img0 = cv2.rectangle(img0, (int(bbox[idx][0]), int(bbox[idx][1])), (int(bbox[idx][2]), int(bbox[idx][3])), color, wt) img0 = cv2.putText(img0, str(idx) + ' ' + names[int(class_id)], (int(bbox[idx][0]), int(bbox[idx][1] + 16)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) img0 = cv2.putText(img0, '{:.4f}'.format(bbox[idx][4]), (int(bbox[idx][0]), int(bbox[idx][1] + 32)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1) det_result_str += '{} {} {} {} {} {}\n'.format(names[bbox[idx][5]], str(bbox[idx][4]), bbox[idx][0], bbox[idx][1], bbox[idx][2], bbox[idx][3])
时间: 2023-12-14 21:04:08 浏览: 32
这段代码使用OpenCV库对图像进行绘制和标注。具体操作如下:
1. 使用`cv2.rectangle()`函数在图像上绘制矩形框,该矩形框的左上角坐标为`(int(bbox[idx][0]), int(bbox[idx][1]))`,右下角坐标为`(int(bbox[idx][2]), int(bbox[idx][3]))`,颜色为`color`,线宽为`wt`。
2. 使用`cv2.putText()`函数在图像上添加文字标注。第一个标注是目标框的索引和类别名称,位置为`(int(bbox[idx][0]), int(bbox[idx][1] + 16))`,字体为`cv2.FONT_HERSHEY_SIMPLEX`,字体大小为0.5,颜色为红色(0, 0, 255),线宽为1。
3. 使用`cv2.putText()`函数在图像上添加文字标注。第二个标注是目标框的置信度,位置为`(int(bbox[idx][0]), int(bbox[idx][1] + 32))`,字体为`cv2.FONT_HERSHEY_SIMPLEX`,字体大小为0.5,颜色为红色(0, 0, 255),线宽为1。
4. 将目标框的类别、置信度、坐标信息以字符串形式拼接,并追加到`det_result_str`变量中。
这段代码的作用是在图像上绘制目标框,并将目标框的信息保存到`det_result_str`中。
相关问题
我有标注好的xml格式数据集,帮我写个可以训练识别出人,球,鞋子这三类物体的目标检测代码,使用 EfficientNet-Lite0 模型进行训练
好的,这里提供一个使用 TensorFlow Object Detection API 进行目标检测的示例代码,使用的模型是 EfficientDet-Lite0。在这之前需要先安装 TensorFlow Object Detection API 和 EfficientDet-Lite0 模型。
```python
import tensorflow as tf
import numpy as np
import cv2
import os
# 定义类别
CLASSES = ['person', 'ball', 'shoe']
# 读取标注数据
def read_annotation(xml_path):
with open(xml_path, 'r') as f:
xml_str = f.read()
xml = tf.compat.v1.compat.v1.string_to_xml(xml_str)
boxes = []
labels = []
for obj in xml.findall('object'):
label = obj.find('name').text
if label not in CLASSES:
continue
xmin = int(obj.find('bndbox/xmin').text)
ymin = int(obj.find('bndbox/ymin').text)
xmax = int(obj.find('bndbox/xmax').text)
ymax = int(obj.find('bndbox/ymax').text)
boxes.append([xmin, ymin, xmax, ymax])
labels.append(CLASSES.index(label))
return np.array(boxes), np.array(labels)
# 定义数据集
class DetectionDataset(tf.keras.utils.Sequence):
def __init__(self, xml_dir, batch_size):
self.xml_files = [os.path.join(xml_dir, f) for f in os.listdir(xml_dir) if f.endswith('.xml')]
self.batch_size = batch_size
def __len__(self):
return len(self.xml_files) // self.batch_size
def __getitem__(self, idx):
batch_xml = self.xml_files[idx*self.batch_size:(idx+1)*self.batch_size]
batch_images = []
batch_boxes = []
batch_labels = []
for xml_path in batch_xml:
image_path = xml_path.replace('.xml', '.jpg')
image = cv2.imread(image_path)
boxes, labels = read_annotation(xml_path)
batch_images.append(image)
batch_boxes.append(boxes)
batch_labels.append(labels)
inputs = {
'image': np.array(batch_images)
}
outputs = {
'bbox': np.array(batch_boxes),
'label': np.array(batch_labels)
}
return inputs, outputs
# 定义模型
def build_model():
image_input = tf.keras.layers.Input(shape=(None, None, 3), name='image')
backbone = tf.keras.applications.EfficientNetLite0(include_top=False, weights='imagenet')
x = backbone(image_input)
x = tf.keras.layers.Conv2D(1280, kernel_size=1, padding='same', activation='relu')(x)
x = tf.keras.layers.Conv2D(256, kernel_size=1, padding='same', activation='relu')(x)
class_output = tf.keras.layers.Conv2D(len(CLASSES), kernel_size=1, padding='same', activation='softmax', name='class_output')(x)
bbox_output = tf.keras.layers.Conv2D(4, kernel_size=1, padding='same', name='bbox_output')(x)
model = tf.keras.models.Model(inputs=image_input, outputs=[class_output, bbox_output])
return model
# 训练模型
def train_model(xml_dir, epochs, steps_per_epoch, batch_size):
dataset = DetectionDataset(xml_dir, batch_size)
model = build_model()
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
model.compile(optimizer=optimizer, loss=['sparse_categorical_crossentropy', 'mse'])
model.fit(dataset, epochs=epochs, steps_per_epoch=steps_per_epoch)
# 测试模型
def test_model(image_path, model_path):
model = tf.keras.models.load_model(model_path)
image = cv2.imread(image_path)
inputs = np.expand_dims(image, axis=0)
outputs = model.predict(inputs)
class_predictions = outputs[0][0]
bbox_predictions = outputs[1][0]
for i in range(len(CLASSES)):
class_prediction = class_predictions[..., i]
bbox_prediction = bbox_predictions[..., i]
class_indices = np.where(class_prediction > 0.5)
bboxes = bbox_prediction[class_indices]
for bbox in bboxes:
xmin, ymin, xmax, ymax = bbox
cv2.rectangle(image, (int(xmin), int(ymin)), (int(xmax), int(ymax)), (0, 255, 0), 2)
cv2.putText(image, CLASSES[i], (int(xmin), int(ymin)), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
cv2.imshow('result', image)
cv2.waitKey(0)
# 训练模型
train_model('path/to/xml/dir', epochs=10, steps_per_epoch=100, batch_size=8)
# 测试模型
test_model('path/to/image', 'path/to/model.h5')
```
其中,`read_annotation` 函数用于读取标注数据,`DetectionDataset` 类用于定义数据集,`build_model` 函数用于构建模型,`train_model` 函数用于训练模型,`test_model` 函数用于测试模型。在训练模型时,需要提供标注数据所在的目录,训练轮数,每轮训练步数和批次大小。在测试模型时,需要提供要测试的图片路径和训练好的模型路径。
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。