def test_get_det_gt(self):
image = Image.load_from_json(self.classes, self.data)
objects_table = Table(np.array([
[0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0],
[2, 2, 2, 2, 2, 0, 0]]), self.columns)
assert(image.get_objects() == objects_table)
data = self.data.copy()
data['objects'][0]['diff'] = 1
data['objects'][1]['trun'] = 1
image = Image.load_from_json(self.classes, data)
objects_table = Table(np.array([
[0, 0, 0, 0, 0, 1, 0],
[1, 1, 1, 1, 1, 0, 1],
[2, 2, 2, 2, 2, 0, 0]]), self.columns)
assert(image.get_objects(
with_diff=True, with_trun=True) == objects_table)
objects_table = Table(np.array([
[1, 1, 1, 1, 1, 0, 1],
[2, 2, 2, 2, 2, 0, 0]]), self.columns)
assert(image.get_objects(
with_diff=False, with_trun=True) == objects_table)
# this should be default behavior
assert(image.get_objects() == objects_table)
objects_table = Table(np.array([
[2, 2, 2, 2, 2, 0, 0]]), self.columns)
assert(image.get_objects(
with_diff=False, with_trun=False) == objects_table)
objects_table = Table(np.array([
[0, 0, 0, 0, 0, 1, 0],
[2, 2, 2, 2, 2, 0, 0]]), self.columns)
assert(image.get_objects(
with_diff=True, with_trun=False) == objects_table)
# What if everything is filtered out?
data['objects'] = data['objects'][:-1]
objects_table = Table(np.array([
[0, 0, 0, 0, 0, 1, 0],
[1, 1, 1, 1, 1, 0, 1]]), self.columns)
image = Image.load_from_json(self.classes, data)
assert(image.get_objects(
with_diff=True, with_trun=True) == objects_table)
assert(
image.get_objects(with_diff=False, with_trun=False).shape[0] == 0)
def test_load_json_data(self):
image = Image.load_from_json(self.classes, self.data)
assert(image.width == 640 and image.height == 480)
assert(image.classes == ['A', 'B', 'C'])
assert(image.name == 'test_image')
objects_table = Table(np.array([
[0, 0, 0, 0, 0, 0, 0],
[1, 1, 1, 1, 1, 0, 0],
[2, 2, 2, 2, 2, 0, 0]]), self.columns)
assert(image.objects_table == objects_table)
def test_get_cls_counts_and_gt(self):
data = self.data.copy()
image = Image.load_from_json(self.classes, data)
assert(np.all(image.get_cls_counts() == np.array([1, 1, 1])))
assert(np.all(image.get_cls_gt() == np.array([True, True, True])))
assert(image.contains_class('A'))
assert(image.contains_class('B'))
data['objects'][0]['class'] = 'B'
image = Image.load_from_json(self.classes, data)
# doesn't actually have to be Series, can be ndarray for comparison
assert(np.all(image.get_cls_counts() == np.array([0, 2, 1])))
assert(np.all(image.get_cls_gt() == np.array([False, True, True])))
assert(not image.contains_class('A'))
assert(image.contains_class('B'))
data['objects'] = []
image = Image.load_from_json(self.classes, data)
assert(np.all(image.get_cls_counts() == np.array([0, 0, 0])))
assert(np.all(image.get_cls_gt() == np.array([False, False, False])))
assert(not image.contains_class('A'))
assert(not image.contains_class('B'))
def load_from_json(self, filename):
"""
Load all parameters of the dataset from a JSON file.
Parameters
----------
filename: string
path to JSON data file
Returns
-------
self
"""
with open(filename) as f:
json_data = json.load(f)
self.classes = json_data['classes']
self.images = [Image.load_from_json(
self.classes, image) for image in json_data['images']]
return self.after_load()