258 lines
8.6 KiB
Plaintext
258 lines
8.6 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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"# 03 — Inference na dlaždicích Hradce Králové\n",
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"\n",
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"Spustíme natrénovaný YOLOv8 model na všech dlaždicích leteckého snímku HK.\n",
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"Každý typ vozidla dostane jinak barevný rámeček:\n",
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"\n",
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"| Třída | Barva |\n",
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"|-------|-------|\n",
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"| car | zelená |\n",
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"| van | žlutá |\n",
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"| truck | červená |\n",
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"| bus | modrá |"
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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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"from pathlib import Path\n",
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"from ultralytics import YOLO\n",
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"from PIL import Image, ImageDraw, ImageFont\n",
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"import json\n",
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"import numpy as np\n",
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"from tqdm.notebook import tqdm\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# Načtení konfigurace modelu\n",
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"CONFIG_PATH = Path(\"model_config.json\")\n",
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"assert CONFIG_PATH.exists(), \"Nejprve spusť 02_training.ipynb!\"\n",
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"\n",
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"with open(CONFIG_PATH) as f:\n",
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" config = json.load(f)\n",
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"\n",
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"MODEL_PATH = Path(config[\"best_model\"])\n",
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"assert MODEL_PATH.exists(), f\"Model nenalezen: {MODEL_PATH}\"\n",
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"\n",
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"model = YOLO(str(MODEL_PATH))\n",
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"print(f\"Model: {MODEL_PATH}\")"
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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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"import torch\n",
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"\n",
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"TILES_DIR = Path(\"tiles\")\n",
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"ANNOTATED_DIR = Path(\"tiles_annotated\")\n",
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"ANNOTATED_DIR.mkdir(exist_ok=True)\n",
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"\n",
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"# Barvy pro každou třídu (RGB)\n",
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"CLASS_COLORS = {\n",
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" 0: (0, 220, 0), # car — zelená\n",
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" 1: (255, 220, 0), # van — žlutá\n",
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" 2: (220, 0, 0), # truck — červená\n",
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" 3: (0, 120, 255), # bus — modrá\n",
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"}\n",
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"CLASS_NAMES = [\"car\", \"van\", \"truck\", \"bus\"]\n",
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"\n",
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"# Detekce zařízení\n",
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"if torch.cuda.is_available():\n",
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" DEVICE = \"cuda\"\n",
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"elif torch.backends.mps.is_available():\n",
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" DEVICE = \"mps\"\n",
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"else:\n",
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" DEVICE = \"cpu\"\n",
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"\n",
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"CONF = 0.25 # minimální confidence\n",
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"IOU = 0.45 # NMS IoU práh\n",
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"\n",
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"tile_files = sorted(TILES_DIR.glob(\"18_*.jpg\"))\n",
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"print(f\"Dlaždice celkem: {len(tile_files)}\")\n",
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"print(f\"Zařízení: {DEVICE}\")"
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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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"from collections import defaultdict\n",
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"\n",
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"def draw_detections(img: Image.Image, boxes, labels, scores) -> Image.Image:\n",
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" \"\"\"Nakreslí boxy s popisky na obrázek.\"\"\"\n",
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" draw = ImageDraw.Draw(img)\n",
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" for box, label, score in zip(boxes, labels, scores):\n",
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" x1, y1, x2, y2 = [int(v) for v in box]\n",
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" color = CLASS_COLORS.get(label, (255, 255, 255))\n",
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" draw.rectangle([x1, y1, x2, y2], outline=color, width=2)\n",
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" text = f\"{CLASS_NAMES[label]} {score:.2f}\"\n",
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" # Bílé pozadí textu pro čitelnost\n",
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" tw, th = draw.textlength(text), 9\n",
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" draw.rectangle([x1, y1-10, x1+tw+2, y1], fill=color)\n",
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" draw.text((x1+1, y1-10), text, fill=(0, 0, 0))\n",
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" return img\n",
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"\n",
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"\n",
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"# Inference s dávkováním pro rychlost\n",
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"BATCH_SIZE = 32\n",
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"all_detections = {} # {tile_name: [{cls, x1,y1,x2,y2,score}]}\n",
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"vehicle_counts = defaultdict(int)\n",
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"\n",
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"# Zpracuj dlaždice v dávkách\n",
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"for i in tqdm(range(0, len(tile_files), BATCH_SIZE), desc=\"Inference\"):\n",
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" batch_paths = tile_files[i:i+BATCH_SIZE]\n",
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" batch_imgs = [str(p) for p in batch_paths]\n",
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"\n",
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" results = model.predict(\n",
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" batch_imgs,\n",
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" conf=CONF,\n",
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" iou=IOU,\n",
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" device=DEVICE,\n",
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" verbose=False,\n",
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" imgsz=256,\n",
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" )\n",
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"\n",
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" for path, result in zip(batch_paths, results):\n",
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" dets = []\n",
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" if result.boxes is not None and len(result.boxes) > 0:\n",
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" boxes = result.boxes.xyxy.cpu().numpy()\n",
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" clss = result.boxes.cls.cpu().numpy().astype(int)\n",
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" confs = result.boxes.conf.cpu().numpy()\n",
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"\n",
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" for box, cls, conf in zip(boxes, clss, confs):\n",
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" dets.append({\"cls\": cls, \"box\": box.tolist(), \"score\": float(conf)})\n",
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" vehicle_counts[CLASS_NAMES[cls]] += 1\n",
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"\n",
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" # Uložení anotované dlaždice\n",
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" img = Image.open(path).convert(\"RGB\")\n",
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" img = draw_detections(img, boxes, clss, confs)\n",
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" img.save(ANNOTATED_DIR / path.name)\n",
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" else:\n",
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" # Dlaždice bez detekcí – jen zkopíruj\n",
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" import shutil\n",
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" shutil.copy(path, ANNOTATED_DIR / path.name)\n",
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"\n",
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" all_detections[path.name] = dets\n",
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"\n",
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"print(\"\\nCelkový počet detekovaných vozidel:\")\n",
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"for cls, cnt in sorted(vehicle_counts.items()):\n",
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" print(f\" {cls:10s}: {cnt:5d}\")\n",
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"print(f\" {'CELKEM':10s}: {sum(vehicle_counts.values()):5d}\")"
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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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"# Uložení detekcí do JSON pro další notebook\n",
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"with open(\"detections.json\", \"w\") as f:\n",
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" json.dump(all_detections, f)\n",
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"\n",
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"print(f\"Detekce uloženy: detections.json ({len(all_detections)} dlaždic)\")"
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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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"# Vizualizace: 6 náhodných anotovaných dlaždic s detekcemi\n",
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"import random\n",
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"\n",
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"tiles_with_detections = [\n",
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" name for name, dets in all_detections.items() if len(dets) > 0\n",
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"]\n",
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"print(f\"Dlaždice s detekcemi: {len(tiles_with_detections)} / {len(all_detections)}\")\n",
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"\n",
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"samples = random.sample(tiles_with_detections, min(6, len(tiles_with_detections)))\n",
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"fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
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"\n",
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"for ax, name in zip(axes.flatten(), samples):\n",
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" img = Image.open(ANNOTATED_DIR / name)\n",
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" dets = all_detections[name]\n",
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" ax.imshow(img)\n",
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" ax.set_title(f\"{name}\\n{len(dets)} vozidel\", fontsize=8)\n",
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" ax.axis(\"off\")\n",
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"\n",
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"plt.suptitle(\"Ukázkové detekce na dlaždicích HK\", fontsize=14)\n",
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"plt.tight_layout()\n",
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"plt.savefig(\"sample_detections.png\", dpi=150, bbox_inches=\"tight\")\n",
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"plt.show()"
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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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"# Tepelná mapa hustoty vozidel\n",
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"import re\n",
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"\n",
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"# Přečtení rozsahu mřížky\n",
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"xs = []; ys = []\n",
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"for name in all_detections:\n",
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" m = re.match(r\"18_(\\d+)_(\\d+)\\.jpg\", name)\n",
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" if m:\n",
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" xs.append(int(m.group(1)))\n",
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" ys.append(int(m.group(2)))\n",
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"\n",
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"x_min, x_max = min(xs), max(xs)\n",
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"y_min, y_max = min(ys), max(ys)\n",
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"cols = x_max - x_min + 1\n",
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"rows = y_max - y_min + 1\n",
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"\n",
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"heatmap = np.zeros((rows, cols), dtype=np.int32)\n",
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"\n",
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"for name, dets in all_detections.items():\n",
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" m = re.match(r\"18_(\\d+)_(\\d+)\\.jpg\", name)\n",
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" if m:\n",
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" xi = int(m.group(1)) - x_min\n",
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" yi = int(m.group(2)) - y_min\n",
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" heatmap[yi, xi] = len(dets)\n",
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"\n",
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"fig, ax = plt.subplots(figsize=(14, 12))\n",
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"im = ax.imshow(heatmap, cmap=\"hot\", interpolation=\"nearest\")\n",
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"plt.colorbar(im, ax=ax, label=\"Počet vozidel\")\n",
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"ax.set_title(\"Hustota vozidel — Hradec Králové (každý pixel = 1 dlaždice)\", fontsize=14)\n",
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"ax.set_xlabel(\"X (západ → východ)\")\n",
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"ax.set_ylabel(\"Y (sever → jih)\")\n",
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"plt.tight_layout()\n",
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"plt.savefig(\"heatmap_vehicles.png\", dpi=150, bbox_inches=\"tight\")\n",
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"plt.show()\n",
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"\n",
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"print(f\"\\nNejhustší dlaždice: {heatmap.max()} vozidel\")\n",
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"print(f\"Průměr na dlaždici: {heatmap[heatmap>0].mean():.1f} vozidel\")"
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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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"name": "python",
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"version": "3.10.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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