def get_servo_dataset(filename, start_index=0, end_index=None):
data = pd.DataFrame.from_csv(filename)
# Outputs
x = []
# Servo ranges from 40-150
servo = []
for i in data.index[start_index:end_index]:
# Don't want noisy data
if data['servo'][i] < -100 or data['servo'][i] > 100 :
continue
# Append
image = deserialize_image(data['image'][i])
minRGB = np.array([0, 0, 41])
maxRGB = np.array([88, 88, 255])
maskRGB = cv2.inRange(image,minRGB,maxRGB)
image = cv2.bitwise_and(image, image, mask = maskRGB)
x.append(image)
servo.append(raw_to_cnn(data['servo'][i]))
return x, servo
def visualize_data(filename, width=72, height=48, depth=3, cnn_model=None):
"""
When cnn_model is specified it'll show what the cnn_model predicts (red)
as opposed to what inputs it actually received (green)
"""
data = pd.DataFrame.from_csv(filename)
for i in data.index:
cur_img = data['image'][i]
cur_throttle = int(data['servo'][i])
cur_motor = int(data['motor'][i])
# [1:-1] is used to remove '[' and ']' from string
cur_img_array = deserialize_image(cur_img)
y_input = cur_img_array.copy() # NN input
# And then rescale it so we can more easily preview it
cur_img_array = cv2.resize(cur_img_array, (480, 320),
interpolation=cv2.INTER_CUBIC)
# Extra debugging info (e.g. steering etc)
cv2.putText(cur_img_array, "frame: %s" % str(i), (5, 35),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
cv2.line(cur_img_array, (240, 300), (240 - (90 - cur_throttle), 200),
(0, 255, 0), 3)
# Motor values
# RGB
cv2.line(cur_img_array, (50, 160), (50, 160 - (90 - cur_motor)),
raw_motor_to_rgb(cur_motor), 3)
# If we wanna visualize our cnn_model
if cnn_model:
y = cnn_model.predict([y_input])
servo_out = cnn_to_raw(y[0])
cv2.line(cur_img_array, (240, 300),
(240 - (90 - int(servo_out)), 200), (0, 0, 255), 3)
# Can determine the motor our with a simple exponential equation
# x = abs(servo_out-90)
# motor_out = (7.64*e^(-0.096*x)) - 1
# motor_out = 90 - motor_out
x_ = abs(servo_out - 90)
motor_out = (7.64 * np.e**(-0.096 * x_)) - 1
motor_out = int(80 - motor_out) # Only wanna go forwards
cv2.line(cur_img_array, (100, 160), (100, 160 - (90 - motor_out)),
raw_motor_to_rgb(motor_out), 3)
print(motor_out, cur_motor)
# Show frame
# Convert to BGR cause thats how OpenCV likes it
cv2.imshow('frame', cv2.cvtColor(cur_img_array, cv2.COLOR_RGB2BGR))
if cv2.waitKey(0) & 0xFF == ord('q'):
break
def visualize_data(filename, width=72, height=48, depth=3, cnn_model=None):
"""
When cnn_model is specified it'll show what the cnn_model predicts (red)
as opposed to what inputs it actually received (green)
"""
data = pd.DataFrame.from_csv(filename)
for i in data.index:
cur_img = data['image'][i]
cur_throttle = int(data['servo'][i])
cur_motor = int(data['motor'][i])
# [1:-1] is used to remove '[' and ']' from string
cur_img_array = deserialize_image(cur_img)
y_input = cur_img_array.copy() # NN input
# And then rescale it so we can more easily preview it
cur_img_array = cv2.resize(cur_img_array, (480, 320), interpolation=cv2.INTER_CUBIC)
# Extra debugging info (e.g. steering etc)
cv2.putText(cur_img_array, "frame: %s" % str(i), (5,35), cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
cv2.line(cur_img_array, (240, 300), (240-(90-cur_throttle), 200), (0, 255, 0), 3)
# Motor values
# RGB
cv2.line(cur_img_array, (50, 160), (50, 160-(90-cur_motor)), raw_motor_to_rgb(cur_motor), 3)
# If we wanna visualize our cnn_model
if cnn_model:
y = cnn_model.predict([y_input])
servo_out = cnn_to_raw(y[0])
cv2.line(cur_img_array, (240, 300), (240-(90-int(servo_out)), 200), (0, 0, 255), 3)
# Can determine the motor our with a simple exponential equation
# x = abs(servo_out-90)
# motor_out = (7.64*e^(-0.096*x)) - 1
# motor_out = 90 - motor_out
x_ = abs(servo_out - 90)
motor_out = (7.64*np.e**(-0.096*x_)) - 1
motor_out = int(80 - motor_out) # Only wanna go forwards
cv2.line(cur_img_array, (100, 160), (100, 160-(90-motor_out)), raw_motor_to_rgb(motor_out), 3)
print(motor_out, cur_motor)
# Show frame
# Convert to BGR cause thats how OpenCV likes it
cv2.imshow('frame', cv2.cvtColor(cur_img_array, cv2.COLOR_RGB2BGR))
if cv2.waitKey(0) & 0xFF == ord('q'):
break
def update_driving(self):
drive_dir = (self.drive_forwards - self.drive_back)
#print('drive_dir = '+str(drive_dir))
if (drive_dir > 0.1) and self.cozmo.is_on_charger:
# cozmo is stuck on the charger, and user is trying to drive off - issue an explicit drive off action
try:
self.cozmo.drive_off_charger_contacts().wait_for_completed()
except cozmo.exceptions.RobotBusy:
# Robot is busy doing another action - try again next time we get a drive impulse
pass
turn_dir = (self.turn_right - self.turn_left) + self.mouse_dir
#print('turn_dir = '+str(turn_dir))
if drive_dir < 0:
# It feels more natural to turn the opposite way when reversing
turn_dir = -turn_dir
forward_speed = self.pick_speed(150, 75, 50)
turn_speed = self.pick_speed(100, 50, 30)
#print(turn_speed)
if not _is_autodrive:
self.cur_motor = drive_dir * forward_speed
self.cur_servo = turn_speed * turn_dir
else:
cur_img_array = deserialize_image(np.asarray(self.logImages[-1]),
SETTINGS['width'],
SETTINGS['height'],
SETTINGS['depth'])
y_input = cur_img_array.copy() # NN input
y = cnn_model.predict([y_input])
print('y[0] = ' + str(y[0]))
self.cur_servo = int(cnn_to_raw([y[0][0]]))
self.cur_motor = int(cnn_to_raw([y[0][1]]))
print('_is_autodrive = ' + str(_is_autodrive))
print('cur_servo = ' + str(self.cur_servo))
print('cur_motor = ' + str(self.cur_motor))
l_wheel_speed = self.cur_motor + self.cur_servo
r_wheel_speed = self.cur_motor - self.cur_servo
self.cozmo.drive_wheels(l_wheel_speed, r_wheel_speed,
l_wheel_speed * 4, r_wheel_speed * 4)
def visualize_data(filename, width=72, height=48, depth=3, cnn_model=None):
"""
When cnn_model is specified it'll show what the cnn_model predicts (red)
as opposed to what inputs it actually received (green)
"""
data = pd.DataFrame.from_csv(filename)
for i in range(30):
cur_img = data['image'][i]
cur_steer = int(data['servo'][i])
cur_throttle = int(data['motor'][i])
# [1:-1] is used to remove '[' and ']' from string
cur_img_array = deserialize_image(cur_img)
# cur_img_array = cv2.resize(cur_img_array, (480, 320), interpolation=cv2.INTER_CUBIC)
image = cv2.cvtColor(cur_img_array, cv2.COLOR_RGB2BGR)
print(i)
cv2.imwrite('test'+str(i)+'.jpg', image)
def get_servo_dataset(filename, start_index=0, end_index=None):
data = pd.DataFrame.from_csv(filename)
# Outputs
x = []
# Servo ranges from 40-150
servo = []
for i in data.index[start_index:end_index]:
# Don't want noisy data
if data['servo'][i] < 40 or data['servo'][i] > 150:
continue
# Append
x.append(deserialize_image(data['image'][i]))
servo.append(raw_to_cnn(data['servo'][i]))
return x, servo
def get_servo_dataset(filename, start_index=0, end_index=None):
data = pd.DataFrame.from_csv(filename)
# Outputs
x = []
# Servo ranges from 40-150
servo = []
for i in data.index[start_index:end_index]:
# Don't want noisy data
if data['servo'][i] < 40 or data['servo'][i] > 150:
continue
# Append
x.append(deserialize_image(data['image'][i]))
servo.append(raw_to_cnn(data['servo'][i]))
return x, servo
def get_dataset(filename,
start_index=0,
end_index=None,
width=72,
height=48,
depth=3):
data = pd.DataFrame.from_csv(filename)
# Outputs
x = []
# Servo and Motor
y = []
for i in data.index[start_index:end_index]:
# Don't want noisy data
#if data['servo'][i] < 40 or data['servo'][i] > 150:
# continue
# Append
x.append(deserialize_image(data['image'][i], width, height, depth))
y.append([raw_to_cnn(data['servo'][i]), raw_to_cnn(data['motor'][i])])
return x, y
def visualize_data(filename, width=72, height=48, depth=3, cnn_model=None):
"""
When cnn_model is specified it'll show what the cnn_model predicts (red)
as opposed to what inputs it actually received (green)
"""
data = pd.DataFrame.from_csv(filename)
frames = []
for i in data.index:
cur_img = data['image'][i]
cur_throttle = int(data['servo'][i])
cur_motor = int(data['motor'][i])
# [1:-1] is used to remove '[' and ']' from string
cur_img_array = deserialize_image(cur_img, width, height, depth)
y_input = cur_img_array.copy() # NN input
# And then rescale it so we can more easily preview it
cur_img_array = cv2.resize(cur_img_array, (480, 320),
interpolation=cv2.INTER_CUBIC)
# Extra debugging info (e.g. steering etc)
cv2.putText(cur_img_array, "frame: %s" % str(i), (5, 35),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
cv2.putText(cur_img_array, "servo: %s" % str(cur_throttle), (5, 70),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
cv2.putText(cur_img_array, "motor: %s" % str(cur_motor), (5, 100),
cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
# Steering values
cv2.line(cur_img_array, (240, 300), (240 + cur_throttle, 200),
(0, 255, 0), 3)
# Motor values, RGB
#cv2.line(cur_img_array, (50, 300), (50, 300-cur_motor), raw_motor_to_rgb(cur_motor), 3)
cv2.line(cur_img_array, (50, 300), (50, 300 - cur_motor), (0, 255, 0),
3)
# If we wanna visualize our cnn_model
if cnn_model:
y = cnn_model.predict([y_input])
print('y[0] = ' + str(y[0]))
# the conversion expect a list (of size 1) for unknown reason
# just play along here
servo_out = int(cnn_to_raw([y[0][0]]))
cv2.line(cur_img_array, (250, 300), (250 + servo_out, 200),
(0, 0, 255), 3)
# Can determine the motor our with a simple exponential equation
# x = abs(servo_out-90)
# motor_out = (7.64*e^(-0.096*x)) - 1
# motor_out = 90 - motor_out
#x_ = abs(servo_out - 90)
#motor_out = (7.64*np.e**(-0.096*x_)) - 1
#motor_out = int(80 - motor_out) # Only wanna go forwards
motor_out = int(cnn_to_raw([y[0][1]]))
cv2.line(cur_img_array, (60, 300), (60, 300 - motor_out),
(0, 0, 255), 3)
#print(motor_out, cur_motor)
cv2.putText(cur_img_array, "pServo: %s" % str(servo_out),
(245, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
cv2.putText(cur_img_array, "pMotor: %s" % str(motor_out),
(245, 100), cv2.FONT_HERSHEY_SIMPLEX, 1, 255)
# Show frame
# Convert to BGR cause thats how OpenCV likes it
cv2.imshow('frame', cv2.cvtColor(cur_img_array, cv2.COLOR_RGB2BGR))
frames += [cur_img_array]
if cv2.waitKey(0) & 0xFF == ord('q'):
break
imageio.mimsave(
os.path.splitext(os.path.basename(filename))[0] + '.gif', frames)
"""
fileoutname = get_new_filename(folder='edata',
filename='output_',
extension='.csv')
outfile = open(fileoutname, 'w')
outfile = open(fileoutname, 'a')
data = pd.DataFrame.from_csv(filename)
frame_results = []
servo_results = []
motorspeed_results = []
for i in data.index:
cur_img = data['image'][i]
servo = data['servo'][i]
motor = data['motor'][i]
cur_img_array = deserialize_image(cur_img)
y_input = cur_img_array.copy() # NN input
#
cur_img_array = bw_rgb_filter(cur_img_array, 48, 72)
cur_img_array = cur_img_array.flatten()
cur_img_array = cur_img_array.astype('uint8')
frame_results.append(serialize_image(cur_img_array))
servo_results.append(servo)
motorspeed_results.append(motor)
raw_data = {
'image': frame_results,
'servo': servo_results,
'motor': motorspeed_results
}