zjsh_yolov11/zhuangtai_class_cls/tuili_f_yuantusave.py

import os
from pathlib import Path
import cv2
import numpy as np
from ultralytics import YOLO

# ---------------------------
# 类别映射
# ---------------------------
CLASS_NAMES = {
    0: "未堆料",
    1: "小堆料",
    2: "大堆料",
    3: "未浇筑满",
    4: "浇筑满"
}

# ---------------------------
# 加载 ROI 列表
# ---------------------------
def load_global_rois(txt_path):
    rois = []
    if not os.path.exists(txt_path):
        print(f"❌ ROI 文件不存在: {txt_path}")
        return rois
    with open(txt_path, 'r') as f:
        for line in f:
            s = line.strip()
            if s:
                try:
                    x, y, w, h = map(int, s.split(','))
                    rois.append((x, y, w, h))
                except Exception as e:
                    print(f"⚠️ 无法解析 ROI 行 '{s}': {e}")
    return rois


# ---------------------------
# 裁剪并 resize ROI
# ---------------------------
def crop_and_resize(img, rois, target_size=640):
    crops = []
    h_img, w_img = img.shape[:2]
    for i, (x, y, w, h) in enumerate(rois):
        # 边界检查
        if x < 0 or y < 0 or x + w > w_img or y + h > h_img:
            print(f"⚠️ ROI 超出图像边界，跳过: ({x}, {y}, {w}, {h})")
            continue
        roi = img[y:y+h, x:x+w]
        roi_resized = cv2.resize(roi, (target_size, target_size), interpolation=cv2.INTER_AREA)
        crops.append((roi_resized, i))
    return crops


# ---------------------------
# class1/class2 加权判断
# ---------------------------
def weighted_small_large(pred_probs, threshold=0.4, w1=0.3, w2=0.7):
    p1 = float(pred_probs[1])
    p2 = float(pred_probs[2])
    total = p1 + p2
    if total > 0:
        score = (w1 * p1 + w2 * p2) / total
    else:
        score = 0.0
    final_class = "大堆料" if score >= threshold else "小堆料"
    return final_class, score, p1, p2


# ---------------------------
# 单张图片推理函数
# ---------------------------
def classify_image_weighted(image, model, threshold=0.5):
    results = model(image)
    pred_probs = results[0].probs.data.cpu().numpy().flatten()
    class_id = int(pred_probs.argmax())
    confidence = float(pred_probs[class_id])
    class_name = CLASS_NAMES.get(class_id, f"未知类别({class_id})")

    # 对于 小堆料/大堆料 使用加权逻辑
    if class_id in [1, 2]:
        final_class, score, p1, p2 = weighted_small_large(pred_probs, threshold=threshold)
    else:
        final_class = class_name
        score = confidence
        p1 = float(pred_probs[1])
        p2 = float(pred_probs[2])

    return final_class, score, p1, p2


# ---------------------------
# 批量推理主函数（保存原图，不改名）
# ---------------------------
def batch_classify_images(model_path, input_folder, output_root, roi_file, target_size=640, threshold=0.5):
    # 加载模型
    print("🚀 加载模型...")
    model = YOLO(model_path)

    # 确保输出根目录存在
    output_root = Path(output_root)
    output_root.mkdir(parents=True, exist_ok=True)
    print(f"📁 输出目录: {output_root}")

    # 为所有类别创建子目录
    class_dirs = {}
    for name in CLASS_NAMES.values():
        d = output_root / name
        d.mkdir(exist_ok=True)
        class_dirs[name] = d

    # 加载 ROI 区域
    rois = load_global_rois(roi_file)
    if not rois:
        print("❌ 没有有效 ROI，退出程序")
        return

    print(f"🎯 已加载 {len(rois)} 个 ROI 区域")

    # 定义缺陷严重性等级（数字越小越严重）
    severity_rank = {
        "未堆料": 0,
        "大堆料": 1,
        "小堆料": 2,
        "未浇筑满": 3,
        "浇筑满": 4
    }

    # 遍历输入文件夹中的所有图片
    input_folder = Path(input_folder)
    image_extensions = {'.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff'}

    processed_count = 0
    for img_path in input_folder.glob("*.*"):
        if img_path.suffix.lower() not in image_extensions:
            continue

        try:
            print(f"\n📄 处理: {img_path.name}")
            img = cv2.imread(str(img_path))
            if img is None:
                print(f"❌ 无法读取图像: {img_path.name}")
                continue

            # 裁剪并缩放 ROI
            crops = crop_and_resize(img, rois, target_size)

            if not crops:
                print(f"⚠️ 无有效 ROI 裁剪区域: {img_path.name}")
                continue

            detected_classes = []

            # 遍历每个 ROI 进行分类
            for roi_img, roi_idx in crops:
                final_class, score, p1, p2 = classify_image_weighted(roi_img, model, threshold=threshold)
                detected_classes.append(final_class)
                print(f"   🔍 ROI{roi_idx}: {final_class} (score={score:.2f})")

            # 选择最严重的类别
            most_severe_class = min(detected_classes, key=lambda x: severity_rank.get(x, 99))

            # 构造目标路径：保持原文件名
            dst_path = class_dirs[most_severe_class] / img_path.name

            # 保存原图（不修改内容，不重命名）
            cv2.imwrite(str(dst_path), img)
            print(f"✅ 保存 -> [{most_severe_class}] {dst_path}")

            processed_count += 1

        except Exception as e:
            print(f"❌ 处理失败 {img_path.name}: {e}")

    print(f"\n🎉 批量处理完成！共处理 {processed_count} 张图像。")


# ---------------------------
# 使用示例（请根据实际情况修改路径）
# ---------------------------
if __name__ == "__main__":
    # 🔧 用户配置区
    model_path = "/home/hx/yolo/ultralytics_yolo11-main/runs/train/cls_resize/exp_cls8/weights/best.pt"
    input_folder = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/cls-new/class01"
    output_root = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/cls-new/class011"
    roi_file = "./roi_coordinates/3.txt"
    target_size = 640
    threshold = 0.4  # 小堆料 vs 大堆料的加权阈值

    # 🚀 开始执行
    batch_classify_images(
        model_path=model_path,
        input_folder=input_folder,
        output_root=output_root,
        roi_file=roi_file,
        target_size=target_size,
        threshold=threshold
    )