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源代码显示的不同输出(机器学习)(Python)

kaggle operating-system machine-learning python

Depleted Money · 技术社区 · 1 年前

我目前正在尝试一个小型的图像机器学习项目。我发现了这个人的 Kaggle code 我试着从头开始复制。然而,即使不是在主要部分,我也已经面临了一个错误。

我确信这一定是一个本地化问题,但我不知道是怎么回事。

我的代码:

#Import Libraries

#Data processing modules
import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt
import cv2
#File directory modules
import glob as gb
import os
#Training and testing (machine learning) modules
import tensorflow as tf 
import keras

#Importing the images into the code

trainDataset = 'melanoma_cancer_dataset/train'
testDataset = 'melanoma_cancer_dataset/test'
predictionDataset = 'melanoma_cancer_dataset/skinTest'

#creating empty lists for the images to fall into for processing
training_List = []
testing_list = []
#making a classification dictionary for the two keys, benign and malignant
#used for inserting into the images
diction = {'benign' : 0, 'malignant' : 1}

#Read through the folder's length contents
for folder in os.listdir(trainDataset):
    data = gb.glob(pathname=str(trainDataset + folder + '/*.jpg'))
    print(f'{len(data)} in folder {folder}')
    #read the images, resize them in a uniform order, and store them in the empty lists
    for data in data:
        image = cv2.imread(data)
        imageList = cv2.resize(image(120,120))
        training_List.append(list(imageList))

笔记本的输出显示它有 0个图像/内容 存储在文件夹中。现在我有点怀疑这里发生了什么,我很想得到一些答案。提前谢谢。我也在使用我自己的VS代码。

这是我的文件的屏幕截图:

1 回复 | 直到 1 年前

Niusoski 1 年前

根据你的文件夹结构和你提供的代码,问题是你没有在文件夹路径的末尾加上斜杠。在提供的代码中,您试图将文件夹名称与路径直接连接起来。但是,如果您错过了斜杠,或者文件夹变量不包括尾部斜杠,这可能会导致路径不正确。

如下更新路径:

trainDataset = 'melanoma_cancer_dataset/train/'
testDataset = 'melanoma_cancer_dataset/test/'
predictionDataset = 'melanoma_cancer_dataset/skinTest/'

您的代码正在执行的操作如下:

for folder in os.listdir(trainDataset):
    data = gb.glob(pathname=str(trainDataset + folder + '/*.jpg'))

它将转到trainDataset的路径,然后使用列出那里的文件夹(分别命名为恶性和良性) os.listdir() . 这些路径被连接以生成具有以下内容的最终图像路径:

data = gb.glob(pathname=str(trainDataset + folder + '/*.jpg'))

此外,行中有轻微语法错误:

imageList = cv2.resize(image(120,120))

应该是

cv2.resize(image, (120, 120))

此外,附加到training_List的方式可能是错误的。在追加之前需要将imageList转换为列表,或者如果要保留图像数组结构,则直接追加imageList。

完整更新代码:

# Data processing modules
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import cv2
# File directory modules
import glob as gb
import os
# Training and testing (machine learning) modules
import tensorflow as tf
import keras

# Directories
trainDataset = 'melanoma_cancer_dataset/train/'
testDataset = 'melanoma_cancer_dataset/test/'
predictionDataset = 'melanoma_cancer_dataset/skinTest/'

# Empty list for the images
training_List = []
testing_list = []

# Classification dictionary
diction = {'benign': 0, 'malignant': 1}

# Read through the folder's contents
for folder in os.listdir(trainDataset):
    # Corrected the path pattern and added a slash
    data = gb.glob(pathname=str(trainDataset + folder + '/*.jpg'))
    print(f'{len(data)} in folder {folder}')
    # Read the images, resize them, and store them in the list
    for file_path in data:
        image = cv2.imread(file_path)
        # Corrected the resize function call
        imageList = cv2.resize(image, (120, 120))
        # Append the image array directly
        training_List.append(imageList)

print(f'Total images in training set: {len(training_List)}')