from __future__ import absolute_import, division, print_function

from os import environ, getcwd

from os.path import join

import keras

import numpy as np

import pandas as pd

import sklearn as skl

import tensorflow as tf

from keras.applications.vgg19 import VGG19

from keras.applications import DenseNet169, InceptionResNetV2, DenseNet201

from keras.applications import NASNetMobile

from keras.layers import Dense, GlobalAveragePooling2D

from keras.metrics import binary_accuracy, binary_crossentropy

from keras.models import Model

from keras.optimizers import Adam

from keras.preprocessing.image import ImageDataGenerator

from mura import Mura

pd.set_option('display.max_rows', 20)

pd.set_option('precision', 4)

np.set_printoptions(precision=4)

environ['TF_CPP_MIN_LOG_LEVEL'] = '2'  # Shut up tensorflow!

print("tf : {}".format(tf.__version__))

print("keras : {}".format(keras.__version__))

print("numpy : {}".format(np.__version__))

print("pandas : {}".format(pd.__version__))

print("sklearn : {}".format(skl.__version__))

# Hyper-parameters / Globals

BATCH_SIZE = 8  # tweak to your GPUs capacity

IMG_HEIGHT = 420  # ResNetInceptionv2 & Xception like 299, ResNet50/VGG/Inception 224, NASM 331

IMG_WIDTH = IMG_HEIGHT

CHANNELS = 3

DIMS = (IMG_HEIGHT, IMG_WIDTH, CHANNELS)  # blame theano

MODEL_TO_EVAL1 = './models/DenseNet169_221_NEW_HIST.hdf5'

MODEL_TO_EVAL2 = './models/DenseNet169_320_NEW_HIST.hdf5'

MODEL_TO_EVAL3 = './models/DenseNet169_420_NEW_HIST.hdf5'

MODEL_TO_EVAL4 = './models/DenseNet169_520_NEW_HIST.hdf5'

MODEL_TO_EVAL5 = './models/DenseNet169_620_NEW_HIST.hdf5'

#MODEL_TO_EVAL6 = './models/DenseNet169_420_SHOULDER_NEW_HIST.hdf5'

#MODEL_TO_EVAL7 = './models/DenseNet169_420_HAND_NEW_HIST.hdf5'

#MODEL_TO_EVAL3 = './models/DenseNet169_420_FOREARM.hdf5'

#MODEL_TO_EVAL4 = './models/DenseNet169_420_HAND.hdf5'

#MODEL_TO_EVAL5 = './models/DenseNet169_420_HUMERUS.hdf5'

#MODEL_TO_EVAL6 = './models/DenseNet169_420_SHOULDER.hdf5'

#MODEL_TO_EVAL7 = './models/DenseNet169_420_WRIST.hdf5'

DATA_DIR = 'MURA-v1.1/'

EVAL_CSV = 'valid.csv'

EVAL_DIR = 'data/val'

# load up our csv with validation factors

data_dir = join(getcwd(), DATA_DIR)

eval_csv = join(data_dir, EVAL_CSV)

df = pd.read_csv(eval_csv, names=['img', 'label'], header=None)

eval_imgs = df.img.values.tolist()

eval_labels = df.label.values.tolist()

eval_datagen = ImageDataGenerator(rescale=1. / 255)

eval_generator = eval_datagen.flow_from_directory(

    EVAL_DIR, class_mode='binary', shuffle=False, target_size=(IMG_HEIGHT, IMG_WIDTH), batch_size=BATCH_SIZE)

n_samples = eval_generator.samples

base_model = DenseNet169(input_shape=DIMS, weights='imagenet', include_top=False)

x = base_model.output

x = GlobalAveragePooling2D(name='avg_pool')(x)  # comment for RESNET

x = Dense(1, activation='sigmoid', name='predictions')(x)

model = Model(inputs=base_model.input, outputs=x)

model.load_weights(MODEL_TO_EVAL1)

model.compile(optimizer=Adam(lr=1e-3), loss=binary_crossentropy, metrics=['binary_accuracy'])

score, acc = model.evaluate_generator(eval_generator, n_samples / BATCH_SIZE)

#print(model.metrics_names)

print('==> Metrics with eval')

print("loss :{:0.4f} \t Accuracy:{:0.4f}".format(score, acc))

y_pred1 = model.predict_generator(eval_generator, n_samples / BATCH_SIZE)

#IMG_HEIGHT = 520

#IMG_WIDTH  = 520

DIMS = (IMG_HEIGHT, IMG_WIDTH, CHANNELS)

eval_generator = eval_datagen.flow_from_directory(

    EVAL_DIR, class_mode='binary', shuffle=False, target_size=(IMG_HEIGHT, IMG_WIDTH), batch_size=BATCH_SIZE)

base_model = DenseNet169(input_shape=DIMS, weights='imagenet', include_top=False)

x = base_model.output

x = GlobalAveragePooling2D(name='avg_pool')(x)  # comment for RESNET

x = Dense(1, activation='sigmoid', name='predictions')(x)

model = Model(inputs=base_model.input, outputs=x)

model.load_weights(MODEL_TO_EVAL2)

model.compile(optimizer=Adam(lr=1e-3), loss=binary_crossentropy, metrics=['binary_accuracy'])

score, acc = model.evaluate_generator(eval_generator, n_samples / BATCH_SIZE)

#print(model.metrics_names)

print('==> Metrics with eval')

print("loss :{:0.4f} \t Accuracy:{:0.4f}".format(score, acc))

y_pred2 = model.predict_generator(eval_generator, n_samples / BATCH_SIZE)

IMG_HEIGHT = 420

IMG_WIDTH  = 420

DIMS = (IMG_HEIGHT, IMG_WIDTH, CHANNELS)

eval_generator = eval_datagen.flow_from_directory(

    EVAL_DIR, class_mode='binary', shuffle=False,target_size=(IMG_HEIGHT, IMG_WIDTH), batch_size =BATCH_SIZE)

base_model = DenseNet169(input_shape=DIMS, weights='imagenet', include_top=False)

x = base_model.output

x = GlobalAveragePooling2D(name='avg_pool')(x)  # comment for RESNET

x = Dense(1, activation='sigmoid', name='predictions')(x)

model = Model(inputs=base_model.input, outputs=x)

model.load_weights(MODEL_TO_EVAL3)

model.compile(optimizer=Adam(lr=1e-3), loss=binary_crossentropy, metrics=['binary_accuracy'])

score, acc = model.evaluate_generator(eval_generator, n_samples / BATCH_SIZE)

#print(model.metrics_names)

print('==> Metrics with eval')

print("loss :{:0.4f} \t Accuracy:{:0.4f}".format(score, acc))

y_pred3 = model.predict_generator(eval_generator, n_samples / BATCH_SIZE)

#IMG_HEIGHT = 520

#IMG_WIDTH  = 520

DIMS = (IMG_HEIGHT, IMG_WIDTH, CHANNELS)

eval_generator = eval_datagen.flow_from_directory(

       EVAL_DIR, class_mode='binary', shuffle=False,target_size=(IMG_HEIGHT, IMG_WIDTH), batch_size=BATCH_SIZE)

base_model = DenseNet169(input_shape=DIMS, weights='imagenet', include_top=False)

x = base_model.output

x = GlobalAveragePooling2D(name='avg_pool')(x)  # comment for RESNET

x = Dense(1, activation='sigmoid', name='predictions')(x)

model = Model(inputs=base_model.input, outputs=x)

model.load_weights(MODEL_TO_EVAL4)

model.compile(optimizer=Adam(lr=1e-3), loss=binary_crossentropy, metrics=['binary_accuracy'])

score, acc = model.evaluate_generator(eval_generator, n_samples / BATCH_SIZE)

#print(model.metrics_names)

print('==> Metrics with eval')

print("loss :{:0.4f} \t Accuracy:{:0.4f}".format(score, acc))

y_pred4 = model.predict_generator(eval_generator, n_samples / BATCH_SIZE)

IMG_HEIGHT = 420

IMG_WIDTH  = 420

DIMS = (IMG_HEIGHT, IMG_WIDTH, CHANNELS)

eval_generator = eval_datagen.flow_from_directory(

     EVAL_DIR, class_mode='binary', shuffle=False,target_size=(IMG_HEIGHT, IMG_WIDTH), batch_size=BATCH_SIZE)

base_model = DenseNet169(input_shape=DIMS, weights='imagenet', include_top=False)

x = base_model.output

x = GlobalAveragePooling2D(name='avg_pool')(x)  # comment for RESNET

x = Dense(1, activation='sigmoid', name='predictions')(x)

model = Model(inputs=base_model.input, outputs=x)

model.load_weights(MODEL_TO_EVAL5)

model.compile(optimizer=Adam(lr=1e-3), loss=binary_crossentropy, metrics=['binary_accuracy'])

score, acc = model.evaluate_generator(eval_generator, n_samples / BATCH_SIZE)

#print(model.metrics_names)

print('==> Metrics with eval')

print("loss :{:0.4f} \t Accuracy:{:0.4f}".format(score, acc))

y_pred5 = model.predict_generator(eval_generator, n_samples / BATCH_SIZE)

#print(y_pred)

df_filenames = pd.Series(np.array(eval_generator.filenames), name='filenames')

df_classes   = pd.Series(np.array(eval_generator.classes), name='classes')

#print(eval_generator.filenames)

#print(eval_generator.classes)

mura = Mura(eval_generator.filenames, y_true=eval_generator.classes, y_pred1=y_pred1, y_pred2=y_pred2, y_pred3=y_pred3, y_pred4=y_pred4, y_pred5=y_pred5)

print('==> Metrics with predict')

print(mura.metrics())

print(mura.metrics_by_encounter())

print(mura.metrics_by_study_type())