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Hyperparameters Tuning/Data_Augumentatin/data-augmentation-by_keras.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # example of zoom image augmentation\n",
+    "# from numpy import expand_dims\n",
+    "# from keras.preprocessing.image import load_img\n",
+    "# from keras.preprocessing.image import img_to_array\n",
+    "# from keras.preprocessing.image import ImageDataGenerator\n",
+    "# from matplotlib import pyplot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# # load the image\n",
+    "# img = load_img('./1.png')\n",
+    "# # convert to numpy array\n",
+    "# data = img_to_array(img)\n",
+    "# # expand dimension to one sample\n",
+    "# samples = expand_dims(data, 0)\n",
+    "# # create image data augmentation generator\n",
+    "# datagen = ImageDataGenerator(zoom_range=[0.5,1.0])\n",
+    "# # prepare iterator\n",
+    "# it = datagen.flow(samples, batch_size=1)\n",
+    "# # generate samples and plot\n",
+    "# for i in range(9):\n",
+    "# \t# define subplot\n",
+    "# \tpyplot.subplot(330 + 1 + i)\n",
+    "# \t# generate batch of images\n",
+    "# \tbatch = it.next()\n",
+    "# \t# convert to unsigned integers for viewing\n",
+    "# \timage = batch[0].astype('uint8')\n",
+    "# \t# plot raw pixel data\n",
+    "# \tpyplot.imshow(image)\n",
+    "# # show the figure\n",
+    "# pyplot.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.preprocessing.image import ImageDataGenerator\n",
+    "\n",
+    "datagen = ImageDataGenerator(\n",
+    "        rotation_range=40,\n",
+    "        width_shift_range=0.2,\n",
+    "        height_shift_range=0.2,\n",
+    "        rescale=1./255,\n",
+    "        shear_range=0.2,\n",
+    "        zoom_range=0.2,\n",
+    "        horizontal_flip=True,\n",
+    "        fill_mode='nearest')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
+    "\n",
+    "datagen = ImageDataGenerator(\n",
+    "        rotation_range=40,\n",
+    "        width_shift_range=0.2,\n",
+    "        height_shift_range=0.2,\n",
+    "        shear_range=0.2,\n",
+    "        zoom_range=0.2,\n",
+    "        horizontal_flip=True,\n",
+    "        fill_mode='nearest')\n",
+    "\n",
+    "img = load_img('./1.png')  # this is a PIL image\n",
+    "x = img_to_array(img)  # this is a Numpy array with shape (3, 150, 150)\n",
+    "x = x.reshape((1,) + x.shape)  # this is a Numpy array with shape (1, 3, 150, 150)\n",
+    "\n",
+    "# the .flow() command below generates batches of randomly transformed images\n",
+    "# and saves the results to the `preview/` directory\n",
+    "i = 0\n",
+    "for batch in datagen.flow(x, batch_size=1,\n",
+    "                          save_to_dir='./', save_prefix='cervix', save_format='png'):\n",
+    "    i += 1\n",
+    "    if i > 20:\n",
+    "        break  # otherwise the generator would loop indefinitely"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}