def test_multiple_crop_and_uncrop_nearest_outside(self):
"""
test combination of multiple crop
"""
shape = [10, 11, 5]
img_in = np.random.random(shape)
crinfo1 = [[2, 8], [3, 9], [2, 5]]
# crinfo2 = [[2, 5], [1, 5], [1, 2]]
img_cropped = imma.crop(img_in, crinfo1)
# img_cropped = imma.crop(img_cropped, crinfo2)
# crinfo_combined = imma.combinecrinfo(crinfo1, crinfo2)
img_uncropped = imma.uncrop(img_cropped, crinfo1, shape, outside_mode="nearest")
# import sed3
# ed = sed3.sed3(img_uncropped)
# ed.show()
self.assertTrue(img_uncropped[4, 4, 3] == img_in[4, 4, 3])
self.assertTrue(img_uncropped[crinfo1[0][0], 5, 3] == img_uncropped[0, 5, 3], msg="pixels under crop")
self.assertTrue(img_uncropped[5, crinfo1[1][0], 3] == img_uncropped[5, 0, 3], msg="pixels under crop")
self.assertTrue(img_uncropped[7, 3, crinfo1[2][0]] == img_uncropped[7, 3, 0], msg="pixels under crop")
self.assertTrue(img_uncropped[crinfo1[0][1] - 1, 5, 3] == img_uncropped[-1, 5, 3], msg="pixels over crop")
self.assertTrue(img_uncropped[5, crinfo1[1][1] - 1, 3] == img_uncropped[5, -1, 3], msg="pixels over crop")
self.assertTrue(img_uncropped[7, 3, crinfo1[2][1] - 1] == img_uncropped[7, 3, -1], msg="pixels over crop")
# self.assertTrue(img_uncropped[crinfo1[0][1], 5 , 3] == img_uncropped[0, 5, 3], msg="pixels over crop")
# self.assertTrue(img_uncropped[crinfo1[1][1], 5 , 3] == img_uncropped[1, 5, 3], msg="pixels over crop")
# self.assertTrue(img_uncropped[crinfo1[2][1], 5 , 3] == img_uncropped[2, 5, 3], msg="pixels over crop")
self.assertEquals(img_in.shape, img_uncropped.shape)
def test_uncrop_with_none_crinfo(self):
shape = [10, 10, 5]
orig_shape = [15, 13, 7]
img_in = np.random.random(shape)
img_uncropped = imma.uncrop(img_in, crinfo=None, orig_shape=orig_shape)
self.assertTrue(img_uncropped[-1, -1, -1] == 0)
self.assertTrue(img_uncropped[4, 4, 3] == img_in[4, 4, 3])
def test_uncrop_with_start_point_crinfo(self):
shape = [10, 10, 5]
orig_shape = [15, 13, 7]
img_in = np.random.random(shape)
crinfo = [5, 2, 1]
img_uncropped = imma.uncrop(img_in, crinfo=crinfo, orig_shape=orig_shape)
self.assertTrue(img_uncropped[-1, -1, -1] == 0)
self.assertTrue(img_uncropped[4 + 5, 4 + 2, 3 + 1] == img_in[4 , 4, 3])
def test_crop_and_uncrop(self):
shape = [10, 10, 5]
img_in = np.random.random(shape)
crinfo = [[2, 8], [3, 9], [2, 5]]
img_cropped = imma.crop(img_in, crinfo)
img_uncropped = imma.uncrop(img_cropped, crinfo, shape)
self.assertTrue(img_uncropped[4, 4, 3] == img_in[4, 4, 3])
def test_multiple_crop_and_uncrop(self):
"""
test combination of multiple crop
"""
shape = [10, 10, 5]
img_in = np.random.random(shape)
crinfo1 = [[2, 8], [3, 9], [2, 5]]
crinfo2 = [[2, 5], [1, 4], [1, 2]]
img_cropped = imma.crop(img_in, crinfo1)
img_cropped = imma.crop(img_cropped, crinfo2)
crinfo_combined = imma.combinecrinfo(crinfo1, crinfo2)
img_uncropped = imma.uncrop(img_cropped, crinfo_combined, shape)
self.assertTrue(img_uncropped[4, 4, 3] == img_in[4, 4, 3])
self.assertEquals(img_in.shape, img_uncropped.shape)
def test_msgc_lo2hi_round_data(self):
"""
Test multiscale segmentation lo2hi round data
"""
np.random.seed(3)
img, seg, seeds = generate_round_data(45, 3, 10, 3)
blocksize = 6
segparams = {
# 'method':'graphcut',
"method": "multiscale_graphcut_lo2hi",
# 'method': 'multiscale_graphcut_hi2lo',
"use_boundary_penalties": False,
"boundary_dilatation_distance": 1,
"boundary_penalties_weight": 1,
"block_size": blocksize,
"tile_zoom_constant": 1,
}
gc = pycut.ImageGraphCut(
img,
segparams=segparams,
keep_graph_properties=True,
# debug_images=True,
debug_images=False,
)
gc.set_seeds(seeds)
gc.run()
# import sed3
# ed = sed3.show_slices(img, gc.segmentation*2)
# gc.debug_show_model()
# gc.debug_show_reconstructed_similarity()
tdata1, tdata2 = gc.debug_get_reconstructed_similarity()
edseeds = seeds
# node_unariesalt, node_neighboor_edges_and_weights, node_neighboor_seeds, node_msindex = gc.interactive_inspect_node()
node_msindex = gc.debug_get_node_msindex(edseeds)
node_unariesalt, node_neighboor_edges_and_weights, node_neighboor_seeds = gc.debug_inspect_node(
node_msindex)
nlinks_max = gc.debug_reconstruct_nlinks_max()
from imcut import image_manipulation as imma
seg_uncr = imma.uncrop(gc.segmentation, None, nlinks_max.shape)
# import sed3
# ed = sed3.sed3(nlinks_max, contour=seg_uncr*2)
# ed.show()
self.assertLess(
np.sum(
np.abs((gc.segmentation == 0).astype(np.int8) -
seg.astype(np.int8))),
100,
)
self.assertEqual(
np.max(nlinks_max),
blocksize,
"nlink maximal value in reconstructed nlink map. "
"It should be size of block if there are at least some"
" lowres blocks.",
)
self.assertEqual(np.min(nlinks_max), 1,
"nlink minimal value in reconstructed nlink map")
self.assertGreaterEqual(
np.min(tdata1), 0,
"tlink minimal value in reconstructed tlink map")
self.assertGreaterEqual(
np.min(tdata2), 0,
"tlink minimal value in reconstructed tlink map")
self.assertGreaterEqual(
node_neighboor_edges_and_weights.shape[0],
3,
"check number of nlink connections",
)
self.assertLessEqual(
node_neighboor_edges_and_weights.shape[0],
6,
"check number of nlink connections",
)
self.assertGreaterEqual(np.min(node_unariesalt), 0,
"selected node nlink minimum")
def test_msgc_lo2hi_round_data(self):
"""
Test multiscale segmentation lo2hi round data
"""
np.random.seed(3)
img, seg, seeds = generate_round_data(45, 3, 10, 3)
blocksize = 6
segparams = {
# 'method':'graphcut',
"method": "multiscale_graphcut_lo2hi",
# 'method': 'multiscale_graphcut_hi2lo',
"use_boundary_penalties": False,
"boundary_dilatation_distance": 1,
"boundary_penalties_weight": 1,
"block_size": blocksize,
"tile_zoom_constant": 1,
}
gc = pycut.ImageGraphCut(
img,
segparams=segparams,
keep_graph_properties=True,
# debug_images=True,
debug_images=False,
)
gc.set_seeds(seeds)
gc.run()
# import sed3
# ed = sed3.show_slices(img, gc.segmentation*2)
# gc.debug_show_model()
# gc.debug_show_reconstructed_similarity()
tdata1, tdata2 = gc.debug_get_reconstructed_similarity()
edseeds = seeds
# node_unariesalt, node_neighboor_edges_and_weights, node_neighboor_seeds, node_msindex = gc.interactive_inspect_node()
node_msindex = gc.debug_get_node_msindex(edseeds)
node_unariesalt, node_neighboor_edges_and_weights, node_neighboor_seeds = gc.debug_inspect_node(
node_msindex
)
nlinks_max = gc.debug_reconstruct_nlinks_max()
from imcut import image_manipulation as imma
seg_uncr = imma.uncrop(gc.segmentation, None, nlinks_max.shape)
# import sed3
# ed = sed3.sed3(nlinks_max, contour=seg_uncr*2)
# ed.show()
self.assertLess(
np.sum(
np.abs((gc.segmentation == 0).astype(np.int8) - seg.astype(np.int8))
),
100,
)
self.assertEqual(
np.max(nlinks_max),
blocksize,
"nlink maximal value in reconstructed nlink map. "
"It should be size of block if there are at least some"
" lowres blocks.",
)
self.assertEqual(
np.min(nlinks_max), 1, "nlink minimal value in reconstructed nlink map"
)
self.assertGreaterEqual(
np.min(tdata1), 0, "tlink minimal value in reconstructed tlink map"
)
self.assertGreaterEqual(
np.min(tdata2), 0, "tlink minimal value in reconstructed tlink map"
)
self.assertGreaterEqual(
node_neighboor_edges_and_weights.shape[0],
3,
"check number of nlink connections",
)
self.assertLessEqual(
node_neighboor_edges_and_weights.shape[0],
6,
"check number of nlink connections",
)
self.assertGreaterEqual(
np.min(node_unariesalt), 0, "selected node nlink minimum"
)