Pine: Neural-Network Aimbot (v2.0 by Wendel for Apex Legends)
==============================================================

[INFO] press '0' to quit or ctrl+C in console...
[INFO] loading neural-network from disk…
[OKAY] OpenCL GPU Detected!
[INFO] loading screencapture device…
[OKAY] Aimbot enabled!

[ WARN:0@1.544] global ocl4dnn_conv_spatial.cpp:1933 cv::dnn::ocl4dnn::OCL4DNNConvSpatial<float>::loadTunedConfig OpenCV(ocl4dnn): consider to specify kernel configuration cache directory through OPENCV_OCL4DNN_CONFIG_PATH parameter.
OpenCL program build log: dnn/dummy
Status -11: CL_BUILD_PROGRAM_FAILURE
-cl-no-subgroup-ifp
Error in processing command line: Don't understand command line argument "-cl-no-subgroup-ifp"!
50 FPS with 19 MS interpolation delay
[INFO] cleaning up…
Traceback (most recent call last):
  File "C:\Users\Wendel\Desktop\Apex_Aimbot_PY\teste\pine.py", line 14, in <module>
    pine.start(ENABLE_AIMBOT=True)
  File "C:\Users\Wendel\Desktop\Apex_Aimbot_PY\teste\lib\pine.py", line 217, in start
    if cv2.waitKey(1) & 0xFF == ord('0'):
       ^^^^^^^^^^^^^^
  File "C:\Users\Wendel\Desktop\Apex_Aimbot_PY\teste\lib\pine.py", line 146, in signal_handler
    sct.close()
    ^^^^^^^^^
AttributeError: 'MSS' object has no attribute 'close'

meu codigo abaixo:

apenas me mostre o trecho que deve ser alterado:

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

import win32gui
import win32api
import win32con
import mouse
import keyboard
from termcolor import colored

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 mouse.is_pressed(button='left') or keyboard.is_pressed('alt'):
        x = int(x - (Wd / 2))
        y = int(y - (Hd / 2))
        
        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))

# Defina os padrões de recoil para cada arma
recoil_patterns = {
    'r301': [(0, -2), (1, -2), (-1, -2), (0, -3), (1, -3), (-1, -4)],
    'r99': [(0, -3), (1, -3), (-1, -3), (0, -4), (1, -4), (-1, -5)],
    'flatline': [(0, -2), (-1, -2), (1, -2), (0, -3), (-1, -3), (1, -4)]
}

def control_recoil(pattern):
    for move_x, move_y in pattern:
        x, y = pyautogui.position()
        pyautogui.moveTo(x + move_x, y + move_y)
        time.sleep(0.05)  # Ajuste baseado na taxa de tiro da arma

def is_firing():
    return mouse.is_pressed(button='left')

def fire_loop():
    weapon = 'r301'  # Definido pelo contexto do jogo (arma atual), pode ser mudado conforme necessário
    while True:
        if is_firing():
            control_recoil(recoil_patterns[weapon])
        time.sleep(0.01)

# Iniciar thread para controle de recoil
fire_thread = threading.Thread(target=fire_loop)
fire_thread.daemon = True
fire_thread.start()

if __name__ == "__main__":
    print("Do not run this file directly.")

def start(ENABLE_AIMBOT):

    YOLO_DIRECTORY = "models"
    CONFIDENCE = 0.25
    THRESHOLD = 0.05
    ACTIVATION_RANGE = 200

    labelsPath = os.path.sep.join([YOLO_DIRECTORY, "obj.names"])
    LABELS = open(labelsPath).read().strip().split("\n")

    np.random.seed(42)
    COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
                               dtype="uint8")

    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"])

    time.sleep(0.4)

    print("[INFO] loading neural-network from disk…")
    net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)

    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()]

    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))

    if not ENABLE_AIMBOT:
        print("[INFO] aimbot disabled, using visualizer only…")
    else:
        print(colored("[OKAY] Aimbot enabled!", "green"))

    def signal_handler(sig, frame):
        print("\n[INFO] cleaning up…")
        sct.close()
        cv2.destroyAllWindows()
        sys.exit(0)
    signal.signal(signal.SIGINT, signal_handler)

    print()

    while True:
        start_time = timeit.default_timer()
        frame = np.array(grab_screen(region=origbox))
        frame = cv2.cvtColor(frame, cv2.COLOR_RGBA2BGR)

        if W is None or H is None:
            (H, W) = frame.shape[: 2]

        frame = cv2.UMat(frame)

        blob = cv2.dnn.blobFromImage(frame, 1/260, (150, 150), 
                                     swapRB=True, crop=True)
        net.setInput(blob)
        layerOutputs = net.forward(ln)

        boxes = []
        confidences = []
        classIDs = []

        for output in layerOutputs:
            for detection in output:
                scores = detection[5:]
                classID = 0 
                confidence = scores[classID]

                if confidence > CONFIDENCE:
                    box = detection[0: 4] * np.array([W, H, W, H])
                    (centerX, centerY, width, height) = box.astype("int")

                    x = int(centerX - (width / 2))
                    y = int(centerY - (height / 2))

                    boxes.append([x, y, int(width), int(height)])
                    confidences.append(float(confidence))
                    classIDs.append(classID)

        idxs = cv2.dnn.NMSBoxes(boxes, confidences, CONFIDENCE, THRESHOLD)

        if len(idxs) > 0:
            bestMatch = confidences[np.argmax(confidences)]

            for i in idxs.flatten():
                (x, y) = (boxes[i][0], boxes[i][1])
                (w, h) = (boxes[i][2], boxes[i][3])

                cv2.circle(frame, (int(x + w / 2), int(y + h / 3)), 2, (0, 0, 255), -1)
                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]:
                    # Ajustar a mira para a cabeça
                    mouseX = origbox[0] + (x + w / 2)
                    mouseY = origbox[1] + (y + h / 4)
                    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

    signal_handler(0, 0)