--- a
+++ b/usage/usage.py
@@ -0,0 +1,138 @@
+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')
+