import sys
from os import path
import SimpleITK as sitk
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import load_model
from focal_loss import BinaryFocalLoss
import json
import matplotlib.pyplot as plt
import numpy as np 

from sfransen.Saliency.base import *
from sfransen.Saliency.integrated_gradients import *
from sfransen.utils_quintin import *  
from sfransen.DWI_exp import preprocess
from sfransen.DWI_exp.helpers import *
from sfransen.DWI_exp.callbacks import dice_coef
from sfransen.FROC.blob_preprocess import *
from sfransen.FROC.cal_froc_from_np import *




# train_10h_t2_b50_b400_b800_b1400_adc
SERIES = ['t2','b50','b400','b800','b1400','adc']
MODEL_PATH = f'./../train_output/train_10h_t2_b50_b400_b800_b1400_adc/models/train_10h_t2_b50_b400_b800_b1400_adc.h5'
YAML_DIR = f'./../train_output/train_10h_t2_b50_b400_b800_b1400_adc'
################ constants ############
DATA_DIR = "./../data/Nijmegen paths/"
TARGET_SPACING = (0.5, 0.5, 3) 
INPUT_SHAPE = (192, 192, 24, len(SERIES))
IMAGE_SHAPE = INPUT_SHAPE[:3]

# import val_indx
# DATA_SPLIT_INDEX = read_yaml_to_dict('./../data/Nijmegen paths/train_val_test_idxs.yml')
# TEST_INDEX = DATA_SPLIT_INDEX['val_set0']

experiment_path = f'./../train_output/train_10h_t2_b50_b400_b800_b1400_adc/froc_metrics.yml'
experiment_metrics = read_yaml_to_dict(experiment_path)
DATA_SPLIT_INDEX = read_yaml_to_dict('./../data/Nijmegen paths/train_val_test_idxs.yml')
TEST_INDEX = DATA_SPLIT_INDEX['test_set0']

top_10_idx = np.argsort(experiment_metrics['roc_pred'])[-10:]
TEST_INDEX = [TEST_INDEX[i] for i in top_10_idx]

########## load images ##############
images, image_paths = {s: [] for s in SERIES}, {}
segmentations = []
print_(f"> Loading images into RAM...")

for s in SERIES:
    with open(path.join(DATA_DIR, f"{s}.txt"), 'r') as f:
        image_paths[s] = [l.strip() for l in f.readlines()]
with open(path.join(DATA_DIR, f"seg.txt"), 'r') as f:
    seg_paths = [l.strip() for l in f.readlines()]
num_images = len(seg_paths)

# Read and preprocess each of the paths for each SERIES, and the segmentations.
for img_idx in TEST_INDEX[:5]: #for less images
    img_s = {s: sitk.ReadImage(image_paths[s][img_idx], sitk.sitkFloat32) 
        for s in SERIES}
    seg_s = sitk.ReadImage(seg_paths[img_idx], sitk.sitkFloat32)
    img_n, seg_n = preprocess(img_s, seg_s, 
        shape=IMAGE_SHAPE, spacing=TARGET_SPACING)
    for seq in img_n:
        images[seq].append(img_n[seq])
    segmentations.append(seg_n)

images_list = [images[s] for s in images.keys()]
images_list = np.transpose(images_list, (1, 2, 3, 4, 0))
 
########### load module ##################
dependencies = {
    'dice_coef': dice_coef
}
reconstructed_model = load_model(MODEL_PATH, custom_objects=dependencies)

# reconstructed_model.layers[-1].activation = tf.keras.activations.linear

print('START prediction')

ig = IntegratedGradients(reconstructed_model)
saliency_map = []
for img_idx in range(len(images_list)):
    input_img = np.resize(images_list[img_idx],(1,192,192,24,len(SERIES)))
    saliency_map.append(ig.get_mask(input_img).numpy())
    print("size saliency map is:",np.shape(saliency_map))

np.save('saliency',saliency_map)

# Christian Roest, [11-3-2022 15:30]
# input_img heeft dimensies (1, 48, 48, 8, 8)

# reconstructed_model.summary(line_length=120)

# make predictions on all val_indx
print('START saliency')
# predictions_blur = reconstructed_model.predict(images_list, batch_size=1)