text: #!/usr/bin/env python
  # -*- coding: utf-8 -*
  
  # test base
  import pytest
  import hypothesis.strategies as st
  from hypothesis import given, settings
  from  hypothesis import HealthCheck as HC
  
  # useful
  import os
  import itk
  import numpy as np
  
  # third part
  from FemurSegmentation.utils import image2array, array2image
  
  #finction to test
  from FemurSegmentation.filters import binary_threshold
  from FemurSegmentation.filters import threshold
  from FemurSegmentation.filters import median_filter
  from FemurSegmentation.filters import gaussian_smoothing
  from FemurSegmentation.filters import hessian_matrix
  from FemurSegmentation.filters import get_eigenvalues_map
  from FemurSegmentation.filters import connected_components #TODO test me
  from FemurSegmentation.filters import relabel_components # TODO test me
  from FemurSegmentation.filters import unsharp_mask
  from FemurSegmentation.filters import normalize_image_gl
  
  
  
  __author__ = ['Riccardo Biondi']
  __email__  = ['riccardo.biondi7@unibo.it']
  
  
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  pixel_types = [itk.UC, itk.SS]
  out_types = [itk.UC, itk.SS, itk.F]
  
  @st.composite
  def random_image_strategy(draw) :
      PixelType = draw(st.sampled_from(pixel_types))
      ImageType = itk.Image[PixelType, 3]
  
      rndImage = itk.RandomImageSource[ImageType].New()
      rndImage.SetSize(200)
      rndImage.Update()
  
      return rndImage.GetOutput()
  
  
  
      # ████████ ███████ ███████ ████████
      #    ██    ██      ██         ██
      #    ██    █████   ███████    ██
      #    ██    ██           ██    ██
      #    ██    ███████ ███████    ██
  
  
  
  @given(random_image_strategy(), st.integers(20, 30), st.integers(200, 250))
  @settings(max_examples = 20, deadline = None,
            suppress_health_check = (HC.too_slow, ))
  def test_binary_threshold_upper_lower(image, lower_thr, upper_thr) :
      '''
      Given :
          - itk image obj
          - lower threshold value
          - upper threshold value
      Then :
          - apply binary threshold
      Assert :
          - binary image as output
          - masked original image value between upper and lower thr
      '''
      thr = binary_threshold(image, upper_thr, lower_thr)
      thr = itk.GetArrayFromImage(thr)
      to_mask = itk.GetArrayFromImage(image)
  
      assert np.all(np.unique(thr) == [0, 1])
      assert np.max(to_mask[thr != 0]) < upper_thr
      assert np.min(to_mask[thr != 0]) > lower_thr
  
  
  @given(random_image_strategy(), st.integers(20, 30), st.integers(200, 250))
  @settings(max_examples = 20, deadline = None,
            suppress_health_check = (HC.too_slow, ))
  def test_binary_threshold_inside_outside(image, inside, outside) :
      '''
      Given:
          - itk image obj
          - inside value
          - outside value
      Then:
          - apply binary threshold
      Assert:
          - binary image values are inside and outside
      '''
      thr = binary_threshold(image, 230, 15, inside, outside)
      thr = itk.GetArrayFromImage(thr)
  
      assert np.all(np.unique(thr) == [inside, outside])
  
  
  
  @given(random_image_strategy(), st.sampled_from(out_types))
  @settings(max_examples = 20, deadline = None,
            suppress_health_check = (HC.too_slow, ))
  def test_binary_threshold_output_type(image, out_type) :
      '''
      Given :
          - itk image obj
          - itk pixel type
      Then :
          - apply threshold specyfiyng the output type
      Assert:
          - output type match the specified one
      '''
      thr = binary_threshold(image, 20, 150, out_type = out_type)
      PixelType, _ = itk.template(thr)[1]
  
      assert PixelType == out_type
  
  
  @given(random_image_strategy(), st.integers(1, 5))
  @settings(max_examples = 20, deadline = None,
            suppress_health_check = (HC.too_slow, ))
  def test_median_filter(image, radius) :
      '''
      Given :
          - itk Image
          - radius
      Then :
          - call median filter function
      Assert :
          - image is correctly setted
          - redius is correctly setted
      '''
      median = median_filter(image, radius)
      inArray, inInfo = image2array(image)
  
      setArray, setInfo = image2array(median.GetInput())
  
      assert np.all(median.GetRadius() == [radius] * 3)
      assert np.all(np.isclose(setArray, inArray))
      assert inInfo == setInfo
  
  
  
  @settings(max_examples = 20, deadline = None,
            suppress_health_check = (HC.too_slow, ))
  @given(random_image_strategy(), st.floats(0.1, 1.5), st.booleans())
  def test_gaussian_smoothing(image, sigma, normalize_across_scale) :
      '''
      Given:
          - image
          - sigma
          - flag
      Then :
          - apply gaussian_smoothing
      Assert :
          - image is correctly setted
          - sigma is correctly setted
          - flag is correctly setted
      '''
      smooth = gaussian_smoothing(image, sigma, normalize_across_scale)
  
      inArr, inInfo = image2array(image)
      setArr, setInfo = image2array(smooth.GetInput())
  
      assert np.all(np.isclose(inArr, setArr))
      assert inInfo == setInfo
      assert smooth.GetSigma() == sigma
      assert smooth.GetNormalizeAcrossScale() == normalize_across_scale
  
  
  
  @settings(max_examples = 20, deadline = None,
            suppress_health_check = (HC.too_slow, ))
  @given(random_image_strategy(), st.floats(0.1, 1.5), st.booleans())
  def test_hessian_matrix(image, sigma, normalize_across_scale) :
      '''
      Given:
          - image
          - sigma
          - flag
      Then :
          - compute hessian
      Assert :
          - image is correctly setted
          - sigma is correctly setted
          - flag is correctly setted
      '''
      hessian = hessian_matrix(image, sigma, normalize_across_scale)
  
      inArr, inInfo = image2array(image)
      setArr, setInfo = image2array(hessian.GetInput())
  
      assert np.all(np.isclose(inArr, setArr))
      assert inInfo == setInfo
      assert hessian.GetSigma() == sigma
      assert hessian.GetNormalizeAcrossScale() == normalize_across_scale
  
  
  
  @settings(max_examples = 20, deadline = None,
            suppress_health_check = (HC.too_slow, ))
  @given(random_image_strategy(), st.floats(0.1, 0.5), st.integers(1, 3))
  def test_eigenvalues(image, sigma, order ) :
      '''
      Given :
          - itk.Image
          - sigma
          - order
      Then :
          - compute the hessian matrix
          - initialize the eigenvalues filter
      Assert :
          - the filter parameters are correctly initialized
      '''
      #create, update and get the output of the hessian filter
      # that because we need to provide an hessian matrix as input to the
      # testing filter
      hess_filter = hessian_matrix(image, sigma)
      _ = hess_filter.Update()
      hess = hess_filter.GetOutput()
  
      arr1, info1 = image2array(hess)
  
      eigen = get_eigenvalues_map(hess, order = order)
  
      arr2, info2 = image2array(eigen.GetInput())
  
      # in this assertion I am using the function np.isclose because the two array
      #to match are of floating point type, so there can be some imprecisions due
      # computer eps
      assert np.all(np.isclose(arr1, arr2))
      assert info1 == info2
  
  
  @given(random_image_strategy(),
      st.floats(0.2, 1.5),
      st.floats(0.2, 2.5),
      st.floats(0., 15.))
  @settings(max_examples=20, deadline=None,
            suppress_health_check=(HC.too_slow, ))
  def test_unsharp_mask_initialization(image, sigma, amount, thr):
      '''
      Given:
          - itk.Image
          - sigma
          - amount
          - threshold
      Then:
          - Initialize the unsharp mask filter
      Assert:
          - Values correctly initialized
      '''
      um = unsharp_mask(image, sigma, amount, thr)
      in_image, in_info = image2array(image)
      set_image, set_info = image2array(um.GetInput())
  
      assert np.all(np.isclose(set_image, in_image))
      assert set_info == in_info
      assert np.isclose(sigma, *um.GetSigmas())
      assert np.isclose(amount, um.GetAmount())
      assert np.isclose(thr, um.GetThreshold())
  
  
  @given(random_image_strategy())
  @settings(max_examples=20, deadline=None,
            suppress_health_check=(HC.too_slow, ))
  def test_normalize_gl_wo_roi(image):
      '''
      Given:
          - itk.Image
      Then:
          - normalize according to mean and standard deviation
      Assert:
          - image info are preserved
          - out image GL mean is close to 0
          - out image GL std dev is close to 1
      '''
      normalized = normalize_image_gl(image)
      _, in_info = image2array(image)
      out_arr, out_info = image2array(normalized)
  
      assert in_info == out_info
      assert np.isclose(np.mean(out_arr), 0., atol = 1e-7)
      assert np.isclose(np.std(out_arr), 1.,  atol = 1e-7)
  
  
  @given(random_image_strategy(), st.integers(20, 30), st.integers(200, 250))
  @settings(max_examples=20, deadline=None,
            suppress_health_check=(HC.too_slow, ))
  def test_normalize_gl_w_roi(image, lower, upper):
      '''
      Given:
          - itk.Image
          - uppert threshold value
          - lower threshold value
      Then:
          - apply a inary threshold to find a ROI
          - normaize the input image according to the mean and std deviation
              inside the ROI
      Assert:
          - image info are preserved
          - the mean inside the ROI is close to zero
          - the std dev inside the ROI in close to 1
          - mean and std dev of the whole image are different from the one
          inside the ROI
      '''
      roi = binary_threshold(image, upper, lower)
      normaized = normalize_image_gl(image, roi)
  
      _, info = image2array(image)
      out_arr, out_info = image2array(normaized)
      r, _ = image2array(roi)
  
      assert out_info == info
      assert np.isclose(np.mean(out_arr[r == 1]), 0.,  atol = 1e-7)
      assert np.isclose(np.std(out_arr[r == 1]), 1.,  atol = 1e-7)
      assert ~np.isclose(np.mean(out_arr[r == 1]), np.mean(out_arr))
      assert ~np.isclose(np.std(out_arr[r == 1]), np.std(out_arr))
  