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 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])
def main():
Config.load_conf()
config = Config.get_dict()
reachy = Reachy()
for motor in reachy.motors:
motor.compliant = True
if config["record"]:
# First part of the script : the user can move the arm and hit return to save the arm position
all_positions = []
action = input("> ")
while action != "save":
# Save the position of the motors
position = {}
for motor in reachy.motors:
print(
f"The motor \"{motor.name}\" is currently in position: {motor.present_position}"
)
position[motor.name] = motor.present_position
all_positions.append(position)
action = input("> ")
# Save the list of position in a file
with open(config["file_path"], "w") as f:
json.dump(all_positions, f)
else:
# If this first part was not executed, then we need to read the json file to load the key points
with open(config["file_path"], "r") as f:
all_positions = json.load(f)
# Move the arm
for motor in reachy.motors:
motor.compliant = False
for position in all_positions:
# Move to position
for motor in reachy.motors:
motor.goal_position = position[motor.name]
# Wait 2 s
time.sleep(2)
for motor in reachy.motors:
motor.compliant = True
def main():
Config.load_conf("config_video_burst.yml")
config = Config.get_dict()
# check if the script can run
assert os.path.isfile(config["file"]), f"Option 'file' need to be provided"
os.makedirs(config["outputdir"], exist_ok=True)
if (config["prefix"] is ""):
config["prefix"] = get_prefix(config["file"])
logging.info(f'prefix: {config["prefix"]}')
frame_id = 0
last_save = -10000
video = cv2.VideoCapture(config["file"])
if not video.isOpened():
raise Exception(f"Cannot open video {config['file']}")
interval_between_pic = int(
video.get(cv2.CAP_PROP_FPS) * config["extract_every"] / 1000)
frame_count = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
logging.info(f'frame_count: {frame_count}')
frame_count_length = len(str(frame_count))
while True:
got_frame, img = video.read()
if not got_frame:
logging.info('end of video')
break
if (frame_id - last_save > interval_between_pic):
picture_path = os.path.join(
config["outputdir"],
f'{config["prefix"]}_{frame_id:0{frame_count_length}}.jpg')
cv2.imwrite(picture_path, img)
last_save = frame_id
logging.info('Saving picture ' + picture_path)
frame_id += 1
def main():
Config.load_conf()
config = Config.get_dict()
assert config["model_path"] != "", "model_path can't be empty"
assert config["input_dir"] != "", "input_dir can't be empty"
assert config["output_dir"] != "", "output_dir can't be empty"
os.makedirs(config["output_dir"], exist_ok=True)
images_list = os.listdir(config["input_dir"])
annotations_list = os.listdir(config["output_dir"])
# Only keep images that aren't processed yet
new_list = []
annotation_ids = [
os.path.splitext(file_name)[0] for file_name in annotations_list
]
for image_name in images_list:
image_id, _ = os.path.splitext(image_name)
if image_id not in annotation_ids:
new_list.append(image_name)
images_list = new_list
images_list.sort()
logging.info("there are {} images to annotate".format(len(images_list)))
# load tensorflow model (must be a frozen model)
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(config["model_path"], 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
with tf.Session() as session:
# Get all tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_scores',
'detection_classes'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
tensor_name)
image_tensor = tf.get_default_graph().get_tensor_by_name(
'image_tensor:0')
# Run inference
first_iter = True
for image_id in tqdm(range(len(images_list))):
image = cv2.cvtColor(
cv2.imread(
os.path.join(config["input_dir"], images_list[image_id])),
cv2.COLOR_BGR2RGB)
if first_iter:
logging.info(f"image.shape: {image.shape}")
first_iter = False
height, width = image.shape[:2]
image_expanded = np.expand_dims(image, axis=0)
output_dict = session.run(tensor_dict,
feed_dict={image_tensor: image_expanded})
good_rectangles = []
for i, detection_score in enumerate(
output_dict["detection_scores"][0]):
if detection_score >= config["object_detection"]["threshold"]:
box = output_dict["detection_boxes"][0][
i] # ymin, xmin, ymax, xmax
if box[3] - box[1] < config["object_detection"][
"max_width"]:
good_rectangles.append({
"xMin":
int(box[1] * width),
"yMin":
int(box[0] * height),
"xMax":
int(box[3] * width),
"yMax":
int(box[2] * height),
"detection_score":
detection_score.item(),
"class":
config["class"]["names"]
[int(output_dict["detection_classes"][0][i]) - 1]
})
else:
break
json_name = os.path.splitext(images_list[image_id])[0] + ".json"
with open(os.path.join(config["output_dir"], json_name),
'w') as outfile:
json.dump({"rectangles": good_rectangles}, outfile)