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removed unnecessary files
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BKHMSI
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5ae501074a
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219
.ipynb_checkpoints/Project_1-checkpoint.ipynb
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219
.ipynb_checkpoints/Project_1-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd \n",
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"import cv2 as cv\n",
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"import scipy.misc\n",
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"\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from keras.models import Sequential, Model\n",
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"from keras.layers import Input, Dense, Conv2D, MaxPooling2D, UpSampling2D\n",
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"from keras.layers.core import Activation, Dropout, Flatten\n",
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"from keras.optimizers import SGD\n",
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"from keras.utils import np_utils\n",
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"from keras.callbacks import TensorBoard\n",
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"\n",
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"np.random.seed(1337) # for reproducibility"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"((1005, 480, 640, 3), (1005, 480, 640, 3))\n"
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]
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}
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],
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"source": [
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"num_images = (1005, 480, 640, 3)\n",
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"X_train = np.zeros(num_images)\n",
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"Y_train = np.zeros(num_images)\n",
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"\n",
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"for i, file in enumerate(os.listdir('ZuBuD_Sketch')):\n",
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" file_path = os.path.join('ZuBuD_Sketch', file)\n",
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" img = cv.imread(file_path)\n",
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" img = cv.cvtColor(img, cv.COLOR_BGR2RGB)\n",
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" X_train[i] = img\n",
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" \n",
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"for i, file in enumerate(os.listdir('ZuBuD')):\n",
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" file_path = os.path.join('ZuBuD', file)\n",
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" img = cv.imread(file_path)\n",
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" img = cv.cvtColor(img, cv.COLOR_BGR2RGB) \n",
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" if img.shape == (240,320,3):\n",
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" img = scipy.misc.imresize(img, (480, 640, 3))\n",
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" Y_train[i] = img\n",
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" \n",
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"print (X_train.shape, Y_train.shape) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"X_val = X_train[:100]\n",
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"Y_val = Y_train[:100]\n",
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"X_train = X_train[100:]\n",
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"Y_train = Y_train[100:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"_________________________________________________________________\n",
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"Layer (type) Output Shape Param # \n",
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"=================================================================\n",
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"input_4 (InputLayer) (None, 480, 640, 3) 0 \n",
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"_________________________________________________________________\n",
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"conv2d_22 (Conv2D) (None, 480, 640, 128) 3584 \n",
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"_________________________________________________________________\n",
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"max_pooling2d_10 (MaxPooling (None, 240, 320, 128) 0 \n",
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"_________________________________________________________________\n",
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"conv2d_23 (Conv2D) (None, 240, 320, 64) 73792 \n",
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"_________________________________________________________________\n",
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"max_pooling2d_11 (MaxPooling (None, 120, 160, 64) 0 \n",
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"_________________________________________________________________\n",
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"conv2d_24 (Conv2D) (None, 120, 160, 64) 36928 \n",
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"_________________________________________________________________\n",
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"max_pooling2d_12 (MaxPooling (None, 60, 80, 64) 0 \n",
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"_________________________________________________________________\n",
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"conv2d_25 (Conv2D) (None, 60, 80, 64) 36928 \n",
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"_________________________________________________________________\n",
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"up_sampling2d_10 (UpSampling (None, 120, 160, 64) 0 \n",
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"_________________________________________________________________\n",
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"conv2d_26 (Conv2D) (None, 120, 160, 64) 36928 \n",
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"_________________________________________________________________\n",
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"up_sampling2d_11 (UpSampling (None, 240, 320, 64) 0 \n",
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"_________________________________________________________________\n",
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"conv2d_27 (Conv2D) (None, 240, 320, 128) 73856 \n",
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"_________________________________________________________________\n",
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"up_sampling2d_12 (UpSampling (None, 480, 640, 128) 0 \n",
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"_________________________________________________________________\n",
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"conv2d_28 (Conv2D) (None, 480, 640, 3) 3459 \n",
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"=================================================================\n",
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"Total params: 265,475.0\n",
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"Trainable params: 265,475.0\n",
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"Non-trainable params: 0.0\n",
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"_________________________________________________________________\n"
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]
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}
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],
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"source": [
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"input_img = Input(shape=(480, 640, 3)) \n",
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"\n",
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"x = Conv2D(128, (3, 3), activation='relu', padding='same')(input_img)\n",
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"x = MaxPooling2D((2, 2), padding='same')(x)\n",
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"x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
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"x = MaxPooling2D((2, 2), padding='same')(x)\n",
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"x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
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"encoded = MaxPooling2D((2, 2), padding='same')(x)\n",
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"\n",
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"# at this point the representation is (4, 4, 8) i.e. 128-dimensional\n",
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"\n",
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"x = Conv2D(64, (3, 3), activation='relu', padding='same')(encoded)\n",
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"x = UpSampling2D((2, 2))(x)\n",
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"x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)\n",
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"x = UpSampling2D((2, 2))(x)\n",
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"x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)\n",
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"x = UpSampling2D((2, 2))(x)\n",
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"decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)\n",
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"\n",
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"autoencoder = Model(input_img, decoded)\n",
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"autoencoder.summary()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Train on 905 samples, validate on 100 samples\n",
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"Epoch 1/50\n"
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]
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}
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],
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"source": [
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"autoencoder.compile(optimizer='adam', loss='mean_squared_error')\n",
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"autoencoder.fit(X_train, Y_train,\n",
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" epochs=12,\n",
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" batch_size=64,\n",
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" shuffle=True,\n",
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" validation_data=(X_val, Y_val))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"Y_test = autoencoder.predict(X_val)\n",
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"\n",
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"fig = plt.figure()\n",
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"a = fig.add_subplot(1,2,1)\n",
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"imgplot = plt.imshow(Y_test[0])\n",
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"a.set_title('Prediction')\n",
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"a = fig.add_subplot(1,2,2)\n",
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"imgplot = plt.imshow(X_val[0])\n",
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"a.set_title('Ground Truth')\n",
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"plt.show()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"import numpy as np\n",
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"import pandas as pd \n",
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"import cv2 as cv\n",
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"import os\n",
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"import scipy.misc\n",
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"\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from keras.models import Sequential, Model\n",
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@ -55,7 +56,7 @@
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" img = cv.imread(file_path)\n",
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" img = cv.cvtColor(img, cv.COLOR_BGR2RGB) \n",
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" if img.shape == (240,320,3):\n",
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" img = np.resize(img, (480, 640, 3))\n",
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" img = scipy.misc.imresize(img, (480, 640, 3))\n",
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" Y_train[i] = img\n",
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" \n",
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"print (X_train.shape, Y_train.shape) "
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@ -167,7 +168,7 @@
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"source": [
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"autoencoder.compile(optimizer='adam', loss='mean_squared_error')\n",
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"autoencoder.fit(X_train, Y_train,\n",
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" epochs=50,\n",
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" epochs=12,\n",
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" batch_size=64,\n",
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" shuffle=True,\n",
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" validation_data=(X_val, Y_val))"
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