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():
global do
while True:
frame = grab_screen([10, 40, 770, 700])
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
roi = frame[300:,100:]
roi = cv2.resize(roi, (80,60))
temp = roi
roi = img_preProcess(roi)
if do == True:
prediction = predict(roi)
prediction = prediction.argmax(axis=1)
if prediction == 0:
right()
elif prediction == 1:
jump()
elif prediction == 2:
rightJump()
print(prediction)
temp = cv2.resize(temp, (320,240))
cv2.imshow('frame', temp)
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
def main():
file_name = 'training_data_takeoff'
training_data = load_data(file_name)
paused = False
print('Initialising Data Collection in:')
countdown()
while True:
if not paused:
try:
screen = grab_screen()
screen = cv2.resize(screen, (240, 135),
interpolation=cv2.INTER_AREA)
output = keys_to_output(key_check())
print(f'Output is : {output}')
training_data.append([screen, output])
if len(training_data) % 1000 == 0:
print(f'Length of training data is : {len(training_data)}')
np.save(file_name, training_data)
except:
print("Window not available. Please try again")
keys = key_check()
if 'T' in keys:
if paused:
countdown()
paused = False
print('Unpaused!')
time.sleep(1)
else:
print('Pausing!')
paused = True
time.sleep(1)
def feeding_img():
global gaa
while (True):
last_time = time.time()
x, y = win32api.GetCursorPos()
# print(x,y)
# screengrab =
# cv2.imshow('screen capture',cv2.cvtColor(np.array(screengrab),cv2.COLOR_BGR2RGB)) # shows the window, but with RGB conversion
# if cv2.waitKey(25) & 0xFF == ord('q'):
# cv2.destroyAllWindows()
# break
gaa = grab_screen([x - 250, y - 250, x + 250, y + 250])
print("time update : {} seconds with FPS : {}".format(
time.time() - last_time, 1 / (time.time() - last_time)))
def main():
file_name = 'training_data_takeoff'
training_data = load_data(file_name)
paused = False
print('Initialising Data Collection in:')
countdown()
while True:
if not paused:
try:
screen = grab_screen()
screen = cv2.resize(screen, (240, 135),
interpolation=cv2.INTER_AREA) #resize
## cv2.imshow('window',screen)
## if cv2.waitKey(25) & 0xFF == ord('q'):
## cv2.destroyAllWindows()
## break
output = keys_to_output(key_check())
print(output)
training_data.append([screen, output])
if len(training_data) % 1000 == 0:
print(len(training_data))
np.save(file_name, training_data)
except:
print("Window not available. Please try again")
keys = key_check()
if 'T' in keys:
if paused:
countdown()
paused = False
print('Unpaused!')
time.sleep(1)
else:
print('Pausing!')
paused = True
time.sleep(1)
def base(path):
return os.path.basename(path)
# Hardcoded from model
class_names = [
'apex', 'csgo', 'dbd', 'eft', 'fifa-21', 'fortnite', 'gtav', 'lol',
'minecraft', 'poe', 'rocket-league', 'rust', 'valorant', 'warzone', 'wow'
]
# Load the bitch
model = tf.keras.models.load_model('models/trained-2k')
while True:
# Use (0, 0, 1920, 1080) for center/main monitor (if 1080p)
screen_raw = grab_screen(region=(-1920, 127, -340, 1016))
# Resize to input into CNN
screen = cv2.resize(screen_raw, (w, h))
# Do something
img_array = keras.preprocessing.image.img_to_array(screen)
img_array = tf.expand_dims(img_array, 0) # Create a batch
# Predict
s = time()
prediction = model.predict(img_array)
e = time()
# Compute confidence
score = tf.nn.softmax(prediction[0])
pred = class_names[np.argmax(score)]
counterclick= 0
clicktimechecker = 0
screenx, screeny = 3840//2 , 2160//2
print("Alive")
while(True):
# x,y = win32api.GetCursorPos()
# screengrab =
screengrab = np.array(grab_screen([screenx-250,screeny-250,screenx+250,screeny+250]))
# cv2.imshow('screen capture',cv2.cvtColor(np.array(gaa),cv2.COLOR_BGR2RGB)) # shows the window, but with RGB conversion
last_time = time.time()
# testing time
mask = cv2.inRange(cv2.cvtColor(screengrab, cv2.COLOR_BGR2HSV), lower_bound, upper_bound)
result_Frame = cv2.bitwise_and(cv2.cvtColor( screengrab,cv2.COLOR_BGR2RGB),cv2.cvtColor( screengrab,cv2.COLOR_BGR2RGB),mask=mask)
cv2.imshow("RESULT", result_Frame)
cv2.imshow('nani',screengrab)
counterclick= 0
clicktimechecker = 0
x, y = 3840//2 , 2160//2
print("Alive")
while(True):
# x,y = win32api.GetCursorPos()
# screengrab =
screengrab = np.array(grab_screen([x-250,y-250,x+250,y+250]))
# cv2.imshow('screen capture',cv2.cvtColor(np.array(gaa),cv2.COLOR_BGR2RGB)) # shows the window, but with RGB conversion
last_time = time.time()
# testing time
mask = cv2.inRange(cv2.cvtColor(screengrab, cv2.COLOR_BGR2HSV), lower_bound, upper_bound)
result_Frame = cv2.bitwise_and(cv2.cvtColor( screengrab,cv2.COLOR_BGR2RGB),cv2.cvtColor( screengrab,cv2.COLOR_BGR2RGB),mask=mask)
cur_image = image.img_to_array(f****e)
cur_image = np.expand_dims(cur_image,axis=0)
# about 0.002 sec
counterclick= 0
clicktimechecker = 0
sx, sy = 3840//2 , 2160//2
print("Alive")
while(True):
# x,y = win32api.GetCursorPos()
# screengrab =
screengrab = np.array(grab_screen([sx-250,sy-250,sx+250,sy+250]))
# cv2.imshow('screen capture',cv2.cvtColor(np.array(gaa),cv2.COLOR_BGR2RGB)) # shows the window, but with RGB conversion
last_time = time.time()
# testing time
mask = cv2.inRange(cv2.cvtColor(screengrab, cv2.COLOR_BGR2HSV), lower_bound, upper_bound)
result_Frame = cv2.bitwise_and(cv2.cvtColor( screengrab,cv2.COLOR_BGR2RGB),cv2.cvtColor( screengrab,cv2.COLOR_BGR2RGB),mask=mask)
cur_image = image.img_to_array(result_Frame)
cur_image = np.expand_dims(cur_image,axis=0)
# about 0.002 sec
folder_creation("ClassifiedData")
folder_creation("ClassifiedData/hit")
folder_creation("ClassifiedData/miss")
while(True):
Lmouse = win32api.GetAsyncKeyState((win32con.VK_LBUTTON))
Ctrl = win32api.GetAsyncKeyState((win32con.VK_CONTROL))
Shift = win32api.GetAsyncKeyState((win32con.VK_SHIFT))
x,y = win32api.GetCursorPos()
print(x,y)
screengrab = grab_screen([x-250,y-250,x+250,y+250])
# printscreen_numpy= np.array(printscreen_pil.getdata(), dtype = 'uint8').reshape( (printscreen_pil.size[1],printscreen_pil.size[0],3))
cv2.imshow('screen capture',cv2.cvtColor( np.array(screengrab),cv2.COLOR_BGR2RGB))
last_time = time.time()
if cv2.waitKey(1) & 0xFF == ord('q'):
cv2.destroyAllWindows()
break
print("time update : {} seconds".format(time.time() - last_time))
print(str(Lmouse) + " " + str(Ctrl) + " " + str(Shift))
if( Shift != 0):
Ctrl = win32api.GetAsyncKeyState((win32con.VK_CONTROL))
Shift = win32api.GetAsyncKeyState((win32con.VK_SHIFT))
MouseB, MouseF = win32api.GetAsyncKeyState(
(win32con.VK_XBUTTON1)), win32api.GetAsyncKeyState(
(win32con.VK_XBUTTON2))
default_resolution_x = win32api.GetSystemMetrics(
win32con.SM_CXVIRTUALSCREEN)
default_resolution_y = win32api.GetSystemMetrics(
win32con.SM_CYVIRTUALSCREEN)
# x,y = win32api.GetCursorPos()
# print(x,y)
screengrab = grab_screen([
default_resolution_x // 2 - 250, default_resolution_y // 2 - 250,
default_resolution_x // 2 + 250, default_resolution_y // 2 + 250
])
# printscreen_numpy= np.array(printscreen_pil.getdata(), dtype = 'uint8').reshape( (printscreen_pil.size[1],printscreen_pil.size[0],3))
cv2.imshow('screen capture',
cv2.cvtColor(np.array(screengrab), cv2.COLOR_BGR2RGB))
mask = cv2.inRange(cv2.cvtColor(screengrab, cv2.COLOR_BGR2HSV),
lower_bound, upper_bound)
result = cv2.bitwise_and(cv2.cvtColor(screengrab, cv2.COLOR_BGR2RGB),
cv2.cvtColor(screengrab, cv2.COLOR_BGR2RGB),
mask=mask)
last_time = time.time()
cv2.imshow("result", result)
if cv2.waitKey(1) & 0xFF == ord('q'):
def forward_right():
PressKey(W)
PressKey(D)
ReleaseKey(A)
ReleaseKey(S)
model = load_model('simpleCNN.h5')
for i in list(range(4))[::-1]:
print(i + 1)
time.sleep(1)
while True:
curr_view = grab_screen([0, 30, 800, 620])
#cv2.imshow('frame', curr_view)
#cv2.imshow('frame',cv2.cvtColor(curr_view, cv2.COLOR_BGR2GRAY))
#screen = cv2.imshow('frame',cv2.cvtColor(curr_view, cv2.COLOR_BGR2GRAY))
screen = cv2.cvtColor(curr_view, cv2.COLOR_BGR2GRAY)
screen = cv2.resize(screen, (60, 60))
cv2.imshow('screen', screen)
screen = screen[np.newaxis, ..., np.newaxis]
prediction = model.predict(screen)
prediction = np.array(prediction)
mode_choice = np.argmax(prediction)
if mode_choice == 0:
straight()
choice_picked = 'straight'
import cv2
import numpy as np
from input import PressKey, Z, X, L, J, ReleaseKey
from screengrab import grab_screen
from encoding import oneHotEncoding
from getKeys import key_check
""" Right+JUMP(L+Z) = [0,0,1]
RIGHT (L) = [1,0,0]
JUMP (Z) = [0,1,0] """
while True:
screen = grab_screen([10, 40, 770, 700])
#screen = cv2.cvtColor(screen, cv2.COLOR_BGR2GRAY)
roi = screen[300:, 100:]
roi = cv2.resize(roi, (80, 60))
cv2.imshow('frame', screen)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()