请帮助更正文件未找到错误

问题描述

我收到这样的错误信息---

FileNotFoundError: [Errno 2] 没有这样的文件或目录：'train-annotations-bBox.csv'

我的代码是

# Faster R-CNN Implementation
from matplotlib import pyplot as plt
from shutil import copyfile
from skimage import io
from tensorflow.keras.preprocessing.image import load_img,img_to_array
import cv2
import numpy as np
import os
import pandas as pd
import random
import sys
import tensorflow as tf
import time

# Url = 'https://storage.googleapis.com/openimages/2018_04/train/train-annotations-bBox.csv'
# Filename = wget.download(url)
import wget
url = 'https://storage.googleapis.com/openimages/2018_04/train/train-images-Boxable-with-rotation.csv'
filename = wget.download(url)

url = 'https://storage.googleapis.com/openimages/v5/class-descriptions-Boxable.csv'
filename = wget.download(url)

annotations_bBox_file = 'train-annotations-bBox.csv'
class_descriptions_file = 'class-descriptions-Boxable.csv'
images_Boxable_file = 'train-images-Boxable-with-rotation.csv'

annotations_bBox = pd.read_csv(annotations_bBox_file)
annotations_bBox.head()

class_descriptions = pd.read_csv(class_descriptions_file,header=None)
class_descriptions.head()

images_Boxable = pd.read_csv(images_Boxable_file)
images_Boxable.head()

请尽快帮我

非常感谢

解决方法

根据您的代码，您似乎在下载 train-annotations-bbox.csv 的文件时注释掉了该行。

此外，您正在覆盖 filename 变量中的数据。如果您可以为每个下载的文件分配唯一的变量，则最好。我希望下面的代码能有所帮助。

import wget
import pandas as pd

url = 'https://storage.googleapis.com/openimages/2018_04/train/train-annotations-bbox.csv'
annotations_bbox_file = wget.download(url)

url = 'https://storage.googleapis.com/openimages/2018_04/train/train-images-boxable-with-rotation.csv'
images_boxable_file = wget.download(url)

url = 'https://storage.googleapis.com/openimages/v5/class-descriptions-boxable.csv'
class_descriptions_file = wget.download(url)

annotations_bbox = pd.read_csv(annotations_bbox_file)
annotations_bbox.head()

class_descriptions = pd.read_csv(class_descriptions_file,header=None)
class_descriptions.head()

images_boxable = pd.read_csv(images_boxable_file)
images_boxable.head()