How to Use Image Augmentation for Deep Learning with Keras

In this Vue tutorial, we learn about How to Use Image Augmentation for Deep Learning with Keras. Data preparation is required when working with neural networks and deep learning models. Increasingly, data augmentation is also required on more complex object recognition tasks.

In this post, you will discover how to use data preparation and data augmentation with your image datasets when developing and evaluating deep learning models in Python with Keras.

After reading this post, you will know:

• About the image augmentation API provided by Keras and how to use it with your models
• How to perform feature standardization
• How to perform ZCA whitening of your images
• How to augment data with random rotations, shifts, and flips
• How to save augmented image data to disk

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

• Jun/2016: First published
• Update Aug/2016: The examples in this post were updated for the latest Keras API. The datagen.next() function was removed
• Update Oct/2016: Updated for Keras 1.1.0, TensorFlow 0.10.0 and scikit-learn v0.18
• Update Jan/2017: Updated for Keras 1.2.0 and TensorFlow 0.12.1
• Update Mar/2017: Updated for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0
• Update Sep/2019: Updated for Keras 2.2.5 API
• Update Jul/2022: Updated for TensorFlow 2.x API with a workaround on the feature standardization issue

Keras Image Augmentation API

Like the rest of Keras, the image augmentation API is simple and powerful.

Keras provides the ImageDataGenerator class that defines the configuration for image data preparation and augmentation. This includes capabilities such as:

• Sample-wise standardization
• Feature-wise standardization
• ZCA whitening
• Random rotation, shifts, shear, and flips
• Dimension reordering
• Save augmented images to disk

An augmented image generator can be created as follows:

from tensorflow.keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator()

Rather than performing the operations on your entire image dataset in memory, the API is designed to be iterated by the deep learning model fitting process, creating augmented image data for you just in time. This reduces your memory overhead but adds some additional time cost during model training.

After you have created and configured your ImageDataGenerator, you must fit it on your data. This will calculate any statistics required to actually perform the transforms to your image data. You can do this by calling the fit() function on the data generator and passing it to your training dataset.

datagen.fit(train)

The data generator itself is, in fact, an iterator, returning batches of image samples when requested. You can configure the batch size and prepare the data generator and get batches of images by calling the flow() function.

X_batch, y_batch = datagen.flow(train, train, batch_size=32)

Finally, you can make use of the data generator. Instead of calling the fit() function on your model, you must call the fit_generator() function and pass in the data generator and the desired length of an epoch as well as the total number of epochs on which to train.

fit_generator(datagen, samples_per_epoch=len(train), epochs=100)

You can learn more about the Keras image data generator API in the Keras documentation.

Point of Comparison for Image Augmentation

Now that you know how the image augmentation API in Keras works, let’s look at some examples.

We will use the MNIST handwritten digit recognition task in these examples. To begin with, let’s take a look at the first nine images in the training dataset.

# Plot images
from tensorflow.keras.datasets import mnist
import matplotlib.pyplot as plt
# load dbata
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# create a grid of 3x3 images
fig, ax = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(4,4))
for i in range(3):
    for j in range(3):
        ax[i][j].imshow(X_train[i*3+j], cmap=plt.get_cmap("gray"))
# show the plot
plt.show()

Running this example provides the following image that you can use as a point of comparison with the image preparation and augmentation in the examples below.

Example MNIST images

Feature Standardization

It is also possible to standardize pixel values across the entire dataset. This is called feature standardization and mirrors the type of standardization often performed for each column in a tabular dataset.

You can perform feature standardization by setting the featurewise_center and featurewise_std_normalization arguments to True on the ImageDataGenerator class. These are set to False by default. However, the recent version of Keras has a bug in the feature standardization so that the mean and standard deviation is calculated across all pixels. If you use the fit() function from the ImageDataGenerator class, you will see an image similar to the one above:

# Standardize images across the dataset, mean=0, stdev=1
from tensorflow.keras.datasets import mnist
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][width][height][channels]
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))
# convert from int to float
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# define data preparation
datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True)
# fit parameters from data
datagen.fit(X_train)
# configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9, shuffle=False):
    print(X_batch.min(), X_batch.mean(), X_batch.max())
    # create a grid of 3x3 images
    fig, ax = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(4,4))
    for i in range(3):
        for j in range(3):
            ax[i][j].imshow(X_batch[i*3+j], cmap=plt.get_cmap("gray"))
    # show the plot
    plt.show()
    break

For example, the minimum, mean, and maximum values from the batch printed above are:

-0.42407447 -0.04093817 2.8215446

And the image displayed is as follows:

Image from feature-wise standardization

The workaround is to compute the feature standardization manually. Each pixel should have a separate mean and standard deviation, and it should be computed across different samples but independent from other pixels in the same sample. You just need to replace the fit() function with your own computation:

# Standardize images across the dataset, every pixel has mean=0, stdev=1
from tensorflow.keras.datasets import mnist
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][width][height][channels]
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))
# convert from int to float
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# define data preparation
datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True)
# fit parameters from data
datagen.mean = X_train.mean(axis=0)
datagen.std = X_train.std(axis=0)
# configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9, shuffle=False):
    print(X_batch.min(), X_batch.mean(), X_batch.max())
    # create a grid of 3x3 images
    fig, ax = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(4,4))
    for i in range(3):
        for j in range(3):
            ax[i][j].imshow(X_batch[i*3+j], cmap=plt.get_cmap("gray"))
    # show the plot
    plt.show()
    break

The minimum, mean, and maximum as printed now have a wider range:

-1.2742625 -0.028436039 17.46127

Running this example, you can see that the effect is different, seemingly darkening and lightening different digits.

ZCA Whitening

A whitening transform of an image is a linear algebraic operation that reduces the redundancy in the matrix of pixel images.

Less redundancy in the image is intended to better highlight the structures and features in the image to the learning algorithm.

Typically, image whitening is performed using the Principal Component Analysis (PCA) technique. More recently, an alternative called ZCA (learn more in Appendix A of this tech report) shows better results in transformed images that keep all the original dimensions. And unlike PCA, the resulting transformed images still look like their originals. Precisely, whitening converts each image into a white noise vector, i.e., each element in the vector has zero mean and unit standard derivation and is statistically independent of each other.

You can perform a ZCA whitening transform by setting the zca_whitening argument to True. But due to the same issue as feature standardization, you must first zero-center your input data separately:

# ZCA Whitening
from tensorflow.keras.datasets import mnist
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][width][height][channels]
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))
# convert from int to float
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# define data preparation
datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True, zca_whitening=True)
# fit parameters from data
X_mean = X_train.mean(axis=0)
datagen.fit(X_train - X_mean)
# configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(X_train - X_mean, y_train, batch_size=9, shuffle=False):
    print(X_batch.min(), X_batch.mean(), X_batch.max())
    # create a grid of 3x3 images
    fig, ax = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(4,4))
    for i in range(3):
        for j in range(3):
            ax[i][j].imshow(X_batch[i*3+j].reshape(28,28), cmap=plt.get_cmap("gray"))
    # show the plot
    plt.show()
    break

Running the example, you can see the same general structure in the images and how the outline of each digit has been highlighted.

ZCA whitening MNIST images

Random Rotations

Sometimes images in your sample data may have varying and different rotations in the scene.

You can train your model to better handle rotations of images by artificially and randomly rotating images from your dataset during training.

The example below creates random rotations of the MNIST digits up to 90 degrees by setting the rotation_range argument.

# Random Rotations
from tensorflow.keras.datasets import mnist
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][width][height][channels]
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))
# convert from int to float
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# define data preparation
datagen = ImageDataGenerator(rotation_range=90)
# configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9, shuffle=False):
    # create a grid of 3x3 images
    fig, ax = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(4,4))
    for i in range(3):
        for j in range(3):
            ax[i][j].imshow(X_batch[i*3+j].reshape(28,28), cmap=plt.get_cmap("gray"))
    # show the plot
    plt.show()
    break

Running the example, you can see that images have been rotated left and right up to a limit of 90 degrees. This is not helpful on this problem because the MNIST digits have a normalized orientation, but this transform might be of help when learning from photographs where the objects may have different orientations.

Random rotations of MNIST images

Random Shifts

Objects in your images may not be centered in the frame. They may be off-center in a variety of different ways.

You can train your deep learning network to expect and currently handle off-center objects by artificially creating shifted versions of your training data. Keras supports separate horizontal and vertical random shifting of training data by the width_shift_range and height_shift_range arguments.

# Random Shifts
from tensorflow.keras.datasets import mnist
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][width][height][channels]
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))
# convert from int to float
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# define data preparation
shift = 0.2
datagen = ImageDataGenerator(width_shift_range=shift, height_shift_range=shift)
# configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9, shuffle=False):
    # create a grid of 3x3 images
    fig, ax = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(4,4))
    for i in range(3):
        for j in range(3):
            ax[i][j].imshow(X_batch[i*3+j].reshape(28,28), cmap=plt.get_cmap("gray"))
    # show the plot
    plt.show()
    break

Running this example creates shifted versions of the digits. Again, this is not required for MNIST as the handwritten digits are already centered, but you can see how this might be useful on more complex problem domains.

Random shifted MNIST images

AD

Random Flips

Another augmentation to your image data that can improve performance on large and complex problems is to create random flips of images in your training data.

Keras supports random flipping along both the vertical and horizontal axes using the vertical_flip and horizontal_flip arguments.

# Random Flips
from tensorflow.keras.datasets import mnist
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][width][height][channels]
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))
# convert from int to float
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# define data preparation
datagen = ImageDataGenerator(horizontal_flip=True, vertical_flip=True)
# configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9, shuffle=False):
    # create a grid of 3x3 images
    fig, ax = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(4,4))
    for i in range(3):
        for j in range(3):
            ax[i][j].imshow(X_batch[i*3+j].reshape(28,28), cmap=plt.get_cmap("gray"))
    # show the plot
    plt.show()
    break

Running this example, you can see flipped digits. Flipping digits is not useful as they will always have the correct left and right orientation, but this may be useful for problems with photographs of objects in a scene that can have a varied orientation.

Randomly flipped MNIST images

Saving Augmented Images to File

The data preparation and augmentation are performed just in time by Keras.

This is efficient in terms of memory, but you may require the exact images used during training. For example, perhaps you would like to use them with a different software package later or only generate them once and use them on multiple different deep learning models or configurations.

Keras allows you to save the images generated during training. The directory, filename prefix, and image file type can be specified to the flow() function before training. Then, during training, the generated images will be written to the file.

The example below demonstrates this and writes nine images to a “images” subdirectory with the prefix “aug” and the file type of PNG.

# Save augmented images to file
from tensorflow.keras.datasets import mnist
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
# load data
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# reshape to be [samples][width][height][channels]
X_train = X_train.reshape((X_train.shape[0], 28, 28, 1))
X_test = X_test.reshape((X_test.shape[0], 28, 28, 1))
# convert from int to float
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
# define data preparation
datagen = ImageDataGenerator(horizontal_flip=True, vertical_flip=True)
# configure batch size and retrieve one batch of images
for X_batch, y_batch in datagen.flow(X_train, y_train, batch_size=9, shuffle=False,
                                     save_to_dir='images', save_prefix='aug', save_format='png'):
    # create a grid of 3x3 images
    fig, ax = plt.subplots(3, 3, sharex=True, sharey=True, figsize=(4,4))
    for i in range(3):
        for j in range(3):
            ax[i][j].imshow(X_batch[i*3+j].reshape(28,28), cmap=plt.get_cmap("gray"))
    # show the plot
    plt.show()
    break

Running the example, you can see that images are only written when they are generated.

Augmented MNIST images saved to file

Tips for Augmenting Image Data with Keras

Image data is unique in that you can review the data and transformed copies of the data and quickly get an idea of how the model may perceive it.

Below are some tips for getting the most from image data preparation and augmentation for deep learning.

• Review Dataset. Take some time to review your dataset in great detail. Look at the images. Take note of image preparation and augmentations that might benefit the training process of your model, such as the need to handle different shifts, rotations, or flips of objects in the scene.
• Review Augmentations. Review sample images after the augmentation has been performed. It is one thing to intellectually know what image transforms you are using; it is a very different thing to look at examples. Review images both with individual augmentations you are using as well as the full set of augmentations you plan to use. You may see ways to simplify or further enhance your model training process.
• Evaluate a Suite of Transforms. Try more than one image data preparation and augmentation scheme. Often you can be surprised by the results of a data preparation scheme you did not think would be beneficial.

Summary

In this post, you discovered image data preparation and augmentation.

You discovered a range of techniques you can use easily in Python with Keras for deep learning models. You learned about:

• The ImageDataGenerator API in Keras for generating transformed images just in time
• Sample-wise and Feature-wise pixel standardization
• The ZCA whitening transform
• Random rotations, shifts, and flips of images
• How to save transformed images to file for later reuse

Do you have any questions about image data augmentation or this post? Ask your questions in the comments, and I will do my best to answer.

Original article sourced at: https://machinelearningmastery.com

#keras #deep-learning 

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EasyMDE: Simple, Beautiful and Embeddable JavaScript Markdown Editor

EasyMDE - Markdown Editor 

This repository is a fork of SimpleMDE, made by Sparksuite. Go to the dedicated section for more information.

A drop-in JavaScript text area replacement for writing beautiful and understandable Markdown. EasyMDE allows users who may be less experienced with Markdown to use familiar toolbar buttons and shortcuts.

In addition, the syntax is rendered while editing to clearly show the expected result. Headings are larger, emphasized words are italicized, links are underlined, etc.

EasyMDE also features both built-in auto saving and spell checking. The editor is entirely customizable, from theming to toolbar buttons and javascript hooks.

Try the demo

Quick access

• EasyMDE - Markdown Editor
  • Quick access
  • Install EasyMDE
  • How to use
    • Loading the editor
    • Editor functions
  • Configuration
    • Options list
    • Options example
    • Toolbar icons
    • Toolbar customization
    • Keyboard shortcuts
  • Advanced use
    • Event handling
    • Removing EasyMDE from text area
    • Useful methods
  • How it works
  • SimpleMDE fork
  • Hacking EasyMDE
  • Contributing
  • License

Install EasyMDE

Via npm:

npm install easymde

Via the UNPKG CDN:

<link rel="stylesheet" href="https://unpkg.com/easymde/dist/easymde.min.css">
<script src="https://unpkg.com/easymde/dist/easymde.min.js"></script>

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<script src="https://cdn.jsdelivr.net/npm/easymde/dist/easymde.min.js"></script>

How to use

Loading the editor

After installing and/or importing the module, you can load EasyMDE onto the first textarea element on the web page:

<textarea></textarea>
<script>
const easyMDE = new EasyMDE();
</script>

Alternatively you can select a specific textarea, via JavaScript:

<textarea id="my-text-area"></textarea>
<script>
const easyMDE = new EasyMDE({element: document.getElementById('my-text-area')});
</script>

Editor functions

Use easyMDE.value() to get the content of the editor:

<script>
easyMDE.value();
</script>

Use easyMDE.value(val) to set the content of the editor:

<script>
easyMDE.value('New input for **EasyMDE**');
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Configuration

Options list

• autoDownloadFontAwesome: If set to true, force downloads Font Awesome (used for icons). If set to false, prevents downloading. Defaults to undefined, which will intelligently check whether Font Awesome has already been included, then download accordingly.
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• initialValue: If set, will customize the initial value of the editor.
• previewImagesInEditor: - EasyMDE will show preview of images, false by default, preview for images will appear only for images on separate lines.
• imagesPreviewHandler: - A custom function for handling the preview of images. Takes the parsed string between the parantheses of the image markdown ![]( ) as argument and returns a string that serves as the src attribute of the <img> tag in the preview. Enables dynamic previewing of images in the frontend without having to upload them to a server, allows copy-pasting of images to the editor with preview.
• insertTexts: Customize how certain buttons that insert text behave. Takes an array with two elements. The first element will be the text inserted before the cursor or highlight, and the second element will be inserted after. For example, this is the default link value: ["[", "](http://)"].
  • horizontalRule
  • image
  • link
  • table
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• lineWrapping: If set to false, disable line wrapping. Defaults to true.
• minHeight: Sets the minimum height for the composition area, before it starts auto-growing. Should be a string containing a valid CSS value like "500px". Defaults to "300px".
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• onToggleFullScreen: A function that gets called when the editor's full screen mode is toggled. The function will be passed a boolean as parameter, true when the editor is currently going into full screen mode, or false.
• parsingConfig: Adjust settings for parsing the Markdown during editing (not previewing).
  • allowAtxHeaderWithoutSpace: If set to true, will render headers without a space after the #. Defaults to false.
  • strikethrough: If set to false, will not process GFM strikethrough syntax. Defaults to true.
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• overlayMode: Pass a custom codemirror overlay mode to parse and style the Markdown during editing.
  • mode: A codemirror mode object.
  • combine: If set to false, will replace CSS classes returned by the default Markdown mode. Otherwise the classes returned by the custom mode will be combined with the classes returned by the default mode. Defaults to true.
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• previewClass: A string or array of strings that will be applied to the preview screen when activated. Defaults to "editor-preview".
• previewRender: Custom function for parsing the plaintext Markdown and returning HTML. Used when user previews.
• promptURLs: If set to true, a JS alert window appears asking for the link or image URL. Defaults to false.
• promptTexts: Customize the text used to prompt for URLs.
  • image: The text to use when prompting for an image's URL. Defaults to URL of the image:.
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• uploadImage: If set to true, enables the image upload functionality, which can be triggered by drag and drop, copy-paste and through the browse-file window (opened when the user click on the upload-image icon). Defaults to false.
• imageMaxSize: Maximum image size in bytes, checked before upload (note: never trust client, always check the image size at server-side). Defaults to 1024 * 1024 * 2 (2 MB).
• imageAccept: A comma-separated list of mime-types used to check image type before upload (note: never trust client, always check file types at server-side). Defaults to image/png, image/jpeg.
• imageUploadFunction: A custom function for handling the image upload. Using this function will render the options imageMaxSize, imageAccept, imageUploadEndpoint and imageCSRFToken ineffective.
  • The function gets a file and onSuccess and onError callback functions as parameters. onSuccess(imageUrl: string) and onError(errorMessage: string)
• imageUploadEndpoint: The endpoint where the images data will be sent, via an asynchronous POST request. The server is supposed to save this image, and return a JSON response.
  • if the request was successfully processed (HTTP 200 OK): {"data": {"filePath": "<filePath>"}} where filePath is the path of the image (absolute if imagePathAbsolute is set to true, relative if otherwise);
  • otherwise: {"error": "<errorCode>"}, where errorCode can be noFileGiven (HTTP 400 Bad Request), typeNotAllowed (HTTP 415 Unsupported Media Type), fileTooLarge (HTTP 413 Payload Too Large) or importError (see errorMessages below). If errorCode is not one of the errorMessages, it is alerted unchanged to the user. This allows for server-side error messages. No default value.
• imagePathAbsolute: If set to true, will treat imageUrl from imageUploadFunction and filePath returned from imageUploadEndpoint as an absolute rather than relative path, i.e. not prepend window.location.origin to it.
• imageCSRFToken: CSRF token to include with AJAX call to upload image. For various instances like Django, Spring and Laravel.
• imageCSRFName: CSRF token filed name to include with AJAX call to upload image, applied when imageCSRFToken has value, defaults to csrfmiddlewaretoken.
• imageCSRFHeader: If set to true, passing CSRF token via header. Defaults to false, which pass CSRF through request body.
• imageTexts: Texts displayed to the user (mainly on the status bar) for the import image feature, where #image_name#, #image_size# and #image_max_size# will replaced by their respective values, that can be used for customization or internationalization:
  • sbInit: Status message displayed initially if uploadImage is set to true. Defaults to Attach files by drag and dropping or pasting from clipboard..
  • sbOnDragEnter: Status message displayed when the user drags a file to the text area. Defaults to Drop image to upload it..
  • sbOnDrop: Status message displayed when the user drops a file in the text area. Defaults to Uploading images #images_names#.
  • sbProgress: Status message displayed to show uploading progress. Defaults to Uploading #file_name#: #progress#%.
  • sbOnUploaded: Status message displayed when the image has been uploaded. Defaults to Uploaded #image_name#.
  • sizeUnits: A comma-separated list of units used to display messages with human-readable file sizes. Defaults to B, KB, MB (example: 218 KB). You can use B,KB,MB instead if you prefer without whitespaces (218KB).
• errorMessages: Errors displayed to the user, using the errorCallback option, where #image_name#, #image_size# and #image_max_size# will replaced by their respective values, that can be used for customization or internationalization:
  • noFileGiven: The server did not receive any file from the user. Defaults to You must select a file..
  • typeNotAllowed: The user send a file type which doesn't match the imageAccept list, or the server returned this error code. Defaults to This image type is not allowed..
  • fileTooLarge: The size of the image being imported is bigger than the imageMaxSize, or if the server returned this error code. Defaults to Image #image_name# is too big (#image_size#).\nMaximum file size is #image_max_size#..
  • importError: An unexpected error occurred when uploading the image. Defaults to Something went wrong when uploading the image #image_name#..
• errorCallback: A callback function used to define how to display an error message. Defaults to (errorMessage) => alert(errorMessage).
• renderingConfig: Adjust settings for parsing the Markdown during previewing (not editing).
  • codeSyntaxHighlighting: If set to true, will highlight using highlight.js. Defaults to false. To use this feature you must include highlight.js on your page or pass in using the hljs option. For example, include the script and the CSS files like:
    <script src="https://cdn.jsdelivr.net/highlight.js/latest/highlight.min.js"></script>
<link rel="stylesheet" href="https://cdn.jsdelivr.net/highlight.js/latest/styles/github.min.css">
  • hljs: An injectible instance of highlight.js. If you don't want to rely on the global namespace (window.hljs), you can provide an instance here. Defaults to undefined.
  • markedOptions: Set the internal Markdown renderer's options. Other renderingConfig options will take precedence.
  • singleLineBreaks: If set to false, disable parsing GitHub Flavored Markdown (GFM) single line breaks. Defaults to true.
  • sanitizerFunction: Custom function for sanitizing the HTML output of Markdown renderer.
• shortcuts: Keyboard shortcuts associated with this instance. Defaults to the array of shortcuts.
• showIcons: An array of icon names to show. Can be used to show specific icons hidden by default without completely customizing the toolbar.
• spellChecker: If set to false, disable the spell checker. Defaults to true. Optionally pass a CodeMirrorSpellChecker-compliant function.
• inputStyle: textarea or contenteditable. Defaults to textarea for desktop and contenteditable for mobile. contenteditable option is necessary to enable nativeSpellcheck.
• nativeSpellcheck: If set to false, disable native spell checker. Defaults to true.
• sideBySideFullscreen: If set to false, allows side-by-side editing without going into fullscreen. Defaults to true.
• status: If set to false, hide the status bar. Defaults to the array of built-in status bar items.
  • Optionally, you can set an array of status bar items to include, and in what order. You can even define your own custom status bar items.
• styleSelectedText: If set to false, remove the CodeMirror-selectedtext class from selected lines. Defaults to true.
• syncSideBySidePreviewScroll: If set to false, disable syncing scroll in side by side mode. Defaults to true.
• tabSize: If set, customize the tab size. Defaults to 2.
• theme: Override the theme. Defaults to easymde.
• toolbar: If set to false, hide the toolbar. Defaults to the array of icons.
• toolbarTips: If set to false, disable toolbar button tips. Defaults to true.
• direction: rtl or ltr. Changes text direction to support right-to-left languages. Defaults to ltr.

Options example

Most options demonstrate the non-default behavior:

const editor = new EasyMDE({
    autofocus: true,
    autosave: {
        enabled: true,
        uniqueId: "MyUniqueID",
        delay: 1000,
        submit_delay: 5000,
        timeFormat: {
            locale: 'en-US',
            format: {
                year: 'numeric',
                month: 'long',
                day: '2-digit',
                hour: '2-digit',
                minute: '2-digit',
            },
        },
        text: "Autosaved: "
    },
    blockStyles: {
        bold: "__",
        italic: "_",
    },
    unorderedListStyle: "-",
    element: document.getElementById("MyID"),
    forceSync: true,
    hideIcons: ["guide", "heading"],
    indentWithTabs: false,
    initialValue: "Hello world!",
    insertTexts: {
        horizontalRule: ["", "\n\n-----\n\n"],
        image: ["![](http://", ")"],
        link: ["[", "](https://)"],
        table: ["", "\n\n| Column 1 | Column 2 | Column 3 |\n| -------- | -------- | -------- |\n| Text     | Text      | Text     |\n\n"],
    },
    lineWrapping: false,
    minHeight: "500px",
    parsingConfig: {
        allowAtxHeaderWithoutSpace: true,
        strikethrough: false,
        underscoresBreakWords: true,
    },
    placeholder: "Type here...",

    previewClass: "my-custom-styling",
    previewClass: ["my-custom-styling", "more-custom-styling"],

    previewRender: (plainText) => customMarkdownParser(plainText), // Returns HTML from a custom parser
    previewRender: (plainText, preview) => { // Async method
        setTimeout(() => {
            preview.innerHTML = customMarkdownParser(plainText);
        }, 250);

        return "Loading...";
    },
    promptURLs: true,
    promptTexts: {
        image: "Custom prompt for URL:",
        link: "Custom prompt for URL:",
    },
    renderingConfig: {
        singleLineBreaks: false,
        codeSyntaxHighlighting: true,
        sanitizerFunction: (renderedHTML) => {
            // Using DOMPurify and only allowing <b> tags
            return DOMPurify.sanitize(renderedHTML, {ALLOWED_TAGS: ['b']})
        },
    },
    shortcuts: {
        drawTable: "Cmd-Alt-T"
    },
    showIcons: ["code", "table"],
    spellChecker: false,
    status: false,
    status: ["autosave", "lines", "words", "cursor"], // Optional usage
    status: ["autosave", "lines", "words", "cursor", {
        className: "keystrokes",
        defaultValue: (el) => {
            el.setAttribute('data-keystrokes', 0);
        },
        onUpdate: (el) => {
            const keystrokes = Number(el.getAttribute('data-keystrokes')) + 1;
            el.innerHTML = `${keystrokes} Keystrokes`;
            el.setAttribute('data-keystrokes', keystrokes);
        },
    }], // Another optional usage, with a custom status bar item that counts keystrokes
    styleSelectedText: false,
    sideBySideFullscreen: false,
    syncSideBySidePreviewScroll: false,
    tabSize: 4,
    toolbar: false,
    toolbarTips: false,
});

Toolbar icons

Below are the built-in toolbar icons (only some of which are enabled by default), which can be reorganized however you like. "Name" is the name of the icon, referenced in the JavaScript. "Action" is either a function or a URL to open. "Class" is the class given to the icon. "Tooltip" is the small tooltip that appears via the title="" attribute. Note that shortcut hints are added automatically and reflect the specified action if it has a key bind assigned to it (i.e. with the value of action set to bold and that of tooltip set to Bold, the final text the user will see would be "Bold (Ctrl-B)").

Additionally, you can add a separator between any icons by adding "|" to the toolbar array.

Name	Action	Tooltip Class
bold	toggleBold	Bold fa fa-bold
italic	toggleItalic	Italic fa fa-italic
strikethrough	toggleStrikethrough	Strikethrough fa fa-strikethrough
heading	toggleHeadingSmaller	Heading fa fa-header
heading-smaller	toggleHeadingSmaller	Smaller Heading fa fa-header
heading-bigger	toggleHeadingBigger	Bigger Heading fa fa-lg fa-header
heading-1	toggleHeading1	Big Heading fa fa-header header-1
heading-2	toggleHeading2	Medium Heading fa fa-header header-2
heading-3	toggleHeading3	Small Heading fa fa-header header-3
code	toggleCodeBlock	Code fa fa-code
quote	toggleBlockquote	Quote fa fa-quote-left
unordered-list	toggleUnorderedList	Generic List fa fa-list-ul
ordered-list	toggleOrderedList	Numbered List fa fa-list-ol
clean-block	cleanBlock	Clean block fa fa-eraser
link	drawLink	Create Link fa fa-link
image	drawImage	Insert Image fa fa-picture-o
table	drawTable	Insert Table fa fa-table
horizontal-rule	drawHorizontalRule	Insert Horizontal Line fa fa-minus
preview	togglePreview	Toggle Preview fa fa-eye no-disable
side-by-side	toggleSideBySide	Toggle Side by Side fa fa-columns no-disable no-mobile
fullscreen	toggleFullScreen	Toggle Fullscreen fa fa-arrows-alt no-disable no-mobile
guide	This link	Markdown Guide fa fa-question-circle
undo	undo	Undo fa fa-undo
redo	redo	Redo fa fa-redo

Toolbar customization

Customize the toolbar using the toolbar option.

Only the order of existing buttons:

const easyMDE = new EasyMDE({
    toolbar: ["bold", "italic", "heading", "|", "quote"]
});

All information and/or add your own icons

const easyMDE = new EasyMDE({
    toolbar: [
        {
            name: "bold",
            action: EasyMDE.toggleBold,
            className: "fa fa-bold",
            title: "Bold",
        },
        "italics", // shortcut to pre-made button
        {
            name: "custom",
            action: (editor) => {
                // Add your own code
            },
            className: "fa fa-star",
            title: "Custom Button",
            attributes: { // for custom attributes
                id: "custom-id",
                "data-value": "custom value" // HTML5 data-* attributes need to be enclosed in quotation marks ("") because of the dash (-) in its name.
            }
        },
        "|" // Separator
        // [, ...]
    ]
});

Put some buttons on dropdown menu

const easyMDE = new EasyMDE({
    toolbar: [{
                name: "heading",
                action: EasyMDE.toggleHeadingSmaller,
                className: "fa fa-header",
                title: "Headers",
            },
            "|",
            {
                name: "others",
                className: "fa fa-blind",
                title: "others buttons",
                children: [
                    {
                        name: "image",
                        action: EasyMDE.drawImage,
                        className: "fa fa-picture-o",
                        title: "Image",
                    },
                    {
                        name: "quote",
                        action: EasyMDE.toggleBlockquote,
                        className: "fa fa-percent",
                        title: "Quote",
                    },
                    {
                        name: "link",
                        action: EasyMDE.drawLink,
                        className: "fa fa-link",
                        title: "Link",
                    }
                ]
            },
        // [, ...]
    ]
});

Keyboard shortcuts

EasyMDE comes with an array of predefined keyboard shortcuts, but they can be altered with a configuration option. The list of default ones is as follows:

Shortcut (Windows / Linux)	Shortcut (macOS)	Action
Ctrl-'	Cmd-'	"toggleBlockquote"
Ctrl-B	Cmd-B	"toggleBold"
Ctrl-E	Cmd-E	"cleanBlock"
Ctrl-H	Cmd-H	"toggleHeadingSmaller"
Ctrl-I	Cmd-I	"toggleItalic"
Ctrl-K	Cmd-K	"drawLink"
Ctrl-L	Cmd-L	"toggleUnorderedList"
Ctrl-P	Cmd-P	"togglePreview"
Ctrl-Alt-C	Cmd-Alt-C	"toggleCodeBlock"
Ctrl-Alt-I	Cmd-Alt-I	"drawImage"
Ctrl-Alt-L	Cmd-Alt-L	"toggleOrderedList"
Shift-Ctrl-H	Shift-Cmd-H	"toggleHeadingBigger"
F9	F9	"toggleSideBySide"
F11	F11	"toggleFullScreen"

Here is how you can change a few, while leaving others untouched:

const editor = new EasyMDE({
    shortcuts: {
        "toggleOrderedList": "Ctrl-Alt-K", // alter the shortcut for toggleOrderedList
        "toggleCodeBlock": null, // unbind Ctrl-Alt-C
        "drawTable": "Cmd-Alt-T", // bind Cmd-Alt-T to drawTable action, which doesn't come with a default shortcut
    }
});

Shortcuts are automatically converted between platforms. If you define a shortcut as "Cmd-B", on PC that shortcut will be changed to "Ctrl-B". Conversely, a shortcut defined as "Ctrl-B" will become "Cmd-B" for Mac users.

The list of actions that can be bound is the same as the list of built-in actions available for toolbar buttons.

Advanced use

Event handling

You can catch the following list of events: https://codemirror.net/doc/manual.html#events

const easyMDE = new EasyMDE();
easyMDE.codemirror.on("change", () => {
    console.log(easyMDE.value());
});

Removing EasyMDE from text area

You can revert to the initial text area by calling the toTextArea method. Note that this clears up the autosave (if enabled) associated with it. The text area will retain any text from the destroyed EasyMDE instance.

const easyMDE = new EasyMDE();
// ...
easyMDE.toTextArea();
easyMDE = null;

If you need to remove registered event listeners (when the editor is not needed anymore), call easyMDE.cleanup().

Useful methods

The following self-explanatory methods may be of use while developing with EasyMDE.

const easyMDE = new EasyMDE();
easyMDE.isPreviewActive(); // returns boolean
easyMDE.isSideBySideActive(); // returns boolean
easyMDE.isFullscreenActive(); // returns boolean
easyMDE.clearAutosavedValue(); // no returned value

How it works

EasyMDE is a continuation of SimpleMDE.

SimpleMDE began as an improvement of lepture's Editor project, but has now taken on an identity of its own. It is bundled with CodeMirror and depends on Font Awesome.

CodeMirror is the backbone of the project and parses much of the Markdown syntax as it's being written. This allows us to add styles to the Markdown that's being written. Additionally, a toolbar and status bar have been added to the top and bottom, respectively. Previews are rendered by Marked using GitHub Flavored Markdown (GFM).

SimpleMDE fork

I originally made this fork to implement FontAwesome 5 compatibility into SimpleMDE. When that was done I submitted a pull request, which has not been accepted yet. This, and the project being inactive since May 2017, triggered me to make more changes and try to put new life into the project.

Changes include:

• FontAwesome 5 compatibility
• Guide button works when editor is in preview mode
• Links are now https:// by default
• Small styling changes
• Support for Node 8 and beyond
• Lots of refactored code
• Links in preview will open in a new tab by default
• TypeScript support

My intention is to continue development on this project, improving it and keeping it alive.

Hacking EasyMDE

You may want to edit this library to adapt its behavior to your needs. This can be done in some quick steps:

1. Follow the prerequisites and installation instructions in the contribution guide;
2. Do your changes;
3. Run gulp command, which will generate files: dist/easymde.min.css and dist/easymde.min.js;
4. Copy-paste those files to your code base, and you are done.

Contributing

Want to contribute to EasyMDE? Thank you! We have a contribution guide just for you!

---

Author: Ionaru
Source Code: https://github.com/Ionaru/easy-markdown-editor
License: MIT license

#react-native #react 

How to Add Splash Screen in Android and iOS with Flutter

When your app is opened, there is a brief time while the native app loads Flutter. By default, during this time, the native app displays a white splash screen. This package automatically generates iOS, Android, and Web-native code for customizing this native splash screen background color and splash image. Supports dark mode, full screen, and platform-specific options.

What's New

[BETA] Support for flavors is in beta. Currently only Android and iOS are supported. See instructions below.

You can now keep the splash screen up while your app initializes! No need for a secondary splash screen anymore. Just use the preserve and remove methods together to remove the splash screen after your initialization is complete. See details below.

Usage

Would you prefer a video tutorial instead? Check out Johannes Milke's tutorial.

First, add flutter_native_splash as a dependency in your pubspec.yaml file.

dependencies:
  flutter_native_splash: ^2.2.19

Don't forget to flutter pub get.

1. Setting the splash screen

 

Customize the following settings and add to your project's pubspec.yaml file or place in a new file in your root project folder named flutter_native_splash.yaml.

flutter_native_splash:
  # This package generates native code to customize Flutter's default white native splash screen
  # with background color and splash image.
  # Customize the parameters below, and run the following command in the terminal:
  # flutter pub run flutter_native_splash:create
  # To restore Flutter's default white splash screen, run the following command in the terminal:
  # flutter pub run flutter_native_splash:remove

  # color or background_image is the only required parameter.  Use color to set the background
  # of your splash screen to a solid color.  Use background_image to set the background of your
  # splash screen to a png image.  This is useful for gradients. The image will be stretch to the
  # size of the app. Only one parameter can be used, color and background_image cannot both be set.
  color: "#42a5f5"
  #background_image: "assets/background.png"

  # Optional parameters are listed below.  To enable a parameter, uncomment the line by removing
  # the leading # character.

  # The image parameter allows you to specify an image used in the splash screen.  It must be a
  # png file and should be sized for 4x pixel density.
  #image: assets/splash.png

  # The branding property allows you to specify an image used as branding in the splash screen.
  # It must be a png file. It is supported for Android, iOS and the Web.  For Android 12,
  # see the Android 12 section below.
  #branding: assets/dart.png

  # To position the branding image at the bottom of the screen you can use bottom, bottomRight,
  # and bottomLeft. The default values is bottom if not specified or specified something else.
  #branding_mode: bottom

  # The color_dark, background_image_dark, image_dark, branding_dark are parameters that set the background
  # and image when the device is in dark mode. If they are not specified, the app will use the
  # parameters from above. If the image_dark parameter is specified, color_dark or
  # background_image_dark must be specified.  color_dark and background_image_dark cannot both be
  # set.
  #color_dark: "#042a49"
  #background_image_dark: "assets/dark-background.png"
  #image_dark: assets/splash-invert.png
  #branding_dark: assets/dart_dark.png

  # Android 12 handles the splash screen differently than previous versions.  Please visit
  # https://developer.android.com/guide/topics/ui/splash-screen
  # Following are Android 12 specific parameter.
  android_12:
    # The image parameter sets the splash screen icon image.  If this parameter is not specified,
    # the app's launcher icon will be used instead.
    # Please note that the splash screen will be clipped to a circle on the center of the screen.
    # App icon with an icon background: This should be 960×960 pixels, and fit within a circle
    # 640 pixels in diameter.
    # App icon without an icon background: This should be 1152×1152 pixels, and fit within a circle
    # 768 pixels in diameter.
    #image: assets/android12splash.png

    # Splash screen background color.
    #color: "#42a5f5"

    # App icon background color.
    #icon_background_color: "#111111"

    # The branding property allows you to specify an image used as branding in the splash screen.
    #branding: assets/dart.png

    # The image_dark, color_dark, icon_background_color_dark, and branding_dark set values that
    # apply when the device is in dark mode. If they are not specified, the app will use the
    # parameters from above.
    #image_dark: assets/android12splash-invert.png
    #color_dark: "#042a49"
    #icon_background_color_dark: "#eeeeee"

  # The android, ios and web parameters can be used to disable generating a splash screen on a given
  # platform.
  #android: false
  #ios: false
  #web: false

  # Platform specific images can be specified with the following parameters, which will override
  # the respective parameter.  You may specify all, selected, or none of these parameters:
  #color_android: "#42a5f5"
  #color_dark_android: "#042a49"
  #color_ios: "#42a5f5"
  #color_dark_ios: "#042a49"
  #color_web: "#42a5f5"
  #color_dark_web: "#042a49"
  #image_android: assets/splash-android.png
  #image_dark_android: assets/splash-invert-android.png
  #image_ios: assets/splash-ios.png
  #image_dark_ios: assets/splash-invert-ios.png
  #image_web: assets/splash-web.png
  #image_dark_web: assets/splash-invert-web.png
  #background_image_android: "assets/background-android.png"
  #background_image_dark_android: "assets/dark-background-android.png"
  #background_image_ios: "assets/background-ios.png"
  #background_image_dark_ios: "assets/dark-background-ios.png"
  #background_image_web: "assets/background-web.png"
  #background_image_dark_web: "assets/dark-background-web.png"
  #branding_android: assets/brand-android.png
  #branding_dark_android: assets/dart_dark-android.png
  #branding_ios: assets/brand-ios.png
  #branding_dark_ios: assets/dart_dark-ios.png

  # The position of the splash image can be set with android_gravity, ios_content_mode, and
  # web_image_mode parameters.  All default to center.
  #
  # android_gravity can be one of the following Android Gravity (see
  # https://developer.android.com/reference/android/view/Gravity): bottom, center,
  # center_horizontal, center_vertical, clip_horizontal, clip_vertical, end, fill, fill_horizontal,
  # fill_vertical, left, right, start, or top.
  #android_gravity: center
  #
  # ios_content_mode can be one of the following iOS UIView.ContentMode (see
  # https://developer.apple.com/documentation/uikit/uiview/contentmode): scaleToFill,
  # scaleAspectFit, scaleAspectFill, center, top, bottom, left, right, topLeft, topRight,
  # bottomLeft, or bottomRight.
  #ios_content_mode: center
  #
  # web_image_mode can be one of the following modes: center, contain, stretch, and cover.
  #web_image_mode: center

  # The screen orientation can be set in Android with the android_screen_orientation parameter.
  # Valid parameters can be found here:
  # https://developer.android.com/guide/topics/manifest/activity-element#screen
  #android_screen_orientation: sensorLandscape

  # To hide the notification bar, use the fullscreen parameter.  Has no effect in web since web
  # has no notification bar.  Defaults to false.
  # NOTE: Unlike Android, iOS will not automatically show the notification bar when the app loads.
  #       To show the notification bar, add the following code to your Flutter app:
  #       WidgetsFlutterBinding.ensureInitialized();
  #       SystemChrome.setEnabledSystemUIOverlays([SystemUiOverlay.bottom, SystemUiOverlay.top]);
  #fullscreen: true

  # If you have changed the name(s) of your info.plist file(s), you can specify the filename(s)
  # with the info_plist_files parameter.  Remove only the # characters in the three lines below,
  # do not remove any spaces:
  #info_plist_files:
  #  - 'ios/Runner/Info-Debug.plist'
  #  - 'ios/Runner/Info-Release.plist'

2. Run the package

After adding your settings, run the following command in the terminal:

flutter pub run flutter_native_splash:create

When the package finishes running, your splash screen is ready.

To specify the YAML file location just add --path with the command in the terminal:

flutter pub run flutter_native_splash:create --path=path/to/my/file.yaml

3. Set up app initialization (optional)

By default, the splash screen will be removed when Flutter has drawn the first frame. If you would like the splash screen to remain while your app initializes, you can use the preserve() and remove() methods together. Pass the preserve() method the value returned from WidgetsFlutterBinding.ensureInitialized() to keep the splash on screen. Later, when your app has initialized, make a call to remove() to remove the splash screen.

import 'package:flutter_native_splash/flutter_native_splash.dart';

void main() {
  WidgetsBinding widgetsBinding = WidgetsFlutterBinding.ensureInitialized();
  FlutterNativeSplash.preserve(widgetsBinding: widgetsBinding);
  runApp(const MyApp());
}

// whenever your initialization is completed, remove the splash screen:
    FlutterNativeSplash.remove();

NOTE: If you do not need to use the preserve() and remove() methods, you can place the flutter_native_splash dependency in the dev_dependencies section of pubspec.yaml.

4. Support the package (optional)

If you find this package useful, you can support it for free by giving it a thumbs up at the top of this page. Here's another option to support the package:

Android 12+ Support

Android 12 has a new method of adding splash screens, which consists of a window background, icon, and the icon background. Note that a background image is not supported.

Be aware of the following considerations regarding these elements:

1: image parameter. By default, the launcher icon is used:

• App icon without an icon background, as shown on the left: This should be 1152×1152 pixels, and fit within a circle 768 pixels in diameter.
• App icon with an icon background, as shown on the right: This should be 960×960 pixels, and fit within a circle 640 pixels in diameter.

2: icon_background_color is optional, and is useful if you need more contrast between the icon and the window background.

3: One-third of the foreground is masked.

4: color the window background consists of a single opaque color.

PLEASE NOTE: The splash screen may not appear when you launch the app from Android Studio on API 31. However, it should appear when you launch by clicking on the launch icon in Android. This seems to be resolved in API 32+.

PLEASE NOTE: There are a number of reports that non-Google launchers do not display the launch image correctly. If the launch image does not display correctly, please try the Google launcher to confirm that this package is working.

PLEASE NOTE: The splash screen does not appear when you launch the app from a notification. Apparently this is the intended behavior on Android 12: core-splashscreen Icon not shown when cold launched from notification.

Flavor Support

If you have a project setup that contains multiple flavors or environments, and you created more than one flavor this would be a feature for you.

Instead of maintaining multiple files and copy/pasting images, you can now, using this tool, create different splash screens for different environments.

Pre-requirements

In order to use the new feature, and generate the desired splash images for you app, a couple of changes are required.

If you want to generate just one flavor and one file you would use either options as described in Step 1. But in order to setup the flavors, you will then be required to move all your setup values to the flutter_native_splash.yaml file, but with a prefix.

Let's assume for the rest of the setup that you have 3 different flavors, Production, Acceptance, Development.

First this you will need to do is to create a different setup file for all 3 flavors with a suffix like so:

flutter_native_splash-production.yaml
flutter_native_splash-acceptance.yaml
flutter_native_splash-development.yaml

You would setup those 3 files the same way as you would the one, but with different assets depending on which environment you would be generating. For example (Note: these are just examples, you can use whatever setup you need for your project that is already supported by the package):

# flutter_native_splash-development.yaml
flutter_native_splash:
  color: "#ffffff"
  image: assets/logo-development.png
  branding: assets/branding-development.png
  color_dark: "#121212"
  image_dark: assets/logo-development.png
  branding_dark: assets/branding-development.png

  android_12:
    image: assets/logo-development.png
    icon_background_color: "#ffffff"
    image_dark: assets/logo-development.png
    icon_background_color_dark: "#121212"

  web: false

# flutter_native_splash-acceptance.yaml
flutter_native_splash:
  color: "#ffffff"
  image: assets/logo-acceptance.png
  branding: assets/branding-acceptance.png
  color_dark: "#121212"
  image_dark: assets/logo-acceptance.png
  branding_dark: assets/branding-acceptance.png

  android_12:
    image: assets/logo-acceptance.png
    icon_background_color: "#ffffff"
    image_dark: assets/logo-acceptance.png
    icon_background_color_dark: "#121212"

  web: false

# flutter_native_splash-production.yaml
flutter_native_splash:
  color: "#ffffff"
  image: assets/logo-production.png
  branding: assets/branding-production.png
  color_dark: "#121212"
  image_dark: assets/logo-production.png
  branding_dark: assets/branding-production.png

  android_12:
    image: assets/logo-production.png
    icon_background_color: "#ffffff"
    image_dark: assets/logo-production.png
    icon_background_color_dark: "#121212"

  web: false

Great, now comes the fun part running the new command!

The new command is:

# If you have a flavor called production you would do this:
flutter pub run flutter_native_splash:create --flavor production

# For a flavor with a name staging you would provide it's name like so:
flutter pub run flutter_native_splash:create --flavor staging

# And if you have a local version for devs you could do that:
flutter pub run flutter_native_splash:create --flavor development

Android setup

You're done! No, really, Android doesn't need any additional setup.

Note: If it didn't work, please make sure that your flavors are named the same as your config files, otherwise the setup will not work.

iOS setup

iOS is a bit tricky, so hang tight, it might look scary but most of the steps are just a single click, explained as much as possible to lower the possibility of mistakes.

When you run the new command, you will need to open xCode and follow the steps bellow:

Assumption

• In order for this setup to work, you would already have 3 different schemes setup; production, acceptance and development.

Preparation

• Open the iOS Flutter project in Xcode (open the Runner.xcworkspace)
• Find the newly created Storyboard files at the same location where the original is {project root}/ios/Runner/Base.lproj
• Select all of them and drag and drop into Xcode, directly to the left hand side where the current LaunchScreen.storyboard is located already
• After you drop your files there Xcode will ask you to link them, make sure you select 'Copy if needed'
• This part is done, you have linked the newly created storyboards in your project.

xCode

Xcode still doesn't know how to use them, so we need to specify for all the current flavors (schemes) which file to use and to use that value inside the Info.plist file.

• Open the iOS Flutter project in Xcode (open the Runner.xcworkspace)
• Click the Runner project in the top left corner (usually the first item in the list)
• In the middle part of the screen, on the left side, select the Runner target
• On the top part of the screen select Build Settings
• Make sure that 'All' and 'Combined' are selected
• Next to 'Combine' you have a '+' button, press it and select 'Add User-Defined Setting'
• Once you do that Xcode will create a new variable for you to name. Suggestion is to name it LAUNCH_SCREEN_STORYBOARD
• Once you do that, you will have the option to define a specific name for each flavor (scheme) that you have defined in the project. Make sure that you input the exact name of the LaunchScreen.storyboard that was created by this tool
  • Example: If you have a flavor Development, there is a Storyboard created name LaunchScreenDevelopment.storyboard, please add that name (without the storyboard part) to the variable value next to the flavor value
• After you finish with that, you need to update Info.plist file to link the newly created variable so that it's used correctly
• Open the Info.plist file
• Find the entry called 'Launch screen interface file base name'
• The default value is 'LaunchScreen', change that to the variable name that you create previously. If you follow these steps exactly, it would be LAUNCH_SCREEN_STORYBOARD, so input this $(LAUNCH_SCREEN_STORYBOARD)
• And your done!

Congrats you finished your setup for multiple flavors,

FAQs

I got the error "A splash screen was provided to Flutter, but this is deprecated."

This message is not related to this package but is related to a change in how Flutter handles splash screens in Flutter 2.5. It is caused by having the following code in your android/app/src/main/AndroidManifest.xml, which was included by default in previous versions of Flutter:

<meta-data
 android:name="io.flutter.embedding.android.SplashScreenDrawable"
 android:resource="@drawable/launch_background"
 />

The solution is to remove the above code. Note that this will also remove the fade effect between the native splash screen and your app.

Are animations/lottie/GIF images supported?

Not at this time. PRs are always welcome!

I got the error AAPT: error: style attribute 'android:attr/windowSplashScreenBackground' not found

This attribute is only found in Android 12, so if you are getting this error, it means your project is not fully set up for Android 12. Did you update your app's build configuration?

I see a flash of the wrong splash screen on iOS

This is caused by an iOS splash caching bug, which can be solved by uninstalling your app, powering off your device, power back on, and then try reinstalling.

I see a white screen between splash screen and app

1. It may be caused by an iOS splash caching bug, which can be solved by uninstalling your app, powering off your device, power back on, and then try reinstalling.
2. It may be caused by the delay due to initialization in your app. To solve this, put any initialization code in the removeAfter method.

Can I base light/dark mode on app settings?

No. This package creates a splash screen that is displayed before Flutter is loaded. Because of this, when the splash screen loads, internal app settings are not available to the splash screen. Unfortunately, this means that it is impossible to control light/dark settings of the splash from app settings.

Notes

If the splash screen was not updated correctly on iOS or if you experience a white screen before the splash screen, run flutter clean and recompile your app. If that does not solve the problem, delete your app, power down the device, power up the device, install and launch the app as per this StackOverflow thread.

This package modifies launch_background.xml and styles.xml files on Android, LaunchScreen.storyboard and Info.plist on iOS, and index.html on Web. If you have modified these files manually, this plugin may not work properly. Please open an issue if you find any bugs.

How it works

Android

• Your splash image will be resized to mdpi, hdpi, xhdpi, xxhdpi and xxxhdpi drawables.
• An <item> tag containing a <bitmap> for your splash image drawable will be added in launch_background.xml
• Background color will be added in colors.xml and referenced in launch_background.xml.
• Code for full screen mode toggle will be added in styles.xml.
• Dark mode variants are placed in drawable-night, values-night, etc. resource folders.

iOS

• Your splash image will be resized to @3x and @2x images.
• Color and image properties will be inserted in LaunchScreen.storyboard.
• The background color is implemented by using a single-pixel png file and stretching it to fit the screen.
• Code for hidden status bar toggle will be added in Info.plist.

Web

• A web/splash folder will be created for splash screen images and CSS files.
• Your splash image will be resized to 1x, 2x, 3x, and 4x sizes and placed in web/splash/img.
• The splash style sheet will be added to the app's web/index.html, as well as the HTML for the splash pictures.

Acknowledgments

This package was originally created by Henrique Arthur and it is currently maintained by Jon Hanson.

Bugs or Requests

If you encounter any problems feel free to open an issue. If you feel the library is missing a feature, please raise a ticket. Pull request are also welcome.

---

Use this package as a library

Depend on it

Run this command:

With Flutter:

 $ flutter pub add flutter_native_splash

This will add a line like this to your package's pubspec.yaml (and run an implicit flutter pub get):

dependencies:
  flutter_native_splash: ^2.2.19

Alternatively, your editor might support flutter pub get. Check the docs for your editor to learn more.

Import it

Now in your Dart code, you can use:

import 'package:flutter_native_splash/flutter_native_splash.dart';

---

example/lib/main.dart

import 'package:flutter/material.dart';
import 'package:flutter_native_splash/flutter_native_splash.dart';

void main() {
  WidgetsBinding widgetsBinding = WidgetsFlutterBinding.ensureInitialized();
  FlutterNativeSplash.preserve(widgetsBinding: widgetsBinding);
  runApp(const MyApp());
}

class MyApp extends StatelessWidget {
  const MyApp({super.key});

  // This widget is the root of your application.
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Flutter Demo',
      theme: ThemeData(
        // This is the theme of your application.
        //
        // Try running your application with "flutter run". You'll see the
        // application has a blue toolbar. Then, without quitting the app, try
        // changing the primarySwatch below to Colors.green and then invoke
        // "hot reload" (press "r" in the console where you ran "flutter run",
        // or simply save your changes to "hot reload" in a Flutter IDE).
        // Notice that the counter didn't reset back to zero; the application
        // is not restarted.
        primarySwatch: Colors.blue,
      ),
      home: const MyHomePage(title: 'Flutter Demo Home Page'),
    );
  }
}

class MyHomePage extends StatefulWidget {
  const MyHomePage({super.key, required this.title});

  // This widget is the home page of your application. It is stateful, meaning
  // that it has a State object (defined below) that contains fields that affect
  // how it looks.

  // This class is the configuration for the state. It holds the values (in this
  // case the title) provided by the parent (in this case the App widget) and
  // used by the build method of the State. Fields in a Widget subclass are
  // always marked "final".

  final String title;

  @override
  State<MyHomePage> createState() => _MyHomePageState();
}

class _MyHomePageState extends State<MyHomePage> {
  int _counter = 0;

  void _incrementCounter() {
    setState(() {
      // This call to setState tells the Flutter framework that something has
      // changed in this State, which causes it to rerun the build method below
      // so that the display can reflect the updated values. If we changed
      // _counter without calling setState(), then the build method would not be
      // called again, and so nothing would appear to happen.
      _counter++;
    });
  }

  @override
  void initState() {
    super.initState();
    initialization();
  }

  void initialization() async {
    // This is where you can initialize the resources needed by your app while
    // the splash screen is displayed.  Remove the following example because
    // delaying the user experience is a bad design practice!
    // ignore_for_file: avoid_print
    print('ready in 3...');
    await Future.delayed(const Duration(seconds: 1));
    print('ready in 2...');
    await Future.delayed(const Duration(seconds: 1));
    print('ready in 1...');
    await Future.delayed(const Duration(seconds: 1));
    print('go!');
    FlutterNativeSplash.remove();
  }

  @override
  Widget build(BuildContext context) {
    // This method is rerun every time setState is called, for instance as done
    // by the _incrementCounter method above.
    //
    // The Flutter framework has been optimized to make rerunning build methods
    // fast, so that you can just rebuild anything that needs updating rather
    // than having to individually change instances of widgets.
    return Scaffold(
      appBar: AppBar(
        // Here we take the value from the MyHomePage object that was created by
        // the App.build method, and use it to set our appbar title.
        title: Text(widget.title),
      ),
      body: Center(
        // Center is a layout widget. It takes a single child and positions it
        // in the middle of the parent.
        child: Column(
          // Column is also a layout widget. It takes a list of children and
          // arranges them vertically. By default, it sizes itself to fit its
          // children horizontally, and tries to be as tall as its parent.
          //
          // Invoke "debug painting" (press "p" in the console, choose the
          // "Toggle Debug Paint" action from the Flutter Inspector in Android
          // Studio, or the "Toggle Debug Paint" command in Visual Studio Code)
          // to see the wireframe for each widget.
          //
          // Column has various properties to control how it sizes itself and
          // how it positions its children. Here we use mainAxisAlignment to
          // center the children vertically; the main axis here is the vertical
          // axis because Columns are vertical (the cross axis would be
          // horizontal).
          mainAxisAlignment: MainAxisAlignment.center,
          children: <Widget>[
            const Text(
              'You have pushed the button this many times:',
            ),
            Text(
              '$_counter',
              style: Theme.of(context).textTheme.headlineMedium,
            ),
          ],
        ),
      ),
      floatingActionButton: FloatingActionButton(
        onPressed: _incrementCounter,
        tooltip: 'Increment',
        child: const Icon(Icons.add),
      ), // This trailing comma makes auto-formatting nicer for build methods.
    );
  }
}

Download Details:
 

Author: jonbhanson
Download Link: Download The Source Code
Official Website: https://github.com/jonbhanson/flutter_native_splash 
License: MIT license

#flutter #ios #android 

Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

1. Design the PCB using your favorite CAD or drawing software.
2. Print the top and bottom copper and top silk screen layers to a PDF file.
3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
4. Use a Gerber viewer to double-check the output against the original PCB design.
5. Make adjustments as needed.
6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

---

pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

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Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl 

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Why Does Image Data Augmentation Work As A Regularizer in Deep Learning?

The problem with deep learning models is they need lots of data to train a model. There are two major problems while training deep learning models is overfitting and underfitting of the model. Those problems are solved by data augmentation is a regularization technique that makes slight modifications to the images and used to generate data.

In this article, we will demonstrate why data augmentation is known as a regularization technique. How to apply data augmentation to our model and whether it is used as a preprocessing technique or post-processing techniques…? All these questions are answered in the below demonstration.

Topics that we will demonstrate in this article:-

• Data augmentation as a regularizer and data generator.
• Implementing Data augmentation techniques.

Data Augmentation As a Regularizer and Data Generator

The regularization is a technique used to reduce the overfitting in the model. unnecessarily. In dealing with deep learning models, too much learning is also bad for the model to make a prediction with unseen data. If we get good results in training data and poor results in unseen data (test data, validation data) then it is framed as an overfitting problem. So now using data augmentation, we perform few transformations to the data like flipping, cropping, adding noise to the data, etc.

As you know, deep learning models are data hungry, if we are lacking data then by using data augmentation transformations of the image we can generate data. Data augmentation is a preprocessing technique because we only work on the data to train our model. In this technique, we generate new instances of images by cropping, flipping, zooming, shearing an original image. So, whenever the training lacks the image dataset, using augmentation, we can create thousands of images to train the model perfectly.

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