Iam new to keras and after testing some tutorials with mnist images I would like to train with my own data set. The data are .png images of numbers from 0-9.
I ordered them into 10 classes, each containing 100 .png images of the numbers separately (so one folder for 0, one folder for 1, one folder for 2 etc ..).
now I am wondering how to load the images with python, for keras to use them ?
You need to use Keras’ ImageDataGenerator().flow_from_directory() to generate batches of your image data from your file system that you will then train your model on. Once you have your images organized in the file system, creating ImageDataGenerator() would be the next step.
This video demonstrates how to prep your image data and create your ImageDataGenerator(), and then this video demonstrates how to train your CNN on the image data.
An example of this would look like
train_batches = ImageDataGenerator().flow_from_directory(directory=<path_to_image_data>, target_size=(224,224), classes=[‘0’, '1', ‘2’, ‘3’, …, ‘9’], batch_size=10)
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I am doing super resolution with resnet in keras and I have split my data into train and test (70-30) and from the test data 20% for validation .i am trying to read the data with datagen.flow_from_directory but its showing 0 images for 0 classes .The main issue is i dont have classes. I only have high resolution images and low resolution images. The high resolution images goes to output and the low resolution images goes to input. How can i load the data without separating them in classess
from keras.preprocessing.image import ImageDataGenerator
import os
train_dir = r'G:\\images\\train'
train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(train_dir)
To resolve 0 images for 0 classes, notice that a common mistake is that the target folder you specify has no subdirectory. ImageDataGenerator splits data to classes, based on each subdirectory under the directory you specify as it's first argument. So, you should have at least one subdirectory under the target.
Furthermore, the generator should label them in order to feed them to your network. By default it uses categorical method as a 2D one-hot encoded labels. But if you want your labels in other ways, set class_mode argument. For example for autoencoders that inputs has no label, you should specify it as class_mode=input.
Base on the docs here, class_mode should be one of these:
categorical will be 2D one-hot encoded labels, (Default Mode)
binary will be 1D binary labels,
sparse will be 1D integer labels,
input will be images identical to input images (mainly used to work with
autoencoders).
None, no labels are returned (the generator will only yield batches of image data, which is useful to use with model.predict())
I am trying to load data from jpeg files to train a convolution network. The images are large, with 24 million pixels however, so loading and using the full resolution is not practical.
To get the images to a more useful format I am trying to load each image, rescale it and then append it to a list. Once this is done, I can then convert the list into a numpy array and feed into the network for training as usual.
My problem is that my data set is very large and it takes about a second to rescale every image, which means it is not feasible to resize every image the way I have currently implemented this:
length_training_DF = 30000
for i in range(length_training_DF):
im = plt.imread(TRAIN_IM_DIR + trainDF.iloc[i]['image_name'] + '.jpg')
image = block_reduce(im, block_size=(10, 10, 1), func=np.max)
trainX.append(image)
I have also used the following:
length_training_DF = 30000
from keras.preprocessing import image
for i in range(50):
img = image.load_img(TRAIN_IM_DIR + trainDF.iloc[0]['image_name'] + '.jpg', target_size=(224, 224))
trainX.append(ima)
Is there any way to load these images more quickly into a format for training a network? I have thought about using a keras dataset, perhaps by using tf.keras.preprocessing.image_dataset_from_directory(), but the directory in which the image data is stored is not formatted correctly into folders containing the same targets as is required by this method.
The images are for a binary classification problem.
The usual way would be to write a preprocessing script that loads the large images, rescales them, applies other operations if needed, and then saves each class to a separate directory, as required by ImageDataGenerator.
There are at least three good reasons to do that:
Typically, you will run your training process dozens of time. You don't want to every time do the rescaling or e.g. auto-white balance.
ImageDataGenerator provides vital methods for augmenting your training data set.
It's a good generator out of the box. Likely you don't want to load entire data set into memory.
I have a folder (on my windows desktop) containing the images I want to use to build my deep learning classifier. I also have one .csv file which has the image number (for example img_1035) and the corresponding class label. How do I load the dataset with the labels into python/jupyter notebooks?
This is the link to the dataset on kaggle (https://www.kaggle.com/debdoot/bdrw).
I would preferably like to use PyTorch to do this but any other ways would also be highly appreciated.
Luckily, PyTorch has a convenient "ImageFolder" class that you can extend to create your own dataset.
Here's an example of a dataset that uses ImageFolder:
class MyDataset(torchvision.datasets.ImageFolder):
def __init__(self, train_folder_path='.', transform=None, target_transform=None):
super().__init__(train_folder_path, transform, target_transform)
# [ Some functions omitted ]
Then you load your set using PyTorch's "DataLoader".
Here's an example for a training set:
training_set = MyDataset(root_path, transform)
train_loader = torch.utils.data.DataLoader(training_set, batch_size=batch_size, shuffle=True)
Using the train loader you can get batches from your dataset. You can then use these batches to train / validate and so on:
batch = next(iter(train_loader))
images, labels = batch
Training is a rather involved process so I'm not entirely sure how deep you want to dive here. I hope this was a nudge in the right direction.
enter image description herei have 3000 images for both training and testing in one folder and i also have the image label in label.csv file which has the five class categories. Can anyone help me how to split this dataset into train and test data so that i can classify the images using convolution neural network. My dataset looks like the following image after the linking with csv and images.
First, you need an association between images and labels (some kind of knowledge of which label belongs to which image). Otherwise it will not work properly. After that you can split your dataset. Here is a toy example, assuming full_dataset contains the whole dataset and SIZE_OF_DATASET is the size of full_dataset:
full_dataset = full_dataset.shuffle()
train_dataset = full_dataset.take(int(0.8*SIZE_OF_DATASET))
test_dataset = full_dataset.skip(int(0.2*SIZE_OF_DATASET))
I'm currently working on a program that can do binary image classification with machine learning. I have a list of labels and a list of images that i'm using as inputs which are then fed into the Inception V3 model.
Will inputting of the dataset this way work with the inception V3 architecture? Is it necessary to organize the images with labeled folders before feeding it into the model?
Thanks for your help!
In your example, you have all the images in memory. You can simply call model.fit(trainX, trainY) to train your model. No need to organize the images in specific folder structures.
What you are referring to, is the flow_from_directory() method of the ImageDataGenerator. This is an object that will yield images from the directories, and automatically infer the labels from the folder structure. In this case, your images should be arranged in one folder per label. Since the ImageDataGenerator is a generator, you should use it in combination with model.fit_generator().
As a third option, you can write your own custom generator that yields both images and labels. This is advised in case you have a more complex label structure than one label per images; for instance in multi-label classification, object detection or semantic segmentation, where the outputs are also images. A custom generator should also be used with model.fit_generator().