data augmentation

codes/python/basics_in_machine_learning/dataaugmentation.py

+# -*- coding: utf-8 -*-
+"""dataaugmentation.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1ibfKtpxC_hIhZlPbefCoqpAS7jTdyiFw
+"""
+
+import tensorflow as tf
+import tensorflow_datasets as tfds # Import TensorFlow datasets
+import urllib
+import tensorflow_datasets as tfds
+import matplotlib.pyplot as plt
+import numpy as np
+# Necessary for dealing with https urls
+import ssl
+ssl._create_default_https_context = ssl._create_unverified_context
+
+# We read only the first 10 training samples
+ds, ds_info = tfds.load('colorectal_histology', split='train', shuffle_files=True, with_info=True, download=True)
+assert isinstance(ds, tf.data.Dataset)
+print(ds_info)
+
+# Visualizing images
+fig = tfds.show_examples(ds, ds_info)
+
+# Reading all images (remove break point to read all)
+for example in tfds.as_numpy(ds):
+  image, label = example['image'], example['label']
+  break
+# take one sample from data
+one_sample = ds.take(1)
+one_sample = list(one_sample.as_numpy_iterator())
+image = one_sample[0]['image']
+label = one_sample[0]['label']
+print(image.shape,label.shape)
+
+# Side by side visualization
+def visualize(im, imAgmented, operation):
+  fig = plt.figure()
+  plt.subplot(1,2,1)
+  plt.title('Original image')
+  plt.imshow(im)
+  plt.subplot(1,2,2)
+  plt.title(operation)
+  plt.imshow(imAgmented)
+
+# Adding Gaussian noise to image
+common_type = tf.float32 # Make noise and image of the same type
+gnoise = tf.random.normal(shape=tf.shape(image), mean=0.0, stddev=0.1, dtype=common_type)
+image_type_converted = tf.image.convert_image_dtype(image, dtype=common_type, saturate=False)
+noisy_image = tf.add(image_type_converted, gnoise)
+visualize(image_type_converted, noisy_image, 'noisyimage')
+
+# Adjusting brighness
+bright = tf.image.adjust_brightness(image, 0.2)
+visualize(image, bright, 'brightened image')
+
+# Flip image
+flipped = tf.image.flip_left_right(image)
+visualize(image, flipped, 'flipped image')
+
+adjusted = tf.image.adjust_jpeg_quality(image, jpeg_quality=20)
+visualize(image, adjusted, 'quality adjusted image')
+
+# Randon cropping of the image (the cropping area is picked at random)
+crop_to_original_ratio = 0.5 # The scale of the cropped area to the original image
+new_size = int(crop_to_original_ratio * image.shape[0])
+cropped = tf.image.random_crop(image, size=[new_size,new_size,3])
+visualize(image, cropped, 'randomly cropped image')
+
+# Center cropping of the image (the cropping area is at the center)
+central_fraction = 0.6 # The scale of the cropped area to the original image
+center_cropped = tf.image.central_crop(image, central_fraction=central_fraction)
+visualize(image, center_cropped, 'centrally cropped image')
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