{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 簡單的圖像分類器\n",
    "\n",
    "此筆記本將教您如何使用預訓練的神經網絡進行圖像分類。\n",
    "\n",
    "**您將學到：**\n",
    "- 如何載入並使用預訓練模型\n",
    "- 圖像預處理\n",
    "- 對圖像進行預測\n",
    "- 理解信心分數\n",
    "\n",
    "**使用案例：** 識別圖像中的物體（例如「貓」、「狗」、「車」等）\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一步：匯入所需的函式庫\n",
    "\n",
    "讓我們匯入所需的工具。不用擔心，如果你現在還不完全理解這些！\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": [
    "## 步驟 2：載入預訓練模型\n",
    "\n",
    "我們將使用 **MobileNetV2**，這是一個已經在數百萬張圖片上訓練過的神經網絡。\n",
    "\n",
    "這被稱為 **遷移學習**——使用別人訓練好的模型！\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": [
    "## 第三步：輔助函數\n",
    "\n",
    "讓我們建立一些函數來載入並準備模型所需的圖片。\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": [
    "## 步驟 4：使用範例圖片進行測試\n",
    "\n",
    "讓我們試著分類一些來自網路的圖片吧！\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": [
    "### 將每張圖片分類\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": [
    "## 第五步：嘗試使用自己的圖片！\n",
    "\n",
    "將下面的 URL 替換為您想要分類的任何圖片 URL。\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": [
    "## 💡 剛剛發生了什麼？\n",
    "\n",
    "1. **我們載入了一個預訓練模型** - MobileNetV2 已經在數百萬張圖片上進行過訓練  \n",
    "2. **我們對圖片進行了預處理** - 將圖片調整大小並格式化以適配模型  \n",
    "3. **模型進行了預測** - 輸出了對 1000 個物件類別的概率  \n",
    "4. **我們解碼了結果** - 將數字轉換為人類可讀的標籤  \n",
    "\n",
    "### 理解信心分數\n",
    "\n",
    "- **90-100%**：非常有信心（幾乎肯定正確）  \n",
    "- **70-90%**：有信心（可能正確）  \n",
    "- **50-70%**：有些信心（可能正確）  \n",
    "- **低於 50%**：信心不足（不確定）  \n",
    "\n",
    "### 為什麼預測可能會出錯？\n",
    "\n",
    "- **不尋常的角度或光線** - 模型是在典型照片上訓練的  \n",
    "- **多個物件** - 模型預期只有一個主要物件  \n",
    "- **罕見物件** - 模型只認識 1000 個類別  \n",
    "- **低品質圖片** - 模糊或像素化的圖片更難辨識  \n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🚀 下一步\n",
    "\n",
    "1. **嘗試不同的圖片：**\n",
    "   - 在 [Unsplash](https://unsplash.com) 上尋找圖片\n",
    "   - 右鍵點擊 → 選擇「複製圖片地址」以獲取 URL\n",
    "\n",
    "2. **進行實驗：**\n",
    "   - 使用抽象藝術會發生什麼？\n",
    "   - 它能否識別不同角度的物體？\n",
    "   - 它如何處理多個物體？\n",
    "\n",
    "3. **深入學習：**\n",
    "   - 探索 [電腦視覺課程](../lessons/4-ComputerVision/README.md)\n",
    "   - 學習如何訓練自己的圖像分類器\n",
    "   - 理解 CNN（卷積神經網絡）的工作原理\n",
    "\n",
    "---\n",
    "\n",
    "## 🎉 恭喜！\n",
    "\n",
    "你剛剛使用最先進的神經網絡構建了一個圖像分類器！\n",
    "\n",
    "這種技術同樣應用於：\n",
    "- Google Photos（整理你的照片）\n",
    "- 自駕車（識別物體）\n",
    "- 醫學診斷（分析 X 光片）\n",
    "- 品質控制（檢測缺陷）\n",
    "\n",
    "繼續探索和學習吧！🚀\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n---\n\n**免責聲明**：  \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，請注意自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵資訊，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。\n"
   ]
  }
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