import matplotlib.pyplot as plt

#from ksvd import ApproximateKSVD

from sklearn.feature_extraction import image

import numpy as np

from sklearn.linear_model import orthogonal_mp_gram

import os, os.path

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import LabelBinarizer

from PIL import Image

#from keras.applications.vgg16 import VGG16

#from keras.preprocessing import image

from keras.applications.xception import preprocess_input

import keras

from keras.models import Sequential

from keras.layers import Dense, Activation, Dropout, Flatten

from keras.layers import Conv2D

from keras.layers import MaxPooling2D

from keras.applications.densenet import DenseNet169

import tensorflow as tf

#from progress.bar import Bar

import time

class TimeHistory(keras.callbacks.Callback):

self.times.append(time.time() - self.epoch_time_start)

def progbar(curr, total, full_progbar):

frac = curr/total

filled_progbar = round(frac*full_progbar)

print('\r', '#'*filled_progbar + '-'*(full_progbar-filled_progbar), '[{:>7.2%}]'.format(frac), end='')

data = pd.read_csv("/home/Projects/Materials/micrograph.csv")

imgs = []

paths = []

img_name = []

img_new_name = []

processed_imgs = []

#dir = os.path.dirname(os.path.realpath('__file__'))

path = '/home/Projects/Materials/micrographs/'

#path = os.path.join(dir, 'micrographs/')

#path = "../micrographs/micrograph"

valid_images = [".tif",".png"]

for f in os.listdir(path):

img_name.append(f)

img_name.sort()

labels = []

for f in img_name:

ext = os.path.splitext(f)[1]

if ext.lower() not in valid_images:

continue

# use PIL to get all uint8 type images, then take the center and convert to float64,

# since apparently matpltlib doesnt care about float32 vs uint8

img = np.asarray(Image.open(os.path.join(path,f)))

imgs.append(img[0:483, 0:645])

l_idx = np.searchsorted(img_name, data['path'])

image_set = np.asarray(imgs)

imgs_ordered = image_set[l_idx]

processed_imgs = []

labels = []

#bar = Bar('Processing', len(imgs_ordered))

print('Generating Images\n')

for i in range(len(imgs_ordered)):

patches = image.extract_patches_2d(imgs_ordered[i], (224,224), max_patches = 100)

label = data['primary_microconstituent'][i]

for patch in patches:

x = Image.fromarray(patch).convert('RGB')

x = np.asarray(x)

#x = np.expand_dims(patch, axis = 2)

x = preprocess_input(x)

processed_imgs.append(x)

labels.append(label)

progbar(i, (len(imgs_ordered)-1), 20)

lb = LabelBinarizer()

lb.fit(np.asarray(data['primary_microconstituent']))

y = lb.transform(labels)

print('\nLabels Binarized, converting array')

input = np.asarray(processed_imgs)

X_train, X_test, y_train, y_test = train_test_split(

input, y, test_size=0.1, random_state=42)

model = DenseNet169(weights=None, classes = 7)

model.summary()

model.compile(loss = 'categorical_crossentropy', optimizer = 'sgd', metrics = ['accuracy'])

time_callback = TimeHistory()

model.fit(X_train, y_train, epochs = 5, batch_size = 32, validation_data=(X_test, y_test), callbacks=[time_callback])

name = 'results/UHCS_DenseNet169_Weights'

score = model.evaluate(X_test, y_test)

print('Test score:', score[0])

print('Test accuracy:', score[1])

model.save_weights(name+'.h5')

times = time_callback.times

file = open('DenseNet169.txt', 'w')

file.write('Test score:'+ str(score[0])+'\n')

file.write('Test accuracy:'+ str(score[1])+'\n')

file.write(str(times))

file.close()