on hold

Augmentation.ipynb

@@ -1,5 +1,50 @@
 {
  "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/subodh_pushkar/anaconda3/lib/python3.5/site-packages/keras_preprocessing/image.py:770: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, which overrides setting of `featurewise_center`.\n",
+      "  warnings.warn('This ImageDataGenerator specifies '\n",
+      "/home/subodh_pushkar/anaconda3/lib/python3.5/site-packages/keras_preprocessing/image.py:959: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
+      "  warnings.warn('This ImageDataGenerator specifies '\n",
+      "/home/subodh_pushkar/anaconda3/lib/python3.5/site-packages/keras_preprocessing/image.py:967: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
+      "  warnings.warn('This ImageDataGenerator specifies '\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
+    "\n",
+    "\n",
+    "\n",
+    "datagen = ImageDataGenerator(featurewise_std_normalization=True)\n",
+    "\n",
+    "import glob\n",
+    "cv_img = []\n",
+    "for fname in glob.glob(\"CROPAUG2/glu/*.jpg\"):\n",
+    "    # n= cv2.imread(img)\n",
+    "    # cv_img.append(n)\n",
+    "\n",
+    "    img = load_img(fname)  # 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='normalized/glu', save_prefix='nor_', save_format='jpg'):\n",
+    "        i += 1\n",
+    "        if i > 0:\n",
+    "            break  # otherwise the generator would loop indefinitely"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,