class RCDriverNNOnly(object):
def __init__(self, model_path):
print('initing...')
# load trained neural network
if model_num == 1:
import model_b as model
self.model = model
elif model_num == 0:
load_model_start = time.time()
from model import NeuralNetwork
self.model = NeuralNetwork(model_path)
load_model_end = time.time()
print('loading model costs {:02f} second(s)'.format(load_model_end - load_model_start))
self.cap = cv2.VideoCapture(0)
self.servo = PyServo.Servo(SerialID,Baudrate)
def drive(self):
step = 0
try:
while True:
ret, img = self.cap.read()
if ret:
#print(img.shape)
img = cv2.resize(img,(320,240),interpolation=cv2.INTER_CUBIC)
#img = cv2.remap(img,map1,map2, interpolation= cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT)
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
height, width = gray.shape
roi_img = gray[roi[0]:roi[1], :]
# Apply GuassianBlur (reduces noise)
blurred = cv2.GaussianBlur(roi_img, (3, 3), 0)
# Apply Canny filter
auto = self.model.auto_canny(blurred)
#print(auto.shape)
global count
if self.servo.isLinsten():
if timer:
timer_prediction = time.time()
prediction_english = None
prediction_english_proba = None
# neural network makes prediction
print("predicting...")
prediction, lines = self.model.predict(auto)
if timer:
print('prediction takes:{} seconds'.format(time.time()-timer_prediction))
timer_action = time.time()
#proba_left, proba_right, proba_forward = probas[0]
if np.all(prediction == [ 0., 0., 1.]):
prediction_english = 'FORWARD'
self.servo.RunGroup(Move_Forward,1)
elif np.all(prediction == [ 1., 0., 0.]):
prediction_english = 'LEFT'
self.servo.RunGroup(Move_Left,1)
elif np.all(prediction == [ 0., 1., 0.]):
prediction_english = 'RIGHT'
self.servo.RunGroup(Move_Right,1)
print(prediction_english)
if write_temp_file:
if model_num ==1:
#auto = self.model.visualize_lines(cv2.cvtColor(roi_img,cv2.COLOR_GRAY2BGR),lines)
pass
cv2.putText(auto, "Prediction: {}".format(prediction_english,
), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, .45, (255, 255, 0), 1)
step += 1
cv2.imwrite('./test_images/step{}.jpg'.format(step), auto)
print('writing images of step{}'.format(step))
if timer:
print('Action takes:{} seconds'.format(time.time()-timer_action))
print(count)
count = 0
else:
while True:
if self.servo.isLinsten():
self.servo.Group_complete = True
break
count+=1
except Exception as e:
print(e)
finally:
print('total {} frames predicted'.format(step))
class RCDriverNNOnly(object):
def __init__(self, host, port, _host, _port, model_path):
# load trained neural network
self.nn = NeuralNetwork(model_path)
self.cap = cv2.VideoCapture(0)
self.rc_car = RCControl(_h, _p)
def drive(self):
stream_bytes = b' '
frame = 0
try:
# stream video frames one by one
while True:
ret, frame = self.cap.read()
if ret:
frame = cv2.resize(frame, (320, 240),
interpolation=cv2.INTER_CUBIC)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
image = frame
height, width = gray.shape
roi = gray[int(height / 2):height, :]
# Apply GuassianBlur (reduces noise)
blurred = cv2.GaussianBlur(roi, (3, 3), 0)
# Apply Canny filter
auto = self.nn.auto_canny(blurred)
cv2.imshow('image', image)
cv2.imshow('What the model sees', auto)
# cv2.imshow('mlp_image', roi)
# Neural network model makes prediciton
# prediction = self.model.predict(auto)
# reshape image
#image_array = roi.reshape(1, int(height/2) * width).astype(np.float32)
global count
frame += 1
if count >= 5:
prediction_english = None
prediction_english_proba = None
# neural network makes prediction
prediction, probas = self.nn.predict(auto)
proba_left, proba_right, proba_forward = probas[0]
if np.all(prediction == [0., 0., 1.]):
prediction_english = 'FORWARD'
prediction_english_proba = proba_forward
elif np.all(prediction == [1., 0., 0.]):
prediction_english = 'LEFT'
prediction_english_proba = proba_left
elif np.all(prediction == [0., 1., 0.]):
prediction_english = 'RIGHT'
prediction_english_proba = proba_right
count = 0
self.rc_car.steer(prediction)
else:
count += 1
if cv2.waitKey(1) & 0xFF == ord('q'):
print("car stopped")
self.rc_car.stop()
break
finally:
cv2.destroyAllWindows()
class RCDriverNNOnly(object):
def __init__(self, host, port, _host, _port, model_path):
#tcp host port
#udp _host _port
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(model_path)
self.rc_car = RCControl(_h, _p)
def drive(self):
stream_bytes = b' '
frame = 0
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, :]
# Apply GuassianBlur (reduces noise)
blurred = cv2.GaussianBlur(roi, (3, 3), 0)
# Apply Canny filter
auto = self.nn.auto_canny(blurred)
cv2.imshow('image', image)
cv2.imshow('What the model sees', auto)
# cv2.imshow('mlp_image', roi)
image = cv2.imdecode(np.frombuffer(jpg, dtype=np.uint8),
cv2.IMREAD_GRAYSCALE)
# Neural network model makes prediciton
# prediction = self.model.predict(auto)
# reshape image
#image_array = roi.reshape(1, int(height/2) * width).astype(np.float32)
prediction_english = None
prediction_english_proba = None
# neural network makes prediction
prediction, probas = self.nn.predict(auto)
proba_left, proba_right, proba_forward = probas[0]
if np.all(prediction == [0., 0., 1.]):
prediction_english = 'FORWARD'
prediction_english_proba = proba_forward
elif np.all(prediction == [1., 0., 0.]):
prediction_english = 'LEFT'
prediction_english_proba = proba_left
elif np.all(prediction == [0., 1., 0.]):
prediction_english = 'RIGHT'
prediction_english_proba = proba_right
global count
frame += 1
cv2.putText(
gray, "Prediction (sig={}): {}, {:>05}".format(
SIGMA, prediction_english,
prediction_english_proba), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, .45, (255, 255, 0), 1)
if write_temp_file:
cv2.imwrite(
'test_frames_temp/frame{:>05}.jpg'.format(frame),
gray)
if count >= 10:
self.rc_car.steer(prediction)
count = 0
else:
count += 1
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()
