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functions.py
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functions.py
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import config_generator
import core
import math
import numpy as np
import cv2
import pyautogui
import random
import time
from shapely.geometry import Polygon
import ctypes
import slyautomation_title
import yaml
from PIL import Image, ImageGrab
import os
import platform
global hwnd
hwnd = 0
global iflag
global icoord
iflag = False
global newTime_break
newTime_break = False
global timer
global timer_break
global ibreak
import pytesseract
with open("pybot-config.yaml", "r") as yamlfile:
data = yaml.load(yamlfile, Loader=yaml.FullLoader)
class bcolors:
OK = '\033[92m' #GREEN
WARNING = '\033[93m' #YELLOW
FAIL = '\033[91m' #RED
RESET = '\033[0m' #RESET COLOR
with open("pybot-config.yaml", "r") as yamlfile:
data = yaml.load(yamlfile, Loader=yaml.FullLoader)
pytesseract.pytesseract.tesseract_cmd = data[0]['Config']['tesseract_path'] + "tesseract"
try:
im = Image.open("images/tynan_shop.png")
text = pytesseract.image_to_string(im)
print(bcolors.OK + "Testing Tesseract is configured: Passed |", text)
except:
pass
os.environ["TESSDATA_PREFIX"] = data[0]['Config']['tesseract_path'] # + "tessdata"
try:
im = Image.open("images/tynan_shop.png")
text = pytesseract.image_to_string(im)
print(bcolors.OK + "Testing Tesseract is configured: Passed |", text)
except:
os.environ["TESSDATA_PREFIX"] = data[0]['Config']['tesseract_path'] + "tessdata"
try:
im = Image.open("images/tynan_shop.png")
text = pytesseract.image_to_string(im)
print(bcolors.OK + "Testing Tesseract is configured: Passed |", text)
except:
print(bcolors.FAIL +"Error setting up tesseract: Check the pyconfig.yaml is set up to your tesseract path or is installed correctly, go here and install latest version: " + 'https://digi.bib.uni-mannheim.de/tesseract/?C=M;O=D')
# Constants
gdi32 = ctypes.WinDLL('gdi32.dll')
VERTRES = 10
DESKTOPVERTRES = 117
class DeviceCap:
VERTRES = 10
DESKTOPVERTRES = 117
def get_scaling_factor():
gdi32 = ctypes.WinDLL('gdi32.dll')
# Get the screen's device context (DC)
desktop = gdi32.CreateDCW('DISPLAY', None, None, None)
# Get the logical and physical screen height
logical_screen_height = gdi32.GetDeviceCaps(desktop, DeviceCap.VERTRES)
physical_screen_height = gdi32.GetDeviceCaps(desktop, DeviceCap.DESKTOPVERTRES)
# Calculate the scaling factor
scaling_factor = physical_screen_height / logical_screen_height
return scaling_factor
# Usage
scaling_factor = get_scaling_factor()
if scaling_factor == 1.0:
print(bcolors.OK +"Scaling Factor:", scaling_factor * 100, "%")
else:
print(bcolors.FAIL +"Scaling Factor: Failed set to 100% | Actual:", scaling_factor * 100, "%")
def get_os_configuration():
# Get scale and layout information
user32 = ctypes.windll.user32
scale_factor = user32.GetDpiForSystem()
# Get font size information
scale = 96 / scale_factor
font_size = user32.SystemParametersInfoW(0x0030, 0, 0, 0) / scale # SPI_GETNONCLIENTMETRICS
# Get display resolution information
width = user32.GetSystemMetrics(0)
height = user32.GetSystemMetrics(1)
return scale_factor, font_size, width, height
# Usage
scale_factor, font_size, width, height = get_os_configuration()
if width == 1920 and height == 1080:
print(bcolors.OK +"Resolution:", width, "x", height)
else:
print(bcolors.FAIL +"Resolution not set correctly: Failed set to 1920 x 1080 | Actual:", width, "x", height)
try:
print(bcolors.OK + "tesseract version:", pytesseract.get_tesseract_version())
except SystemExit:
print(bcolors.FAIL +"tesseract version detailed: not found")
print(bcolors.RESET)
filename = data[0]['Config']['pc_profile']
osrs = data[0]['Config']['file_path_to_client']
live_file = "jagex_cl_oldschool_LIVE.dat"
random_file = "random.dat"
try:
os.remove(filename + osrs + live_file)
os.remove(filename + osrs + random_file)
except OSError:
pass
except FileNotFoundError:
pass
if platform.system() == 'Linux' or platform.system() == 'Mac':
filename = filename + osrs + "/jagexcache/oldschool/LIVE/"
else:
filename = filename + osrs + "\\jagexcache\\oldschool\\LIVE\\"
try:
for f in os.listdir(filename):
try:
if not f.startswith("main_file"):
continue
os.remove(os.path.join(filename, f))
except OSError:
pass
except FileNotFoundError:
pass
except OSError:
pass
except FileNotFoundError:
pass
#
print('jagex files deleted')
def deposit_all_Bank():
banker = 50
b = random.uniform(0.1, 0.77)
x = random.randrange(480, 500) # x = random.randrange(1040, 1050)
y = random.randrange(626, 647) # y = random.randrange(775, 805)
pyautogui.moveTo(x, y, duration=b)
b = random.uniform(0.01, 0.1)
pyautogui.click(x, y, duration=b, button='left')
c = random.uniform(0.1, 4.5)
time.sleep(c)
def invent_crop():
return screen_Image(620, 480, 820, 750, 'inventshot.png')
def random_inventory():
global newTime_break, actions
actions = 'inventory tab'
b = random.uniform(0.1, 15)
pyautogui.press('f4')
time.sleep(b)
pyautogui.press('f4')
b = random.uniform(0.1, 2)
time.sleep(b)
pyautogui.press('esc')
newTime_break = True
def random_combat():
global newTime_break, actions
actions = 'combat tab'
b = random.uniform(0.1, 15)
pyautogui.press('f1')
time.sleep(b)
pyautogui.press('f1')
b = random.uniform(0.1, 2)
time.sleep(b)
pyautogui.press('esc')
newTime_break = True
def random_skills():
global newTime_break, actions
actions = 'skills tab'
b = random.uniform(0.1, 15)
pyautogui.press('f2')
time.sleep(b)
pyautogui.press('f2')
b = random.uniform(0.1, 2)
time.sleep(b)
pyautogui.press('esc')
newTime_break = True
def random_quests():
global newTime_break, actions
actions = 'quest tab'
b = random.uniform(0.1, 15)
pyautogui.press('f3')
time.sleep(b)
pyautogui.press('f3')
b = random.uniform(0.1, 2)
time.sleep(b)
pyautogui.press('esc')
newTime_break = True
def resize_quick():
left = 40
top = 49
right = 105
bottom = 67
im = ImageGrab.grab(bbox=(left, top, right, bottom))
im.save('images/screen_resize.png', 'png')
def resizeImage():
resize_quick()
png = 'images/screen_resize.png'
im = Image.open(png)
# saves new cropped image
width, height = im.size
new_size = (width * 4, height * 4)
im1 = im.resize(new_size)
im1.save('images/textshot.png')
def Miner_Image_quick():
left = 150
top = 150
right = 600
bottom = 750
im = ImageGrab.grab(bbox=(left, top, right, bottom))
im.save('images/miner_img.png', 'png')
def Image_to_Text(preprocess, image, parse_config='--psm 7'):
resizeImage()
change_brown_black()
# construct the argument parse and parse the arguments
image = cv2.imread('images/' + image)
image = cv2.bitwise_not(image)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# check to see if we should apply thresholding to preprocess the
# image
if preprocess == "thresh":
gray = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
# make a check to see if median blurring should be done to remove
# noise
if preprocess == "blur":
gray = cv2.medianBlur(gray, 3)
if preprocess == 'adaptive':
gray = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 31, 2)
# write the grayscale image to disk as a temporary file so we can
# apply OCR to it
filename = "{}.png".format(os.getpid())
cv2.imwrite(filename, gray)
# load the image as a PIL/Pillow image, apply OCR, and then delete
# the temporary file
with Image.open(filename) as im:
text = pytesseract.image_to_string(im, config=parse_config)
os.remove(filename)
#print(text)
return text
def screen_Image_new(name='screenshot.png'):
x, y, w, h = core.getWindow(data[0]['Config']['client_title'])
im = ImageGrab.grab(bbox=(x, y, x+w, y+h))
im.save('images/' + name, 'png')
def screen_Image(left=0, top=0, right=0, bottom=0, name='screenshot.png'):
if left != 0 or top != 0 or right != 0 or bottom != 0:
myScreenshot = ImageGrab.grab(bbox=(left, top, right, bottom))
else:
myScreenshot = ImageGrab.grab()
myScreenshot.save('images/' + name)
def Image_color_new():
screen_Image_new('images/screenshot2.png')
image = cv2.imread('images/screenshot2.png')
image = cv2.rectangle(image, pt1=(600, 0), pt2=(850, 200), color=(0, 0, 0), thickness=-1)
image = cv2.rectangle(image, pt1=(0, 0), pt2=(150, 100), color=(0, 0, 0), thickness=-1)
# define the list of boundaries
red = ([0, 0, 180], [80, 80, 255]) # 0 Index
green = ([0, 180, 0], [80, 255, 80]) # 1 Index
amber = ([0, 170, 170], [170, 255, 255]) # 2 Index
pickup_high = ([150, 0, 100], [255, 60, 160]) # 3 Index
attack_blue = ([200, 200, 0], [255, 255, 5])
boundaries = [
red, green, amber, pickup_high, attack_blue
]
# loop over the boundaries
for (lower, upper) in boundaries:
# create NumPy arrays from the boundaries
lower = np.array(lower, dtype="uint8")
upper = np.array(upper, dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask=mask)
# show the images
cv2.imshow("images", np.hstack([image, output]))
cv2.moveWindow("images", 20, 20);
cv2.waitKey(0)
def Image_color(left=0, top=0, right=0, bottom=0):
screen_Image(left, top, right, bottom)
image = cv2.imread('images/screenshot.png')
image = cv2.rectangle(image, pt1=(600, 0), pt2=(850, 200), color=(0, 0, 0), thickness=-1)
image = cv2.rectangle(image, pt1=(0, 0), pt2=(150, 100), color=(0, 0, 0), thickness=-1)
# define the list of boundaries
red = ([0, 0, 180], [80, 80, 255]) # 0 Index
green = ([0, 180, 0], [80, 255, 80]) # 1 Index
amber = ([0, 170, 170], [170, 255, 255]) # 2 Index
pickup_high = ([150, 0, 100], [255, 60, 160]) # 3 Index
attack_blue = ([200, 200, 0], [255, 255, 5])
boundaries = [
red, green, amber, pickup_high, attack_blue
]
# loop over the boundaries
for (lower, upper) in boundaries:
# create NumPy arrays from the boundaries
lower = np.array(lower, dtype="uint8")
upper = np.array(upper, dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask=mask)
# show the images
cv2.imshow("images", np.hstack([image, output]))
cv2.moveWindow("images", 20, 20);
cv2.waitKey(0)
def exit_bank(Debug=False):
if Debug:
print('exit bank')
c = random.uniform(0.1, 0.7)
x = random.randrange(523, 540)
if Debug:
print('x: ', x)
y = random.randrange(40, 55)
if Debug:
print('y: ', y)
b = random.uniform(0.1, 0.6)
pyautogui.moveTo(x, y, duration=b)
b = random.uniform(0.01, 0.3)
pyautogui.click(duration=b, button='left')
time.sleep(c)
def teleport_home_new():
x, y, w, h = core.getWindow(data[0]['Config']['client_title'])
pyautogui.press('esc')
random_breaks(0.1, 0.3)
pyautogui.press('f6')
random_breaks(0.1, 0.3)
pick_item(x+w - 170, y+h - 305)
def teleport_home():
pyautogui.press('esc')
random_breaks(0.1, 0.3)
pyautogui.press('f6')
random_breaks(0.1, 0.3)
pick_item(1928-1280, 498)
def change_brown_black():
# Load the aerial image and convert to HSV colourspace
image = cv2.imread("images/textshot.png")
#hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
# define the list of boundaries
# BGR
# Define lower and uppper limits of what we call "brown"
brown_lo = np.array([0, 0, 0])
brown_hi = np.array([60, 80, 85])
# Mask image to only select browns
mask = cv2.inRange(image, brown_lo, brown_hi)
# Change image to red where we found brown
image[mask > 0] = (0, 0, 0)
cv2.imwrite("images/textshot.png", image)
def find_Object_precise(item, left=0, top=0, right=0, bottom=0):
screen_Image(left, top, right, bottom)
image = cv2.imread('images/screenshot.png')
image = cv2.rectangle(image, pt1=(600, 0), pt2=(850, 200), color=(0, 0, 0), thickness=-1)
image = cv2.rectangle(image, pt1=(0, 0), pt2=(150, 100), color=(0, 0, 0), thickness=-1)
# define the list of boundaries
# B, G, R
red = ([0, 0, 180], [80, 80, 255]) # 0 Index
green = ([0, 180, 0], [80, 255, 80]) # 1 Index
amber = ([0, 200, 200], [60, 255, 255]) # 2 Index
pickup_high = ([250, 0, 167], [255, 5, 172]) # 3 Index
attack_blue = ([200, 200, 0], [255, 255, 5])
object_list = [red, green, amber, pickup_high, attack_blue]
boundaries = [object_list[item]]
# loop over the boundaries
for (lower, upper) in boundaries:
# create NumPy arrays from the boundaries
lower = np.array(lower, dtype="uint8")
upper = np.array(upper, dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask=mask)
ret, thresh = cv2.threshold(mask, 40, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if len(contours) != 0:
# find the biggest countour (c) by the area
c = max(contours, key=cv2.contourArea)
#print(c)
#print(np.squeeze(c))
#print(Polygon(np.squeeze(c)))
minx, miny, maxx, maxy = Polygon(np.squeeze(c)).bounds
#print(minx, miny, maxx, maxy)
x = random.randrange(minx + 1, max(minx + 2,maxx - 1))
y = random.randrange(miny + 1, max(miny + 2,maxy - 1))
#print('y: ', y)
b = random.uniform(0.1, 0.4)
pyautogui.moveTo(x, y, duration=b)
b = random.uniform(0.01, 0.05)
pyautogui.click(duration=b)
return (x, y)
return False
def add_blank_square_to_image(filename, left, top):
image_name_output = 'images/blank_image.png'
mode = 'RGBA'
size = (25, 25)
color = (255, 255, 255, 255)
im = Image.new(mode, size, color)
im.save(image_name_output, 'PNG')
im.close()
pos = (400 - left, 415 - top)
frontImage = Image.open(image_name_output)
background = Image.open(filename)
background.paste(frontImage, pos, frontImage.convert('RGBA'))
background.save('images/screenshot.png', format='png')
def find_Object_closest(item, left=0, top=0, right=0, bottom=0, clicker='left', size=1):
screen_Image(left, top, right, bottom)
add_blank_square_to_image('images/screenshot.png', left, top)
image = cv2.imread('images/screenshot.png')
image = cv2.rectangle(image, pt1=(600, 0), pt2=(850, 200), color=(0, 0, 0), thickness=-1)
image = cv2.rectangle(image, pt1=(0, 0), pt2=(150, 100), color=(0, 0, 0), thickness=-1)
# define the list of boundaries
# B, G, R
red = ([0, 0, 180], [80, 80, 255]) # 0 Index
green = ([0, 180, 0], [80, 255, 80]) # 1 Index
amber = ([0, 200, 200], [60, 255, 255]) # 2 Index
pickup_high = ([250, 0, 167], [255, 5, 172]) # 3 Index
attack_blue = ([200, 200, 0], [255, 255, 5]) # 4
object_list = [red, green, amber, pickup_high, attack_blue]
boundaries = [object_list[item]]
close_list = []
close_points = []
pos = (pyautogui.position().x, pyautogui.position().y)
# loop over the boundaries
for (lower, upper) in boundaries:
# create NumPy arrays from the boundaries
lower = np.array(lower, dtype="uint8")
upper = np.array(upper, dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask=mask)
cv2.imwrite("res1.png", np.hstack([image, output]))
ret, thresh = cv2.threshold(mask, 40, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnt = contours
for c in cnt:
#print(cv2.contourArea(c))
#print(cv2.boundingRect(c))
if cv2.contourArea(c) > size:
x1, y1, w1, h1 = cv2.boundingRect(c)
close_list.append(abs(abs(pos[0] - x1) + abs(pos[1] - y1)))
close_points.append((x1, y1))
if len(contours) == 0:
#print('not found')
return False
#print(close_list)
#print(close_points)
if len(close_list) == 0:
return False
min_value = min(close_list)
min_index = close_list.index(min_value)
coords = close_points[min_index]
#print(coords)
#print('min_value:', min_value, '| min_index:', min_index)
x = random.randrange(5, 20)
y = random.randrange(5, 20)
icoord = coords[0] + x + left
icoord = (icoord, coords[1] + y + top)
b = random.uniform(0.1, 0.7)
pyautogui.moveTo(icoord, duration=b)
b = random.uniform(0.01, 0.3)
pyautogui.click(icoord, duration=b, button=clicker)
return close_points
def find_Object(item, left=0, top=0, right=0, bottom=0):
screen_Image(left, top, right, bottom)
image = cv2.imread('images/screenshot.png')
image = cv2.rectangle(image, pt1=(600, 0), pt2=(850, 200), color=(0, 0, 0), thickness=-1)
image = cv2.rectangle(image, pt1=(0, 0), pt2=(150, 100), color=(0, 0, 0), thickness=-1)
#cv2.imwrite('images/screenshot3.png', image)
# define the list of boundaries
# B, G, R
red = ([0, 0, 180], [80, 80, 255]) # 0 Index
green = ([0, 180, 0], [80, 255, 80]) # 1 Index
amber = ([0, 200, 200], [60, 255, 255]) # 2 Index
pickup_high = ([150, 0, 100], [255, 60, 160]) # 3 Index
attack_blue = ([200, 200, 0], [255, 255, 5])
object_list = [red, green, amber, pickup_high, attack_blue]
boundaries = [object_list[item]]
# loop over the boundaries
for (lower, upper) in boundaries:
# create NumPy arrays from the boundaries
lower = np.array(lower, dtype="uint8")
upper = np.array(upper, dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask=mask)
ret, thresh = cv2.threshold(mask, 40, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if len(contours) != 0:
#print(len(contours))
# find the biggest countour (c) by the area
c = max(contours, key=cv2.contourArea)
#print(contours)
x, y, w, h = cv2.boundingRect(c)
x = random.randrange(x + 5, x + max(w - 5, 6)) + left # 950,960
#print('x: ', x)
y = random.randrange(y + 5, y + max(h - 5, 6)) + top # 490,500
#print('y: ', y)
b = random.uniform(0.1, 0.4)
pyautogui.moveTo(x, y, duration=b)
b = random.uniform(0.01, 0.05)
pyautogui.click(duration=b)
return (x, y)
else:
return False
def find_Object_right_quick(item, left=0, top=0, right=0, bottom=0, y_range=40):
screen_Image(left, top, right, bottom)
image = cv2.imread('images/screenshot.png')
image = cv2.rectangle(image, pt1=(600, 0), pt2=(850, 200), color=(0, 0, 0), thickness=-1)
image = cv2.rectangle(image, pt1=(0, 0), pt2=(150, 100), color=(0, 0, 0), thickness=-1)
# define the list of boundaries
# B, G, R
red = ([0, 0, 180], [80, 80, 255]) # 0 Index
green = ([0, 180, 0], [80, 255, 80]) # 1 Index
amber = ([0, 200, 200], [60, 255, 255]) # 2 Index
pickup_high = ([250, 0, 167], [255, 5, 172]) # 3 Index
attack_blue = ([200, 200, 0], [255, 255, 5])
object_list = [red, green, amber, pickup_high, attack_blue]
boundaries = [object_list[item]]
# loop over the boundaries
for (lower, upper) in boundaries:
# create NumPy arrays from the boundaries
lower = np.array(lower, dtype="uint8")
upper = np.array(upper, dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask=mask)
ret, thresh = cv2.threshold(mask, 40, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnt = contours[0]
a = cv2.pointPolygonTest(cnt, (pyautogui.position()[0], pyautogui.position()[1]), True)
#print(a)
if len(contours) != 0:
#print(len(contours))
# find the biggest countour (c) by the area
c = max(contours, key=cv2.contourArea)
#print('max:', c)
x, y, w, h = cv2.boundingRect(c)
x = random.randrange(x + 5, x + max(w - 5, 6)) # 950,960
#print('x: ', x)
y = random.randrange(y + 5, y + max(h - 5, 6)) # 490,500
#print('y: ', y)
b = random.uniform(0.01, 0.1)
pyautogui.moveTo(x, y, duration=b)
b = random.uniform(0.01, 0.05)
pyautogui.click(duration=b, button='right')
d = random.uniform(0.1,0.4)
time.sleep(d)
b = random.uniform(0.01, 0.05)
c = random.randrange(0, 40)
y = random.randrange(y_range, y_range+5)
pyautogui.move(c, y, duration=b)
b = random.uniform(0.01, 0.1)
pyautogui.click(duration=b)
return True
else:
return False
def find_Object_right(item, left=0, top=0, right=0, bottom=0, y_range=40):
screen_Image(left, top, right, bottom)
image = cv2.imread('images/screenshot.png')
image = cv2.rectangle(image, pt1=(600, 0), pt2=(850, 200), color=(0, 0, 0), thickness=-1)
image = cv2.rectangle(image, pt1=(0, 0), pt2=(150, 100), color=(0, 0, 0), thickness=-1)
# define the list of boundaries
# B, G, R
red = ([0, 0, 180], [80, 80, 255]) # 0 Index
green = ([0, 180, 0], [80, 255, 80]) # 1 Index
amber = ([0, 200, 200], [60, 255, 255]) # 2 Index
pickup_high = ([250, 0, 167], [255, 5, 172]) # 3 Index
attack_blue = ([200, 200, 0], [255, 255, 5])
object_list = [red, green, amber, pickup_high, attack_blue]
boundaries = [object_list[item]]
# loop over the boundaries
for (lower, upper) in boundaries:
# create NumPy arrays from the boundaries
lower = np.array(lower, dtype="uint8")
upper = np.array(upper, dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask=mask)
ret, thresh = cv2.threshold(mask, 40, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnt = contours[0]
a = cv2.pointPolygonTest(cnt, (pyautogui.position()[0], pyautogui.position()[1]), True)
#print(a)
if len(contours) != 0:
#print(len(contours))
# find the biggest countour (c) by the area
c = max(contours, key=cv2.contourArea)
#print('max:', c)
x, y, w, h = cv2.boundingRect(c)
x = random.randrange(x + 5, x + max(w - 5, 6)) # 950,960
#print('x: ', x)
y = random.randrange(y + 5, y + max(h - 5, 6)) # 490,500
#print('y: ', y)
b = random.uniform(0.1, 0.4)
pyautogui.moveTo(x, y, duration=b)
b = random.uniform(0.01, 0.05)
pyautogui.click(duration=b, button='right')
d = random.uniform(0.1,0.5)
time.sleep(d)
b = random.uniform(0.01, 0.05)
c = random.randrange(0, 40)
y = random.randrange(y_range, y_range+5)
pyautogui.move(c, y, duration=b)
b = random.uniform(0.01, 0.3)
pyautogui.click(duration=b)
return True
else:
return False
def spaces(a):
global actions
if a == 1:
d = random.uniform(0.05, 0.1)
time.sleep(d)
pyautogui.press('space')
if a == 0:
actions = "none"
if a == 2:
d = random.uniform(0.05, 0.1)
time.sleep(d)
pyautogui.press('space')
d = random.uniform(0.05, 0.1)
time.sleep(d)
pyautogui.press('space')
def skill_lvl_up():
counter = 0
myScreenshot = ImageGrab.grab()
myScreenshot.save(r"images/screen.png")
img_rgb = cv2.imread(r"images/screen.png")
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('images/Congrats_flag.png', 0)
w, h = template.shape[::-1]
res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
threshold = 0.8
loc = np.where(res >= threshold)
for pt in zip(*loc[::-1]):
cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2)
counter += 1
# cv2.imwrite('res.png', img_rgb)
return counter
def skill_lvl_up_new():
counter = 0
screen_Image_new("images/screen.png")
img_rgb = cv2.imread(r"images/screen.png")
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('images/Congrats_flag.png', 0)
w, h = template.shape[::-1]
res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
threshold = 0.8
loc = np.where(res >= threshold)
for pt in zip(*loc[::-1]):
cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0, 0, 255), 2)
counter += 1
# cv2.imwrite('images/res.png', img_rgb)
return counter
def pick_item_right(v, u, option=1):
c = random.uniform(0.1, 0.7)
d = random.uniform(0.01, 0.15)
x = random.randrange(v - 10, v + 10)
#print('x: ', x)
y = random.randrange(u - 5, u + 5)
b = random.uniform(0.1, 0.7)
pyautogui.moveTo(x, y, duration=b)
time.sleep(d)
pyautogui.click(button='right')
time.sleep(c)
w = random.randrange(0, 10) + x
#print('x: ', x)
one = random.randrange(40, 45) + y
two = random.randrange(50, 55) + y
three = random.randrange(60, 65) + y
four = random.randrange(70, 75) + y
five = random.randrange(80, 85) + y
six = random.randrange(90,95) + y
seven = random.randrange(100,105) + y
eight = random.randrange(110,115) + y
right_order = {1: one,
2: two,
3: three,
4: four,
5: five,
6: six,
7: seven,
8: eight
}
z = right_order[option]
#print('y: ', y)
pyautogui.moveTo(w, z, duration=b)
b = random.uniform(0.1, 0.19)
pyautogui.click(duration=b)
c = random.uniform(0.1, 0.4)
time.sleep(c)
def pick_item_new(v, u):
c = random.uniform(0.1, 0.7)
d = random.uniform(0.01, 0.15)
x = random.randrange(v, v + 1)
#print('x: ', x)
y = random.randrange(u, u + 1)
b = random.uniform(0.1, 0.6)
pyautogui.moveTo(x, y, duration=b)
time.sleep(d)
pyautogui.click(button='left')
time.sleep(c)
def pick_item(v, u):
c = random.uniform(0.1, 0.7)
d = random.uniform(0.01, 0.15)
x = random.randrange(v - 10, v + 10)
#print('x: ', x)
y = random.randrange(u - 5, u + 5)
b = random.uniform(0.1, 0.6)
pyautogui.moveTo(x, y, duration=b)
time.sleep(d)
pyautogui.click(button='left')
time.sleep(c)
def deposit_secondItem_new():
x, y, w, h = core.getWindow(data[0]['Config']['client_title'])
if w > 940:
pick_item(x+w - 115, y+h - 255)
else:
pick_item(x+w - 115, y+h - 295)
def deposit_secondItem():
c = random.uniform(0.1, 0.7)
x = random.randrange(690, 715) # 950,960
z = x
#print('x: ', x)
y = random.randrange(495, 515) # 490,500
w = y
#print('y: ', y)
b = random.uniform(0.1, 0.7)
pyautogui.moveTo(x, y, duration=b)
b = random.uniform(0.01, 0.3)
pyautogui.click(duration=b, button='left')
time.sleep(c)
def max_point(x, y, radius):
# Calculate the distance between the point (x, y) and the center of the circle
x = x - 745
y = y - 110
print(x,y)
distance = math.sqrt(x**2 + y**2)
print(distance)
# If the distance is less than or equal to the radius, the point is inside the circle
if distance <= radius:
x = x + 745
y = y + 110
return x, y, True
# If the distance is greater than the radius, the point is outside the circle
# Calculate the angle between the point and the x-axis
angle = math.atan2(y, x)
# Calculate the maximum x and y coordinates that are on the circle
max_x = radius * math.cos(angle)
max_y = radius * math.sin(angle)
max_x = max_x + 744
max_y = max_y + 109
return max_x, max_y, False
def mini_map_image(image, iwidth=0, iheight=0, threshold=0.7, clicker='left', xspace=0, yspace=0, Debug=True):
screen_Image(661, 27, 826, 190, 'mini_map.png')
img_rgb = cv2.imread('images/mini_map.png')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('images/' + image, 0)
#w, h = template.shape[::-1]
pt = None
res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
threshold = threshold
loc = np.where(res >= threshold)
for pt in zip(*loc[::-1]):
if Debug:
cv2.imwrite('images/res.png', img_rgb)
x_1 = random.randrange(iwidth, iwidth + 1 + xspace)
y_1 = random.randrange(iheight, iheight + 1 + yspace)
if Debug:
print("random points:", x_1,y_1)
print("final points:", 661 + pt[0] + x_1, 30 + pt[1] + y_1)
x, y, within_circle = max_point(661 + pt[0] + x_1, 30 + pt[1] + y_1, 74)
icoord = (x, y)
if Debug:
print(icoord, within_circle)
b = random.uniform(0.1, 0.7)
pyautogui.moveTo(icoord, duration=b)
b = random.uniform(0.01, 0.3)
pyautogui.click(icoord, duration=b, button=clicker)
if Debug:
print(True)
return True
if Debug:
print(False)
return False
def mini_map_bool(image, threshold=0.7):
screen_Image(1941 - 1280, 27, 2106 - 1280, 190, 'mini_map.png')
global icoord
global iflag
img_rgb = cv2.imread('images/mini_map.png')
# print('screenshot taken')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('images/' + image, 0)
w, h = template.shape[::-1]
pt = None
res = cv2.matchTemplate(img_gray, template, cv2.TM_CCOEFF_NORMED)
threshold = threshold
loc = np.where(res >= threshold)
for pt in zip(*loc[::-1]):
return True
return False
def xp_quick():
left = 560
top = 95
right = 595
bottom = 220
im = ImageGrab.grab(bbox=(left, top, right, bottom))
im.save('images/xp_gain.png', 'png')
def xp_gain_check(image, threshold=0.95, showCoords=False):
xp_quick()
# read screenshot
img = cv2.imread('images/xp_gain.png')
template = cv2.imread('images/' + image, cv2.IMREAD_UNCHANGED)
hh, ww = template.shape[:2]
temp_a = template[:, :, 0:3]
alpha = template[:, :, 3]
alpha = cv2.merge([alpha, alpha, alpha])
# set threshold
threshold = threshold
# do masked template matching and save correlation image
corr_img = cv2.matchTemplate(img, temp_a, cv2.TM_CCORR_NORMED, mask=alpha)
# search for max score
#result = img.copy()
max_val = 0
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(corr_img)
if showCoords:
print(max_val, max_loc)
if max_val > threshold:
# draw match on copy of input
return True
else:
return False
def McropImage_quick():
left = 150
top = 150
right = 600
bottom = 750
im = ImageGrab.grab(bbox=(left, top, right, bottom))
im.save('images/screenshot2.png', 'png')
def findarea_attack_quick(object, deep=20):
McropImage_quick()
image = cv2.imread(r"images/screenshot2.png")
# B, G, R
# --------------------- ADD OBJECTS -------------------
red = ([0, 0, 180], [80, 80, 255])
green = ([0, 180, 0], [80, 255, 80])
pickup_high = ([200, 0, 100], [255, 30, 190])
attack_blue = ([250, 250, 0], [255, 255, 5])
amber = ([0, 160, 160], [80, 255, 255])
# --------------------- ADD OBJECTS -------------------
ore_list = [red, green, pickup_high, attack_blue, amber]
boundaries = [ore_list[object]]
# loop over the boundaries
for (lower, upper) in boundaries:
# create NumPy arrays from the boundaries
lower = np.array(lower, dtype="uint8")
upper = np.array(upper, dtype="uint8")
# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask=mask)
ret, thresh = cv2.threshold(mask, 40, 255, 0)
# if (cv2.__version__[0] > 3):
# contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# else:
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
if len(contours) != 0:
# draw in blue the contours that were founded
# cv2.drawContours(output, contours, -1, 255, 3)
# find the biggest countour (c) by the area
c = max(contours, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(c)
# draw the biggest contour (c) in green
whalf = max(round(w / 2), 1)
hhalf = max(round(h / 2), 1)
# cv2.rectangle(output, (x, y), (x + w, y + h), (0, 255, 0), 2)
x = random.randrange(x + 150 + whalf - deep, x + 150 + max(whalf + deep, 1)) # 950,960
#print('attack x: ', x)