zjsh_yolov11/zhuangtai_class_cls/resize_main.py

import os
import shutil
from pathlib import Path
from ultralytics import YOLO
import cv2

# ---------------------------
# ROI 裁剪函数
# ---------------------------
def load_global_rois(txt_path):
    """加载全局 ROI 坐标"""
    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:
            line = line.strip()
            if line:
                try:
                    x, y, w, h = map(int, line.split(','))
                    rois.append((x, y, w, h))
                    print(f"📌 加载 ROI: (x={x}, y={y}, w={w}, h={h})")
                except Exception as e:
                    print(f"⚠️ 无法解析 ROI 行: {line}, 错误: {e}")
    return rois

def crop_and_resize(img, rois, target_size=640):
    """根据 ROI 裁剪并 resize"""
    crops = []
    for i, (x, y, w, h) in enumerate(rois):
        h_img, w_img = img.shape[:2]
        if x < 0 or y < 0 or x + w > w_img or y + h > h_img:
            print(f"⚠️ ROI 越界，跳过: {x},{y},{w},{h}")
            continue
        roi_img = img[y:y+h, x:x+w]
        roi_resized = cv2.resize(roi_img, (target_size, target_size), interpolation=cv2.INTER_AREA)
        crops.append((roi_resized, i))
    return crops

# ---------------------------
# 分类函数
# ---------------------------
def classify_and_save_images(model_path, input_folder, output_root, roi_file, target_size=640):
    # 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)

    # 创建类别子文件夹 (class0 到 class4)
    class_dirs = []
    for i in range(5):  # 假设有5个类别 (0-4)
        class_dir = output_root / f"class{i}"
        class_dir.mkdir(exist_ok=True)
        class_dirs.append(class_dir)

    # 加载 ROI
    rois = load_global_rois(roi_file)
    if len(rois) == 0:
        print("❌ 没有有效 ROI，退出")
        return

    # 遍历输入文件夹
    for img_path in Path(input_folder).glob("*.*"):
        if img_path.suffix.lower() not in ['.jpg', '.jpeg', '.png', '.bmp', '.tif']:
            continue

        try:
            # 读取原图
            img = cv2.imread(str(img_path))
            if img is None:
                print(f"❌ 无法读取图像: {img_path}")
                continue

            # 根据 ROI 裁剪
            crops = crop_and_resize(img, rois, target_size)

            for roi_img, roi_idx in crops:
                # YOLO 推理
                results = model(roi_img)

                pred = results[0].probs.data  # 获取概率分布
                class_id = int(pred.argmax())

                # 保存到对应类别文件夹
                suffix = f"_roi{roi_idx}" if len(crops) > 1 else ""
                dst_path = class_dirs[class_id] / f"{img_path.stem}{suffix}{img_path.suffix}"
                cv2.imwrite(dst_path, roi_img)  # 保存裁剪后的 ROI 图像
                print(f"Processed {img_path.name}{suffix} -> Class {class_id}")

        except Exception as e:
            print(f"Error processing {img_path.name}: {str(e)}")

# ---------------------------
# 主程序
# ---------------------------
if __name__ == "__main__":
    model_path = r"best.pt"
    input_folder = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/f6"
    output_root = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/class44"
    roi_file = "./roi_coordinates/1_rois.txt"  # 训练时使用的 ROI 文件
    target_size = 640

    classify_and_save_images(model_path, input_folder, output_root, roi_file, target_size)