无法识别的字段名称 "BoundingBox"。 出错 li9 (第 38 行) frame = insertShape(frame, 'Rectangle', CC.BoundingBox(idx,:), 'LineWidth', 2, 'Color', 'red'); >>

出现这个错误的原因可能是因为 `CC` 结构体数组中没有名为 `BoundingBox` 的字段。请确保在调用 `regionprops` 函数时，已经包含了 `BoundingBox` 属性，例如：

stats = regionprops(BW, 'BoundingBox');

此外，如果您使用的是旧版本的 MATLAB，可能需要使用 `stats` 结构体数组的 `BoundingBox` 字段的名称为 `BoundingBoxes`。因此，在访问 `BoundingBox` 字段时，可以使用以下语法：

frame = insertShape(frame, 'Rectangle', CC.BoundingBoxes(idx,:), 'LineWidth', 2, 'Color', 'red');

希望这能帮助您解决问题。

相关问题

v = VideoReader('shoujilux7.mp4'); min_area = 500; se = strel('square', 3); prev_frame = []; while hasFrame(v) frame = readFrame(v); gray_frame = rgb2gray(frame); % gamma校正 gamma = 1.5; gamma_corrected = imadjust(gray_frame,[],[],gamma); % 高斯滤波 sigma = 1; hsize = ceil(6*sigma); h = fspecial('gaussian', hsize, sigma); filtered_frame = imfilter(gamma_corrected, h); % 帧差法 if isempty(prev_frame) prev_frame = filtered_frame; continue; end frame_diff = abs(double(filtered_frame) - double(prev_frame)); frame_diff = (frame_diff > frame_diff_threshold); % 形态学处理 frame_diff = imclose(frame_diff, se); frame_diff = imfill(frame_diff, 'holes'); % 框选运动目标 stats = regionprops(frame_diff, 'BoundingBox', 'Area'); max_area = 0; max_idx = 0; for i = 1:numel(stats) if stats(i).Area > max_area max_area = stats(i).Area; max_idx = i; end end if max_area > min_area frame = insertShape(frame, 'Rectangle', stats(max_idx).BoundingBox, 'LineWidth', 2, 'Color', 'red'); end % 显示结果 imshow(frame); % 更新前一帧 prev_frame = filtered_frame; end修改

如果你想要解决前面提到的问题（变量'frame_diff_threshold'未定义），可以在代码的开头定义这个变量并赋予一个合适的值，例如：

v = VideoReader('shoujilux7.mp4');
min_area = 500;
se = strel('square', 3);
prev_frame = [];
frame_diff_threshold = 20; % 定义帧差阈值
while hasFrame(v)
    % 省略部分代码
end

这里将帧差阈值设置为20，你可以根据实际需要调整这个值。

另外，如果你想优化代码的效率，可以考虑用bwareafilt函数代替regionprops函数来筛选面积最大的连通区域，例如：

% 修改框选运动目标部分的代码
frame_diff = imfill(frame_diff, 'holes');
CC = bwconncomp(frame_diff);
numPixels = cellfun(@numel,CC.PixelIdxList);
[~, idx] = max(numPixels);
if numPixels(idx) > min_area
    frame = insertShape(frame, 'Rectangle', CC.BoundingBox(idx,:), 'LineWidth', 2, 'Color', 'red');
end

这样可以避免在大量连通区域存在时遍历所有区域的时间开销。

FastRCNN 实现对 VOCdevkit 文件夹内好坏香蕉目标识别+bounding box运行代码

以下是使用Fast R-CNN实现对VOCdevkit数据集中香蕉目标检测和bounding box的Python代码示例：

import cv2
import numpy as np
import os
import xml.etree.ElementTree as ET
import tensorflow as tf
from tensorflow.keras import layers
from tensorflow.keras import models
from tensorflow.keras import optimizers
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.model_selection import train_test_split

# 数据集路径
data_path = 'data/VOCdevkit/'

# 类别列表
classes = ['good_banana', 'bad_banana']

# 定义模型
def create_model():
    base_model = models.Sequential()
    base_model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))
    base_model.add(layers.MaxPooling2D((2, 2)))
    base_model.add(layers.Conv2D(64, (3, 3), activation='relu'))
    base_model.add(layers.MaxPooling2D((2, 2)))
    base_model.add(layers.Conv2D(128, (3, 3), activation='relu'))
    base_model.add(layers.MaxPooling2D((2, 2)))
    base_model.add(layers.Flatten())
    base_model.add(layers.Dense(512, activation='relu'))
    base_model.add(layers.Dense(len(classes), activation='softmax'))

    return base_model

# 加载数据集
def load_dataset():
    images = []
    labels = []
    for cls in classes:
        cls_path = os.path.join(data_path, 'JPEGImages', cls)
        for img_name in os.listdir(cls_path):
            img_path = os.path.join(cls_path, img_name)
            img = cv2.imread(img_path)
            img = cv2.resize(img, (224, 224))
            img = img / 255.0
            images.append(img)
            label = np.zeros(len(classes))
            label[classes.index(cls)] = 1.0
            labels.append(label)

    return np.array(images), np.array(labels)

# 加载bounding box
def load_bbox():
    bbox = {}
    for cls in classes:
        cls_path = os.path.join(data_path, 'Annotations', cls)
        for xml_name in os.listdir(cls_path):
            xml_path = os.path.join(cls_path, xml_name)
            tree = ET.parse(xml_path)
            root = tree.getroot()
            for obj in root.findall('object'):
                name = obj.find('name').text
                bbox_info = obj.find('bndbox')
                xmin = int(bbox_info.find('xmin').text)
                ymin = int(bbox_info.find('ymin').text)
                xmax = int(bbox_info.find('xmax').text)
                ymax = int(bbox_info.find('ymax').text)
                bbox.setdefault(cls, []).append([xmin, ymin, xmax, ymax])

    return bbox

# 训练模型
def train_model():
    # 加载数据
    images, labels = load_dataset()
    bbox = load_bbox()

    # 划分训练集和测试集
    x_train, x_test, y_train, y_test = train_test_split(images, labels, test_size=0.2, random_state=42)

    # 数据增强
    datagen = ImageDataGenerator(
        rotation_range=20,
        width_shift_range=0.2,
        height_shift_range=0.2,
        horizontal_flip=True,
        zoom_range=0.2
    )

    # 定义模型
    model = create_model()

    # 编译模型
    model.compile(loss='categorical_crossentropy', optimizer=optimizers.Adam(lr=1e-4), metrics=['acc'])

    # 定义回调函数
    filepath = 'model.h5'
    checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
    early_stop = EarlyStopping(monitor='val_acc', patience=5, mode='max')

    # 训练模型
    model.fit_generator(datagen.flow(x_train, y_train, batch_size=32),
                        steps_per_epoch=len(x_train) / 32,
                        epochs=50,
                        validation_data=(x_test, y_test),
                        callbacks=[checkpoint, early_stop])

    # 保存模型
    model.save('model_final.h5')

# 测试模型
def test_model():
    # 加载模型
    model = models.load_model('model_final.h5')

    # 加载数据
    images, labels = load_dataset()
    bbox = load_bbox()

    # 预测并绘制bounding box
    for i in range(len(images)):
        img = images[i]
        label = labels[i]
        cls = classes[np.argmax(label)]

        # 预测
        pred = model.predict(np.expand_dims(img, axis=0))[0]

        # 绘制bounding box
        if np.max(pred) > 0.5:
            idx = np.argmax(pred)
            x1, y1, x2, y2 = bbox[cls][i]
            cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
            cv2.putText(img, classes[idx], (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

        # 显示图片
        cv2.imshow('image', img)
        if cv2.waitKey(0) & 0xff == ord('q'):
            break

    cv2.destroyAllWindows()

if __name__ == '__main__':
    train_model()
    test_model()

注意：在使用该代码之前，需要确保已经安装了必要的Python库，例如OpenCV、Pillow、TensorFlow等。