def get_classes():
logging.info(Config.get_var("class.names"))
return jsonify({
"classNames": Config.get_var("class.names"),
"classColors": Config.get_var("class.colors"),
"minHeight": Config.get_var("min_height"),
"minWidth": Config.get_var("min_width")
})
def test_load_conf_os_env(self, mock_args):
Config.clear()
Config.define_bool("VaR", False, "turn me true")
Config.define_int("v1", 0, "var")
Config.define_int("nm.v2", 0, "var")
Config.load_conf()
self.assertEqual(Config.get_var("v1"), 1)
self.assertEqual(Config.get_var("nm.v2"), 2)
self.assertEqual(Config.get_var("VAr"), True)
def main():
global image_provider
Config.load_conf()
image_provider = ImageProvider()
# Write txt file containing class info
with open(
os.path.join(Config.get_var("human_annotations_path"),
"class_names.txt"), "w") as f:
f.write(','.join(Config.get_var("class.names")))
app.run(host='0.0.0.0', threaded=True)
def set_image():
message = flask.request.get_json()
logging.info(message)
for shape in message["detectedObjects"]:
assert shape["class"] in Config.get_var("class.names")
json_name = os.path.splitext(os.path.split(
message['imageSrc'])[1])[0] + ".json"
with open(
os.path.join(Config.get_var("human_annotations_path"), json_name),
'w') as outfile:
json.dump({"rectangles": message["detectedObjects"]}, outfile)
return "ok"
def __init__(self):
"""class providing path of images to process
"""
# user-provided attributes
self.images_path = Config.get_var("images_path")
self.human_annotations_path = Config.get_var("human_annotations_path")
self.model_annotations_path = Config.get_var("model_annotations_path")
# other attributes
self.lock = threading.Lock()
self.current_image = 0
# Handle paths
assert os.path.exists(self.images_path)
os.makedirs(self.human_annotations_path, exist_ok=True)
if self.model_annotations_path:
os.makedirs(self.model_annotations_path, exist_ok=True)
# Handle lists
self.images_list = os.listdir(self.images_path)
self.human_annotations_list = os.listdir(self.human_annotations_path)
if self.model_annotations_path:
self.model_annotations_list = os.listdir(
self.model_annotations_path)
else:
self.model_annotations_list = []
logging.info(f"there are {len(self.images_list)} images in total")
# Only keep images that where not yet processed
new_list = []
human_annotation_ids = [
os.path.splitext(file_name)[0]
for file_name in self.human_annotations_list
]
for image_name in self.images_list:
image_id, _ = os.path.splitext(image_name)
if image_id not in human_annotation_ids:
new_list.append(image_name)
self.images_list = new_list
logging.info(f"there are {len(self.images_list)} images to annotate")
self.images_list.sort()
self.n_images = len(self.images_list)
def test_load_conf_cmd(self, mock_args):
Config.clear()
Config.define_bool("VaR", False, "turn me true")
Config.define_int("v1", 1, "var")
Config.define_int("v2", 2, "var")
Config.define_float_list("list", [5., 6, "7."], "A test for list")
with Config.namespace("n1"):
Config.define_int("v1", 1, "var")
Config.define_int("v2", 2, "var")
Config.define_bool("V3", True, "turn me false")
Config.load_conf()
self.assertEqual(Config.get_var("v1"), 2)
self.assertEqual(Config.get_var("v2"), 3)
self.assertEqual(Config.get_var("n1.v1"), 2)
self.assertEqual(Config.get_var("n1.v2"), 3)
self.assertEqual(Config.get_var("n1.v3"), False)
self.assertEqual(Config.get_var("VAr"), True)
self.assertEqual(Config.get_var("list"), [1.0, 2.0, 3.0])