def main():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# Train model
loaded_model = load()
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 = np.array(ImageGrab.grab(bbox=(0,40,800,640)))
screen = grab_screen(region=(0, 40, 800, 640))
print('loop took {} seconds'.format(time.time() - last_time))
last_time = time.time()
screen = cv2.resize(screen, (299, 299))
prediction = loaded_model.predict([screen.reshape(1, 299, 299, 3)])
print(prediction)
turn_thresh = .75
fwd_thresh = 0.70
if prediction[0][1] > fwd_thresh:
straight()
elif prediction[0][0] > turn_thresh:
left()
elif prediction[0][2] > turn_thresh:
right()
elif prediction[0][3] > turn_thresh:
stop()
else:
straight()
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)
ReleaseKey(S)
time.sleep(1)
pyautogui.click()
# Create some setup variables
sct = mss.mss()
goingRight = True
count = 0
kernel = np.ones((3,3), np.uint8)
# Used to hold images until game is over then we can
# write them to disk.
image_holder = []
# Start game loop
# holding h will break loop
while True:
keys = key_check()
if keys == "H":
break
count += 1
# Define our area and grab screen.
scr = sct.grab({
'left': 0,
'top': 390,
'width': 440,
'height': 50
})
# Turn screen grab into numpy array.
img = np.array(scr)
def main():
last_time = time.time()
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
paused = False
mode_choice = 0
screen = grabscreen(region=(0, 40, GAME_WIDTH, GAME_HEIGHT))
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)
t_minus = screen
t_now = screen
t_plus = screen
while (True):
if not paused:
screen = grabscreen(region=(0, 40, GAME_WIDTH, GAME_HEIGHT + 40))
screen = cv2.cvtColor(screen, cv2.COLOR_BGR2RGB)
print(screen.shape)
last_time = time.time()
delta_count = motion_detection(t_minus, t_now, t_plus)
t_minus = t_now
t_now = t_plus
t_plus = screen
t_plus = cv2.blur(t_plus, (4, 4))
prediction = model.predict(screen.reshape(1, HEIGHT, WIDTH, 3))[0]
prediction = np.array(prediction)# * np.array([1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2])
second = np.argpartition(a, -2)[1]
mode_choice = np.argmax(prediction)
print('Choice: {}'.format(mode_choice))
print('Second: {}'.format(second))
print(prediction)
if mode_choice == 0:
straight()
choice_picked = 'straight'
elif mode_choice == 1:
reverse()
choice_picked = 'reverse'
elif mode_choice == 2:
left()
choice_picked = 'left'
elif mode_choice == 3:
right()
choice_picked = 'right'
elif mode_choice == 4:
forward_left()
choice_picked = 'forward+left'
elif mode_choice == 5:
forward_right()
choice_picked = 'forward+right'
elif mode_choice == 6:
reverse_left()
choice_picked = 'reverse+left'
elif mode_choice == 7:
reverse_right()
choice_picked = 'reverse+right'
elif mode_choice == 8:
no_keys()
choice_picked = 'nokeys'
motion_log.append(delta_count)
motion_avg = round(mean(motion_log), 3)
print('loop took {} seconds. Motion: {}. Choice: {}'.format(round(time.time() - last_time, 3), motion_avg,
choice_picked))
if motion_avg < motion_req and len(motion_log) >= log_len:
print('WERE PROBABLY STUCK FFS, initiating some evasive maneuvers.')
# 0 = reverse straight, turn left out
# 1 = reverse straight, turn right out
# 2 = reverse left, turn right out
# 3 = reverse right, turn left out
quick_choice = random.randrange(0, 4)
if quick_choice == 0:
reverse()
time.sleep(random.uniform(1, 2))
forward_left()
time.sleep(random.uniform(1, 2))
elif quick_choice == 1:
reverse()
time.sleep(random.uniform(1, 2))
forward_right()
time.sleep(random.uniform(1, 2))
elif quick_choice == 2:
reverse_left()
time.sleep(random.uniform(1, 2))
forward_right()
time.sleep(random.uniform(1, 2))
elif quick_choice == 3:
reverse_right()
time.sleep(random.uniform(1, 2))
forward_left()
time.sleep(random.uniform(1, 2))
for i in range(log_len - 2):
del motion_log[0]
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(self):
print("Running on: {}x{}".format(self.res[0], self.res[1]))
print("Using {} key".format(self.key))
print("Autosend: {}".format(self.autosend))
print("Zoom: {}".format(self.zoom))
###################################################################################################
# L=152,98+I*8,36 -> para achar largura em função do número da imagem
# C=593,81+I*31,62 -> para achar comprimento em função do número da imagem
# zoom_dict = {
# '-5': (113,447), ok
# '-4': (121,474), ok
# '-3': (121,479), not ok
# '-2': (134,509), not ok
# '-1': (148,571), ok
# '0': (156,600), ok
# '1': (161,629),
# '2': (169,659),
# '3': (177,687),
# '4': (187,721),
# '5': (195, 752) # Linear aproximation (I couldn't measure it)
# }
file_name = 'Data\\training_data.npy'
if os.path.isfile(file_name):
print("Training file exists, loading previos data!")
training_data = list(np.load(file_name))
else:
print("Training file does not exist, starting fresh!")
training_data = []
frame_file = 'Data\\frames.npy'
if os.path.isfile(frame_file):
print("Frames file exists, loading previos data!")
frames = list(np.load(frame_file))
else:
print("Frames file does not exist, starting fresh!")
frames = []
###################################################################################################
fishing_region_file = 'media\\Images\\fr {}.png'.format(self.zoom)
if os.path.exists(fishing_region_file):
region_template = cv2.imread('media\\Images\\fr {}.png'.format(self.zoom))
print(fishing_region_file)
else:
quit()
region_template_gray = cv2.cvtColor(region_template, cv2.COLOR_BGR2GRAY)
# region_template_gray = cv2.resize(region_template_gray, zoom_dict[str(self.zoom)])
wr, hr = region_template_gray.shape[::-1] # 121, 474
print("w: 121 h: 474".format(wr, hr), end='\t')
# resized = zoom_dict[str(self.zoom)]
# print(resized)
was_fishing = False
counter = 1
while self.run:
res_x, res_y = self.res
screen = np.array(ImageGrab.grab(bbox=(0, 40, res_x, res_y+40 )))
fishing, green_bar_window, floor_height = fishing_region(screen, region_template_gray, wr, hr)
if fishing:
if counter == 1:
initial_time = datetime.datetime.now()
counter = 2
contour, green_bar_height, lowest_point = process_img(screen,
green_bar_window) # process every frame (would be nice if it could process every 5 or so frames, so the process becomes faster).
fish_detected, fish_height, searching_nemo = fish(green_bar_window)
d_rect_fish = fish_height - green_bar_height # if result is + : fish is below the green bar, if result is - : fish is above the green bar
d_rect_floor = floor_height - lowest_point # always +
key_pressed = key_check(self.key)
data = [d_rect_fish, d_rect_floor, key_pressed] # example key pressed: [231, 456, 1]
training_data.append(data)
print(data)
was_fishing = True
if not fishing and was_fishing:
if len(frames) == 0:
# print('list of frames is new')
frames.append(len(training_data))
print("Frames analysed:\t", len(training_data))
np.save(frame_file, frames)
print("Saving...")
np.save(file_name, training_data)
was_fishing = False
self.score.emit(sum(frames))
if self.autosend:
with open("config.json", 'r') as f:
output = json.loads(f.read())
BASE_URL = 'http://192.168.1.102'
response_code = SendFiles.send_data(BASE_URL, output['User'], output['Password'])
self.data_response_code.emit(response_code)
else:
frame = len(training_data) - sum(frames)
frames.append(frame)
print("Frames analysed:\t", frames[-1])
np.save(frame_file, frames)
print("Saving...")
np.save(file_name, training_data)
was_fishing = False
self.score.emit(sum(frames))
if self.autosend:
with open("config.json", 'r') as f:
output = json.loads(f.read())
BASE_URL = 'http://192.168.1.102'
response_code = SendFiles.send_data(BASE_URL, output['User'], output['Password'])
self.data_response_code.emit(response_code)
final_time = datetime.datetime.now()
time_delta = final_time - initial_time
print(time_delta.total_seconds())
frame_yield = 100 * frames[-1] / (time_delta.total_seconds() * 30)
print("Rendimento: {}%".format(frame_yield))
def main(self):
# Unique file name
file_name = 'Data\\Training Data\\%s.npy' % uuid4()
logger.info("Training data file created")
training_data = np.empty(shape=[0, 2])
region_template, wr, hr = self.load_template(
zoom_level=-4
) # Default value is -4, but it cycles trough. could be from -5 to +5
# Variáveis de controle:
# was_fishing: sinaliza se o frame anterior foi ou não um frame da sessão de pescaria. Caso o mini-game seja
# detectado, isso é setado para True no final do loop. Assim, no frame seguinte, caso não tenha sido detectado a
# região e was_fishing for True, isso signifca que a pescaria acabou, e deve ser feito o processo de finalização
# da captura de dados.
# coords: coordenadas da região reduzida encontrada do mini-game.
was_fishing = False
coords = None
logger.info("Data started")
while self.run:
# res_x, res_y = self.res
screen = grabscreen.grab_screen() # Return BGR screen
if screen is not None:
# Finds the thin area the fish stays at
if coords is not None:
region = fishing_region(
screen[coords[0]:coords[1], coords[2]:coords[3]],
region_template, wr, hr)
else:
region = fishing_region(screen, region_template, wr, hr)
zoom_dict = self.find_zoom(screen)
if zoom_dict['Found']:
# In subsequent fishing sessions, it will start by trying this zoom level
logger.info(f"Zoom used: {zoom_dict['Zoom']}")
region_template, wr, hr = self.load_template(
zoom_dict['Zoom'])
if region["Detected"]:
# Se a área for detectada, salvar na np.array o frame e a o key-press do jogador.
window = region["Region"]
key_pressed = key_check(self.key) # return 1 or 0
data = [
window, key_pressed
] # or data = np.array([key_pressed, window], dtype=object)
training_data = np.vstack((training_data, data))
# Contants for the next loop
was_fishing = True
bgr_screen_last = region["BGR Region"]
# For the first frame of the detected region, get its coordinates to reduce the area to look for it again
if coords is None:
print("Found")
bgr_screen_first = region["BGR Region"]
coords = region["Coords"]
logger.info("Coordinates found: %s" % coords)
initial_time = datetime.datetime.now()
# If area not detected this frame, but was on the last one, this means fishing is over.
if not region["Detected"] and was_fishing:
logger.info("Fishing finished")
final_time = datetime.datetime.now()
new_frames = np.float64(len(training_data))
print("Frames analysed: %s" % new_frames)
# Apenas salva caso houver mais de 75 frames
if new_frames >= 75:
validated = self.validate(bgr_screen_first,
bgr_screen_last)
verified = verify_too_similar_frames(training_data)
if validated and verified:
np.save(file_name, training_data)
# Sinaliza ao main_thread que deve enviar os dados coletados
self.send_data.emit()
print("Session saved!")
# Necessary to reset the region coordinates after every fishing session.
training_data = np.empty(shape=[0, 2])
file_name = 'Data\\Training Data\\%s.npy' % uuid4()
coords = None
was_fishing = False
# Measurements (debug):
time_delta = (final_time - initial_time).total_seconds()
median_fps = round(new_frames / time_delta, 2)
print(f"FPS: {median_fps}\n")
method = 'np.vstack'
w_img, h_img = window.shape[::-1]
with open("Data\\log.txt", 'a') as f:
f.write(
f"Method: {method}\nMedian FPS: {median_fps}\ndTime: {time_delta}s\n"
f"Frames: {new_frames}\nSize: ({w_img}, {h_img})\n\n"
)
# Caso o usuário clique em "Stop" na GUI
self.finished.emit()