重构目录结构：调整项目布局

vision/resize_tuili_image_main.py

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+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):
+        if x < 0 or y < 0 or x + w > w_img or y + h > h_img:
+            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=0.4):
+    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})")
+
+    # class1/class2 使用加权得分
+    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
+
+
+# ---------------------------
+# 实时视频流推理函数
+# ---------------------------
+def real_time_inference(rtsp_url, model_path, roi_file, target_size=640, threshold=0.4):
+    """
+    从RTSP流实时推理
+    :param rtsp_url: RTSP流URL
+    :param model_path: 模型路径
+    :param roi_file: ROI文件路径
+    :param target_size: 目标尺寸
+    :param threshold: 分类阈值
+    """
+    # 加载模型
+    model = YOLO(model_path)
+
+    # 加载ROI
+    rois = load_global_rois(roi_file)
+    if not rois:
+        print("❌ 没有有效 ROI，退出")
+        return
+
+    # 打开RTSP流
+    cap = cv2.VideoCapture(rtsp_url)
+
+    if not cap.isOpened():
+        print(f"❌ 无法打开视频流: {rtsp_url}")
+        return
+
+    print(f"✅ 成功连接到视频流: {rtsp_url}")
+    print("按 'q' 键退出，按 's' 键保存当前帧")
+
+    frame_count = 0
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            print("❌ 无法读取帧，可能连接已断开")
+            break
+
+        frame_count += 1
+        print(f"\n处理第 {frame_count} 帧")
+
+        try:
+            # 裁剪并调整ROI
+            crops = crop_and_resize(frame, rois, target_size)
+
+            for roi_resized, roi_idx in crops:
+                final_class, score, p1, p2 = classify_image_weighted(roi_resized, model, threshold=threshold)
+
+                print(f"ROI {roi_idx} -> 类别: {final_class}, 加权分数: {score:.2f}, "
+                      f"class1 置信度: {p1:.2f}, class2 置信度: {p2:.2f}")
+
+                # 判断是否溢料
+                if "大堆料" in final_class or "浇筑满" in final_class:
+                    print(f"🚨 检测到溢料: ROI {roi_idx} - {final_class}")
+
+                # 可视化（可选）
+                cv2.imshow(f'ROI {roi_idx}', roi_resized)
+
+            # 显示原始帧
+            cv2.imshow('Original Frame', frame)
+
+        except Exception as e:
+            print(f"处理帧时出错: {e}")
+            continue
+
+        # 键盘控制
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord('q'):  # 按q退出
+            break
+        elif key == ord('s'):  # 按s保存当前帧
+            cv2.imwrite(f"frame_{frame_count}.jpg", frame)
+            print(f"保存帧到 frame_{frame_count}.jpg")
+
+    # 清理资源
+    cap.release()
+    cv2.destroyAllWindows()
+    print("✅ 视频流处理结束")
+
+
+# ---------------------------
+# 主函数 - 实时推理示例
+# ---------------------------
+# if __name__ == "__main__":
+#     # RTSP流URL
+#     rtsp_url = "rtsp://admin:XJ123456@192.168.1.51:554/streaming/channels/101"
+#
+#     # 配置参数
+#     model_path = r"models/overflow.pt"
+#     roi_file = r"./roi_coordinates/1_rois.txt"
+#     target_size = 640
+#     threshold = 0.4
+#
+#     print("开始实时视频流推理...")
+#     real_time_inference(rtsp_url, model_path, roi_file, target_size, threshold)