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)