python写一个Bicycle(自行车)类,有run(骑行)方法, 调用时显示骑行里程km

好的，这个问题可以回答。以下是一个Python自行车类的示例代码：

class Bicycle:
    def __init__(self):
        self.km = 0
        
    def run(self, km):
        self.km += km
        print("骑行里程为:", self.km, "km")

使用这个类可以创建一个自行车对象，并调用它的run()方法来设置骑行里程。例如：

b = Bicycle()
b.run(5)  # 骑行里程为: 5 km
b.run(3)  # 骑行里程为: 8 km

希望能够帮助你解决问题。

相关问题

写一个调用segment anything model的python代码

Segment Anything Model可以指很多不同的模型，因此调用方式也不同。以下是使用Mask R-CNN进行实例分割的示例代码：

import os
import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt

# Root directory of the project
ROOT_DIR = os.path.abspath("../")

# Import Mask RCNN
sys.path.append(ROOT_DIR)  # To find local version of the library
from mrcnn.config import Config
from mrcnn import model as modellib, utils


# Directory to save logs and trained model
MODEL_DIR = os.path.join(ROOT_DIR, "logs")

# Path to trained weights file
COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")

# Download COCO trained weights from Releases if needed
if not os.path.exists(COCO_MODEL_PATH):
    utils.download_trained_weights(COCO_MODEL_PATH)

# Directory of images to run detection on
IMAGE_DIR = os.path.join(ROOT_DIR, "images")


class InferenceConfig(Config):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1
    NAME = "segment_anything_model"

    # Number of classes (including background)
    NUM_CLASSES = 1 + 80  # COCO has 80 classes


config = InferenceConfig()
config.display()

# Create model object in inference mode.
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

# Load weights trained on MS-COCO
model.load_weights(COCO_MODEL_PATH, by_name=True)

# COCO Class names
# Index of the class in the list is its ID. For example, to get ID of
# the teddy bear class, use: class_names.index('teddy bear')
class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
               'bus', 'train', 'truck', 'boat', 'traffic light',
               'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
               'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
               'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
               'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
               'kite', 'baseball bat', 'baseball glove', 'skateboard',
               'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
               'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
               'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
               'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
               'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
               'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
               'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
               'teddy bear', 'hair drier', 'toothbrush']

# Load a random image from the images folder
file_names = next(os.walk(IMAGE_DIR))[2]
image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names)))

# Run detection
results = model.detect([image], verbose=1)

# Visualize results
r = results[0]
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'],
                            class_names, r['scores'])

这段代码会在指定的文件夹中随机选取一张图片进行实例分割，并将结果可视化显示。你需要将代码中的`ROOT_DIR`、`MODEL_DIR`、`COCO_MODEL_PATH`、`IMAGE_DIR`等路径修改为你自己的路径。同时，如果你使用的是其他的Segment Anything Model，那么需要根据具体的模型进行修改。

帮我用Python openCV 写一个垃圾分类的程序

当然可以，以下是一个简单的垃圾分类程序的 Python 代码：

import cv2
import numpy as np

# 定义分类器
net = cv2.dnn.readNetFromCaffe('models/MobileNetSSD_deploy.prototxt.txt', 'models/MobileNetSSD_deploy.caffemodel')
CLASSES = ["background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]

# 加载图像
image = cv2.imread('test.jpg')

# 对图像进行预处理
(h, w) = image.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 0.007843, (300, 300), 127.5)

# 通过分类器进行分类
net.setInput(blob)
detections = net.forward()

# 遍历分类结果
for i in np.arange(0, detections.shape[2]):
    confidence = detections[0, 0, i, 2]
    if confidence > 0.5:
        idx = int(detections[0, 0, i, 1])
        if CLASSES[idx] == 'bottle':
            print('这是一个可回收垃圾')
        elif CLASSES[idx] == 'person':
            print('这是一个有害垃圾')
        else:
            print('这是一个其他垃圾')

这个程序使用了 OpenCV 的深度学习模块，通过 MobileNet SSD 模型对图像进行分类，可以识别出可回收垃圾、有害垃圾和其他垃圾。