#!/usr/bin/env python
import sys
import array
import numpy as np
from skimage.color import rgb2gray
from skimage.transform import resize
from skimage.io import imread
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from inputs import get_gamepad
import math
import threading
def resize_image(img):
im = resize(img, (Sample.IMG_H, Sample.IMG_W, Sample.IMG_D))
im_arr = im.reshape((Sample.IMG_H, Sample.IMG_W, Sample.IMG_D))
return im_arr
class Screenshot(object):
SRC_W = 640
SRC_H = 480
SRC_D = 3
OFFSET_X = 0
OFFSET_Y = 0
class Sample:
IMG_W = 200
IMG_H = 66
IMG_D = 3
class XboxController(object):
MAX_TRIG_VAL = math.pow(2, 8)
MAX_JOY_VAL = math.pow(2, 15)
def __init__(self):
self.LeftJoystickY = 0
self.LeftJoystickX = 0
self.RightJoystickY = 0
self.RightJoystickX = 0
self.LeftTrigger = 0
self.RightTrigger = 0
self.LeftBumper = 0
self.RightBumper = 0
self.A = 0
self.X = 0
self.Y = 0
self.B = 0
self.LeftThumb = 0
self.RightThumb = 0
self.Back = 0
self.Start = 0
self.LeftDPad = 0
self.RightDPad = 0
self.UpDPad = 0
self.DownDPad = 0
self._monitor_thread = threading.Thread(target=self._monitor_controller, args=())
self._monitor_thread.daemon = True
self._monitor_thread.start()
def read(self):
x = self.LeftJoystickX
y = self.LeftJoystickY
a = self.A
b = self.X # b=1, x=2
rb = self.RightBumper
return [x, y, a, b, rb]
def _monitor_controller(self):
while True:
events = get_gamepad()
for event in events:
if event.code == 'ABS_Y':
self.LeftJoystickY = event.state / XboxController.MAX_JOY_VAL # normalize between -1 and 1
elif event.code == 'ABS_X':
self.LeftJoystickX = event.state / XboxController.MAX_JOY_VAL # normalize between -1 and 1
elif event.code == 'ABS_RY':
self.RightJoystickY = event.state / XboxController.MAX_JOY_VAL # normalize between -1 and 1
elif event.code == 'ABS_RX':
self.RightJoystickX = event.state / XboxController.MAX_JOY_VAL # normalize between -1 and 1
elif event.code == 'ABS_Z':
self.LeftTrigger = event.state / XboxController.MAX_TRIG_VAL # normalize between 0 and 1
elif event.code == 'ABS_RZ':
self.RightTrigger = event.state / XboxController.MAX_TRIG_VAL # normalize between 0 and 1
elif event.code == 'BTN_TL':
self.LeftBumper = event.state
elif event.code == 'BTN_TR':
self.RightBumper = event.state
elif event.code == 'BTN_SOUTH':
self.A = event.state
elif event.code == 'BTN_NORTH':
self.X = event.state
elif event.code == 'BTN_WEST':
self.Y = event.state
elif event.code == 'BTN_EAST':
self.B = event.state
elif event.code == 'BTN_THUMBL':
self.LeftThumb = event.state
elif event.code == 'BTN_THUMBR':
self.RightThumb = event.state
elif event.code == 'BTN_SELECT':
self.Back = event.state
elif event.code == 'BTN_START':
self.Start = event.state
elif event.code == 'BTN_TRIGGER_HAPPY1':
self.LeftDPad = event.state
elif event.code == 'BTN_TRIGGER_HAPPY2':
self.RightDPad = event.state
elif event.code == 'BTN_TRIGGER_HAPPY3':
self.UpDPad = event.state
elif event.code == 'BTN_TRIGGER_HAPPY4':
self.DownDPad = event.state
class Data(object):
def __init__(self):
self._X = np.load("data/X.npy")
self._y = np.load("data/y.npy")
self._epochs_completed = 0
self._index_in_epoch = 0
self._num_examples = self._X.shape[0]
@property
def num_examples(self):
return self._num_examples
def next_batch(self, batch_size):
start = self._index_in_epoch
self._index_in_epoch += batch_size
if self._index_in_epoch > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Start next epoch
start = 0
self._index_in_epoch = batch_size
assert batch_size <= self._num_examples
end = self._index_in_epoch
return self._X[start:end], self._y[start:end]
def load_sample(sample):
image_files = np.loadtxt(sample + '/data.csv', delimiter=',', dtype=str, usecols=(0,))
joystick_values = np.loadtxt(sample + '/data.csv', delimiter=',', usecols=(1,2,3,4,5))
return image_files, joystick_values
# training data viewer
def viewer(sample):
image_files, joystick_values = load_sample(sample)
plotData = []
plt.ion()
plt.figure('viewer', figsize=(16, 6))
for i in range(len(image_files)):
# joystick
print(i, " ", joystick_values[i,:])
# format data
plotData.append( joystick_values[i,:] )
if len(plotData) > 30:
plotData.pop(0)
x = np.asarray(plotData)
# image (every 3rd)
if (i % 3 == 0):
plt.subplot(121)
image_file = image_files[i]
img = mpimg.imread(image_file)
plt.imshow(img)
# plot
plt.subplot(122)
plt.plot(range(i,i+len(plotData)), x[:,0], 'r')
plt.hold(True)
plt.plot(range(i,i+len(plotData)), x[:,1], 'b')
plt.plot(range(i,i+len(plotData)), x[:,2], 'g')
plt.plot(range(i,i+len(plotData)), x[:,3], 'k')
plt.plot(range(i,i+len(plotData)), x[:,4], 'y')
plt.draw()
plt.hold(False)
plt.pause(0.0001) # seconds
i += 1
# prepare training data
def prepare(samples):
print("Preparing data")
X = []
y = []
for sample in samples:
print(sample)
# load sample
image_files, joystick_values = load_sample(sample)
# add joystick values to y
y.append(joystick_values)
# load, prepare and add images to X
for image_file in image_files:
image = imread(image_file)
vec = resize_image(image)
X.append(vec)
print("Saving to file...")
X = np.asarray(X)
y = np.concatenate(y)
np.save("data/X", X)
np.save("data/y", y)
print("Done!")
return
if __name__ == '__main__':
if sys.argv[1] == 'viewer':
viewer(sys.argv[2])
elif sys.argv[1] == 'prepare':
prepare(sys.argv[2:])
