def main(file_name, starting_value):
file_name = file_name
starting_value = starting_value
training_data = []
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
last_time = time.time()
paused = False
print('STARTING!!!')
while (True):
if not paused:
screen = grab_screen(region=(0, 40, GAME_WIDTH, GAME_HEIGHT + 40))
last_time = time.time()
# resize to something a bit more acceptable for a CNN
screen = cv2.resize(screen, (80, 60))
# run a color convert:
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)
keys = key_check()
output = keys_to_output(keys)
training_data.append([screen, output])
# print('loop took {} seconds'.format(time.time()-last_time))
last_time = time.time()
## cv2.imshow('window',cv2.resize(screen,(640,360)))
## if cv2.waitKey(25) & 0xFF == ord('q'):
## cv2.destroyAllWindows()
## break
if len(training_data) % 100 == 0:
print(len(training_data))
if len(training_data) == 500:
np.save(file_name, training_data)
print('SAVED')
training_data = []
starting_value += 1
file_name = 'C:/Users/stepanovep/PycharmProjects/PyGta5/phase7-larger-color/training_data-{}.npy'.format(
starting_value)
keys = key_check()
if 'T' in keys:
if paused:
paused = False
print('unpaused!')
time.sleep(1)
else:
print('Pausing!')
paused = True
time.sleep(1)
def main(file_name, starting_value):
file_name = file_name
starting_value = starting_value
training_data = []
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
last_time = time.time()
paused = False
print('STARTING!!!')
while (True):
time.sleep(0.1)
if not paused:
screen = grab_screen(region=(100, 100, 1350, 1030))
last_time = time.time()
# resize to something a bit more acceptable for a CNN
screen = cv2.resize(screen, (480, 270))
# run a color convert:
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)
keys = key_check()
output = keys_to_output(keys)
training_data.append([screen, output])
# print('loop took {} seconds'.format(time.time()-last_time))
last_time = time.time()
# cv2.imshow('window',cv2.resize(screen,(640,360)))
# if cv2.waitKey(25) & 0xFF == ord('q'):
# cv2.destroyAllWindows()
# break
if len(training_data) % 100 == 0:
print(len(training_data))
if len(training_data) == 500:
np.save(file_name, training_data)
print('SAVED')
training_data = []
starting_value += 1
file_name = 'C:/Users/ADR/Documents/Most_Wanted_Self_Driving_Car/TrainingData/training_data-{}.npy'.format(
starting_value)
keys = key_check()
if 'T' in keys:
if paused:
paused = False
print('unpaused!')
time.sleep(1)
else:
print('Pausing!')
paused = True
time.sleep(1)
def main():
file_name = "training_data.npy"
if os.path.isfile(file_name):
training_data = list(np.load(file_name))
else:
training_data = []
for i in list(range(4))[::-1]:
print(i+1)
time.sleep(1)
lst = time.time()
while(True):
screen = np.array(sct.grab((0,40,800,640)))
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
screen = cv2.resize(screen, (80,60))
keys = key_check()
output = keys_to_output(keys)
training_data.append([screen, output])
# print("Loop took {}".format((time.time()-lst)))
lst = time.time()
if len(training_data) % 500 == 0:
print("Saving data")
np.save(file_name, training_data)
def main():
# countdown of 5
for i in range(6)[::-1]:
print('Stating in {}'.format(i))
time.sleep(1)
initial_time = time.time()
# main loop
while True:
# grabbing the screen
screen = grab_screen([10, 40, 770, 700])
# converting to gray
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
# getting the region of interest
roi = screen[300:, 100:]
# resizing the frame to (80, 60)
roi = cv2.resize(roi, (480, 270))
# checking the keystroke
key = key_check()
# getting the one hot encoding of the key
key = oneHotEncoding(key)
# appending the frame with particular key stoke
training_data.append([roi, key])
print('Fps: {}'.format(time.time() - initial_time))
initial_time = time.time()
# if length of training data is 500, we stop and save
if len(training_data) % 200 == 0:
print('Length is {}, now saving the data'.format(
len(training_data)))
np.save(trainFile, training_data)
def main():
A_counter = 10674
W_counter = 21641
D_counter = 14518
for i in range(5, 0, -1):
print(i)
time.sleep(1)
while True:
game_frame = grab_screen()
game_frame = cv2.cvtColor(game_frame, cv2.COLOR_BGR2GRAY)
key_pressed = key_check()
if key_pressed == 'A':
cv2.imwrite(DATADIR + "A/" + str(A_counter) + ".jpg", game_frame)
A_counter = A_counter + 1
elif key_pressed == 'W':
cv2.imwrite(DATADIR + "W/" + str(W_counter) + ".jpg", game_frame)
W_counter = W_counter + 1
if key_pressed == 'D':
cv2.imwrite(DATADIR + "D/" + str(D_counter) + ".jpg", game_frame)
D_counter = D_counter + 1
else:
pass
cv2.imshow('game_frame', game_frame)
if cv2.waitKey(1) == 27:
break
cv2.destroyAllWindows()
def screen_record():
lc = 0
rc = 0
sc = 0
count = 0
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
# 800x600 windowed mode
while (True):
last_time = time.time()
screen = grab_screen(region=(0, 400, 800, 600))
#screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
screen = cv2.resize(screen, (128, 128))
keys = key_check()
keystrokes = presskeys_to_keystrokes(keys)
#print("Frames capturing at {} fps".format(1.0 / time.time()-last_time))
last_time = time.time()
path = "C:/Users/ajeya/PycharmProjects/Self Driving Car/data"
name = 'image' + str(count) + '.png'
if 'A' in keys:
lc += 1
name = 'image' + str(lc) + '.png'
print(keys)
path = path + "/left"
cv2.imwrite(os.path.join(path, name), screen)
elif 'W' in keys:
sc += 1
name = 'image' + str(sc) + '.png'
print(keys)
path = path + "/straight"
cv2.imwrite(os.path.join(path, name), screen)
elif 'D' in keys:
rc += 1
name = 'image' + str(rc) + '.png'
print(keys)
path = path + "/right"
cv2.imwrite(os.path.join(path, name), screen)
else:
print(keys)
path = path + "/slow"
pass
#cv2.imwrite(os.path.join(path, name), screen)
count += 1
#new_img = cv2.resize(screen,(500,400))
#backtorgb = cv2.cvtColor(new_screen, cv2.COLOR_GRAY2RGB)
#cv2.imshow('Window', cv2.cvtColor(screen, cv2.COLOR_BGR2RGB))
cv2.imshow('window', screen)
#cv2.imwrite(os.path.join(path,name), new_screen)
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
def main():
last_time = time.time()
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
paused = False
while (True):
if not paused:
# 800x600 windowed mode
screen = grab_screen(region=(0, 40, 800, 640))
print('loop took {} seconds'.format(time.time() - last_time))
last_time = time.time()
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
screen = cv2.resize(screen, (160, 120))
prediction = model.predict([screen.reshape(160, 120, 1)])[0]
print(prediction)
if np.argmax(prediction) == np.argmax(w):
straight()
elif np.argmax(prediction) == np.argmax(s):
reverse()
if np.argmax(prediction) == np.argmax(a):
left()
if np.argmax(prediction) == np.argmax(d):
right()
if np.argmax(prediction) == np.argmax(wa):
forward_left()
if np.argmax(prediction) == np.argmax(wd):
forward_right()
if np.argmax(prediction) == np.argmax(sa):
reverse_left()
if np.argmax(prediction) == np.argmax(sd):
reverse_right()
if np.argmax(prediction) == np.argmax(nk):
no_keys()
keys = key_check()
# p pauses game and can get annoying.
if 'T' in keys:
if paused:
paused = False
time.sleep(1)
else:
paused = True
ReleaseKey(A)
ReleaseKey(W)
ReleaseKey(D)
time.sleep(1)
def main():
for i in list(range(4))[::-1]:
print(i+1)
time.sleep(1)
last_time = time.time()
while True:
screen = grab_screen(region=(0,40,800,640))
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
screen = cv2.resize(screen,(80,60))
keys = key_check()
output = keys_to_output(keys)
training_data.append([screen,output])
print('Frame took {} seconds'.format(time.time()-last_time))
last_time = time.time()
#cv2.imshow('window2',cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))
if len(training_data) % 500 == 0:
print(len(training_data))
np.save(file_name,training_data)
def streamingAndCollectData(self):
saved_frame = 0
total_frame = 0
print('Start collecting images...')
e1 = cv2.getTickCount()
try:
print("Connection from: ", self.client_address)
print("Streaming...")
stream_bytes = b''
frame = 1
while True:
stream_bytes += self.connection.read(1024)
first = stream_bytes.find(b'\xff\xd8')
last = stream_bytes.find(b'\xff\xd9')
self.conn.sendall(b'WA')
if first != -1 and last != -1:
jpg = stream_bytes[first:last + 2]
stream_bytes = stream_bytes[last + 2:]
image = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
height, width = image.shape
# select lower half of the image
roi = image[int(height/2):height, :]
# Predict the data based on image frame received.
prediction = self.predict(roi)
# Control based on prediction
self.navigate(prediction)
# Press Q to Quit runnuing the car..
keys = key_check()
if 'Q' in keys:
payload = dict(data='x')
response = requests.post(self.restUrl, params=payload)
print(response, payload, 'sent to server.')
exit(0)
finally:
def main(file_name, set_count):
count = 0
start_collecting = False
while (True):
#start = time.time()
screen = cv2.cvtColor(grab_screen(region=(0, 100, 800, 540)),
cv2.COLOR_BGR2GRAY)
mini_screen = cv2.resize(screen, (160, 108))
cv2.imshow('screen', screen)
if (cv2.waitKey(25) & 0xFF == ord('q')):
cv2.destroyAllWindows()
break
keys = key_check()
label = keys_to_array(keys)
if ('P' in keys):
start_collecting = not (start_collecting)
if (start_collecting):
print("unpaused")
else:
print("paused")
if (start_collecting and label is not None):
train_data.append([mini_screen, label])
count += 1
if (count % 500 == 0):
print("have collected {} data points".format(str(count)))
#save after every 5000 datapoints
if (count % 20000 == 0):
print("have collected {} data points".format(str(count)))
np.save(file_name, train_data)
if (count == 40000):
set_count += 1
file_name = "training_data" + str(set_count) + ".npy"
def screen_record():
count = 0
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
# 800x600 windowed mode
while(True):
last_time = time.time()
screen = grab_screen(region=(0, 40, 800, 600))
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
screen = cv2.resize(screen, (128, 128))
keys = key_check()
keystrokes = presskeys_to_keystrokes(keys)
training_data.append([screen, keystrokes])
#print("Frames capturing at {} fps".format(1.0 / time.time()-last_time))
last_time = time.time()
path = "C:/Users/ajeya/PycharmProjects/Self Driving Car/images"
name = 'image'+str(count)+'.png'
count += 1
#new_img = cv2.resize(screen,(500,400))
#backtorgb = cv2.cvtColor(new_screen, cv2.COLOR_GRAY2RGB)
#cv2.imshow('Window', cv2.cvtColor(screen, cv2.COLOR_BGR2RGB))
cv2.imshow('window', screen)
#cv2.imwrite(os.path.join(path,name), new_screen)
if len(training_data) % 400 == 0:
print(len(training_data))
np.save(data_file, training_data)
if cv2.waitKey(25) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
crashing the vjoy driver
"""
vj = vJoy()
x_range = 16393
z_range = 32786
wAxisX = 16393
wAxisY = 16393
wAxisZ = 0
wAxisXRot = 16393
wAxisYRot = 16393
wAxisZRot = 0
keys = key_check()
ultimate_release()
while True:
vj.open()
btn = 1
joystickPosition = vj.generateJoystickPosition(wAxisX=12000, wAxisZ=32000, wAxisZRot=0)
vj.update(joystickPosition)
print("running")
time.sleep(0.1)
vj.sendButtons(0)
keys = key_check()
if 'P' in keys:
joystickPosition = vj.generateJoystickPosition(wAxisX=16000, wAxisY=16000)
vj.update(joystickPosition)
def streamingAndCollectData(self):
saved_frame = 0
total_frame = 0
# collect images for training
print('Start collecting images...')
e1 = cv2.getTickCount()
image_array = np.zeros((1, 38400))
label_array = np.zeros((1, 4), 'float')
try:
print("Connection from: ", self.client_address)
print("Streaming Pi Camera...")
print("Press 'Q' to exit")
stream_bytes = b''
frame = 1
while True:
stream_bytes += self.connection.read(1024)
first = stream_bytes.find(b'\xff\xd8')
last = stream_bytes.find(b'\xff\xd9')
self.conn.sendall(b'WA')
if first != -1 and last != -1:
jpg = stream_bytes[first:last + 2]
stream_bytes = stream_bytes[last + 2:]
image = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
imageBW = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
height, width = imageBW.shape
# select lower half of the image
roi = imageBW[int(height/2):height, :]
# Show Image
cv2.imshow('image', image)
temp_array = roi.flatten().astype(np.float32)
frame += 1
total_frame += 1
# Listen to pygame event
for event in pygame.event.get():
keystate = pygame.key.get_pressed()
if event.type == pygame.QUIT:
print("Quit")
break
if event.type == pygame.KEYDOWN:
print("Start Controlling...")
if keystate[pygame.K_UP]:
print("Forward")
cv2.imwrite('training_images/Imageframe{:>05}.jpg'.format(frame), roi)
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[2]))
saved_frame += 1
payload = dict(data='s')
response = requests.post(self.restUrl, params=payload)
print(response, payload, 'sent to server.')
elif keystate[pygame.K_DOWN]:
print("Reverse")
cv2.imwrite('training_images/Imageframe{:>05}.jpg'.format(frame), roi)
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[3]))
saved_frame += 1
payload = dict(data='w')
response = requests.post(self.restUrl, params=payload)
print(response, payload, 'sent to server.')
elif keystate[pygame.K_LEFT]:
print("Left")
cv2.imwrite('training_images/Imageframe{:>05}.jpg'.format(frame), roi)
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[0]))
saved_frame += 1
payload = dict(data='a')
response = requests.post(self.restUrl, params=payload)
print(response, payload, 'sent to server.')
elif keystate[pygame.K_RIGHT]:
print("Right")
cv2.imwrite('training_images/Imageframe{:>05}.jpg'.format(frame), roi)
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[1]))
saved_frame += 1
payload = dict(data='d')
response = requests.post(self.restUrl, params=payload)
print(response, payload, 'sent to server.')
elif keystate[pygame.K_UP] and keystate[pygame.K_RIGHT]:
print("Forward Right")
cv2.imwrite('training_images/Imageframe{:>05}.jpg'.format(frame), roi)
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[1]))
saved_frame += 1
payload = dict(data='q')
response = requests.post(self.restUrl, params=payload)
print(response, payload, 'sent to server.')
elif keystate[pygame.K_UP] and keystate[pygame.K_LEFT]:
print("Forward Left")
cv2.imwrite('training_images/Imageframe{:>05}.jpg'.format(frame), roi)
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[0]))
saved_frame += 1
payload = dict(data='e')
response = requests.post(self.restUrl, params=payload)
print(response, payload, 'sent to server.')
if event.type == pygame.KEYUP:
print("release")
payload = dict(data='x')
response = requests.post(self.restUrl, params=payload)
print(response, payload, 'sent to server.')
pygame.display.update()
keys = key_check()
if 'Q' in keys:
print('exit')
self.send_inst = False
break
# save training images and labels
train = image_array[1:, :]
train_labels = label_array[1:, :]
# save training data as a numpy file
file_name = str(int(time.time()))
directory = "training_data"
if not os.path.exists(directory):
os.makedirs(directory)
try:
np.savez(directory + '/' + file_name + '.npz', train=train, train_labels=train_labels)
except IOError as e:
print(e)
e2 = cv2.getTickCount()
# calculate streaming metrics
time0 = (e2 - e1) / cv2.getTickFrequency()
print('Streaming duration:', time0)
print(train.shape)
print(train_labels.shape)
print('Total frame:', total_frame)
print('Saved frame:', saved_frame)
print('Dropped frame', total_frame - saved_frame)
finally:
self.connection.close()
self.server_socket.close()
def streamingAndCollectData(self):
saved_frame = 0
total_frame = 0
# collect images for training
print('Start collecting images...')
e1 = cv2.getTickCount()
image_array = np.zeros((1, 38400))
label_array = np.zeros((1, 4), 'float')
try:
print("Connection from: ", self.client_address)
print("Streaming...")
print("Press 'Q' to exit")
stream_bytes = b''
frame = 1
while True:
stream_bytes += self.connection.read(1024)
first = stream_bytes.find(b'\xff\xd8')
last = stream_bytes.find(b'\xff\xd9')
self.conn.sendall(b'WA')
if first != -1 and last != -1:
jpg = stream_bytes[first:last + 2]
stream_bytes = stream_bytes[last + 2:]
image = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8),
cv2.IMREAD_UNCHANGED)
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
height, width = image.shape
# select lower half of the image
roi = image[int(height / 2):height, :]
# select lower half of the image
# cv2.imshow('image', roi)
# save streamed images
cv2.imwrite(
'training_images/Imageframe{:>05}.jpg'.format(frame),
roi)
# reshape the roi image into one row array
# temp_array = roi.reshape(1, 38400).astype(np.float32)
temp_array = roi.flatten().astype(np.float32)
frame += 1
total_frame += 1
# Check key stroke while streaming
keys = key_check()
if 'W' in keys and 'A' in keys:
print("Forward Left")
image_array = np.vstack((image_array, temp_array))
# Remember to check label_array later
label_array = np.vstack((label_array, self.k[0]))
saved_frame += 1
# Send key to rest api
payload = dict(data='WA')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'W' in keys and 'D' in keys:
print("Forward Right")
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[1]))
saved_frame += 1
# Send key to rest api
payload = dict(data='WD')
headers = {'content-type': 'application/json'}
response = requests.post(self.restUrl,
data=json.dumps(payload),
headers=headers)
print(response, payload, 'sent to server.')
elif 'S' in keys and 'A' in keys:
print("Reverse Left")
# Send key to rest api
payload = dict(data='SA')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'S' in keys and 'D' in keys:
print("Reverse Right")
# Send key to rest api
payload = dict(data='SD')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'W' in keys:
print("Forward")
saved_frame += 1
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[2]))
# Send key to rest api
payload = dict(data='W')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'S' in keys:
print("Reverse")
saved_frame += 1
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[3]))
# Send key to rest api
payload = dict(data='S')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'D' in keys:
print("Right")
saved_frame += 1
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[1]))
# Send key to rest api
payload = dict(data='D')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'A' in keys:
print("Left")
saved_frame += 1
image_array = np.vstack((image_array, temp_array))
label_array = np.vstack((label_array, self.k[0]))
# Send key to rest api
payload = dict(data='A')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'Q' in keys:
print('exit')
self.send_inst = False
# Send key to rest api
payload = dict(data='Q')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
break
# save training images and labels
train = image_array[1:, :]
train_labels = label_array[1:, :]
# save training data as a numpy file
np.savez('training_data_temp/test00001.npz',
train=train,
train_labels=train_labels)
e2 = cv2.getTickCount()
# calculate streaming duration
time0 = (e2 - e1) / cv2.getTickFrequency()
print('Streaming duration:', time0)
print(train.shape)
print(train_labels.shape)
print('Total frame:', total_frame)
print('Saved frame:', saved_frame)
print('Dropped frame', total_frame - saved_frame)
finally:
self.connection.close()
self.server_socket.close()
def screen_record():
paused = True
starting_value = 0
training_data = []
while True:
file_name = 'training_data/training_data-{}.npy'.format(starting_value)
if os.path.isfile(file_name):
print('File exists.', starting_value)
starting_value += 1
else:
print('File does not exist.', starting_value)
break
while (True):
if not paused:
# start = time.time()
with mss.mss() as sct:
mon = sct.monitors[1]
monitor = {
"top": mon["top"] + 405,
"left": mon["left"] + 720,
"width": 480,
"height": 270,
"mon": 1,
}
screen = np.array(sct.grab(monitor))
keys = key_check()
get_key_output(keys)
get_mouse_output()
print(output)
training_data.append(
[screen,
str(output),
getattr(CSserver, 'get_reward')()])
reset_output()
CSserver.reset_reward()
# print(time.time() - start)
if len(training_data) % 500 == 0:
np.save(file_name, training_data)
print('SAVED', starting_value)
training_data = []
starting_value += 1
file_name = 'training_data/training_data-{}.npy'.format(
starting_value)
keys = key_check()
if 'T' in keys:
if paused:
paused = False
print('unpaused!')
time.sleep(1)
else:
print('Pausing!')
paused = True
time.sleep(1)
def screen_record():
last_time = time.time()
count = 0
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
# 800x600 windowed mode
stopped = False
while (True):
if not stopped:
screen = grab_screen(region=(0, 400, 800, 600))
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
screen = cv2.resize(screen, (128, 128))
pred = trained_model.predict([screen.reshape(1, 128, 128, 1)])[0]
moves = list(np.around(pred))
# moves = np.array(pred)*np.array([4.5, 0.1, 0.1])
# moves = np.argmax(moves)
print("Move {0} with prediction {1}".format(moves, pred))
turn_prob = 0.75
no_turn_prob = 0.70
# if pred[1] > no_turn_prob:
# go_straight()
# elif pred[0] > turn_prob:
# go_left()
# elif pred[2] > turn_prob:
# go_right()
# else:
# go_straight()
# if moves == 0:
# go_straight()
# choice_picked = 'straight'
#
# elif moves == 1:
# go_reverse()
# choice_picked = 'reverse'
# w
# elif moves == 2:
# go_left()
# choice_picked = 'left'
# elif moves == 3:
# go_right()
# choice_picked = 'right'
# elif moves == 4:
# go_forward_left()
# choice_picked = 'forward+left'
# elif moves == 5:
# go_forward_right()
# sss choice_picked = 'forward+right'
# elif moves == 6:
# go_reverse_left()
# choice_picked = 'reverse+left'
# elif moves == 7:
# go_reverse_right()
# choice_picked = 'reverse+right'
# elif moves == 8:
# no_keys()
# choice_picked = 'nokeys'
if moves == [1, 0, 0]:
go_left()
elif moves == [0, 1, 0]:
go_straight()
elif moves == [0, 0, 1]:
go_right()
else:
go_straight()
pass
key = key_check()
if 'T' in key:
if stopped:
stopped = False
time.sleep(1)
else:
stopped = True
ReleaseKey(A)
ReleaseKey(W)
ReleaseKey(D)
#print("Frames capturing at {} fps".format(time.time()-last_time))
last_time = time.time()
def streamingAndCollectData(self):
saved_frame = 0
total_frame = 0
# collect images for training
print('Start collecting images...')
e1 = cv2.getTickCount()
file_name = self.file_name
starting_value = self.starting_value
training_data = []
for i in list(range(4))[::-1]:
print(i+1)
time.sleep(1)
last_time = time.time()
paused = False
print('STARTING!!!')
try:
print("Connection from: ", self.client_address)
print("Streaming...")
print("Press 'Q' to exit")
stream_bytes = b''
frame = 1
while True:
stream_bytes += self.connection.read(1024)
first = stream_bytes.find(b'\xff\xd8')
last = stream_bytes.find(b'\xff\xd9')
self.conn.sendall(b'WA')
if first != -1 and last != -1:
jpg = stream_bytes[first:last + 2]
stream_bytes = stream_bytes[last + 2:]
image = cv2.imdecode(np.fromstring(jpg, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
# select lower half of the image
roi = image[120:240, :]
last_time = time.time()
# run a color convert:
screen = cv2.cvtColor(roi, cv2.COLOR_BGR2RGB)
cv2.imshow('image', screen)
cv2.imwrite('training_images/Imageframe{:>05}.jpg'.format(frame), screen)
frame += 1
total_frame += 1
# Check key stroke while streaming
keys = key_check()
if 'W' in keys and 'A' in keys:
print("Forward Left")
training_data.append([screen,self.wa])
saved_frame += 1
# Send key to rest api
payload = dict(data='WA')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'W' in keys and 'D' in keys:
print("Forward Right")
training_data.append([screen,self.wd])
saved_frame += 1
# Send key to rest api
payload = dict(data='WD')
headers = { 'content-type': 'application/json' }
response = requests.post(self.restUrl, data=json.dumps(payload), headers=headers )
print(response, payload, 'sent to server.')
elif 'S' in keys and 'A' in keys:
print("Reverse Left")
training_data.append([screen,self.sa])
# Send key to rest api
payload = dict(data='SA')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'S' in keys and 'D' in keys:
print("Reverse Right")
training_data.append([screen,self.sd])
# Send key to rest api
payload = dict(data='SD')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'W' in keys:
print("Forward")
saved_frame += 1
training_data.append([screen,self.w])
# Send key to rest api
payload = dict(data='W')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'S' in keys:
print("Reverse")
saved_frame += 1
training_data.append([screen,self.s])
# Send key to rest api
payload = dict(data='S')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'D' in keys:
print("Right")
saved_frame += 1
training_data.append([screen,self.d])
# Send key to rest api
payload = dict(data='D')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'A' in keys:
print("Left")
saved_frame += 1
training_data.append([screen,self.a])
# Send key to rest api
payload = dict(data='A')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
elif 'Q' in keys:
print('exit')
training_data.append([screen,self.nk])
self.send_inst = False
# Send key to rest api
payload = dict(data='Q')
response = requests.post(self.restUrl, data=payload)
print(response, payload, 'sent to server.')
break
np.save(file_name,training_data)
e2 = cv2.getTickCount()
# calculate streaming duration
time0 = (e2 - e1) / cv2.getTickFrequency()
print('Streaming duration:', time0)
print('Total frame:', total_frame)
print('Saved frame:', saved_frame)
print('Dropped frame', total_frame - saved_frame)
finally:
self.connection.close()
self.server_socket.close()
