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# Copyright 2017 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 object_detection.utils.visualization_utils."""
import logging
import os
import numpy as np
import PIL.Image as Image
import tensorflow as tf
from object_detection.core import standard_fields as fields
from object_detection.utils import visualization_utils
_TESTDATA_PATH = 'object_detection/test_images'
class VisualizationUtilsTest(tf.test.TestCase):
def create_colorful_test_image(self):
"""This function creates an image that can be used to test vis functions.
It makes an image composed of four colored rectangles.
Returns:
colorful test numpy array image.
"""
ch255 = np.full([100, 200, 1], 255, dtype=np.uint8)
ch128 = np.full([100, 200, 1], 128, dtype=np.uint8)
ch0 = np.full([100, 200, 1], 0, dtype=np.uint8)
imr = np.concatenate((ch255, ch128, ch128), axis=2)
img = np.concatenate((ch255, ch255, ch0), axis=2)
imb = np.concatenate((ch255, ch0, ch255), axis=2)
imw = np.concatenate((ch128, ch128, ch128), axis=2)
imu = np.concatenate((imr, img), axis=1)
imd = np.concatenate((imb, imw), axis=1)
image = np.concatenate((imu, imd), axis=0)
return image
def create_test_image_with_five_channels(self):
return np.full([100, 200, 5], 255, dtype=np.uint8)
def create_test_grayscale_image(self):
return np.full([100, 200, 1], 255, dtype=np.uint8)
def test_draw_bounding_box_on_image(self):
test_image = self.create_colorful_test_image()
test_image = Image.fromarray(test_image)
width_original, height_original = test_image.size
ymin = 0.25
ymax = 0.75
xmin = 0.4
xmax = 0.6
visualization_utils.draw_bounding_box_on_image(test_image, ymin, xmin, ymax,
xmax)
width_final, height_final = test_image.size
self.assertEqual(width_original, width_final)
self.assertEqual(height_original, height_final)
def test_draw_bounding_box_on_image_array(self):
test_image = self.create_colorful_test_image()
width_original = test_image.shape[0]
height_original = test_image.shape[1]
ymin = 0.25
ymax = 0.75
xmin = 0.4
xmax = 0.6
visualization_utils.draw_bounding_box_on_image_array(
test_image, ymin, xmin, ymax, xmax)
width_final = test_image.shape[0]
height_final = test_image.shape[1]
self.assertEqual(width_original, width_final)
self.assertEqual(height_original, height_final)
def test_draw_bounding_boxes_on_image(self):
test_image = self.create_colorful_test_image()
test_image = Image.fromarray(test_image)
width_original, height_original = test_image.size
boxes = np.array([[0.25, 0.75, 0.4, 0.6],
[0.1, 0.1, 0.9, 0.9]])
visualization_utils.draw_bounding_boxes_on_image(test_image, boxes)
width_final, height_final = test_image.size
self.assertEqual(width_original, width_final)
self.assertEqual(height_original, height_final)
def test_draw_bounding_boxes_on_image_array(self):
test_image = self.create_colorful_test_image()
width_original = test_image.shape[0]
height_original = test_image.shape[1]
boxes = np.array([[0.25, 0.75, 0.4, 0.6],
[0.1, 0.1, 0.9, 0.9]])
visualization_utils.draw_bounding_boxes_on_image_array(test_image, boxes)
width_final = test_image.shape[0]
height_final = test_image.shape[1]
self.assertEqual(width_original, width_final)
self.assertEqual(height_original, height_final)
def test_draw_bounding_boxes_on_image_tensors(self):
"""Tests that bounding box utility produces reasonable results."""
category_index = {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}}
fname = os.path.join(_TESTDATA_PATH, 'image1.jpg')
image_np = np.array(Image.open(fname))
images_np = np.stack((image_np, image_np), axis=0)
original_image_shape = [[636, 512], [636, 512]]
with tf.Graph().as_default():
images_tensor = tf.constant(value=images_np, dtype=tf.uint8)
image_shape = tf.constant(original_image_shape, dtype=tf.int32)
boxes = tf.constant([[[0.4, 0.25, 0.75, 0.75], [0.5, 0.3, 0.6, 0.9]],
[[0.25, 0.25, 0.75, 0.75], [0.1, 0.3, 0.6, 1.0]]])
classes = tf.constant([[1, 1], [1, 2]], dtype=tf.int64)
scores = tf.constant([[0.8, 0.1], [0.6, 0.5]])
images_with_boxes = (
visualization_utils.draw_bounding_boxes_on_image_tensors(
images_tensor,
boxes,
classes,
scores,
category_index,
original_image_spatial_shape=image_shape,
true_image_shape=image_shape,
min_score_thresh=0.2))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
# Write output images for visualization.
images_with_boxes_np = sess.run(images_with_boxes)
self.assertEqual(images_np.shape[0], images_with_boxes_np.shape[0])
self.assertEqual(images_np.shape[3], images_with_boxes_np.shape[3])
self.assertEqual(
tuple(original_image_shape[0]), images_with_boxes_np.shape[1:3])
for i in range(images_with_boxes_np.shape[0]):
img_name = 'image_' + str(i) + '.png'
output_file = os.path.join(self.get_temp_dir(), img_name)
logging.info('Writing output image %d to %s', i, output_file)
image_pil = Image.fromarray(images_with_boxes_np[i, ...])
image_pil.save(output_file)
def test_draw_bounding_boxes_on_image_tensors_with_additional_channels(self):
"""Tests the case where input image tensor has more than 3 channels."""
category_index = {1: {'id': 1, 'name': 'dog'}}
image_np = self.create_test_image_with_five_channels()
images_np = np.stack((image_np, image_np), axis=0)
with tf.Graph().as_default():
images_tensor = tf.constant(value=images_np, dtype=tf.uint8)
boxes = tf.constant(0, dtype=tf.float32, shape=[2, 0, 4])
classes = tf.constant(0, dtype=tf.int64, shape=[2, 0])
scores = tf.constant(0, dtype=tf.float32, shape=[2, 0])
images_with_boxes = (
visualization_utils.draw_bounding_boxes_on_image_tensors(
images_tensor,
boxes,
classes,
scores,
category_index,
min_score_thresh=0.2))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
final_images_np = sess.run(images_with_boxes)
self.assertEqual((2, 100, 200, 3), final_images_np.shape)
def test_draw_bounding_boxes_on_image_tensors_grayscale(self):
"""Tests the case where input image tensor has one channel."""
category_index = {1: {'id': 1, 'name': 'dog'}}
image_np = self.create_test_grayscale_image()
images_np = np.stack((image_np, image_np), axis=0)
with tf.Graph().as_default():
images_tensor = tf.constant(value=images_np, dtype=tf.uint8)
image_shape = tf.constant([[100, 200], [100, 200]], dtype=tf.int32)
boxes = tf.constant(0, dtype=tf.float32, shape=[2, 0, 4])
classes = tf.constant(0, dtype=tf.int64, shape=[2, 0])
scores = tf.constant(0, dtype=tf.float32, shape=[2, 0])
images_with_boxes = (
visualization_utils.draw_bounding_boxes_on_image_tensors(
images_tensor,
boxes,
classes,
scores,
category_index,
original_image_spatial_shape=image_shape,
true_image_shape=image_shape,
min_score_thresh=0.2))
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
final_images_np = sess.run(images_with_boxes)
self.assertEqual((2, 100, 200, 3), final_images_np.shape)
def test_draw_keypoints_on_image(self):
test_image = self.create_colorful_test_image()
test_image = Image.fromarray(test_image)
width_original, height_original = test_image.size
keypoints = [[0.25, 0.75], [0.4, 0.6], [0.1, 0.1], [0.9, 0.9]]
visualization_utils.draw_keypoints_on_image(test_image, keypoints)
width_final, height_final = test_image.size
self.assertEqual(width_original, width_final)
self.assertEqual(height_original, height_final)
def test_draw_keypoints_on_image_array(self):
test_image = self.create_colorful_test_image()
width_original = test_image.shape[0]
height_original = test_image.shape[1]
keypoints = [[0.25, 0.75], [0.4, 0.6], [0.1, 0.1], [0.9, 0.9]]
visualization_utils.draw_keypoints_on_image_array(test_image, keypoints)
width_final = test_image.shape[0]
height_final = test_image.shape[1]
self.assertEqual(width_original, width_final)
self.assertEqual(height_original, height_final)
def test_draw_mask_on_image_array(self):
test_image = np.asarray([[[0, 0, 0], [0, 0, 0]],
[[0, 0, 0], [0, 0, 0]]], dtype=np.uint8)
mask = np.asarray([[0, 1],
[1, 1]], dtype=np.uint8)
expected_result = np.asarray([[[0, 0, 0], [0, 0, 127]],
[[0, 0, 127], [0, 0, 127]]], dtype=np.uint8)
visualization_utils.draw_mask_on_image_array(test_image, mask,
color='Blue', alpha=.5)
self.assertAllEqual(test_image, expected_result)
def test_add_cdf_image_summary(self):
values = [0.1, 0.2, 0.3, 0.4, 0.42, 0.44, 0.46, 0.48, 0.50]
visualization_utils.add_cdf_image_summary(values, 'PositiveAnchorLoss')
cdf_image_summary = tf.get_collection(key=tf.GraphKeys.SUMMARIES)[0]
with self.test_session():
cdf_image_summary.eval()
def test_add_hist_image_summary(self):
values = [0.1, 0.2, 0.3, 0.4, 0.42, 0.44, 0.46, 0.48, 0.50]
bins = [0.01 * i for i in range(101)]
visualization_utils.add_hist_image_summary(values, bins,
'ScoresDistribution')
hist_image_summary = tf.get_collection(key=tf.GraphKeys.SUMMARIES)[0]
with self.test_session():
hist_image_summary.eval()
def test_eval_metric_ops(self):
category_index = {1: {'id': 1, 'name': 'dog'}, 2: {'id': 2, 'name': 'cat'}}
max_examples_to_draw = 4
metric_op_base = 'Detections_Left_Groundtruth_Right'
eval_metric_ops = visualization_utils.VisualizeSingleFrameDetections(
category_index,
max_examples_to_draw=max_examples_to_draw,
summary_name_prefix=metric_op_base)
original_image = tf.placeholder(tf.uint8, [4, None, None, 3])
original_image_spatial_shape = tf.placeholder(tf.int32, [4, 2])
true_image_shape = tf.placeholder(tf.int32, [4, 3])
detection_boxes = tf.random_uniform([4, 20, 4],
minval=0.0,
maxval=1.0,
dtype=tf.float32)
detection_classes = tf.random_uniform([4, 20],
minval=1,
maxval=3,
dtype=tf.int64)
detection_scores = tf.random_uniform([4, 20],
minval=0.,
maxval=1.,
dtype=tf.float32)
groundtruth_boxes = tf.random_uniform([4, 8, 4],
minval=0.0,
maxval=1.0,
dtype=tf.float32)
groundtruth_classes = tf.random_uniform([4, 8],
minval=1,
maxval=3,
dtype=tf.int64)
eval_dict = {
fields.DetectionResultFields.detection_boxes:
detection_boxes,
fields.DetectionResultFields.detection_classes:
detection_classes,
fields.DetectionResultFields.detection_scores:
detection_scores,
fields.InputDataFields.original_image:
original_image,
fields.InputDataFields.original_image_spatial_shape: (
original_image_spatial_shape),
fields.InputDataFields.true_image_shape: (true_image_shape),
fields.InputDataFields.groundtruth_boxes:
groundtruth_boxes,
fields.InputDataFields.groundtruth_classes:
groundtruth_classes
}
metric_ops = eval_metric_ops.get_estimator_eval_metric_ops(eval_dict)
_, update_op = metric_ops[metric_ops.keys()[0]]
with self.test_session() as sess:
sess.run(tf.global_variables_initializer())
value_ops = {}
for key, (value_op, _) in metric_ops.iteritems():
value_ops[key] = value_op
# First run enough update steps to surpass `max_examples_to_draw`.
for i in range(max_examples_to_draw):
# Use a unique image shape on each eval image.
sess.run(
update_op,
feed_dict={
original_image:
np.random.randint(
low=0,
high=256,
size=(4, 6 + i, 7 + i, 3),
dtype=np.uint8),
original_image_spatial_shape: [[6 + i, 7 + i], [6 + i, 7 + i],
[6 + i, 7 + i], [6 + i, 7 + i]],
true_image_shape: [[6 + i, 7 + i, 3], [6 + i, 7 + i, 3],
[6 + i, 7 + i, 3], [6 + i, 7 + i, 3]]
})
value_ops_out = sess.run(value_ops)
for key, value_op in value_ops_out.iteritems():
self.assertNotEqual('', value_op)
# Now run fewer update steps than `max_examples_to_draw`. A single value
# op will be the empty string, since not enough image summaries can be
# produced.
for i in range(max_examples_to_draw - 1):
# Use a unique image shape on each eval image.
sess.run(
update_op,
feed_dict={
original_image:
np.random.randint(
low=0,
high=256,
size=(4, 6 + i, 7 + i, 3),
dtype=np.uint8),
original_image_spatial_shape: [[6 + i, 7 + i], [6 + i, 7 + i],
[6 + i, 7 + i], [6 + i, 7 + i]],
true_image_shape: [[6 + i, 7 + i, 3], [6 + i, 7 + i, 3],
[6 + i, 7 + i, 3], [6 + i, 7 + i, 3]]
})
value_ops_out = sess.run(value_ops)
self.assertEqual(
'',
value_ops_out[metric_op_base + '/' + str(max_examples_to_draw - 1)])
if __name__ == '__main__':
tf.test.main()
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