test_generator.py
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"""
Copyright 2017-2018 Fizyr (https://fizyr.com)
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.
"""
from keras_retinanet.preprocessing.generator import Generator
import numpy as np
import pytest
class SimpleGenerator(Generator):
def __init__(self, bboxes, labels, num_classes=0, image=None):
assert(len(bboxes) == len(labels))
self.bboxes = bboxes
self.labels = labels
self.num_classes_ = num_classes
self.image = image
super(SimpleGenerator, self).__init__(group_method='none', shuffle_groups=False)
def num_classes(self):
return self.num_classes_
def load_image(self, image_index):
return self.image
def image_path(self, image_index):
return ''
def size(self):
return len(self.bboxes)
def load_annotations(self, image_index):
annotations = {'labels': self.labels[image_index], 'bboxes': self.bboxes[image_index]}
return annotations
class TestLoadAnnotationsGroup(object):
def test_simple(self):
input_bboxes_group = [
np.array([
[ 0, 0, 10, 10],
[150, 150, 350, 350]
]),
]
input_labels_group = [
np.array([
1,
3
]),
]
expected_bboxes_group = input_bboxes_group
expected_labels_group = input_labels_group
simple_generator = SimpleGenerator(input_bboxes_group, input_labels_group)
annotations = simple_generator.load_annotations_group(simple_generator.groups[0])
assert('bboxes' in annotations[0])
assert('labels' in annotations[0])
np.testing.assert_equal(expected_bboxes_group[0], annotations[0]['bboxes'])
np.testing.assert_equal(expected_labels_group[0], annotations[0]['labels'])
def test_multiple(self):
input_bboxes_group = [
np.array([
[ 0, 0, 10, 10],
[150, 150, 350, 350]
]),
np.array([
[0, 0, 50, 50],
]),
]
input_labels_group = [
np.array([
1,
0
]),
np.array([
3
])
]
expected_bboxes_group = input_bboxes_group
expected_labels_group = input_labels_group
simple_generator = SimpleGenerator(input_bboxes_group, input_labels_group)
annotations_group_0 = simple_generator.load_annotations_group(simple_generator.groups[0])
annotations_group_1 = simple_generator.load_annotations_group(simple_generator.groups[1])
assert('bboxes' in annotations_group_0[0])
assert('bboxes' in annotations_group_1[0])
assert('labels' in annotations_group_0[0])
assert('labels' in annotations_group_1[0])
np.testing.assert_equal(expected_bboxes_group[0], annotations_group_0[0]['bboxes'])
np.testing.assert_equal(expected_labels_group[0], annotations_group_0[0]['labels'])
np.testing.assert_equal(expected_bboxes_group[1], annotations_group_1[0]['bboxes'])
np.testing.assert_equal(expected_labels_group[1], annotations_group_1[0]['labels'])
class TestFilterAnnotations(object):
def test_simple_filter(self):
input_bboxes_group = [
np.array([
[ 0, 0, 10, 10],
[150, 150, 50, 50]
]),
]
input_labels_group = [
np.array([
3,
1
]),
]
input_image = np.zeros((500, 500, 3))
expected_bboxes_group = [
np.array([
[0, 0, 10, 10],
]),
]
expected_labels_group = [
np.array([
3,
]),
]
simple_generator = SimpleGenerator(input_bboxes_group, input_labels_group)
annotations = simple_generator.load_annotations_group(simple_generator.groups[0])
# expect a UserWarning
with pytest.warns(UserWarning):
image_group, annotations_group = simple_generator.filter_annotations([input_image], annotations, simple_generator.groups[0])
np.testing.assert_equal(expected_bboxes_group[0], annotations_group[0]['bboxes'])
np.testing.assert_equal(expected_labels_group[0], annotations_group[0]['labels'])
def test_multiple_filter(self):
input_bboxes_group = [
np.array([
[ 0, 0, 10, 10],
[150, 150, 50, 50],
[150, 150, 350, 350],
[350, 350, 150, 150],
[ 1, 1, 2, 2],
[ 2, 2, 1, 1]
]),
np.array([
[0, 0, -1, -1]
]),
np.array([
[-10, -10, 0, 0],
[-10, -10, -100, -100],
[ 10, 10, 100, 100]
]),
np.array([
[ 10, 10, 100, 100],
[ 10, 10, 600, 600]
]),
]
input_labels_group = [
np.array([
6,
5,
4,
3,
2,
1
]),
np.array([
0
]),
np.array([
10,
11,
12
]),
np.array([
105,
107
]),
]
input_image = np.zeros((500, 500, 3))
expected_bboxes_group = [
np.array([
[ 0, 0, 10, 10],
[150, 150, 350, 350],
[ 1, 1, 2, 2]
]),
np.zeros((0, 4)),
np.array([
[10, 10, 100, 100]
]),
np.array([
[ 10, 10, 100, 100]
]),
]
expected_labels_group = [
np.array([
6,
4,
2
]),
np.zeros((0,)),
np.array([
12
]),
np.array([
105
]),
]
simple_generator = SimpleGenerator(input_bboxes_group, input_labels_group)
# expect a UserWarning
annotations_group_0 = simple_generator.load_annotations_group(simple_generator.groups[0])
with pytest.warns(UserWarning):
image_group, annotations_group_0 = simple_generator.filter_annotations([input_image], annotations_group_0, simple_generator.groups[0])
annotations_group_1 = simple_generator.load_annotations_group(simple_generator.groups[1])
with pytest.warns(UserWarning):
image_group, annotations_group_1 = simple_generator.filter_annotations([input_image], annotations_group_1, simple_generator.groups[1])
annotations_group_2 = simple_generator.load_annotations_group(simple_generator.groups[2])
with pytest.warns(UserWarning):
image_group, annotations_group_2 = simple_generator.filter_annotations([input_image], annotations_group_2, simple_generator.groups[2])
np.testing.assert_equal(expected_bboxes_group[0], annotations_group_0[0]['bboxes'])
np.testing.assert_equal(expected_labels_group[0], annotations_group_0[0]['labels'])
np.testing.assert_equal(expected_bboxes_group[1], annotations_group_1[0]['bboxes'])
np.testing.assert_equal(expected_labels_group[1], annotations_group_1[0]['labels'])
np.testing.assert_equal(expected_bboxes_group[2], annotations_group_2[0]['bboxes'])
np.testing.assert_equal(expected_labels_group[2], annotations_group_2[0]['labels'])
def test_complete(self):
input_bboxes_group = [
np.array([
[ 0, 0, 50, 50],
[150, 150, 50, 50], # invalid bbox
], dtype=float)
]
input_labels_group = [
np.array([
5, # one object of class 5
3, # one object of class 3 with an invalid box
], dtype=float)
]
input_image = np.zeros((500, 500, 3), dtype=np.uint8)
simple_generator = SimpleGenerator(input_bboxes_group, input_labels_group, image=input_image, num_classes=6)
# expect a UserWarning
with pytest.warns(UserWarning):
_, [_, labels_batch] = simple_generator[0]
# test that only object with class 5 is present in labels_batch
labels = np.unique(np.argmax(labels_batch == 5, axis=2))
assert(len(labels) == 1 and labels[0] == 0), 'Expected only class 0 to be present, but got classes {}'.format(labels)