def q(debug_type, debugImg, threshold):
hsv_filter_vals = HsvFilter(114, 0, 0, 179, 255, 255, 0, 0, 62, 233)
if (debug_type == 1):
''' When we're not looking for processed images
'''
while (True):
if (cv.waitKey(1) == ord('q')):
cv.destroyAllWindows()
break
screenshot = wincap.get_screenshot()
rectangles = debugImg.find(screenshot, threshold)
output_img = debugImg.drawRectangles(screenshot, rectangles)
cv.imshow('Matches', output_img)
if (debug_type == 2):
''' When we want to test for processed images
'''
debugImg.init_control_gui()
while (True):
if (cv.waitKey(1) == ord('q')):
cv.destroyAllWindows()
break
screenshot = wincap.get_screenshot()
processed_img = debugImg.apply_hsv_filter(screenshot)
cv.imshow('Bot vision', processed_img)
if (debug_type == 3):
''' When we want to see what the bot sees
'''
while (True):
if (cv.waitKey(1) == ord('q')):
cv.destroyAllWindows()
break
screenshot = wincap.get_screenshot()
processed_img = debugImg.apply_hsv_filter(screenshot,
hsv_filter_vals)
cv.imshow('Bot vision', processed_img)
if (debug_type == 4):
''' When we want to detect needles in processed img haystack (rectangles)
'''
while (True):
if (cv.waitKey(1) == ord('q')):
cv.destroyAllWindows()
break
screenshot = wincap.get_screenshot()
processed_img = debugImg.apply_hsv_filter(screenshot,
hsv_filter_vals)
rectangles_points = debugImg.find(processed_img, threshold)
output_img_regu = debugImg.drawRectangles(screenshot,
rectangles_points)
cv.imshow('Matches', output_img_regu)
if (debug_type == 5):
''' When we want to detect needles in processed img haystack (crosshairs)
'''
while (True):
if (cv.waitKey(1) == ord('q')):
cv.destroyAllWindows()
break
screenshot = wincap.get_screenshot()
processed_img = debugImg.apply_hsv_filter(screenshot,
hsv_filter_vals)
rectangles_points = debugImg.find(processed_img, threshold)
crosshair_points = debugImg.getClickPoints(rectangles_points)
output_img_proc = debugImg.drawCrosshairs(processed_img,
crosshair_points)
cv.imshow('Matches', output_img_proc)
def get_hsv_filter_from_controls(self):
# Get current positions of all trackbars
hsv_filter = HsvFilter()
hsv_filter.hMin = cv.getTrackbarPos('HMin', self.TRACKBAR_WINDOW)
hsv_filter.sMin = cv.getTrackbarPos('SMin', self.TRACKBAR_WINDOW)
hsv_filter.vMin = cv.getTrackbarPos('VMin', self.TRACKBAR_WINDOW)
hsv_filter.hMax = cv.getTrackbarPos('HMax', self.TRACKBAR_WINDOW)
hsv_filter.sMax = cv.getTrackbarPos('SMax', self.TRACKBAR_WINDOW)
hsv_filter.vMax = cv.getTrackbarPos('VMax', self.TRACKBAR_WINDOW)
hsv_filter.sAdd = cv.getTrackbarPos('SAdd', self.TRACKBAR_WINDOW)
hsv_filter.sSub = cv.getTrackbarPos('SSub', self.TRACKBAR_WINDOW)
hsv_filter.vAdd = cv.getTrackbarPos('VAdd', self.TRACKBAR_WINDOW)
hsv_filter.vSub = cv.getTrackbarPos('VSub', self.TRACKBAR_WINDOW)
return hsv_filter
from vision import Vision
from hsvfilter import HsvFilter
# Change the working directory to the folder this script is in.
# Doing this because I'll be putting the files from each video in their own folder on GitHub
os.chdir(os.path.dirname(os.path.abspath(__file__)))
# initialize the WindowCapture class
wincap = WindowCapture('Albion Online Client')
# initialize the Vision class
vision_limestone = Vision('albion_limestone_processed.jpg')
# initialize the trackbar window
vision_limestone.init_control_gui()
# limestone HSV filter
hsv_filter = HsvFilter(0, 180, 129, 15, 229, 243, 143, 0, 67, 0)
loop_time = time()
while (True):
# get an updated image of the game
screenshot = wincap.get_screenshot()
# pre-process the image
processed_image = vision_limestone.apply_hsv_filter(screenshot, hsv_filter)
# do object detection
rectangles = vision_limestone.find(processed_image, 0.46)
# draw the detection results onto the original image
output_image = vision_limestone.draw_rectangles(screenshot, rectangles)
class FishingBot:
#properties
fish_pos_x = None
fish_pos_y = None
fish_last_time = None
detect_text_enable = False
botting = False
FISH_RANGE = 74
FISH_VELO_PREDICT = 30
BAIT_POSITION = (473, 750)
FISH_POSITION = (440, 750)
FILTER_CONFIG = [49, 0, 58, 134, 189, 189, 0, 0, 0, 0]
FISH_WINDOW_CLOSE = (430, 115)
# set position of the fish windows
# this value can be diferent by the sizes of the game window
FISH_WINDOW_SIZE = (280, 226)
FISH_WINDOW_POSITION = (163, 125)
wincap = None
fishfilter = Filter() if detect_text_enable else None
# Load the needle image
needle_img = cv.imread('images/fiss.jpg', cv.IMREAD_UNCHANGED)
needle_img_clock = cv.imread('images/clock.jpg', cv.IMREAD_UNCHANGED)
# Some time cooldowns
detect_text = True
# Limit time
initial_time = None
end_time_enable = False
end_time = 0
# for fps
loop_time = time()
# The mouse click cooldown
timer_mouse = time()
# The timer beteween the states
timer_action = time()
bait_time = 2
throw_time = 2
game_time = 2
# This is the filter parameters, this help to find the right image
hsv_filter = HsvFilter(*FILTER_CONFIG)
state = 0
def detect(self, haystack_img):
# match the needle_image with the hasytack image
result = cv.matchTemplate(haystack_img, self.needle_img,
cv.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(result)
# needle_image's dimensions
needle_w = self.needle_img.shape[1]
needle_h = self.needle_img.shape[0]
# get the position of the match image
top_left = max_loc
bottom_right = (top_left[0] + needle_w, top_left[1] + needle_h)
# Draw the circle of the fish limits
cv.circle(
haystack_img,
(int(haystack_img.shape[1] / 2), int(haystack_img.shape[0] / 2)),
self.FISH_RANGE,
color=(0, 0, 255),
thickness=1)
# Only the max level of match is greater than 0.5
if max_val > 0.5:
pos_x = (top_left[0] + bottom_right[0]) / 2
pos_y = (top_left[1] + bottom_right[1]) / 2
if self.fish_last_time:
dist = math.sqrt((pos_x - self.fish_pos_x)**2 +
(self.fish_pos_y - pos_y)**2)
cv.rectangle(haystack_img,
top_left,
bottom_right,
color=(0, 255, 0),
thickness=2,
lineType=cv.LINE_4)
# Calculate the fish velocity
velo = dist / (time() - self.fish_last_time)
if velo == 0.0:
return (pos_x, pos_y, True)
elif velo >= 150:
# With this velocity the fish position will be predict
pro = self.FISH_VELO_PREDICT / dist
destiny_x = int(pos_x + (pos_x - self.fish_pos_x) * pro)
destiny_y = int(pos_y + (pos_y - self.fish_pos_y) * pro)
# Draw the predict line
cv.line(haystack_img, (int(pos_x), int(pos_y)),
(destiny_x, destiny_y), (0, 255, 0),
thickness=3)
return (destiny_x, destiny_y, False)
# get the fish position and the time
self.fish_pos_x = pos_x
self.fish_pos_y = pos_y
self.fish_last_time = time()
return None
def detect_minigame(self, haystack_img):
result = cv.matchTemplate(haystack_img, self.needle_img_clock,
cv.TM_CCOEFF_NORMED)
min_val, max_val, min_loc, max_loc = cv.minMaxLoc(result)
if max_val > 0.9:
return True
return False
def set_to_begin(self, values):
if values['-ENDTIMEP-']:
self.end_time_enable = True
try:
self.end_time = int(values['-ENDTIME-']) * 60
except:
self.end_time = 0
self.bait_time = values['-BAITTIME-']
self.throw_time = values['-THROWTIME-']
self.game_time = values['-STARTGAME-']
self.wincap = WindowCapture('METIN2')
self.state = 0
self.initial_time = time()
self.timer_action = time()
def runHack(self):
screenshot = self.wincap.get_screenshot()
# crop and aply hsv filter
crop_img = screenshot[
self.FISH_WINDOW_POSITION[1]:self.FISH_WINDOW_POSITION[1] +
self.FISH_WINDOW_SIZE[1],
self.FISH_WINDOW_POSITION[0]:self.FISH_WINDOW_POSITION[0] +
self.FISH_WINDOW_SIZE[0]]
detect_end_img = screenshot[
self.FISH_WINDOW_POSITION[1]:self.FISH_WINDOW_POSITION[1] +
self.FISH_WINDOW_SIZE[1],
self.FISH_WINDOW_POSITION[0]:self.FISH_WINDOW_POSITION[0] +
self.FISH_WINDOW_SIZE[0]]
crop_img = self.hsv_filter.apply_hsv_filter(crop_img)
cv.putText(crop_img, 'FPS: ' + str(1 / (time() - self.loop_time))[:2],
(10, 200), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv.putText(
crop_img, 'State: ' + str(self.state) + ' ' +
str(time() - self.timer_action)[:5], (10, 160),
cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
self.loop_time = time()
# Verify total time
if self.end_time_enable and time() - self.initial_time > self.end_time:
self.botting = False
# State to click put the bait in the rod
if self.state == 0:
mouse_x = int(self.BAIT_POSITION[0] + self.wincap.offset_x)
mouse_y = int(self.BAIT_POSITION[1] + self.wincap.offset_y)
if time() - self.timer_action > self.bait_time:
self.detect_text = True
pydirectinput.click(x=mouse_x, y=mouse_y, button='right')
self.state = 1
self.timer_action = time()
# State to throw the bait
if self.state == 1:
if time() - self.timer_action > self.throw_time:
mouse_x = int(self.FISH_POSITION[0] + self.wincap.offset_x)
mouse_y = int(self.FISH_POSITION[1] + self.wincap.offset_y)
pydirectinput.click(x=mouse_x, y=mouse_y, button='right')
self.state = 2
self.timer_action = time()
# Delay to start the clicks
if self.state == 2:
if time() - self.timer_action > self.game_time:
self.state = 3
self.timer_action = time()
# Countdown to finish the state
detected_end = self.detect_minigame(detect_end_img)
if self.state == 3:
if time() - self.timer_action > 15:
self.timer_action = time()
self.state = 0
if time() - self.timer_action > 5 and detected_end is False:
self.timer_action = time()
self.state = 0
if self.detect_text_enable and time() - self.timer_action > 1.5:
if self.detect_text:
if self.fishfilter.match_with_text(screenshot) is False:
mouse_x = int(self.wincap.offset_x +
self.FISH_WINDOW_CLOSE[0])
mouse_y = int(self.wincap.offset_y +
self.FISH_WINDOW_CLOSE[1])
pydirectinput.click(x=mouse_x,
y=mouse_y,
button='left')
pydirectinput.click(x=mouse_x,
y=mouse_y,
button='left')
self.detect_text = False
# make the click
if (time() -
self.timer_mouse) > 0.3 and self.state == 3 and detected_end:
# Detect the fish
square_pos = self.detect(crop_img)
if square_pos:
# Recalculate the mouse position with the fish position
pos_x = square_pos[0]
pos_y = square_pos[1]
center_x = self.FISH_WINDOW_SIZE[0] / 2
center_y = self.FISH_WINDOW_SIZE[1] / 2
mouse_x = int(pos_x)
mouse_y = int(pos_y)
# Verify if the fish is in range
d = self.FISH_RANGE**2 - ((center_x - mouse_x)**2 +
(center_y - mouse_y)**2)
# Make the click
if (d > 0):
self.timer_mouse = time()
mouse_x = int(pos_x + self.FISH_WINDOW_POSITION[0] +
self.wincap.offset_x)
mouse_y = int(pos_y + self.FISH_WINDOW_POSITION[1] +
self.wincap.offset_y)
pydirectinput.click(x=mouse_x, y=mouse_y)
'''
cv.imshow('Minha Janela', crop_img)
if cv.waitKey(1) == ord('q'):
cv.destroyAllWindows()
return True
'''
return crop_img
def get_hsv_filter_from_controls(self):
hsv_filter = HsvFilter()
hsv_filter.Hmin = cv2.getTrackbarPos("Hmin", self.TRACKBAR_WINDOW)
hsv_filter.Smin = cv2.getTrackbarPos("Smin", self.TRACKBAR_WINDOW)
hsv_filter.Vmin = cv2.getTrackbarPos("Vmin", self.TRACKBAR_WINDOW)
hsv_filter.Hmax = cv2.getTrackbarPos("Hmax", self.TRACKBAR_WINDOW)
hsv_filter.Smax = cv2.getTrackbarPos("Smax", self.TRACKBAR_WINDOW)
hsv_filter.Vmax = cv2.getTrackbarPos("Vmax", self.TRACKBAR_WINDOW)
hsv_filter.Sadd = cv2.getTrackbarPos("Sadd", self.TRACKBAR_WINDOW)
hsv_filter.Ssub = cv2.getTrackbarPos("Ssub", self.TRACKBAR_WINDOW)
hsv_filter.Vadd = cv2.getTrackbarPos("Vadd", self.TRACKBAR_WINDOW)
hsv_filter.Vsub = cv2.getTrackbarPos("Vsub", self.TRACKBAR_WINDOW)
return hsv_filter
# Note: Neural Net reponse images placed after image 5380
mouse = Controller()
CATEGORIES = ['wait', 'shoot']
# img=data_tools().data_img_read_test(filepath='images/just missed/3642.jpg')
model=tf.keras.models.load_model('models/32x4_v3.2-CNN.model')
wincap = WindowCapture('VALORANT ')
selected_vision = VisionAdjust()
# new_vision = selected_vision.init_control_gui()
hsv_filter = HsvFilter(0,141,0,179,255,255,0,0,0,0)
y = 442
x = 898
width = 104
height = 169
count=10006
loop_time = time()
while(True):
screenshot = wincap.get_screenshot()
crop_img = screenshot[y:y+height, x:x+width]
output_image_og = selected_vision.apply_hsv_filter(crop_img, hsv_filter)
output_image = output_image_og.reshape(-1, 104, 169, 3)
prediction=model.predict([output_image])
cap = cv.VideoCapture('mario.mp4')
# load the trained models
neutral_b = cv.CascadeClassifier('cascade_models/cascade_copy.xml')
jab = cv.CascadeClassifier('cascade_models/jab.xml')
sheild = cv.CascadeClassifier('cascade_models/sheild.xml')
count = MoveCounter()
t = TimeTracker()
labels = MoveLabels()
tracker = Tracker()
# This stores the locations at which images are recognized.
move_locs = {labels.neutral_b: [], labels.jab: [], labels.shield: []}
mario_training_filter = HsvFilter(0, 5, 0, 179, 255, 255, 0, 17, 0, 0)
# initialize vision class
vision = Vision('')
while (True):
# get an updated image of the game
# USE THIS IF YOU ARE USING SCREENCAPTURE
# smash_screenshot = wincap.get_screenshot()
# USE THIS IF YOU ARE READING A VIDEO FILE
ret, smash_screenshot = cap.read()
# apply filter to img.
output_image = vision.apply_hsv_filter(smash_screenshot,
def getHsvFilterFromControls(self):
hsv_filter = HsvFilter()
hsv_filter.hMin = cv.getTrackbarPos('HMin', self.TRACKBAR_WINDOW)
hsv_filter.sMin = cv.getTrackbarPos('SMin', self.TRACKBAR_WINDOW)
hsv_filter.vMin = cv.getTrackbarPos('VMin', self.TRACKBAR_WINDOW)
hsv_filter.hMax = cv.getTrackbarPos('HMax', self.TRACKBAR_WINDOW)
hsv_filter.sMax = cv.getTrackbarPos('SMax', self.TRACKBAR_WINDOW)
hsv_filter.vMax = cv.getTrackbarPos('VMax', self.TRACKBAR_WINDOW)
hsv_filter.sAdd = cv.getTrackbarPos('SAdd', self.TRACKBAR_WINDOW)
hsv_filter.sSub = cv.getTrackbarPos('SSub', self.TRACKBAR_WINDOW)
hsv_filter.vAdd = cv.getTrackbarPos('VAdd', self.TRACKBAR_WINDOW)
hsv_filter.vSub = cv.getTrackbarPos('VSub', self.TRACKBAR_WINDOW)
return hsv_filter