Python | OpenCV 專案：天堂私服 YOLOv8 物件偵測實戰

# window_capture.py
from ctypes import windll
import numpy as np
import pygetwindow as gw
import win32gui
import win32ui

def find_game_window(title_keyword):
    """回傳符合標題關鍵字、可見、未最小化的視窗；找不到回傳 None"""
    for w in gw.getAllWindows():
        if (title_keyword.lower() in w.title.lower()
                and w.visible and not w.isMinimized
                and w.width > 0 and w.height > 0):
            return w
    return None

def list_visible_windows():
    """列出所有可見且有標題的視窗，方便診斷"""
    return [w.title for w in gw.getAllWindows() if w.title.strip() and w.visible]

def capture_window(hwnd):
    """用 PrintWindow 擷取視窗畫面：被遮擋也能抓，最小化會拿到黑畫面"""
    left, top, right, bottom = win32gui.GetWindowRect(hwnd)
    w, h = right - left, bottom - top
    if w <= 0 or h <= 0:
        return None

    hwnd_dc  = win32gui.GetWindowDC(hwnd)
    mfc_dc   = win32ui.CreateDCFromHandle(hwnd_dc)
    save_dc  = mfc_dc.CreateCompatibleDC()
    save_bmp = win32ui.CreateBitmap()
    save_bmp.CreateCompatibleBitmap(mfc_dc, w, h)
    save_dc.SelectObject(save_bmp)

    # flag=2 (PW_RENDERFULLCONTENT)：支援 DWM 合成畫面，對多數現代應用必要
    result = windll.user32.PrintWindow(hwnd, save_dc.GetSafeHdc(), 2)
    img = np.frombuffer(save_bmp.GetBitmapBits(True), dtype=np.uint8).reshape((h, w, 4))

    win32gui.DeleteObject(save_bmp.GetHandle())
    save_dc.DeleteDC()
    mfc_dc.DeleteDC()
    win32gui.ReleaseDC(hwnd, hwnd_dc)

    return img[:, :, :3].copy() if result == 1 else None   # BGRA → BGR

# collect_screenshots.py
import cv2
import os
import time
from window_capture import find_game_window, capture_window

SAVE_DIR     = "dataset/raw_images"
INTERVAL     = 0.5          # 每隔 0.5 秒截一張
TARGET       = 500          # 目標張數
WINDOW_TITLE = "Lineage"    # 遊戲視窗標題（部分符合即可）

os.makedirs(SAVE_DIR, exist_ok=True)

game_win = find_game_window(WINDOW_TITLE)
if game_win is None:
    print(f"找不到『{WINDOW_TITLE}』視窗，請先開啟遊戲")
    exit()

print(f"開始蒐集，目標 {TARGET} 張，間隔 {INTERVAL} 秒（Ctrl+C 停止）")

count = 0
while count < TARGET:
    frame = capture_window(game_win._hWnd)
    if frame is None:
        time.sleep(INTERVAL)
        continue

    cv2.imwrite(f"{SAVE_DIR}/{count:05d}.jpg", frame)
    count += 1
    if count % 50 == 0:
        print(f"已蒐集：{count}/{TARGET}")
    time.sleep(INTERVAL)

print(f"完成！共 {count} 張，儲存於 {SAVE_DIR}")

# split_dataset.py
import os
import shutil
import random

SRC_IMAGES = "dataset/raw_images"
SRC_LABELS = "dataset/raw_labels"
DST_ROOT   = "dataset"
VAL_RATIO  = 0.2
random.seed(42)

for split in ["train", "val"]:
    os.makedirs(f"{DST_ROOT}/images/{split}", exist_ok=True)
    os.makedirs(f"{DST_ROOT}/labels/{split}", exist_ok=True)

files = [f for f in os.listdir(SRC_IMAGES)
         if f.lower().endswith((".jpg", ".png"))]
random.shuffle(files)

val_set = set(files[:int(len(files) * VAL_RATIO)])

for fname in files:
    split = "val" if fname in val_set else "train"
    stem  = os.path.splitext(fname)[0]

    shutil.copy(f"{SRC_IMAGES}/{fname}",
                f"{DST_ROOT}/images/{split}/{fname}")

    lbl = f"{SRC_LABELS}/{stem}.txt"
    if os.path.exists(lbl):
        shutil.copy(lbl, f"{DST_ROOT}/labels/{split}/{stem}.txt")
    else:
        open(f"{DST_ROOT}/labels/{split}/{stem}.txt", "w").close()

print(f"train: {len(files)-len(val_set)}, val: {len(val_set)}")

# create_yaml.py
import os

# 從 LabelImg 產生的 classes.txt 讀取類別
with open("dataset/raw_labels/classes.txt") as f:
    classes = [line.strip() for line in f if line.strip()]

yaml_content = f"""path: ./dataset
train: images/train
val:   images/val

nc: {len(classes)}
names:
"""
for i, cls in enumerate(classes):
    yaml_content += f"  {i}: {cls}\n"

with open("data.yaml", "w") as f:
    f.write(yaml_content)

print("data.yaml 已產生：")
print(yaml_content)

# train.py
from ultralytics import YOLO
import torch

print(f"GPU：{'可用 - ' + torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'CPU'}")

model = YOLO("yolov8s.pt")

model.train(
    data="data.yaml",
    epochs=100,
    imgsz=640,
    batch=16,
    device=0 if torch.cuda.is_available() else "cpu",
    workers=0,          # Windows 必設 0
    patience=20,
    name="lineage_detector",
    plots=True,
)

print(f"\n訓練完成！best.pt：{model.trainer.best}")