有没有直接在模型中获得roi的方法呀

import sensor, image, time, ml, uos, gc
from image import SEARCH_EX, SEARCH_DS
# 初始化传感器
sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)    # 使用灰度图像减少内存占用
sensor.set_framesize(sensor.QQVGA)        # 设置为 QQVGA（160x120）进一步减少内存占用
sensor.skip_frames(time=2000)             # 等待传感器稳定

# 加载模型和标签
net = None
labels = None
templates_folder = "/number1" #保存多个模板

try:
    # 加载模型文件
    net = ml.Model("ei-edge_num_identify1-openmv-v3/trained.tflite",
                   load_to_fb=uos.stat('ei-edge_num_identify1-openmv-v3/trained.tflite')[6] > (gc.mem_free() - (64*1024)))
except Exception as e:
    raise Exception('模型加载失败: ' + str(e))

try:
    :face_with_tears_of_joy: # 加载标签文件
    labels = [line.rstrip('\n') for line in open("ei-edge_num_identify1-openmv-v3/labels.txt")]
except Exception as e:
    raise Exception('标签加载失败: ' + str(e))
try:
    templates = [f for f in uos.listdir(templates_folder) if f.endswith('.pgm')]
except Exception as e:
    raise Exception("无法加载模板文件夹: " + str(e))
clock = time.clock()

while True:
    clock.tick()

    # 捕获灰度图像
    gray_img = sensor.snapshot()

    # 创建边缘图像作为备份
    # try:
    edge_img = gray_img.copy()  # 备份原图像
    edge_img = edge_img.find_edges(image.EDGE_CANNY, threshold=(50, 80))  # 应用边缘检测
    for t in templates:
        template_path = templates_folder + "/" + t
        template = image.Image(template_path)
           #对每个模板遍历进行模板匹配
        r = edge_img.find_template(template, 0.70, step=4, search=SEARCH_EX)
        if r:
            gray_img.draw_rectangle(r)
    # except MemoryError:
    #     print("内存不足，跳过此帧")
    #     continue

    # 使用模型预测
    predictions_list = list(zip(labels, net.predict([gray_img])[0].flatten().tolist()))
    max_prediction = max(predictions_list, key=lambda x: x[1])
    detected_label = max_prediction[0]  # 获取预测结果
    print("识别结果: %s (置信度: %f)" % (detected_label, max_prediction[1]))

    # 检测 ROI 区域并绘制矩形框
    # for blob in edge_img.find_blobs([(0, 255)], pixels_threshold=50, area_threshold=50, merge=True):
    #     roi = blob.rect()  # 获取 ROI 矩形
    #     gray_img.draw_rectangle(roi, color=255)  # 在灰度图像上绘制矩形框
    #     gray_img.draw_string(roi[0], roi[1] - 10, detected_label, color=255, scale=1)  # 绘制标签

    # 显示灰度图像（包含矩形框和标签）
    print(clock.fps(), "fps")  # 打印帧率

@kidswong999 https://docs.singtown.com/micropython/zh/latest/openmvcam/library/omv.image.html这个呀

我这个是最新的固件库，ide的版本也是最新的，为什么还会有找不到属性的错误，查函数手册也有啊