EfficientNetV2による画像分類（入力：動画像）（ImageNet 1000クラス）（ソースコードと説明と利用ガイド）

REM Python をシステム領域にインストール
winget install --scope machine --id Python.Python.3.12 -e --silent
REM Python のパス設定
set "PYTHON_PATH=C:\Program Files\Python312"
set "PYTHON_SCRIPTS_PATH=C:\Program Files\Python312\Scripts"
echo "%PATH%" | find /i "%PYTHON_PATH%" >nul
if errorlevel 1 setx PATH "%PATH%;%PYTHON_PATH%" /M >nul
echo "%PATH%" | find /i "%PYTHON_SCRIPTS_PATH%" >nul
if errorlevel 1 setx PATH "%PATH%;%PYTHON_SCRIPTS_PATH%" /M >nul

winget install --scope machine Codeium.Windsurf -e --silent

# EfficientNetV2による画像分類プログラム（ImageNet 1000クラス）
# 特徴技術名: EfficientNetV2
# 出典: Tan, M., & Le, Q. V. (2021). EfficientNetV2: Smaller models and faster training.
#       In International Conference on Machine Learning (pp. 10096-10106). PMLR.
# 特徴機能: Progressive Learning（段階的学習）- 学習時の画像サイズを段階的に増加させつつ
#           正則化を適応的に調整することで、学習速度と精度を向上
# 学習済みモデル: efficientnetv2_rw_s.ra2_in1k（timm実装版、ImageNet-1k事前学習済み、約20Mパラメータ）
#                URL: https://huggingface.co/timm/efficientnetv2_rw_s.ra2_in1k
# 特徴技術および学習済モデルの利用制限:
#   - timmライブラリ: Apache 2.0ライセンス（商用利用可能）
#   - EfficientNetV2アーキテクチャ: Apache 2.0ライセンス（商用利用可能）
#   - 学習済みモデルの重み: ImageNet-1kで学習されており、ImageNetライセンスが適用される
#     ImageNetは非商用研究目的のみでリリースされているため、商用利用する場合は法的助言が必要
#   - 必ず利用者自身で利用制限を確認すること
# 方式設計
#   - 関連利用技術:
#     * timm（PyTorch Image Models）: 学習済みモデル提供
#     * OpenCV: 動画・カメラ入力とリアルタイム表示
#     * PIL/Pillow: 画像前処理と日本語フォント描画
#     * tkinter: ファイル選択UI
#   - 入力と出力:
#     入力: 動画（ユーザは「0:動画ファイル、1:カメラ、2:サンプル動画」のメニューで選択。0:動画ファイルの場合はtkinterでファイル選択。1の場合はOpenCVでカメラが開く。2の場合はhttps://raw.githubusercontent.com/opencv/opencv/master/samples/data/vtest.aviを使用）
#     出力: OpenCV画面でリアルタイム表示、各フレームごとにprint()による分類結果表示、プログラム終了時result.txtファイル保存
#   - 処理手順:
#     1. 動画入力の取得・前処理（RGB変換、timm標準変換）
#     2. EfficientNetV2モデルによる推論実行
#     3. Top-5分類結果の算出・日本語表示
#     4. リアルタイム画面描画・結果保存
#   - 前処理: timm標準データ変換（正規化、リサイズ）によるImageNet互換形式変換
#   - 後処理: ソフトマックス確率変換、Top-k選択、日本語フォント描画
#   - 追加処理: フレームバッファクリア（最新フレーム取得）、日本語結果表示（PIL/OpenCV併用）
#   - 調整を必要とする設定値: MODEL_NAME（学習済みモデル選択）、FONT_SIZE（表示サイズ）
# 将来方策: プログラム内でのモデル性能比較機能（複数EfficientNetV2モデルの精度・速度測定）
# その他の重要事項: Windows環境対応、DirectShowバックエンド使用（Windows環境時）
# 前準備: pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
#         pip install timm opencv-python pillow

import cv2
import numpy as np
import torch
import timm
import torch.nn.functional as F
import tkinter as tk
from tkinter import filedialog
from PIL import Image, ImageDraw, ImageFont
import urllib.request
import time
import sys
import io
from datetime import datetime
import threading

# Windows文字エンコーディング設定
sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', line_buffering=True)

# GPU/CPU自動選択
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'デバイス: {str(device)}')

# GPU使用時の最適化
if device.type == 'cuda':
    torch.backends.cudnn.benchmark = True

# 定数定義
MODEL_NAME = 'efficientnetv2_rw_s.ra2_in1k'
FONT_PATH = 'C:/Windows/Fonts/meiryo.ttc'
FONT_SIZE_MAIN = 18
TOP_K = 5
WINDOW_NAME = "ImageNet Classification"

# ImageNet クラス名リスト
IMAGENET_CLASSES = [
    'tench', 'goldfish', 'great white shark', 'tiger shark', 'hammerhead', 'electric ray', 'stingray', 'cock', 'hen', 'ostrich',
    'brambling', 'goldfinch', 'house finch', 'junco', 'indigo bunting', 'robin', 'bulbul', 'jay', 'magpie', 'chickadee',
    'water ouzel', 'kite', 'bald eagle', 'vulture', 'great grey owl', 'European fire salamander', 'common newt', 'eft', 'spotted salamander', 'axolotl',
    'bullfrog', 'tree frog', 'tailed frog', 'loggerhead', 'leatherback turtle', 'mud turtle', 'terrapin', 'box turtle', 'banded gecko', 'common iguana',
    'American chameleon', 'whiptail', 'agama', 'frilled lizard', 'alligator lizard', 'Gila monster', 'green lizard', 'African chameleon', 'Komodo dragon', 'African crocodile',
    'American alligator', 'triceratops', 'thunder snake', 'ringneck snake', 'hognose snake', 'green snake', 'king snake', 'garter snake', 'water snake', 'vine snake',
    'night snake', 'boa constrictor', 'rock python', 'Indian cobra', 'green mamba', 'sea snake', 'horned viper', 'diamondback', 'sidewinder', 'trilobite',
    'harvestman', 'scorpion', 'black and gold garden spider', 'barn spider', 'garden spider', 'black widow', 'tarantula', 'wolf spider', 'tick', 'centipede',
    'black grouse', 'ptarmigan', 'ruffed grouse', 'prairie chicken', 'peacock', 'quail', 'partridge', 'African grey', 'macaw', 'sulphur-crested cockatoo',
    'lorikeet', 'coucal', 'bee eater', 'hornbill', 'hummingbird', 'jacamar', 'toucan', 'drake', 'red-breasted merganser', 'goose',
    'black swan', 'tusker', 'echidna', 'platypus', 'wallaby', 'koala', 'wombat', 'jellyfish', 'sea anemone', 'brain coral',
    'flatworm', 'nematode', 'conch', 'snail', 'slug', 'sea slug', 'chiton', 'chambered nautilus', 'Dungeness crab', 'rock crab',
    'fiddler crab', 'king crab', 'American lobster', 'spiny lobster', 'crayfish', 'hermit crab', 'isopod', 'white stork', 'black stork', 'spoonbill',
    'flamingo', 'little blue heron', 'American egret', 'bittern', 'crane', 'limpkin', 'European gallinule', 'American coot', 'bustard', 'ruddy turnstone',
    'red-backed sandpiper', 'redshank', 'dowitcher', 'oystercatcher', 'pelican', 'king penguin', 'albatross', 'grey whale', 'killer whale', 'dugong',
    'sea lion', 'Chihuahua', 'Japanese spaniel', 'Maltese dog', 'Pekinese', 'Shih-Tzu', 'Blenheim spaniel', 'papillon', 'toy terrier', 'Rhodesian ridgeback',
    'Afghan hound', 'basset', 'beagle', 'bloodhound', 'bluetick', 'black-and-tan coonhound', 'Walker hound', 'English foxhound', 'redbone', 'borzoi',
    'Irish wolfhound', 'Italian greyhound', 'whippet', 'Ibizan hound', 'Norwegian elkhound', 'otterhound', 'Saluki', 'Scottish deerhound', 'Weimaraner', 'Staffordshire bullterrier',
    'American Staffordshire terrier', 'Bedlington terrier', 'Border terrier', 'Kerry blue terrier', 'Irish terrier', 'Norfolk terrier', 'Norwich terrier', 'Yorkshire terrier', 'wire-haired fox terrier', 'Lakeland terrier',
    'Sealyham terrier', 'Airedale', 'cairn', 'Australian terrier', 'Dandie Dinmont', 'Boston bull', 'miniature schnauzer', 'giant schnauzer', 'standard schnauzer', 'Scotch terrier',
    'Tibetan terrier', 'silky terrier', 'soft-coated wheaten terrier', 'West Highland white terrier', 'Lhasa', 'flat-coated retriever', 'curly-coated retriever', 'golden retriever', 'Labrador retriever', 'Chesapeake Bay retriever',
    'German short-haired pointer', 'vizsla', 'English setter', 'Irish setter', 'Gordon setter', 'Brittany spaniel', 'clumber', 'English springer', 'Welsh springer spaniel', 'cocker spaniel',
    'Sussex spaniel', 'Irish water spaniel', 'kuvasz', 'schipperke', 'groenendael', 'malinois', 'briard', 'kelpie', 'komondor', 'Old English sheepdog',
    'Shetland sheepdog', 'collie', 'Border collie', 'Bouvier des Flandres', 'Rottweiler', 'German shepherd', 'Doberman', 'miniature pinscher', 'Greater Swiss Mountain dog', 'Bernese mountain dog',
    'Appenzeller', 'EntleBucher', 'boxer', 'bull mastiff', 'Tibetan mastiff', 'French bulldog', 'Great Dane', 'Saint Bernard', 'Eskimo dog', 'malamute',
    'Siberian husky', 'dalmatian', 'affenpinscher', 'basenji', 'pug', 'Leonberg', 'Newfoundland', 'Great Pyrenees', 'Samoyed', 'Pomeranian',
    'chow', 'keeshond', 'Brabancon griffon', 'Pembroke', 'Cardigan', 'toy poodle', 'miniature poodle', 'standard poodle', 'Mexican hairless', 'timber wolf',
    'white wolf', 'red wolf', 'coyote', 'dingo', 'dhole', 'African hunting dog', 'hyena', 'red fox', 'kit fox', 'Arctic fox',
    'grey fox', 'tabby', 'tiger cat', 'Persian cat', 'Siamese cat', 'Egyptian cat', 'cougar', 'lynx', 'leopard', 'snow leopard',
    'jaguar', 'lion', 'tiger', 'cheetah', 'brown bear', 'American black bear', 'ice bear', 'sloth bear', 'mongoose', 'meerkat',
    'tiger beetle', 'ladybug', 'ground beetle', 'long-horned beetle', 'leaf beetle', 'dung beetle', 'rhinoceros beetle', 'weevil', 'fly', 'bee',
    'ant', 'grasshopper', 'cricket', 'walking stick', 'cockroach', 'mantis', 'cicada', 'leafhopper', 'lacewing', 'dragonfly',
    'damselfly', 'admiral', 'ringlet', 'monarch', 'cabbage butterfly', 'sulphur butterfly', 'lycaenid', 'starfish', 'sea urchin', 'sea cucumber',
    'wood rabbit', 'hare', 'Angora', 'hamster', 'porcupine', 'fox squirrel', 'marmot', 'beaver', 'guinea pig', 'sorrel',
    'zebra', 'hog', 'wild boar', 'warthog', 'hippopotamus', 'ox', 'water buffalo', 'bison', 'ram', 'bighorn',
    'ibex', 'hartebeest', 'impala', 'gazelle', 'Arabian camel', 'llama', 'weasel', 'mink', 'polecat', 'black-footed ferret',
    'otter', 'skunk', 'badger', 'armadillo', 'three-toed sloth', 'orangutan', 'gorilla', 'chimpanzee', 'gibbon', 'siamang',
    'guenon', 'patas', 'baboon', 'macaque', 'langur', 'colobus', 'proboscis monkey', 'marmoset', 'capuchin', 'howler monkey',
    'titi', 'spider monkey', 'squirrel monkey', 'Madagascar cat', 'indri', 'Indian elephant', 'African elephant', 'lesser panda', 'giant panda', 'barracouta',
    'eel', 'coho', 'rock beauty', 'anemone fish', 'sturgeon', 'gar', 'lionfish', 'puffer', 'abacus', 'abaya',
    'academic gown', 'accordion', 'acoustic guitar', 'aircraft carrier', 'airliner', 'airship', 'altar', 'ambulance', 'amphibian', 'analog clock',
    'apiary', 'apron', 'ashcan', 'assault rifle', 'backpack', 'bakery', 'balance beam', 'balloon', 'ballpoint', 'Band Aid',
    'banjo', 'bannister', 'barbell', 'barber chair', 'barbershop', 'barn', 'barometer', 'barrel', 'barrow', 'baseball',
    'basketball', 'bassinet', 'bassoon', 'bathing cap', 'bath towel', 'bathtub', 'beach wagon', 'beacon', 'beaker', 'bearskin',
    'beer bottle', 'beer glass', 'bell cote', 'bib', 'bicycle-built-for-two', 'bikini', 'binder', 'binoculars', 'birdhouse', 'boathouse',
    'bobsled', 'bolo tie', 'bonnet', 'bookcase', 'bookshop', 'bottlecap', 'bow', 'bow tie', 'brass', 'brassiere',
    'breakwater', 'breastplate', 'broom', 'bucket', 'buckle', 'bulletproof vest', 'bullet train', 'butcher shop', 'cab', 'caldron',
    'candle', 'cannon', 'canoe', 'can opener', 'cardigan', 'car mirror', 'carousel', 'carpenter\'s kit', 'carton', 'car wheel',
    'cash machine', 'cassette', 'cassette player', 'castle', 'catamaran', 'CD player', 'cello', 'cellular telephone', 'chain', 'chainlink fence',
    'chain mail', 'chain saw', 'chest', 'chiffonier', 'chime', 'china cabinet', 'Christmas stocking', 'church', 'cinema', 'cleaver',
    'cliff dwelling', 'cloak', 'clog', 'cocktail shaker', 'coffee mug', 'coffeepot', 'coil', 'combination lock', 'computer keyboard', 'confectionery',
    'container ship', 'convertible', 'corkscrew', 'cornet', 'cowboy boot', 'cowboy hat', 'cradle', 'crane', 'crash helmet', 'crate',
    'crib', 'Crock Pot', 'croquet ball', 'crutch', 'cuirass', 'dam', 'desk', 'desktop computer', 'dial telephone', 'diaper',
    'digital clock', 'digital watch', 'dining table', 'dishrag', 'dishwasher', 'disk brake', 'dock', 'dogsled', 'dome', 'doormat',
    'drilling platform', 'drum', 'drumstick', 'dumbbell', 'Dutch oven', 'electric fan', 'electric guitar', 'electric locomotive', 'entertainment center', 'envelope',
    'espresso maker', 'face powder', 'feather boa', 'file', 'fireboat', 'fire engine', 'fire screen', 'flagpole', 'flute', 'folding chair',
    'football helmet', 'forklift', 'fountain', 'fountain pen', 'four-poster', 'freight car', 'French horn', 'frying pan', 'fur coat', 'garbage truck',
    'gasmask', 'gas pump', 'goblet', 'go-kart', 'golf ball', 'golfcart', 'gondola', 'gong', 'gown', 'grand piano',
    'greenhouse', 'grille', 'grocery store', 'guillotine', 'hair slide', 'hair spray', 'half track', 'hammer', 'hamper', 'hand blower',
    'hand-held computer', 'handkerchief', 'hard disc', 'harmonica', 'harp', 'harvester', 'hatchet', 'holster', 'home theater', 'honeycomb',
    'hook', 'hoopskirt', 'horizontal bar', 'horse cart', 'hourglass', 'iPod', 'iron', 'jack-o\'-lantern', 'jean', 'jeep',
    'jersey', 'jigsaw puzzle', 'jinrikisha', 'joystick', 'kimono', 'knee pad', 'knot', 'lab coat', 'ladle', 'lampshade',
    'laptop', 'lawn mower', 'lens cap', 'letter opener', 'library', 'lifeboat', 'lighter', 'limousine', 'liner', 'lipstick',
    'Loafer', 'lotion', 'loudspeaker', 'loupe', 'lumbermill', 'magnetic compass', 'mailbag', 'mailbox', 'maillot', 'maillot (tank suit)',
    'manhole cover', 'maraca', 'marimba', 'mask', 'matchstick', 'maypole', 'maze', 'measuring cup', 'medicine chest', 'megalith',
    'microphone', 'microwave', 'military uniform', 'milk can', 'minibus', 'miniskirt', 'minivan', 'missile', 'mitten', 'mixing bowl',
    'mobile home', 'Model T', 'modem', 'monastery', 'monitor', 'moped', 'mortar', 'mortarboard', 'mosque', 'mosquito net',
    'motor scooter', 'mountain bike', 'mountain tent', 'mouse', 'mousetrap', 'moving van', 'muzzle', 'nail', 'neck brace', 'necklace',
    'nipple', 'notebook', 'obelisk', 'oboe', 'ocarina', 'odometer', 'oil filter', 'organ', 'oscilloscope', 'overskirt',
    'oxcart', 'oxygen mask', 'packet', 'paddle', 'paddlewheel', 'padlock', 'paintbrush', 'pajama', 'palace', 'panpipe',
    'paper towel', 'parachute', 'parallel bars', 'park bench', 'parking meter', 'passenger car', 'patio', 'pay-phone', 'pedestal', 'pencil box',
    'pencil sharpener', 'perfume', 'Petri dish', 'photocopier', 'pick', 'pickelhaube', 'picket fence', 'pickup', 'pier', 'piggy bank',
    'pill bottle', 'pillow', 'ping-pong ball', 'pinwheel', 'pirate', 'pitcher', 'plane', 'planetarium', 'plastic bag', 'plate rack',
    'plow', 'plunger', 'Polaroid camera', 'pole', 'police van', 'poncho', 'pool table', 'pop bottle', 'pot', 'potter\'s wheel',
    'power drill', 'prayer rug', 'printer', 'prison', 'projectile', 'projector', 'puck', 'punching bag', 'purse', 'quill',
    'quilt', 'racer', 'racket', 'radiator', 'radio', 'radio telescope', 'rain barrel', 'recreational vehicle', 'reel', 'reflex camera',
    'refrigerator', 'remote control', 'restaurant', 'revolver', 'rifle', 'rocking chair', 'rotisserie', 'rubber eraser', 'rugby ball', 'rule',
    'running shoe', 'safe', 'safety pin', 'saltshaker', 'sandal', 'sarong', 'sax', 'scabbard', 'scale', 'school bus',
    'schooner', 'scoreboard', 'screen', 'screw', 'screwdriver', 'seat belt', 'sewing machine', 'shield', 'shoe shop', 'shoji',
    'shopping basket', 'shopping cart', 'shovel', 'shower cap', 'shower curtain', 'ski', 'ski mask', 'sleeping bag', 'slide rule', 'sliding door',
    'slot', 'snorkel', 'snowmobile', 'snowplow', 'soap dispenser', 'soccer ball', 'sock', 'solar dish', 'sombrero', 'soup bowl',
    'space bar', 'space heater', 'space shuttle', 'spatula', 'speedboat', 'spider web', 'spindle', 'sports car', 'spotlight', 'stage',
    'steam locomotive', 'steel arch bridge', 'steel drum', 'stethoscope', 'stole', 'stone wall', 'stopwatch', 'stove', 'strainer', 'streetcar',
    'stretcher', 'studio couch', 'stupa', 'submarine', 'suit', 'sundial', 'sunglass', 'sunglasses', 'sunscreen', 'suspension bridge',
    'swab', 'sweatshirt', 'swimming trunks', 'swing', 'switch', 'syringe', 'table lamp', 'tank', 'tape player', 'teapot',
    'teddy', 'television', 'tennis ball', 'thatch', 'theater curtain', 'thimble', 'thresher', 'throne', 'tile roof', 'toaster',
    'tobacco shop', 'toilet seat', 'torch', 'totem pole', 'tow truck', 'toyshop', 'tractor', 'trailer truck', 'tray', 'trench coat',
    'tricycle', 'trimaran', 'tripod', 'triumphal arch', 'trolleybus', 'trombone', 'tub', 'turnstile', 'typewriter keyboard', 'umbrella',
    'unicycle', 'upright', 'vacuum', 'vase', 'vault', 'velvet', 'vending machine', 'vestment', 'viaduct', 'violin',
    'volleyball', 'waffle iron', 'wall clock', 'wallet', 'wardrobe', 'warplane', 'washbasin', 'washer', 'water bottle', 'water jug',
    'water tower', 'whiskey jug', 'whistle', 'wig', 'window screen', 'window shade', 'Windsor tie', 'wine bottle', 'wing', 'wok',
    'wooden spoon', 'wool', 'worm fence', 'wreck', 'yawl', 'yurt', 'web site', 'comic book', 'crossword puzzle', 'street sign',
    'traffic light', 'book jacket', 'menu', 'plate', 'guacamole', 'consomme', 'hot pot', 'trifle', 'ice cream', 'ice lolly',
    'French loaf', 'bagel', 'pretzel', 'cheeseburger', 'hotdog', 'mashed potato', 'head cabbage', 'broccoli', 'cauliflower', 'zucchini',
    'spaghetti squash', 'acorn squash', 'butternut squash', 'cucumber', 'artichoke', 'bell pepper', 'cardoon', 'mushroom', 'Granny Smith', 'strawberry',
    'orange', 'lemon', 'fig', 'pineapple', 'banana', 'jackfruit', 'custard apple', 'pomegranate', 'hay', 'carbonara',
    'chocolate sauce', 'dough', 'meat loaf', 'pizza', 'potpie', 'burrito', 'red wine', 'espresso', 'cup', 'eggnog',
    'alp', 'bubble', 'cliff', 'coral reef', 'geyser', 'lakeside', 'promontory', 'sandbar', 'seashore', 'valley',
    'volcano', 'ballplayer', 'groom', 'scuba diver', 'rapeseed', 'daisy', 'yellow lady\'s slipper', 'corn', 'acorn', 'hip',
    'buckeye', 'coral fungus', 'agaric', 'gyromitra', 'stinkhorn', 'earthstar', 'hen-of-the-woods', 'bolete', 'ear', 'toilet tissue'
]

# 日本語フォント設定
font_main = ImageFont.truetype(FONT_PATH, FONT_SIZE_MAIN)

# グローバル変数
frame_count = 0
results_log = []
class_counts = {}
model = None
transforms = None


class ThreadedVideoCapture:
    """スレッド化されたVideoCapture（常に最新フレームを取得）"""
    def __init__(self, src, is_camera=False):
        if is_camera:
            self.cap = cv2.VideoCapture(src, cv2.CAP_DSHOW)
            fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
            self.cap.set(cv2.CAP_PROP_FOURCC, fourcc)
            self.cap.set(cv2.CAP_PROP_FPS, 60)
        else:
            self.cap = cv2.VideoCapture(src)

        self.grabbed, self.frame = self.cap.read()
        self.stopped = False
        self.lock = threading.Lock()
        self.thread = threading.Thread(target=self.update, args=())
        self.thread.daemon = True
        self.thread.start()

    def update(self):
        """バックグラウンドでフレームを取得し続ける"""
        while not self.stopped:
            grabbed, frame = self.cap.read()
            with self.lock:
                self.grabbed = grabbed
                if grabbed:
                    self.frame = frame

    def read(self):
        """最新フレームを返す"""
        with self.lock:
            return self.grabbed, self.frame.copy() if self.grabbed else None

    def isOpened(self):
        return self.cap.isOpened()

    def get(self, prop):
        return self.cap.get(prop)

    def release(self):
        self.stopped = True
        self.thread.join()
        self.cap.release()


def display_program_header():
    print('=' * 60)
    print('=== EfficientNetV2画像分類プログラム ===')
    print('=' * 60)
    print('概要: ImageNet 1000クラス分類をリアルタイムで実行')
    print('機能: EfficientNetV2による画像分類（ImageNet 1000クラス）')
    print('技術: timm標準データ変換、Progressive Learning')
    print('操作: qキーで終了')
    print('出力: 各フレームごとに処理結果を表示し、終了時にresult.txtへ保存')
    print()


def draw_texts_with_pillow(bgr_frame, texts):
    """テキスト描画, texts: list of dict with keys {text, org, color, font_type}"""
    img_pil = Image.fromarray(cv2.cvtColor(bgr_frame, cv2.COLOR_BGR2RGB))
    draw = ImageDraw.Draw(img_pil)

    for item in texts:
        text = item['text']
        x, y = item['org']
        color = item['color']
        draw.text((x, y), text, font=font_main, fill=color)

    return cv2.cvtColor(np.array(img_pil), cv2.COLOR_RGB2BGR)


def draw_classification_results(frame, classifications):
    """画像分類の描画処理"""
    texts_to_draw = []
    texts_to_draw.append({
        'text': f'画像分類結果 (上位{TOP_K}位):',
        'org': (10, 30),
        'color': (0, 255, 0),
        'font_type': 'main'
    })

    for i, classification in enumerate(classifications):
        result_text = f'{i+1}位: {classification["name"]} ({classification["conf"]:.3f})'
        texts_to_draw.append({
            'text': result_text,
            'org': (10, 60 + i * 25),
            'color': (255, 255, 255),
            'font_type': 'main'
        })

    frame = draw_texts_with_pillow(frame, texts_to_draw)
    return frame


def format_classification_output(classifications):
    """画像分類の出力フォーマット"""
    if len(classifications) == 0:
        return '分類なし'
    else:
        result = classifications[0]['name'] + f' ({classifications[0]["conf"]:.3f})'
        return result


def classify_image(frame):
    """共通の分類処理（前処理、推論、分類を実行）"""
    global model, transforms

    pil_image = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
    input_tensor = transforms(pil_image).unsqueeze(0).to(device)

    with torch.no_grad():
        outputs = model(input_tensor)
        probabilities = F.softmax(outputs, dim=1)
        topk_prob, topk_indices = torch.topk(probabilities, TOP_K)
        topk_prob = topk_prob.cpu().numpy()[0]
        topk_indices = topk_indices.cpu().numpy()[0]

    curr_classifications = []
    for i, (class_index, confidence) in enumerate(zip(topk_indices, topk_prob)):
        if class_index < len(IMAGENET_CLASSES):
            class_name = IMAGENET_CLASSES[class_index]
            curr_classifications.append({
                'name': class_name,
                'conf': float(confidence),
                'class': int(class_index)
            })

    return curr_classifications


def process_video_frame(frame, timestamp_ms, is_camera):
    """動画用ラッパー"""
    classifications = classify_image(frame)

    global class_counts
    for classification in classifications:
        name = classification['name']
        if name not in class_counts:
            class_counts[name] = 0
        class_counts[name] += 1

    frame = draw_classification_results(frame, classifications)
    result = format_classification_output(classifications)

    return frame, result


def video_frame_processing(frame, timestamp_ms, is_camera):
    """動画フレーム処理（標準形式）"""
    global frame_count
    current_time = time.time()
    frame_count += 1

    processed_frame, result = process_video_frame(frame, timestamp_ms, is_camera)
    return processed_frame, result, current_time


# プログラムヘッダー表示
display_program_header()

print(f'モデル {MODEL_NAME} をロード中...')
model = timm.create_model(MODEL_NAME, pretrained=True)
model.to(device)
model.eval()

# timm標準のデータ変換設定
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)

print('モデルのロード完了')

# 入力選択
print("\n=== EfficientNetV2リアルタイム画像分類（ImageNet 1000クラス） ===")
print("0: 動画ファイル")
print("1: カメラ")
print("2: サンプル動画")

choice = input("選択: ")

is_camera = (choice == '1')

if choice == '0':
    root = tk.Tk()
    root.withdraw()
    path = filedialog.askopenfilename()
    if not path:
        raise SystemExit(1)
    cap = cv2.VideoCapture(path)
elif choice == '1':
    cap = ThreadedVideoCapture(0, is_camera=True)
else:
    SAMPLE_URL = 'https://raw.githubusercontent.com/opencv/opencv/master/samples/data/vtest.avi'
    SAMPLE_FILE = 'vtest.avi'
    print('サンプル動画をダウンロード中...')
    urllib.request.urlretrieve(SAMPLE_URL, SAMPLE_FILE)
    cap = cv2.VideoCapture(SAMPLE_FILE)

if not cap.isOpened():
    print('動画ファイル・カメラを開けませんでした')
    raise SystemExit(1)

# フレームレートの取得とタイムスタンプ増分の計算
if is_camera:
    actual_fps = cap.get(cv2.CAP_PROP_FPS)
    print(f'カメラのfps: {actual_fps}')
    timestamp_increment = int(1000 / actual_fps) if actual_fps > 0 else 33
else:
    video_fps = cap.get(cv2.CAP_PROP_FPS)
    timestamp_increment = int(1000 / video_fps) if video_fps > 0 else 33

# メイン処理
print('\n=== 動画処理開始 ===')
print('操作方法:')
print('  q キー: プログラム終了')

start_time = time.time()
last_info_time = start_time
info_interval = 10.0
timestamp_ms = 0
total_processing_time = 0.0

try:
    while True:
        ret, frame = cap.read()
        if not ret:
            break

        timestamp_ms += timestamp_increment

        processing_start = time.time()
        processed_frame, result, current_time = video_frame_processing(frame, timestamp_ms, is_camera)
        processing_time = time.time() - processing_start
        total_processing_time += processing_time
        cv2.imshow(WINDOW_NAME, processed_frame)

        if result:
            if is_camera:
                timestamp = datetime.fromtimestamp(current_time).strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
                print(f'{timestamp}, {result}')
            else:
                print(f'Frame {frame_count}: {result}')

            results_log.append(result)

        if is_camera:
            elapsed = current_time - last_info_time
            if elapsed >= info_interval:
                total_elapsed = current_time - start_time
                actual_fps = frame_count / total_elapsed if total_elapsed > 0 else 0
                avg_processing_time = (total_processing_time / frame_count * 1000) if frame_count > 0 else 0
                print(f'[情報] 経過時間: {total_elapsed:.1f}秒, 処理フレーム数: {frame_count}, 実測fps: {actual_fps:.1f}, 平均処理時間: {avg_processing_time:.1f}ms')
                last_info_time = current_time

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

finally:
    print('\n=== プログラム終了 ===')
    cap.release()
    cv2.destroyAllWindows()

    if results_log:
        with open('result.txt', 'w', encoding='utf-8') as f:
            f.write('=== EfficientNetV2画像分類結果 ===\n')
            f.write(f'処理フレーム数: {frame_count}\n')
            f.write(f'使用モデル: {MODEL_NAME}\n')
            f.write(f'使用デバイス: {str(device).upper()}\n')
            if device.type == 'cuda':
                f.write(f'GPU: {torch.cuda.get_device_name(0)}\n')
            f.write(f'前処理: timm標準データ変換（正規化、リサイズ）\n')
            f.write(f'上位表示数: {TOP_K}\n')
            f.write(f'\n分類されたクラス一覧:\n')
            for class_name, count in sorted(class_counts.items()):
                f.write(f'  {class_name}: {count}回\n')
            f.write('\n')
            if is_camera:
                f.write('形式: タイムスタンプ, クラス名 (信頼度)\n')
            else:
                f.write('形式: フレーム番号, クラス名 (信頼度)\n')
            f.write('\n')
            f.write('\n'.join(results_log))
        print(f'\n処理結果をresult.txtに保存しました')
        print(f'分類されたクラス数: {len(class_counts)}')