class RCDriverNNOnly(object):
def __init__(self, host, port, serial_port, model_path):
self.server_socket = socket.socket()
self.server_socket.bind((host, port))
self.server_socket.listen(0)
# accept a single connection
self.connection = self.server_socket.accept()[0].makefile('rb')
# connect to a seral port
self.ser = serial.Serial(serial_port, 115200, timeout=1)
# load trained neural network
self.nn = NeuralNetwork()
self.nn.load_model(model_path)
self.rc_car = RCControl(serial_port)
def drive(self):
stream_bytes = b' '
try:
# stream video frames one by one
while True:
stream_bytes += self.connection.read(1024)
first = stream_bytes.find(b'\xff\xd8')
last = stream_bytes.find(b'\xff\xd9')
if first != -1 and last != -1:
jpg = stream_bytes[first:last + 2]
stream_bytes = stream_bytes[last + 2:]
gray = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8),
cv2.IMREAD_GRAYSCALE)
image = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8),
cv2.IMREAD_COLOR)
# lower half of the image
height, width = gray.shape
roi = gray[int(height / 2):height, :]
cv2.imshow('image', image)
# cv2.imshow('mlp_image', roi)
# reshape image
image_array = roi.reshape(1,
int(height / 2) * width).astype(
np.float32)
# neural network makes prediction
prediction = self.nn.predict(image_array)
self.rc_car.steer(prediction)
if cv2.waitKey(1) & 0xFF == ord('q'):
print("car stopped")
self.rc_car.stop()
break
finally:
cv2.destroyAllWindows()
self.connection.close()
self.server_socket.close()
class RCDriverNNOnly(object):
def __init__(self, host, port, serial_port, model_path):
self.server_socket = socket.socket()
self.server_socket.bind((host, port))
self.server_socket.listen(0)
# accept a single connection
self.connection = self.server_socket.accept()[0].makefile('rb')
# load trained neural network
self.nn = NeuralNetwork()
self.nn.load_model(model_path)
self.rc_car = RCControl(serial_port)
def drive(self):
stream_bytes = b' '
try:
# stream video frames one by one
while True:
stream_bytes += self.connection.read(1024)
first = stream_bytes.find(b'\xff\xd8')
last = stream_bytes.find(b'\xff\xd9')
if first != -1 and last != -1:
jpg = stream_bytes[first:last + 2]
stream_bytes = stream_bytes[last + 2:]
gray = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8), cv2.IMREAD_GRAYSCALE)
image = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8), cv2.IMREAD_COLOR)
# lower half of the image
height, width = gray.shape
roi = gray[int(height/2):height, :]
cv2.imshow('image', image)
# cv2.imshow('mlp_image', roi)
# reshape image
image_array = roi.reshape(1, int(height/2) * width).astype(np.float32)
# neural network makes prediction
prediction = self.nn.predict(image_array)
self.rc_car.steer(prediction)
if cv2.waitKey(1) & 0xFF == ord('q'):
print("car stopped")
self.rc_car.stop()
break
finally:
cv2.destroyAllWindows()
self.connection.close()
self.server_socket.close()
def __init__(self, host, port, serial_port, model_path):
self.server_socket = socket.socket()
self.server_socket.bind((host, port))
self.server_socket.listen(0)
# accept a single connection
self.connection = self.server_socket.accept()[0].makefile('rb')
# load trained neural network
self.nn = NeuralNetwork()
self.nn.load_model(model_path)
self.rc_car = RCControl(serial_port)
def __init__(self, host, port, serial_port, model_path):
self.server_socket = socket.socket()
self.server_socket.bind((host, port))
self.server_socket.listen(0)
# accept a single connection
self.connection = self.server_socket.accept()[0].makefile('rb')
# load trained neural network
self.nn = NeuralNetwork()
self.nn.load_model(model_path)
self.rc_car = RCControl(serial_port)