else:
if way > 0:
color = (1, 0, 0)
elif way < 0:
color = (0, 0, 1)
cv2.line(self.pmap, p1, p2, color, thickness=3)
if __name__ == "__main__":
import os
base_path = os.path.expanduser("~") + "\\random_data"
Dataset = dataset_json.Dataset(["direction", "speed", "throttle", "time"])
# direction_comp = Dataset.get_component("speed")
# direction_comp.offset = 0
# direction_comp.scale = 3.6
paths = Dataset.load_dos_sorted(f"{base_path}\\donkey\\1\\")
sequence_to_study = (2000, 5000)
paths = paths[sequence_to_study[0]:sequence_to_study[1]]
annotations = np.array([Dataset.load_annotation(path) for path in paths])
directions = annotations[:, 0][:-1]
speeds = len(annotations[:, 1]) * [1]
dates = annotations[:, -1]
delta_times = dates[1:] - dates[:-1]
it += 1
idxs.append(it)
return idxs
if __name__ == "__main__":
import os
base_path = os.path.expanduser("~") + "\\random_data"
current_file = os.path.abspath(os.getcwd())
model = load_model(
os.path.normpath(f"{current_file}..\\test_model\\models\\fe.h5"))
Dataset = dataset_json.Dataset(["time"])
dos = f"{base_path}\\json_dataset\\20 checkpoint patch\\"
paths = Dataset.load_dos_sorted(dos)
initial_len = len(paths)
latents = get_latents(Dataset, model, paths)
index = 0
del_threshold = 0.1
while index < initial_len:
nearests = find_nearest(latents, index=index)
if len(nearests) > 0:
nearests.sort()
nearests = list(reversed(nearests))
for cmp_key in values:
if "__" not in cmp_key:
to_save[cmp_key] = self.Dataset.get_component(
cmp_key).from_string(values[cmp_key])
to_save["dos"] = self.dos
to_save["img_path"] = img_path
new_annotation_path = self.Dataset.save_annotation_dict(
to_save)
self.img_paths_mapping[img_path] = new_annotation_path
i += 1
break
self.window.close()
if __name__ == "__main__":
import os
base_path = os.path.expanduser("~") + "\\random_data"
# path = f"{base_path}\\1 ironcar driving\\"
# path = 'C:\\Users\\maxim\\recorded_imgs\\0_1600008448.0622997\\'
path = f"{base_path}\\test_scene\\0_1611408252.2687962\\"
Dataset = dataset_json.Dataset(
["direction", "speed", "throttle", "left_lane", "right_lane"])
output_components = [0, 1, 2, 3, 4] # indexes to labelise
labeliser = Labeliser(Dataset, output_components, path, mode="union")
px = self.last_packet.get('pos_x')
py = self.last_packet.get('pos_y')
pz = self.last_packet.get('pos_z')
if (px, py, pz) != self.last_point:
self.log(px, py, pz)
self.last_point = (px, py, pz)
if __name__ == "__main__":
model = model_utils.safe_load_model(
'C:\\Users\\maxim\\GITHUB\\AutonomousCar\\test_model\\models\\test_scene.h5', compile=False)
model_utils.apply_predict_decorator(model)
model.summary()
dataset = dataset_json.Dataset(
['direction', 'speed', 'throttle', 'time'])
input_components = [1]
hosts = ['127.0.0.1', 'donkey-sim.roboticist.dev', 'sim.diyrobocars.fr']
host = hosts[0]
port = 9091
window = windowInterface() # create a window
config = {
'host': host,
'port': port,
'window': window,
'use_speed': (True, True),
'sleep_time': 0.01,
'PID_settings': [17, 0.5, 0.3, 1.0, 1.0],
import cv2
from custom_modules.datasets import dataset_json
if __name__ == "__main__":
Dataset = dataset_json.Dataset(["direction", "time"])
paths = Dataset.load_dos_sorted(
"C:\\Users\\maxim\\random_data\\ironcar\\ironcar\\")
for path in paths:
img, annotation = Dataset.load_img_and_annotation(path)
img = cv2.resize(img, (480, 360))
cv2.imshow("img", img)
cv2.waitKey(1)
