for i in range(len(labels['cls'])): if int(labels['cls'][i][0]) == 0: labels['cls'][i] = [1] if int(labels['cls'][i][0]) == 1: labels['cls'][i] = [0]
时间: 2023-10-07 18:10:30 浏览: 27
这段代码的作用是将一个名为`labels`的数据结构中的`cls`标签进行修改。代码的逻辑如下:
- 对`labels`中每个`cls`标签进行遍历,使用`range(len(labels['cls']))`来获取索引值。
- 如果索引处的`cls`标签的值为0,将其修改为[1]。
- 如果索引处的`cls`标签的值为1,将其修改为[0]。
该代码片段可能是为了将标签从0和1转换为二进制分类问题中的类别标签。
相关问题
但是labels['cls'][i] = [1]這段無效 無法存取
抱歉,我犯了一个错误。确实,直接将`labels['cls'][i]`赋值为[1]或[0]是无效的。这是因为`labels['cls'][i]`是一个值,无法通过直接赋值来修改它。
如果想要修改`labels['cls'][i]`的值,可以使用索引来访问和修改列表中的元素。下面是一种可能的修改方式:
```python
for i in range(len(labels['cls'])):
if int(labels['cls'][i][0]) == 0:
labels['cls'][i][0] = 1
if int(labels['cls'][i][0]) == 1:
labels['cls'][i][0] = 0
```
这样,通过使用`labels['cls'][i][0]`来访问和修改列表中的元素,就可以达到修改的目的了。再次对我之前的错误表示抱歉。
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:
```python
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。