397 lines
13 KiB
Plaintext
397 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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"# Clasificador de Imágenes Simple\n",
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"\n",
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"Este cuaderno te muestra cómo clasificar imágenes utilizando una red neuronal preentrenada.\n",
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"\n",
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"**Lo que aprenderás:**\n",
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"- Cómo cargar y usar un modelo preentrenado\n",
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"- Preprocesamiento de imágenes\n",
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"- Realizar predicciones en imágenes\n",
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"- Comprender los puntajes de confianza\n",
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"\n",
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"**Caso de uso:** Identificar objetos en imágenes (como \"gato\", \"perro\", \"coche\", etc.)\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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"## Paso 1: Importar las bibliotecas necesarias\n",
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"\n",
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"Vamos a importar las herramientas que necesitamos. ¡No te preocupes si aún no entiendes todas!\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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"## Paso 2: Cargar el modelo preentrenado\n",
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"\n",
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"Usaremos **MobileNetV2**, una red neuronal ya entrenada con millones de imágenes.\n",
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"\n",
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"Esto se llama **Aprendizaje por Transferencia**: ¡usar un modelo que alguien más entrenó!\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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"## Paso 3: Funciones Auxiliares\n",
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"\n",
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"Vamos a crear funciones para cargar y preparar imágenes para nuestro modelo.\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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"## Paso 4: Prueba con imágenes de muestra\n",
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"\n",
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"¡Vamos a intentar clasificar algunas imágenes de internet!\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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"### Clasifica Cada Imagen\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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"## Paso 5: ¡Prueba con tus propias imágenes!\n",
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"\n",
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"Reemplaza la URL a continuación con cualquier URL de imagen que desees clasificar.\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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"## 💡 ¿Qué acaba de pasar?\n",
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"\n",
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"1. **Cargamos un modelo preentrenado** - MobileNetV2 fue entrenado con millones de imágenes. \n",
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"2. **Preprocesamos las imágenes** - Las redimensionamos y las formateamos para el modelo. \n",
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"3. **El modelo hizo predicciones** - Generó probabilidades para 1000 clases de objetos. \n",
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"4. **Decodificamos los resultados** - Convertimos los números en etiquetas comprensibles para humanos. \n",
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"\n",
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"### Entendiendo los niveles de confianza\n",
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"\n",
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"- **90-100%**: Muy confiado (casi seguro que es correcto). \n",
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"- **70-90%**: Confiado (probablemente correcto). \n",
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"- **50-70%**: Algo confiado (podría ser correcto). \n",
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"- **Menos del 50%**: No muy confiado (incierto). \n",
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"\n",
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"### ¿Por qué podrían estar equivocadas las predicciones?\n",
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"\n",
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"- **Ángulo o iluminación inusual** - El modelo fue entrenado con fotos típicas. \n",
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"- **Múltiples objetos** - El modelo espera un objeto principal. \n",
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"- **Objetos raros** - El modelo solo reconoce 1000 categorías. \n",
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"- **Imagen de baja calidad** - Las imágenes borrosas o pixeladas son más difíciles de interpretar. \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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"## 🚀 Próximos pasos\n",
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"\n",
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"1. **Prueba con diferentes imágenes:**\n",
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" - Encuentra imágenes en [Unsplash](https://unsplash.com)\n",
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" - Haz clic derecho → \"Copiar dirección de imagen\" para obtener la URL\n",
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"\n",
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"2. **Experimenta:**\n",
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" - ¿Qué sucede con arte abstracto?\n",
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" - ¿Puede reconocer objetos desde diferentes ángulos?\n",
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" - ¿Cómo maneja múltiples objetos?\n",
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"\n",
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"3. **Aprende más:**\n",
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" - Explora las [lecciones de Visión por Computadora](../lessons/4-ComputerVision/README.md)\n",
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" - Aprende a entrenar tu propio clasificador de imágenes\n",
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" - Entiende cómo funcionan las CNNs (Redes Neuronales Convolucionales)\n",
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"\n",
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"---\n",
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"\n",
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"## 🎉 ¡Felicidades!\n",
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"\n",
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"¡Acabas de construir un clasificador de imágenes utilizando una red neuronal de última generación!\n",
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"\n",
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"Esta misma técnica impulsa:\n",
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"- Google Photos (organización de tus fotos)\n",
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"- Autos autónomos (reconocimiento de objetos)\n",
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"- Diagnóstico médico (análisis de radiografías)\n",
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"- Control de calidad (detección de defectos)\n",
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"\n",
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"¡Sigue explorando y aprendiendo! 🚀\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**Descargo de responsabilidad**: \nEste documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Aunque nos esforzamos por garantizar la precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse como la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción.\n"
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]
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}
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