def test_rotated_img():
    """
    The harris filter should yield the same results with an image and it's
    rotation.
    """
    im = img_as_float(data.astronaut().mean(axis=2))
    im_rotated = im.T

    # Moravec
    results = peak_local_max(corner_moravec(im),
                             min_distance=10, threshold_rel=0)
    results_rotated = peak_local_max(corner_moravec(im_rotated),
                                     min_distance=10, threshold_rel=0)
    assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
    assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()

    # Harris
    results = peak_local_max(corner_harris(im),
                             min_distance=10, threshold_rel=0)
    results_rotated = peak_local_max(corner_harris(im_rotated),
                                     min_distance=10, threshold_rel=0)
    assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
    assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()

    # Shi-Tomasi
    results = peak_local_max(corner_shi_tomasi(im),
                             min_distance=10, threshold_rel=0)
    results_rotated = peak_local_max(corner_shi_tomasi(im_rotated),
                                     min_distance=10, threshold_rel=0)
    assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
    assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()

def test_rotated_lena():
    """
    The harris filter should yield the same results with an image and it's
    rotation.
    """
    im = img_as_float(data.lena().mean(axis=2))
    im_rotated = im.T

    # Moravec
    results = peak_local_max(corner_moravec(im))
    results_rotated = peak_local_max(corner_moravec(im_rotated))
    assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
    assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()

    # Harris
    results = peak_local_max(corner_harris(im))
    results_rotated = peak_local_max(corner_harris(im_rotated))
    assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
    assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()

    # Shi-Tomasi
    results = peak_local_max(corner_shi_tomasi(im))
    results_rotated = peak_local_max(corner_shi_tomasi(im_rotated))
    assert (np.sort(results[:, 0]) == np.sort(results_rotated[:, 1])).all()
    assert (np.sort(results[:, 1]) == np.sort(results_rotated[:, 0])).all()

def test_square_image():
    im = np.zeros((50, 50)).astype(float)
    im[:25, :25] = 1.

    # Moravec
    results = peak_local_max(corner_moravec(im),
                             min_distance=10, threshold_rel=0)
    # interest points along edge
    assert len(results) == 57

    # Harris
    results = peak_local_max(corner_harris(im, method='k'),
                             min_distance=10, threshold_rel=0)
    # interest at corner
    assert len(results) == 1

    results = peak_local_max(corner_harris(im, method='eps'),
                             min_distance=10, threshold_rel=0)
    # interest at corner
    assert len(results) == 1

    # Shi-Tomasi
    results = peak_local_max(corner_shi_tomasi(im),
                             min_distance=10, threshold_rel=0)
    # interest at corner
    assert len(results) == 1

    def __call__(self, im1, im2, maxiter=10,
                 weigh_by_shitomasi = False,
                 smooth_vfield = 2):

        mesh = self.mesh
        xgrid, ygrid = np.unique(mesh[:,1]), np.unique(mesh[:,0])

        gshape = (len(xgrid), len(ygrid))
        p = np.zeros((2,)+gshape)
        imx = im1.copy()
        for niter in xrange(maxiter):
            vx = lk_opflow(imx,im2, mesh, wsize=self.wsize)

            if weigh_by_shitomasi:
                st_resp = skfeature.corner_shi_tomasi(im1)
                st_resp =  lib.clip_and_rescale(st_resp, 5000)
                weights = np.array([st_resp[tuple(l)] for l in mesh])
                vx = vx*weights[:,None]

            vfields = vx.T.reshape((2,)+gshape)

            if smooth_vfield:
                vfields = map(partial(atrous.smooth, level=smooth_vfield), vfields)

            p += vfields
            imx = self.warp_image(im1, p)
        return imx, p

def test_squared_dot():
    im = np.zeros((50, 50))
    im[4:8, 4:8] = 1
    im = img_as_float(im)

    # Moravec fails

    # Harris
    results = peak_local_max(corner_harris(im))
    assert (results == np.array([[6, 6]])).all()

    # Shi-Tomasi
    results = peak_local_max(corner_shi_tomasi(im))
    assert (results == np.array([[6, 6]])).all()

def test_noisy_square_image():
    im = np.zeros((50, 50)).astype(float)
    im[:25, :25] = 1.
    im = im + np.random.uniform(size=im.shape) * .2

    # Moravec
    results = peak_local_max(corner_moravec(im))
    # undefined number of interest points
    assert results.any()

    # Harris
    results = peak_local_max(corner_harris(im, sigma=1.5))
    assert len(results) == 1

    # Shi-Tomasi
    results = peak_local_max(corner_shi_tomasi(im, sigma=1.5))
    assert len(results) == 1

    def get_corner_distances(self): 
        a = corner_shi_tomasi(color.rgb2grey(self.img))
#        val = filters.threshold_otsu(self.img)
#        mask = self.img < val 
#        a = peak_local_max(mask)
        print(a.shape)
        print(a)
        d1 = self.get_coord_dist(a[0], a[1])
        d2 = self.get_coord_dist(a[1], a[2])
        d3 = self.get_coord_dist(a[2], a[3])
        d4 = self.get_coord_dist(a[3], a[0])
        print('corner distances')
        print(d1)
        print(d2)
        print(d3)
        print(d4)
        print('std dev: ' + str(np.std([d1,d2,d3,4])))

def test_square_image():
    im = np.zeros((50, 50)).astype(float)
    im[:25, :25] = 1.

    # Moravec
    results = peak_local_max(corner_moravec(im))
    # interest points along edge
    assert len(results) == 57

    # Harris
    results = peak_local_max(corner_harris(im))
    # interest at corner
    assert len(results) == 1

    # Shi-Tomasi
    results = peak_local_max(corner_shi_tomasi(im))
    # interest at corner
    assert len(results) == 1

def test_noisy_square_image():
    im = np.zeros((50, 50)).astype(float)
    im[:25, :25] = 1.
    np.random.seed(seed=1234)
    im = im + np.random.uniform(size=im.shape) * .2

    # Moravec
    results = peak_local_max(corner_moravec(im),
                             min_distance=10, threshold_rel=0)
    # undefined number of interest points
    assert results.any()

    # Harris
    results = peak_local_max(corner_harris(im, method='k'),
                             min_distance=10, threshold_rel=0)
    assert len(results) == 1
    results = peak_local_max(corner_harris(im, method='eps'),
                             min_distance=10, threshold_rel=0)
    assert len(results) == 1

    # Shi-Tomasi
    results = peak_local_max(corner_shi_tomasi(im, sigma=1.5),
                             min_distance=10, threshold_rel=0)
    assert len(results) == 1

def _corner_shi_tomasi_feature(im):
    return feature.corner_shi_tomasi(im)