ailai_image_point_diff/ailai_pc/detet_pc_f.py

from ultralytics import YOLO
from ultralytics.utils.ops import non_max_suppression
import torch
import os
import time
import shutil
from pathlib import Path

# ======================
# 配置参数
# ======================
MODEL_PATH = '/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_ailai_detect/weights/best.pt'
INPUT_FOLDER = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/ailaidete/train/bag'
OUTPUT_FOLDER = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/ailaidete/train/bag'

CONF_BUCKETS = [0.93, 0.95]   # ← ⭐ 自己改这里
IOU_THRESH = 0.45
CLASS_NAMES = ['bag']

DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
IMG_SIZE = 640

IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.tiff', '.webp'}


# ======================
# 获取图片路径
# ======================
def get_image_paths(folder):
    folder = Path(folder)
    return sorted([p for p in folder.iterdir() if p.suffix.lower() in IMG_EXTENSIONS])


# ======================
# 防止重名覆盖
# ======================
def safe_move(src, dst_dir):
    os.makedirs(dst_dir, exist_ok=True)
    dst = os.path.join(dst_dir, os.path.basename(src))
    if not os.path.exists(dst):
        shutil.move(src, dst)
        return dst

    stem, suffix = os.path.splitext(os.path.basename(src))
    i = 1
    while True:
        new_dst = os.path.join(dst_dir, f"{stem}_{i}{suffix}")
        if not os.path.exists(new_dst):
            shutil.move(src, new_dst)
            return new_dst
        i += 1


# ======================
# 根据置信度选择目录
# ======================
def get_bucket_dir(max_conf, output_root, buckets):
    for th in sorted(buckets, reverse=True):
        if max_conf >= th:
            return os.path.join(output_root, f"bag_{th}")
    return os.path.join(output_root, "delet")


# ======================
# 主逻辑
# ======================
def main():
    print(f"✅ 使用设备: {DEVICE}")

    model = YOLO(MODEL_PATH).to(DEVICE)

    img_paths = get_image_paths(Path(INPUT_FOLDER))
    if not img_paths:
        print("⚠️ 没有图片")
        return

    print(f"📸 共 {len(img_paths)} 张图片")
    print(f"📊 置信度档位: {CONF_BUCKETS}\n")

    for idx, img_path in enumerate(img_paths, 1):
        print(f"{'='*50}")
        print(f"🖼️ {idx}/{len(img_paths)}: {img_path.name}")

        start_time = time.time()

        results = model(
            str(img_path),
            imgsz=IMG_SIZE,
            conf=min(CONF_BUCKETS),
            device=DEVICE,
            verbose=False
        )

        r = results[0]
        pred = r.boxes.data

        det = non_max_suppression(
            pred.unsqueeze(0),
            conf_thres=min(CONF_BUCKETS),
            iou_thres=IOU_THRESH,
            classes=None,
            agnostic=False,
            max_det=100
        )[0]

        if det is not None and len(det):
            det = det.cpu().numpy()
        else:
            det = []

        max_conf = 0.0
        for *_, conf, cls_id in det:
            if int(cls_id) == 0:
                max_conf = max(max_conf, float(conf))

        dst_dir = get_bucket_dir(max_conf, OUTPUT_FOLDER, CONF_BUCKETS)
        new_path = safe_move(str(img_path), dst_dir)

        if max_conf > 0:
            print(f"✅ bag max_conf={max_conf:.3f} → {os.path.basename(dst_dir)}")
        else:
            print("❌ 未检测到 bag")

        print(f"🚚 已移动到: {new_path}")
        print(f"⏱️ {(time.time() - start_time)*1000:.1f} ms")

    print("\n🎉 全部处理完成")


if __name__ == '__main__':
    main()