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added keras code
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BKHMSI
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PencilSketch/__pycache__/filters.cpython-36.pyc
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PencilSketch/__pycache__/filters.cpython-36.pyc
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PencilSketch/dancing.jpg
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PencilSketch/filters.py
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PencilSketch/filters.py
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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""" A module containing a number of interesting image filter effects,
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such as:
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* Black-and-white pencil sketch
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* Warming/cooling filters
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* Cartoonizer
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"""
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import numpy as np
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import cv2
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from scipy.interpolate import UnivariateSpline
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__author__ = "Michael Beyeler"
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__license__ = "GNU GPL 3.0 or later"
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class PencilSketch:
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"""Pencil sketch effect
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A class that applies a pencil sketch effect to an image.
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The processed image is overlayed over a background image for visual
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effect.
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"""
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def __init__(self, width, height, bg_gray='pencilsketch_bg.jpg'):
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"""Initialize parameters
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:param (width, height): Image size.
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:param bg_gray: Optional background image to improve the illusion
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that the pencil sketch was drawn on a canvas.
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"""
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self.width = width
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self.height = height
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# try to open background canvas (if it exists)
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self.canvas = cv2.imread(bg_gray, cv2.CV_8UC1)
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if self.canvas is not None:
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self.canvas = cv2.resize(self.canvas, (self.width, self.height))
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def render(self, img_rgb):
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"""Applies pencil sketch effect to an RGB image
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:param img_rgb: RGB image to be processed
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:returns: Processed RGB image
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"""
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img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)
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img_blur = cv2.GaussianBlur(img_gray, (21, 21), 0, 0)
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img_blend = cv2.divide(img_gray, img_blur, scale=256)
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# if available, blend with background canvas
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if self.canvas is not None:
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img_blend = cv2.multiply(img_blend, self.canvas, scale=1./256)
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return cv2.cvtColor(img_blend, cv2.COLOR_GRAY2RGB)
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PencilSketch/sketch.jpg
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PencilSketch/sketch.jpg
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PencilSketch/sketch.py
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PencilSketch/sketch.py
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import cv2 as cv
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import numpy as np
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import os
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from filters import PencilSketch
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for file in os.listdir('ZuBuD'):
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file_path = os.path.join('ZuBuD', file)
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img = cv.imread(file_path)
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img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
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print (img.shape)
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height, width, channels = img.shape
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if (height, width, channels) == (240,320,3):
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img = np.resize(img, (480, 640, 3))
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pencil = PencilSketch(width, height)
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sketch = pencil.render(img)
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write_path = os.path.join('ZuBuD_Sketch', file)
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cv.imwrite(write_path, sketch)
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Project_1.ipynb
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Project_1.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 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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"\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 = np.resize(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=50,\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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