├── Cv2_image_preprocessing.ipynb ├── Image_Preprocessing_Numpy.ipynb ├── Keras Image_Preprocessing.ipynb ├── README.md └── TF_Image_Preprocessing.ipynb /Keras Image_Preprocessing.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 29, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import pandas as pd\n", 10 | "import numpy as np\n", 11 | "import os\n", 12 | "from tensorflow.keras.preprocessing.image import ImageDataGenerator, img_to_array, load_img, array_to_img\n", 13 | "import tensorflow as tf\n" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 12, 19 | "metadata": {}, 20 | "outputs": [], 21 | "source": [ 22 | "IMG_WIDTH=200\n", 23 | "IMG_HEIGHT=200\n", 24 | "batch_size=4\n", 25 | "\n", 26 | "train_dir = r'C:\\data\\CV\\Intel_Images\\seg_train\\seg_train'\n", 27 | "test_dir = r'C:\\data\\CV\\Intel_Images\\seg_pred\\seg_pred'\n", 28 | "val_dir = r'C:\\data\\CV\\Intel_Images\\seg_test\\seg_test'" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 9, 34 | "metadata": {}, 35 | "outputs": [], 36 | "source": [ 37 | "import matplotlib.image as mpimg\n", 38 | "from matplotlib import pyplot as plt\n", 39 | "%matplotlib inline" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 10, 45 | "metadata": {}, 46 | "outputs": [ 47 | { 48 | "data": { 49 | "text/plain": [ 50 | "" 51 | ] 52 | }, 53 | "execution_count": 10, 54 | "metadata": {}, 55 | "output_type": "execute_result" 56 | }, 57 | { 58 | "data": { 59 | "image/png": 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\n", 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" 62 | ] 63 | }, 64 | "metadata": { 65 | "needs_background": "light" 66 | }, 67 | "output_type": "display_data" 68 | } 69 | ], 70 | "source": [ 71 | "\n", 72 | "test_image=r'C:\\data\\CV\\Intel_Images\\seg_train\\seg_train\\mountain\\414.jpg'\n", 73 | "\n", 74 | "img=mpimg.imread(test_image)\n", 75 | "plt.imshow(img)" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": 43, 81 | "metadata": {}, 82 | "outputs": [], 83 | "source": [ 84 | "\n", 85 | "image_gen_train = ImageDataGenerator(rescale=1./255, \n", 86 | " zoom_range=0.2, \n", 87 | " rotation_range=65,\n", 88 | " shear_range=0.09,\n", 89 | " horizontal_flip=True,\n", 90 | " vertical_flip=True)\n" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 44, 96 | "metadata": {}, 97 | "outputs": [ 98 | { 99 | "name": "stdout", 100 | "output_type": "stream", 101 | "text": [ 102 | "Found 14034 images belonging to 6 classes.\n" 103 | ] 104 | } 105 | ], 106 | "source": [ 107 | "train_data_gen = image_gen_train.flow_from_directory(batch_size=batch_size,\n", 108 | " directory=train_dir,\n", 109 | " shuffle=True,\n", 110 | " target_size=(IMG_HEIGHT, IMG_WIDTH),\n", 111 | " class_mode='sparse')" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 45, 117 | "metadata": {}, 118 | "outputs": [ 119 | { 120 | "name": "stdout", 121 | "output_type": "stream", 122 | "text": [ 123 | "Found 3000 images belonging to 6 classes.\n" 124 | ] 125 | } 126 | ], 127 | "source": [ 128 | "image_gen_val = ImageDataGenerator(rescale=1./255)\n", 129 | "val_data_gen = image_gen_val.flow_from_directory(batch_size=batch_size,\n", 130 | " directory=val_dir,\n", 131 | " target_size=(IMG_HEIGHT, IMG_WIDTH),\n", 132 | " class_mode='sparse')" 133 | ] 134 | }, 135 | { 136 | "cell_type": "code", 137 | "execution_count": 46, 138 | "metadata": {}, 139 | "outputs": [ 140 | { 141 | "data": { 142 | "text/plain": [ 143 | "dict_keys(['buildings', 'forest', 'glacier', 'mountain', 'sea', 'street'])" 144 | ] 145 | }, 146 | "execution_count": 46, 147 | "metadata": {}, 148 | "output_type": "execute_result" 149 | } 150 | ], 151 | "source": [ 152 | "train_data_gen.class_indices.keys()" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 47, 158 | "metadata": { 159 | "scrolled": true 160 | }, 161 | "outputs": [], 162 | "source": [ 163 | "model=tf.keras.Sequential(\n", 164 | " [\n", 165 | " tf.keras.layers.InputLayer(input_shape=(200, 200, 3)),\n", 166 | " tf.keras.layers.Conv2D(\n", 167 | " filters=32, kernel_size=3, strides=(2, 2), activation='relu'),\n", 168 | " tf.keras.layers.Conv2D(\n", 169 | " filters=64, kernel_size=3, strides=(2, 2), activation='relu'),\n", 170 | " tf.keras.layers.Flatten(),\n", 171 | " # No activation\n", 172 | " tf.keras.layers.Dense(6)])\n", 173 | "\n" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 48, 179 | "metadata": {}, 180 | "outputs": [ 181 | { 182 | "name": "stdout", 183 | "output_type": "stream", 184 | "text": [ 185 | "Model: \"sequential_2\"\n", 186 | "_________________________________________________________________\n", 187 | "Layer (type) Output Shape Param # \n", 188 | "=================================================================\n", 189 | "conv2d_4 (Conv2D) (None, 99, 99, 32) 896 \n", 190 | "_________________________________________________________________\n", 191 | "conv2d_5 (Conv2D) (None, 49, 49, 64) 18496 \n", 192 | "_________________________________________________________________\n", 193 | "flatten_2 (Flatten) (None, 153664) 0 \n", 194 | "_________________________________________________________________\n", 195 | "dense_2 (Dense) (None, 6) 921990 \n", 196 | "=================================================================\n", 197 | "Total params: 941,382\n", 198 | "Trainable params: 941,382\n", 199 | "Non-trainable params: 0\n", 200 | "_________________________________________________________________\n" 201 | ] 202 | } 203 | ], 204 | "source": [ 205 | "model.summary()" 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": 49, 211 | "metadata": {}, 212 | "outputs": [], 213 | "source": [ 214 | "model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n" 215 | ] 216 | }, 217 | { 218 | "cell_type": "code", 219 | "execution_count": 50, 220 | "metadata": {}, 221 | "outputs": [ 222 | { 223 | "name": "stdout", 224 | "output_type": "stream", 225 | "text": [ 226 | "Train for 877 steps, validate for 750 steps\n", 227 | "Epoch 1/2\n", 228 | "877/877 [==============================] - 149s 170ms/step - loss: 2.7023 - accuracy: 0.1676 - val_loss: 1.7918 - val_accuracy: 0.1750\n", 229 | "Epoch 2/2\n", 230 | "877/877 [==============================] - 147s 168ms/step - loss: 1.7918 - accuracy: 0.1787 - val_loss: 1.7918 - val_accuracy: 0.1750\n" 231 | ] 232 | } 233 | ], 234 | "source": [ 235 | "\n", 236 | "history = model.fit(train_data_gen,steps_per_epoch=len(train_data_gen)//batch_size, validation_data=val_data_gen, epochs=2)\n" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": null, 242 | "metadata": {}, 243 | "outputs": [], 244 | "source": [] 245 | } 246 | ], 247 | "metadata": { 248 | "kernelspec": { 249 | "display_name": "Python 3", 250 | "language": "python", 251 | "name": "python3" 252 | }, 253 | "language_info": { 254 | "codemirror_mode": { 255 | "name": "ipython", 256 | "version": 3 257 | }, 258 | "file_extension": ".py", 259 | "mimetype": "text/x-python", 260 | "name": "python", 261 | "nbconvert_exporter": "python", 262 | "pygments_lexer": "ipython3", 263 | "version": "3.7.4" 264 | } 265 | }, 266 | "nbformat": 4, 267 | "nbformat_minor": 2 268 | } 269 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Load_Dataset 2 | # To load dataset using PIL, CV2, Keras andTensorflow 3 | ## Typical steps for loading custom dataset for Deep Learning Models 4 | 1.Open the image file. The format of the file can be JPEG, PNG, BMP, etc. 5 | 6 | 2.Resize the image to match the input size for the Input layer of the Deep Learning model. 7 | 8 | 3.Convert the image pixels to float datatype. 9 | 10 | 4.Normalize the image to have pixel values scaled down between 0 and 1 from 0 to 255. 11 | 12 | 5.Image data for Deep Learning models should be either a numpy array or a tensor object. 13 | -------------------------------------------------------------------------------- /TF_Image_Preprocessing.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "metadata": {}, 7 | "outputs": [ 8 | { 9 | "name": "stderr", 10 | "output_type": "stream", 11 | "text": [ 12 | "Using TensorFlow backend.\n" 13 | ] 14 | } 15 | ], 16 | "source": [ 17 | "import pandas as pd\n", 18 | "import numpy as np\n", 19 | "import os\n", 20 | "from keras.layers import Dense, Flatten, Reshape, Input, InputLayer\n", 21 | "from keras.models import Sequential, Model\n", 22 | "#from keras.preprocessing.image import load_img\n", 23 | "##from keras.preprocessing.image import img_to_array\n", 24 | "#from keras.preprocessing.image import array_to_img\n", 25 | "from matplotlib import pyplot as plt\n", 26 | "\n", 27 | "import tensorflow as tf\n", 28 | "import os\n", 29 | "import PIL\n", 30 | "import tensorflow as tf\n", 31 | "import cv2\n", 32 | "\n", 33 | "from tensorflow import keras\n", 34 | "from tensorflow.keras import layers\n", 35 | "from tensorflow.keras.models import Sequential\n", 36 | "%matplotlib inline" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 7, 42 | "metadata": {}, 43 | "outputs": [], 44 | "source": [ 45 | "IMG_WIDTH=200\n", 46 | "IMG_HEIGHT=200\n", 47 | "img_folder=r'C:\\data\\CV\\Intel_Images\\seg_train\\seg_train'" 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": 8, 53 | "metadata": {}, 54 | "outputs": [], 55 | "source": [ 56 | "import matplotlib.image as mpimg" 57 | ] 58 | }, 59 | { 60 | "cell_type": "code", 61 | "execution_count": 9, 62 | "metadata": {}, 63 | "outputs": [ 64 | { 65 | "data": { 66 | "text/plain": [ 67 | "" 68 | ] 69 | }, 70 | "execution_count": 9, 71 | "metadata": {}, 72 | "output_type": "execute_result" 73 | }, 74 | { 75 | "data": { 76 | "image/png": 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\n", 77 | "text/plain": [ 78 | "
" 79 | ] 80 | }, 81 | "metadata": { 82 | "needs_background": "light" 83 | }, 84 | "output_type": "display_data" 85 | } 86 | ], 87 | "source": [ 88 | "\n", 89 | "test_image=r'C:\\data\\CV\\Intel_Images\\seg_train\\seg_train\\mountain\\480.jpg'\n", 90 | "\n", 91 | "img=mpimg.imread(test_image)\n", 92 | "plt.imshow(img)" 93 | ] 94 | }, 95 | { 96 | "cell_type": "markdown", 97 | "metadata": {}, 98 | "source": [ 99 | "## https://keras.io/api/preprocessing/image/\n", 100 | "## https://www.tensorflow.org/tutorials/load_data/images" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": 18, 106 | "metadata": {}, 107 | "outputs": [], 108 | "source": [ 109 | "\n", 110 | "def create_dataset_tf(img_folder):\n", 111 | " class_name=[]\n", 112 | " tf_img_data_array=[] \n", 113 | " \n", 114 | " for dir1 in os.listdir(img_folder):\n", 115 | " for file in os.listdir(os.path.join(img_folder, dir1)):\n", 116 | " image= os.path.join(img_folder,dir1, file)\n", 117 | " image = tf.io.read_file(image)\n", 118 | " image = tf.io.decode_jpeg(image, channels=3)\n", 119 | " image = tf.image.resize(image, (200,200))\n", 120 | " image = tf.cast(image / 255., tf.float32)\n", 121 | " tf_img_data_array.append(image)\n", 122 | " class_name.append(dir1) \n", 123 | " return tf.stack(tf_img_data_array, axis=0),class_name" 124 | ] 125 | }, 126 | { 127 | "cell_type": "code", 128 | "execution_count": 19, 129 | "metadata": {}, 130 | "outputs": [], 131 | "source": [ 132 | "img_folder=r'C:\\data\\CV\\Intel_Images\\seg_train\\seg_train'\n", 133 | "tf_img_data, class_name=create_dataset_tf(img_folder)" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": 20, 139 | "metadata": {}, 140 | "outputs": [ 141 | { 142 | "name": "stdout", 143 | "output_type": "stream", 144 | "text": [ 145 | "\n", 146 | "\n" 147 | ] 148 | } 149 | ], 150 | "source": [ 151 | "print(type(tf_img_data))\n", 152 | "print(type(class_name))\n" 153 | ] 154 | }, 155 | { 156 | "cell_type": "code", 157 | "execution_count": 22, 158 | "metadata": {}, 159 | "outputs": [ 160 | { 161 | "data": { 162 | "text/plain": [ 163 | "{'buildings': 0,\n", 164 | " 'forest': 1,\n", 165 | " 'glacier': 2,\n", 166 | " 'mountain': 3,\n", 167 | " 'sea': 4,\n", 168 | " 'street': 5}" 169 | ] 170 | }, 171 | "execution_count": 22, 172 | "metadata": {}, 173 | "output_type": "execute_result" 174 | } 175 | ], 176 | "source": [ 177 | "\n", 178 | "target_dict={k: v for v, k in enumerate(np.unique(class_name))}\n", 179 | "target_dict\n" 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "execution_count": 23, 185 | "metadata": {}, 186 | "outputs": [], 187 | "source": [ 188 | "\n", 189 | "target_val= [target_dict[class_name[i]] for i in range(len(class_name))]" 190 | ] 191 | }, 192 | { 193 | "cell_type": "code", 194 | "execution_count": 24, 195 | "metadata": { 196 | "scrolled": true 197 | }, 198 | "outputs": [], 199 | "source": [ 200 | "model=tf.keras.Sequential(\n", 201 | " [\n", 202 | " tf.keras.layers.InputLayer(input_shape=(200, 200, 3)),\n", 203 | " tf.keras.layers.Conv2D(\n", 204 | " filters=32, kernel_size=3, strides=(2, 2), activation='relu'),\n", 205 | " tf.keras.layers.Conv2D(\n", 206 | " filters=64, kernel_size=3, strides=(2, 2), activation='relu'),\n", 207 | " tf.keras.layers.Flatten(),\n", 208 | " # No activation\n", 209 | " tf.keras.layers.Dense(32)])\n", 210 | "\n" 211 | ] 212 | }, 213 | { 214 | "cell_type": "code", 215 | "execution_count": 25, 216 | "metadata": {}, 217 | "outputs": [ 218 | { 219 | "name": "stdout", 220 | "output_type": "stream", 221 | "text": [ 222 | "Model: \"sequential\"\n", 223 | "_________________________________________________________________\n", 224 | "Layer (type) Output Shape Param # \n", 225 | "=================================================================\n", 226 | "conv2d (Conv2D) (None, 99, 99, 32) 896 \n", 227 | "_________________________________________________________________\n", 228 | "conv2d_1 (Conv2D) (None, 49, 49, 64) 18496 \n", 229 | "_________________________________________________________________\n", 230 | "flatten (Flatten) (None, 153664) 0 \n", 231 | "_________________________________________________________________\n", 232 | "dense (Dense) (None, 32) 4917280 \n", 233 | "=================================================================\n", 234 | "Total params: 4,936,672\n", 235 | "Trainable params: 4,936,672\n", 236 | "Non-trainable params: 0\n", 237 | "_________________________________________________________________\n" 238 | ] 239 | } 240 | ], 241 | "source": [ 242 | "model.summary()" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 27, 248 | "metadata": {}, 249 | "outputs": [], 250 | "source": [ 251 | "model.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": 28, 257 | "metadata": {}, 258 | "outputs": [ 259 | { 260 | "data": { 261 | "text/plain": [ 262 | "tensorflow.python.framework.ops.EagerTensor" 263 | ] 264 | }, 265 | "execution_count": 28, 266 | "metadata": {}, 267 | "output_type": "execute_result" 268 | } 269 | ], 270 | "source": [ 271 | "type(tf_img_data)" 272 | ] 273 | }, 274 | { 275 | "cell_type": "code", 276 | "execution_count": 29, 277 | "metadata": {}, 278 | "outputs": [ 279 | { 280 | "name": "stdout", 281 | "output_type": "stream", 282 | "text": [ 283 | "Train on 14034 samples\n", 284 | "Epoch 1/2\n", 285 | "14034/14034 [==============================] - 69s 5ms/sample - loss: 9.2072 - accuracy: 0.1723\n", 286 | "Epoch 2/2\n", 287 | "14034/14034 [==============================] - 67s 5ms/sample - loss: 9.2036 - accuracy: 0.1730\n" 288 | ] 289 | } 290 | ], 291 | "source": [ 292 | "\n", 293 | "history = model.fit(x=tf_img_data, y=tf.cast(list(map(int,target_val)),tf.int32), epochs=2)\n" 294 | ] 295 | }, 296 | { 297 | "cell_type": "code", 298 | "execution_count": null, 299 | "metadata": {}, 300 | "outputs": [], 301 | "source": [] 302 | } 303 | ], 304 | "metadata": { 305 | "kernelspec": { 306 | "display_name": "Python 3", 307 | "language": "python", 308 | "name": "python3" 309 | }, 310 | "language_info": { 311 | "codemirror_mode": { 312 | "name": "ipython", 313 | "version": 3 314 | }, 315 | "file_extension": ".py", 316 | "mimetype": "text/x-python", 317 | "name": "python", 318 | "nbconvert_exporter": "python", 319 | "pygments_lexer": "ipython3", 320 | "version": "3.7.4" 321 | } 322 | }, 323 | "nbformat": 4, 324 | "nbformat_minor": 2 325 | } 326 | --------------------------------------------------------------------------------