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python - 从其他 .py 文件导入类时 Flask 不起作用

问题描述

我正在使用 Flask 创建一个 Web 应用程序，并尝试从与我的 app.py 文件位于同一目录中的 .py 文件导入另一个类，但导入不起作用。

详细说明：

这是我的代码，它运行良好。

from flask import Flask
import os

from flask import Flask, request, render_template, redirect, url_for, flash
from flask import send_from_directory
#from moleimages import MoleImages
import random
from werkzeug.utils import secure_filename
from keras.models import load_model
import tensorflow as tf
from keras.models import model_from_json

app = Flask(__name__)

@app.route('/')
def index():
    return 'hi'

if __name__ == "__main__":
    app.run(debug=True)

现在，我第二次取消注释“从 moleimages 导入 MoleImages”，命令提示符不会为烧瓶应用程序生成任何输出，并且它会终止。

终端图片：

工作正常： 在此处输入图像描述

现在，当我取消注释 moleimages 导入时：

在此处输入图像描述

烧瓶应用程序只是自行终止。

有谁知道为什么会这样？moleimages 文件本身可以正常工作并且不会产生错误。这是摩尔图像文件。

'''
Author@PranavEranki
'''
import numpy as np
from skimage import io
from skimage.transform import resize
import glob
import h5py
import os


'''
This is the helper method for image prep
'''
class MoleImages():
    def __init__(self, dir=None):
        self.dir = dir
        self.size = None

    # Resizing multiple images to a 128 x 128 size
    def resize_bulk(self, wtype, size=(128,128)):
        '''
        Resize Images and create matrix
        Input: size of the images (128,128)
        Output: Numpy array of (size,num_images)
        '''
        self.size = size
        X = []
        image_list = glob.glob(self.dir) #Getting images we need to resize
        n_images = len(image_list)
        most = 2000
        if n_images > most:
            n_images = most
            image_list = image_list[:n_images]
        # Resizing of the images with verbosity
        print('Resizing {} images:'.format(n_images))
        for i, imgfile in enumerate(image_list):
            print('Resizing image {} of {}'.format(i+1, n_images))
            img = io.imread(imgfile)
            img = resize(img, self.size)

            X.append(img)


        return np.array(X)

    def load_test_images(self, dir_b, dir_m):
        X = []
        image_list_b = glob.glob(os.path.join(os.getcwd(), dir_b + '/*.png'))

        n_images_b = len(image_list_b)
        print('Loading {} images of class benign:'.format(n_images_b))
        for i, imgfile in enumerate(image_list_b):
            print('Loading image {} of {}'.format(i+1, n_images_b))
            img = io.imread(imgfile)
            X.append(img)
        image_list_m = glob.glob(os.path.join(os.getcwd(), dir_m + '/*.png'))

        n_images_m = len(image_list_m)
        print('Loading {} images of class benign:'.format(n_images_m))
        for i, imgfile in enumerate(image_list_m):
            print('Loading image {} of {}'.format(i+1, n_images_m))
            img = io.imread(imgfile)
            X.append(img)

        y = np.hstack((np.zeros(n_images_b), np.ones(n_images_m)))

        return np.array(X), y.reshape(len(y),1)

    def load_image(self, filename, size=(128,128)):
        self.size = size
        img = io.imread(filename) #Getting image
        img = resize(img, self.size, mode='constant') * 255 # Resizing image
        if img.shape[2] == 4: #Making sure everything is 3 channels only
            img = img[:,:,0:3]
        return img.reshape(1, self.size[0], self.size[1], 3)

    def save_h5(self, X, filename, dataset):
        '''
        Save a numpy array to a data.h5 file specified.
        Input:
        X: Numpy array to save
        filename: name of h5 file
        dataset: label for the dataset
        '''
        with h5py.File(filename, 'w') as hf:
            hf.create_dataset(dataset, data=X)
        print('File {} saved'.format(filename))

    def load_h5(self, filename, dataset):
        '''
        Load a data.h5 file specified.
        Input: filename, dataset
        Output: Data
        '''
        with h5py.File(filename, 'r') as hf:
            return hf[dataset][:]

    def save_png(self, matrix, dir, tag='img', format='png'):

        # Saving the picture to the directory
        for i, img in enumerate(matrix):
            current_dir = os.getcwd()
            # getting the appropriate filename and directory
            if dir[-1] != '/':
                current_dir = (os.path.join(current_dir, dir + "/"))
                filename = tag + str(i) + '.' + format
            else:
                current_dir = (os.path.join(current_dir, dir))
                filename = tag + str(i) + '.' + format

            # this is some verbosity which I implemented for bug testing - not important
            print('Saving file {}'.format(filename))
            print(current_dir)

            # Making rhe dir benign / malign for data scaled if not present
            if not os.path.exists(current_dir):
                os.makedirs(current_dir)

            # Saving the image to the proper directory
            current_dir = os.path.join(current_dir, filename)

            io.imsave(current_dir, img)



if __name__ == '__main__':
    pass
    #benign = MoleImages()
    #X = benign.load_h5('benigns.h5','benign')

标签： pythonpython-import

解决方案

如果moleimages.py在同一目录中，您可能需要将导入更改为from .moleimages import MoleImages

这样你告诉 python 从同一个文件夹中加载具有该名称的文件。否则 python 正在寻找一个名为moleimages.

另一种方法是使用绝对导入，但如果不知道您的应用层次结构，我无法告诉您这会是什么样子。

您可以在PEP 328中阅读有关相对和绝对导入的更多信息

编辑：还要确保您__init__.py在目录中

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