Complete Smart Farm Photo Keyword Tagging AI System - All deliverables ready
This commit is contained in:
Aherobo Ovie Victor
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Smart Farm Photo Keyword Tagging AI - Analysis\n",
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"\n",
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"This notebook demonstrates the agricultural photo keyword generation system using AI.\n",
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"\n",
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"## Overview\n",
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"- **Goal**: Automate keyword tagging for agricultural stock photos\n",
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"- **Model**: BLIP-2 for image captioning and keyword extraction\n",
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"- **Output**: 5-10 relevant agricultural keywords per image\n",
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"- **Scale**: Process 1,000+ photos/month in batches"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"import os\n",
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"sys.path.append('../')\n",
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"\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"from PIL import Image\n",
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"import numpy as np\n",
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"\n",
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"# Import our custom modules\n",
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"from src.data.image_processor import ImageProcessor\n",
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"from src.model.keyword_generator import AgricultureKeywordGenerator\n",
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"\n",
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"print(\"📚 Libraries loaded successfully!\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Data Exploration"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Initialize image processor\n",
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"processor = ImageProcessor('../data/raw')\n",
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"\n",
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"# Get image files\n",
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"image_files = processor.get_image_files('../data/raw')\n",
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"print(f\"Found {len(image_files)} image files\")\n",
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"\n",
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"if image_files:\n",
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" for img_file in image_files[:5]: # Show first 5\n",
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" print(f\" - {os.path.basename(img_file)}\")\nelse:\n",
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" print(\"No images found. Creating sample data...\")\n",
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" processor.create_sample_data('../data/raw')\n",
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" image_files = processor.get_image_files('../data/raw')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. AI Keyword Generation Demo"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Initialize keyword generator\n",
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"keyword_gen = AgricultureKeywordGenerator()\n",
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"\n",
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"# Process first image as example\n",
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"if image_files:\n",
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" sample_image = image_files[0]\n",
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" print(f\"Processing sample image: {os.path.basename(sample_image)}\")\n",
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" \n",
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" # Generate keywords\n",
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" results = keyword_gen.generate_keywords(sample_image)\n",
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" \n",
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" print(f\"\\n📝 Caption: {results['caption']}\")\n",
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" print(f\"🏷️ Keywords: {', '.join(results['keywords'])}\")\n",
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" print(f\"📰 Title: {results['title']}\")\n",
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" \n",
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" # Display image\n",
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" img = Image.open(sample_image)\n",
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" plt.figure(figsize=(8, 6))\n",
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" plt.imshow(img)\n",
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" plt.title(f\"Sample: {os.path.basename(sample_image)}\")\n",
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" plt.axis('off')\n",
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" plt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Batch Processing Analysis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Process all images\n",
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"results_list = []\n",
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"\n",
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"for img_path in image_files[:5]: # Process first 5 for demo\n",
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" try:\n",
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" filename = os.path.basename(img_path)\n",
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" print(f\"Processing {filename}...\")\n",
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" \n",
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" ai_results = keyword_gen.generate_keywords(img_path)\n",
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" location = processor.extract_location_metadata(img_path)\n",
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" \n",
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" result = {\n",
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" 'filename': filename,\n",
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" 'ai_keywords': ', '.join(ai_results['keywords']),\n",
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" 'keyword_count': len(ai_results['keywords']),\n",
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" 'ai_title': ai_results['title'],\n",
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" 'location': location or 'Not available',\n",
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" 'caption': ai_results['caption']\n",
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" }\n",
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" \n",
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" results_list.append(result)\n",
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" \n",
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" except Exception as e:\n",
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" print(f\"Error processing {filename}: {e}\")\n",
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"\n",
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"# Create DataFrame\n",
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"results_df = pd.DataFrame(results_list)\n",
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"print(f\"\\n✅ Processed {len(results_df)} images successfully\")\n",
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"results_df.head()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4. Keyword Analysis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Analyze keyword distribution\n",
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"if not results_df.empty:\n",
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" # Keyword count distribution\n",
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" plt.figure(figsize=(10, 6))\n",
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" \n",
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" plt.subplot(1, 2, 1)\n",
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" plt.hist(results_df['keyword_count'], bins=range(1, 12), alpha=0.7, color='green')\n",
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" plt.xlabel('Number of Keywords')\n",
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" plt.ylabel('Frequency')\n",
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" plt.title('Distribution of Keyword Counts')\n",
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" plt.grid(True, alpha=0.3)\n",
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" \n",
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" # Most common keywords\n",
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" all_keywords = []\n",
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" for keywords_str in results_df['ai_keywords']:\n",
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" keywords = [k.strip() for k in keywords_str.split(',')]\n",
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" all_keywords.extend(keywords)\n",
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" \n",
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" keyword_counts = pd.Series(all_keywords).value_counts().head(10)\n",
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" \n",
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" plt.subplot(1, 2, 2)\n",
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" keyword_counts.plot(kind='barh', color='lightgreen')\n",
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" plt.xlabel('Frequency')\n",
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" plt.title('Top 10 Most Common Keywords')\n",
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" plt.tight_layout()\n",
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" plt.show()\n",
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" \n",
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" print(f\"\\n📊 Keyword Statistics:\")\n",
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" print(f\"Average keywords per image: {results_df['keyword_count'].mean():.1f}\")\n",
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" print(f\"Total unique keywords: {len(set(all_keywords))}\")\n",
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" print(f\"Most common keyword: '{keyword_counts.index[0]}' ({keyword_counts.iloc[0]} times)\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 5. Export Results"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Save results to CSV\n",
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"if not results_df.empty:\n",
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" output_file = '../outputs/notebook_analysis_results.csv'\n",
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" os.makedirs('../outputs', exist_ok=True)\n",
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" \n",
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" # Add human keywords column for comparison (empty for now)\n",
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" results_df['human_keywords'] = ''\n",
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" \n",
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" # Reorder columns to match specification\n",
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" final_df = results_df[['filename', 'human_keywords', 'ai_keywords', 'ai_title', 'location']]\n",
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" \n",
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" final_df.to_csv(output_file, index=False)\n",
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" print(f\"✅ Results exported to: {output_file}\")\n",
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" \n",
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" # Display final results\n",
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" print(\"\\n📋 Final Results Preview:\")\n",
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" print(final_df.to_string(index=False, max_colwidth=50))\nelse:\n",
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" print(\"No results to export\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 6. Conclusions\n",
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"\n",
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"### System Performance:\n",
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"- ✅ Successfully generates 5-10 keywords per agricultural image\n",
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"- ✅ Creates descriptive titles for stock photo use\n",
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"- ✅ Processes images in batch format\n",
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"- ✅ Outputs results in CSV format as specified\n",
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"\n",
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"### Next Steps for Production:\n",
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"1. **Fine-tune model** on 30,000 agricultural photos for better accuracy\n",
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"2. **Enhance location extraction** from EXIF GPS data\n",
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"3. **Improve agriculture-specific distinctions** (farmer vs rancher)\n",
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"4. **Scale testing** with larger batches (500+ images)\n",
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"5. **Add quality validation** metrics\n",
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"\n",
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"### Current Capabilities:\n",
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"- Processes any number of agricultural photos\n",
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"- Generates relevant keywords using state-of-the-art AI\n",
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"- Ready for integration into existing workflow\n",
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"- Scalable to 1,000+ photos/month requirement"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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