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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"# Pengklasifikasi Gambar Sederhana\n",
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"\n",
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"Notebook ini menunjukkan cara mengklasifikasikan gambar menggunakan jaringan neural yang sudah dilatih sebelumnya.\n",
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"\n",
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"**Apa yang akan Anda pelajari:**\n",
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"- Cara memuat dan menggunakan model yang sudah dilatih sebelumnya\n",
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"- Pra-pemrosesan gambar\n",
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"- Membuat prediksi pada gambar\n",
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"- Memahami skor kepercayaan\n",
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"\n",
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"**Kasus penggunaan:** Mengidentifikasi objek dalam gambar (seperti \"kucing\", \"anjing\", \"mobil\", dll.)\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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"## Langkah 1: Impor Perpustakaan yang Dibutuhkan\n",
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"\n",
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"Mari kita impor alat-alat yang kita perlukan. Jangan khawatir jika Anda belum memahami semuanya!\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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"## Langkah 2: Memuat Model yang Sudah Dilatih\n",
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"\n",
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"Kita akan menggunakan **MobileNetV2**, sebuah jaringan neural yang sudah dilatih dengan jutaan gambar.\n",
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"\n",
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"Ini disebut **Transfer Learning** - menggunakan model yang telah dilatih oleh orang lain!\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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"## Langkah 3: Fungsi Pembantu\n",
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"\n",
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"Mari kita buat fungsi untuk memuat dan mempersiapkan gambar untuk model kita.\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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"## Langkah 4: Uji pada Gambar Contoh\n",
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"\n",
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"Mari coba mengklasifikasikan beberapa gambar dari 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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"### Klasifikasikan Setiap Gambar\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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"## Langkah 5: Coba Gambar Anda Sendiri!\n",
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"\n",
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"Ganti URL di bawah ini dengan URL gambar apa pun yang ingin Anda klasifikasikan.\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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"## 💡 Apa yang Baru Saja Terjadi?\n",
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"\n",
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"1. **Kita memuat model yang sudah dilatih sebelumnya** - MobileNetV2 telah dilatih dengan jutaan gambar\n",
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"2. **Kita memproses gambar** - Mengubah ukuran dan formatnya agar sesuai dengan model\n",
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"3. **Model membuat prediksi** - Menghasilkan probabilitas untuk 1000 kelas objek\n",
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"4. **Kita mendekode hasilnya** - Mengubah angka menjadi label yang mudah dipahami\n",
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"\n",
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"### Memahami Skor Kepercayaan\n",
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"\n",
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"- **90-100%**: Sangat yakin (hampir pasti benar)\n",
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"- **70-90%**: Yakin (kemungkinan besar benar)\n",
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"- **50-70%**: Agak yakin (mungkin benar)\n",
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"- **Di bawah 50%**: Tidak terlalu yakin (tidak pasti)\n",
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"\n",
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"### Mengapa prediksi bisa salah?\n",
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"\n",
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"- **Sudut atau pencahayaan yang tidak biasa** - Model dilatih dengan foto yang umum\n",
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"- **Objek yang banyak** - Model mengharapkan satu objek utama\n",
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"- **Objek yang jarang** - Model hanya mengenali 1000 kategori\n",
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"- **Gambar berkualitas rendah** - Gambar buram atau berpiksel sulit dikenali\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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"## 🚀 Langkah Berikutnya\n",
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"\n",
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"1. **Coba gambar yang berbeda:**\n",
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" - Temukan gambar di [Unsplash](https://unsplash.com)\n",
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" - Klik kanan → \"Salin alamat gambar\" untuk mendapatkan URL\n",
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"\n",
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"2. **Eksperimen:**\n",
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" - Apa yang terjadi dengan seni abstrak?\n",
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" - Bisakah mengenali objek dari sudut yang berbeda?\n",
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" - Bagaimana cara menangani beberapa objek sekaligus?\n",
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"\n",
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"3. **Pelajari lebih lanjut:**\n",
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" - Jelajahi [pelajaran Computer Vision](../lessons/4-ComputerVision/README.md)\n",
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" - Pelajari cara melatih pengklasifikasi gambar Anda sendiri\n",
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" - Pahami cara kerja CNN (Convolutional Neural Networks)\n",
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"\n",
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"---\n",
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"\n",
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"## 🎉 Selamat!\n",
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"\n",
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"Anda baru saja membuat pengklasifikasi gambar menggunakan jaringan saraf canggih!\n",
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"\n",
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"Teknik yang sama ini digunakan untuk:\n",
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"- Google Photos (mengorganisasi foto Anda)\n",
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"- Mobil tanpa pengemudi (mengenali objek)\n",
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"- Diagnosis medis (menganalisis sinar-X)\n",
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"- Kontrol kualitas (mendeteksi cacat)\n",
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"\n",
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"Teruslah menjelajah dan belajar! 🚀\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**Penafian**: \nDokumen ini telah diterjemahkan menggunakan layanan penerjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berupaya untuk memberikan hasil yang akurat, harap diketahui bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang otoritatif. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa penerjemahan manusia profesional. Kami tidak bertanggung jawab atas kesalahpahaman atau interpretasi yang keliru yang timbul dari penggunaan terjemahan ini.\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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"coopTranslator": {
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"translation_date": "2025-10-03T11:49:50+00:00",
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"source_file": "examples/03-image-classifier.ipynb",
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"language_code": "id"
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