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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.
# ==============================================================================
r"""Tests for detection_inference.py."""
import os
import StringIO
import numpy as np
from PIL import Image
import tensorflow as tf
from object_detection.core import standard_fields
from object_detection.inference import detection_inference
from object_detection.utils import dataset_util
def get_mock_tfrecord_path():
return os.path.join(tf.test.get_temp_dir(), 'mock.tfrec')
def create_mock_tfrecord():
pil_image = Image.fromarray(np.array([[[123, 0, 0]]], dtype=np.uint8), 'RGB')
image_output_stream = StringIO.StringIO()
pil_image.save(image_output_stream, format='png')
encoded_image = image_output_stream.getvalue()
feature_map = {
'test_field':
dataset_util.float_list_feature([1, 2, 3, 4]),
standard_fields.TfExampleFields.image_encoded:
dataset_util.bytes_feature(encoded_image),
}
tf_example = tf.train.Example(features=tf.train.Features(feature=feature_map))
with tf.python_io.TFRecordWriter(get_mock_tfrecord_path()) as writer:
writer.write(tf_example.SerializeToString())
def get_mock_graph_path():
return os.path.join(tf.test.get_temp_dir(), 'mock_graph.pb')
def create_mock_graph():
g = tf.Graph()
with g.as_default():
in_image_tensor = tf.placeholder(
tf.uint8, shape=[1, None, None, 3], name='image_tensor')
tf.constant([2.0], name='num_detections')
tf.constant(
[[[0, 0.8, 0.7, 1], [0.1, 0.2, 0.8, 0.9], [0.2, 0.3, 0.4, 0.5]]],
name='detection_boxes')
tf.constant([[0.1, 0.2, 0.3]], name='detection_scores')
tf.identity(
tf.constant([[1.0, 2.0, 3.0]]) *
tf.reduce_sum(tf.cast(in_image_tensor, dtype=tf.float32)),
name='detection_classes')
graph_def = g.as_graph_def()
with tf.gfile.Open(get_mock_graph_path(), 'w') as fl:
fl.write(graph_def.SerializeToString())
class InferDetectionsTests(tf.test.TestCase):
def test_simple(self):
create_mock_graph()
create_mock_tfrecord()
serialized_example_tensor, image_tensor = detection_inference.build_input(
[get_mock_tfrecord_path()])
self.assertAllEqual(image_tensor.get_shape().as_list(), [1, None, None, 3])
(detected_boxes_tensor, detected_scores_tensor,
detected_labels_tensor) = detection_inference.build_inference_graph(
image_tensor, get_mock_graph_path())
with self.test_session(use_gpu=False) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
tf.train.start_queue_runners()
tf_example = detection_inference.infer_detections_and_add_to_example(
serialized_example_tensor, detected_boxes_tensor,
detected_scores_tensor, detected_labels_tensor, False)
self.assertProtoEquals(r"""
features {
feature {
key: "image/detection/bbox/ymin"
value { float_list { value: [0.0, 0.1] } } }
feature {
key: "image/detection/bbox/xmin"
value { float_list { value: [0.8, 0.2] } } }
feature {
key: "image/detection/bbox/ymax"
value { float_list { value: [0.7, 0.8] } } }
feature {
key: "image/detection/bbox/xmax"
value { float_list { value: [1.0, 0.9] } } }
feature {
key: "image/detection/label"
value { int64_list { value: [123, 246] } } }
feature {
key: "image/detection/score"
value { float_list { value: [0.1, 0.2] } } }
feature {
key: "image/encoded"
value { bytes_list { value:
"\211PNG\r\n\032\n\000\000\000\rIHDR\000\000\000\001\000\000"
"\000\001\010\002\000\000\000\220wS\336\000\000\000\022IDATx"
"\234b\250f`\000\000\000\000\377\377\003\000\001u\000|gO\242"
"\213\000\000\000\000IEND\256B`\202" } } }
feature {
key: "test_field"
value { float_list { value: [1.0, 2.0, 3.0, 4.0] } } } }
""", tf_example)
def test_discard_image(self):
create_mock_graph()
create_mock_tfrecord()
serialized_example_tensor, image_tensor = detection_inference.build_input(
[get_mock_tfrecord_path()])
(detected_boxes_tensor, detected_scores_tensor,
detected_labels_tensor) = detection_inference.build_inference_graph(
image_tensor, get_mock_graph_path())
with self.test_session(use_gpu=False) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
tf.train.start_queue_runners()
tf_example = detection_inference.infer_detections_and_add_to_example(
serialized_example_tensor, detected_boxes_tensor,
detected_scores_tensor, detected_labels_tensor, True)
self.assertProtoEquals(r"""
features {
feature {
key: "image/detection/bbox/ymin"
value { float_list { value: [0.0, 0.1] } } }
feature {
key: "image/detection/bbox/xmin"
value { float_list { value: [0.8, 0.2] } } }
feature {
key: "image/detection/bbox/ymax"
value { float_list { value: [0.7, 0.8] } } }
feature {
key: "image/detection/bbox/xmax"
value { float_list { value: [1.0, 0.9] } } }
feature {
key: "image/detection/label"
value { int64_list { value: [123, 246] } } }
feature {
key: "image/detection/score"
value { float_list { value: [0.1, 0.2] } } }
feature {
key: "test_field"
value { float_list { value: [1.0, 2.0, 3.0, 4.0] } } } }
""", tf_example)
if __name__ == '__main__':
tf.test.main()
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