import os

import time

import glob

import sys

import numpy as np

import openslide

import matplotlib.pyplot as plt

import cv2

import matplotlib.gridspec as gridspec

from mpl_toolkits.axes_grid1 import make_axes_locatable

from PIL import Image

sys.path.append('..')

from DigiPathAI.Segmentation import getSegmentation

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

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

for path in digestpath_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  = 256, 

          stride_size = 128,

          batch_size  = 4,

          quick       = quick,

          tta_list    = tta_list,

          crf         = False,

          probs_path  = base_path + '-DigiPathAI_{}_probs'.format(model) + '.tiff',

          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        = 'colon')

    """

    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'))

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

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

    probs = np.array(probs.read_region((0,0), level, img_dimensions).convert('L'))/255.0

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

    fig, ax = plt.subplots(2, 2, figsize=(14, 20))

    fig.tight_layout()

    im_ = ax[0][0].imshow(img)

    ax[0][0].set_xticklabels([])

    ax[0][0].set_yticklabels([])

    ax[0][0].set_xticks([])

    ax[0][0].set_yticks([])

    ax[0][0].set_aspect('equal')

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

    # ax[0][0].title.set_text("WSI Slide")

    gt_ = ax[0][1].imshow(gt,cmap='gray')

    ax[0][1].set_xticklabels([])

    ax[0][1].set_yticklabels([])

    ax[0][1].set_xticks([])

    ax[0][1].set_yticks([])

    ax[0][1].set_aspect('equal')

    ax[0][1].tick_params(bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off' )

    # ax[0][1].title.set_text("Ground Truth")

    pred_ = ax[1][1].imshow(mask,cmap='gray')

    ax[1][1].set_xticklabels([])

    ax[1][1].set_yticklabels([])

    ax[1][1].set_xticks([])

    ax[1][1].set_yticks([])

    ax[1][1].set_aspect('equal')

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

    # ax[1][1].title.set_text("Ground Truth")

    prob_map_ = ax[1][0].imshow(probs, cmap=plt.cm.jet)

    ax[1][0].set_xticklabels([])

    ax[1][0].set_yticklabels([])

    ax[1][0].set_xticks([])

    ax[1][0].set_yticks([])

    ax[1][0].set_aspect('equal')

    ax[1][0].tick_params(bottom='off', top='off', labelbottom='off', right='off', left='off', labelleft='off' )

    # ax[1][0].title.set_text("Probability Map")

    cax = fig.add_axes([ax[1][0].get_position().x1 + 0.01,

              ax[1][0].get_position().y0,

              0.01,

              ax[1][0].get_position().y1-ax[1][0].get_position().y0])

    fig.colorbar(prob_map_, cax=cax)

    plt.savefig('im2.png',bbox_inches = 'tight',pad_inches = 0.1)