{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Jednoduchý klasifikátor obrázkov\n", "\n", "Tento notebook vám ukáže, ako klasifikovať obrázky pomocou predtrénovanej neurónovej siete.\n", "\n", "**Čo sa naučíte:**\n", "- Ako načítať a používať predtrénovaný model\n", "- Predspracovanie obrázkov\n", "- Vytváranie predpovedí na obrázkoch\n", "- Pochopenie skóre dôveryhodnosti\n", "\n", "**Použitie:** Identifikácia objektov na obrázkoch (napríklad \"mačka\", \"pes\", \"auto\" atď.)\n", "\n", "---\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Krok 1: Importovanie potrebných knižníc\n", "\n", "Poďme si importovať nástroje, ktoré budeme potrebovať. Nemajte obavy, ak zatiaľ nerozumiete všetkým!\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: Načítanie predtrénovaného modelu\n", "\n", "Použijeme **MobileNetV2**, neurónovú sieť, ktorá už bola natrénovaná na miliónoch obrázkov.\n", "\n", "Toto sa nazýva **Transfer Learning** - použitie modelu, ktorý natrénoval niekto iný!\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 funkcie\n", "\n", "Vytvorme funkcie na načítanie a prípravu obrázkov pre 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: Testovanie na vzorových obrázkoch\n", "\n", "Skúsme klasifikovať niektoré 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 obrázok\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: Vyskúšajte vlastné obrázky!\n", "\n", "Nahraďte URL nižšie akoukoľvek URL obrázka, ktorý chcete klasifikovať.\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": [ "## 💡 Čo sa práve stalo?\n", "\n", "1. **Načítali sme predtrénovaný model** - MobileNetV2 bol natrénovaný na miliónoch obrázkov \n", "2. **Predspracovali sme obrázky** - Zmenili sme ich veľkosť a upravili ich pre model \n", "3. **Model vykonal predikcie** - Vrátil pravdepodobnosti pre 1000 kategórií objektov \n", "4. **Dekódovali sme výsledky** - Preložili sme čísla na čitateľné označenia \n", "\n", "### Pochopenie skóre dôveryhodnosti\n", "\n", "- **90-100%**: Veľmi isté (takmer určite správne) \n", "- **70-90%**: Isté (pravdepodobne správne) \n", "- **50-70%**: Čiastočne isté (môže byť správne) \n", "- **Pod 50%**: Málo isté (neisté) \n", "\n", "### Prečo môžu byť predikcie nesprávne?\n", "\n", "- **Neobvyklý uhol alebo osvetlenie** - Model bol trénovaný na typických fotografiách \n", "- **Viacero objektov** - Model očakáva jeden hlavný objekt \n", "- **Zriedkavé objekty** - Model pozná iba 1000 kategórií \n", "- **Nekvalitný obrázok** - Rozmazané alebo pixelované obrázky sú náročnejšie \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 🚀 Ďalšie kroky\n", "\n", "1. **Vyskúšajte rôzne obrázky:**\n", " - Nájdite obrázky na [Unsplash](https://unsplash.com)\n", " - Kliknite pravým tlačidlom → „Kopírovať adresu obrázka“ na získanie URL\n", "\n", "2. **Experimentujte:**\n", " - Čo sa stane s abstraktným umením?\n", " - Dokáže rozpoznať objekty z rôznych uhlov?\n", " - Ako si poradí s viacerými objektmi?\n", "\n", "3. **Získajte viac informácií:**\n", " - Preskúmajte [lekcie o počítačovom videní](../lessons/4-ComputerVision/README.md)\n", " - Naučte sa trénovať vlastný klasifikátor obrázkov\n", " - Pochopte, ako fungujú CNN (Konvolučné neurónové siete)\n", "\n", "---\n", "\n", "## 🎉 Gratulujeme!\n", "\n", "Práve ste vytvorili klasifikátor obrázkov pomocou špičkovej neurónovej siete!\n", "\n", "Táto technika poháňa:\n", "- Google Photos (organizovanie vašich fotografií)\n", "- Autonómne vozidlá (rozpoznávanie objektov)\n", "- Lekársku diagnostiku (analýza röntgenových snímok)\n", "- Kontrolu kvality (detekcia chýb)\n", "\n", "Pokračujte v objavovaní a učení! 🚀\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**Upozornenie**: \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Hoci sa snažíme o presnosť, upozorňujeme, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nenesieme zodpovednosť za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\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": "2025-10-03T11:52:32+00:00", "source_file": "examples/03-image-classifier.ipynb", "language_code": "sk" } }, "nbformat": 4, "nbformat_minor": 4 }