import numpy as np
from termcolor import colored
import timeit
import threading
import imutils
import time
import mss
import cv2
import os
import signal
import sys
import pynput
import ctypes
import pyautogui
from lib.grab import grab_screen

import win32gui
import win32api
import win32con
import mouse
import keyboard
import itertools

sct = mss.mss()
Wd, Hd = sct.monitors[1][“width”], sct.monitors[1][“height”]
SendInput = ctypes.windll.user32.SendInput
PUL = ctypes.POINTER(ctypes.c_ulong)

class KeyBdInput(ctypes.Structure):
fields = [(“wVk”, ctypes.c_ushort),
(“wScan”, ctypes.c_ushort),
(“dwFlags”, ctypes.c_ulong),
(“time”, ctypes.c_ulong),
(“dwExtraInfo”, PUL)]
class HardwareInput(ctypes.Structure):
fields = [(“uMsg”, ctypes.c_ulong),
(“wParamL”, ctypes.c_short),
(“wParamH”, ctypes.c_ushort)]
class MouseInput(ctypes.Structure):
fields = [(“dx”, ctypes.c_long),
(“dy”, ctypes.c_long),
(“mouseData”, ctypes.c_ulong),
(“dwFlags”, ctypes.c_ulong),
(“time”, ctypes.c_ulong),
(“dwExtraInfo”, PUL)]
class Input_I(ctypes.Union):
fields = [(“ki”, KeyBdInput),
(“mi”, MouseInput),
(“hi”, HardwareInput)]
class Input(ctypes.Structure):
fields = [(“type”, ctypes.c_ulong),
(“ii”, Input_I)]

def set_pos(x, y):
if keyboard.is_pressed(‘alt’):
x = int(x - (Wd / 2))
y = int(y - (Hd / 2))

# Recoil pattern for R-301
RECOIL_PATTERN = [(0, -1), (0, -1), (0, -1), (1, -1), (1, -1), (1, -1)]
recoil_cycle = itertools.cycle(RECOIL_PATTERN)

dx, dy = next(recoil_cycle)
x += dx
y += dy

extra = ctypes.c_ulong(0)
ii = pynput._util.win32.INPUT_union()
ii.mi = pynput._util.win32.MOUSEINPUT(x, y, 0, (0x0001 | 0x8000), 0, ctypes.cast(ctypes.pointer(extra), ctypes.c_void_p))
command = pynput._util.win32.INPUT(ctypes.c_ulong(0), ii)
SendInput(1, ctypes.pointer(command), ctypes.sizeof(command))

if name == “main”:
print(“Do not run this file directly.”)

def start(ENABLE_AIMBOT):

# Config
CONFIDENCE = 0.20
THRESHOLD = 0.01

YOLO_DIRECTORY = “models”

ACTIVATION_RANGE = 200

# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join([YOLO_DIRECTORY, “obj.names”])
LABELS = open(labelsPath).read().strip().split(“\n”)

# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
dtype=“uint8”)

# derive the paths to the YOLO weights and model configuration
weightsPath = os.path.sep.join([YOLO_DIRECTORY, “PESOCNN/yolov4-tiny-obj_final.weights”])
configPath = os.path.sep.join([YOLO_DIRECTORY, “PESOCNN/yolov4-tiny-obj.cfg”])

# Wait for buffering
time.sleep(0.4)

# load our YOLO object detector trained on COCO dataset (80 classes)
# and determine only the output layer names that we need from YOLO
print(“[INFO] loading neural-network from disk…”)
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

# Check if GPU is available and use it
if cv2.cuda.getCudaEnabledDeviceCount() > 0:
print(colored(“[OKAY] CUDA GPU Detected!”, “green”))
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_CUDA)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CUDA)
elif cv2.ocl.haveOpenCL():
print(colored(“[OKAY] OpenCL GPU Detected!”, “green”))
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_DEFAULT)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_OPENCL)
else:
print(colored(“[WARNING] No GPU detected, using CPU.”, “yellow”))
net.setPreferableBackend(cv2.dnn.DNN_BACKEND_DEFAULT)
net.setPreferableTarget(cv2.dnn.DNN_TARGET_CPU)

ln = net.getLayerNames()
ln = [ln[i - 1] for i in net.getUnconnectedOutLayers()]

# Define screen capture area
print(“[INFO] loading screencapture device…”)
W, H = None, None
origbox = (int(Wd/2 - ACTIVATION_RANGE/2),
int(Hd/2 - ACTIVATION_RANGE/2),
int(Wd/2 + ACTIVATION_RANGE/2),
int(Hd/2 + ACTIVATION_RANGE/2))

# Log whether aimbot is enabled
if not ENABLE_AIMBOT:
print(“[INFO] aimbot disabled, using visualizer only…”)
else:
print(colored(“[OKAY] Aimbot enabled!”, “green”))

# Handle Ctrl+C in terminal, release pointers
def signal_handler(sig, frame):
# release the file pointers
print(“\n[INFO] cleaning up…”)
sct.close() # .exit() does not exist for mss.mss()
cv2.destroyAllWindows()
sys.exit(0)
signal.signal(signal.SIGINT, signal_handler)

print()

# loop over frames from the video file stream
while True:
start_time = timeit.default_timer()
frame = np.array(grab_screen(region=origbox))
frame = cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR)

# if the frame dimensions are empty, grab them
if W is None or H is None:
(H, W) = frame.shape[: 2]

frame = cv2.UMat(frame)

# construct a blob from the input frame and then perform a forward
# pass of the YOLO object detector, giving us our bounding boxes
# and associated probabilities
blob = cv2.dnn.blobFromImage(frame, 1/260, (150, 150),
swapRB=True, crop=True)
net.setInput(blob)
layerOutputs = net.forward(ln)

# initialize our lists of detected bounding boxes, confidences,
# and class IDs, respectively
boxes = []
confidences = []
classIDs = []

# loop over each of the layer outputs
for output in layerOutputs:
# loop over each of the detections
for detection in output:
# extract the class ID and confidence (i.e., probability)
# of the current object detection
scores = detection[5:]
classID = 0 # person = 0
confidence = scores[classID]

# filter out weak predictions by ensuring the detected
# probability is greater than the minimum probability
if confidence > CONFIDENCE:
box = detection[0: 4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype(“int”)

# use the center (x, y)-coordinates to derive the top
# and and left corner of the bounding box
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))

# update our list of bounding box coordinates,
# confidences, and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)

# apply non-maxima suppression to suppress weak, overlapping
# bounding boxes
idxs = cv2.dnn.NMSBoxes(boxes, confidences, CONFIDENCE, THRESHOLD)

# ensure at least one detection exists
if len(idxs) > 0:
# Find best player match
bestMatch = confidences[np.argmax(confidences)]

# loop over the indexes we are keeping
for i in idxs.flatten():
# extract the bounding box coordinates
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])

# draw target dot on the frame
cv2.circle(frame, (int(x + w / 2), int(y + h / 3)), 2, (0, 0, 255), -1)

# draw a bounding box rectangle and label on the frame
cv2.rectangle(frame, (x, y),
(x + w, y + h), (0, 255, 0), 1)

text = “enemy {}%”.format(int(confidences[i] * 100))
cv2.putText(frame, text, (x, y - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)

if ENABLE_AIMBOT and bestMatch == confidences[i]:
mouseX = origbox[0] + (x + w/1.55)
mouseY = origbox[1] + (y + h/2.50)
set_pos(mouseX, mouseY)

cv2.imshow(“Neural Net Vision (Pine)”, frame)
elapsed = timeit.default_timer() - start_time
sys.stdout.write(
“\r{1} FPS with {0} MS interpolation delay \t”.format(int(elapsed*1000), int(1/elapsed)))
sys.stdout.flush()
if cv2.waitKey(1) & 0xFF == ord(‘0’):
break

# Clean up on exit
signal_handler(0, 0)


percebi que a tentativa de controlar o recoil nao funcionou com esse padrao!
remova o padrao de recoil e faça que todas as armas fiquem sem recoil!
nao endo solavancos quando ativa nem para cima nem para baixo nem para os lados