from overdrive import Overdrive
def locationChangeCallback(addr, location, piece, speed, clockwise):
# Print out addr, piece ID, location ID of the vehicle, this print everytime when location changed
print("Location from " + addr + " : " + "Piece=" + str(piece) +
" Location=" + str(location) + " Clockwise=" + str(clockwise))
car = Overdrive("F5:64:F7:27:C4:70")
car.setLocationChangeCallback(
locationChangeCallback) # Set location change callback to function above
car.changeSpeed(200,
1000) # Set car speed with speed = 500, acceleration = 1000
#car.changeLaneRight(1000, 1000) # Switch to next right lane with speed = 1000, acceleration = 1000
input() # Hold the program so it won't end abruptly
from overdrive import Overdrive
def locationChangeCallback(addr, params):
# Print out addr, piece ID, location ID of the vehicle, this print everytime when location changed
print(
"Location from {addr} : Piece={piece} Location={location} Clockwise={clockwise}"
.format(addr=addr, **params))
car = Overdrive("xx:xx:xx:xx:xx:xx")
car.setLocationChangeCallback(
locationChangeCallback) # Set location change callback to function above
car.changeSpeed(500, 500) # Set car speed with speed = 500, acceleration = 500
car.changeLaneRight(
500, 500) # Switch to next right lane with speed = 500, acceleration = 500
input() # Hold the program so it won't end abruptly
tmp_clockwise = True
tmp_piece = 0
def locationChangeCallback(addr, location, piece, speed, clockwise):
global tmp_clockwise, tmp_piece
print(" Location from " + addr + " : " + "Piece=" + str(piece) +
" Location=" + str(location) + " Clockwise=" + str(clockwise))
tmp_clockwise = str(clockwise)
tmp_piece = str(piece)
# UDP
client = socket.socket(socket.AF_INET, socket.SOCK_DGRAM) # UDP
client.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)
client.settimeout(0.1)
client.bind(("", 37020))
# Anki
car = Overdrive("E2:81:E9:52:65:97")
car.setLocationChangeCallback(locationChangeCallback)
car.changeSpeed(200, 300)
while True:
message = str(tmp_clockwise) + " " + str(tmp_piece)
client.sendto(message, ('172.27.0.3', 44444))
data, addr = client.recvfrom(1024)
if data == "0":
car.changeSpeed(0, 1000)
input()
def main(input_speed):
if not os.path.exists(MODEL_NAME + '/frozen_inference_graph.pb'):
print('Downloading the model')
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
file_name = os.path.basename(file.name)
if 'frozen_inference_graph.pb' in file_name:
tar_file.extract(file, os.getcwd())
print('Download complete')
else:
print('Model already exists')
# ## Load a (frozen) Tensorflow model into memory.
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# Loading label map:
# Label maps map indices to category names, so that when our convolution
# network predicts `5`, we know that this corresponds to `airplane`. Here
# we use internal utility functions, but anything that returns a dictionary
# mapping integers to appropriate string labels would be fine
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
# Intializing the web camera device: 0 for internal and 1 for external camera
# of the laptop used
cap = cv2.VideoCapture(1)
# Running the tensorflow session
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
ret = True
# Select which cars to use on the track using MAC address of the device
#car2 = Overdrive("CD:5A:27:DC:41:89")
car3 = Overdrive("DE:83:21:EB:1B:2E")
car2 = Overdrive("FB:76:00:CB:82:63")
#car3 = Overdrive("DB:DE:FF:52:CB:9E")
# Set initial car speed and acceleration for the two cars
initial_car_speed = input_speed
initial_car_acceleration = 800
car2.changeSpeed(initial_car_speed, initial_car_acceleration)
car3.changeSpeed(initial_car_speed, initial_car_acceleration)
while (ret):
ret, image_np = cap.read()
print(image_np)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
print(image_np_expanded)
image_tensor = detection_graph.get_tensor_by_name(
'image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name(
'detection_scores:0')
classes = detection_graph.get_tensor_by_name(
'detection_classes:0')
num_detections = detection_graph.get_tensor_by_name(
'num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
classes_detected = classes[scores > 0.5]
if 13 in classes_detected:
#print('detected')
car3.changeSpeed(0, initial_car_acceleration)
car2.changeSpeed(0, initial_car_acceleration)
#car3.changeLaneRight(250, 250)
car3.setLocationChangeCallback(locationChangeCallback)
#print(car3.piece)
elif 10 in classes_detected:
print('verkeerslicht detected')
#print('detected')
car3.changeSpeed(0, initial_car_acceleration)
car2.changeSpeed(0, initial_car_acceleration)
#car3.changeLaneRight(250, 250)
car3.setLocationChangeCallback(locationChangeCallback)
"""
elif 1 in classes_detected:
print('car detected')
car3.changeSpeed(int(initial_car_speed/2), initial_car_acceleration)
car2.changeSpeed(int(initial_car_speed/2), initial_car_acceleration)
car3.setLocationChangeCallback(locationChangeCallback)
"""
else:
car3.changeSpeed(initial_car_speed,
initial_car_acceleration)
car2.changeSpeed(initial_car_speed,
initial_car_acceleration)
car3.setLocationChangeCallback(locationChangeCallback)
#print(car3.piece)
#drive(cv2)
#print(image_np,boxes,classes,scores,category_index)
#plt.figure(figsize=IMAGE_SIZE)
#plt.imshow(image_np)
cv2.imshow('image', cv2.resize(image_np, (1280, 960)))
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
cap.release()
break
'Location from {addr} : Piece={piece} Location={location} Speed={speed} Clockwise={clockwise}'
.format(**params))
car.setLocationChangeCallback(locationChangeCallback)
def transitionChangeCallback(addr, params):
params['addr'] = addr
print('Transition from {addr} From={piecePrev} To={piece} Offset={offset}'.
format(**params))
car.setTransitionCallback(transitionChangeCallback)
car.changeSpeed(500, 800)
while True:
inpt = input(
'[ to change lane to left, ] to change lane to right, enter to stop.\n'
)
if inpt == '[':
car.changeLaneLeft(500, 800)
elif inpt == ']':
car.changeLaneRight(500, 800)
else:
break
car.changeSpeed(0, 800)
car.disconnect()
numa = 0
numl = 0
numr = 0
mcs = 800
nums = 0
if car.addr == caradress:
print("car connected")
while 1:
events = get_gamepad()
for event in events:
if not event.ev_type == "Sync":
if event.code == "ABS_RZ":
carspeed = event.state
if carspeed > mcs:
carspeed = mcs
car.changeSpeed(carspeed, 1000)
print(carspeed)
else:
if event.code == "BTN_TR":
numr = numr+1
if numr == 2:
car.changeLaneRight(500, 800)
print("RIGHT")
numr = 0
else:
if event.code == "BTN_TL":
numl= numl+1
if numl == 2:
car.changeLaneLeft(500, 800)
print("LEFT")
numl = 0
category_index = label_map_util.create_category_index(categories)
#intializing the web camera device
import cv2
cap = cv2.VideoCapture(1)
# Running the tensorflow session
with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
ret = True
#car2 = Overdrive("CD:5A:27:DC:41:89")
car3 = Overdrive("DE:83:21:EB:1B:2E")
car2 = Overdrive("FB:76:00:CB:82:63")
#car3 = Overdrive("DB:DE:FF:52:CB:9E")
car2.changeSpeed(500, 1000)
car3.changeSpeed(500, 1000)
while (ret):
ret,image_np = cap.read()
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
from overdrive import Overdrive
# Global variables for the cars and the directionchange
# Select which cars to use on the track using MAC address of the device
#car2 = Overdrive("CD:5A:27:DC:41:89") #Brandbaar
#car3 = Overdrive("DE:83:21:EB:1B:2E") #GAS
car3 = Overdrive("FB:76:00:CB:82:63") #Explosief
car2 = Overdrive("DB:DE:FF:52:CB:9E") #Radioactief
initial_car_speed = 300
initial_car_acceleration = 800
car2.changeSpeed(initial_car_speed, initial_car_acceleration)
car3.changeSpeed(initial_car_speed, initial_car_acceleration)