395 lines
13 KiB
Plaintext
395 lines
13 KiB
Plaintext
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# Jednoduchý klasifikátor obrázků\n",
|
|
"\n",
|
|
"Tento notebook vám ukáže, jak klasifikovat obrázky pomocí předem natrénované neuronové sítě.\n",
|
|
"\n",
|
|
"**Co se naučíte:**\n",
|
|
"- Jak načíst a použít předem natrénovaný model\n",
|
|
"- Předzpracování obrázků\n",
|
|
"- Provádění predikcí na obrázcích\n",
|
|
"- Porozumění skóre důvěryhodnosti\n",
|
|
"\n",
|
|
"**Použití:** Identifikace objektů na obrázcích (například \"kočka\", \"pes\", \"auto\" atd.)\n",
|
|
"\n",
|
|
"---\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Krok 1: Importujte potřebné knihovny\n",
|
|
"\n",
|
|
"Importujeme nástroje, které budeme potřebovat. Nemějte obavy, pokud zatím všemu nerozumíte!\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Core libraries\n",
|
|
"import numpy as np\n",
|
|
"from PIL import Image\n",
|
|
"import requests\n",
|
|
"from io import BytesIO\n",
|
|
"\n",
|
|
"# TensorFlow for deep learning\n",
|
|
"try:\n",
|
|
" import tensorflow as tf\n",
|
|
" from tensorflow.keras.applications import MobileNetV2\n",
|
|
" from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions\n",
|
|
" print(\"✅ TensorFlow loaded successfully!\")\n",
|
|
" print(f\" Version: {tf.__version__}\")\n",
|
|
"except ImportError:\n",
|
|
" print(\"❌ Please install TensorFlow: pip install tensorflow\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Krok 2: Načtení předem natrénovaného modelu\n",
|
|
"\n",
|
|
"Použijeme **MobileNetV2**, neuronovou síť, která byla již natrénována na milionech obrázků.\n",
|
|
"\n",
|
|
"Tomu se říká **Transfer Learning** - využití modelu, který natrénoval někdo jiný!\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print(\"📦 Loading pre-trained MobileNetV2 model...\")\n",
|
|
"print(\" This may take a minute on first run (downloading weights)...\")\n",
|
|
"\n",
|
|
"# Load the model\n",
|
|
"# include_top=True means we use the classification layer\n",
|
|
"# weights='imagenet' means it was trained on ImageNet dataset\n",
|
|
"model = MobileNetV2(weights='imagenet', include_top=True)\n",
|
|
"\n",
|
|
"print(\"✅ Model loaded!\")\n",
|
|
"print(f\" The model can recognize 1000 different object categories\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Krok 3: Pomocné funkce\n",
|
|
"\n",
|
|
"Vytvořme funkce pro načítání a přípravu obrázků pro náš model.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def load_image_from_url(url):\n",
|
|
" \"\"\"\n",
|
|
" Load an image from a URL.\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" url: Web address of the image\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" PIL Image object\n",
|
|
" \"\"\"\n",
|
|
" response = requests.get(url)\n",
|
|
" img = Image.open(BytesIO(response.content))\n",
|
|
" return img\n",
|
|
"\n",
|
|
"\n",
|
|
"def prepare_image(img):\n",
|
|
" \"\"\"\n",
|
|
" Prepare an image for the model.\n",
|
|
" \n",
|
|
" Steps:\n",
|
|
" 1. Resize to 224x224 (model's expected size)\n",
|
|
" 2. Convert to array\n",
|
|
" 3. Add batch dimension\n",
|
|
" 4. Preprocess for MobileNetV2\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" img: PIL Image\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" Preprocessed image array\n",
|
|
" \"\"\"\n",
|
|
" # Resize to 224x224 pixels\n",
|
|
" img = img.resize((224, 224))\n",
|
|
" \n",
|
|
" # Convert to numpy array\n",
|
|
" img_array = np.array(img)\n",
|
|
" \n",
|
|
" # Add batch dimension (model expects multiple images)\n",
|
|
" img_array = np.expand_dims(img_array, axis=0)\n",
|
|
" \n",
|
|
" # Preprocess for MobileNetV2\n",
|
|
" img_array = preprocess_input(img_array)\n",
|
|
" \n",
|
|
" return img_array\n",
|
|
"\n",
|
|
"\n",
|
|
"def classify_image(img):\n",
|
|
" \"\"\"\n",
|
|
" Classify an image and return top predictions.\n",
|
|
" \n",
|
|
" Args:\n",
|
|
" img: PIL Image\n",
|
|
" \n",
|
|
" Returns:\n",
|
|
" List of (class_name, confidence) tuples\n",
|
|
" \"\"\"\n",
|
|
" # Prepare the image\n",
|
|
" img_array = prepare_image(img)\n",
|
|
" \n",
|
|
" # Make prediction\n",
|
|
" predictions = model.predict(img_array, verbose=0)\n",
|
|
" \n",
|
|
" # Decode predictions to human-readable labels\n",
|
|
" # top=5 means we get the top 5 most likely classes\n",
|
|
" decoded = decode_predictions(predictions, top=5)[0]\n",
|
|
" \n",
|
|
" # Convert to simpler format\n",
|
|
" results = [(label, float(confidence)) for (_, label, confidence) in decoded]\n",
|
|
" \n",
|
|
" return results\n",
|
|
"\n",
|
|
"\n",
|
|
"print(\"✅ Helper functions ready!\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Krok 4: Testování na vzorových obrázcích\n",
|
|
"\n",
|
|
"Zkusme klasifikovat některé obrázky z internetu!\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Sample images to classify\n",
|
|
"# These are from Unsplash (free stock photos)\n",
|
|
"test_images = [\n",
|
|
" {\n",
|
|
" \"url\": \"https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=400\",\n",
|
|
" \"description\": \"A cat\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"url\": \"https://images.unsplash.com/photo-1552053831-71594a27632d?w=400\",\n",
|
|
" \"description\": \"A dog\"\n",
|
|
" },\n",
|
|
" {\n",
|
|
" \"url\": \"https://images.unsplash.com/photo-1511919884226-fd3cad34687c?w=400\",\n",
|
|
" \"description\": \"A car\"\n",
|
|
" },\n",
|
|
"]\n",
|
|
"\n",
|
|
"print(f\"🧪 Testing on {len(test_images)} images...\")\n",
|
|
"print(\"=\" * 70)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Klasifikujte každý obrázek\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"for i, img_data in enumerate(test_images, 1):\n",
|
|
" print(f\"\\n📸 Image {i}: {img_data['description']}\")\n",
|
|
" print(\"-\" * 70)\n",
|
|
" \n",
|
|
" try:\n",
|
|
" # Load image\n",
|
|
" img = load_image_from_url(img_data['url'])\n",
|
|
" \n",
|
|
" # Display image\n",
|
|
" display(img.resize((200, 200))) # Show smaller version\n",
|
|
" \n",
|
|
" # Classify\n",
|
|
" results = classify_image(img)\n",
|
|
" \n",
|
|
" # Show predictions\n",
|
|
" print(\"\\n🎯 Top 5 Predictions:\")\n",
|
|
" for rank, (label, confidence) in enumerate(results, 1):\n",
|
|
" # Create a visual bar\n",
|
|
" bar_length = int(confidence * 50)\n",
|
|
" bar = \"█\" * bar_length\n",
|
|
" \n",
|
|
" print(f\" {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n",
|
|
" \n",
|
|
" except Exception as e:\n",
|
|
" print(f\"❌ Error: {e}\")\n",
|
|
"\n",
|
|
"print(\"\\n\" + \"=\" * 70)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Krok 5: Vyzkoušejte své vlastní obrázky!\n",
|
|
"\n",
|
|
"Nahraďte níže uvedenou URL libovolnou URL obrázku, který chcete klasifikovat.\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# Try your own image!\n",
|
|
"# Replace this URL with any image URL\n",
|
|
"custom_image_url = \"https://images.unsplash.com/photo-1472491235688-bdc81a63246e?w=400\" # A flower\n",
|
|
"\n",
|
|
"print(\"🖼️ Classifying your custom image...\")\n",
|
|
"print(\"=\" * 70)\n",
|
|
"\n",
|
|
"try:\n",
|
|
" # Load and show image\n",
|
|
" img = load_image_from_url(custom_image_url)\n",
|
|
" display(img.resize((300, 300)))\n",
|
|
" \n",
|
|
" # Classify\n",
|
|
" results = classify_image(img)\n",
|
|
" \n",
|
|
" # Show results\n",
|
|
" print(\"\\n🎯 Top 5 Predictions:\")\n",
|
|
" print(\"-\" * 70)\n",
|
|
" for rank, (label, confidence) in enumerate(results, 1):\n",
|
|
" bar_length = int(confidence * 50)\n",
|
|
" bar = \"█\" * bar_length\n",
|
|
" print(f\" {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n",
|
|
" \n",
|
|
" # Highlight top prediction\n",
|
|
" top_label, top_confidence = results[0]\n",
|
|
" print(\"\\n\" + \"=\" * 70)\n",
|
|
" print(f\"\\n🏆 Best guess: {top_label} ({top_confidence*100:.2f}% confident)\")\n",
|
|
" \n",
|
|
"except Exception as e:\n",
|
|
" print(f\"❌ Error: {e}\")\n",
|
|
" print(\" Make sure the URL points to a valid image!\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 💡 Co se právě stalo?\n",
|
|
"\n",
|
|
"1. **Načetli jsme předtrénovaný model** - MobileNetV2 byl natrénován na milionech obrázků \n",
|
|
"2. **Předzpracovali jsme obrázky** - Změnili jsme jejich velikost a formát pro model \n",
|
|
"3. **Model provedl predikce** - Vygeneroval pravděpodobnosti pro 1000 kategorií objektů \n",
|
|
"4. **Dekódovali jsme výsledky** - Převedli čísla na čitelné popisky \n",
|
|
"\n",
|
|
"### Porozumění skóre jistoty\n",
|
|
"\n",
|
|
"- **90-100 %**: Velmi jisté (téměř určitě správné) \n",
|
|
"- **70-90 %**: Jisté (pravděpodobně správné) \n",
|
|
"- **50-70 %**: Částečně jisté (může být správné) \n",
|
|
"- **Pod 50 %**: Málo jisté (nejisté) \n",
|
|
"\n",
|
|
"### Proč mohou být predikce chybné?\n",
|
|
"\n",
|
|
"- **Neobvyklý úhel nebo osvětlení** - Model byl trénován na běžných fotografiích \n",
|
|
"- **Více objektů** - Model očekává jeden hlavní objekt \n",
|
|
"- **Vzácné objekty** - Model zná pouze 1000 kategorií \n",
|
|
"- **Nízká kvalita obrázku** - Rozmazané nebo pixelované obrázky jsou obtížnější \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 🚀 Další kroky\n",
|
|
"\n",
|
|
"1. **Vyzkoušejte různé obrázky:**\n",
|
|
" - Najděte obrázky na [Unsplash](https://unsplash.com)\n",
|
|
" - Klikněte pravým tlačítkem → „Kopírovat adresu obrázku“ pro získání URL\n",
|
|
"\n",
|
|
"2. **Experimentujte:**\n",
|
|
" - Co se stane s abstraktním uměním?\n",
|
|
" - Dokáže rozpoznat objekty z různých úhlů?\n",
|
|
" - Jak si poradí s více objekty?\n",
|
|
"\n",
|
|
"3. **Zjistěte více:**\n",
|
|
" - Prozkoumejte [lekce o počítačovém vidění](../lessons/4-ComputerVision/README.md)\n",
|
|
" - Naučte se trénovat vlastní klasifikátor obrázků\n",
|
|
" - Pochopte, jak fungují CNNs (Konvoluční neuronové sítě)\n",
|
|
"\n",
|
|
"---\n",
|
|
"\n",
|
|
"## 🎉 Gratulujeme!\n",
|
|
"\n",
|
|
"Právě jste vytvořili klasifikátor obrázků pomocí špičkové neuronové sítě!\n",
|
|
"\n",
|
|
"Tato stejná technika pohání:\n",
|
|
"- Google Photos (organizace vašich fotografií)\n",
|
|
"- Autonomní auta (rozpoznávání objektů)\n",
|
|
"- Lékařskou diagnostiku (analýza rentgenových snímků)\n",
|
|
"- Kontrolu kvality (detekce vad)\n",
|
|
"\n",
|
|
"Pokračujte v objevování a učení! 🚀\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n---\n\n**Prohlášení**: \nTento dokument byl přeložen pomocí služby AI pro překlady [Co-op Translator](https://github.com/Azure/co-op-translator). I když se snažíme o přesnost, mějte prosím na paměti, že automatizované překlady mohou obsahovat chyby nebo nepřesnosti. Původní dokument v jeho původním jazyce by měl být považován za autoritativní zdroj. Pro důležité informace doporučujeme profesionální lidský překlad. Neodpovídáme za žádná nedorozumění nebo nesprávné interpretace vyplývající z použití tohoto překladu.\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.0"
|
|
},
|
|
"coopTranslator": {
|
|
"original_hash": "1d472141d9df46b751542b3c29f88677",
|
|
"translation_date": "2025-10-03T11:52:07+00:00",
|
|
"source_file": "examples/03-image-classifier.ipynb",
|
|
"language_code": "cs"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
} |