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example_cifar10_processing.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Example lbp application on Cifar10 using TensorFlow
"""
from tensorflow.keras.datasets import cifar10
from lib.lbplib import py_lbp,tf_lbp
import numpy as np
import time
import matplotlib.pyplot as plt
def py_extract_lbp_rgb(x_train):
[N,Rows,Cols,Channels]=x_train.shape
x_train_lbp=np.zeros(shape=(N,Rows,Cols,Channels),dtype='uint8')
for i in range(N):
x_train_lbp[i,:,:,0]=py_lbp(x_train[i,:,:,0])
x_train_lbp[i,:,:,1]=py_lbp(x_train[i,:,:,1])
x_train_lbp[i,:,:,2]=py_lbp(x_train[i,:,:,2])
return x_train_lbp
def tf_extract_lbp_rgb(x_train):
[N,Rows,Cols,Channels]=x_train.shape
x_train_lbp=np.zeros(shape=(N,Rows,Cols,Channels),dtype='uint8')
x_train_lbp[:,:,:,0]=tf_lbp(x_train[:,:,:,0].astype('uint8')).numpy()
x_train_lbp[:,:,:,1]=tf_lbp(x_train[:,:,:,1].astype('uint8')).numpy()
x_train_lbp[:,:,:,2]=tf_lbp(x_train[:,:,:,2].astype('uint8')).numpy()
return x_train_lbp
"""
def tf_extract_lbp_gray(x_train):
x_train_lbp=tf_lbp(x_train.astype('uint8')).numpy()
return x_train_lbp
"""
# Load test dataset
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
#use first 100 images for test
x_train=x_train[:100,:,:,:]
# Process complete dataset
# Extract lbp features for Cifar10 dataset using TensorFlow
start_time = time.time()
#process all images in the Cifar10 --------------------------------------------
x_train_lbp_tf = tf_extract_lbp_rgb(x_train)
elapsed_tf = time.time() - start_time
print('tensor flow elapsed_time=',elapsed_tf)
# Extract lbp features for Cifar10 dataset ------------------------------------
start_time = time.time()
#process all images in the Cifar10
x_train_lbp_py = py_extract_lbp_rgb(x_train)
elapsed_py = time.time() - start_time
print('python elapsed_time=',elapsed_py)
# Check error
print('error=',np.sum(x_train_lbp_py-x_train_lbp_tf))
# Example images---------------------------------------------------------------
#input images
plt.figure(1)
figs, axes = plt.subplots(4, 6)
for i in range(4):
for j in range(6):
axes[i, j].imshow(x_train[i*6+j,:,:,:])
axes[i, j].set_xticks([])
axes[i, j].set_yticks([])
# LBP images
plt.figure(2)
figs, axes = plt.subplots(4, 6)
for i in range(4):
for j in range(6):
axes[i, j].imshow(x_train_lbp_tf[i*6+j,:,:,:])
axes[i, j].set_xticks([])
axes[i, j].set_yticks([])