import os

import time

import glob

import sys

import numpy as np

import openslide

import matplotlib.pyplot as plt

import matplotlib.gridspec as gridspec

from mpl_toolkits.axes_grid1 import make_axes_locatable

import cv2

kernel = np.ones((5,5), np.uint8) 

from PIL import Image

sys.path.append('..')

from DigiPathAI.Segmentation import getSegmentation

digestpath_imgs = ['../examples/colon-cancer-1.tiff']

paip_imgs       = ['../examples/examples/tcga/liver-1.svs']

tcga_imgs       = ['../examples/examples/tcga/TCGA-CM.svs']

camelyon_imgs   = ['../examples/Camelyon_16_Test_samples/camelyon-1.tif']

models = ['dense', 'inception', 'deeplabv3', 'ensemble', 'epistemic']

def iou(gt, mask):

    gt = np.uint8(np.array(gt>0.1))

    mask = np.uint8(np.array(mask>0.1))

    nr = np.sum(gt*mask)*2.0

    dr = np.sum(gt + mask)*1.0

    return nr/dr 

for path in tcga_imgs:

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

  base_path = os.path.splitext(path)[0]

  print (ext, base_path, base_path[:-5])

  quick = True

  tta_list = ['FLIP_LEFT_RIGHT', 'ROTATE_90'] #, 'ROTATE_180', 'ROTATE_270']

  for model in models:

    print (model, quick, path, "======================================")

    if model == 'ensemble': 

      quick = False

    elif model == 'epistemic':

      quick = False

      tta_list = None

    """

    getSegmentation(path, 

          patch_size  = 512, 

          stride_size = 512,

          batch_size  = 4,

          quick       = quick,

          tta_list    = tta_list,

          crf         = False,

          mask_path   = base_path + '-DigiPathAI_{}_mask'.format(model) + '.tiff',

          uncertainty_path   = base_path + '-DigiPathAI_{}_uncertainty'.format(model)+ '.tiff',

          status      = None,

          mask_level = 4,

          model       = model,

          mode        = 'breast')

    """

    slide = openslide.OpenSlide(path)

    level = len(slide.level_dimensions) - 1

    img_dimensions = slide.level_dimensions[-1]

    img = np.array(slide.read_region((0,0), level, img_dimensions).convert('RGB'))

    mask = openslide.OpenSlide(base_path + '-DigiPathAI_{}_mask'.format(model) + '.tiff')

    level = np.where([1 if ((dim[0] == img_dimensions[0])*(dim[1] == img_dimensions[1])) else 0 for dim in mask.level_dimensions])[0]

    mask = np.array(mask.read_region((0,0), level, img_dimensions).convert('L'))

    mask = cv2.dilate(mask, kernel, iterations=2) 

    uncertainty = openslide.OpenSlide(base_path + '-DigiPathAI_{}_uncertainty'.format(model) + '.tiff')

    level = np.where([1 if ((dim[0] == img_dimensions[0])*(dim[1] == img_dimensions[1])) else 0 for dim in uncertainty.level_dimensions])[0]

    uncertainty = np.array(uncertainty.read_region((0,0), level, img_dimensions).convert('L'))

    gt = openslide.OpenSlide(glob.glob(base_path + '-gt*')[0])

    level = np.where([1 if ((dim[0] == img_dimensions[0])*(dim[1] == img_dimensions[1])) else 0 for dim in gt.level_dimensions])[0]

    gt = np.array(gt.read_region((0,0), level, img_dimensions).convert('L'))*255

    gt = np.array(Image.fromarray(gt).resize(img_dimensions, Image.NEAREST))

    mask = np.array(Image.fromarray(mask).resize(img_dimensions, Image.NEAREST))

    uncertainty = np.array(Image.fromarray(uncertainty).resize(img_dimensions))/255.0

    # gt = np.array(Image.open(base_path + 'gt.jpg').convert('L').resize(img_dimensions))

    print ("path: {}, model: {}, IoU: {}".format(path, model, iou(gt, mask)))

    fig, ax = plt.subplots(1, 4, figsize=(10, 40))

    im_ = ax[0].imshow(img)

    ax[0].set_xticklabels([])

    ax[0].set_yticklabels([])

    ax[0].set_xticks([])

    ax[0].set_yticks([])

    ax[0].set_aspect('equal')

    ax[0].tick_params(bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off' )

    im_ = ax[1].imshow(img)

    gt_ = ax[1].imshow(gt, alpha = 0.5, cmap='gray')

    ax[1].set_xticklabels([])

    ax[1].set_yticklabels([])

    ax[1].set_xticks([])

    ax[1].set_yticks([])

    ax[1].set_aspect('equal')

    ax[1].tick_params(bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off' )

    im_ = ax[2].imshow(img)

    mask_ = ax[2].imshow(mask, alpha = 0.5, cmap='gray')

    ax[2].set_xticklabels([])

    ax[2].set_yticklabels([])

    ax[2].set_xticks([])

    ax[2].set_yticks([])

    ax[2].set_aspect('equal')

    ax[2].tick_params(bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off' )

    im_ = ax[3].imshow(img)

    uncertain_ = ax[3].imshow(uncertainty, alpha = 0.5, cmap=plt.cm.RdBu_r)

    ax[3].set_xticklabels([])

    ax[3].set_yticklabels([])

    ax[3].set_xticks([])

    ax[3].set_yticks([])

    ax[3].set_aspect('equal')

    ax[3].tick_params(bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off' )

    cax = fig.add_axes([ax[3].get_position().x1 + 0.01,

              ax[3].get_position().y0,

              0.01,

              ax[3].get_position().y1-ax[3].get_position().y0])

    fig.colorbar(uncertain_, cax=cax)

    plt.savefig(base_path+'DigiPath_Results_{}.png'.format(model), bbox_inches='tight')