import numpy as np
from termcolor import colored
import timeit
import threading # thread -> 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

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))

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
#YOLO_DIRECTORY = “models”
#CONFIDENCE = 0.20 #Reduzir CONFIDENCE permitirá que o modelo detecte mais objetos, mas pode aumentar os falsos positivos:
#THRESHOLD = 0.01
#THRESHOLD = 0.22 Estava usando esete antes de 0.01


# Config
YOLO_DIRECTORY = “models”
CONFIDENCE = 0.25 #Reduzir CONFIDENCE permitirá que o modelo detecte mais objetos, mas pode aumentar os falsos positivos:
THRESHOLD = 0.05
#THRESHOLD = 0.22 Estava usando esete antes de 0.01




#
# Size (in pixels) of the screen capture box to feed the neural net.
# This box is in the center of your screen. Lower value makes the network faster.
#
# Example: “ACTIVATION_RANGE = 400” means a 400x400 pixel box.
#
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)


preciso que o aimbot mire sempre na cabeça do oponente mesmo se estiver perto ou de longa distancia, nao remova outras partes do codigo apenas implemente oque pedi