Python自动驾驶无人机与飞行控制

 from djitellopy import Tello

    
  
    
 # 创建无人机对象
    
 tello = Tello()
    
  
    
 # 连接无人机和计算机
    
 tello.connect()
    
  
    
 # 启动无人机
    
 tello.takeoff()
    
  
    
 # 控制无人机向前飞行1米
    
 tello.move_forward(100)
    
  
    
 # 控制无人机向右飞行1米
    
 tello.move_right(100)
    
  
    
 # 控制无人机降落
    
 tello.land()
    
  
    
 # 断开连接
    
 tello.end()
    
    
    
    
    代码解读

 import cv2

    
  
    
 # 获取摄像头图像
    
 cap = cv2.VideoCapture(0)
    
  
    
 while True:
    
     ret, frame = cap.read()
    
     # 进行图像处理
    
  
    
     # 显示图像
    
     cv2.imshow('frame', frame)
    
     if cv2.waitKey(1) & 0xFF == ord('q'):
    
     break
    
  
    
 # 释放摄像头
    
 cap.release()
    
 cv2.destroyAllWindows()
    
    
    
    
    代码解读

 import tensorflow as tf

    
 from tensorflow.keras.layers import Dense, Flatten, Conv2D
    
 from tensorflow.keras import Model
    
  
    
 # 加载数据集
    
 mnist = tf.keras.datasets.mnist
    
  
    
 (x_train, y_train), (x_test, y_test) = mnist.load_data()
    
 x_train, x_test = x_train / 255.0, x_test / 255.0
    
  
    
 # 定义模型
    
 class MyModel(Model):
    
     def __init__(self):
    
     super(MyModel, self).__init__()
    
     self.conv1 = Conv2D(32, 3, activation='relu')
    
     self.flatten = Flatten()
    
     self.d1 = Dense(128, activation='relu')
    
     self.d2 = Dense(10)
    
  
    
     def call(self, x):
    
     x = self.conv1(x)
    
     x = self.flatten(x)
    
     x = self.d1(x)
    
     return self.d2(x)
    
  
    
 model = MyModel()
    
  
    
 # 定义损失函数和优化器
    
 loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    
 optimizer = tf.keras.optimizers.Adam()
    
  
    
 # 定义评估指标
    
 train_loss = tf.keras.metrics.Mean(name='train_loss')
    
 train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')
    
  
    
 test_loss = tf.keras.metrics.Mean(name='test_loss')
    
 test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')
    
  
    
 # 定义训练函数
    
 @tf.function
    
 def train_step(images, labels):
    
     with tf.GradientTape() as tape:
    
     predictions = model(images)
    
     loss = loss_object(labels, predictions)
    
     gradients = tape.gradient(loss, model.trainable_variables)
    
     optimizer.apply_gradients(zip(gradients, model.trainable_variables))
    
  
    
     train_loss(loss)
    
     train_accuracy(labels, predictions)
    
  
    
 # 定义测试函数
    
 @tf.function
    
 def test_step(images, labels):
    
     predictions = model(images)
    
     t_loss = loss_object(labels, predictions)
    
  
    
     test_loss(t_loss)
    
     test_accuracy(labels, predictions)
    
  
    
 # 训练模型
    
 EPOCHS = 5
    
 for epoch in range(EPOCHS):
    
     for images, labels in train_ds:
    
     train_step(images, labels)
    
  
    
     for test_images, test_labels in test_ds:
    
     test_step(test_images, test_labels)
    
  
    
     template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
    
     print(template.format(epoch+1,
    
                       train_loss.result(),
    
                       train_accuracy.result()*100,
    
                       test_loss.result(),
    
                       test_accuracy.result()*100))
    
    
    
    
    代码解读