在 keras 中创建混合数据生成器(图像，csv) [英] Create a mixed data generator (images,csv) in keras

问题描述

我正在构建一个具有多个输入的模型，如 pyimagesearch，但是我无法将所有图像加载到 RAM 中，我正在尝试创建一个使用 flow_from_directory 的生成器，并从 CSV 文件中获取每个图像的所有额外属性正在处理的图像.

I am building a model with multiple inputs as shown in pyimagesearch, however I can't load all images into RAM and I am trying to create a generator that uses flow_from_directory and get from a CSV file all the extra attributes for each image being processed.

问题:如何从 CSV 中获取与图像生成器中每批图像对应的属性?

Question: How do I get the attributes from the CSV to correspond with the images in each batch from the image generator?

def get_combined_generator(images_dir, csv_dir, split, *args):
    """
    Creates train/val generators on images and csv data.

    Arguments:

    images_dir : string
        Path to a directory with subdirectories for each class.

    csv_dir : string
        Path to a directory containing train/val csv files with extra attributes.

    split : string
        Current split being used (train, val or test)
    """
    img_width, img_height, batch_size = args

    datagen = ImageDataGenerator(
        rescale=1. / 255)

    generator = datagen.flow_from_directory(
        f'{images_dir}/{split}',
        target_size=(img_width, img_height),
        batch_size=batch_size,
        shuffle=True,
        class_mode='categorical')

    df = pd.read_csv(f'{csv_dir}/{split}.csv', index_col='image')

    def my_generator(image_gen, data):
        while True:
            i = image_gen.batch_index
            batch = image_gen.batch_size
            row = data[i * batch:(i + 1) * batch]
            images, labels = image_gen.next()
            yield [images, row], labels

    csv_generator = my_generator(generator, df)

    return csv_generator

推荐答案

我使用自定义生成器根据 Luke 的回答找到了一个解决方案

I found a solution based on Luke's answer using a custom generator

import random
import pandas as pd
import numpy as np
from glob import glob
from keras.preprocessing import image as krs_image

# Create the arguments for image preprocessing
data_gen_args = dict(
    horizontal_flip=True,
    brightness_range=[0.5, 1.5],
    shear_range=10,
    channel_shift_range=50,
    rescale=1. / 255,
)

# Create an empty data generator
datagen = ImageDataGenerator()

# Read the image list and csv
image_file_list = glob(f'{images_dir}/{split}/**/*.JPG', recursive=True)
df = pd.read_csv(f'{csv_dir}/{split}.csv', index_col=csv_data[0])
random.shuffle(image_file_list)

def custom_generator(images_list, dataframe, batch_size):
    i = 0
    while True:
        batch = {'images': [], 'csv': [], 'labels': []}
        for b in range(batch_size):
            if i == len(images_list):
                i = 0
                random.shuffle(images_list)
            # Read image from list and convert to array
            image_path = images_list[i]
            image_name = os.path.basename(image_path).replace('.JPG', '')
            image = krs_image.load_img(image_path, target_size=(img_height, img_width))
            image = datagen.apply_transform(image, data_gen_args)
            image = krs_image.img_to_array(image)

            # Read data from csv using the name of current image
            csv_row = dataframe.loc[image_name, :]
            label = csv_row['class']
            csv_features = csv_row.drop(labels='class')

            batch['images'].append(image)
            batch['csv'].append(csv_features)
            batch['labels'].append(label)

            i += 1

        batch['images'] = np.array(batch['images'])
        batch['csv'] = np.array(batch['csv'])
        # Convert labels to categorical values
        batch['labels'] = np.eye(num_classes)[batch['labels']]

        yield [batch['images'], batch['csv']], batch['labels']

这篇关于在 keras 中创建混合数据生成器(图像，csv)的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！