395 lines
13 KiB
Plaintext
395 lines
13 KiB
Plaintext
{
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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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"# Jednoduchý klasifikátor obrázkov\n",
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"\n",
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"Tento notebook vám ukáže, ako klasifikovať obrázky pomocou predtrénovanej neurónovej siete.\n",
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"\n",
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"**Čo sa naučíte:**\n",
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"- Ako načítať a používať predtrénovaný model\n",
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"- Predspracovanie obrázkov\n",
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"- Vytváranie predpovedí na obrázkoch\n",
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"- Pochopenie skóre dôveryhodnosti\n",
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"\n",
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"**Použitie:** Identifikácia objektov na obrázkoch (napríklad \"mačka\", \"pes\", \"auto\" atď.)\n",
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"\n",
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"---\n"
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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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"## Krok 1: Importovanie potrebných knižníc\n",
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"\n",
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"Poďme si importovať nástroje, ktoré budeme potrebovať. Nemajte obavy, ak zatiaľ nerozumiete všetkým!\n"
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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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"# Core libraries\n",
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"import numpy as np\n",
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"from PIL import Image\n",
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"import requests\n",
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"from io import BytesIO\n",
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"\n",
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"# TensorFlow for deep learning\n",
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"try:\n",
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" import tensorflow as tf\n",
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" from tensorflow.keras.applications import MobileNetV2\n",
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" from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions\n",
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" print(\"✅ TensorFlow loaded successfully!\")\n",
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" print(f\" Version: {tf.__version__}\")\n",
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"except ImportError:\n",
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" print(\"❌ Please install TensorFlow: pip install tensorflow\")"
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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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"## Krok 2: Načítanie predtrénovaného modelu\n",
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"\n",
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"Použijeme **MobileNetV2**, neurónovú sieť, ktorá už bola natrénovaná na miliónoch obrázkov.\n",
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"\n",
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"Toto sa nazýva **Transfer Learning** - použitie modelu, ktorý natrénoval niekto iný!\n"
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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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"print(\"📦 Loading pre-trained MobileNetV2 model...\")\n",
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"print(\" This may take a minute on first run (downloading weights)...\")\n",
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"\n",
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"# Load the model\n",
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"# include_top=True means we use the classification layer\n",
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"# weights='imagenet' means it was trained on ImageNet dataset\n",
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"model = MobileNetV2(weights='imagenet', include_top=True)\n",
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"\n",
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"print(\"✅ Model loaded!\")\n",
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"print(f\" The model can recognize 1000 different object categories\")"
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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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"## Krok 3: Pomocné funkcie\n",
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"\n",
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"Vytvorme funkcie na načítanie a prípravu obrázkov pre náš model.\n"
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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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"def load_image_from_url(url):\n",
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" \"\"\"\n",
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" Load an image from a URL.\n",
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" \n",
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" Args:\n",
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" url: Web address of the image\n",
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" \n",
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" Returns:\n",
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" PIL Image object\n",
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" \"\"\"\n",
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" response = requests.get(url)\n",
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" img = Image.open(BytesIO(response.content))\n",
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" return img\n",
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"\n",
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"\n",
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"def prepare_image(img):\n",
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" \"\"\"\n",
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" Prepare an image for the model.\n",
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" \n",
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" Steps:\n",
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" 1. Resize to 224x224 (model's expected size)\n",
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" 2. Convert to array\n",
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" 3. Add batch dimension\n",
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" 4. Preprocess for MobileNetV2\n",
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" \n",
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" Args:\n",
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" img: PIL Image\n",
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" \n",
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" Returns:\n",
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" Preprocessed image array\n",
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" \"\"\"\n",
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" # Resize to 224x224 pixels\n",
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" img = img.resize((224, 224))\n",
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" \n",
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" # Convert to numpy array\n",
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" img_array = np.array(img)\n",
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" \n",
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" # Add batch dimension (model expects multiple images)\n",
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" img_array = np.expand_dims(img_array, axis=0)\n",
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" \n",
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" # Preprocess for MobileNetV2\n",
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" img_array = preprocess_input(img_array)\n",
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" \n",
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" return img_array\n",
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"\n",
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"\n",
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"def classify_image(img):\n",
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" \"\"\"\n",
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" Classify an image and return top predictions.\n",
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" \n",
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" Args:\n",
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" img: PIL Image\n",
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" \n",
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" Returns:\n",
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" List of (class_name, confidence) tuples\n",
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" \"\"\"\n",
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" # Prepare the image\n",
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" img_array = prepare_image(img)\n",
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" \n",
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" # Make prediction\n",
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" predictions = model.predict(img_array, verbose=0)\n",
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" \n",
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" # Decode predictions to human-readable labels\n",
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" # top=5 means we get the top 5 most likely classes\n",
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" decoded = decode_predictions(predictions, top=5)[0]\n",
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" \n",
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" # Convert to simpler format\n",
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" results = [(label, float(confidence)) for (_, label, confidence) in decoded]\n",
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" \n",
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" return results\n",
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"\n",
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"\n",
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"print(\"✅ Helper functions ready!\")"
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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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"## Krok 4: Testovanie na vzorových obrázkoch\n",
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"\n",
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"Skúsme klasifikovať niektoré obrázky z internetu!\n"
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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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"# Sample images to classify\n",
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"# These are from Unsplash (free stock photos)\n",
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"test_images = [\n",
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" {\n",
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" \"url\": \"https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=400\",\n",
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" \"description\": \"A cat\"\n",
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" },\n",
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" {\n",
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" \"url\": \"https://images.unsplash.com/photo-1552053831-71594a27632d?w=400\",\n",
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" \"description\": \"A dog\"\n",
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" },\n",
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" {\n",
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" \"url\": \"https://images.unsplash.com/photo-1511919884226-fd3cad34687c?w=400\",\n",
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" \"description\": \"A car\"\n",
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" },\n",
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"]\n",
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"\n",
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"print(f\"🧪 Testing on {len(test_images)} images...\")\n",
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"print(\"=\" * 70)"
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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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"### Klasifikujte každý obrázok\n"
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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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"for i, img_data in enumerate(test_images, 1):\n",
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" print(f\"\\n📸 Image {i}: {img_data['description']}\")\n",
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" print(\"-\" * 70)\n",
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" \n",
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" try:\n",
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" # Load image\n",
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" img = load_image_from_url(img_data['url'])\n",
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" \n",
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" # Display image\n",
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" display(img.resize((200, 200))) # Show smaller version\n",
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" \n",
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" # Classify\n",
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" results = classify_image(img)\n",
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" \n",
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" # Show predictions\n",
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" print(\"\\n🎯 Top 5 Predictions:\")\n",
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" for rank, (label, confidence) in enumerate(results, 1):\n",
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" # Create a visual bar\n",
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" bar_length = int(confidence * 50)\n",
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" bar = \"█\" * bar_length\n",
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" \n",
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" print(f\" {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n",
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" \n",
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" except Exception as e:\n",
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" print(f\"❌ Error: {e}\")\n",
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"\n",
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"print(\"\\n\" + \"=\" * 70)"
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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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"## Krok 5: Vyskúšajte vlastné obrázky!\n",
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"\n",
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"Nahraďte URL nižšie akoukoľvek URL obrázka, ktorý chcete klasifikovať.\n"
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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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"# Try your own image!\n",
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"# Replace this URL with any image URL\n",
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"custom_image_url = \"https://images.unsplash.com/photo-1472491235688-bdc81a63246e?w=400\" # A flower\n",
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"\n",
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"print(\"🖼️ Classifying your custom image...\")\n",
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"print(\"=\" * 70)\n",
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"\n",
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"try:\n",
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" # Load and show image\n",
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" img = load_image_from_url(custom_image_url)\n",
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" display(img.resize((300, 300)))\n",
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" \n",
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" # Classify\n",
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" results = classify_image(img)\n",
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" \n",
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" # Show results\n",
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" print(\"\\n🎯 Top 5 Predictions:\")\n",
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" print(\"-\" * 70)\n",
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" for rank, (label, confidence) in enumerate(results, 1):\n",
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" bar_length = int(confidence * 50)\n",
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" bar = \"█\" * bar_length\n",
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" print(f\" {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n",
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" \n",
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" # Highlight top prediction\n",
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" top_label, top_confidence = results[0]\n",
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" print(\"\\n\" + \"=\" * 70)\n",
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" print(f\"\\n🏆 Best guess: {top_label} ({top_confidence*100:.2f}% confident)\")\n",
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" \n",
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"except Exception as e:\n",
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" print(f\"❌ Error: {e}\")\n",
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" print(\" Make sure the URL points to a valid image!\")"
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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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"## 💡 Čo sa práve stalo?\n",
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"\n",
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"1. **Načítali sme predtrénovaný model** - MobileNetV2 bol natrénovaný na miliónoch obrázkov \n",
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"2. **Predspracovali sme obrázky** - Zmenili sme ich veľkosť a upravili ich pre model \n",
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"3. **Model vykonal predikcie** - Vrátil pravdepodobnosti pre 1000 kategórií objektov \n",
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"4. **Dekódovali sme výsledky** - Preložili sme čísla na čitateľné označenia \n",
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"\n",
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"### Pochopenie skóre dôveryhodnosti\n",
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"\n",
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"- **90-100%**: Veľmi isté (takmer určite správne) \n",
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"- **70-90%**: Isté (pravdepodobne správne) \n",
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"- **50-70%**: Čiastočne isté (môže byť správne) \n",
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"- **Pod 50%**: Málo isté (neisté) \n",
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"\n",
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"### Prečo môžu byť predikcie nesprávne?\n",
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"\n",
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"- **Neobvyklý uhol alebo osvetlenie** - Model bol trénovaný na typických fotografiách \n",
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"- **Viacero objektov** - Model očakáva jeden hlavný objekt \n",
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"- **Zriedkavé objekty** - Model pozná iba 1000 kategórií \n",
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"- **Nekvalitný obrázok** - Rozmazané alebo pixelované obrázky sú náročnejšie \n"
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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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"## 🚀 Ďalšie kroky\n",
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"\n",
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"1. **Vyskúšajte rôzne obrázky:**\n",
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" - Nájdite obrázky na [Unsplash](https://unsplash.com)\n",
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" - Kliknite pravým tlačidlom → „Kopírovať adresu obrázka“ na získanie URL\n",
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"\n",
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"2. **Experimentujte:**\n",
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" - Čo sa stane s abstraktným umením?\n",
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" - Dokáže rozpoznať objekty z rôznych uhlov?\n",
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" - Ako si poradí s viacerými objektmi?\n",
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"\n",
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"3. **Získajte viac informácií:**\n",
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" - Preskúmajte [lekcie o počítačovom videní](../lessons/4-ComputerVision/README.md)\n",
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" - Naučte sa trénovať vlastný klasifikátor obrázkov\n",
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" - Pochopte, ako fungujú CNN (Konvolučné neurónové siete)\n",
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"\n",
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"---\n",
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"\n",
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"## 🎉 Gratulujeme!\n",
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"\n",
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"Práve ste vytvorili klasifikátor obrázkov pomocou špičkovej neurónovej siete!\n",
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"\n",
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"Táto technika poháňa:\n",
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"- Google Photos (organizovanie vašich fotografií)\n",
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"- Autonómne vozidlá (rozpoznávanie objektov)\n",
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"- Lekársku diagnostiku (analýza röntgenových snímok)\n",
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"- Kontrolu kvality (detekcia chýb)\n",
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"\n",
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"Pokračujte v objavovaní a učení! 🚀\n"
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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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"\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"
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]
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}
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