更新料带目标检测，判断料带到位逻辑

ailai_pc/chose_ROI.py

@@ -0,0 +1,93 @@
+import cv2
+import numpy as np
+import os
+
+# 全局变量
+drawing = False  # 是否正在绘制
+ix, iy = -1, -1  # 起始点
+roi_list = []    # 存储多个 ROI 坐标 [(x, y, w, h), ...]
+image_path = "1.jpg"  # <<< 修改为你自己的图像路径
+save_dir = "./roi_111/1.txt"  # 保存坐标的目录
+
+# 创建保存目录
+os.makedirs(save_dir, exist_ok=True)
+
+def draw_rectangle(event, x, y, flags, param):
+    global ix, iy, drawing, img_copy, roi_list
+
+    if event == cv2.EVENT_LBUTTONDOWN:
+        drawing = True
+        ix, iy = x, y
+
+    elif event == cv2.EVENT_MOUSEMOVE:
+        if drawing:
+            # 每次移动都恢复原始图像，重新画矩形
+            img_copy = img.copy()
+            cv2.rectangle(img_copy, (ix, iy), (x, y), (0, 255, 0), 2)
+            cv2.imshow("Select ROI", img_copy)
+
+    elif event == cv2.EVENT_LBUTTONUP:
+        drawing = False
+        w = x - ix
+        h = y - iy
+        if w != 0 and h != 0:
+            # 确保宽高为正
+            x_start = min(ix, x)
+            y_start = min(iy, y)
+            w = abs(w)
+            h = abs(h)
+            cv2.rectangle(img_copy, (x_start, y_start), (x_start + w, y_start + h), (0, 255, 0), 2)
+            cv2.imshow("Select ROI", img_copy)
+            # 添加到列表
+            roi_list.append((x_start, y_start, w, h))
+            print(f"已选择 ROI: (x={x_start}, y={y_start}, w={w}, h={h})")
+
+# 保存坐标到 .txt 文件的函数
+def save_rois_to_txt(rois, filepath):
+    with open(filepath, 'w') as file:
+        for roi in rois:
+            # 将每个 ROI 转换为字符串并写入文件，每行一个 ROI
+            line = ','.join(map(str, roi)) + '\n'
+            file.write(line)
+    print(f"💾 ROI 坐标已保存至: {filepath}")
+
+def select_roi(image_path):
+    global img, img_copy
+
+    img = cv2.imread(image_path)
+    if img is None:
+        print(f"❌ 无法读取图像: {image_path}")
+        return
+
+    img_copy = img.copy()
+    cv2.namedWindow("Select ROI")
+    cv2.setMouseCallback("Select ROI", draw_rectangle)
+
+    print("📌 使用鼠标左键拖拽选择 ROI")
+    print("✅ 选择完成后按 's' 键保存坐标")
+    print("⏭️  按 'n' 键跳过/下一步（可自定义）")
+    print("🚪 按 'q' 键退出")
+
+    while True:
+        cv2.imshow("Select ROI", img_copy)
+        key = cv2.waitKey(1) & 0xFF
+
+        if key == ord('s'):
+            # 保存坐标
+            base_name = os.path.splitext(os.path.basename(image_path))[0]
+            save_path = os.path.join(save_dir, f"{base_name}_rois1.txt")  # 修改了扩展名为 .txt
+            save_rois_to_txt(roi_list, save_path)  # 使用新的保存函数
+
+        elif key == ord('n'):
+            print("⏭️  跳到下一张图片（此处可扩展）")
+            break
+
+        elif key == ord('q'):
+            print("👋 退出程序")
+            cv2.destroyAllWindows()
+            return
+
+    cv2.destroyAllWindows()
+
+if __name__ == "__main__":
+    select_roi(image_path)

ailai_pc/divid_conf—box.py

@@ -0,0 +1,65 @@
+import cv2
+import os
+import shutil
+from ultralytics import YOLO
+
+# ====================== 配置 ======================
+MODEL_PATH = 'point.pt'
+IMAGE_SOURCE_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/ailaipoint'
+
+OUTPUT_ROOT = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/ailaipoint/train_split'
+OUTPUT_DIR_0 = os.path.join(OUTPUT_ROOT, '0')   # 无目标 / conf=0
+OUTPUT_DIR_1 = os.path.join(OUTPUT_ROOT, '1')   # 0 < conf < 0.5
+OUTPUT_DIR_2 = os.path.join(OUTPUT_ROOT, '2')   # conf >= 0.5
+
+for d in [OUTPUT_DIR_0, OUTPUT_DIR_1, OUTPUT_DIR_2]:
+    os.makedirs(d, exist_ok=True)
+
+IMG_EXTENSIONS = {'.jpg', '.jpeg', '.png', '.bmp', '.webp'}
+
+# ====================== 主程序 ======================
+if __name__ == "__main__":
+    print("🚀 bbox 置信度分桶（移动原图，含无目标图像）")
+
+    model = YOLO(MODEL_PATH)
+
+    image_files = [
+        f for f in os.listdir(IMAGE_SOURCE_DIR)
+        if os.path.splitext(f.lower())[1] in IMG_EXTENSIONS
+    ]
+
+    print(f"📸 找到图片 {len(image_files)} 张")
+
+    for img_name in image_files:
+        src_path = os.path.join(IMAGE_SOURCE_DIR, img_name)
+
+        img = cv2.imread(src_path)
+        if img is None:
+            continue
+
+        results = model(img, verbose=False)
+
+        # ====================== 关键修复点 ======================
+        if not results or results[0].boxes is None or len(results[0].boxes.conf) == 0:
+            # 没有任何检测框 → 当作 conf = 0
+            bbox_conf = 0.0
+        else:
+            # 有检测框 → 取第一个（或最大 conf）
+            bbox_conf = float(results[0].boxes.conf[0].cpu().item())
+
+        # ====================== 分桶 ======================
+        if bbox_conf == 0:
+            dst_dir = OUTPUT_DIR_0
+        elif bbox_conf < 0.5:
+            dst_dir = OUTPUT_DIR_1
+        else:
+            dst_dir = OUTPUT_DIR_2
+
+        dst_path = os.path.join(dst_dir, img_name)
+
+        # ====================== 移动文件 ======================
+        shutil.move(src_path, dst_path)
+
+        print(f"{img_name} -> conf={bbox_conf:.3f} -> {os.path.basename(dst_dir)}")
+
+    print("✅ 完成（含无目标图片）")

ailai_pc/point_error_test.py

@@ -6,8 +6,8 @@ from ultralytics import YOLO
 # ====================== 用户配置 ======================
 #MODEL_PATH = '/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_ailai2/weights/best.pt'
 MODEL_PATH = 'point.pt'
-IMAGE_SOURCE_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/20251212'   # 验证集图片目录
-LABEL_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/20251212'          # 标签目录（与图片同名 .txt）
+IMAGE_SOURCE_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/20251214'   # 验证集图片目录
+LABEL_DIR = '/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/20251214'          # 标签目录（与图片同名 .txt）
 OUTPUT_DIR = './output_images'