from IPython.display import HTML, Image

url = 'http://stratospark.com/demos/food-101/'
el = '<' + 'iframe src="{}"'.format(url) + ' width="100%" height=600>' # prevent notebook render bug
HTML(el)

Image('demo.jpg')

import matplotlib.pyplot as plt
import matplotlib.image as img
import numpy as np
from scipy.misc import imresize

%matplotlib inline

import os
from os import listdir
from os.path import isfile, join
import shutil
import stat
import collections
from collections import defaultdict

from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets

import h5py
from sklearn.model_selection import train_test_split
from keras.utils.np_utils import to_categorical
from keras.applications.inception_v3 import preprocess_input
from keras.models import load_model

# !wget http://data.vision.ee.ethz.ch/cvl/food-101.tar.gz

# !tar xzvf food-101.tar.gz

!ls food-101/images

!ls food-101/images/apple_pie/ | head -10

root_dir = 'food-101/images/'
rows = 17
cols = 6
fig, ax = plt.subplots(rows, cols, frameon=False, figsize=(15, 25))
fig.suptitle('Random Image from Each Food Class', fontsize=20)
sorted_food_dirs = sorted(os.listdir(root_dir))
for i in range(rows):
    for j in range(cols):
        try:
            food_dir = sorted_food_dirs[i*cols + j]
        except:
            break
        all_files = os.listdir(os.path.join(root_dir, food_dir))
        rand_img = np.random.choice(all_files)
        img = plt.imread(os.path.join(root_dir, food_dir, rand_img))
        ax[i][j].imshow(img)
        ec = (0, .6, .1)
        fc = (0, .7, .2)
        ax[i][j].text(0, -20, food_dir, size=10, rotation=0,
                ha="left", va="top", 
                bbox=dict(boxstyle="round", ec=ec, fc=fc))
plt.setp(ax, xticks=[], yticks=[])
plt.tight_layout(rect=[0, 0.03, 1, 0.95])

# Setup multiprocessing pool
# Do this early, as once images are loaded into memory there will be Errno 12
# http://stackoverflow.com/questions/14749897/python-multiprocessing-memory-usage
import multiprocessing as mp

num_processes = 6
pool = mp.Pool(processes=num_processes)

class_to_ix = {}
ix_to_class = {}
with open('food-101/meta/classes.txt', 'r') as txt:
    classes = [l.strip() for l in txt.readlines()]
    class_to_ix = dict(zip(classes, range(len(classes))))
    ix_to_class = dict(zip(range(len(classes)), classes))
    class_to_ix = {v: k for k, v in ix_to_class.items()}
sorted_class_to_ix = collections.OrderedDict(sorted(class_to_ix.items()))

# Only split files if haven't already
if not os.path.isdir('./food-101/test') and not os.path.isdir('./food-101/train'):

    def copytree(src, dst, symlinks = False, ignore = None):
        if not os.path.exists(dst):
            os.makedirs(dst)
            shutil.copystat(src, dst)
        lst = os.listdir(src)
        if ignore:
            excl = ignore(src, lst)
            lst = [x for x in lst if x not in excl]
        for item in lst:
            s = os.path.join(src, item)
            d = os.path.join(dst, item)
            if symlinks and os.path.islink(s):
                if os.path.lexists(d):
                    os.remove(d)
                os.symlink(os.readlink(s), d)
                try:
                    st = os.lstat(s)
                    mode = stat.S_IMODE(st.st_mode)
                    os.lchmod(d, mode)
                except:
                    pass # lchmod not available
            elif os.path.isdir(s):
                copytree(s, d, symlinks, ignore)
            else:
                shutil.copy2(s, d)

    def generate_dir_file_map(path):
        dir_files = defaultdict(list)
        with open(path, 'r') as txt:
            files = [l.strip() for l in txt.readlines()]
            for f in files:
                dir_name, id = f.split('/')
                dir_files[dir_name].append(id + '.jpg')
        return dir_files

    train_dir_files = generate_dir_file_map('food-101/meta/train.txt')
    test_dir_files = generate_dir_file_map('food-101/meta/test.txt')


    def ignore_train(d, filenames):
        print(d)
        subdir = d.split('/')[-1]
        to_ignore = train_dir_files[subdir]
        return to_ignore

    def ignore_test(d, filenames):
        print(d)
        subdir = d.split('/')[-1]
        to_ignore = test_dir_files[subdir]
        return to_ignore

    copytree('food-101/images', 'food-101/test', ignore=ignore_train)
    copytree('food-101/images', 'food-101/train', ignore=ignore_test)
    
else:
    print('Train/Test files already copied into separate folders.')

%%time

# Load dataset images and resize to meet minimum width and height pixel size
def load_images(root, min_side=299):
    all_imgs = []
    all_classes = []
    resize_count = 0
    invalid_count = 0
    for i, subdir in enumerate(listdir(root)):
        imgs = listdir(join(root, subdir))
        class_ix = class_to_ix[subdir]
        print(i, class_ix, subdir)
        for img_name in imgs:
            img_arr = img.imread(join(root, subdir, img_name))
            img_arr_rs = img_arr
            try:
                w, h, _ = img_arr.shape
                if w < min_side:
                    wpercent = (min_side/float(w))
                    hsize = int((float(h)*float(wpercent)))
                    #print('new dims:', min_side, hsize)
                    img_arr_rs = imresize(img_arr, (min_side, hsize))
                    resize_count += 1
                elif h < min_side:
                    hpercent = (min_side/float(h))
                    wsize = int((float(w)*float(hpercent)))
                    #print('new dims:', wsize, min_side)
                    img_arr_rs = imresize(img_arr, (wsize, min_side))
                    resize_count += 1
                all_imgs.append(img_arr_rs)
                all_classes.append(class_ix)
            except:
                print('Skipping bad image: ', subdir, img_name)
                invalid_count += 1
    print(len(all_imgs), 'images loaded')
    print(resize_count, 'images resized')
    print(invalid_count, 'images skipped')
    return np.array(all_imgs), np.array(all_classes)
    
X_test, y_test = load_images('food-101/test', min_side=299)

%%time
X_train, y_train = load_images('food-101/train', min_side=299)

print('X_train shape', X_train.shape)
print('y_train shape', y_train.shape)
print('X_test shape', X_test.shape)
print('y_test shape', y_test.shape)

@interact(n=(0, len(X_train)))
def show_pic(n):
    plt.imshow(X_train[n])
    print('class:', y_train[n], ix_to_class[y_train[n]])

@interact(n=(0, len(X_test)))
def show_pic(n):
    plt.imshow(X_test[n])
    print('class:', y_test[n], ix_to_class[y_test[n]])

@interact(n_class=sorted_class_to_ix)
def show_random_images_of_class(n_class=0):
    print(n_class)
    nrows = 4
    ncols = 8
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
    fig.set_size_inches(12, 8)
    #fig.tight_layout()
    imgs = np.random.choice((y_train == n_class).nonzero()[0], nrows * ncols)
    for i, ax in enumerate(axes.flat):
        im = ax.imshow(X_train[imgs[i]])
        ax.set_axis_off()
        ax.title.set_visible(False)
        ax.xaxis.set_ticks([])
        ax.yaxis.set_ticks([])
        for spine in ax.spines.values():
            spine.set_visible(False)
    plt.subplots_adjust(left=0, wspace=0, hspace=0)
    plt.show()

@interact(n_class=sorted_class_to_ix)
def show_random_images_of_class(n_class=0):
    print(n_class)
    nrows = 4
    ncols = 8
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
    fig.set_size_inches(12, 8)
    #fig.tight_layout()
    imgs = np.random.choice((y_test == n_class).nonzero()[0], nrows * ncols)
    for i, ax in enumerate(axes.flat):
        im = ax.imshow(X_test[imgs[i]])
        ax.set_axis_off()
        ax.title.set_visible(False)
        ax.xaxis.set_ticks([])
        ax.yaxis.set_ticks([])
        for spine in ax.spines.values():
            spine.set_visible(False)
    plt.subplots_adjust(left=0, wspace=0, hspace=0)
    plt.show()

from keras.utils.np_utils import to_categorical

n_classes = 101
y_train_cat = to_categorical(y_train, nb_classes=n_classes)
y_test_cat = to_categorical(y_test, nb_classes=n_classes)

from keras.applications.inception_v3 import InceptionV3
from keras.applications.inception_v3 import preprocess_input, decode_predictions
from keras.preprocessing import image
from keras.layers import Input

import tools.image_gen_extended as T

# Useful for checking the output of the generators after code change
#from importlib import reload
#reload(T)

display(Image('./gpu.png'))

%%time

# this is the augmentation configuration we will use for training
train_datagen = T.ImageDataGenerator(
    featurewise_center=False,  # set input mean to 0 over the dataset
    samplewise_center=False,  # set each sample mean to 0
    featurewise_std_normalization=False,  # divide inputs by std of the dataset
    samplewise_std_normalization=False,  # divide each input by its std
    zca_whitening=False,  # apply ZCA whitening
    rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
    width_shift_range=0.2,  # randomly shift images horizontally (fraction of total width)
    height_shift_range=0.2,  # randomly shift images vertically (fraction of total height)
    horizontal_flip=True,  # randomly flip images
    vertical_flip=False, # randomly flip images
    zoom_range=[.8, 1],
    channel_shift_range=30,
    fill_mode='reflect')
train_datagen.config['random_crop_size'] = (299, 299)
train_datagen.set_pipeline([T.random_transform, T.random_crop, T.preprocess_input])
train_generator = train_datagen.flow(X_train, y_train_cat, batch_size=64, seed=11, pool=pool)

test_datagen = T.ImageDataGenerator()
test_datagen.config['random_crop_size'] = (299, 299)
test_datagen.set_pipeline([T.random_transform, T.random_crop, T.preprocess_input])
test_generator = test_datagen.flow(X_test, y_test_cat, batch_size=64, seed=11, pool=pool)

def reverse_preprocess_input(x0):
    x = x0 / 2.0
    x += 0.5
    x *= 255.
    return x

%%time
@interact()
def show_images(unprocess=True):
    for x in test_generator:
        fig, axes = plt.subplots(nrows=8, ncols=4)
        fig.set_size_inches(8, 8)
        page = 0
        page_size = 32
        start_i = page * page_size
        for i, ax in enumerate(axes.flat):
            img = x[0][i+start_i]
            if unprocess:
                im = ax.imshow( reverse_preprocess_input(img).astype('uint8') )
            else:
                im = ax.imshow(img)
            ax.set_axis_off()
            ax.title.set_visible(False)
            ax.xaxis.set_ticks([])
            ax.yaxis.set_ticks([])
            for spine in ax.spines.values():
                spine.set_visible(False)

        plt.subplots_adjust(left=0, wspace=0, hspace=0)
        plt.show()
        break

%%time
show_images(unprocess=False)

%%time
from keras.models import Sequential, Model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, CSVLogger, LearningRateScheduler, ReduceLROnPlateau
from keras.optimizers import SGD
from keras.regularizers import l2
import keras.backend as K
import math

K.clear_session()

base_model = InceptionV3(weights='imagenet', include_top=False, input_tensor=Input(shape=(299, 299, 3)))
x = base_model.output
x = AveragePooling2D(pool_size=(8, 8))(x)
x = Dropout(.4)(x)
x = Flatten()(x)
predictions = Dense(n_classes, init='glorot_uniform', W_regularizer=l2(.0005), activation='softmax')(x)

model = Model(input=base_model.input, output=predictions)

opt = SGD(lr=.01, momentum=.9)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])

checkpointer = ModelCheckpoint(filepath='model4.{epoch:02d}-{val_loss:.2f}.hdf5', verbose=1, save_best_only=True)
csv_logger = CSVLogger('model4.log')

def schedule(epoch):
    if epoch < 15:
        return .01
    elif epoch < 28:
        return .002
    else:
        return .0004
lr_scheduler = LearningRateScheduler(schedule)

model.fit_generator(train_generator,
                    validation_data=test_generator,
                    nb_val_samples=X_test.shape[0],
                    samples_per_epoch=X_train.shape[0],
                    nb_epoch=32,
                    verbose=2,
                    callbacks=[lr_scheduler, csv_logger, checkpointer])

%%time
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D, GlobalAveragePooling2D, AveragePooling2D
from keras.layers.normalization import BatchNormalization
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint, CSVLogger, LearningRateScheduler, ReduceLROnPlateau
from keras.optimizers import SGD
from keras.regularizers import l2
import keras.backend as K
import math

model = load_model(filepath='./model4.29-0.69.hdf5')

opt = SGD(lr=.01, momentum=.9)
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])

checkpointer = ModelCheckpoint(filepath='model4b.{epoch:02d}-{val_loss:.2f}.hdf5', verbose=1, save_best_only=True)
csv_logger = CSVLogger('model4b.log')

def schedule(epoch):
    if epoch < 10:
        return .00008
    elif epoch < 20:
        return .000016
    else:
        return .0000032
    
lr_scheduler = LearningRateScheduler(schedule)

model.fit_generator(train_generator,
                    validation_data=test_generator,
                    nb_val_samples=X_test.shape[0],
                    samples_per_epoch=X_train.shape[0],
                    nb_epoch=32,
                    verbose=2,
                    callbacks=[lr_scheduler, csv_logger, checkpointer])

%%time
#model = load_model(filepath='./model4.29-0.69.hdf5') # 86.8039 10-crop Top-1 test accuracy
model = load_model(filepath='./model4b.10-0.68.hdf5') # 86.9703

def center_crop(x, center_crop_size, **kwargs):
    centerw, centerh = x.shape[0]//2, x.shape[1]//2
    halfw, halfh = center_crop_size[0]//2, center_crop_size[1]//2
    return x[centerw-halfw:centerw+halfw+1,centerh-halfh:centerh+halfh+1, :]

def predict_10_crop(img, ix, top_n=5, plot=False, preprocess=True, debug=False):
    flipped_X = np.fliplr(img)
    crops = [
        img[:299,:299, :], # Upper Left
        img[:299, img.shape[1]-299:, :], # Upper Right
        img[img.shape[0]-299:, :299, :], # Lower Left
        img[img.shape[0]-299:, img.shape[1]-299:, :], # Lower Right
        center_crop(img, (299, 299)),
        
        flipped_X[:299,:299, :],
        flipped_X[:299, flipped_X.shape[1]-299:, :],
        flipped_X[flipped_X.shape[0]-299:, :299, :],
        flipped_X[flipped_X.shape[0]-299:, flipped_X.shape[1]-299:, :],
        center_crop(flipped_X, (299, 299))
    ]
    if preprocess:
        crops = [preprocess_input(x.astype('float32')) for x in crops]

    if plot:
        fig, ax = plt.subplots(2, 5, figsize=(10, 4))
        ax[0][0].imshow(crops[0])
        ax[0][1].imshow(crops[1])
        ax[0][2].imshow(crops[2])
        ax[0][3].imshow(crops[3])
        ax[0][4].imshow(crops[4])
        ax[1][0].imshow(crops[5])
        ax[1][1].imshow(crops[6])
        ax[1][2].imshow(crops[7])
        ax[1][3].imshow(crops[8])
        ax[1][4].imshow(crops[9])
    
    y_pred = model.predict(np.array(crops))
    preds = np.argmax(y_pred, axis=1)
    top_n_preds= np.argpartition(y_pred, -top_n)[:,-top_n:]
    if debug:
        print('Top-1 Predicted:', preds)
        print('Top-5 Predicted:', top_n_preds)
        print('True Label:', y_test[ix])
    return preds, top_n_preds

    
ix = 13001
predict_10_crop(X_test[ix], ix, top_n=5, plot=True, preprocess=False, debug=True)

ix = 13001
predict_10_crop(X_test[ix], ix, top_n=5, plot=True, preprocess=True, debug=True)

%%time
preds_10_crop = {}
for ix in range(len(X_test)):
    if ix % 1000 == 0:
        print(ix)
    preds_10_crop[ix] = predict_10_crop(X_test[ix], ix)

preds_uniq = {k: np.unique(v[0]) for k, v in preds_10_crop.items()}
preds_hist = np.array([len(x) for x in preds_uniq.values()])

plt.hist(preds_hist, bins=11)
plt.title('Number of unique predictions per image')

preds_top_1 = {k: collections.Counter(v[0]).most_common(1) for k, v in preds_10_crop.items()}

top_5_per_ix = {k: collections.Counter(preds_10_crop[k][1].reshape(-1)).most_common(5) 
                for k, v in preds_10_crop.items()}
preds_top_5 = {k: [y[0] for y in v] for k, v in top_5_per_ix.items()}

%%time
right_counter = 0
for i in range(len(y_test)):
    guess, actual = preds_top_1[i][0][0], y_test[i]
    if guess == actual:
        right_counter += 1
        
print('Top-1 Accuracy, 10-Crop: {0:.2f}%'.format(right_counter / len(y_test) * 100))

%%time
top_5_counter = 0
for i in range(len(y_test)):
    guesses, actual = preds_top_5[i], y_test[i]
    if actual in guesses:
        top_5_counter += 1
        
print('Top-5 Accuracy, 10-Crop: {0:.2f}%'.format(top_5_counter / len(y_test) * 100))

y_pred = [x[0][0] for x in preds_top_1.values()]

@interact(page=[0, int(len(X_test)/20)])
def show_images_prediction(page=0):
    page_size = 20
    nrows = 4
    ncols = 5
    fig, axes = plt.subplots(nrows=nrows, ncols=ncols, figsize=(12, 12))
    fig.set_size_inches(12, 8)
    #fig.tight_layout()
    #imgs = np.random.choice((y_all == n_class).nonzero()[0], nrows * ncols)
    start_i = page * page_size
    for i, ax in enumerate(axes.flat):
        im = ax.imshow(X_test[i+start_i])
        ax.set_axis_off()
        ax.title.set_visible(False)
        ax.xaxis.set_ticks([])
        ax.yaxis.set_ticks([])
        for spine in ax.spines.values():
            spine.set_visible(False)
        predicted = ix_to_class[y_pred[i+start_i]]
        match = predicted ==  ix_to_class[y_test[start_i + i]]
        ec = (1, .5, .5)
        fc = (1, .8, .8)
        if match:
            ec = (0, .6, .1)
            fc = (0, .7, .2)
        # predicted label
        ax.text(0, 400, 'P: ' + predicted, size=10, rotation=0,
            ha="left", va="top",
             bbox=dict(boxstyle="round",
                   ec=ec,
                   fc=fc,
                   )
             )
        if not match:
            # true label
            ax.text(0, 480, 'A: ' + ix_to_class[y_test[start_i + i]], size=10, rotation=0,
                ha="left", va="top",
                 bbox=dict(boxstyle="round",
                       ec=ec,
                       fc=fc,
                       )
                 )
    plt.subplots_adjust(left=0, wspace=1, hspace=0)
    plt.show()

%%time
from sklearn.metrics import confusion_matrix
import itertools

def plot_confusion_matrix(cm, classes,
                          normalize=False,
                          title='Confusion matrix',
                          cmap=plt.cm.Blues):
    """
    This function prints and plots the confusion matrix.
    Normalization can be applied by setting `normalize=True`.
    """
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=90)
    plt.yticks(tick_marks, classes)

    if normalize:
        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
        print("Normalized confusion matrix")
    else:
        print('Confusion matrix, without normalization')

    print(cm)

    thresh = cm.max() / 2.
    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j, i, cm[i, j],
                 horizontalalignment="center",
                 color="white" if cm[i, j] > thresh else "black")

    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predicted label')
    

# Compute confusion matrix
cnf_matrix = confusion_matrix(y_test, y_pred)
np.set_printoptions(precision=2)

class_names = [ix_to_class[i] for i in range(101)]

plt.figure()
fig = plt.gcf()
fig.set_size_inches(32, 32)
plot_confusion_matrix(cnf_matrix, classes=class_names,
                      title='Confusion matrix, without normalization',
                      cmap=plt.cm.cool)
plt.show()

corrects = collections.defaultdict(int)
incorrects = collections.defaultdict(int)
for (pred, actual) in zip(y_pred, y_test):
    if pred == actual:
        corrects[actual] += 1
    else:
        incorrects[actual] += 1

class_accuracies = {}
for ix in range(101):
    class_accuracies[ix] = corrects[ix]/250

plt.hist(list(class_accuracies.values()), bins=20)
plt.title('Accuracy by Class histogram')

sorted_class_accuracies = sorted(class_accuracies.items(), key=lambda x: -x[1])
[(ix_to_class[c[0]], c[1]) for c in sorted_class_accuracies]

pic_path = '/home/stratospark/Downloads/soup.jpg'
pic = img.imread(pic_path)
preds = predict_10_crop(np.array(pic), 0)[0]
best_pred = collections.Counter(preds).most_common(1)[0][0]
print(ix_to_class[best_pred])
plt.imshow(pic)

import urllib.request

@interact
def predict_remote_image(url='http://themodelhouse.tv/wp-content/uploads/2016/08/hummus.jpg'):
    with urllib.request.urlopen(url) as f:
        pic = plt.imread(f, format='jpg')
        preds = predict_10_crop(np.array(pic), 0)[0]
        best_pred = collections.Counter(preds).most_common(1)[0][0]
        print(ix_to_class[best_pred])
        plt.imshow(pic)

with open('model.json', 'w') as f:
    f.write(model.to_json())

import json

json.dumps(ix_to_class)