重构目录结构：调整项目布局

vision/resize_main.py

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+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"models/overflow.pt"
+    input_folder = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/f6"
+    output_root = "/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/class111"
+    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)