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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for connected component analysis."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import logging
import numpy as np
from tensorflow.contrib.image.python.ops import image_ops
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import googletest
# Image for testing connected_components, with a single, winding component.
SNAKE = np.asarray(
[[0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 1, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 1, 1, 1, 1, 1, 0],
[0, 1, 0, 0, 0, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0]]) # pyformat: disable
class SegmentationTest(test_util.TensorFlowTestCase):
def testDisconnected(self):
arr = math_ops.cast(
[[1, 0, 0, 1, 0, 0, 0, 0, 1],
[0, 1, 0, 0, 0, 1, 0, 1, 0],
[1, 0, 1, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 1, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0]],
dtypes.bool) # pyformat: disable
expected = (
[[1, 0, 0, 2, 0, 0, 0, 0, 3],
[0, 4, 0, 0, 0, 5, 0, 6, 0],
[7, 0, 8, 0, 0, 0, 9, 0, 0],
[0, 0, 0, 0, 10, 0, 0, 0, 0],
[0, 0, 11, 0, 0, 0, 0, 0, 0]]) # pyformat: disable
with self.test_session():
self.assertAllEqual(image_ops.connected_components(arr).eval(), expected)
def testSimple(self):
arr = [[0, 1, 0], [1, 1, 1], [0, 1, 0]]
with self.test_session():
# Single component with id 1.
self.assertAllEqual(
image_ops.connected_components(math_ops.cast(
arr, dtypes.bool)).eval(), arr)
def testSnake(self):
with self.test_session():
# Single component with id 1.
self.assertAllEqual(
image_ops.connected_components(math_ops.cast(
SNAKE, dtypes.bool)).eval(), SNAKE)
def testSnake_disconnected(self):
for i in range(SNAKE.shape[0]):
for j in range(SNAKE.shape[1]):
with self.test_session():
# If we disconnect any part of the snake except for the endpoints,
# there will be 2 components.
if SNAKE[i, j] and (i, j) not in [(1, 1), (6, 3)]:
disconnected_snake = SNAKE.copy()
disconnected_snake[i, j] = 0
components = image_ops.connected_components(
math_ops.cast(disconnected_snake, dtypes.bool)).eval()
self.assertEqual(components.max(), 2, 'disconnect (%d, %d)' % (i,
j))
bins = np.bincount(components.ravel())
# Nonzero number of pixels labeled 0, 1, or 2.
self.assertGreater(bins[0], 0)
self.assertGreater(bins[1], 0)
self.assertGreater(bins[2], 0)
def testMultipleImages(self):
images = [[[1, 1, 1, 1],
[1, 0, 0, 1],
[1, 0, 0, 1],
[1, 1, 1, 1]],
[[1, 0, 0, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 0, 0, 1]],
[[1, 1, 0, 1],
[0, 1, 1, 0],
[1, 0, 1, 0],
[0, 0, 1, 1]]] # pyformat: disable
expected = [[[1, 1, 1, 1],
[1, 0, 0, 1],
[1, 0, 0, 1],
[1, 1, 1, 1]],
[[2, 0, 0, 3],
[0, 0, 0, 0],
[0, 0, 0, 0],
[4, 0, 0, 5]],
[[6, 6, 0, 7],
[0, 6, 6, 0],
[8, 0, 6, 0],
[0, 0, 6, 6]]] # pyformat: disable
with self.test_session():
self.assertAllEqual(
image_ops.connected_components(math_ops.cast(
images, dtypes.bool)).eval(), expected)
def testZeros(self):
with self.test_session():
self.assertAllEqual(
image_ops.connected_components(
array_ops.zeros((100, 20, 50), dtypes.bool)).eval(),
np.zeros((100, 20, 50)))
def testOnes(self):
with self.test_session():
self.assertAllEqual(
image_ops.connected_components(
array_ops.ones((100, 20, 50), dtypes.bool)).eval(),
np.tile(np.arange(100)[:, None, None] + 1, [1, 20, 50]))
def testOnes_small(self):
with self.test_session():
self.assertAllEqual(
image_ops.connected_components(array_ops.ones((3, 5),
dtypes.bool)).eval(),
np.ones((3, 5)))
def testRandom_scipy(self):
np.random.seed(42)
images = np.random.randint(0, 2, size=(10, 100, 200)).astype(np.bool)
expected = connected_components_reference_implementation(images)
if expected is None:
return
with self.test_session():
self.assertAllEqual(
image_ops.connected_components(images).eval(), expected)
def connected_components_reference_implementation(images):
try:
# pylint: disable=g-import-not-at-top
from scipy.ndimage import measurements
except ImportError:
logging.exception('Skipping test method because scipy could not be loaded')
return
image_or_images = np.asarray(images)
if len(image_or_images.shape) == 2:
images = image_or_images[None, :, :]
elif len(image_or_images.shape) == 3:
images = image_or_images
components = np.asarray([measurements.label(image)[0] for image in images])
# Get the count of nonzero ids for each image, and offset each image's nonzero
# ids using the cumulative sum.
num_ids_per_image = components.reshape(
[-1, components.shape[1] * components.shape[2]]).max(axis=-1)
positive_id_start_per_image = np.cumsum(num_ids_per_image)
for i in range(components.shape[0]):
new_id_start = positive_id_start_per_image[i - 1] if i > 0 else 0
components[i, components[i] > 0] += new_id_start
if len(image_or_images.shape) == 2:
return components[0, :, :]
else:
return components
if __name__ == '__main__':
googletest.main()
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