv
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# 4. Analyze with notebook
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jupyter notebook notebooks/agricultural_keyword_analysis.ipynb
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```
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## 🔄 Integration with Existing Workflow
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### For Stock Photo Businesses:
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1. **Upload**: Place new photos in `data/raw/`
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2. **Process**: Run batch processing monthly
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3. **Review**: Check AI keywords against human keywords
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4. **Export**: Use CSV for your photo management system
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### Scaling Up:
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- Process 1,000+ photos by running multiple batches
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- Monitor processing time and adjust batch sizes
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- Consider upgrading hardware for faster processing
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## 📋 Next Steps for Production
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1. **Fine-tune model** on your 30,000 tagged photos
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2. **Add location services** for GPS coordinate conversion
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3. **Implement quality scoring** for keyword confidence
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4. **Create web interface** for easier use
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5. **Add batch scheduling** for automated processing
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---
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**Need help?** Check the notebook examples or review the code documentation in `src/` directory.
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